data
dict |
|---|
{
"issue": {
"id": "12OmNAZx8Ow",
"title": "June",
"year": "2015",
"issueNum": "06",
"idPrefix": "tg",
"pubType": "journal",
"volume": "21",
"label": "June",
"downloadables": {
"hasCover": false,
"__typename": "PeriodicalIssueDownloadablesType"
},
"__typename": "PeriodicalIssue"
},
"article": {
"id": "13rRUxAASTe",
"doi": "10.1109/TVCG.2015.2396064",
"abstract": "We present a new framework for fast edge-aware processing of images and videos. The proposed smoothing method is based on an optimization formulation with a non-convex sparse regularization for a better smoothing behavior near strong edges. We develop mathematical tools based on first order approximation of proximal operators to accelerate the proposed method while maintaining high-quality smoothing. The first order approximation is used to estimate a solution of the proximal form in a half-quadratic solver, and also to derive a warm-start solution that can be calculated quickly when the image is loaded by the user. We extend the method to large-scale processing by estimating the smoothing operation with independent 1D convolution operations. This approach linearly scales to the size of the image and can fully take advantage of parallel processing. The method supports full color filtering and turns out to be temporally coherent for fast video processing. We demonstrate the performance of the proposed method on various applications including image smoothing, detail manipulation, HDR tone-mapping, fast edge simplification and video edge-aware processing.",
"abstracts": [
{
"abstractType": "Regular",
"content": "We present a new framework for fast edge-aware processing of images and videos. The proposed smoothing method is based on an optimization formulation with a non-convex sparse regularization for a better smoothing behavior near strong edges. We develop mathematical tools based on first order approximation of proximal operators to accelerate the proposed method while maintaining high-quality smoothing. The first order approximation is used to estimate a solution of the proximal form in a half-quadratic solver, and also to derive a warm-start solution that can be calculated quickly when the image is loaded by the user. We extend the method to large-scale processing by estimating the smoothing operation with independent 1D convolution operations. This approach linearly scales to the size of the image and can fully take advantage of parallel processing. The method supports full color filtering and turns out to be temporally coherent for fast video processing. We demonstrate the performance of the proposed method on various applications including image smoothing, detail manipulation, HDR tone-mapping, fast edge simplification and video edge-aware processing.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "We present a new framework for fast edge-aware processing of images and videos. The proposed smoothing method is based on an optimization formulation with a non-convex sparse regularization for a better smoothing behavior near strong edges. We develop mathematical tools based on first order approximation of proximal operators to accelerate the proposed method while maintaining high-quality smoothing. The first order approximation is used to estimate a solution of the proximal form in a half-quadratic solver, and also to derive a warm-start solution that can be calculated quickly when the image is loaded by the user. We extend the method to large-scale processing by estimating the smoothing operation with independent 1D convolution operations. This approach linearly scales to the size of the image and can fully take advantage of parallel processing. The method supports full color filtering and turns out to be temporally coherent for fast video processing. We demonstrate the performance of the proposed method on various applications including image smoothing, detail manipulation, HDR tone-mapping, fast edge simplification and video edge-aware processing.",
"title": "Fast Edge-Aware Processing via First Order Proximal Approximation",
"normalizedTitle": "Fast Edge-Aware Processing via First Order Proximal Approximation",
"fno": "07018984",
"hasPdf": true,
"idPrefix": "tg",
"keywords": [
"Smoothing Methods",
"Image Edge Detection",
"Equations",
"Mathematical Model",
"Laplace Equations",
"Approximation Methods",
"Convolution",
"Edge Aware Processing",
"Fast Image Smoothing",
"Sparsity",
"Edge Aware Processing",
"Fast Image Smoothing",
"Sparsity"
],
"authors": [
{
"givenName": "Hicham",
"surname": "Badri",
"fullName": "Hicham Badri",
"affiliation": "Bordeaux Sud-Ouest, INRIA, Talence, Aquitaine, France",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Hussein",
"surname": "Yahia",
"fullName": "Hussein Yahia",
"affiliation": "INRIA Bordeaux Sud-Ouest",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Driss",
"surname": "Aboutajdine",
"fullName": "Driss Aboutajdine",
"affiliation": "Mohammed V-Agdal University—LRIT, Associated Unit to CNRST (URAC 29)",
"__typename": "ArticleAuthorType"
}
],
"replicability": null,
"showBuyMe": true,
"showRecommendedArticles": true,
"isOpenAccess": false,
"issueNum": "06",
"pubDate": "2015-06-01 00:00:00",
"pubType": "trans",
"pages": "743-755",
"year": "2015",
"issn": "1077-2626",
"isbn": null,
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"recommendedArticles": [
{
"id": "proceedings/icpr/1994/6265/1/00576223",
"title": "Performance evaluation of first order operators",
"doi": null,
"abstractUrl": "/proceedings-article/icpr/1994/00576223/12OmNCcKQlX",
"parentPublication": {
"id": "proceedings/icpr/1994/6265/1",
"title": "Proceedings of 12th International Conference on Pattern Recognition",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icccnt/2013/3926/0/06726831",
"title": "Improving the denoising performance of Perona Malik filter using adaptive edge indicator",
"doi": null,
"abstractUrl": "/proceedings-article/icccnt/2013/06726831/12OmNCxbXDp",
"parentPublication": {
"id": "proceedings/icccnt/2013/3926/0",
"title": "2013 Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icpr/1992/2920/0/00202093",
"title": "A study on the forms of smoothing filters for step and ramp edge detection",
"doi": null,
"abstractUrl": "/proceedings-article/icpr/1992/00202093/12OmNwEJ0DT",
"parentPublication": {
"id": "proceedings/icpr/1992/2920/0",
"title": "11th IAPR International Conference on Pattern Recognition. Vol. III. Conference C: Image, Speech and Signal Analysis,",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icicta/2015/7644/0/7644a933",
"title": "The Application of Edge Detection Algorithm in Bedload Contour Extraction",
"doi": null,
"abstractUrl": "/proceedings-article/icicta/2015/7644a933/12OmNxw5B9x",
"parentPublication": {
"id": "proceedings/icicta/2015/7644/0",
"title": "2015 8th International Conference on Intelligent Computation Technology and Automation (ICICTA)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2014/5118/0/5118c838",
"title": "Edge-Aware Gradient Domain Optimization Framework for Image Filtering by Local Propagation",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2014/5118c838/12OmNyeECG5",
"parentPublication": {
"id": "proceedings/cvpr/2014/5118/0",
"title": "2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccnea/2017/3981/0/3981a183",
"title": "New Method of Image Smoothing and Edge Detection Based on Nonlinear Ambiguity Function",
"doi": null,
"abstractUrl": "/proceedings-article/iccnea/2017/3981a183/12OmNzC5T3d",
"parentPublication": {
"id": "proceedings/iccnea/2017/3981/0",
"title": "2017 International Conference on Computer Network, Electronic and Automation (ICCNEA)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tp/2013/06/ttp2013061397",
"title": "Guided Image Filtering",
"doi": null,
"abstractUrl": "/journal/tp/2013/06/ttp2013061397/13rRUxYrbNs",
"parentPublication": {
"id": "trans/tp",
"title": "IEEE Transactions on Pattern Analysis & Machine Intelligence",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/wi-iat/2022/9402/0/940200a315",
"title": "Pointspectrum: Equivariance Meets Laplacian Filtering for Graph Representation Learning",
"doi": null,
"abstractUrl": "/proceedings-article/wi-iat/2022/940200a315/1MBEFjeVoje",
"parentPublication": {
"id": "proceedings/wi-iat/2022/9402/0",
"title": "2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "mags/it/2019/03/08720136",
"title": "The Rise of Proximal Mobile Edge Servers",
"doi": null,
"abstractUrl": "/magazine/it/2019/03/08720136/1aeWSXFBlBK",
"parentPublication": {
"id": "mags/it",
"title": "IT Professional",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icdm/2020/8316/0/831600a731",
"title": "FeatureNorm: L2 Feature Normalization for Dynamic Graph Embedding",
"doi": null,
"abstractUrl": "/proceedings-article/icdm/2020/831600a731/1r54CyDOUFi",
"parentPublication": {
"id": "proceedings/icdm/2020/8316/0",
"title": "2020 IEEE International Conference on Data Mining (ICDM)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"adjacentArticles": {
"previous": {
"fno": "07015617",
"articleId": "13rRUwwJWFO",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "07018997",
"articleId": "13rRUy0HYRr",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"webExtras": [],
"articleVideos": []
}
|
{
"issue": {
"id": "12OmNwc3wwx",
"title": "PrePrints",
"year": "5555",
"issueNum": "01",
"idPrefix": "tq",
"pubType": "journal",
"volume": null,
"label": "PrePrints",
"downloadables": {
"hasCover": false,
"__typename": "PeriodicalIssueDownloadablesType"
},
"__typename": "PeriodicalIssue"
},
"article": {
"id": "1DSuouD5lh6",
"doi": "10.1109/TDSC.2022.3179698",
"abstract": "Increasingly, information systems rely on computational, storage, and network resources deployed in third-party facilities such as cloud centers and edge nodes. Such an approach further exacerbates cybersecurity concerns constantly raised by numerous incidents of security and privacy attacks resulting in data leakage and identity theft, among others. These have, in turn, forced the creation of stricter security and privacy-related regulations and have eroded the trust in cyberspace. In particular, security-related services and infrastructures, such as Certificate Authorities (CAs) that provide digital certificate services and Third-Party Authorities (TPAs) that provide cryptographic key services, are critical components for establishing trust in crypto-based privacy-preserving applications and services. To address such trust issues, various transparency frameworks and approaches have been recently proposed in the literature. This paper proposes TAB framework that provides transparency and trustworthiness of third-party authority and third-party facilities using blockchain techniques for emerging crypto-based privacy-preserving applications. TAB employs the Ethereum blockchain as the underlying public ledger and also includes a novel smart contract to automate accountability with an incentive mechanism that motivates users to participate in auditing, and punishes unintentional or malicious behaviors. We implement TAB and show through experimental evaluation in the Ethereum official test network, Rinkeby, that the framework is efficient. We also formally show the security guarantee provided by TAB, and analyze the privacy guarantee and trustworthiness it provides.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Increasingly, information systems rely on computational, storage, and network resources deployed in third-party facilities such as cloud centers and edge nodes. Such an approach further exacerbates cybersecurity concerns constantly raised by numerous incidents of security and privacy attacks resulting in data leakage and identity theft, among others. These have, in turn, forced the creation of stricter security and privacy-related regulations and have eroded the trust in cyberspace. In particular, security-related services and infrastructures, such as Certificate Authorities (CAs) that provide digital certificate services and Third-Party Authorities (TPAs) that provide cryptographic key services, are critical components for establishing trust in crypto-based privacy-preserving applications and services. To address such trust issues, various transparency frameworks and approaches have been recently proposed in the literature. This paper proposes TAB framework that provides transparency and trustworthiness of third-party authority and third-party facilities using blockchain techniques for emerging crypto-based privacy-preserving applications. TAB employs the Ethereum blockchain as the underlying public ledger and also includes a novel smart contract to automate accountability with an incentive mechanism that motivates users to participate in auditing, and punishes unintentional or malicious behaviors. We implement TAB and show through experimental evaluation in the Ethereum official test network, Rinkeby, that the framework is efficient. We also formally show the security guarantee provided by TAB, and analyze the privacy guarantee and trustworthiness it provides.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Increasingly, information systems rely on computational, storage, and network resources deployed in third-party facilities such as cloud centers and edge nodes. Such an approach further exacerbates cybersecurity concerns constantly raised by numerous incidents of security and privacy attacks resulting in data leakage and identity theft, among others. These have, in turn, forced the creation of stricter security and privacy-related regulations and have eroded the trust in cyberspace. In particular, security-related services and infrastructures, such as Certificate Authorities (CAs) that provide digital certificate services and Third-Party Authorities (TPAs) that provide cryptographic key services, are critical components for establishing trust in crypto-based privacy-preserving applications and services. To address such trust issues, various transparency frameworks and approaches have been recently proposed in the literature. This paper proposes TAB framework that provides transparency and trustworthiness of third-party authority and third-party facilities using blockchain techniques for emerging crypto-based privacy-preserving applications. TAB employs the Ethereum blockchain as the underlying public ledger and also includes a novel smart contract to automate accountability with an incentive mechanism that motivates users to participate in auditing, and punishes unintentional or malicious behaviors. We implement TAB and show through experimental evaluation in the Ethereum official test network, Rinkeby, that the framework is efficient. We also formally show the security guarantee provided by TAB, and analyze the privacy guarantee and trustworthiness it provides.",
"title": "Blockchain-based Transparency Framework for Privacy Preserving Third-party Services",
"normalizedTitle": "Blockchain-based Transparency Framework for Privacy Preserving Third-party Services",
"fno": "09787357",
"hasPdf": true,
"idPrefix": "tq",
"keywords": [
"Iron",
"Encryption",
"Blockchains",
"Public Key",
"Privacy",
"Smart Contracts",
"Behavioral Sciences",
"Transparency",
"Trustworthiness",
"Third Party Authority",
"Blockchain",
"Ethereum",
"Smart Contract",
"Functional Encryption"
],
"authors": [
{
"givenName": "Runhua",
"surname": "Xu",
"fullName": "Runhua Xu",
"affiliation": "IBM Research, San Jose, CA, United States, 95120",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Chao",
"surname": "Li",
"fullName": "Chao Li",
"affiliation": "Beijing Key Laboratory of Security and Privacy in Intelligent Transportation, Beijing Jiaotong University, Beijing, China, 100044",
"__typename": "ArticleAuthorType"
},
{
"givenName": "James",
"surname": "Joshi",
"fullName": "James Joshi",
"affiliation": "School of Computing and Information, University of Pittsburgh, Pittsburgh, PA, United States, 15260",
"__typename": "ArticleAuthorType"
}
],
"replicability": null,
"showBuyMe": true,
"showRecommendedArticles": true,
"isOpenAccess": false,
"issueNum": "01",
"pubDate": "2022-06-01 00:00:00",
"pubType": "trans",
"pages": "1-1",
"year": "5555",
"issn": "1545-5971",
"isbn": null,
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"recommendedArticles": [
{
"id": "proceedings/isme/2010/7669/2/05573901",
"title": "An Empirical Study of Online Third Party Guarantee Service Effect",
"doi": null,
"abstractUrl": "/proceedings-article/isme/2010/05573901/12OmNqHItB2",
"parentPublication": {
"id": "proceedings/isme/2010/7669/2",
"title": "2010 International Conference of Information Science and Management Engineering. ISME 2010",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/dsc/2017/1600/0/1600a431",
"title": "Anonymous Fair Exchange Protocol with a Semitrusted Third Party",
"doi": null,
"abstractUrl": "/proceedings-article/dsc/2017/1600a431/12OmNwJgANQ",
"parentPublication": {
"id": "proceedings/dsc/2017/1600/0",
"title": "2017 IEEE Second International Conference on Data Science in Cyberspace (DSC)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/sp/2012/4681/0/06234427",
"title": "Third-Party Web Tracking: Policy and Technology",
"doi": null,
"abstractUrl": "/proceedings-article/sp/2012/06234427/12OmNwseEXI",
"parentPublication": {
"id": "proceedings/sp/2012/4681/0",
"title": "2012 IEEE Symposium on Security and Privacy",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/apsec/2001/1408/0/14080431",
"title": "Framework for Third Party Testing of Component Software",
"doi": null,
"abstractUrl": "/proceedings-article/apsec/2001/14080431/12OmNxEBz8G",
"parentPublication": {
"id": "proceedings/apsec/2001/1408/0",
"title": "Proceedings Eighth Asia-Pacific Software Engineering Conference",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icme/2011/348/0/06012033",
"title": "A privacy preserving content distribution mechanism for drm without trusted third parties",
"doi": null,
"abstractUrl": "/proceedings-article/icme/2011/06012033/12OmNxvO02c",
"parentPublication": {
"id": "proceedings/icme/2011/348/0",
"title": "2011 IEEE International Conference on Multimedia and Expo",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/isme/2010/4132/2/4132b109",
"title": "An Empirical Study of Online Third Party Guarantee Service Effect",
"doi": null,
"abstractUrl": "/proceedings-article/isme/2010/4132b109/12OmNzvQHRm",
"parentPublication": {
"id": "proceedings/isme/2010/4132/2",
"title": "Information Science and Management Engineering, International Conference of",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/nt/2022/03/09686356",
"title": "PPQC: A Blockchain-Based Privacy-Preserving Quality Control Mechanism in Crowdsensing Applications",
"doi": null,
"abstractUrl": "/journal/nt/2022/03/09686356/1Ai9pD0iL84",
"parentPublication": {
"id": "trans/nt",
"title": "IEEE/ACM Transactions on Networking",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icbctis/2022/9691/0/969100a005",
"title": "Trustworthy Internet Based on Generalized Blockchain",
"doi": null,
"abstractUrl": "/proceedings-article/icbctis/2022/969100a005/1GqgZz8LyPS",
"parentPublication": {
"id": "proceedings/icbctis/2022/9691/0",
"title": "2022 International Conference on Blockchain Technology and Information Security (ICBCTIS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ispa-bdcloud-socialcom-sustaincom/2022/6497/0/649700a371",
"title": "A Blockchain-Based Multi-Cloud Storage Data Consistency Verification Scheme",
"doi": null,
"abstractUrl": "/proceedings-article/ispa-bdcloud-socialcom-sustaincom/2022/649700a371/1LKwoMBb3gs",
"parentPublication": {
"id": "proceedings/ispa-bdcloud-socialcom-sustaincom/2022/6497/0",
"title": "2022 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/trustcom/2020/4380/0/438000a683",
"title": "A Privacy-Preserving Data Collection and Processing Framework for Third-Party UAV Services",
"doi": null,
"abstractUrl": "/proceedings-article/trustcom/2020/438000a683/1r54d414iFq",
"parentPublication": {
"id": "proceedings/trustcom/2020/4380/0",
"title": "2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"adjacentArticles": {
"previous": {
"fno": "09786611",
"articleId": "1DQPDH0wh3y",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "09786740",
"articleId": "1DSuoGwfC4E",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"webExtras": [],
"articleVideos": []
}
|
{
"issue": {
"id": "12OmNwCsdFw",
"title": "PrePrints",
"year": "5555",
"issueNum": "01",
"idPrefix": "tk",
"pubType": "journal",
"volume": null,
"label": "PrePrints",
"downloadables": {
"hasCover": false,
"__typename": "PeriodicalIssueDownloadablesType"
},
"__typename": "PeriodicalIssue"
},
"article": {
"id": "1FJ0RquRClq",
"doi": "10.1109/TKDE.2022.3197985",
"abstract": "Recently, aligning users among different social networks has received significant attention. However, most of the existing studies do not consider users' behavior information during the aligning procedure and thus still suffer from poor learning performance. In fact, we observe that social network alignment and user behavior analysis can benefit from each other. Motivated by such an observation, we propose to jointly study the social network alignment and user behavior analysis problem in this paper. We design a novel framework named BANANA-RGB. In this framework, to capture users' multi-scale behavior information in each social network, we train a variant of the hierarchical periodic memory network with personalized memorization. To leverage behavior analysis for social network alignment, we design a tensor fusion network-based alignment component to improve the performance. To further leverage social network alignment for behavior analysis, we design a gating-based cross-network behavior fusion component to integrate users' behavior information in different social networks based on the alignment result. We iteratively train the above two components to make the two tasks benefit from each other. Extensive experiments on real-world datasets demonstrate that our proposed approach outperforms the state-of-the-art methods.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Recently, aligning users among different social networks has received significant attention. However, most of the existing studies do not consider users' behavior information during the aligning procedure and thus still suffer from poor learning performance. In fact, we observe that social network alignment and user behavior analysis can benefit from each other. Motivated by such an observation, we propose to jointly study the social network alignment and user behavior analysis problem in this paper. We design a novel framework named BANANA-RGB. In this framework, to capture users' multi-scale behavior information in each social network, we train a variant of the hierarchical periodic memory network with personalized memorization. To leverage behavior analysis for social network alignment, we design a tensor fusion network-based alignment component to improve the performance. To further leverage social network alignment for behavior analysis, we design a gating-based cross-network behavior fusion component to integrate users' behavior information in different social networks based on the alignment result. We iteratively train the above two components to make the two tasks benefit from each other. Extensive experiments on real-world datasets demonstrate that our proposed approach outperforms the state-of-the-art methods.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Recently, aligning users among different social networks has received significant attention. However, most of the existing studies do not consider users' behavior information during the aligning procedure and thus still suffer from poor learning performance. In fact, we observe that social network alignment and user behavior analysis can benefit from each other. Motivated by such an observation, we propose to jointly study the social network alignment and user behavior analysis problem in this paper. We design a novel framework named BANANA-RGB. In this framework, to capture users' multi-scale behavior information in each social network, we train a variant of the hierarchical periodic memory network with personalized memorization. To leverage behavior analysis for social network alignment, we design a tensor fusion network-based alignment component to improve the performance. To further leverage social network alignment for behavior analysis, we design a gating-based cross-network behavior fusion component to integrate users' behavior information in different social networks based on the alignment result. We iteratively train the above two components to make the two tasks benefit from each other. Extensive experiments on real-world datasets demonstrate that our proposed approach outperforms the state-of-the-art methods.",
"title": "When Behavior Analysis Meets Social Network Alignment",
"normalizedTitle": "When Behavior Analysis Meets Social Network Alignment",
"fno": "09854153",
"hasPdf": true,
"idPrefix": "tk",
"keywords": [
"Behavioral Sciences",
"Social Networking Online",
"Correlation",
"Data Mining",
"Tensors",
"Task Analysis",
"Predictive Analytics",
"Social Network Alignment",
"Behavior Analysis",
"Social Networks",
"Data Mining"
],
"authors": [
{
"givenName": "Zhongbao",
"surname": "Zhang",
"fullName": "Zhongbao Zhang",
"affiliation": "State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, China",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Fuxin",
"surname": "Ren",
"fullName": "Fuxin Ren",
"affiliation": "State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, China",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Jiawei",
"surname": "Zhang",
"fullName": "Jiawei Zhang",
"affiliation": "Department of Computer Science, University of California, Davis, USA",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Sen",
"surname": "Su",
"fullName": "Sen Su",
"affiliation": "State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, China",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Yang",
"surname": "Yan",
"fullName": "Yang Yan",
"affiliation": "State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, China",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Qian",
"surname": "Wei",
"fullName": "Qian Wei",
"affiliation": "State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, China",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Li",
"surname": "Sun",
"fullName": "Li Sun",
"affiliation": "School of Control and Computer Engineering, North China Electric Power University, China",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Guozhen",
"surname": "Zhu",
"fullName": "Guozhen Zhu",
"affiliation": "State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, China",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Congying",
"surname": "Guo",
"fullName": "Congying Guo",
"affiliation": "State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, China",
"__typename": "ArticleAuthorType"
}
],
"replicability": null,
"showBuyMe": true,
"showRecommendedArticles": true,
"isOpenAccess": false,
"issueNum": "01",
"pubDate": "2022-08-01 00:00:00",
"pubType": "trans",
"pages": "1-18",
"year": "5555",
"issn": "1041-4347",
"isbn": null,
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"recommendedArticles": [
{
"id": "trans/tk/2023/06/09716810",
"title": "Aligning Dynamic Social Networks: An Optimization Over Dynamic Graph Autoencoder",
"doi": null,
"abstractUrl": "/journal/tk/2023/06/09716810/1B5WxZW7dUQ",
"parentPublication": {
"id": "trans/tk",
"title": "IEEE Transactions on Knowledge & Data Engineering",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/sc/5555/01/09852695",
"title": "Time-aware Service Recommendation with Social-powered Graph Hierarchical Attention Network",
"doi": null,
"abstractUrl": "/journal/sc/5555/01/09852695/1FHlMV0x9Ly",
"parentPublication": {
"id": "trans/sc",
"title": "IEEE Transactions on Services Computing",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icim/2022/5174/0/517400a092",
"title": "Within-individual Increases in Solver Participation Level on Crowdsourcing Contests Platform: A Social Cognitive Perspective",
"doi": null,
"abstractUrl": "/proceedings-article/icim/2022/517400a092/1FHqDkgZCxi",
"parentPublication": {
"id": "proceedings/icim/2022/5174/0",
"title": "2022 8th International Conference on Information Management (ICIM)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ithings-greencom-cpscom-smartdata-cybermatics/2022/5417/0/541700a444",
"title": "Mining Composite Spatio-Temporal Lifestyle Patterns from Geotagged Social Data",
"doi": null,
"abstractUrl": "/proceedings-article/ithings-greencom-cpscom-smartdata-cybermatics/2022/541700a444/1HcmQIdzjLq",
"parentPublication": {
"id": "proceedings/ithings-greencom-cpscom-smartdata-cybermatics/2022/5417/0",
"title": "2022 IEEE International Conferences on Internet of Things (iThings) and IEEE Green Computing & Communications (GreenCom) and IEEE Cyber, Physical & Social Computing (CPSCom) and IEEE Smart Data (SmartData) and IEEE Congress on Cybermatics (Cybermatics)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/nana/2022/6131/0/613100a459",
"title": "An efficient method for restraining negative information cascades in online social networks",
"doi": null,
"abstractUrl": "/proceedings-article/nana/2022/613100a459/1JwPJnr4FVu",
"parentPublication": {
"id": "proceedings/nana/2022/6131/0",
"title": "2022 International Conference on Networking and Network Applications (NaNA)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icdmw/2022/4609/0/460900a475",
"title": "Personalized User Recommendation based on Various User Behavior in Local Domain",
"doi": null,
"abstractUrl": "/proceedings-article/icdmw/2022/460900a475/1KBqRLH7UhG",
"parentPublication": {
"id": "proceedings/icdmw/2022/4609/0",
"title": "2022 IEEE International Conference on Data Mining Workshops (ICDMW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ispa-bdcloud-socialcom-sustaincom/2022/6497/0/649700a621",
"title": "Learning Dynamic Behavior Patterns for Fraud Detection",
"doi": null,
"abstractUrl": "/proceedings-article/ispa-bdcloud-socialcom-sustaincom/2022/649700a621/1LKwrlsAjF6",
"parentPublication": {
"id": "proceedings/ispa-bdcloud-socialcom-sustaincom/2022/6497/0",
"title": "2022 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/hpcc-dss-smartcity-dependsys/2022/1993/0/199300a619",
"title": "Dynamic Behavior Pattern: Mining the Fraudsters in Telecom Network",
"doi": null,
"abstractUrl": "/proceedings-article/hpcc-dss-smartcity-dependsys/2022/199300a619/1LSPh3KinNm",
"parentPublication": {
"id": "proceedings/hpcc-dss-smartcity-dependsys/2022/1993/0",
"title": "2022 IEEE 24th Int Conf on High Performance Computing & Communications; 8th Int Conf on Data Science & Systems; 20th Int Conf on Smart City; 8th Int Conf on Dependability in Sensor, Cloud & Big Data Systems & Application (HPCC/DSS/SmartCity/DependSys)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/wi-iat/2022/9402/0/940200a519",
"title": "Social CRM to Reduce Perceived Risks in Online Purchase Behavior",
"doi": null,
"abstractUrl": "/proceedings-article/wi-iat/2022/940200a519/1MBEOYJK6ze",
"parentPublication": {
"id": "proceedings/wi-iat/2022/9402/0",
"title": "2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icscde/2021/0142/0/014200a188",
"title": "Interactive Behavior Analysis Based on Social Network",
"doi": null,
"abstractUrl": "/proceedings-article/icscde/2021/014200a188/1xtSDXTFp4s",
"parentPublication": {
"id": "proceedings/icscde/2021/0142/0",
"title": "2021 International Conference of Social Computing and Digital Economy (ICSCDE)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"adjacentArticles": {
"previous": {
"fno": "09852662",
"articleId": "1FHlNLEQbBu",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "09854129",
"articleId": "1FJ0RCmBTuE",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"webExtras": [],
"articleVideos": []
}
|
{
"issue": {
"id": "1Iu2NOQ0pXi",
"title": "Jan.",
"year": "2023",
"issueNum": "01",
"idPrefix": "td",
"pubType": "journal",
"volume": "34",
"label": "Jan.",
"downloadables": {
"hasCover": false,
"__typename": "PeriodicalIssueDownloadablesType"
},
"__typename": "PeriodicalIssue"
},
"article": {
"id": "1HQ8fynB0di",
"doi": "10.1109/TPDS.2022.3217824",
"abstract": "Tensor Cores have been an important unit to accelerate Fused Matrix Multiplication Accumulation (MMA) in all NVIDIA GPUs since Volta Architecture. To program Tensor Cores, users have to use either legacy wmma APIs or current mma APIs. Legacy wmma APIs are more easy-to-use but can only exploit limited features and power of Tensor Cores. Specifically, wmma APIs support fewer operand shapes and can not leverage the new sparse matrix multiplication feature of the newest Ampere Tensor Cores. However, the performance of current programming interface has not been well explored. Furthermore, the computation numeric behaviors of low-precision floating points (TF32, BF16, and FP16) supported by the newest Ampere Tensor Cores are also mysterious. In this paper, we explore the throughput and latency of current programming APIs. We also intuitively study the numeric behaviors of Tensor Cores MMA and profile the intermediate operations including multiplication, addition of inner product, and accumulation. All codes used in this work can be found in <uri>https://github.com/sunlex0717/DissectingTensorCores</uri>.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Tensor Cores have been an important unit to accelerate Fused Matrix Multiplication Accumulation (MMA) in all NVIDIA GPUs since Volta Architecture. To program Tensor Cores, users have to use either legacy wmma APIs or current mma APIs. Legacy wmma APIs are more easy-to-use but can only exploit limited features and power of Tensor Cores. Specifically, wmma APIs support fewer operand shapes and can not leverage the new sparse matrix multiplication feature of the newest Ampere Tensor Cores. However, the performance of current programming interface has not been well explored. Furthermore, the computation numeric behaviors of low-precision floating points (TF32, BF16, and FP16) supported by the newest Ampere Tensor Cores are also mysterious. In this paper, we explore the throughput and latency of current programming APIs. We also intuitively study the numeric behaviors of Tensor Cores MMA and profile the intermediate operations including multiplication, addition of inner product, and accumulation. All codes used in this work can be found in <uri>https://github.com/sunlex0717/DissectingTensorCores</uri>.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Tensor Cores have been an important unit to accelerate Fused Matrix Multiplication Accumulation (MMA) in all NVIDIA GPUs since Volta Architecture. To program Tensor Cores, users have to use either legacy wmma APIs or current mma APIs. Legacy wmma APIs are more easy-to-use but can only exploit limited features and power of Tensor Cores. Specifically, wmma APIs support fewer operand shapes and can not leverage the new sparse matrix multiplication feature of the newest Ampere Tensor Cores. However, the performance of current programming interface has not been well explored. Furthermore, the computation numeric behaviors of low-precision floating points (TF32, BF16, and FP16) supported by the newest Ampere Tensor Cores are also mysterious. In this paper, we explore the throughput and latency of current programming APIs. We also intuitively study the numeric behaviors of Tensor Cores MMA and profile the intermediate operations including multiplication, addition of inner product, and accumulation. All codes used in this work can be found in https://github.com/sunlex0717/DissectingTensorCores.",
"title": "Dissecting Tensor Cores via Microbenchmarks: Latency, Throughput and Numeric Behaviors",
"normalizedTitle": "Dissecting Tensor Cores via Microbenchmarks: Latency, Throughput and Numeric Behaviors",
"fno": "09931992",
"hasPdf": true,
"idPrefix": "td",
"keywords": [
"Application Program Interfaces",
"Floating Point Arithmetic",
"Graphics Processing Units",
"Matrix Multiplication",
"Sparse Matrices",
"Tensors",
"Ampere Tensor Cores",
"Computation Numeric Behaviors",
"Current Mma AP Is",
"Legacy Wmma AP Is",
"Low Precision Floating Points",
"Matrix Multiplication Accumulation",
"NVIDIA GP Us",
"Sparse Matrix Multiplication Feature",
"Tensor Cores MMA",
"Volta Architecture",
"Tensors",
"Graphics Processing Units",
"Programming",
"Hidden Markov Models",
"Computer Architecture",
"Behavioral Sciences",
"Registers",
"GPU",
"Tensor Cores",
"Numeric Profiling",
"Ampere",
"Turing",
"Microbenchmark"
],
"authors": [
{
"givenName": "Wei",
"surname": "Sun",
"fullName": "Wei Sun",
"affiliation": "Electronic System Group, Eindhoven University of Technology, Eindhoven, AZ, The Netherlands",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Ang",
"surname": "Li",
"fullName": "Ang Li",
"affiliation": "Physical and Computational Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, USA",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Tong",
"surname": "Geng",
"fullName": "Tong Geng",
"affiliation": "Physical and Computational Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, USA",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Sander",
"surname": "Stuijk",
"fullName": "Sander Stuijk",
"affiliation": "Electronic System Group, Eindhoven University of Technology, Eindhoven, AZ, The Netherlands",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Henk",
"surname": "Corporaal",
"fullName": "Henk Corporaal",
"affiliation": "Electronic System Group, Eindhoven University of Technology, Eindhoven, AZ, The Netherlands",
"__typename": "ArticleAuthorType"
}
],
"replicability": null,
"showBuyMe": true,
"showRecommendedArticles": true,
"isOpenAccess": false,
"issueNum": "01",
"pubDate": "2023-01-01 00:00:00",
"pubType": "trans",
"pages": "246-261",
"year": "2023",
"issn": "1045-9219",
"isbn": null,
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"recommendedArticles": [
{
"id": "proceedings/iceccs/2022/0162/0/016200a189",
"title": "Extending Tensor Virtual Machine to Support Deep-Learning Accelerators with Convolution Cores",
"doi": null,
"abstractUrl": "/proceedings-article/iceccs/2022/016200a189/1D4NnNyCenC",
"parentPublication": {
"id": "proceedings/iceccs/2022/0162/0",
"title": "2022 26th International Conference on Engineering of Complex Computer Systems (ICECCS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/sc/2021/8442/0/09910132",
"title": "APNN-TC: Accelerating Arbitrary Precision Neural Networks on Ampere GPU Tensor Cores",
"doi": null,
"abstractUrl": "/proceedings-article/sc/2021/09910132/1HzBHL46Dks",
"parentPublication": {
"id": "proceedings/sc/2021/8442/0",
"title": "SC21: International Conference for High Performance Computing, Networking, Storage and Analysis",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/isvlsi/2022/6605/0/660500a223",
"title": "Improving GPU Throughput through Parallel Execution Using Tensor Cores and CUDA Cores",
"doi": null,
"abstractUrl": "/proceedings-article/isvlsi/2022/660500a223/1HzC1V495Ze",
"parentPublication": {
"id": "proceedings/isvlsi/2022/6605/0",
"title": "2022 IEEE Computer Society Annual Symposium on VLSI (ISVLSI)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/sc/2022/5444/0/544400a509",
"title": "Efficient Quantized Sparse Matrix Operations on Tensor Cores",
"doi": null,
"abstractUrl": "/proceedings-article/sc/2022/544400a509/1I0bSXj3pVm",
"parentPublication": {
"id": "proceedings/sc/2022/5444/0/",
"title": "SC22: International Conference for High Performance Computing, Networking, Storage and Analysis",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tc/2023/06/09987675",
"title": "High-Performance Tensor Learning Primitives Using GPU Tensor Cores",
"doi": null,
"abstractUrl": "/journal/tc/2023/06/09987675/1J7RPYvN6YU",
"parentPublication": {
"id": "trans/tc",
"title": "IEEE Transactions on Computers",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/sc/2022/5444/0/544400a509",
"title": "Efficient Quantized Sparse Matrix Operations on Tensor Cores",
"doi": null,
"abstractUrl": "/proceedings-article/sc/2022/544400a509/1L07tZkoN6E",
"parentPublication": {
"id": "proceedings/sc/2022/5444/0/",
"title": "SC22: International Conference for High Performance Computing, Networking, Storage and Analysis",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/hipc/2022/9423/0/942300a135",
"title": "Leveraging GPU Tensor Cores for Double Precision Euclidean Distance Calculations",
"doi": null,
"abstractUrl": "/proceedings-article/hipc/2022/942300a135/1MEXfs1oJWM",
"parentPublication": {
"id": "proceedings/hipc/2022/9423/0",
"title": "2022 IEEE 29th International Conference on High Performance Computing, Data, and Analytics (HiPC)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ipdps/2020/6876/0/09139835",
"title": "Demystifying Tensor Cores to Optimize Half-Precision Matrix Multiply",
"doi": null,
"abstractUrl": "/proceedings-article/ipdps/2020/09139835/1lss7yS2gAU",
"parentPublication": {
"id": "proceedings/ipdps/2020/6876/0",
"title": "2020 IEEE International Parallel and Distributed Processing Symposium (IPDPS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/micro/2020/7383/0/738300a725",
"title": "Duplo: Lifting Redundant Memory Accesses of Deep Neural Networks for GPU Tensor Cores",
"doi": null,
"abstractUrl": "/proceedings-article/micro/2020/738300a725/1oFGBI8PUek",
"parentPublication": {
"id": "proceedings/micro/2020/7383/0",
"title": "2020 53rd Annual IEEE/ACM International Symposium on Microarchitecture (MICRO)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ipdps/2021/4066/0/406600a507",
"title": "Accelerating non-power-of-2 size Fourier transforms with GPU Tensor Cores",
"doi": null,
"abstractUrl": "/proceedings-article/ipdps/2021/406600a507/1uOw61PIheE",
"parentPublication": {
"id": "proceedings/ipdps/2021/4066/0",
"title": "2021 IEEE International Parallel and Distributed Processing Symposium (IPDPS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"adjacentArticles": {
"previous": {
"fno": "09925644",
"articleId": "1HCR6qtPJKM",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "09932018",
"articleId": "1HQ8fofZTCE",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"webExtras": [
{
"id": "1Iu2QPtgrni",
"name": "ttd202301-09931992s1-supp1-3217824.pdf",
"location": "https://www.computer.org/csdl/api/v1/extra/ttd202301-09931992s1-supp1-3217824.pdf",
"extension": "pdf",
"size": "56.1 kB",
"__typename": "WebExtraType"
}
],
"articleVideos": []
}
|
{
"issue": {
"id": "12OmNznkK71",
"title": "Dec.",
"year": "2018",
"issueNum": "04",
"idPrefix": "bd",
"pubType": "journal",
"volume": "4",
"label": "Dec.",
"downloadables": {
"hasCover": false,
"__typename": "PeriodicalIssueDownloadablesType"
},
"__typename": "PeriodicalIssue"
},
"article": {
"id": "17D45Xq6dD1",
"doi": "10.1109/TBDATA.2017.2723899",
"abstract": "Many countries are suffering from severe air pollution. Understanding how different air pollutants accumulate and propagate is critical to making relevant public policies. In this paper, we use urban big data (air quality data and meteorological data) to identify the spatiotemporal (ST) causal pathways for air pollutants. This problem is challenging because: (1) there are numerous noisy and low-pollution periods in the raw air quality data, which may lead to unreliable causality analysis; (2) for large-scale data in the ST space, the computational complexity of constructing a causal structure is very high; and (3) the ST causal pathways are complex due to the interactions of multiple pollutants and the influence of environmental factors. Therefore, we present pg-Causality, a novel pattern-aided graphical causality analysis approach that combines the strengths of pattern mining and Bayesian learning to efficiently identify the ST causal pathways. First, pattern mining helps suppress the noise by capturing frequent evolving patterns (FEPs) of each monitoring sensor, and greatly reduce the complexity by selecting the pattern-matched sensors as “causers”. Then, Bayesian learning carefully encodes the local and ST causal relations with a Gaussian Bayesian Network (GBN)-based graphical model, which also integrates environmental influences to minimize biases in the final results. We evaluate our approach with three real-world data sets containing 982 air quality sensors in 128 cities, in three regions of China from 01-Jun-2013 to 31-Dec-2016. Results show that our approach outperforms the traditional causal structure learning methods in time efficiency, inference accuracy and interpretability.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Many countries are suffering from severe air pollution. Understanding how different air pollutants accumulate and propagate is critical to making relevant public policies. In this paper, we use urban big data (air quality data and meteorological data) to identify the spatiotemporal (ST) causal pathways for air pollutants. This problem is challenging because: (1) there are numerous noisy and low-pollution periods in the raw air quality data, which may lead to unreliable causality analysis; (2) for large-scale data in the ST space, the computational complexity of constructing a causal structure is very high; and (3) the ST causal pathways are complex due to the interactions of multiple pollutants and the influence of environmental factors. Therefore, we present pg-Causality, a novel pattern-aided graphical causality analysis approach that combines the strengths of pattern mining and Bayesian learning to efficiently identify the ST causal pathways. First, pattern mining helps suppress the noise by capturing frequent evolving patterns (FEPs) of each monitoring sensor, and greatly reduce the complexity by selecting the pattern-matched sensors as “causers”. Then, Bayesian learning carefully encodes the local and ST causal relations with a Gaussian Bayesian Network (GBN)-based graphical model, which also integrates environmental influences to minimize biases in the final results. We evaluate our approach with three real-world data sets containing 982 air quality sensors in 128 cities, in three regions of China from 01-Jun-2013 to 31-Dec-2016. Results show that our approach outperforms the traditional causal structure learning methods in time efficiency, inference accuracy and interpretability.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Many countries are suffering from severe air pollution. Understanding how different air pollutants accumulate and propagate is critical to making relevant public policies. In this paper, we use urban big data (air quality data and meteorological data) to identify the spatiotemporal (ST) causal pathways for air pollutants. This problem is challenging because: (1) there are numerous noisy and low-pollution periods in the raw air quality data, which may lead to unreliable causality analysis; (2) for large-scale data in the ST space, the computational complexity of constructing a causal structure is very high; and (3) the ST causal pathways are complex due to the interactions of multiple pollutants and the influence of environmental factors. Therefore, we present pg-Causality, a novel pattern-aided graphical causality analysis approach that combines the strengths of pattern mining and Bayesian learning to efficiently identify the ST causal pathways. First, pattern mining helps suppress the noise by capturing frequent evolving patterns (FEPs) of each monitoring sensor, and greatly reduce the complexity by selecting the pattern-matched sensors as “causers”. Then, Bayesian learning carefully encodes the local and ST causal relations with a Gaussian Bayesian Network (GBN)-based graphical model, which also integrates environmental influences to minimize biases in the final results. We evaluate our approach with three real-world data sets containing 982 air quality sensors in 128 cities, in three regions of China from 01-Jun-2013 to 31-Dec-2016. Results show that our approach outperforms the traditional causal structure learning methods in time efficiency, inference accuracy and interpretability.",
"title": "pg-Causality: Identifying Spatiotemporal Causal Pathways for Air Pollutants with Urban Big Data",
"normalizedTitle": "pg-Causality: Identifying Spatiotemporal Causal Pathways for Air Pollutants with Urban Big Data",
"fno": "07970191",
"hasPdf": true,
"idPrefix": "bd",
"keywords": [
"Air Pollution Control",
"Air Quality",
"Bayes Methods",
"Belief Networks",
"Big Data",
"Computational Complexity",
"Data Mining",
"Environmental Science Computing",
"Gaussian Processes",
"Inference Mechanisms",
"Learning Artificial Intelligence",
"Noise Abatement",
"Town And Country Planning",
"Pg Causality",
"Urban Big Data",
"Meteorological Data",
"Pattern Mining",
"Frequent Evolving Patterns",
"ST Causal Relations",
"Air Pollution",
"Public Policies",
"Air Quality Sensors",
"Air Pollutants Accumulate",
"Pattern Aided Graphical Causality Analysis Approach",
"Gaussian Bayesian Network",
"Causal Structure Learning Methods",
"Spatiotemporal Causal Pathways",
"Computational Complexity",
"Environmental Factors",
"Bayesian Learning",
"FE Ps",
"China",
"Inference Accuracy",
"Air Pollution",
"Bayes Methods",
"Data Mining",
"Atmospheric Modeling",
"Time Series Analysis",
"Big Data",
"Urban Areas",
"Causality",
"Pattern Mining",
"Bayesian Learning",
"Spatiotemporal ST Big Data",
"Urban Computing"
],
"authors": [
{
"givenName": "Julie Yixuan",
"surname": "Zhu",
"fullName": "Julie Yixuan Zhu",
"affiliation": "Department of Electrical and Electronic Engineering, University of Hong Kong, Pok Fu Lam, Hong Kong",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Chao",
"surname": "Zhang",
"fullName": "Chao Zhang",
"affiliation": "Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Huichu",
"surname": "Zhang",
"fullName": "Huichu Zhang",
"affiliation": "Apex Data & Knowledge Management Lab, Shanghai Jiao Tong University, Shanghai, China",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Shi",
"surname": "Zhi",
"fullName": "Shi Zhi",
"affiliation": "Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Victor O.K.",
"surname": "Li",
"fullName": "Victor O.K. Li",
"affiliation": "Department of Electrical and Electronic Engineering, University of Hong Kong, Pok Fu Lam, Hong Kong",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Jiawei",
"surname": "Han",
"fullName": "Jiawei Han",
"affiliation": "Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Yu",
"surname": "Zheng",
"fullName": "Yu Zheng",
"affiliation": "Microsoft Research, Beijing, China",
"__typename": "ArticleAuthorType"
}
],
"replicability": null,
"showBuyMe": true,
"showRecommendedArticles": true,
"isOpenAccess": false,
"issueNum": "04",
"pubDate": "2018-10-01 00:00:00",
"pubType": "trans",
"pages": "571-585",
"year": "2018",
"issn": "2332-7790",
"isbn": null,
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"recommendedArticles": [
{
"id": "proceedings/snpd/2015/8676/0/07176184",
"title": "Analysis and classification of respiratory health risks with respect to air pollution levels",
"doi": null,
"abstractUrl": "/proceedings-article/snpd/2015/07176184/12OmNAgGwha",
"parentPublication": {
"id": "proceedings/snpd/2015/8676/0",
"title": "2015 16th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/bd/2017/03/07817820",
"title": "An Extended Spatio-Temporal Granger Causality Model for Air Quality Estimation with Heterogeneous Urban Big Data",
"doi": null,
"abstractUrl": "/journal/bd/2017/03/07817820/13rRUwvT9lg",
"parentPublication": {
"id": "trans/bd",
"title": "IEEE Transactions on Big Data",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "mags/ex/2016/03/mex2016030108",
"title": "A Web-Based Approach for Classifying Environmental Pollutants Using Portable E-nose Devices",
"doi": null,
"abstractUrl": "/magazine/ex/2016/03/mex2016030108/13rRUxYIMQX",
"parentPublication": {
"id": "mags/ex",
"title": "IEEE Intelligent Systems",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/sccic/2018/6208/0/08584551",
"title": "A Hybrid Measurement Kit for Real-time Air Quality Monitoring Across Senegal Cities",
"doi": null,
"abstractUrl": "/proceedings-article/sccic/2018/08584551/17D45W9KVHc",
"parentPublication": {
"id": "proceedings/sccic/2018/6208/0",
"title": "2018 1st International Conference on Smart Cities and Communities (SCCIC)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cse-euc/2017/3220/1/08005841",
"title": "Impact Analysis of Air Pollutants on the Air Quality Index in Jinan Winter",
"doi": null,
"abstractUrl": "/proceedings-article/cse-euc/2017/08005841/17D45XacGk3",
"parentPublication": {
"id": "proceedings/cse-euc/2017/3220/1",
"title": "2017 IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/itme/2018/7744/0/774400a275",
"title": "Relationship Between Air Pollutants and Outpatient Visits for Respiratory Diseases in Hangzhou",
"doi": null,
"abstractUrl": "/proceedings-article/itme/2018/774400a275/17D45XeKgvP",
"parentPublication": {
"id": "proceedings/itme/2018/7744/0",
"title": "2018 9th International Conference on Information Technology in Medicine and Education (ITME)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icisce/2018/5500/0/550000a407",
"title": "Quantitative Correlation and Causality Analysis on the Cause of Haze in Henan Province",
"doi": null,
"abstractUrl": "/proceedings-article/icisce/2018/550000a407/17D45Xq6dAV",
"parentPublication": {
"id": "proceedings/icisce/2018/5500/0",
"title": "2018 5th International Conference on Information Science and Control Engineering (ICISCE)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icisce/2018/5500/0/550000a301",
"title": "Long Term Causality Analyses of Industrial Pollutants and Meteorological Factors on PM2.5 Concentrations in Zhejiang Province",
"doi": null,
"abstractUrl": "/proceedings-article/icisce/2018/550000a301/17D45XuDNGZ",
"parentPublication": {
"id": "proceedings/icisce/2018/5500/0",
"title": "2018 5th International Conference on Information Science and Control Engineering (ICISCE)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iri/2022/6603/0/660300a069",
"title": "Multilayer Meta-Learning Approach to Forecasting Air Pollutants",
"doi": null,
"abstractUrl": "/proceedings-article/iri/2022/660300a069/1GvdMinpknS",
"parentPublication": {
"id": "proceedings/iri/2022/6603/0",
"title": "2022 IEEE 23rd International Conference on Information Reuse and Integration for Data Science (IRI)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/2022/01/09557222",
"title": "Compass: Towards Better Causal Analysis of Urban Time Series",
"doi": null,
"abstractUrl": "/journal/tg/2022/01/09557222/1xlvZ1jrwLC",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"adjacentArticles": {
"previous": {
"fno": "07954992",
"articleId": "17D45Vu1TyX",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "08012464",
"articleId": "17D45Xi9rWt",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"webExtras": [],
"articleVideos": []
}
|
{
"issue": {
"id": "1zBamVZHyne",
"title": "Jan.",
"year": "2022",
"issueNum": "01",
"idPrefix": "tg",
"pubType": "journal",
"volume": "28",
"label": "Jan.",
"downloadables": {
"hasCover": false,
"__typename": "PeriodicalIssueDownloadablesType"
},
"__typename": "PeriodicalIssue"
},
"article": {
"id": "1xlvZ1jrwLC",
"doi": "10.1109/TVCG.2021.3114875",
"abstract": "The spatial time series generated by city sensors allow us to observe urban phenomena like environmental pollution and traffic congestion at an unprecedented scale. However, recovering causal relations from these observations to explain the sources of urban phenomena remains a challenging task because these causal relations tend to be time-varying and demand proper time series partitioning for effective analyses. The prior approaches extract one causal graph given long-time observations, which cannot be directly applied to capturing, interpreting, and validating dynamic urban causality. This paper presents Compass, a novel visual analytics approach for in-depth analyses of the dynamic causality in urban time series. To develop Compass, we identify and address three challenges: detecting urban causality, interpreting dynamic causal relations, and unveiling suspicious causal relations. First, multiple causal graphs over time among urban time series are obtained with a causal detection framework extended from the Granger causality test. Then, a dynamic causal graph visualization is designed to reveal the time-varying causal relations across these causal graphs and facilitate the exploration of the graphs along the time. Finally, a tailored multi-dimensional visualization is developed to support the identification of spurious causal relations, thereby improving the reliability of causal analyses. The effectiveness of Compass is evaluated with two case studies conducted on the real-world urban datasets, including the air pollution and traffic speed datasets, and positive feedback was received from domain experts.",
"abstracts": [
{
"abstractType": "Regular",
"content": "The spatial time series generated by city sensors allow us to observe urban phenomena like environmental pollution and traffic congestion at an unprecedented scale. However, recovering causal relations from these observations to explain the sources of urban phenomena remains a challenging task because these causal relations tend to be time-varying and demand proper time series partitioning for effective analyses. The prior approaches extract one causal graph given long-time observations, which cannot be directly applied to capturing, interpreting, and validating dynamic urban causality. This paper presents Compass, a novel visual analytics approach for in-depth analyses of the dynamic causality in urban time series. To develop Compass, we identify and address three challenges: detecting urban causality, interpreting dynamic causal relations, and unveiling suspicious causal relations. First, multiple causal graphs over time among urban time series are obtained with a causal detection framework extended from the Granger causality test. Then, a dynamic causal graph visualization is designed to reveal the time-varying causal relations across these causal graphs and facilitate the exploration of the graphs along the time. Finally, a tailored multi-dimensional visualization is developed to support the identification of spurious causal relations, thereby improving the reliability of causal analyses. The effectiveness of Compass is evaluated with two case studies conducted on the real-world urban datasets, including the air pollution and traffic speed datasets, and positive feedback was received from domain experts.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "The spatial time series generated by city sensors allow us to observe urban phenomena like environmental pollution and traffic congestion at an unprecedented scale. However, recovering causal relations from these observations to explain the sources of urban phenomena remains a challenging task because these causal relations tend to be time-varying and demand proper time series partitioning for effective analyses. The prior approaches extract one causal graph given long-time observations, which cannot be directly applied to capturing, interpreting, and validating dynamic urban causality. This paper presents Compass, a novel visual analytics approach for in-depth analyses of the dynamic causality in urban time series. To develop Compass, we identify and address three challenges: detecting urban causality, interpreting dynamic causal relations, and unveiling suspicious causal relations. First, multiple causal graphs over time among urban time series are obtained with a causal detection framework extended from the Granger causality test. Then, a dynamic causal graph visualization is designed to reveal the time-varying causal relations across these causal graphs and facilitate the exploration of the graphs along the time. Finally, a tailored multi-dimensional visualization is developed to support the identification of spurious causal relations, thereby improving the reliability of causal analyses. The effectiveness of Compass is evaluated with two case studies conducted on the real-world urban datasets, including the air pollution and traffic speed datasets, and positive feedback was received from domain experts.",
"title": "Compass: Towards Better Causal Analysis of Urban Time Series",
"normalizedTitle": "Compass: Towards Better Causal Analysis of Urban Time Series",
"fno": "09557222",
"hasPdf": true,
"idPrefix": "tg",
"keywords": [
"Time Series Analysis",
"Visual Analytics",
"Compass",
"Air Pollution",
"Correlation",
"Urban Planning",
"Indexes",
"Visual Causal Analysis",
"Urban Time Series",
"Causal Graph Analysis"
],
"authors": [
{
"givenName": "Zikun",
"surname": "Deng",
"fullName": "Zikun Deng",
"affiliation": "State Key Lab of CAD & CG, Zhejiang University, Hangzhou and Zhejiang Lab, Hangzhou, China",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Di",
"surname": "Weng",
"fullName": "Di Weng",
"affiliation": "State Key Lab of CAD & CG, Zhejiang University, Hangzhou and Zhejiang Lab, Hangzhou, China",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Xiao",
"surname": "Xie",
"fullName": "Xiao Xie",
"affiliation": "Department of Sport Science, Zhejiang University, Hangzhou, China",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Jie",
"surname": "Bao",
"fullName": "Jie Bao",
"affiliation": "JD Intelligent Cities Research, JD Tech, Beijing, China",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Yu",
"surname": "Zheng",
"fullName": "Yu Zheng",
"affiliation": "JD Intelligent Cities Research, JD Tech, Beijing, China",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Mingliang",
"surname": "Xu",
"fullName": "Mingliang Xu",
"affiliation": "School of Information Engineering, Zhengzhou University, Zhengzhou, China",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Wei",
"surname": "Chen",
"fullName": "Wei Chen",
"affiliation": "State Key Lab of CAD & CG, Zhejiang University, Hangzhou and Zhejiang Lab, Hangzhou, China",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Yingcai",
"surname": "Wu",
"fullName": "Yingcai Wu",
"affiliation": "State Key Lab of CAD & CG, Zhejiang University, Hangzhou and Zhejiang Lab, Hangzhou, China",
"__typename": "ArticleAuthorType"
}
],
"replicability": null,
"showBuyMe": true,
"showRecommendedArticles": true,
"isOpenAccess": false,
"issueNum": "01",
"pubDate": "2022-01-01 00:00:00",
"pubType": "trans",
"pages": "1051-1061",
"year": "2022",
"issn": "1077-2626",
"isbn": null,
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"recommendedArticles": [
{
"id": "proceedings/iiai-aai/2017/0621/0/0621a814",
"title": "Construction of Linked Urban Problem Data with Causal Relations Using Crowdsourcing",
"doi": null,
"abstractUrl": "/proceedings-article/iiai-aai/2017/0621a814/12OmNylboJO",
"parentPublication": {
"id": "proceedings/iiai-aai/2017/0621/0",
"title": "2017 6th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ictai/2002/1849/0/18490375",
"title": "TimeSleuth: A Tool for Discovering Causal and Temporal Rules",
"doi": null,
"abstractUrl": "/proceedings-article/ictai/2002/18490375/12OmNyrIaJZ",
"parentPublication": {
"id": "proceedings/ictai/2002/1849/0",
"title": "14th IEEE International Conference on Tools with Artificial Intelligence, 2002. (ICTAI 2002). Proceedings.",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/bd/2017/03/07817820",
"title": "An Extended Spatio-Temporal Granger Causality Model for Air Quality Estimation with Heterogeneous Urban Big Data",
"doi": null,
"abstractUrl": "/journal/bd/2017/03/07817820/13rRUwvT9lg",
"parentPublication": {
"id": "trans/bd",
"title": "IEEE Transactions on Big Data",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tb/2019/05/08307225",
"title": "Comorbidity Scoring with Causal Disease Networks",
"doi": null,
"abstractUrl": "/journal/tb/2019/05/08307225/13rRUx0geys",
"parentPublication": {
"id": "trans/tb",
"title": "IEEE/ACM Transactions on Computational Biology and Bioinformatics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/bd/2018/04/07970191",
"title": "pg-Causality: Identifying Spatiotemporal Causal Pathways for Air Pollutants with Urban Big Data",
"doi": null,
"abstractUrl": "/journal/bd/2018/04/07970191/17D45Xq6dD1",
"parentPublication": {
"id": "trans/bd",
"title": "IEEE Transactions on Big Data",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cain/2022/9275/0/927500a043",
"title": "Structural Causal Models as Boundary Objects in AI System Development",
"doi": null,
"abstractUrl": "/proceedings-article/cain/2022/927500a043/1Ehsjt2rGRq",
"parentPublication": {
"id": "proceedings/cain/2022/9275/0",
"title": "2022 IEEE/ACM 1st International Conference on AI Engineering – Software Engineering for AI (CAIN)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/5555/01/09895311",
"title": "DOMINO: Visual Causal Reasoning With Time-Dependent Phenomena",
"doi": null,
"abstractUrl": "/journal/tg/5555/01/09895311/1GNprsVfaFi",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ickg/2020/8156/0/09194510",
"title": "Causal Extraction from the Literature of Pressure Injury and Risk Factors",
"doi": null,
"abstractUrl": "/proceedings-article/ickg/2020/09194510/1n2nkO9teN2",
"parentPublication": {
"id": "proceedings/ickg/2020/8156/0",
"title": "2020 IEEE International Conference on Knowledge Graph (ICKG)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/2021/02/09216629",
"title": "A Visual Analytics Approach for Exploratory Causal Analysis: Exploration, Validation, and Applications",
"doi": null,
"abstractUrl": "/journal/tg/2021/02/09216629/1nJsGFc8lUY",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icdm/2020/8316/0/831600a972",
"title": "Inductive Granger Causal Modeling for Multivariate Time Series",
"doi": null,
"abstractUrl": "/proceedings-article/icdm/2020/831600a972/1r54F2RY7qE",
"parentPublication": {
"id": "proceedings/icdm/2020/8316/0",
"title": "2020 IEEE International Conference on Data Mining (ICDM)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"adjacentArticles": {
"previous": {
"fno": "09552909",
"articleId": "1xibW2zLd9C",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "09552191",
"articleId": "1xic2jmfPOg",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"webExtras": [
{
"id": "1zBaxoBpmVi",
"name": "ttg202201-09557222s1-supp1-3114875.mp4",
"location": "https://www.computer.org/csdl/api/v1/extra/ttg202201-09557222s1-supp1-3114875.mp4",
"extension": "mp4",
"size": "35.7 MB",
"__typename": "WebExtraType"
}
],
"articleVideos": []
}
|
{
"issue": {
"id": "12OmNzICEFz",
"title": "July-Sept.",
"year": "2017",
"issueNum": "03",
"idPrefix": "th",
"pubType": "journal",
"volume": "10",
"label": "July-Sept.",
"downloadables": {
"hasCover": false,
"__typename": "PeriodicalIssueDownloadablesType"
},
"__typename": "PeriodicalIssue"
},
"article": {
"id": "13rRUxNW1TZ",
"doi": "10.1109/TOH.2017.2650221",
"abstract": "This survey provides an overview of work on haptic technology for social touch. Social touch has been studied extensively in psychology and neuroscience. With the development of new technologies, it is now possible to engage in social touch at a distance or engage in social touch with artificial social agents. Social touch research has inspired research into technology mediated social touch, and this line of research has found effects similar to actual social touch. The importance of haptic stimulus qualities, multimodal cues, and contextual factors in technology mediated social touch is discussed. This survey is concluded by reflecting on the current state of research into social touch technology, and providing suggestions for future research and applications.",
"abstracts": [
{
"abstractType": "Regular",
"content": "This survey provides an overview of work on haptic technology for social touch. Social touch has been studied extensively in psychology and neuroscience. With the development of new technologies, it is now possible to engage in social touch at a distance or engage in social touch with artificial social agents. Social touch research has inspired research into technology mediated social touch, and this line of research has found effects similar to actual social touch. The importance of haptic stimulus qualities, multimodal cues, and contextual factors in technology mediated social touch is discussed. This survey is concluded by reflecting on the current state of research into social touch technology, and providing suggestions for future research and applications.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "This survey provides an overview of work on haptic technology for social touch. Social touch has been studied extensively in psychology and neuroscience. With the development of new technologies, it is now possible to engage in social touch at a distance or engage in social touch with artificial social agents. Social touch research has inspired research into technology mediated social touch, and this line of research has found effects similar to actual social touch. The importance of haptic stimulus qualities, multimodal cues, and contextual factors in technology mediated social touch is discussed. This survey is concluded by reflecting on the current state of research into social touch technology, and providing suggestions for future research and applications.",
"title": "Social Touch Technology: A Survey of Haptic Technology for Social Touch",
"normalizedTitle": "Social Touch Technology: A Survey of Haptic Technology for Social Touch",
"fno": "07811300",
"hasPdf": true,
"idPrefix": "th",
"keywords": [
"Haptic Interfaces",
"Skin",
"Neurophysiology",
"Manipulators",
"Psychology",
"Visualization",
"Bonding",
"Haptics And Haptic Interfaces",
"Social Touch",
"Affective Touch",
"Medaited Social Touch",
"Simulated Social Touch"
],
"authors": [
{
"givenName": "Gijs",
"surname": "Huisman",
"fullName": "Gijs Huisman",
"affiliation": "Human Media Interaction Group, University of Twente, Enschede, NB, the Netherlands",
"__typename": "ArticleAuthorType"
}
],
"replicability": null,
"showBuyMe": true,
"showRecommendedArticles": true,
"isOpenAccess": false,
"issueNum": "03",
"pubDate": "2017-07-01 00:00:00",
"pubType": "trans",
"pages": "391-408",
"year": "2017",
"issn": "1939-1412",
"isbn": null,
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"recommendedArticles": [
{
"id": "proceedings/ism/2010/4217/0/4217a228",
"title": "Bridging the Gap between Virtual and Real World by Bringing an Interpersonal Haptic Communication System in Second Life",
"doi": null,
"abstractUrl": "/proceedings-article/ism/2010/4217a228/12OmNB9t6wX",
"parentPublication": {
"id": "proceedings/ism/2010/4217/0",
"title": "2010 IEEE International Symposium on Multimedia",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/acii/2015/9953/0/07344656",
"title": "A warm touch of affect?",
"doi": null,
"abstractUrl": "/proceedings-article/acii/2015/07344656/12OmNBIWXCi",
"parentPublication": {
"id": "proceedings/acii/2015/9953/0",
"title": "2015 International Conference on Affective Computing and Intelligent Interaction (ACII)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/acii/2015/9953/0/07344694",
"title": "Design of a wearable research tool for warm mediated social touches",
"doi": null,
"abstractUrl": "/proceedings-article/acii/2015/07344694/12OmNBhHtio",
"parentPublication": {
"id": "proceedings/acii/2015/9953/0",
"title": "2015 International Conference on Affective Computing and Intelligent Interaction (ACII)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/acii/2013/5048/0/5048a769",
"title": "International Workshop on Mediated Touch and Affect (MeTA 2013): Introduction",
"doi": null,
"abstractUrl": "/proceedings-article/acii/2013/5048a769/12OmNqJq4xv",
"parentPublication": {
"id": "proceedings/acii/2013/5048/0",
"title": "2013 Humaine Association Conference on Affective Computing and Intelligent Interaction (ACII)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/acii/2013/5048/0/5048a780",
"title": "How to Touch Humans: Guidelines for Social Agents and Robots That Can Touch",
"doi": null,
"abstractUrl": "/proceedings-article/acii/2013/5048a780/12OmNrMZpkm",
"parentPublication": {
"id": "proceedings/acii/2013/5048/0",
"title": "2013 Humaine Association Conference on Affective Computing and Intelligent Interaction (ACII)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/vr/2009/3943/0/04811019",
"title": "Virtual Humans That Touch Back: Enhancing Nonverbal Communication with Virtual Humans through Bidirectional Touch",
"doi": null,
"abstractUrl": "/proceedings-article/vr/2009/04811019/12OmNwMXnsz",
"parentPublication": {
"id": "proceedings/vr/2009/3943/0",
"title": "2009 IEEE Virtual Reality Conference",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/th/2014/03/06809998",
"title": "Haptic Invitation of Textures: Perceptually Prominent Properties of Materials Determine Human Touch Motions",
"doi": null,
"abstractUrl": "/journal/th/2014/03/06809998/13rRUxOdD8p",
"parentPublication": {
"id": "trans/th",
"title": "IEEE Transactions on Haptics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/th/2011/04/tth2011040295",
"title": "The Role of Gesture Types and Spatial Feedback in Haptic Communication",
"doi": null,
"abstractUrl": "/journal/th/2011/04/tth2011040295/13rRUxOve9U",
"parentPublication": {
"id": "trans/th",
"title": "IEEE Transactions on Haptics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/th/2009/03/tth2009030136",
"title": "The Virtual Midas Touch: Helping Behavior After a Mediated Social Touch",
"doi": null,
"abstractUrl": "/journal/th/2009/03/tth2009030136/13rRUygT7n5",
"parentPublication": {
"id": "trans/th",
"title": "IEEE Transactions on Haptics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/ta/5555/01/09444587",
"title": "Receiving a mediated touch from your partner vs. a male stranger: How visual feedback of touch and its sender influence touch experience",
"doi": null,
"abstractUrl": "/journal/ta/5555/01/09444587/1u3mvTnIRKo",
"parentPublication": {
"id": "trans/ta",
"title": "IEEE Transactions on Affective Computing",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"adjacentArticles": {
"previous": {
"fno": "07797128",
"articleId": "13rRUx0geA5",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "07795197",
"articleId": "13rRUxD9h5l",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"webExtras": [],
"articleVideos": []
}
|
{
"issue": {
"id": "12OmNs0TKPu",
"title": "Jan.-March",
"year": "2017",
"issueNum": "01",
"idPrefix": "ta",
"pubType": "journal",
"volume": "8",
"label": "Jan.-March",
"downloadables": {
"hasCover": false,
"__typename": "PeriodicalIssueDownloadablesType"
},
"__typename": "PeriodicalIssue"
},
"article": {
"id": "13rRUxYINdD",
"doi": "10.1109/TAFFC.2015.2509985",
"abstract": "The human touch has long been recognized to promote physical, emotional, social, and spiritual comfort. There are situations, however, when touch cannot be exchanged. Although mobile phones and web-based communication are ubiquitous, touch—a communication modality that conveys powerful messages—is inexistent in modern communications media. This paper describes a tele-touch device that transfers affective touch to another person through the internet. Commands for vibration, warmth, and tickle were sent over the internet to a haptic device at the subjects’ forearm. With a heart rate (HR) monitor and a galvanic skin response (GSR) sensor, the physiological effect of the tele-touch device was evaluated as the subjects watched an emotionally-laden movie. We compared these to one group of subjects who were touched by their spouse or girlfriend and to subjects of a control group where no touch was provided. Results show that the HR of the subjects with the tele-touch device was not significantly different from those subjects who were touched by their loved ones. These results were in contrast to the subjects who were not provided with any form of touch. On the other hand, the GSR results revealed that all the three touch conditions were different from one another.",
"abstracts": [
{
"abstractType": "Regular",
"content": "The human touch has long been recognized to promote physical, emotional, social, and spiritual comfort. There are situations, however, when touch cannot be exchanged. Although mobile phones and web-based communication are ubiquitous, touch—a communication modality that conveys powerful messages—is inexistent in modern communications media. This paper describes a tele-touch device that transfers affective touch to another person through the internet. Commands for vibration, warmth, and tickle were sent over the internet to a haptic device at the subjects’ forearm. With a heart rate (HR) monitor and a galvanic skin response (GSR) sensor, the physiological effect of the tele-touch device was evaluated as the subjects watched an emotionally-laden movie. We compared these to one group of subjects who were touched by their spouse or girlfriend and to subjects of a control group where no touch was provided. Results show that the HR of the subjects with the tele-touch device was not significantly different from those subjects who were touched by their loved ones. These results were in contrast to the subjects who were not provided with any form of touch. On the other hand, the GSR results revealed that all the three touch conditions were different from one another.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "The human touch has long been recognized to promote physical, emotional, social, and spiritual comfort. There are situations, however, when touch cannot be exchanged. Although mobile phones and web-based communication are ubiquitous, touch—a communication modality that conveys powerful messages—is inexistent in modern communications media. This paper describes a tele-touch device that transfers affective touch to another person through the internet. Commands for vibration, warmth, and tickle were sent over the internet to a haptic device at the subjects’ forearm. With a heart rate (HR) monitor and a galvanic skin response (GSR) sensor, the physiological effect of the tele-touch device was evaluated as the subjects watched an emotionally-laden movie. We compared these to one group of subjects who were touched by their spouse or girlfriend and to subjects of a control group where no touch was provided. Results show that the HR of the subjects with the tele-touch device was not significantly different from those subjects who were touched by their loved ones. These results were in contrast to the subjects who were not provided with any form of touch. On the other hand, the GSR results revealed that all the three touch conditions were different from one another.",
"title": "Physiological Responses to Affective Tele-Touch during Induced Emotional Stimuli",
"normalizedTitle": "Physiological Responses to Affective Tele-Touch during Induced Emotional Stimuli",
"fno": "07360126",
"hasPdf": true,
"idPrefix": "ta",
"keywords": [
"Haptic Interfaces",
"Internet",
"Vibrations",
"Biomedical Monitoring",
"Heart Rate",
"Motion Pictures",
"Actuators",
"Galvanic Skin Response",
"Affective Touch",
"Human Touch",
"Tele Touch",
"Heart Rate"
],
"authors": [
{
"givenName": "John-John",
"surname": "Cabibihan",
"fullName": "John-John Cabibihan",
"affiliation": "Department of Mechanical and Industrial Engineering, Qatar University, Doha, Qatar",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Sushil Singh",
"surname": "Chauhan",
"fullName": "Sushil Singh Chauhan",
"affiliation": "Department of Electrical and Computer Engineering, National University of Singapore, Singapore",
"__typename": "ArticleAuthorType"
}
],
"replicability": null,
"showBuyMe": false,
"showRecommendedArticles": true,
"isOpenAccess": true,
"issueNum": "01",
"pubDate": "2017-01-01 00:00:00",
"pubType": "trans",
"pages": "108-118",
"year": "2017",
"issn": "1949-3045",
"isbn": null,
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"recommendedArticles": [
{
"id": "proceedings/intetain/2015/0061/0/07325487",
"title": "How does it feel like? An exploratory study of a prototype system to convey emotion through haptic wearable devices",
"doi": null,
"abstractUrl": "/proceedings-article/intetain/2015/07325487/12OmNBNM97o",
"parentPublication": {
"id": "proceedings/intetain/2015/0061/0",
"title": "2015 7th International Conference on Intelligent Technologies for Interactive Entertainment (INTETAIN)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/acii/2017/0563/0/08273612",
"title": "Emotional responses of vibrotactile-thermal stimuli: Effects of constant-temperature thermal stimuli",
"doi": null,
"abstractUrl": "/proceedings-article/acii/2017/08273612/12OmNqMPfQu",
"parentPublication": {
"id": "proceedings/acii/2017/0563/0",
"title": "2017 Seventh International Conference on Affective Computing and Intelligent Interaction (ACII)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/vr/2015/1727/0/07223412",
"title": "A modified tactile brush algorithm for complex touch gestures",
"doi": null,
"abstractUrl": "/proceedings-article/vr/2015/07223412/12OmNyS6RDd",
"parentPublication": {
"id": "proceedings/vr/2015/1727/0",
"title": "2015 IEEE Virtual Reality (VR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/ta/2014/02/06825835",
"title": "Emotion Recognition Based on Multi-Variant Correlation of Physiological Signals",
"doi": null,
"abstractUrl": "/journal/ta/2014/02/06825835/13rRUIJuxnO",
"parentPublication": {
"id": "trans/ta",
"title": "IEEE Transactions on Affective Computing",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/2019/09/08409988",
"title": "HapTable: An Interactive Tabletop Providing Online Haptic Feedback for Touch Gestures",
"doi": null,
"abstractUrl": "/journal/tg/2019/09/08409988/13rRUIM2VBO",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/ta/2016/03/07452588",
"title": "Real-Time Tele-Monitoring of Patients with Chronic Heart-Failure Using a Smartphone: Lessons Learned",
"doi": null,
"abstractUrl": "/journal/ta/2016/03/07452588/13rRUwInvjC",
"parentPublication": {
"id": "trans/ta",
"title": "IEEE Transactions on Affective Computing",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/th/2017/04/07929357",
"title": "Effect of Waveform on Tactile Perception by Electrovibration Displayed on Touch Screens",
"doi": null,
"abstractUrl": "/journal/th/2017/04/07929357/13rRUx0geq7",
"parentPublication": {
"id": "trans/th",
"title": "IEEE Transactions on Haptics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/th/2016/01/07377107",
"title": "Lower Extremity Lateral Skin Stretch Perception for Haptic Feedback",
"doi": null,
"abstractUrl": "/journal/th/2016/01/07377107/13rRUxDIthq",
"parentPublication": {
"id": "trans/th",
"title": "IEEE Transactions on Haptics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/percomw/2018/3227/0/08480170",
"title": "Physiological Impact of Vibro-Acoustic Therapy on Stress and Emotions through Wearable Sensors",
"doi": null,
"abstractUrl": "/proceedings-article/percomw/2018/08480170/17D45Xtvp8r",
"parentPublication": {
"id": "proceedings/percomw/2018/3227/0",
"title": "2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ismar-adjunct/2022/5365/0/536500a727",
"title": "Phantom Touch phenomenon as a manifestation of the Visual-Auditory-Tactile Synaesthesia and its impact on the users in virtual reality",
"doi": null,
"abstractUrl": "/proceedings-article/ismar-adjunct/2022/536500a727/1J7WmsXKzxS",
"parentPublication": {
"id": "proceedings/ismar-adjunct/2022/5365/0",
"title": "2022 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"adjacentArticles": {
"previous": {
"fno": "07368100",
"articleId": "13rRUxjQyfN",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "07355319",
"articleId": "13rRUwh80Bi",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"webExtras": [
{
"id": "17ShDTXWRWq",
"name": "tta201701-07360126s1.zip",
"location": "https://www.computer.org/csdl/api/v1/extra/tta201701-07360126s1.zip",
"extension": "zip",
"size": "73.4 MB",
"__typename": "WebExtraType"
}
],
"articleVideos": []
}
|
{
"issue": {
"id": "12OmNvmXJ4d",
"title": "Oct.-Dec.",
"year": "2018",
"issueNum": "04",
"idPrefix": "ta",
"pubType": "journal",
"volume": "9",
"label": "Oct.-Dec.",
"downloadables": {
"hasCover": false,
"__typename": "PeriodicalIssueDownloadablesType"
},
"__typename": "PeriodicalIssue"
},
"article": {
"id": "17D45XeKgr8",
"doi": "10.1109/TAFFC.2016.2631469",
"abstract": "We conducted an experimental study to investigate how the participants combine touch and facial expression to evaluate emotional valence. In this study, visual and haptic stimuli were presented separately and then presented together. The visual stimuli comprised pictures of facial expressions with different emotional levels, and the touch stimuli consisted of air jet tactile stimulation performed on the arms of the participants. The participants were asked to evaluate the communicated emotional valence on a continuous scale. Information Integration Theory was used to model the algebraic rule (addition, multiplication, averaging, and so on) that underlies the multimodal perception of emotional valence. Analyses showed that the participants usually integrated both visual and touch information to evaluate the emotional valence. The main integration rule was averaging, with nonsystematic predominance of each modality over the other modality.",
"abstracts": [
{
"abstractType": "Regular",
"content": "We conducted an experimental study to investigate how the participants combine touch and facial expression to evaluate emotional valence. In this study, visual and haptic stimuli were presented separately and then presented together. The visual stimuli comprised pictures of facial expressions with different emotional levels, and the touch stimuli consisted of air jet tactile stimulation performed on the arms of the participants. The participants were asked to evaluate the communicated emotional valence on a continuous scale. Information Integration Theory was used to model the algebraic rule (addition, multiplication, averaging, and so on) that underlies the multimodal perception of emotional valence. Analyses showed that the participants usually integrated both visual and touch information to evaluate the emotional valence. The main integration rule was averaging, with nonsystematic predominance of each modality over the other modality.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "We conducted an experimental study to investigate how the participants combine touch and facial expression to evaluate emotional valence. In this study, visual and haptic stimuli were presented separately and then presented together. The visual stimuli comprised pictures of facial expressions with different emotional levels, and the touch stimuli consisted of air jet tactile stimulation performed on the arms of the participants. The participants were asked to evaluate the communicated emotional valence on a continuous scale. Information Integration Theory was used to model the algebraic rule (addition, multiplication, averaging, and so on) that underlies the multimodal perception of emotional valence. Analyses showed that the participants usually integrated both visual and touch information to evaluate the emotional valence. The main integration rule was averaging, with nonsystematic predominance of each modality over the other modality.",
"title": "Combining Facial Expression and Touch for Perceiving Emotional Valence",
"normalizedTitle": "Combining Facial Expression and Touch for Perceiving Emotional Valence",
"fno": "07752812",
"hasPdf": true,
"idPrefix": "ta",
"keywords": [
"Behavioural Sciences Computing",
"Emotion Recognition",
"Face Recognition",
"Haptic Interfaces",
"Touch Physiological",
"Facial Expression",
"Haptic Stimuli",
"Touch Stimuli",
"Communicated Emotional Valence",
"Visual Stimuli",
"Algebraic Rule",
"Information Integration Theory",
"Face Recognition",
"Visualization",
"Haptic Interfaces",
"Emotion Recognition",
"Human Computer Interaction",
"Speech Processing",
"Facial Expression",
"Tactile Stimulation",
"Emotional Valence",
"Information Integration Theory",
"Multimodality"
],
"authors": [
{
"givenName": "Mohamed Yassine",
"surname": "Tsalamlal",
"fullName": "Mohamed Yassine Tsalamlal",
"affiliation": "Université Paris-Saclay, Orsay Cedex, France",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Michel-Ange",
"surname": "Amorim",
"fullName": "Michel-Ange Amorim",
"affiliation": "Université Paris-Saclay, Orsay Cedex, France",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Jean-Claude",
"surname": "Martin",
"fullName": "Jean-Claude Martin",
"affiliation": "Université Paris-Saclay, Orsay Cedex, France",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Mehdi",
"surname": "Ammi",
"fullName": "Mehdi Ammi",
"affiliation": "Université Paris-Saclay, Orsay Cedex, France",
"__typename": "ArticleAuthorType"
}
],
"replicability": null,
"showBuyMe": true,
"showRecommendedArticles": true,
"isOpenAccess": false,
"issueNum": "04",
"pubDate": "2018-10-01 00:00:00",
"pubType": "trans",
"pages": "437-449",
"year": "2018",
"issn": "1949-3045",
"isbn": null,
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"recommendedArticles": [
{
"id": "proceedings/fg/2011/9140/0/05771353",
"title": "Expression of emotional states during locomotion based on canonical parameters",
"doi": null,
"abstractUrl": "/proceedings-article/fg/2011/05771353/12OmNAqU4VX",
"parentPublication": {
"id": "proceedings/fg/2011/9140/0",
"title": "Face and Gesture 2011",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/acii/2017/0563/0/08273636",
"title": "Predicting speaker recognition reliability by considering emotional content",
"doi": null,
"abstractUrl": "/proceedings-article/acii/2017/08273636/12OmNBqdr7d",
"parentPublication": {
"id": "proceedings/acii/2017/0563/0",
"title": "2017 Seventh International Conference on Affective Computing and Intelligent Interaction (ACII)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icme/2008/2570/0/04607572",
"title": "The Vera am Mittag German audio-visual emotional speech database",
"doi": null,
"abstractUrl": "/proceedings-article/icme/2008/04607572/12OmNqI04KI",
"parentPublication": {
"id": "proceedings/icme/2008/2570/0",
"title": "2008 IEEE International Conference on Multimedia and Expo (ICME)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/acii/2017/0563/0/08273612",
"title": "Emotional responses of vibrotactile-thermal stimuli: Effects of constant-temperature thermal stimuli",
"doi": null,
"abstractUrl": "/proceedings-article/acii/2017/08273612/12OmNqMPfQu",
"parentPublication": {
"id": "proceedings/acii/2017/0563/0",
"title": "2017 Seventh International Conference on Affective Computing and Intelligent Interaction (ACII)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cw/2012/4814/0/4814a053",
"title": "EEG-based Valence Level Recognition for Real-Time Applications",
"doi": null,
"abstractUrl": "/proceedings-article/cw/2012/4814a053/12OmNwp74MG",
"parentPublication": {
"id": "proceedings/cw/2012/4814/0",
"title": "2012 International Conference on Cyberworlds",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icme/2014/4761/0/06890208",
"title": "Location of an emotionally neutral region in valence-arousal space: Two-class vs. three-class cross corpora emotion recognition evaluations",
"doi": null,
"abstractUrl": "/proceedings-article/icme/2014/06890208/12OmNxVlTEM",
"parentPublication": {
"id": "proceedings/icme/2014/4761/0",
"title": "2014 IEEE International Conference on Multimedia and Expo (ICME)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iciip/2015/0148/0/07414739",
"title": "Emotion recognition based on physiological signals using valence-arousal model",
"doi": null,
"abstractUrl": "/proceedings-article/iciip/2015/07414739/12OmNyQYte9",
"parentPublication": {
"id": "proceedings/iciip/2015/0148/0",
"title": "2015 Third International Conference on Image Information Processing (ICIIP)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/ta/2022/01/08859243",
"title": "MES-P: An Emotional Tonal Speech Dataset in Mandarin with Distal and Proximal Labels",
"doi": null,
"abstractUrl": "/journal/ta/2022/01/08859243/1dR0QzhXnIA",
"parentPublication": {
"id": "trans/ta",
"title": "IEEE Transactions on Affective Computing",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/ta/2022/03/09122472",
"title": "Leaders and Followers Identified by Emotional Mimicry During Collaborative Learning: A Facial Expression Recognition Study on Emotional Valence",
"doi": null,
"abstractUrl": "/journal/ta/2022/03/09122472/1kRRmlS9Z2o",
"parentPublication": {
"id": "trans/ta",
"title": "IEEE Transactions on Affective Computing",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/ta/2023/01/09258960",
"title": "Touching Virtual Humans: Haptic Responses Reveal the Emotional Impact of Affective Agents",
"doi": null,
"abstractUrl": "/journal/ta/2023/01/09258960/1oIW8klCOiY",
"parentPublication": {
"id": "trans/ta",
"title": "IEEE Transactions on Affective Computing",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"adjacentArticles": {
"previous": {
"fno": "07820164",
"articleId": "17D45WB0qc8",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "07867778",
"articleId": "17D45XwUAMY",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"webExtras": [],
"articleVideos": []
}
|
{
"issue": {
"id": "12OmNqIhFTu",
"title": "July-Sept.",
"year": "2013",
"issueNum": "03",
"idPrefix": "pc",
"pubType": "magazine",
"volume": "12",
"label": "July-Sept.",
"downloadables": {
"hasCover": false,
"__typename": "PeriodicalIssueDownloadablesType"
},
"__typename": "PeriodicalIssue"
},
"article": {
"id": "13rRUwInvir",
"doi": "10.1109/MPRV.2013.55",
"abstract": "Current technology supports only special-purpose, low-volume textiles, garments, and electronics. Moreover, the textile, electronic, and software industries have different product cycles, cultures, and price models, creating scores of practical problems for smart textiles. Mass producing smart cloth will require decoupling the textile production from concrete sensing apps and moving the complexity to generic electronics and software--creating wearable sensing as an app.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Current technology supports only special-purpose, low-volume textiles, garments, and electronics. Moreover, the textile, electronic, and software industries have different product cycles, cultures, and price models, creating scores of practical problems for smart textiles. Mass producing smart cloth will require decoupling the textile production from concrete sensing apps and moving the complexity to generic electronics and software--creating wearable sensing as an app.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Current technology supports only special-purpose, low-volume textiles, garments, and electronics. Moreover, the textile, electronic, and software industries have different product cycles, cultures, and price models, creating scores of practical problems for smart textiles. Mass producing smart cloth will require decoupling the textile production from concrete sensing apps and moving the complexity to generic electronics and software--creating wearable sensing as an app.",
"title": "Smart Textiles: From Niche to Mainstream",
"normalizedTitle": "Smart Textiles: From Niche to Mainstream",
"fno": "mpc2013030081",
"hasPdf": true,
"idPrefix": "pc",
"keywords": [
"Sensors",
"Clothing",
"Textiles",
"Wearable Computers",
"Fabrics",
"Software Engineering",
"Smart Clothing",
"Wearable Computing",
"Pervasive Computing",
"Smart Cloth",
"Smart Textiles",
"Wearable Sensing"
],
"authors": [
{
"givenName": "Jingyuan",
"surname": "Cheng",
"fullName": "Jingyuan Cheng",
"affiliation": "DFKI Kaiserslautern",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Paul",
"surname": "Lukowicz",
"fullName": "Paul Lukowicz",
"affiliation": "DFKI Kaiserslautern",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Niels",
"surname": "Henze",
"fullName": "Niels Henze",
"affiliation": "University of Stuttgart",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Albrecht",
"surname": "Schmidt",
"fullName": "Albrecht Schmidt",
"affiliation": "University of Stuttgart",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Oliver",
"surname": "Amft",
"fullName": "Oliver Amft",
"affiliation": "TU Eindhoven",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Giovanni A.",
"surname": "Salvatore",
"fullName": "Giovanni A. Salvatore",
"affiliation": "ETH Zurich",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Gerhard",
"surname": "Troster",
"fullName": "Gerhard Troster",
"affiliation": "ETH Zurich",
"__typename": "ArticleAuthorType"
}
],
"replicability": null,
"showBuyMe": true,
"showRecommendedArticles": true,
"isOpenAccess": false,
"issueNum": "03",
"pubDate": "2013-07-01 00:00:00",
"pubType": "mags",
"pages": "81-84",
"year": "2013",
"issn": "1536-1268",
"isbn": null,
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"recommendedArticles": [
{
"id": "proceedings/iswc/2010/9046/0/05665874",
"title": "Weaving integrated circuits into textiles",
"doi": null,
"abstractUrl": "/proceedings-article/iswc/2010/05665874/12OmNrYCXTr",
"parentPublication": {
"id": "proceedings/iswc/2010/9046/0",
"title": "International Symposium on Wearable Computers (ISWC) 2010",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iswc/2012/4697/0/4697a060",
"title": "Textile Interfaces: Embroidered Jog-Wheel, Beaded Tilt Sensor, Twisted Pair Ribbon, and Sound Sequins",
"doi": null,
"abstractUrl": "/proceedings-article/iswc/2012/4697a060/12OmNro0HRJ",
"parentPublication": {
"id": "proceedings/iswc/2012/4697/0",
"title": "2012 16th International Symposium on Wearable Computers",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iswc/2006/0597/0/04067731",
"title": "A Construction Kit for Electronic Textiles",
"doi": null,
"abstractUrl": "/proceedings-article/iswc/2006/04067731/12OmNwoPtwG",
"parentPublication": {
"id": "proceedings/iswc/2006/0597/0",
"title": "2006 10th IEEE International Symposium on Wearable Computers",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/dac/2002/2402/0/24020175",
"title": "Challenges and Opportunities in Electronic Textiles Modeling and Optimization",
"doi": null,
"abstractUrl": "/proceedings-article/dac/2002/24020175/12OmNx8fiiL",
"parentPublication": {
"id": "proceedings/dac/2002/2402/0",
"title": "Design Automation Conference",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icn/2010/3979/0/3979a157",
"title": "Application of Shielding Textiles for Increasing Safety Airborne Systems - Limitation of GSM Interference",
"doi": null,
"abstractUrl": "/proceedings-article/icn/2010/3979a157/12OmNz2kqpe",
"parentPublication": {
"id": "proceedings/icn/2010/3979/0",
"title": "International Conference on Networking",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "mags/pc/2011/02/mpc2011020087",
"title": "Smart Clothes—The Unfulfilled Pledge?",
"doi": null,
"abstractUrl": "/magazine/pc/2011/02/mpc2011020087/13rRUxOdD5n",
"parentPublication": {
"id": "mags/pc",
"title": "IEEE Pervasive Computing",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/th/2015/02/07060666",
"title": "Surface-Roughness-Based Virtual Textiles: Evaluation Using a Multi-Contactor Display",
"doi": null,
"abstractUrl": "/journal/th/2015/02/07060666/13rRUxly95L",
"parentPublication": {
"id": "trans/th",
"title": "IEEE Transactions on Haptics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/5555/01/09891797",
"title": "Yarn-Level Simulation of Hygroscopicity of Woven Textiles",
"doi": null,
"abstractUrl": "/journal/tg/5555/01/09891797/1GF6PmosQr6",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icid/2020/1481/0/440500a172",
"title": "A Study of the Intelligent Design of Smart Textile in Future Life",
"doi": null,
"abstractUrl": "/proceedings-article/icid/2020/440500a172/1taFr8A5cmA",
"parentPublication": {
"id": "proceedings/icid/2020/1481/0",
"title": "2020 International Conference on Intelligent Design (ICID)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/smartcomp/2021/1252/0/125200a129",
"title": "Baby-Guard: An IoT-based Neonatal Monitoring System Integrated with Smart Textiles",
"doi": null,
"abstractUrl": "/proceedings-article/smartcomp/2021/125200a129/1xxcA63YIfu",
"parentPublication": {
"id": "proceedings/smartcomp/2021/1252/0",
"title": "2021 IEEE International Conference on Smart Computing (SMARTCOMP)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"adjacentArticles": {
"previous": {
"fno": "mpc2013030074",
"articleId": "13rRUx0gesx",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "mpc2013030085",
"articleId": "13rRUyeCk7n",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"webExtras": [],
"articleVideos": []
}
|
{
"issue": {
"id": "12OmNzFdtcg",
"title": "Sept.",
"year": "2017",
"issueNum": "03",
"idPrefix": "ci",
"pubType": "journal",
"volume": "9",
"label": "Sept.",
"downloadables": {
"hasCover": false,
"__typename": "PeriodicalIssueDownloadablesType"
},
"__typename": "PeriodicalIssue"
},
"article": {
"id": "13rRUy2YM1h",
"doi": "10.1109/TCIAIG.2016.2546063",
"abstract": "Computers are often used as tools to design, implement, and even visualize a variety of narrative forms. Many researchers and artists are now further attempting to engage the computer actively throughout the development of the narrative itself. Any form of computational narrative authoring is at some level always mixed-initiative , meaning that the processing capabilities of the computer are utilized with a varying degree to automate certain features of the authoring process. We structure this survey by focusing on two key components of stories, plot and space, and more specifically the degree to which these are either automated by the computer or authored manually. By examining the successes of existing research, we identify potential new research directions in the field of computational narrative. We also identify the advantages of developing a standard model of narrative to allow for collaboration between plot and space automation techniques. This would likely benefit the field of automated space generation with the strengths in the field of automated plot generation.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Computers are often used as tools to design, implement, and even visualize a variety of narrative forms. Many researchers and artists are now further attempting to engage the computer actively throughout the development of the narrative itself. Any form of computational narrative authoring is at some level always mixed-initiative , meaning that the processing capabilities of the computer are utilized with a varying degree to automate certain features of the authoring process. We structure this survey by focusing on two key components of stories, plot and space, and more specifically the degree to which these are either automated by the computer or authored manually. By examining the successes of existing research, we identify potential new research directions in the field of computational narrative. We also identify the advantages of developing a standard model of narrative to allow for collaboration between plot and space automation techniques. This would likely benefit the field of automated space generation with the strengths in the field of automated plot generation.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Computers are often used as tools to design, implement, and even visualize a variety of narrative forms. Many researchers and artists are now further attempting to engage the computer actively throughout the development of the narrative itself. Any form of computational narrative authoring is at some level always mixed-initiative , meaning that the processing capabilities of the computer are utilized with a varying degree to automate certain features of the authoring process. We structure this survey by focusing on two key components of stories, plot and space, and more specifically the degree to which these are either automated by the computer or authored manually. By examining the successes of existing research, we identify potential new research directions in the field of computational narrative. We also identify the advantages of developing a standard model of narrative to allow for collaboration between plot and space automation techniques. This would likely benefit the field of automated space generation with the strengths in the field of automated plot generation.",
"title": "A Survey on Story Generation Techniques for Authoring Computational Narratives",
"normalizedTitle": "A Survey on Story Generation Techniques for Authoring Computational Narratives",
"fno": "07439785",
"hasPdf": true,
"idPrefix": "ci",
"keywords": [
"Computers",
"Automation",
"Manuals",
"Computational Modeling",
"Collaboration",
"Aerospace Electronics",
"Visualization",
"Automated Storytelling",
"Computational Narrative Authoring",
"Procedural Content Generation",
"Story Generation"
],
"authors": [
{
"givenName": "Ben",
"surname": "Kybartas",
"fullName": "Ben Kybartas",
"affiliation": "Computer Graphics and Visualization Group, Delft University of Technology, CD Delft, The Netherlands",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Rafael",
"surname": "Bidarra",
"fullName": "Rafael Bidarra",
"affiliation": "Computer Graphics and Visualization Group, Delft University of Technology, CD Delft, The Netherlands",
"__typename": "ArticleAuthorType"
}
],
"replicability": null,
"showBuyMe": true,
"showRecommendedArticles": true,
"isOpenAccess": false,
"issueNum": "03",
"pubDate": "2017-07-01 00:00:00",
"pubType": "trans",
"pages": "239-253",
"year": "2017",
"issn": "1943-068X",
"isbn": null,
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"recommendedArticles": [
{
"id": "proceedings/isda/2008/3382/2/3382b067",
"title": "Design of an IDML-Based Interactive Agent Drama Authoring Tool",
"doi": null,
"abstractUrl": "/proceedings-article/isda/2008/3382b067/12OmNqJZgGR",
"parentPublication": {
"id": "proceedings/isda/2008/3382/2",
"title": "2008 Eighth International Conference on Intelligent Systems Design and Applications",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/vs-games/2016/2722/0/07590383",
"title": "Yoway: Coupling Narrative Structure with Physical Exploration in Multi-Linear Locative Narratives",
"doi": null,
"abstractUrl": "/proceedings-article/vs-games/2016/07590383/12OmNqN6R42",
"parentPublication": {
"id": "proceedings/vs-games/2016/2722/0",
"title": "2016 8th International Conference on Games and Virtual Worlds for Serious Applications (VS-Games)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iswc/2003/2034/0/20340118",
"title": "Authoring 3D Hypermedia for Wearable Augmented and Virtual Reality",
"doi": null,
"abstractUrl": "/proceedings-article/iswc/2003/20340118/12OmNwIHoqo",
"parentPublication": {
"id": "proceedings/iswc/2003/2034/0",
"title": "Seventh IEEE International Symposium on Wearable Computers, 2003. Proceedings.",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/aamas/2004/2092/1/20920186",
"title": "An Intent-Driven Planner for Multi-Agent Story Generation",
"doi": null,
"abstractUrl": "/proceedings-article/aamas/2004/20920186/12OmNyRPgHr",
"parentPublication": {
"id": "proceedings/aamas/2004/2092/1",
"title": "Autonomous Agents and Multiagent Systems, International Joint Conference on",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/ci/2014/02/06604407",
"title": "Personalized Interactive Narratives via Sequential Recommendation of Plot Points",
"doi": null,
"abstractUrl": "/journal/ci/2014/02/06604407/13rRUxASuk7",
"parentPublication": {
"id": "trans/ci",
"title": "IEEE Transactions on Computational Intelligence and AI in Games",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/2018/01/08017584",
"title": "Visualizing Nonlinear Narratives with Story Curves",
"doi": null,
"abstractUrl": "/journal/tg/2018/01/08017584/13rRUyueghe",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/5555/01/10081398",
"title": "How Does Automation Shape the Process of Narrative Visualization: A Survey of Tools",
"doi": null,
"abstractUrl": "/journal/tg/5555/01/10081398/1LRbRjcZeLK",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iv/2019/2838/0/283800a339",
"title": "An Online Authoring Tool for Interactive Fiction",
"doi": null,
"abstractUrl": "/proceedings-article/iv/2019/283800a339/1cMFcA4O1q0",
"parentPublication": {
"id": "proceedings/iv/2019/2838/0",
"title": "2019 23rd International Conference Information Visualisation (IV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/aivr/2020/7463/0/746300a331",
"title": "Story ARtist",
"doi": null,
"abstractUrl": "/proceedings-article/aivr/2020/746300a331/1qpzByTcXqU",
"parentPublication": {
"id": "proceedings/aivr/2020/7463/0",
"title": "2020 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/2023/02/09547737",
"title": "ChartStory: Automated Partitioning, Layout, and Captioning of Charts into Comic-Style Narratives",
"doi": null,
"abstractUrl": "/journal/tg/2023/02/09547737/1x9TL0bvSlq",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"adjacentArticles": {
"previous": {
"fno": "07435292",
"articleId": "13rRUxCitB2",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "07442549",
"articleId": "13rRUwInvMT",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"webExtras": [],
"articleVideos": []
}
|
{
"issue": {
"id": "12OmNz5apx6",
"title": "June",
"year": "2014",
"issueNum": "02",
"idPrefix": "ci",
"pubType": "journal",
"volume": "6",
"label": "June",
"downloadables": {
"hasCover": false,
"__typename": "PeriodicalIssueDownloadablesType"
},
"__typename": "PeriodicalIssue"
},
"article": {
"id": "13rRUyuNszA",
"doi": "10.1109/TCIAIG.2013.2290088",
"abstract": "Using procedural narrative generation in video games provides a flexible way to extend game play and provide more depth to the game world at low cost to the developers. Current examples of narrative generation in commercial games, however, tend to be simplistic, resulting in repetitive and uninteresting stories. In this paper, we develop a system for narrative generation using a context-aware graph-rewriting framework. We use a graph representation of the game world to create narratives which reflect and modify the current world state. Using a novel set of metrics to evaluate narrative quality, we validate our approach by comparing our generated narratives to other procedurally generated stories, as well as to authored narratives from commercially successful and critically praised games. The results show that our narratives compare favorably to the authored narratives. Our metrics provide a new approach to narrative analysis, and our system provides a unique and practical approach to story generation.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Using procedural narrative generation in video games provides a flexible way to extend game play and provide more depth to the game world at low cost to the developers. Current examples of narrative generation in commercial games, however, tend to be simplistic, resulting in repetitive and uninteresting stories. In this paper, we develop a system for narrative generation using a context-aware graph-rewriting framework. We use a graph representation of the game world to create narratives which reflect and modify the current world state. Using a novel set of metrics to evaluate narrative quality, we validate our approach by comparing our generated narratives to other procedurally generated stories, as well as to authored narratives from commercially successful and critically praised games. The results show that our narratives compare favorably to the authored narratives. Our metrics provide a new approach to narrative analysis, and our system provides a unique and practical approach to story generation.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Using procedural narrative generation in video games provides a flexible way to extend game play and provide more depth to the game world at low cost to the developers. Current examples of narrative generation in commercial games, however, tend to be simplistic, resulting in repetitive and uninteresting stories. In this paper, we develop a system for narrative generation using a context-aware graph-rewriting framework. We use a graph representation of the game world to create narratives which reflect and modify the current world state. Using a novel set of metrics to evaluate narrative quality, we validate our approach by comparing our generated narratives to other procedurally generated stories, as well as to authored narratives from commercially successful and critically praised games. The results show that our narratives compare favorably to the authored narratives. Our metrics provide a new approach to narrative analysis, and our system provides a unique and practical approach to story generation.",
"title": "Analysis of ReGEN as a Graph-Rewriting System for Quest Generation",
"normalizedTitle": "Analysis of ReGEN as a Graph-Rewriting System for Quest Generation",
"fno": "06657816",
"hasPdf": true,
"idPrefix": "ci",
"keywords": [
"Computer Games",
"Graph Theory",
"Humanities",
"Rewriting Systems",
"Re GEN Analysis",
"Quest Generation",
"Procedural Narrative Generation",
"Video Games",
"Game Play",
"Narrative Generation",
"Commercial Games",
"Context Aware Graph Rewriting Framework",
"Graph Representation",
"Narrative Quality Evaluation",
"Narrative Analysis",
"Story Generation",
"Games",
"Grammar",
"Measurement",
"Planning",
"Complexity Theory",
"Abstracts",
"Context",
"Computer Games",
"Graph Rewriting",
"Narrative",
"Procedural Content Generation"
],
"authors": [
{
"givenName": "Ben",
"surname": "Kybartas",
"fullName": "Ben Kybartas",
"affiliation": "School of Computer Science, McGill University, Montréal, Canada",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Clark",
"surname": "Verbrugge",
"fullName": "Clark Verbrugge",
"affiliation": "School of Computer Science, McGill University, Montréal, Canada",
"__typename": "ArticleAuthorType"
}
],
"replicability": null,
"showBuyMe": true,
"showRecommendedArticles": true,
"isOpenAccess": false,
"issueNum": "02",
"pubDate": "2014-04-01 00:00:00",
"pubType": "trans",
"pages": "228-242",
"year": "2014",
"issn": "1943-068X",
"isbn": null,
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"recommendedArticles": [
{
"id": "proceedings/icci-cc/2012/2795/0/06311156",
"title": "Macro structure and basic methods in the integrated narrative generation system by introducing narratological knowledge",
"doi": null,
"abstractUrl": "/proceedings-article/icci-cc/2012/06311156/12OmNBpVQdR",
"parentPublication": {
"id": "proceedings/icci-cc/2012/2795/0",
"title": "2012 11th IEEE International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/sbgames/2017/4846/0/484601a144",
"title": "Network Traversal as an Aid to Plot Analysis and Composition",
"doi": null,
"abstractUrl": "/proceedings-article/sbgames/2017/484601a144/12OmNC4O4Cs",
"parentPublication": {
"id": "proceedings/sbgames/2017/4846/0",
"title": "2017 16th Brazilian Symposium on Computer Games and Digital Entertainment (SBGames)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cw/2013/2246/0/2246a214",
"title": "Designing Narrative Interface with a Function of Narrative Generation",
"doi": null,
"abstractUrl": "/proceedings-article/cw/2013/2246a214/12OmNsdo6u8",
"parentPublication": {
"id": "proceedings/cw/2013/2246/0",
"title": "2013 International Conference on Cyberworlds (CW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iiai-aai/2015/9957/0/07373881",
"title": "A Hierarchical Visualization Program for Computer-Generated Narratives",
"doi": null,
"abstractUrl": "/proceedings-article/iiai-aai/2015/07373881/12OmNwHQB26",
"parentPublication": {
"id": "proceedings/iiai-aai/2015/9957/0",
"title": "2015 IIAI 4th International Congress on Advanced Applied Informatics (IIAI-AAI)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/ci/2012/03/06266725",
"title": "Experience-driven procedural music generation for games",
"doi": null,
"abstractUrl": "/journal/ci/2012/03/06266725/13rRUNvPLcr",
"parentPublication": {
"id": "trans/ci",
"title": "IEEE Transactions on Computational Intelligence and AI in Games",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "mags/cg/2006/03/mcg2006030023",
"title": "From Linear Story Generation to Branching Story Graphs",
"doi": null,
"abstractUrl": "/magazine/cg/2006/03/mcg2006030023/13rRUwInv91",
"parentPublication": {
"id": "mags/cg",
"title": "IEEE Computer Graphics and Applications",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/ci/2015/01/06815689",
"title": "Suspenser: a story generation system for suspense",
"doi": null,
"abstractUrl": "/journal/ci/2015/01/06815689/13rRUxk89gI",
"parentPublication": {
"id": "trans/ci",
"title": "IEEE Transactions on Computational Intelligence and AI in Games",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/ci/2014/02/06832695",
"title": "Guest Editorial: Computational Narrative and Games",
"doi": null,
"abstractUrl": "/journal/ci/2014/02/06832695/13rRUzpQPNY",
"parentPublication": {
"id": "trans/ci",
"title": "IEEE Transactions on Computational Intelligence and AI in Games",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/aivr/2018/9269/0/926900a100",
"title": "A Two-Level Planning Framework for Mixed Reality Interactive Narratives with User Engagement",
"doi": null,
"abstractUrl": "/proceedings-article/aivr/2018/926900a100/17D45Xh13v2",
"parentPublication": {
"id": "proceedings/aivr/2018/9269/0",
"title": "2018 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/gi/2021/4466/0/446600a039",
"title": "(Genetically) Improving Novelty in Procedural Story Generation",
"doi": null,
"abstractUrl": "/proceedings-article/gi/2021/446600a039/1v2QLKRKBiM",
"parentPublication": {
"id": "proceedings/gi/2021/4466/0",
"title": "2021 IEEE/ACM International Workshop on Genetic Improvement (GI)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"adjacentArticles": {
"previous": {
"fno": "06571208",
"articleId": "13rRUxEhFv8",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "06832683",
"articleId": "13rRUyhaIjd",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"webExtras": [],
"articleVideos": []
}
|
{
"issue": {
"id": "12OmNz5apx6",
"title": "June",
"year": "2014",
"issueNum": "02",
"idPrefix": "ci",
"pubType": "journal",
"volume": "6",
"label": "June",
"downloadables": {
"hasCover": false,
"__typename": "PeriodicalIssueDownloadablesType"
},
"__typename": "PeriodicalIssue"
},
"article": {
"id": "13rRUzpQPNY",
"doi": "10.1109/TCIAIG.2014.2325879",
"abstract": "The eleven articles in this special issue focus on the use of computational modeling in developing the narratives for video programs and online games. Narratives are perceived to be central to cultures, to the ways people communicate, and, many have argued, to cognition itself. The articles in this issue explore these issues and reports on technologies and computer applications that support narrative programming.",
"abstracts": [
{
"abstractType": "Regular",
"content": "The eleven articles in this special issue focus on the use of computational modeling in developing the narratives for video programs and online games. Narratives are perceived to be central to cultures, to the ways people communicate, and, many have argued, to cognition itself. The articles in this issue explore these issues and reports on technologies and computer applications that support narrative programming.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "The eleven articles in this special issue focus on the use of computational modeling in developing the narratives for video programs and online games. Narratives are perceived to be central to cultures, to the ways people communicate, and, many have argued, to cognition itself. The articles in this issue explore these issues and reports on technologies and computer applications that support narrative programming.",
"title": "Guest Editorial: Computational Narrative and Games",
"normalizedTitle": "Guest Editorial: Computational Narrative and Games",
"fno": "06832695",
"hasPdf": true,
"idPrefix": "ci",
"keywords": [
"Special Issues And Sections",
"Games",
"Artificial Intelligence",
"Supervised Learning",
"Computational Modeling",
"Cognition",
"Interactive Systems"
],
"authors": [
{
"givenName": "Ian D.",
"surname": "Horswill",
"fullName": "Ian D. Horswill",
"affiliation": "Department of Electrical Engineering and Computer Science, Northwestern University, Evanston, IL, USA",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Nick",
"surname": "Montfort",
"fullName": "Nick Montfort",
"affiliation": "Comparative Media Studies and Writing, Massachusetts Institute of Technology, Cambridge, MA, USA",
"__typename": "ArticleAuthorType"
},
{
"givenName": "R. Michael",
"surname": "Young",
"fullName": "R. Michael Young",
"affiliation": "Computer Science Department, North Carolina State University, Raleigh, NC, USA",
"__typename": "ArticleAuthorType"
}
],
"replicability": null,
"showBuyMe": false,
"showRecommendedArticles": true,
"isOpenAccess": true,
"issueNum": "02",
"pubDate": "2014-04-01 00:00:00",
"pubType": "trans",
"pages": "93-96",
"year": "2014",
"issn": "1943-068X",
"isbn": null,
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"recommendedArticles": [
{
"id": "trans/lt/2012/02/tlt2012020102",
"title": "Guest Editorial: Special Section on Semantic Technologies for Learning and Teaching Support in Higher Education",
"doi": null,
"abstractUrl": "/journal/lt/2012/02/tlt2012020102/13rRUEgarxF",
"parentPublication": {
"id": "trans/lt",
"title": "IEEE Transactions on Learning Technologies",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/th/2012/01/tth2012010004",
"title": "Guest Editorial for Special Section on Consumer Electronics",
"doi": null,
"abstractUrl": "/journal/th/2012/01/tth2012010004/13rRUIJcWlw",
"parentPublication": {
"id": "trans/th",
"title": "IEEE Transactions on Haptics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/ta/2014/01/06812170",
"title": "Guest Editorial: Emotion in Games",
"doi": null,
"abstractUrl": "/journal/ta/2014/01/06812170/13rRUILtJk9",
"parentPublication": {
"id": "trans/ta",
"title": "IEEE Transactions on Affective Computing",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/ci/2016/02/07491393",
"title": "Guest Editorial: Physics-Based Simulation Games",
"doi": null,
"abstractUrl": "/journal/ci/2016/02/07491393/13rRUNvya3A",
"parentPublication": {
"id": "trans/ci",
"title": "IEEE Transactions on Computational Intelligence and AI in Games",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/ci/2011/03/06017222",
"title": "Guest Editorial: Procedural Content Generation in Games",
"doi": null,
"abstractUrl": "/journal/ci/2011/03/06017222/13rRUwkfB22",
"parentPublication": {
"id": "trans/ci",
"title": "IEEE Transactions on Computational Intelligence and AI in Games",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/ci/2012/03/06299016",
"title": "Guest Editorial: Special Issue on Computational Aesthetics in Games",
"doi": null,
"abstractUrl": "/journal/ci/2012/03/06299016/13rRUx0gehT",
"parentPublication": {
"id": "trans/ci",
"title": "IEEE Transactions on Computational Intelligence and AI in Games",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "mags/cs/2014/03/mcs2014030006",
"title": "Software Engineering for Computational Science and Engineering [Guest editors' introduction]",
"doi": null,
"abstractUrl": "/magazine/cs/2014/03/mcs2014030006/13rRUx0xQ3x",
"parentPublication": {
"id": "mags/cs",
"title": "Computing in Science & Engineering",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/ci/2014/04/06951356",
"title": "Guest Editorial: General Games",
"doi": null,
"abstractUrl": "/journal/ci/2014/04/06951356/13rRUxC0SK1",
"parentPublication": {
"id": "trans/ci",
"title": "IEEE Transactions on Computational Intelligence and AI in Games",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "mags/cs/2013/04/mcs2013040008",
"title": "Cloud Computing [Guest editorial]",
"doi": null,
"abstractUrl": "/magazine/cs/2013/04/mcs2013040008/13rRUxYINc4",
"parentPublication": {
"id": "mags/cs",
"title": "Computing in Science & Engineering",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/2012/04/ttg20120400vi",
"title": "Message from the Paper Chairs and Guest Editors",
"doi": null,
"abstractUrl": "/journal/tg/2012/04/ttg20120400vi/13rRUxly9dS",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"adjacentArticles": {
"previous": {
"fno": "06832706",
"articleId": "13rRUxbCbsL",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "06732913",
"articleId": "13rRUB7a13J",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"webExtras": [],
"articleVideos": []
}
|
{
"issue": {
"id": "1KEthkkyOw8",
"title": "Feb.",
"year": "2023",
"issueNum": "02",
"idPrefix": "co",
"pubType": "magazine",
"volume": "56",
"label": "Feb.",
"downloadables": {
"hasCover": true,
"__typename": "PeriodicalIssueDownloadablesType"
},
"__typename": "PeriodicalIssue"
},
"article": {
"id": "1KEti7i59w4",
"doi": "10.1109/MC.2022.3212091",
"abstract": "We identify the maturity level of the different requirements for artificial intelligence (AI) in autonomous driving and outline the main challenges to be addressed in the future to ensure that automotive AI systems are developed in a trustworthy way.",
"abstracts": [
{
"abstractType": "Regular",
"content": "We identify the maturity level of the different requirements for artificial intelligence (AI) in autonomous driving and outline the main challenges to be addressed in the future to ensure that automotive AI systems are developed in a trustworthy way.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "We identify the maturity level of the different requirements for artificial intelligence (AI) in autonomous driving and outline the main challenges to be addressed in the future to ensure that automotive AI systems are developed in a trustworthy way.",
"title": "Trustworthy Artificial Intelligence Requirements in the Autonomous Driving Domain",
"normalizedTitle": "Trustworthy Artificial Intelligence Requirements in the Autonomous Driving Domain",
"fno": "10042093",
"hasPdf": true,
"idPrefix": "co",
"keywords": [
"Advanced Driver Assistance Systems",
"Artificial Intelligence",
"Trusted Computing",
"Automotive AI Systems",
"Autonomous Driving Domain",
"Trustworthy Artificial Intelligence Requirements",
"Artificial Intelligence",
"Autonomous Vehicles",
"Automotive Engineering",
"Trust Computing"
],
"authors": [
{
"givenName": "David",
"surname": "Fernández-Llorca",
"fullName": "David Fernández-Llorca",
"affiliation": "Department of Computer Engineering, University of Alcalá, Alcalá de Henares, Spain",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Emilia",
"surname": "Gómez",
"fullName": "Emilia Gómez",
"affiliation": "Guest Professor, Pompeu Fabra University, Barcelona, Spain",
"__typename": "ArticleAuthorType"
}
],
"replicability": null,
"showBuyMe": false,
"showRecommendedArticles": true,
"isOpenAccess": true,
"issueNum": "02",
"pubDate": "2023-02-01 00:00:00",
"pubType": "mags",
"pages": "29-39",
"year": "2023",
"issn": "0018-9162",
"isbn": null,
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"recommendedArticles": [
{
"id": "proceedings/issrew/2017/2387/0/2387a286",
"title": "Challenges in Certification of Autonomous Driving Systems",
"doi": null,
"abstractUrl": "/proceedings-article/issrew/2017/2387a286/12OmNzIl3Cu",
"parentPublication": {
"id": "proceedings/issrew/2017/2387/0",
"title": "2017 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/synasc/2017/2626/0/262600a385",
"title": "Algorithm Fusion for Windscreen Obstruction Detection in Autonomous Driving",
"doi": null,
"abstractUrl": "/proceedings-article/synasc/2017/262600a385/17D45Xbl4P9",
"parentPublication": {
"id": "proceedings/synasc/2017/2626/0",
"title": "2017 19th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ichi/2022/6845/0/684500a626",
"title": "Towards Trustworthy Artificial Intelligence in Healthcare",
"doi": null,
"abstractUrl": "/proceedings-article/ichi/2022/684500a626/1GvdugO5Qdi",
"parentPublication": {
"id": "proceedings/ichi/2022/6845/0",
"title": "2022 IEEE 10th International Conference on Healthcare Informatics (ICHI)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "mags/co/2023/02/10042116",
"title": "Wasabi: A Conceptual Model for Trustworthy Artificial Intelligence",
"doi": null,
"abstractUrl": "/magazine/co/2023/02/10042116/1KEtjXFzM7S",
"parentPublication": {
"id": "mags/co",
"title": "Computer",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "mags/co/2023/02/10042118",
"title": "Trustworthy Autonomous Systems Through Verifiability",
"doi": null,
"abstractUrl": "/magazine/co/2023/02/10042118/1KEtmpDv8Dm",
"parentPublication": {
"id": "mags/co",
"title": "Computer",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/big-data/2022/8045/0/10021117",
"title": "Automated Guided Vehicles Challenges for Artificial Intelligence",
"doi": null,
"abstractUrl": "/proceedings-article/big-data/2022/10021117/1KfRUcjNogo",
"parentPublication": {
"id": "proceedings/big-data/2022/8045/0",
"title": "2022 IEEE International Conference on Big Data (Big Data)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cogmi/2022/7406/0/740600a027",
"title": "Inference for Trustworthy Machine Intelligence: Challenges and Solutions",
"doi": null,
"abstractUrl": "/proceedings-article/cogmi/2022/740600a027/1Lu4j0krhzq",
"parentPublication": {
"id": "proceedings/cogmi/2022/7406/0",
"title": "2022 IEEE 4th International Conference on Cognitive Machine Intelligence (CogMI)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "mags/co/2023/04/10098202",
"title": "Quality Attributes of Trustworthy Artificial Intelligence in Normative Documents and Secondary Studies: A Preliminary Review",
"doi": null,
"abstractUrl": "/magazine/co/2023/04/10098202/1Mg6hMJL75m",
"parentPublication": {
"id": "mags/co",
"title": "Computer",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icsa-c/2019/1876/0/08712153",
"title": "System/ Software Architecture for Autonomous Driving Systems",
"doi": null,
"abstractUrl": "/proceedings-article/icsa-c/2019/08712153/1a3x2y00kRq",
"parentPublication": {
"id": "proceedings/icsa-c/2019/1876/0",
"title": "2019 IEEE International Conference on Software Architecture Companion (ICSA-C)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/isvlsi/2020/5775/0/577500a476",
"title": "Towards Artificial-Intelligence-Based Cybersecurity for Robustifying Automated Driving Systems Against Camera Sensor Attacks",
"doi": null,
"abstractUrl": "/proceedings-article/isvlsi/2020/577500a476/1m1iWigdu6Y",
"parentPublication": {
"id": "proceedings/isvlsi/2020/5775/0",
"title": "2020 IEEE Computer Society Annual Symposium on VLSI (ISVLSI)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"adjacentArticles": {
"previous": {
"fno": "10042116",
"articleId": "1KEtjXFzM7S",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "10042118",
"articleId": "1KEtmpDv8Dm",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"webExtras": [],
"articleVideos": []
}
|
{
"issue": {
"id": "12OmNyoiZ12",
"title": "Feb.",
"year": "2015",
"issueNum": "02",
"idPrefix": "tg",
"pubType": "journal",
"volume": "21",
"label": "Feb.",
"downloadables": {
"hasCover": false,
"__typename": "PeriodicalIssueDownloadablesType"
},
"__typename": "PeriodicalIssue"
},
"article": {
"id": "13rRUwInuWw",
"doi": "10.1109/TVCG.2014.2354039",
"abstract": "This paper presents an approach for reconstructing polyhedral objects from single-view line drawings. Our approach separates a complex line drawing representing a manifold object into a series of simpler line drawings, based on the degree of reconstruction freedom (DRF). We then progressively reconstruct a complete 3D model from these simpler line drawings. Our experiments show that our decomposition algorithm is able to handle complex drawings which are challenging for the state of the art. The advantages of the presented progressive 3D reconstruction method over the existing reconstruction methods in terms of both robustness and efficiency are also demonstrated.",
"abstracts": [
{
"abstractType": "Regular",
"content": "This paper presents an approach for reconstructing polyhedral objects from single-view line drawings. Our approach separates a complex line drawing representing a manifold object into a series of simpler line drawings, based on the degree of reconstruction freedom (DRF). We then progressively reconstruct a complete 3D model from these simpler line drawings. Our experiments show that our decomposition algorithm is able to handle complex drawings which are challenging for the state of the art. The advantages of the presented progressive 3D reconstruction method over the existing reconstruction methods in terms of both robustness and efficiency are also demonstrated.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "This paper presents an approach for reconstructing polyhedral objects from single-view line drawings. Our approach separates a complex line drawing representing a manifold object into a series of simpler line drawings, based on the degree of reconstruction freedom (DRF). We then progressively reconstruct a complete 3D model from these simpler line drawings. Our experiments show that our decomposition algorithm is able to handle complex drawings which are challenging for the state of the art. The advantages of the presented progressive 3D reconstruction method over the existing reconstruction methods in terms of both robustness and efficiency are also demonstrated.",
"title": "Progressive 3D Reconstruction of Planar-Faced Manifold Objects with DRF-Based Line Drawing Decomposition",
"normalizedTitle": "Progressive 3D Reconstruction of Planar-Faced Manifold Objects with DRF-Based Line Drawing Decomposition",
"fno": "06891368",
"hasPdf": true,
"idPrefix": "tg",
"keywords": [
"Computer Graphics",
"Image Reconstruction",
"Planar Faced Manifold Objects",
"DRF Based Line Drawing Decomposition",
"Polyhedral Object Reconstruction",
"Single View Line Drawings",
"Complex Line Drawing",
"Degree Of Reconstruction Freedom",
"Complex Drawings",
"Progressive 3 D Reconstruction Method",
"Three Dimensional Displays",
"Manifolds",
"Shape",
"Solid Modeling",
"Educational Institutions",
"Approximation Algorithms",
"Electronic Mail",
"3 D Reconstruction",
"Degree Of Reconstruction Freedom",
"Decomposition",
"Line Drawing",
"Optimization Based"
],
"authors": [
{
"givenName": "Changqing",
"surname": "Zou",
"fullName": "Changqing Zou",
"affiliation": "Department of Physics and Electronic Information Science, Hengyang Normal University, Hengyang, China",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Shifeng",
"surname": "Chen",
"fullName": "Shifeng Chen",
"affiliation": "Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Hongbo",
"surname": "Fu",
"fullName": "Hongbo Fu",
"affiliation": "School of Creative Media, City University of Hong Kong, Hong Kong",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Jianzhuang",
"surname": "Liu",
"fullName": "Jianzhuang Liu",
"affiliation": "Media Laboratory, Huawei Technologies Co., Ltd., The Chinese University of Hong Kong, Shenzhen, China",
"__typename": "ArticleAuthorType"
}
],
"replicability": null,
"showBuyMe": true,
"showRecommendedArticles": true,
"isOpenAccess": false,
"issueNum": "02",
"pubDate": "2015-02-01 00:00:00",
"pubType": "trans",
"pages": "252-263",
"year": "2015",
"issn": "1077-2626",
"isbn": null,
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"recommendedArticles": [
{
"id": "proceedings/3dui/2009/3965/0/04811229",
"title": "Poster: Spatially augmented tape drawing",
"doi": null,
"abstractUrl": "/proceedings-article/3dui/2009/04811229/12OmNASraBm",
"parentPublication": {
"id": "proceedings/3dui/2009/3965/0",
"title": "2009 IEEE Symposium on 3D User Interfaces",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2010/6984/0/05540087",
"title": "Object cut: Complex 3D object reconstruction through line drawing separation",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2010/05540087/12OmNAYGlyJ",
"parentPublication": {
"id": "proceedings/cvpr/2010/6984/0",
"title": "2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccv/2007/1630/0/04409059",
"title": "A Divide-and-Conquer Approach to 3D Object Reconstruction from Line Drawings",
"doi": null,
"abstractUrl": "/proceedings-article/iccv/2007/04409059/12OmNBh8gRx",
"parentPublication": {
"id": "proceedings/iccv/2007/1630/0",
"title": "2007 11th IEEE International Conference on Computer Vision",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/pg/2001/1227/0/12270108",
"title": "Progressive 3D Reconstruction from a Sketch Drawing",
"doi": null,
"abstractUrl": "/proceedings-article/pg/2001/12270108/12OmNvmXJ7d",
"parentPublication": {
"id": "proceedings/pg/2001/1227/0",
"title": "Computer Graphics and Applications, Pacific Conference on",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccv/2013/2840/0/2840b433",
"title": "Complex 3D General Object Reconstruction from Line Drawings",
"doi": null,
"abstractUrl": "/proceedings-article/iccv/2013/2840b433/12OmNyfdOMu",
"parentPublication": {
"id": "proceedings/iccv/2013/2840/0",
"title": "2013 IEEE International Conference on Computer Vision (ICCV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2014/5118/0/5118a692",
"title": "Separation of Line Drawings Based on Split Faces for 3D Object Reconstruction",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2014/5118a692/12OmNzxyiDL",
"parentPublication": {
"id": "proceedings/cvpr/2014/5118/0",
"title": "2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tp/2002/12/i1579",
"title": "Identifying Faces in a 2D Line Drawing Representing a Manifold Object",
"doi": null,
"abstractUrl": "/journal/tp/2002/12/i1579/13rRUwhHcRL",
"parentPublication": {
"id": "trans/tp",
"title": "IEEE Transactions on Pattern Analysis & Machine Intelligence",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tp/2011/01/ttp2011010003",
"title": "Decomposition of Complex Line Drawings with Hidden Lines for 3D Planar-Faced Manifold Object Reconstruction",
"doi": null,
"abstractUrl": "/journal/tp/2011/01/ttp2011010003/13rRUx0xQ0E",
"parentPublication": {
"id": "trans/tp",
"title": "IEEE Transactions on Pattern Analysis & Machine Intelligence",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/5555/01/09852696",
"title": "CreatureShop: Interactive 3D Character Modeling and Texturing from a Single Color Drawing",
"doi": null,
"abstractUrl": "/journal/tg/5555/01/09852696/1FHlT4i4Pmw",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2022/6946/0/694600b612",
"title": "Neural Face Identification in a 2D Wireframe Projection of a Manifold Object",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2022/694600b612/1H0NCOwSVkQ",
"parentPublication": {
"id": "proceedings/cvpr/2022/6946/0",
"title": "2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"adjacentArticles": {
"previous": {
"fno": "06905831",
"articleId": "13rRUxASu0M",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "06905844",
"articleId": "13rRUyeCkaj",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"webExtras": [],
"articleVideos": []
}
|
{
"issue": {
"id": "12OmNxvO04X",
"title": "PrePrints",
"year": "5555",
"issueNum": "01",
"idPrefix": "tp",
"pubType": "journal",
"volume": null,
"label": "PrePrints",
"downloadables": {
"hasCover": false,
"__typename": "PeriodicalIssueDownloadablesType"
},
"__typename": "PeriodicalIssue"
},
"article": {
"id": "1KL6TgYfsLC",
"doi": "10.1109/TPAMI.2023.3244828",
"abstract": "Point clouds are characterized by irregularity and unstructuredness, which pose challenges in efficient data exploitation and discriminative feature extraction. In this paper, we present an unsupervised deep neural architecture called Flattening-Net to represent irregular 3D point clouds of arbitrary geometry and topology as a completely regular 2D point geometry image (PGI) structure, in which coordinates of spatial points are captured in colors of image pixels. Intuitively, Flattening-Net implicitly approximates a locally smooth 3D-to-2D surface flattening process while effectively preserving neighborhood consistency. As a generic representation modality, PGI inherently encodes the intrinsic property of the underlying manifold structure and facilitates surface-style point feature aggregation. To demonstrate its potential, we construct a unified learning framework directly operating on PGIs to achieve diverse types of high-level and low-level downstream applications driven by specific task networks, including classification, segmentation, reconstruction, and upsampling. Extensive experiments demonstrate that our methods perform favorably against the current state-of-the-art competitors. The source code and data are publicly available at <uri>https://github.com/keeganhk/Flattening-Net</uri>.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Point clouds are characterized by irregularity and unstructuredness, which pose challenges in efficient data exploitation and discriminative feature extraction. In this paper, we present an unsupervised deep neural architecture called Flattening-Net to represent irregular 3D point clouds of arbitrary geometry and topology as a completely regular 2D point geometry image (PGI) structure, in which coordinates of spatial points are captured in colors of image pixels. Intuitively, Flattening-Net implicitly approximates a locally smooth 3D-to-2D surface flattening process while effectively preserving neighborhood consistency. As a generic representation modality, PGI inherently encodes the intrinsic property of the underlying manifold structure and facilitates surface-style point feature aggregation. To demonstrate its potential, we construct a unified learning framework directly operating on PGIs to achieve diverse types of high-level and low-level downstream applications driven by specific task networks, including classification, segmentation, reconstruction, and upsampling. Extensive experiments demonstrate that our methods perform favorably against the current state-of-the-art competitors. The source code and data are publicly available at <uri>https://github.com/keeganhk/Flattening-Net</uri>.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Point clouds are characterized by irregularity and unstructuredness, which pose challenges in efficient data exploitation and discriminative feature extraction. In this paper, we present an unsupervised deep neural architecture called Flattening-Net to represent irregular 3D point clouds of arbitrary geometry and topology as a completely regular 2D point geometry image (PGI) structure, in which coordinates of spatial points are captured in colors of image pixels. Intuitively, Flattening-Net implicitly approximates a locally smooth 3D-to-2D surface flattening process while effectively preserving neighborhood consistency. As a generic representation modality, PGI inherently encodes the intrinsic property of the underlying manifold structure and facilitates surface-style point feature aggregation. To demonstrate its potential, we construct a unified learning framework directly operating on PGIs to achieve diverse types of high-level and low-level downstream applications driven by specific task networks, including classification, segmentation, reconstruction, and upsampling. Extensive experiments demonstrate that our methods perform favorably against the current state-of-the-art competitors. The source code and data are publicly available at https://github.com/keeganhk/Flattening-Net.",
"title": "Flattening-Net: Deep Regular 2D Representation for 3D Point Cloud Analysis",
"normalizedTitle": "Flattening-Net: Deep Regular 2D Representation for 3D Point Cloud Analysis",
"fno": "10044160",
"hasPdf": true,
"idPrefix": "tp",
"keywords": [
"Point Cloud Compression",
"Three Dimensional Displays",
"Feature Extraction",
"Geometry",
"Task Analysis",
"Surface Treatment",
"Solid Modeling",
"Deep Neural Network",
"Point Geometry Image",
"Regular Representation",
"Unsupervised Learning",
"Point Cloud"
],
"authors": [
{
"givenName": "Qijian",
"surname": "Zhang",
"fullName": "Qijian Zhang",
"affiliation": "Department of Computer Science, City University of Hong Kong, Hong Kong SAR",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Junhui",
"surname": "Hou",
"fullName": "Junhui Hou",
"affiliation": "Department of Computer Science, City University of Hong Kong, Hong Kong SAR",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Yue",
"surname": "Qian",
"fullName": "Yue Qian",
"affiliation": "Department of Computer Science, City University of Hong Kong, Hong Kong SAR",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Yiming",
"surname": "Zeng",
"fullName": "Yiming Zeng",
"affiliation": "Department of Computer Science, City University of Hong Kong, Hong Kong SAR",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Juyong",
"surname": "Zhang",
"fullName": "Juyong Zhang",
"affiliation": "School of Mathematical Sciences, University of Science and Technology of China, Hefei, Anhui, China",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Ying",
"surname": "He",
"fullName": "Ying He",
"affiliation": "School of Computer Science and Engineering, Nanyang Technological University, Singapore",
"__typename": "ArticleAuthorType"
}
],
"replicability": null,
"showBuyMe": true,
"showRecommendedArticles": true,
"isOpenAccess": false,
"issueNum": "01",
"pubDate": "2023-02-01 00:00:00",
"pubType": "trans",
"pages": "1-17",
"year": "5555",
"issn": "0162-8828",
"isbn": null,
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"recommendedArticles": [
{
"id": "trans/tg/2006/01/v0026",
"title": "Distance Preserving Flattening of Surface Sections",
"doi": null,
"abstractUrl": "/journal/tg/2006/01/v0026/13rRUyYjK59",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tp/2023/01/09693131",
"title": "Refine-Net: Normal Refinement Neural Network for Noisy Point Clouds",
"doi": null,
"abstractUrl": "/journal/tp/2023/01/09693131/1As6TjLcxmU",
"parentPublication": {
"id": "trans/tp",
"title": "IEEE Transactions on Pattern Analysis & Machine Intelligence",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tp/2023/01/09735342",
"title": "PMP-Net++: Point Cloud Completion by Transformer-Enhanced Multi-Step Point Moving Paths",
"doi": null,
"abstractUrl": "/journal/tp/2023/01/09735342/1BLmVZBJX6o",
"parentPublication": {
"id": "trans/tp",
"title": "IEEE Transactions on Pattern Analysis & Machine Intelligence",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tp/2023/03/09804752",
"title": "Intrinsic and Isotropic Resampling for 3D Point Clouds",
"doi": null,
"abstractUrl": "/journal/tp/2023/03/09804752/1ErlhDR4iI0",
"parentPublication": {
"id": "trans/tp",
"title": "IEEE Transactions on Pattern Analysis & Machine Intelligence",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/5555/01/09850404",
"title": "PU-Flow: a Point Cloud Upsampling Network with Normalizing Flows",
"doi": null,
"abstractUrl": "/journal/tg/5555/01/09850404/1Fz4SEQnoiY",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icme/2022/8563/0/09859668",
"title": "HFF-Net: Hierarchical Feature Fusion Network for Point Cloud Generation with Point Transformers",
"doi": null,
"abstractUrl": "/proceedings-article/icme/2022/09859668/1G9DKBzb6I8",
"parentPublication": {
"id": "proceedings/icme/2022/8563/0",
"title": "2022 IEEE International Conference on Multimedia and Expo (ICME)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icme/2022/8563/0/09859772",
"title": "LGP-Net: Local Geometry Preserving Network for Point Cloud Completion",
"doi": null,
"abstractUrl": "/proceedings-article/icme/2022/09859772/1G9EQKPLOpO",
"parentPublication": {
"id": "proceedings/icme/2022/8563/0",
"title": "2022 IEEE International Conference on Multimedia and Expo (ICME)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2022/6946/0/694600g328",
"title": "IDEA-Net: Dynamic 3D Point Cloud Interpolation via Deep Embedding Alignment",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2022/694600g328/1H1hTQlrJkY",
"parentPublication": {
"id": "proceedings/cvpr/2022/6946/0",
"title": "2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icvrv/2017/2636/0/263600a073",
"title": "Surface Flattening Based on Energy Fabric Deformation Model in Garment Design",
"doi": null,
"abstractUrl": "/proceedings-article/icvrv/2017/263600a073/1ap5xx2ft5e",
"parentPublication": {
"id": "proceedings/icvrv/2017/2636/0",
"title": "2017 International Conference on Virtual Reality and Visualization (ICVRV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/hpbd&is/2021/1327/0/09658452",
"title": "2D-3DMatchingNet: Multimodal Point Completion with 2D Geometry Matching",
"doi": null,
"abstractUrl": "/proceedings-article/hpbd&is/2021/09658452/1zRFmc9ALyo",
"parentPublication": {
"id": "proceedings/hpbd&is/2021/1327/0",
"title": "2021 International Conference on High Performance Big Data and Intelligent Systems (HPBD&IS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"adjacentArticles": {
"previous": {
"fno": "10044259",
"articleId": "1KL6SJ4jOzS",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "10045797",
"articleId": "1KOqIiHm4G4",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"webExtras": [
{
"id": "1KMLSNQ3X8s",
"name": "ttp555501-010044160s1-supp1-3244828.pdf",
"location": "https://www.computer.org/csdl/api/v1/extra/ttp555501-010044160s1-supp1-3244828.pdf",
"extension": "pdf",
"size": "11.2 MB",
"__typename": "WebExtraType"
}
],
"articleVideos": []
}
|
{
"issue": {
"id": "12OmNCm7Bxu",
"title": "July",
"year": "2011",
"issueNum": "07",
"idPrefix": "tg",
"pubType": "journal",
"volume": "17",
"label": "July",
"downloadables": {
"hasCover": false,
"__typename": "PeriodicalIssueDownloadablesType"
},
"__typename": "PeriodicalIssue"
},
"article": {
"id": "13rRUwInvJd",
"doi": "10.1109/TVCG.2010.248",
"abstract": "The display units integrated in today's head-mounted displays (HMDs) provide only a limited field of view (FOV) to the virtual world. In order to present an undistorted view to the virtual environment (VE), the perspective projection used to render the VE has to be adjusted to the limitations caused by the HMD characteristics. In particular, the geometric field of view (GFOV), which defines the virtual aperture angle used for rendering of the 3D scene, is set up according to the display field of view (DFOV). A discrepancy between these two fields of view distorts the geometry of the VE in a way that either minifies or magnifies the imagery displayed to the user. It has been shown that this distortion has the potential to affect a user's perception of the virtual space, sense of presence, and performance on visual search tasks. In this paper, we analyze the user's perception of a VE displayed in a HMD, which is rendered with different GFOVs. We introduce a psychophysical calibration method to determine the HMD's actual field of view, which may vary from the nominal values specified by the manufacturer. Furthermore, we conducted two experiments to identify perspective projections for HMDs, which are identified as natural by subjects—even if these perspectives deviate from the perspectives that are inherently defined by the DFOV. In the first experiment, subjects had to adjust the GFOV for a rendered virtual laboratory such that their perception of the virtual replica matched the perception of the real laboratory, which they saw before the virtual one. In the second experiment, we displayed the same virtual laboratory, but restricted the viewing condition in the real world to simulate the limited viewing condition in a HMD environment. We found that subjects evaluate a GFOV as natural when it is larger than the actual DFOV of the HMD—in some cases up to 50 percent—even when subjects viewed the real space with a limited field of view.",
"abstracts": [
{
"abstractType": "Regular",
"content": "The display units integrated in today's head-mounted displays (HMDs) provide only a limited field of view (FOV) to the virtual world. In order to present an undistorted view to the virtual environment (VE), the perspective projection used to render the VE has to be adjusted to the limitations caused by the HMD characteristics. In particular, the geometric field of view (GFOV), which defines the virtual aperture angle used for rendering of the 3D scene, is set up according to the display field of view (DFOV). A discrepancy between these two fields of view distorts the geometry of the VE in a way that either minifies or magnifies the imagery displayed to the user. It has been shown that this distortion has the potential to affect a user's perception of the virtual space, sense of presence, and performance on visual search tasks. In this paper, we analyze the user's perception of a VE displayed in a HMD, which is rendered with different GFOVs. We introduce a psychophysical calibration method to determine the HMD's actual field of view, which may vary from the nominal values specified by the manufacturer. Furthermore, we conducted two experiments to identify perspective projections for HMDs, which are identified as natural by subjects—even if these perspectives deviate from the perspectives that are inherently defined by the DFOV. In the first experiment, subjects had to adjust the GFOV for a rendered virtual laboratory such that their perception of the virtual replica matched the perception of the real laboratory, which they saw before the virtual one. In the second experiment, we displayed the same virtual laboratory, but restricted the viewing condition in the real world to simulate the limited viewing condition in a HMD environment. We found that subjects evaluate a GFOV as natural when it is larger than the actual DFOV of the HMD—in some cases up to 50 percent—even when subjects viewed the real space with a limited field of view.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "The display units integrated in today's head-mounted displays (HMDs) provide only a limited field of view (FOV) to the virtual world. In order to present an undistorted view to the virtual environment (VE), the perspective projection used to render the VE has to be adjusted to the limitations caused by the HMD characteristics. In particular, the geometric field of view (GFOV), which defines the virtual aperture angle used for rendering of the 3D scene, is set up according to the display field of view (DFOV). A discrepancy between these two fields of view distorts the geometry of the VE in a way that either minifies or magnifies the imagery displayed to the user. It has been shown that this distortion has the potential to affect a user's perception of the virtual space, sense of presence, and performance on visual search tasks. In this paper, we analyze the user's perception of a VE displayed in a HMD, which is rendered with different GFOVs. We introduce a psychophysical calibration method to determine the HMD's actual field of view, which may vary from the nominal values specified by the manufacturer. Furthermore, we conducted two experiments to identify perspective projections for HMDs, which are identified as natural by subjects—even if these perspectives deviate from the perspectives that are inherently defined by the DFOV. In the first experiment, subjects had to adjust the GFOV for a rendered virtual laboratory such that their perception of the virtual replica matched the perception of the real laboratory, which they saw before the virtual one. In the second experiment, we displayed the same virtual laboratory, but restricted the viewing condition in the real world to simulate the limited viewing condition in a HMD environment. We found that subjects evaluate a GFOV as natural when it is larger than the actual DFOV of the HMD—in some cases up to 50 percent—even when subjects viewed the real space with a limited field of view.",
"title": "Natural Perspective Projections for Head-Mounted Displays",
"normalizedTitle": "Natural Perspective Projections for Head-Mounted Displays",
"fno": "ttg2011070888",
"hasPdf": true,
"idPrefix": "tg",
"keywords": [
"Virtual Reality",
"Head Mounted Displays",
"Field Of View"
],
"authors": [
{
"givenName": "Frank",
"surname": "Steinicke",
"fullName": "Frank Steinicke",
"affiliation": "University of Münster, Münster",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Gerd",
"surname": "Bruder",
"fullName": "Gerd Bruder",
"affiliation": "University of Münster, Münster",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Scott",
"surname": "Kuhl",
"fullName": "Scott Kuhl",
"affiliation": "Michigan Technical University, Houghton",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Pete",
"surname": "Willemsen",
"fullName": "Pete Willemsen",
"affiliation": "University of Minnesota Duluth, Duluth",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Markus",
"surname": "Lappe",
"fullName": "Markus Lappe",
"affiliation": "University of Münster, Münster",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Klaus H.",
"surname": "Hinrichs",
"fullName": "Klaus H. Hinrichs",
"affiliation": "University of Münster, Münster",
"__typename": "ArticleAuthorType"
}
],
"replicability": null,
"showBuyMe": true,
"showRecommendedArticles": true,
"isOpenAccess": false,
"issueNum": "07",
"pubDate": "2011-07-01 00:00:00",
"pubType": "trans",
"pages": "888-899",
"year": "2011",
"issn": "1077-2626",
"isbn": null,
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"recommendedArticles": [
{
"id": "proceedings/ismar/2012/4660/0/06402574",
"title": "Occlusion capable optical see-through head-mounted display using freeform optics",
"doi": null,
"abstractUrl": "/proceedings-article/ismar/2012/06402574/12OmNBEpnEt",
"parentPublication": {
"id": "proceedings/ismar/2012/4660/0",
"title": "2012 IEEE International Symposium on Mixed and Augmented Reality (ISMAR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/vrais/1995/7084/0/70840084",
"title": "Design and applications of a high-resolution insert head-mounted-display",
"doi": null,
"abstractUrl": "/proceedings-article/vrais/1995/70840084/12OmNCcbE1b",
"parentPublication": {
"id": "proceedings/vrais/1995/7084/0",
"title": "Virtual Reality Annual International Symposium",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/wevr/2017/3881/0/07957709",
"title": "Immersive eating: evaluating the use of head-mounted displays for mixed reality meal sessions",
"doi": null,
"abstractUrl": "/proceedings-article/wevr/2017/07957709/12OmNwK7o9G",
"parentPublication": {
"id": "proceedings/wevr/2017/3881/0",
"title": "2017 IEEE 3rd Workshop on Everyday Virtual Reality (WEVR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/3dui/2008/2047/0/04476604",
"title": "Poster: Sliding Viewport for Head Mounted Displays in Interactive Environments",
"doi": null,
"abstractUrl": "/proceedings-article/3dui/2008/04476604/12OmNzdoMAW",
"parentPublication": {
"id": "proceedings/3dui/2008/2047/0",
"title": "2008 IEEE Symposium on 3D User Interfaces",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/2012/04/ttg2012040589",
"title": "Geometric Calibration of Head-Mounted Displays and its Effects on Distance Estimation",
"doi": null,
"abstractUrl": "/journal/tg/2012/04/ttg2012040589/13rRUwbs2b1",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/vrw/2022/8402/0/840200a640",
"title": "Towards Eye-Perspective Rendering for Optical See-Through Head-Mounted Displays",
"doi": null,
"abstractUrl": "/proceedings-article/vrw/2022/840200a640/1CJewzlI3CM",
"parentPublication": {
"id": "proceedings/vrw/2022/8402/0",
"title": "2022 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/5555/01/09850416",
"title": "Distance Perception in Virtual Reality: A Meta-Analysis of the Effect of Head-Mounted Display Characteristics",
"doi": null,
"abstractUrl": "/journal/tg/5555/01/09850416/1Fz4SPLVTMY",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ismar/2022/5325/0/532500a470",
"title": "Perceptibility of Jitter in Augmented Reality Head-Mounted Displays",
"doi": null,
"abstractUrl": "/proceedings-article/ismar/2022/532500a470/1JrQZ2SKCuQ",
"parentPublication": {
"id": "proceedings/ismar/2022/5325/0",
"title": "2022 IEEE International Symposium on Mixed and Augmented Reality (ISMAR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ismar/2020/8508/0/850800a649",
"title": "Comparing World and Screen Coordinate Systems in Optical See-Through Head-Mounted Displays for Text Readability while Walking",
"doi": null,
"abstractUrl": "/proceedings-article/ismar/2020/850800a649/1pysvKFdazS",
"parentPublication": {
"id": "proceedings/ismar/2020/8508/0",
"title": "2020 IEEE International Symposium on Mixed and Augmented Reality (ISMAR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ismar/2021/0158/0/015800a413",
"title": "Selective Foveated Ray Tracing for Head-Mounted Displays",
"doi": null,
"abstractUrl": "/proceedings-article/ismar/2021/015800a413/1yeD8bFOZos",
"parentPublication": {
"id": "proceedings/ismar/2021/0158/0",
"title": "2021 IEEE International Symposium on Mixed and Augmented Reality (ISMAR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"adjacentArticles": {
"previous": {
"fno": "ttg2011070875",
"articleId": "13rRUyYSWsP",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "ttg2011070900",
"articleId": "13rRUytWF9h",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"webExtras": [],
"articleVideos": []
}
|
{
"issue": {
"id": "1D34Iu3iR1e",
"title": "June",
"year": "2022",
"issueNum": "06",
"idPrefix": "tg",
"pubType": "journal",
"volume": "28",
"label": "June",
"downloadables": {
"hasCover": false,
"__typename": "PeriodicalIssueDownloadablesType"
},
"__typename": "PeriodicalIssue"
},
"article": {
"id": "1nTqKKPoy5i",
"doi": "10.1109/TVCG.2020.3030330",
"abstract": "In the game and film industries, modeling 3D heads plays a very important role in designing characters. Although human head modeling has been researched for a long time, few works have focused on animal-like heads, which are of more diverse shapes and richer geometric details. In this article, we present <italic>SAniHead</italic>, an interactive system for creating animal-like heads with a mesh representation from dual-view sketches. Our core technical contribution is a view-surface collaborative mesh generative network. Initially, a graph convolutional neural network (GCNN) is trained to learn the deformation of a template mesh to fit the shape of sketches, giving rise to a coarse model. It is then projected into vertex maps where image-to-image translation networks are performed for detail inference. After back-projecting the inferred details onto the meshed surface, a new GCNN is trained for further detail refinement. The modules of view-based detail inference and surface-based detail refinement are conducted in an alternating cascaded fashion, collaboratively improving the model. A refinement sketching interface is also implemented to support direct mesh manipulation. Experimental results show the superiority of our approach and the usability of our interactive system. Our work also contributes a 3D animal head dataset with corresponding line drawings.",
"abstracts": [
{
"abstractType": "Regular",
"content": "In the game and film industries, modeling 3D heads plays a very important role in designing characters. Although human head modeling has been researched for a long time, few works have focused on animal-like heads, which are of more diverse shapes and richer geometric details. In this article, we present <italic>SAniHead</italic>, an interactive system for creating animal-like heads with a mesh representation from dual-view sketches. Our core technical contribution is a view-surface collaborative mesh generative network. Initially, a graph convolutional neural network (GCNN) is trained to learn the deformation of a template mesh to fit the shape of sketches, giving rise to a coarse model. It is then projected into vertex maps where image-to-image translation networks are performed for detail inference. After back-projecting the inferred details onto the meshed surface, a new GCNN is trained for further detail refinement. The modules of view-based detail inference and surface-based detail refinement are conducted in an alternating cascaded fashion, collaboratively improving the model. A refinement sketching interface is also implemented to support direct mesh manipulation. Experimental results show the superiority of our approach and the usability of our interactive system. Our work also contributes a 3D animal head dataset with corresponding line drawings.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "In the game and film industries, modeling 3D heads plays a very important role in designing characters. Although human head modeling has been researched for a long time, few works have focused on animal-like heads, which are of more diverse shapes and richer geometric details. In this article, we present SAniHead, an interactive system for creating animal-like heads with a mesh representation from dual-view sketches. Our core technical contribution is a view-surface collaborative mesh generative network. Initially, a graph convolutional neural network (GCNN) is trained to learn the deformation of a template mesh to fit the shape of sketches, giving rise to a coarse model. It is then projected into vertex maps where image-to-image translation networks are performed for detail inference. After back-projecting the inferred details onto the meshed surface, a new GCNN is trained for further detail refinement. The modules of view-based detail inference and surface-based detail refinement are conducted in an alternating cascaded fashion, collaboratively improving the model. A refinement sketching interface is also implemented to support direct mesh manipulation. Experimental results show the superiority of our approach and the usability of our interactive system. Our work also contributes a 3D animal head dataset with corresponding line drawings.",
"title": "<italic>SAniHead:</italic> Sketching Animal-Like 3D Character Heads Using a View-Surface Collaborative Mesh Generative Network",
"normalizedTitle": "SAniHead: Sketching Animal-Like 3D Character Heads Using a View-Surface Collaborative Mesh Generative Network",
"fno": "09222121",
"hasPdf": true,
"idPrefix": "tg",
"keywords": [
"Computer Animation",
"Mesh Generation",
"Neural Nets",
"Solid Modelling",
"Dual View Sketches",
"View Surface Collaborative Mesh Generative Network",
"Graph Convolutional Neural Network",
"Template Mesh",
"Coarse Model",
"Image To Image Translation Networks",
"Meshed Surface",
"View Based Detail Inference",
"Surface Based Detail Refinement",
"Refinement Sketching Interface",
"Direct Mesh Manipulation",
"Interactive System",
"3 D Animal Head Dataset",
"S Ani Head",
"3 D Character Heads",
"Human Head Modeling",
"Animal Like Heads",
"Mesh Representation",
"Three Dimensional Displays",
"Shape",
"Solid Modeling",
"Head",
"Computational Modeling",
"Collaboration",
"Strain",
"Sketch Based 3 D Modeling",
"Graph Convolutional Neural Network",
"Animal Like Character Heads"
],
"authors": [
{
"givenName": "Dong",
"surname": "Du",
"fullName": "Dong Du",
"affiliation": "University of Science and Technology of China, Hefei, Anhui, China",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Xiaoguang",
"surname": "Han",
"fullName": "Xiaoguang Han",
"affiliation": "Chinese University of Hong Kong, Shenzhen",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Hongbo",
"surname": "Fu",
"fullName": "Hongbo Fu",
"affiliation": "City University of Hong Kong, Hong Kong",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Feiyang",
"surname": "Wu",
"fullName": "Feiyang Wu",
"affiliation": "Chinese University of Hong Kong, Shenzhen",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Yizhou",
"surname": "Yu",
"fullName": "Yizhou Yu",
"affiliation": "University of Hong Kong, Hong Kong",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Shuguang",
"surname": "Cui",
"fullName": "Shuguang Cui",
"affiliation": "Chinese University of Hong Kong, Shenzhen",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Ligang",
"surname": "Liu",
"fullName": "Ligang Liu",
"affiliation": "University of Science and Technology of China, Hefei, Anhui, China",
"__typename": "ArticleAuthorType"
}
],
"replicability": null,
"showBuyMe": true,
"showRecommendedArticles": true,
"isOpenAccess": false,
"issueNum": "06",
"pubDate": "2022-06-01 00:00:00",
"pubType": "trans",
"pages": "2415-2429",
"year": "2022",
"issn": "1077-2626",
"isbn": null,
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"recommendedArticles": [
{
"id": "proceedings/iiki/2016/5952/0/5952a255",
"title": "Biomechanical Model for Detection of Vertigo Disease",
"doi": null,
"abstractUrl": "/proceedings-article/iiki/2016/5952a255/12OmNrYlmJw",
"parentPublication": {
"id": "proceedings/iiki/2016/5952/0",
"title": "2016 International Conference on Identification, Information and Knowledge in the Internet of Things (IIKI)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/taai/2012/4976/0/06395035",
"title": "A Visualization System for Animating Vertebrate Animal Models",
"doi": null,
"abstractUrl": "/proceedings-article/taai/2012/06395035/12OmNwKGAo5",
"parentPublication": {
"id": "proceedings/taai/2012/4976/0",
"title": "2012 Conference on Technologies and Applications of Artificial Intelligence (TAAI)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvprw/2011/0529/0/05981742",
"title": "Deformable image alignment as a source of stereo correspondences on portraits",
"doi": null,
"abstractUrl": "/proceedings-article/cvprw/2011/05981742/12OmNyL0TMs",
"parentPublication": {
"id": "proceedings/cvprw/2011/0529/0",
"title": "CVPR 2011 WORKSHOPS",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/fg/2018/2335/0/233501a404",
"title": "A Data-Augmented 3D Morphable Model of the Ear",
"doi": null,
"abstractUrl": "/proceedings-article/fg/2018/233501a404/12OmNz5apCl",
"parentPublication": {
"id": "proceedings/fg/2018/2335/0",
"title": "2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2018/6420/0/642000d955",
"title": "Lions and Tigers and Bears: Capturing Non-rigid, 3D, Articulated Shape from Images",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2018/642000d955/17D45WHONjC",
"parentPublication": {
"id": "proceedings/cvpr/2018/6420/0",
"title": "2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/2020/07/08580421",
"title": "CaricatureShop: Personalized and Photorealistic Caricature Sketching",
"doi": null,
"abstractUrl": "/journal/tg/2020/07/08580421/17D45XfSEU4",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2020/7168/0/716800f232",
"title": "Transferring Dense Pose to Proximal Animal Classes",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2020/716800f232/1m3nBjZH6xi",
"parentPublication": {
"id": "proceedings/cvpr/2020/7168/0",
"title": "2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2020/7168/0/716800g468",
"title": "Learning to Dress 3D People in Generative Clothing",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2020/716800g468/1m3nwUHFD68",
"parentPublication": {
"id": "proceedings/cvpr/2020/7168/0",
"title": "2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icalt/2021/4106/0/410600a422",
"title": "VEA: A Virtual Environment for Animal experimentation",
"doi": null,
"abstractUrl": "/proceedings-article/icalt/2021/410600a422/1vK09wwDOBW",
"parentPublication": {
"id": "proceedings/icalt/2021/4106/0",
"title": "2021 International Conference on Advanced Learning Technologies (ICALT)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2021/4509/0/450900m2798",
"title": "i3DMM: Deep Implicit 3D Morphable Model of Human Heads",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2021/450900m2798/1yeLR7aJqiA",
"parentPublication": {
"id": "proceedings/cvpr/2021/4509/0",
"title": "2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"adjacentArticles": {
"previous": {
"fno": "09226608",
"articleId": "1nYoYAgcD5e",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "09234096",
"articleId": "1o546fdr6GA",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"webExtras": [
{
"id": "1D34UoHxGI8",
"name": "ttg202206-09222121s1-supp1-3030330.mp4",
"location": "https://www.computer.org/csdl/api/v1/extra/ttg202206-09222121s1-supp1-3030330.mp4",
"extension": "mp4",
"size": "26.6 MB",
"__typename": "WebExtraType"
},
{
"id": "1D34TIOi0xO",
"name": "ttg202206-09222121s1-supp2-3030330.pdf",
"location": "https://www.computer.org/csdl/api/v1/extra/ttg202206-09222121s1-supp2-3030330.pdf",
"extension": "pdf",
"size": "392 kB",
"__typename": "WebExtraType"
}
],
"articleVideos": []
}
|
{
"issue": {
"id": "12OmNwMob9C",
"title": "April",
"year": "2018",
"issueNum": "04",
"idPrefix": "tg",
"pubType": "journal",
"volume": "24",
"label": "April",
"downloadables": {
"hasCover": false,
"__typename": "PeriodicalIssueDownloadablesType"
},
"__typename": "PeriodicalIssue"
},
"article": {
"id": "13rRUwInvJm",
"doi": "10.1109/TVCG.2018.2793680",
"abstract": "This article investigates the effects of visual warning presentation methods on human performance in augmented reality (AR) driving. An experimental user study was conducted in a parking lot where participants drove a test vehicle while braking for any cross traffic with assistance from AR visual warnings presented on a monoscopic and volumetric head-up display (HUD). Results showed that monoscopic displays can be as effective as volumetric displays for human performance in AR braking tasks. The experiment also demonstrated the benefits of conformal graphics, which are tightly integrated into the real world, such as their ability to guide drivers' attention and their positive consequences on driver behavior and performance. These findings suggest that conformal graphics presented via monoscopic HUDs can enhance driver performance by leveraging the effectiveness of monocular depth cues. The proposed approaches and methods can be used and further developed by future researchers and practitioners to better understand driver performance in AR as well as inform usability evaluation of future automotive AR applications.",
"abstracts": [
{
"abstractType": "Regular",
"content": "This article investigates the effects of visual warning presentation methods on human performance in augmented reality (AR) driving. An experimental user study was conducted in a parking lot where participants drove a test vehicle while braking for any cross traffic with assistance from AR visual warnings presented on a monoscopic and volumetric head-up display (HUD). Results showed that monoscopic displays can be as effective as volumetric displays for human performance in AR braking tasks. The experiment also demonstrated the benefits of conformal graphics, which are tightly integrated into the real world, such as their ability to guide drivers' attention and their positive consequences on driver behavior and performance. These findings suggest that conformal graphics presented via monoscopic HUDs can enhance driver performance by leveraging the effectiveness of monocular depth cues. The proposed approaches and methods can be used and further developed by future researchers and practitioners to better understand driver performance in AR as well as inform usability evaluation of future automotive AR applications.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "This article investigates the effects of visual warning presentation methods on human performance in augmented reality (AR) driving. An experimental user study was conducted in a parking lot where participants drove a test vehicle while braking for any cross traffic with assistance from AR visual warnings presented on a monoscopic and volumetric head-up display (HUD). Results showed that monoscopic displays can be as effective as volumetric displays for human performance in AR braking tasks. The experiment also demonstrated the benefits of conformal graphics, which are tightly integrated into the real world, such as their ability to guide drivers' attention and their positive consequences on driver behavior and performance. These findings suggest that conformal graphics presented via monoscopic HUDs can enhance driver performance by leveraging the effectiveness of monocular depth cues. The proposed approaches and methods can be used and further developed by future researchers and practitioners to better understand driver performance in AR as well as inform usability evaluation of future automotive AR applications.",
"title": "Driver Behavior and Performance with Augmented Reality Pedestrian Collision Warning: An Outdoor User Study",
"normalizedTitle": "Driver Behavior and Performance with Augmented Reality Pedestrian Collision Warning: An Outdoor User Study",
"fno": "08302393",
"hasPdf": true,
"idPrefix": "tg",
"keywords": [
"Three Dimensional Displays",
"Visualization",
"Vehicles",
"Task Analysis",
"Stereo Image Processing",
"Observers",
"Augmented Reality",
"Human Performance",
"Automotive",
"Depth Cues",
"Three Dimensional Displays"
],
"authors": [
{
"givenName": "Hyungil",
"surname": "Kim",
"fullName": "Hyungil Kim",
"affiliation": "Virginia Tech.",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Joseph L.",
"surname": "Gabbard",
"fullName": "Joseph L. Gabbard",
"affiliation": "Virginia Tech.",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Alexandre Miranda",
"surname": "Anon",
"fullName": "Alexandre Miranda Anon",
"affiliation": "Honda Research Institute, USA",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Teruhisa",
"surname": "Misu",
"fullName": "Teruhisa Misu",
"affiliation": "Honda Research Institute, USA",
"__typename": "ArticleAuthorType"
}
],
"replicability": null,
"showBuyMe": true,
"showRecommendedArticles": true,
"isOpenAccess": false,
"issueNum": "04",
"pubDate": "2018-04-01 00:00:00",
"pubType": "trans",
"pages": "1515-1524",
"year": "2018",
"issn": "1077-2626",
"isbn": null,
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"recommendedArticles": [
{
"id": "proceedings/vr/2016/0836/0/07504725",
"title": "Casting shadows: Ecological interface design for augmented reality pedestrian collision warning",
"doi": null,
"abstractUrl": "/proceedings-article/vr/2016/07504725/12OmNC8uRtR",
"parentPublication": {
"id": "proceedings/vr/2016/0836/0",
"title": "2016 IEEE Virtual Reality (VR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/is3c/2016/3071/0/3071a740",
"title": "Pedestrian Collision Warning of Advanced Driver Assistance Systems",
"doi": null,
"abstractUrl": "/proceedings-article/is3c/2016/3071a740/12OmNscOUdQ",
"parentPublication": {
"id": "proceedings/is3c/2016/3071/0",
"title": "2016 International Symposium on Computer, Consumer and Control (IS3C)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ismar/2015/7660/0/7660a180",
"title": "[POSTER] Interactive Visualizations for Monoscopic Eyewear to Assist in Manually Orienting Objects in 3D",
"doi": null,
"abstractUrl": "/proceedings-article/ismar/2015/7660a180/12OmNvDI3Y2",
"parentPublication": {
"id": "proceedings/ismar/2015/7660/0",
"title": "2015 IEEE International Symposium on Mixed and Augmented Reality (ISMAR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/re/2014/3031/0/06912275",
"title": "Simulation-based requirements discovery for smart driver assistive technologies",
"doi": null,
"abstractUrl": "/proceedings-article/re/2014/06912275/12OmNx3HI93",
"parentPublication": {
"id": "proceedings/re/2014/3031/0",
"title": "2014 IEEE 22nd International Requirements Engineering Conference (RE)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/fg/2015/6026/1/07163164",
"title": "3D interaction design: Increasing the stimulus-response correspondence by using stereoscopic vision",
"doi": null,
"abstractUrl": "/proceedings-article/fg/2015/07163164/12OmNxR5UKi",
"parentPublication": {
"id": "proceedings/fg/2015/6026/5",
"title": "2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/compsac/2015/6564/2/6564b563",
"title": "Real-Time Lane Detection and Rear-End Collision Warning System on a Mobile Computing Platform",
"doi": null,
"abstractUrl": "/proceedings-article/compsac/2015/6564b563/12OmNxecRQi",
"parentPublication": {
"id": "compsac/2015/6564/2",
"title": "2015 IEEE 39th Annual Computer Software and Applications Conference (COMPSAC)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/3dvis/2014/6826/0/07160095",
"title": "Beyond the classical monoscopic 3D in graph analytics: An experimental study of the impact of stereoscopy",
"doi": null,
"abstractUrl": "/proceedings-article/3dvis/2014/07160095/12OmNxwENmo",
"parentPublication": {
"id": "proceedings/3dvis/2014/6826/0",
"title": "2014 IEEE VIS International Workshop on 3DVis (3DVis)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/dasc-picom-datacom-cyberscitech/2017/1956/0/08328376",
"title": "Analysis of the Impact of Driver Behavior Models on Performance of Forward Collision Warning Systems",
"doi": null,
"abstractUrl": "/proceedings-article/dasc-picom-datacom-cyberscitech/2017/08328376/17D45WgziRa",
"parentPublication": {
"id": "proceedings/dasc-picom-datacom-cyberscitech/2017/1956/0",
"title": "2017 IEEE 15th Intl Conf on Dependable, Autonomic and Secure Computing, 15th Intl Conf on Pervasive Intelligence and Computing, 3rd Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/PiCom/DataCom/CyberSciTech)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/vrw/2020/6532/0/09090511",
"title": "Evaluating Automotive Augmented Reality Head-up Display Effects on Driver Performance and Distraction",
"doi": null,
"abstractUrl": "/proceedings-article/vrw/2020/09090511/1jIxviTG03C",
"parentPublication": {
"id": "proceedings/vrw/2020/6532/0",
"title": "2020 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/vrw/2020/6532/0/09090631",
"title": "Framing the Scene: An Examination of Augmented Reality Head Worn Displays in Construction Assembly Tasks",
"doi": null,
"abstractUrl": "/proceedings-article/vrw/2020/09090631/1jIxyGx0KXK",
"parentPublication": {
"id": "proceedings/vrw/2020/6532/0",
"title": "2020 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"adjacentArticles": {
"previous": {
"fno": "08267106",
"articleId": "13rRUwIF6dW",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "08302409",
"articleId": "13rRUxcbnCw",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"webExtras": [],
"articleVideos": []
}
|
{
"issue": {
"id": "12OmNxb5hpi",
"title": "February",
"year": "1994",
"issueNum": "01",
"idPrefix": "ex",
"pubType": "magazine",
"volume": "9",
"label": "February",
"downloadables": {
"hasCover": false,
"__typename": "PeriodicalIssueDownloadablesType"
},
"__typename": "PeriodicalIssue"
},
"article": {
"id": "13rRUwcAqna",
"doi": "10.1109/64.295134",
"abstract": "Processing natural language such as English has always been one of the central research issues of artificial intelligence, both because of the key role language plays in human intelligence and because of the wealth of potential applications. Many of the knowledge representation and inference techniques that have been applied successfully in knowledge-based systems were originally developed for processing natural language, but the language-processing applications themselves have always seemed far from being realized. The special series on natural-language processing is an attempt to bring language processing and its applications into focus/spl minus/to demonstrate techniques that have recently been applied to real-world problems, to identify research ripe for practical exploitation, and to illustrate some promising combinations of natural-language processing with other emerging technologies. Each of the four articles in the series provides some insight into the state of the art and conveys the practical significance of recent research in the field.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Processing natural language such as English has always been one of the central research issues of artificial intelligence, both because of the key role language plays in human intelligence and because of the wealth of potential applications. Many of the knowledge representation and inference techniques that have been applied successfully in knowledge-based systems were originally developed for processing natural language, but the language-processing applications themselves have always seemed far from being realized. The special series on natural-language processing is an attempt to bring language processing and its applications into focus/spl minus/to demonstrate techniques that have recently been applied to real-world problems, to identify research ripe for practical exploitation, and to illustrate some promising combinations of natural-language processing with other emerging technologies. Each of the four articles in the series provides some insight into the state of the art and conveys the practical significance of recent research in the field.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Processing natural language such as English has always been one of the central research issues of artificial intelligence, both because of the key role language plays in human intelligence and because of the wealth of potential applications. Many of the knowledge representation and inference techniques that have been applied successfully in knowledge-based systems were originally developed for processing natural language, but the language-processing applications themselves have always seemed far from being realized. The special series on natural-language processing is an attempt to bring language processing and its applications into focus/spl minus/to demonstrate techniques that have recently been applied to real-world problems, to identify research ripe for practical exploitation, and to illustrate some promising combinations of natural-language processing with other emerging technologies. Each of the four articles in the series provides some insight into the state of the art and conveys the practical significance of recent research in the field.",
"title": "Guest Editor's Introduction: Natural-Language Processing",
"normalizedTitle": "Guest Editor's Introduction: Natural-Language Processing",
"fno": "x1035",
"hasPdf": true,
"idPrefix": "ex",
"keywords": [],
"authors": [
{
"givenName": "Terry",
"surname": "Patten",
"fullName": "Terry Patten",
"affiliation": null,
"__typename": "ArticleAuthorType"
},
{
"givenName": "Paul",
"surname": "Jacobs",
"fullName": "Paul Jacobs",
"affiliation": null,
"__typename": "ArticleAuthorType"
}
],
"replicability": null,
"showBuyMe": false,
"showRecommendedArticles": false,
"isOpenAccess": true,
"issueNum": "01",
"pubDate": "1994-01-01 00:00:00",
"pubType": "mags",
"pages": "35",
"year": "1994",
"issn": "1541-1672",
"isbn": null,
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"recommendedArticles": [],
"adjacentArticles": {
"previous": {
"fno": "x1028",
"articleId": "13rRUy3gn1L",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "x1036",
"articleId": "13rRUyfbwuN",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"webExtras": [],
"articleVideos": []
}
|
{
"issue": {
"id": "12OmNwFid72",
"title": "April",
"year": "1994",
"issueNum": "02",
"idPrefix": "ex",
"pubType": "magazine",
"volume": "9",
"label": "April",
"downloadables": {
"hasCover": false,
"__typename": "PeriodicalIssueDownloadablesType"
},
"__typename": "PeriodicalIssue"
},
"article": {
"id": "13rRUxBJhzA",
"doi": "10.1109/64.294135",
"abstract": "Improved numerical weather prediction simulations have led weather services to examine how and where human forecasters add value to forecast production. The Forecast Production Assistant (FPA) was developed with that in mind. The authors discuss the Forecast Generator (FOG), the first application developed on the FPA. FOG is a bilingual report generator that produces routine and special purpose forecast directly from the FPA's graphical weather predictions. Using rules and a natural-language generator, FOG converts weather maps into forecast text. The natural-language issues involved are relevant to anyone designing a similar system.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Improved numerical weather prediction simulations have led weather services to examine how and where human forecasters add value to forecast production. The Forecast Production Assistant (FPA) was developed with that in mind. The authors discuss the Forecast Generator (FOG), the first application developed on the FPA. FOG is a bilingual report generator that produces routine and special purpose forecast directly from the FPA's graphical weather predictions. Using rules and a natural-language generator, FOG converts weather maps into forecast text. The natural-language issues involved are relevant to anyone designing a similar system.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Improved numerical weather prediction simulations have led weather services to examine how and where human forecasters add value to forecast production. The Forecast Production Assistant (FPA) was developed with that in mind. The authors discuss the Forecast Generator (FOG), the first application developed on the FPA. FOG is a bilingual report generator that produces routine and special purpose forecast directly from the FPA's graphical weather predictions. Using rules and a natural-language generator, FOG converts weather maps into forecast text. The natural-language issues involved are relevant to anyone designing a similar system.",
"title": "Using Natural-Language Processing to Produce Weather Forecasts",
"normalizedTitle": "Using Natural-Language Processing to Produce Weather Forecasts",
"fno": "x2045",
"hasPdf": true,
"idPrefix": "ex",
"keywords": [],
"authors": [
{
"givenName": "Eli",
"surname": "Goldberg",
"fullName": "Eli Goldberg",
"affiliation": null,
"__typename": "ArticleAuthorType"
},
{
"givenName": "Norbert",
"surname": "Driedger",
"fullName": "Norbert Driedger",
"affiliation": null,
"__typename": "ArticleAuthorType"
},
{
"givenName": "Richard I.",
"surname": "Kittredge",
"fullName": "Richard I. Kittredge",
"affiliation": null,
"__typename": "ArticleAuthorType"
}
],
"replicability": null,
"showBuyMe": true,
"showRecommendedArticles": false,
"isOpenAccess": false,
"issueNum": "02",
"pubDate": "1994-03-01 00:00:00",
"pubType": "mags",
"pages": "45-53",
"year": "1994",
"issn": "1541-1672",
"isbn": null,
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"recommendedArticles": [],
"adjacentArticles": {
"previous": {
"fno": "x2040",
"articleId": "13rRUwbaqQu",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "x2054",
"articleId": "13rRUxZRbtD",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"webExtras": [],
"articleVideos": []
}
|
{
"issue": {
"id": "12OmNAPBbcB",
"title": "Jan.-Feb.",
"year": "2019",
"issueNum": "01",
"idPrefix": "tb",
"pubType": "journal",
"volume": "16",
"label": "Jan.-Feb.",
"downloadables": {
"hasCover": false,
"__typename": "PeriodicalIssueDownloadablesType"
},
"__typename": "PeriodicalIssue"
},
"article": {
"id": "17D45X2fUFf",
"doi": "10.1109/TCBB.2018.2849968",
"abstract": "This article reviews recent advances in applying natural language processing (NLP) to Electronic Health Records (EHRs) for computational phenotyping. NLP-based computational phenotyping has numerous applications including diagnosis categorization, novel phenotype discovery, clinical trial screening, pharmacogenomics, drug-drug interaction (DDI), and adverse drug event (ADE) detection, as well as genome-wide and phenome-wide association studies. Significant progress has been made in algorithm development and resource construction for computational phenotyping. Among the surveyed methods, well-designed keyword search and rule-based systems often achieve good performance. However, the construction of keyword and rule lists requires significant manual effort, which is difficult to scale. Supervised machine learning models have been favored because they are capable of acquiring both classification patterns and structures from data. Recently, deep learning and unsupervised learning have received growing attention, with the former favored for its performance and the latter for its ability to find novel phenotypes. Integrating heterogeneous data sources have become increasingly important and have shown promise in improving model performance. Often, better performance is achieved by combining multiple modalities of information. Despite these many advances, challenges and opportunities remain for NLP-based computational phenotyping, including better model interpretability and generalizability, and proper characterization of feature relations in clinical narratives.",
"abstracts": [
{
"abstractType": "Regular",
"content": "This article reviews recent advances in applying natural language processing (NLP) to Electronic Health Records (EHRs) for computational phenotyping. NLP-based computational phenotyping has numerous applications including diagnosis categorization, novel phenotype discovery, clinical trial screening, pharmacogenomics, drug-drug interaction (DDI), and adverse drug event (ADE) detection, as well as genome-wide and phenome-wide association studies. Significant progress has been made in algorithm development and resource construction for computational phenotyping. Among the surveyed methods, well-designed keyword search and rule-based systems often achieve good performance. However, the construction of keyword and rule lists requires significant manual effort, which is difficult to scale. Supervised machine learning models have been favored because they are capable of acquiring both classification patterns and structures from data. Recently, deep learning and unsupervised learning have received growing attention, with the former favored for its performance and the latter for its ability to find novel phenotypes. Integrating heterogeneous data sources have become increasingly important and have shown promise in improving model performance. Often, better performance is achieved by combining multiple modalities of information. Despite these many advances, challenges and opportunities remain for NLP-based computational phenotyping, including better model interpretability and generalizability, and proper characterization of feature relations in clinical narratives.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "This article reviews recent advances in applying natural language processing (NLP) to Electronic Health Records (EHRs) for computational phenotyping. NLP-based computational phenotyping has numerous applications including diagnosis categorization, novel phenotype discovery, clinical trial screening, pharmacogenomics, drug-drug interaction (DDI), and adverse drug event (ADE) detection, as well as genome-wide and phenome-wide association studies. Significant progress has been made in algorithm development and resource construction for computational phenotyping. Among the surveyed methods, well-designed keyword search and rule-based systems often achieve good performance. However, the construction of keyword and rule lists requires significant manual effort, which is difficult to scale. Supervised machine learning models have been favored because they are capable of acquiring both classification patterns and structures from data. Recently, deep learning and unsupervised learning have received growing attention, with the former favored for its performance and the latter for its ability to find novel phenotypes. Integrating heterogeneous data sources have become increasingly important and have shown promise in improving model performance. Often, better performance is achieved by combining multiple modalities of information. Despite these many advances, challenges and opportunities remain for NLP-based computational phenotyping, including better model interpretability and generalizability, and proper characterization of feature relations in clinical narratives.",
"title": "Natural Language Processing for EHR-Based Computational Phenotyping",
"normalizedTitle": "Natural Language Processing for EHR-Based Computational Phenotyping",
"fno": "08395074",
"hasPdf": true,
"idPrefix": "tb",
"keywords": [
"Drugs",
"Electronic Health Records",
"Genomics",
"Natural Language Processing",
"Supervised Learning",
"Unsupervised Learning",
"EHR Based Computational",
"Natural Language Processing",
"NLP Based Computational Phenotyping",
"Numerous Applications Including Diagnosis Categorization",
"Novel Phenotype Discovery",
"Drug Drug Interaction",
"Adverse Drug Event Detection",
"Phenome Wide Association Studies",
"Well Designed Keyword Search",
"Rule Based Systems",
"Supervised Machine Learning Models",
"Electronic Health Records",
"Clinical Trials",
"Drugs",
"Bioinformatics",
"Genomics",
"Diseases",
"Natural Language Processing",
"Keyword Search",
"Electronic Health Records",
"Natural Language Processing",
"Computational Phenotyping",
"Machine Learning"
],
"authors": [
{
"givenName": "Zexian",
"surname": "Zeng",
"fullName": "Zexian Zeng",
"affiliation": "Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Yu",
"surname": "Deng",
"fullName": "Yu Deng",
"affiliation": "Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Xiaoyu",
"surname": "Li",
"fullName": "Xiaoyu Li",
"affiliation": "Department of Social and Behavioral Sciences, Harvard T. H. Chan School of Public Health, Harvard University, Boston, MA, USA",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Tristan",
"surname": "Naumann",
"fullName": "Tristan Naumann",
"affiliation": "Computer Science and Artificial Intelligence Lab, Massachusetts Institue of Technology, Cambridge, MA, USA",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Yuan",
"surname": "Luo",
"fullName": "Yuan Luo",
"affiliation": "Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA",
"__typename": "ArticleAuthorType"
}
],
"replicability": null,
"showBuyMe": true,
"showRecommendedArticles": true,
"isOpenAccess": false,
"issueNum": "01",
"pubDate": "2019-01-01 00:00:00",
"pubType": "trans",
"pages": "139-153",
"year": "2019",
"issn": "1545-5963",
"isbn": null,
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"recommendedArticles": [
{
"id": "proceedings/bibm/2011/1799/0/06120519",
"title": "Hypothesis Generation and Evaluation in Clinical Trial Design",
"doi": null,
"abstractUrl": "/proceedings-article/bibm/2011/06120519/12OmNvrvjd8",
"parentPublication": {
"id": "proceedings/bibm/2011/1799/0",
"title": "2011 IEEE International Conference on Bioinformatics and Biomedicine",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ichi/2016/6117/0/6117a399",
"title": "Deep State Space Models for Computational Phenotyping",
"doi": null,
"abstractUrl": "/proceedings-article/ichi/2016/6117a399/12OmNyQ7FGr",
"parentPublication": {
"id": "proceedings/ichi/2016/6117/0",
"title": "2016 IEEE International Conference on Healthcare Informatics (ICHI)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/big-data/2021/3902/0/09671766",
"title": "Drug-Drug Interaction Prediction: a Purely SMILES Based Approach",
"doi": null,
"abstractUrl": "/proceedings-article/big-data/2021/09671766/1A8iXG3IPIY",
"parentPublication": {
"id": "proceedings/big-data/2021/3902/0",
"title": "2021 IEEE International Conference on Big Data (Big Data)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bibm/2021/0126/0/09669340",
"title": "Self-Supervised Learning with Heterogeneous Graph Neural Network for COVID-19 Drug Recommendation",
"doi": null,
"abstractUrl": "/proceedings-article/bibm/2021/09669340/1A9VePaj9WE",
"parentPublication": {
"id": "proceedings/bibm/2021/0126/0",
"title": "2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cmbs/2022/6770/0/677000a435",
"title": "Drug repositioning with gender perspective focused on Adverse Drug Reactions",
"doi": null,
"abstractUrl": "/proceedings-article/cmbs/2022/677000a435/1GhVXDoWPFS",
"parentPublication": {
"id": "proceedings/cmbs/2022/6770/0",
"title": "2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ictai/2022/9744/0/974400b093",
"title": "Drug Side Effects Prediction via Heterogeneous Multi-Relational Graph Convolutional Networks",
"doi": null,
"abstractUrl": "/proceedings-article/ictai/2022/974400b093/1MrFMg4BxVm",
"parentPublication": {
"id": "proceedings/ictai/2022/9744/0",
"title": "2022 IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/aiccsa/2019/5052/0/09035341",
"title": "Exploiting Ethereum Smart Contracts for Clinical Trial Management",
"doi": null,
"abstractUrl": "/proceedings-article/aiccsa/2019/09035341/1ifhurmqzSM",
"parentPublication": {
"id": "proceedings/aiccsa/2019/5052/0",
"title": "2019 IEEE/ACS 16th International Conference on Computer Systems and Applications (AICCSA)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iv/2020/9134/0/913400a344",
"title": "Comparison of four visual analytics techniques for the visualization of adverse drug event rates in clinical trials",
"doi": null,
"abstractUrl": "/proceedings-article/iv/2020/913400a344/1rSRc4omAj6",
"parentPublication": {
"id": "proceedings/iv/2020/9134/0",
"title": "2020 24th International Conference Information Visualisation (IV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ichi/2020/5382/0/09374395",
"title": "Detecting Potential Adverse Drug Reactions of Preschool ADHD Treatment Using Health Consumer-Generated Content",
"doi": null,
"abstractUrl": "/proceedings-article/ichi/2020/09374395/1rUJ0jlaToQ",
"parentPublication": {
"id": "proceedings/ichi/2020/5382/0",
"title": "2020 IEEE International Conference on Healthcare Informatics (ICHI)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/jcdl/2021/1770/0/177000a291",
"title": "Building the COVID-19 Portal By Integrating Literature, Clinical Trials, and Knowledge Graphs",
"doi": null,
"abstractUrl": "/proceedings-article/jcdl/2021/177000a291/1zJmRq80SaY",
"parentPublication": {
"id": "proceedings/jcdl/2021/1770/0",
"title": "2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"adjacentArticles": {
"previous": {
"fno": "08382215",
"articleId": "17D45XuDNIt",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "08350090",
"articleId": "17D45XeKgra",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"webExtras": [],
"articleVideos": []
}
|
{
"issue": {
"id": "1AH2Z6Gevo4",
"title": "Jan.-Feb.",
"year": "2022",
"issueNum": "01",
"idPrefix": "tb",
"pubType": "journal",
"volume": "19",
"label": "Jan.-Feb.",
"downloadables": {
"hasCover": false,
"__typename": "PeriodicalIssueDownloadablesType"
},
"__typename": "PeriodicalIssue"
},
"article": {
"id": "1s2FZRmSST6",
"doi": "10.1109/TCBB.2021.3065986",
"abstract": "Cryo-electron tomography, combined with subtomogram averaging (STA), can reveal three-dimensional (3D) macromolecule structures in the near-native state from cells and other biological samples. In STA, to get a high-resolution 3D view of macromolecule structures, diverse macromolecules captured by the cellular tomograms need to be accurately classified. However, due to the poor signal-to-noise-ratio (SNR) and severe ray artifacts in the tomogram, it remains a major challenge to classify macromolecules with high accuracy. In this paper, we propose a new convolutional neural network, named 3D-Dilated-DenseNet, to improve the performance of macromolecule classification. In 3D-Dilated-DenseNet, there are two key strategies to guarantee macromolecule classification accuracy: 1) Using dense connections to enhance feature map utilization (corresponding to the baseline 3D-C-DenseNet); 2) Adopting dilated convolution to enrich multi-level information in feature maps. We tested 3D-Dilated-DenseNet and 3D-C-DenseNet both on synthetic data and experimental data. The results show that, on synthetic data, compared with the state-of-the-art method in the SHREC contest (SHREC-CNN), both 3D-C-DenseNet and 3D-Dilated-DenseNet outperform SHREC-CNN. In particular, 3D-Dilated-DenseNet improves 0.393 of F1 metric on tiny-size macromolecules and 0.213 on small-size macromolecules. On experimental data, compared with 3D-C-DenseNet, 3D-Dilated-DenseNet can increase classification performance by 2.1 percent.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Cryo-electron tomography, combined with subtomogram averaging (STA), can reveal three-dimensional (3D) macromolecule structures in the near-native state from cells and other biological samples. In STA, to get a high-resolution 3D view of macromolecule structures, diverse macromolecules captured by the cellular tomograms need to be accurately classified. However, due to the poor signal-to-noise-ratio (SNR) and severe ray artifacts in the tomogram, it remains a major challenge to classify macromolecules with high accuracy. In this paper, we propose a new convolutional neural network, named 3D-Dilated-DenseNet, to improve the performance of macromolecule classification. In 3D-Dilated-DenseNet, there are two key strategies to guarantee macromolecule classification accuracy: 1) Using dense connections to enhance feature map utilization (corresponding to the baseline 3D-C-DenseNet); 2) Adopting dilated convolution to enrich multi-level information in feature maps. We tested 3D-Dilated-DenseNet and 3D-C-DenseNet both on synthetic data and experimental data. The results show that, on synthetic data, compared with the state-of-the-art method in the SHREC contest (SHREC-CNN), both 3D-C-DenseNet and 3D-Dilated-DenseNet outperform SHREC-CNN. In particular, 3D-Dilated-DenseNet improves 0.393 of F1 metric on tiny-size macromolecules and 0.213 on small-size macromolecules. On experimental data, compared with 3D-C-DenseNet, 3D-Dilated-DenseNet can increase classification performance by 2.1 percent.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Cryo-electron tomography, combined with subtomogram averaging (STA), can reveal three-dimensional (3D) macromolecule structures in the near-native state from cells and other biological samples. In STA, to get a high-resolution 3D view of macromolecule structures, diverse macromolecules captured by the cellular tomograms need to be accurately classified. However, due to the poor signal-to-noise-ratio (SNR) and severe ray artifacts in the tomogram, it remains a major challenge to classify macromolecules with high accuracy. In this paper, we propose a new convolutional neural network, named 3D-Dilated-DenseNet, to improve the performance of macromolecule classification. In 3D-Dilated-DenseNet, there are two key strategies to guarantee macromolecule classification accuracy: 1) Using dense connections to enhance feature map utilization (corresponding to the baseline 3D-C-DenseNet); 2) Adopting dilated convolution to enrich multi-level information in feature maps. We tested 3D-Dilated-DenseNet and 3D-C-DenseNet both on synthetic data and experimental data. The results show that, on synthetic data, compared with the state-of-the-art method in the SHREC contest (SHREC-CNN), both 3D-C-DenseNet and 3D-Dilated-DenseNet outperform SHREC-CNN. In particular, 3D-Dilated-DenseNet improves 0.393 of F1 metric on tiny-size macromolecules and 0.213 on small-size macromolecules. On experimental data, compared with 3D-C-DenseNet, 3D-Dilated-DenseNet can increase classification performance by 2.1 percent.",
"title": "Macromolecules Structural Classification With a 3D Dilated Dense Network in Cryo-Electron Tomography",
"normalizedTitle": "Macromolecules Structural Classification With a 3D Dilated Dense Network in Cryo-Electron Tomography",
"fno": "09380401",
"hasPdf": true,
"idPrefix": "tb",
"keywords": [
"Biological Techniques",
"Cellular Biophysics",
"Convolutional Neural Nets",
"Electron Microscopy",
"Image Reconstruction",
"Image Resolution",
"Macromolecules",
"Medical Image Processing",
"Molecular Biophysics",
"Pattern Classification",
"Tomography",
"Macromolecules Structural Classification",
"Cryo Electron Tomography",
"Three Dimensional Macromolecule Structures",
"High Resolution 3 D View",
"Diverse Macromolecules",
"Severe Ray Artifacts",
"Macromolecule Classification Accuracy",
"Baseline 3 D C Dense Net",
"3 D Dilated Dense Net Outperform SHREC CNN",
"Tiny Size Macromolecules",
"Small Size Macromolecules",
"Convolutional Neural Network",
"Feature Extraction",
"Computational Modeling",
"Convolution",
"Three Dimensional Displays",
"Signal To Noise Ratio",
"Tomography",
"Training",
"Cryo Electron Tomography",
"Image Classification",
"Convolution Neural Network"
],
"authors": [
{
"givenName": "Shan",
"surname": "Gao",
"fullName": "Shan Gao",
"affiliation": "High Performance Computer Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Renmin",
"surname": "Han",
"fullName": "Renmin Han",
"affiliation": "Research Center for Mathematics and Interdisciplinary Sciences, Shandong University, Qingdao, China",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Xiangrui",
"surname": "Zeng",
"fullName": "Xiangrui Zeng",
"affiliation": "Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Zhiyong",
"surname": "Liu",
"fullName": "Zhiyong Liu",
"affiliation": "High Performance Computer Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Min",
"surname": "Xu",
"fullName": "Min Xu",
"affiliation": "Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Fa",
"surname": "Zhang",
"fullName": "Fa Zhang",
"affiliation": "High Performance Computer Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China",
"__typename": "ArticleAuthorType"
}
],
"replicability": null,
"showBuyMe": false,
"showRecommendedArticles": true,
"isOpenAccess": true,
"issueNum": "01",
"pubDate": "2022-01-01 00:00:00",
"pubType": "trans",
"pages": "209-219",
"year": "2022",
"issn": "1545-5963",
"isbn": null,
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"recommendedArticles": [
{
"id": "proceedings/ictai/2017/3876/0/387601b272",
"title": "Dilated Deep Residual Network for Image Denoising",
"doi": null,
"abstractUrl": "/proceedings-article/ictai/2017/387601b272/12OmNBajTHm",
"parentPublication": {
"id": "proceedings/ictai/2017/3876/0",
"title": "2017 IEEE 29th International Conference on Tools with Artificial Intelligence (ICTAI)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icpr/2018/3788/0/08546106",
"title": "Spatial Pyramid Dilated Network for Pulmonary Nodule Malignancy Classification",
"doi": null,
"abstractUrl": "/proceedings-article/icpr/2018/08546106/17D45WcjjQw",
"parentPublication": {
"id": "proceedings/icpr/2018/3788/0",
"title": "2018 24th International Conference on Pattern Recognition (ICPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bibm/2021/0126/0/09669318",
"title": "Tracing Filaments in Simulated 3D Cryo-Electron Tomography Maps Using a Fast Dynamic Programming Algorithm",
"doi": null,
"abstractUrl": "/proceedings-article/bibm/2021/09669318/1A9VqrWnZPG",
"parentPublication": {
"id": "proceedings/bibm/2021/0126/0",
"title": "2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccv/2021/2812/0/2.812E41",
"title": "Self-Supervised Cryo-Electron Tomography Volumetric Image Restoration from Single Noisy Volume with Sparsity Constraint",
"doi": null,
"abstractUrl": "/proceedings-article/iccv/2021/2.812E41/1BmL53XVH0c",
"parentPublication": {
"id": "proceedings/iccv/2021/2812/0",
"title": "2021 IEEE/CVF International Conference on Computer Vision (ICCV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/aipr/2021/2471/0/09762209",
"title": "Practical Analysis of Macromolecule Identity from Cryo-electron Tomography Images using Deep Learning",
"doi": null,
"abstractUrl": "/proceedings-article/aipr/2021/09762209/1CT9aP80A1i",
"parentPublication": {
"id": "proceedings/aipr/2021/2471/0",
"title": "2021 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/5555/01/09806341",
"title": "Finding Nano-Ötzi: Cryo-Electron Tomography Visualization Guided by Learned Segmentation",
"doi": null,
"abstractUrl": "/journal/tg/5555/01/09806341/1Et0iwB480M",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bibm/2019/1867/0/08982954",
"title": "Regularized Adversarial Training (RAT) for Robust Cellular Electron Cryo Tomograms Classification",
"doi": null,
"abstractUrl": "/proceedings-article/bibm/2019/08982954/1hgukgM2cRa",
"parentPublication": {
"id": "proceedings/bibm/2019/1867/0",
"title": "2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icme/2020/1331/0/09102945",
"title": "Rddan: A Residual Dense Dilated Aggregated Network For Single Image Deraining",
"doi": null,
"abstractUrl": "/proceedings-article/icme/2020/09102945/1kwqYM0HzIA",
"parentPublication": {
"id": "proceedings/icme/2020/1331/0",
"title": "2020 IEEE International Conference on Multimedia and Expo (ICME)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bibm/2020/6215/0/09313185",
"title": "Efficient Cryo-Electron Tomogram Simulation of Macromolecular Crowding with Application to SARS-CoV-2",
"doi": null,
"abstractUrl": "/proceedings-article/bibm/2020/09313185/1qmfUw4iPgA",
"parentPublication": {
"id": "proceedings/bibm/2020/6215/0",
"title": "2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2021/4509/0/450900a993",
"title": "Densely connected multidilated convolutional networks for dense prediction tasks",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2021/450900a993/1yeJNqz1Gfe",
"parentPublication": {
"id": "proceedings/cvpr/2021/4509/0",
"title": "2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"adjacentArticles": {
"previous": {
"fno": "09702708",
"articleId": "1AH375DQaGY",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "09557831",
"articleId": "1xquspaYq88",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"webExtras": [],
"articleVideos": []
}
|
{
"issue": {
"id": "1M2IpVB2R3i",
"title": "May",
"year": "2023",
"issueNum": "05",
"idPrefix": "tp",
"pubType": "journal",
"volume": "45",
"label": "May",
"downloadables": {
"hasCover": false,
"__typename": "PeriodicalIssueDownloadablesType"
},
"__typename": "PeriodicalIssue"
},
"article": {
"id": "1HL9mk8rEKk",
"doi": "10.1109/TPAMI.2022.3217161",
"abstract": "Most existing point cloud completion methods suffer from the discrete nature of point clouds and the unstructured prediction of points in local regions, which makes it difficult to reveal fine local geometric details. To resolve this issue, we propose SnowflakeNet with snowflake point deconvolution (SPD) to generate complete point clouds. SPD models the generation of point clouds as the snowflake-like growth of points, where child points are generated progressively by splitting their parent points after each SPD. Our insight into the detailed geometry is to introduce a skip-transformer in the SPD to learn the point splitting patterns that can best fit the local regions. The skip-transformer leverages attention mechanism to summarize the splitting patterns used in the previous SPD layer to produce the splitting in the current layer. The locally compact and structured point clouds generated by SPD precisely reveal the structural characteristics of the 3D shape in local patches, which enables us to predict highly detailed geometries. Moreover, since SPD is a general operation that is not limited to completion, we explore its applications in other generative tasks, including point cloud auto-encoding, generation, single image reconstruction, and upsampling. Our experimental results outperform state-of-the-art methods under widely used benchmarks.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Most existing point cloud completion methods suffer from the discrete nature of point clouds and the unstructured prediction of points in local regions, which makes it difficult to reveal fine local geometric details. To resolve this issue, we propose SnowflakeNet with snowflake point deconvolution (SPD) to generate complete point clouds. SPD models the generation of point clouds as the snowflake-like growth of points, where child points are generated progressively by splitting their parent points after each SPD. Our insight into the detailed geometry is to introduce a skip-transformer in the SPD to learn the point splitting patterns that can best fit the local regions. The skip-transformer leverages attention mechanism to summarize the splitting patterns used in the previous SPD layer to produce the splitting in the current layer. The locally compact and structured point clouds generated by SPD precisely reveal the structural characteristics of the 3D shape in local patches, which enables us to predict highly detailed geometries. Moreover, since SPD is a general operation that is not limited to completion, we explore its applications in other generative tasks, including point cloud auto-encoding, generation, single image reconstruction, and upsampling. Our experimental results outperform state-of-the-art methods under widely used benchmarks.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Most existing point cloud completion methods suffer from the discrete nature of point clouds and the unstructured prediction of points in local regions, which makes it difficult to reveal fine local geometric details. To resolve this issue, we propose SnowflakeNet with snowflake point deconvolution (SPD) to generate complete point clouds. SPD models the generation of point clouds as the snowflake-like growth of points, where child points are generated progressively by splitting their parent points after each SPD. Our insight into the detailed geometry is to introduce a skip-transformer in the SPD to learn the point splitting patterns that can best fit the local regions. The skip-transformer leverages attention mechanism to summarize the splitting patterns used in the previous SPD layer to produce the splitting in the current layer. The locally compact and structured point clouds generated by SPD precisely reveal the structural characteristics of the 3D shape in local patches, which enables us to predict highly detailed geometries. Moreover, since SPD is a general operation that is not limited to completion, we explore its applications in other generative tasks, including point cloud auto-encoding, generation, single image reconstruction, and upsampling. Our experimental results outperform state-of-the-art methods under widely used benchmarks.",
"title": "Snowflake Point Deconvolution for Point Cloud Completion and Generation With Skip-Transformer",
"normalizedTitle": "Snowflake Point Deconvolution for Point Cloud Completion and Generation With Skip-Transformer",
"fno": "09928787",
"hasPdf": true,
"idPrefix": "tp",
"keywords": [
"Computational Geometry",
"Deconvolution",
"Image Reconstruction",
"Learning Artificial Intelligence",
"Solid Modelling",
"Child Points",
"Complete Point Clouds",
"Existing Point Cloud Completion Methods",
"Fine Local Geometric Details",
"Generative Tasks",
"Local Patches",
"Local Regions",
"Locally Compact Point Clouds",
"Parent Points",
"Point Cloud Auto Encoding",
"Point Splitting Patterns",
"Previous SPD Layer",
"Skip Transformer Leverages Attention Mechanism",
"Snowflake Point Deconvolution",
"SPD Models",
"Structured Point Clouds",
"Point Cloud Compression",
"Shape",
"Three Dimensional Displays",
"Task Analysis",
"Decoding",
"Transformers",
"Image Reconstruction",
"Point Clouds",
"3 D Shape Completion",
"Generation",
"Reconstruction",
"Upsampling",
"Transformer"
],
"authors": [
{
"givenName": "Peng",
"surname": "Xiang",
"fullName": "Peng Xiang",
"affiliation": "School of Software, Tsinghua University, Beijing, China",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Xin",
"surname": "Wen",
"fullName": "Xin Wen",
"affiliation": "School of Software, Tsinghua University, Beijing, China",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Yu-Shen",
"surname": "Liu",
"fullName": "Yu-Shen Liu",
"affiliation": "School of Software, BNRist, Tsinghua University, Beijing, China",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Yan-Pei",
"surname": "Cao",
"fullName": "Yan-Pei Cao",
"affiliation": "ARC Lab, Tencent PCG, China",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Pengfei",
"surname": "Wan",
"fullName": "Pengfei Wan",
"affiliation": "Y-Tech, Kuaishou Technology, Beijing, China",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Wen",
"surname": "Zheng",
"fullName": "Wen Zheng",
"affiliation": "Y-Tech, Kuaishou Technology, Beijing, China",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Zhizhong",
"surname": "Han",
"fullName": "Zhizhong Han",
"affiliation": "Department of Computer Science, Wayne State University, Detroit, MI, USA",
"__typename": "ArticleAuthorType"
}
],
"replicability": null,
"showBuyMe": true,
"showRecommendedArticles": true,
"isOpenAccess": false,
"issueNum": "05",
"pubDate": "2023-05-01 00:00:00",
"pubType": "trans",
"pages": "6320-6338",
"year": "2023",
"issn": "0162-8828",
"isbn": null,
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"recommendedArticles": [
{
"id": "trans/tp/2023/01/09735342",
"title": "PMP-Net++: Point Cloud Completion by Transformer-Enhanced Multi-Step Point Moving Paths",
"doi": null,
"abstractUrl": "/journal/tp/2023/01/09735342/1BLmVZBJX6o",
"parentPublication": {
"id": "trans/tp",
"title": "IEEE Transactions on Pattern Analysis & Machine Intelligence",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccv/2021/2812/0/281200g078",
"title": "Pyramid Point Cloud Transformer for Large-Scale Place Recognition",
"doi": null,
"abstractUrl": "/proceedings-article/iccv/2021/281200g078/1BmFDZdzHwY",
"parentPublication": {
"id": "proceedings/iccv/2021/2812/0",
"title": "2021 IEEE/CVF International Conference on Computer Vision (ICCV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccv/2021/2812/0/281200f479",
"title": "SnowflakeNet: Point Cloud Completion by Snowflake Point Deconvolution with Skip-Transformer",
"doi": null,
"abstractUrl": "/proceedings-article/iccv/2021/281200f479/1BmL45zCYda",
"parentPublication": {
"id": "proceedings/iccv/2021/2812/0",
"title": "2021 IEEE/CVF International Conference on Computer Vision (ICCV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/5555/01/09804851",
"title": "Point Cloud Completion Via Skeleton-Detail Transformer",
"doi": null,
"abstractUrl": "/journal/tg/5555/01/09804851/1ErlpBk8JBS",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2022/6946/0/694600q6928",
"title": "Fast Point Transformer",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2022/694600q6928/1H0MTxeBHwY",
"parentPublication": {
"id": "proceedings/cvpr/2022/6946/0",
"title": "2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2022/6946/0/694600l1133",
"title": "Geometric Transformer for Fast and Robust Point Cloud Registration",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2022/694600l1133/1H0NxQCVYxW",
"parentPublication": {
"id": "proceedings/cvpr/2022/6946/0",
"title": "2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2022/6946/0/694600b558",
"title": "Learning Local Displacements for Point Cloud Completion",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2022/694600b558/1H0OdBujprG",
"parentPublication": {
"id": "proceedings/cvpr/2022/6946/0",
"title": "2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2022/6946/0/694600l1820",
"title": "An MIL-Derived Transformer for Weakly Supervised Point Cloud Segmentation",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2022/694600l1820/1H1hJaELWaA",
"parentPublication": {
"id": "proceedings/cvpr/2022/6946/0",
"title": "2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/5555/01/10015045",
"title": "CSDN: Cross-Modal Shape-Transfer Dual-Refinement Network for Point Cloud Completion",
"doi": null,
"abstractUrl": "/journal/tg/5555/01/10015045/1JR6dVW7wJi",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iscsic/2022/5488/0/548800a159",
"title": "MLFT-Net: Point Cloud Completion Using Multi-Level Feature Transformer",
"doi": null,
"abstractUrl": "/proceedings-article/iscsic/2022/548800a159/1LvAmC051qo",
"parentPublication": {
"id": "proceedings/iscsic/2022/5488/0",
"title": "2022 6th International Symposium on Computer Science and Intelligent Control (ISCSIC)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"adjacentArticles": {
"previous": {
"fno": "09916156",
"articleId": "1Hojz1AFgI0",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "09925617",
"articleId": "1HCQR0DkOha",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"webExtras": [
{
"id": "1M2IzFMwOvm",
"name": "ttp202305-09928787s1-supp1-3217161.pdf",
"location": "https://www.computer.org/csdl/api/v1/extra/ttp202305-09928787s1-supp1-3217161.pdf",
"extension": "pdf",
"size": "6.03 MB",
"__typename": "WebExtraType"
}
],
"articleVideos": []
}
|
{
"issue": {
"id": "12OmNxvO04X",
"title": "PrePrints",
"year": "5555",
"issueNum": "01",
"idPrefix": "tp",
"pubType": "journal",
"volume": null,
"label": "PrePrints",
"downloadables": {
"hasCover": false,
"__typename": "PeriodicalIssueDownloadablesType"
},
"__typename": "PeriodicalIssue"
},
"article": {
"id": "1MwAn9y4Ozu",
"doi": "10.1109/TPAMI.2023.3268305",
"abstract": "Real-scanned point clouds are often incomplete due to viewpoint, occlusion, and noise, which hampers 3D geometric modeling and perception. Existing point cloud completion methods tend to generate global shape skeletons and hence lack fine local details. Furthermore, they mostly learn a deterministic partial-to-complete mapping, but overlook structural relations in man-made objects. To tackle these challenges, this paper proposes a variational framework, Variational Relational point Completion network (VRCNet) with two appealing properties: 1) Probabilistic Modeling. In particular, we propose a dual-path architecture to enable principled probabilistic modeling across partial and complete clouds. One path consumes complete point clouds for reconstruction by learning a point VAE. The other path generates complete shapes for partial point clouds, whose embedded distribution is guided by distribution obtained from the reconstruction path during training. 2) Relational Enhancement. Specifically, we carefully design point self-attention kernel and point selective kernel module to exploit relational point features, which refines local shape details conditioned on the coarse completion. In addition, we contribute multi-view partial point cloud datasets (MVP and MVP-40 dataset) containing over 200,000 high-quality scans, which render partial 3D shapes from 26 uniformly distributed camera poses for each 3D CAD model. Extensive experiments demonstrate that VRCNet outperforms state-of-the-art methods on all standard point cloud completion benchmarks. Notably, VRCNet shows great generalizability and robustness on real-world point cloud scans. Moreover, we can achieve robust 3D classification for partial point clouds with the help of VRCNet, which can highly increase classification accuracy. Our project is available at <uri>https://paul007pl.github.io/projects/VRCNet</uri>.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Real-scanned point clouds are often incomplete due to viewpoint, occlusion, and noise, which hampers 3D geometric modeling and perception. Existing point cloud completion methods tend to generate global shape skeletons and hence lack fine local details. Furthermore, they mostly learn a deterministic partial-to-complete mapping, but overlook structural relations in man-made objects. To tackle these challenges, this paper proposes a variational framework, Variational Relational point Completion network (VRCNet) with two appealing properties: 1) Probabilistic Modeling. In particular, we propose a dual-path architecture to enable principled probabilistic modeling across partial and complete clouds. One path consumes complete point clouds for reconstruction by learning a point VAE. The other path generates complete shapes for partial point clouds, whose embedded distribution is guided by distribution obtained from the reconstruction path during training. 2) Relational Enhancement. Specifically, we carefully design point self-attention kernel and point selective kernel module to exploit relational point features, which refines local shape details conditioned on the coarse completion. In addition, we contribute multi-view partial point cloud datasets (MVP and MVP-40 dataset) containing over 200,000 high-quality scans, which render partial 3D shapes from 26 uniformly distributed camera poses for each 3D CAD model. Extensive experiments demonstrate that VRCNet outperforms state-of-the-art methods on all standard point cloud completion benchmarks. Notably, VRCNet shows great generalizability and robustness on real-world point cloud scans. Moreover, we can achieve robust 3D classification for partial point clouds with the help of VRCNet, which can highly increase classification accuracy. Our project is available at <uri>https://paul007pl.github.io/projects/VRCNet</uri>.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Real-scanned point clouds are often incomplete due to viewpoint, occlusion, and noise, which hampers 3D geometric modeling and perception. Existing point cloud completion methods tend to generate global shape skeletons and hence lack fine local details. Furthermore, they mostly learn a deterministic partial-to-complete mapping, but overlook structural relations in man-made objects. To tackle these challenges, this paper proposes a variational framework, Variational Relational point Completion network (VRCNet) with two appealing properties: 1) Probabilistic Modeling. In particular, we propose a dual-path architecture to enable principled probabilistic modeling across partial and complete clouds. One path consumes complete point clouds for reconstruction by learning a point VAE. The other path generates complete shapes for partial point clouds, whose embedded distribution is guided by distribution obtained from the reconstruction path during training. 2) Relational Enhancement. Specifically, we carefully design point self-attention kernel and point selective kernel module to exploit relational point features, which refines local shape details conditioned on the coarse completion. In addition, we contribute multi-view partial point cloud datasets (MVP and MVP-40 dataset) containing over 200,000 high-quality scans, which render partial 3D shapes from 26 uniformly distributed camera poses for each 3D CAD model. Extensive experiments demonstrate that VRCNet outperforms state-of-the-art methods on all standard point cloud completion benchmarks. Notably, VRCNet shows great generalizability and robustness on real-world point cloud scans. Moreover, we can achieve robust 3D classification for partial point clouds with the help of VRCNet, which can highly increase classification accuracy. Our project is available at https://paul007pl.github.io/projects/VRCNet.",
"title": "Variational Relational Point Completion Network for Robust 3D Classification",
"normalizedTitle": "Variational Relational Point Completion Network for Robust 3D Classification",
"fno": "10106495",
"hasPdf": true,
"idPrefix": "tp",
"keywords": [
"Point Cloud Compression",
"Three Dimensional Displays",
"Shape",
"Probabilistic Logic",
"Kernel",
"Solid Modeling",
"Training",
"3 D Perception",
"Multi View Partial Point Clouds",
"Point Cloud Completion",
"Self Attention Operations"
],
"authors": [
{
"givenName": "Liang",
"surname": "Pan",
"fullName": "Liang Pan",
"affiliation": "S-Lab, Nanyang Technological University, Singapore",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Xinyi",
"surname": "Chen",
"fullName": "Xinyi Chen",
"affiliation": "S-Lab, Nanyang Technological University, Singapore",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Zhongang",
"surname": "Cai",
"fullName": "Zhongang Cai",
"affiliation": "SenseTime Research, Hong Kong",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Junzhe",
"surname": "Zhang",
"fullName": "Junzhe Zhang",
"affiliation": "S-Lab, Nanyang Technological University, Singapore",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Haiyu",
"surname": "Zhao",
"fullName": "Haiyu Zhao",
"affiliation": "SenseTime Research, Hong Kong",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Shuai",
"surname": "Yi",
"fullName": "Shuai Yi",
"affiliation": "SenseTime Research, Hong Kong",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Ziwei",
"surname": "Liu",
"fullName": "Ziwei Liu",
"affiliation": "S-Lab, Nanyang Technological University, Singapore",
"__typename": "ArticleAuthorType"
}
],
"replicability": null,
"showBuyMe": true,
"showRecommendedArticles": true,
"isOpenAccess": false,
"issueNum": "01",
"pubDate": "2023-04-01 00:00:00",
"pubType": "trans",
"pages": "1-12",
"year": "5555",
"issn": "0162-8828",
"isbn": null,
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"recommendedArticles": [
{
"id": "proceedings/3dv/2018/8425/0/842500a728",
"title": "PCN: Point Completion Network",
"doi": null,
"abstractUrl": "/proceedings-article/3dv/2018/842500a728/17D45VTRoAx",
"parentPublication": {
"id": "proceedings/3dv/2018/8425/0",
"title": "2018 International Conference on 3D Vision (3DV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccv/2021/2812/0/281200f806",
"title": "3D Shape Generation and Completion through Point-Voxel Diffusion",
"doi": null,
"abstractUrl": "/proceedings-article/iccv/2021/281200f806/1BmHiEgI4q4",
"parentPublication": {
"id": "proceedings/iccv/2021/2812/0",
"title": "2021 IEEE/CVF International Conference on Computer Vision (ICCV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccv/2021/2812/0/281200m2478",
"title": "PoinTr: Diverse Point Cloud Completion with Geometry-Aware Transformers",
"doi": null,
"abstractUrl": "/proceedings-article/iccv/2021/281200m2478/1BmIkQ0yRrO",
"parentPublication": {
"id": "proceedings/iccv/2021/2812/0",
"title": "2021 IEEE/CVF International Conference on Computer Vision (ICCV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccv/2021/2812/0/281200f479",
"title": "SnowflakeNet: Point Cloud Completion by Snowflake Point Deconvolution with Skip-Transformer",
"doi": null,
"abstractUrl": "/proceedings-article/iccv/2021/281200f479/1BmL45zCYda",
"parentPublication": {
"id": "proceedings/iccv/2021/2812/0",
"title": "2021 IEEE/CVF International Conference on Computer Vision (ICCV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2022/6946/0/694600f533",
"title": "Learning a Structured Latent Space for Unsupervised Point Cloud Completion",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2022/694600f533/1H0KOsU2FZC",
"parentPublication": {
"id": "proceedings/cvpr/2022/6946/0",
"title": "2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2022/6946/0/694600b716",
"title": "LAKe-Net: Topology-Aware Point Cloud Completion by Localizing Aligned Keypoints",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2022/694600b716/1H0Kwo5tABi",
"parentPublication": {
"id": "proceedings/cvpr/2022/6946/0",
"title": "2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2022/6946/0/694600b558",
"title": "Learning Local Displacements for Point Cloud Completion",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2022/694600b558/1H0OdBujprG",
"parentPublication": {
"id": "proceedings/cvpr/2022/6946/0",
"title": "2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icpr/2022/9062/0/09956459",
"title": "Multi-view Based 3D Point Cloud Completion Algorithm for Vehicles",
"doi": null,
"abstractUrl": "/proceedings-article/icpr/2022/09956459/1IHpSLoC9yM",
"parentPublication": {
"id": "proceedings/icpr/2022/9062/0",
"title": "2022 26th International Conference on Pattern Recognition (ICPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2021/4509/0/450900i520",
"title": "Variational Relational Point Completion Network",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2021/450900i520/1yeLNkSQJX2",
"parentPublication": {
"id": "proceedings/cvpr/2021/4509/0",
"title": "2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/hpbd&is/2021/1327/0/09658452",
"title": "2D-3DMatchingNet: Multimodal Point Completion with 2D Geometry Matching",
"doi": null,
"abstractUrl": "/proceedings-article/hpbd&is/2021/09658452/1zRFmc9ALyo",
"parentPublication": {
"id": "proceedings/hpbd&is/2021/1327/0",
"title": "2021 International Conference on High Performance Big Data and Intelligent Systems (HPBD&IS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"adjacentArticles": {
"previous": {
"fno": "10106423",
"articleId": "1MwAg23iMg0",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "10107423",
"articleId": "1MDFd4rHtF6",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"webExtras": [
{
"id": "1MDFdbeWcGQ",
"name": "ttp555501-010106495s1-supp1-3268305.pdf",
"location": "https://www.computer.org/csdl/api/v1/extra/ttp555501-010106495s1-supp1-3268305.pdf",
"extension": "pdf",
"size": "87.5 kB",
"__typename": "WebExtraType"
}
],
"articleVideos": []
}
|
{
"issue": {
"id": "12OmNvH7fgf",
"title": "July-Dec.",
"year": "2020",
"issueNum": "02",
"idPrefix": "ca",
"pubType": "journal",
"volume": "19",
"label": "July-Dec.",
"downloadables": {
"hasCover": false,
"__typename": "PeriodicalIssueDownloadablesType"
},
"__typename": "PeriodicalIssue"
},
"article": {
"id": "1oDXEHhZgek",
"doi": "10.1109/LCA.2020.3031907",
"abstract": "Neural network (NN) inference is an essential part of modern systems and is found at the heart of numerous applications ranging from image recognition to natural language processing. In situ NN accelerators can efficiently perform NN inference using resistive crossbars, which makes them a promising solution to the data movement challenges faced by conventional architectures. Although such accelerators demonstrate significant potential for dense NNs, they often do not benefit from sparse NNs, which contain relatively few non-zero weights. Processing sparse NNs on in situ accelerators results in wasted energy to charge the entire crossbar where most elements are zeros. To address this limitation, this letter proposes Granular Matrix Reordering (GMR): a preprocessing technique that enables an energy-efficient computation of sparse NNs on in situ accelerators. GMR reorders the rows and columns of sparse weight matrices to maximize the crossbars' utilization and minimize the total number of crossbars needed to be charged. The reordering process does not rely on sparsity patterns and incurs no accuracy loss. Overall, GMR achieves an average of 28 percent and up to 34 percent reduction in energy consumption over seven pruned NNs across four different pruning methods and network architectures.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Neural network (NN) inference is an essential part of modern systems and is found at the heart of numerous applications ranging from image recognition to natural language processing. In situ NN accelerators can efficiently perform NN inference using resistive crossbars, which makes them a promising solution to the data movement challenges faced by conventional architectures. Although such accelerators demonstrate significant potential for dense NNs, they often do not benefit from sparse NNs, which contain relatively few non-zero weights. Processing sparse NNs on in situ accelerators results in wasted energy to charge the entire crossbar where most elements are zeros. To address this limitation, this letter proposes Granular Matrix Reordering (GMR): a preprocessing technique that enables an energy-efficient computation of sparse NNs on in situ accelerators. GMR reorders the rows and columns of sparse weight matrices to maximize the crossbars' utilization and minimize the total number of crossbars needed to be charged. The reordering process does not rely on sparsity patterns and incurs no accuracy loss. Overall, GMR achieves an average of 28 percent and up to 34 percent reduction in energy consumption over seven pruned NNs across four different pruning methods and network architectures.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Neural network (NN) inference is an essential part of modern systems and is found at the heart of numerous applications ranging from image recognition to natural language processing. In situ NN accelerators can efficiently perform NN inference using resistive crossbars, which makes them a promising solution to the data movement challenges faced by conventional architectures. Although such accelerators demonstrate significant potential for dense NNs, they often do not benefit from sparse NNs, which contain relatively few non-zero weights. Processing sparse NNs on in situ accelerators results in wasted energy to charge the entire crossbar where most elements are zeros. To address this limitation, this letter proposes Granular Matrix Reordering (GMR): a preprocessing technique that enables an energy-efficient computation of sparse NNs on in situ accelerators. GMR reorders the rows and columns of sparse weight matrices to maximize the crossbars' utilization and minimize the total number of crossbars needed to be charged. The reordering process does not rely on sparsity patterns and incurs no accuracy loss. Overall, GMR achieves an average of 28 percent and up to 34 percent reduction in energy consumption over seven pruned NNs across four different pruning methods and network architectures.",
"title": "Adapting In Situ Accelerators for Sparsity with Granular Matrix Reordering",
"normalizedTitle": "Adapting In Situ Accelerators for Sparsity with Granular Matrix Reordering",
"fno": "09234705",
"hasPdf": true,
"idPrefix": "ca",
"keywords": [
"Image Recognition",
"Matrix Algebra",
"Natural Language Processing",
"Neural Nets",
"Network Architectures",
"Sparse Weight Matrices",
"Energy Efficient Computation",
"GMR",
"Conventional Architectures",
"Data Movement",
"Natural Language Processing",
"Image Recognition",
"Neural Network Inference",
"Granular Matrix Reordering",
"Artificial Neural Networks",
"Sparse Matrices",
"Energy Consumption",
"Computer Architecture",
"Deep Learning",
"Sorting",
"Sparse Neural Networks",
"Matrix Reordering",
"In Situ Computing",
"Hardware Accelerators",
"Resistive Crossbars"
],
"authors": [
{
"givenName": "Darya",
"surname": "Mikhailenko",
"fullName": "Darya Mikhailenko",
"affiliation": "Department of Electrical Engineering, University of Rochester, Rochester, NY, USA",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Yujin",
"surname": "Nakamoto",
"fullName": "Yujin Nakamoto",
"affiliation": "Department of Electrical Engineering, University of Rochester, Rochester, NY, USA",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Ben",
"surname": "Feinberg",
"fullName": "Ben Feinberg",
"affiliation": "Sandia National Laboratories, Albuquerque, NM, USA",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Engin",
"surname": "Ipek",
"fullName": "Engin Ipek",
"affiliation": "Department of Electrical Engineering, University of Rochester, Rochester, NY, USA",
"__typename": "ArticleAuthorType"
}
],
"replicability": null,
"showBuyMe": true,
"showRecommendedArticles": true,
"isOpenAccess": false,
"issueNum": "02",
"pubDate": "2020-07-01 00:00:00",
"pubType": "letters",
"pages": "143-146",
"year": "2020",
"issn": "1556-6056",
"isbn": null,
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"recommendedArticles": [
{
"id": "proceedings/iccd/2014/6492/0/06974716",
"title": "An energy efficient column-major backend for FPGA SpMV accelerators",
"doi": null,
"abstractUrl": "/proceedings-article/iccd/2014/06974716/12OmNAio72P",
"parentPublication": {
"id": "proceedings/iccd/2014/6492/0",
"title": "2014 32nd IEEE International Conference on Computer Design (ICCD)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/isca/2016/8947/0/8947a014",
"title": "ISAAC: A Convolutional Neural Network Accelerator with In-Situ Analog Arithmetic in Crossbars",
"doi": null,
"abstractUrl": "/proceedings-article/isca/2016/8947a014/12OmNCga1RR",
"parentPublication": {
"id": "proceedings/isca/2016/8947/0",
"title": "2016 ACM/IEEE 43rd Annual International Symposium on Computer Architecture (ISCA)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/isca/2018/5984/0/598401a367",
"title": "Enabling Scientific Computing on Memristive Accelerators",
"doi": null,
"abstractUrl": "/proceedings-article/isca/2018/598401a367/12OmNrYlmwN",
"parentPublication": {
"id": "proceedings/isca/2018/5984/0",
"title": "2018 ACM/IEEE 45th Annual International Symposium on Computer Architecture (ISCA)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "mags/mi/2019/01/08649807",
"title": "Memristive Accelerators for Dense and Sparse Linear Algebra: From Machine Learning to High-Performance Scientific Computing",
"doi": null,
"abstractUrl": "/magazine/mi/2019/01/08649807/17ShDQ5OdCo",
"parentPublication": {
"id": "mags/mi",
"title": "IEEE Micro",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/isvlsi/2022/6605/0/660500a320",
"title": "A DNN Protection Solution for PIM accelerators with Model Compression",
"doi": null,
"abstractUrl": "/proceedings-article/isvlsi/2022/660500a320/1HzBT18h43u",
"parentPublication": {
"id": "proceedings/isvlsi/2022/6605/0",
"title": "2022 IEEE Computer Society Annual Symposium on VLSI (ISVLSI)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tc/5555/01/09964075",
"title": "ADC-Free ReRAM-Based In-Situ Accelerator for Energy-Efficient Binary Neural Networks",
"doi": null,
"abstractUrl": "/journal/tc/5555/01/09964075/1IAFMtcQrvi",
"parentPublication": {
"id": "trans/tc",
"title": "IEEE Transactions on Computers",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tc/2020/07/09040430",
"title": "Crane: Mitigating Accelerator Under-utilization Caused by Sparsity Irregularities in CNNs",
"doi": null,
"abstractUrl": "/journal/tc/2020/07/09040430/1iix6N5wiDm",
"parentPublication": {
"id": "trans/tc",
"title": "IEEE Transactions on Computers",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/emc2-nips/2019/2418/0/241800a001",
"title": "Exploring Bit-Slice Sparsity in Deep Neural Networks for Efficient ReRAM-Based Deployment",
"doi": null,
"abstractUrl": "/proceedings-article/emc2-nips/2019/241800a001/1uPzikcn25W",
"parentPublication": {
"id": "proceedings/emc2-nips/2019/2418/0",
"title": "2019 Fifth Workshop on Energy Efficient Machine Learning and Cognitive Computing - NeurIPS Edition (EMC2-NIPS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/isca/2021/3333/0/333300a265",
"title": "FORMS: Fine-grained Polarized ReRAM-based In-situ Computation for Mixed-signal DNN Accelerator",
"doi": null,
"abstractUrl": "/proceedings-article/isca/2021/333300a265/1vNjO70ltBu",
"parentPublication": {
"id": "proceedings/isca/2021/3333/0",
"title": "2021 ACM/IEEE 48th Annual International Symposium on Computer Architecture (ISCA)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccd/2021/3219/0/321900a417",
"title": "SME: ReRAM-based Sparse-Multiplication-Engine to Squeeze-Out Bit Sparsity of Neural Network",
"doi": null,
"abstractUrl": "/proceedings-article/iccd/2021/321900a417/1zuveGd9KsU",
"parentPublication": {
"id": "proceedings/iccd/2021/3219/0",
"title": "2021 IEEE 39th International Conference on Computer Design (ICCD)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"adjacentArticles": {
"previous": {
"fno": "09195730",
"articleId": "1o8m69Olp7i",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "09246215",
"articleId": "1pyolsmR5Pq",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"webExtras": [],
"articleVideos": []
}
|
{
"issue": {
"id": "1HBI1WcWK9G",
"title": "Oct.",
"year": "2022",
"issueNum": "05",
"idPrefix": "ai",
"pubType": "journal",
"volume": "3",
"label": "Oct.",
"downloadables": {
"hasCover": false,
"__typename": "PeriodicalIssueDownloadablesType"
},
"__typename": "PeriodicalIssue"
},
"article": {
"id": "1xgx5fmhGzC",
"doi": "10.1109/TAI.2021.3115992",
"abstract": "Style is an integral component of a sentence indicated by the choice of words a person makes. Different people have different ways of expressing themselves; however, they adjust their speaking and writing style to a social context, an audience, an interlocutor, or the formality of an occasion. Text style transfer is defined as a task of adapting and/or changing the stylistic manner in which a sentence is written, while preserving the meaning of the original sentence. A systematic review of text style transfer methodologies using deep learning is presented in this article. We point out the technological advances in deep neural networks that have been the driving force behind current successes in the fields of natural language understanding and generation. The review is structured around two key stages in the text style transfer process, namely, representation learning and sentence generation in a new style. The discussion highlights the commonalities and differences between proposed solutions as well as challenges and opportunities that are expected to direct and foster further research in the field.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Style is an integral component of a sentence indicated by the choice of words a person makes. Different people have different ways of expressing themselves; however, they adjust their speaking and writing style to a social context, an audience, an interlocutor, or the formality of an occasion. Text style transfer is defined as a task of adapting and/or changing the stylistic manner in which a sentence is written, while preserving the meaning of the original sentence. A systematic review of text style transfer methodologies using deep learning is presented in this article. We point out the technological advances in deep neural networks that have been the driving force behind current successes in the fields of natural language understanding and generation. The review is structured around two key stages in the text style transfer process, namely, representation learning and sentence generation in a new style. The discussion highlights the commonalities and differences between proposed solutions as well as challenges and opportunities that are expected to direct and foster further research in the field.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Style is an integral component of a sentence indicated by the choice of words a person makes. Different people have different ways of expressing themselves; however, they adjust their speaking and writing style to a social context, an audience, an interlocutor, or the formality of an occasion. Text style transfer is defined as a task of adapting and/or changing the stylistic manner in which a sentence is written, while preserving the meaning of the original sentence. A systematic review of text style transfer methodologies using deep learning is presented in this article. We point out the technological advances in deep neural networks that have been the driving force behind current successes in the fields of natural language understanding and generation. The review is structured around two key stages in the text style transfer process, namely, representation learning and sentence generation in a new style. The discussion highlights the commonalities and differences between proposed solutions as well as challenges and opportunities that are expected to direct and foster further research in the field.",
"title": "A Review of Text Style Transfer Using Deep Learning",
"normalizedTitle": "A Review of Text Style Transfer Using Deep Learning",
"fno": "09551764",
"hasPdf": true,
"idPrefix": "ai",
"keywords": [
"Deep Learning Artificial Intelligence",
"Natural Language Processing",
"Text Analysis",
"Deep Learning",
"Deep Neural Networks",
"Integral Component",
"Original Sentence",
"Representation Learning",
"Speaking Style",
"Systematic Review",
"Text Style Transfer Methodologies",
"Writing Style",
"Writing",
"Deep Learning",
"Natural Language Processing",
"Linguistics",
"Neural Networks",
"Deep Learning DL",
"Natural Language Generation NLG",
"Natural Language Processing",
"Neural Networks",
"Text Style Transfer"
],
"authors": [
{
"givenName": "Martina",
"surname": "Toshevska",
"fullName": "Martina Toshevska",
"affiliation": "Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University, Skopje, North Macedonia",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Sonja",
"surname": "Gievska",
"fullName": "Sonja Gievska",
"affiliation": "Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University, Skopje, North Macedonia",
"__typename": "ArticleAuthorType"
}
],
"replicability": null,
"showBuyMe": true,
"showRecommendedArticles": true,
"isOpenAccess": false,
"issueNum": "05",
"pubDate": "2022-10-01 00:00:00",
"pubType": "trans",
"pages": "669-684",
"year": "2022",
"issn": "2691-4581",
"isbn": null,
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"recommendedArticles": [
{
"id": "proceedings/icdar/2017/3586/5/3586f051",
"title": "Neural Font Style Transfer",
"doi": null,
"abstractUrl": "/proceedings-article/icdar/2017/3586f051/12OmNy68ELb",
"parentPublication": {
"id": "icdar/2017/3586/5",
"title": "2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/big-data/2021/3902/0/09671820",
"title": "Non-Parallel Text Style Transfer using Self-Attentional Discriminator as Supervisor",
"doi": null,
"abstractUrl": "/proceedings-article/big-data/2021/09671820/1A8hr4FqzAY",
"parentPublication": {
"id": "proceedings/big-data/2021/3902/0",
"title": "2021 IEEE International Conference on Big Data (Big Data)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2022/6946/0/694600s8041",
"title": "CLIPstyler: Image Style Transfer with a Single Text Condition",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2022/694600s8041/1H0KEFdpA2c",
"parentPublication": {
"id": "proceedings/cvpr/2022/6946/0",
"title": "2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icdarw/2019/5054/5/505405a008",
"title": "Synthesizing Scene Text Images for Recognition with Style Transfer",
"doi": null,
"abstractUrl": "/proceedings-article/icdarw/2019/505405a008/1eLyhkltBSM",
"parentPublication": {
"id": "icdarw/2019/5054/5",
"title": "2019 International Conference on Document Analysis and Recognition Workshops (ICDARW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icdar/2019/3014/0/301400a805",
"title": "Selective Style Transfer for Text",
"doi": null,
"abstractUrl": "/proceedings-article/icdar/2019/301400a805/1h81zIkqfeM",
"parentPublication": {
"id": "proceedings/icdar/2019/3014/0",
"title": "2019 International Conference on Document Analysis and Recognition (ICDAR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccv/2019/4803/0/480300e441",
"title": "Controllable Artistic Text Style Transfer via Shape-Matching GAN",
"doi": null,
"abstractUrl": "/proceedings-article/iccv/2019/480300e441/1hVlRglbrk4",
"parentPublication": {
"id": "proceedings/iccv/2019/4803/0",
"title": "2019 IEEE/CVF International Conference on Computer Vision (ICCV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ictai/2019/3798/0/379800b563",
"title": "Text Style Transfer Using Partly-Shared Decoder",
"doi": null,
"abstractUrl": "/proceedings-article/ictai/2019/379800b563/1hrLSokpXYk",
"parentPublication": {
"id": "proceedings/ictai/2019/3798/0",
"title": "2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bigcomp/2021/8924/0/892400a350",
"title": "Text Style Transfer Using DRG Framework of Combined Retrieval Strategy",
"doi": null,
"abstractUrl": "/proceedings-article/bigcomp/2021/892400a350/1rRccaS2Sl2",
"parentPublication": {
"id": "proceedings/bigcomp/2021/8924/0",
"title": "2021 IEEE International Conference on Big Data and Smart Computing (BigComp)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ifeea/2020/9627/0/962700a451",
"title": "Artistic Text Style Transfer based on Generative Adversarial Networks",
"doi": null,
"abstractUrl": "/proceedings-article/ifeea/2020/962700a451/1rvCEvTEN7W",
"parentPublication": {
"id": "proceedings/ifeea/2020/9627/0",
"title": "2020 7th International Forum on Electrical Engineering and Automation (IFEEA)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/mipr/2021/1865/0/186500a063",
"title": "Multi-Style Transfer Generative Adversarial Network for Text Images",
"doi": null,
"abstractUrl": "/proceedings-article/mipr/2021/186500a063/1xPsjXkDspq",
"parentPublication": {
"id": "proceedings/mipr/2021/1865/0",
"title": "2021 IEEE 4th International Conference on Multimedia Information Processing and Retrieval (MIPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"adjacentArticles": {
"previous": {
"fno": "09543519",
"articleId": "1x4UMqXvFok",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "09583879",
"articleId": "1xSHUaVawZW",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"webExtras": [],
"articleVideos": []
}
|
{
"issue": {
"id": "1DFd8NuTZXW",
"title": "Nov.",
"year": "2022",
"issueNum": "11",
"idPrefix": "td",
"pubType": "journal",
"volume": "33",
"label": "Nov.",
"downloadables": {
"hasCover": false,
"__typename": "PeriodicalIssueDownloadablesType"
},
"__typename": "PeriodicalIssue"
},
"article": {
"id": "1zYf6SHCZKE",
"doi": "10.1109/TPDS.2022.3140681",
"abstract": "Neural networks (NN) are used in high-performance computing and high-throughput analysis to extract knowledge from datasets. Neural architecture search (NAS) automates NN design by generating, training, and analyzing thousands of NNs. However, NAS requires massive computational power for NN training. To address challenges of efficiency and scalability, we propose <italic>PENGUIN</italic>, a decoupled fitness prediction engine that informs the search without interfering in it. <italic>PENGUIN</italic> uses parametric modeling to predict fitness of NNs. Existing NAS methods and parametric modeling functions can be plugged into <italic>PENGUIN</italic> to build flexible NAS workflows. Through this decoupling and flexible parametric modeling, <italic>PENGUIN</italic> reduces training costs: it predicts the fitness of NNs, enabling NAS to terminate training NNs early. Early termination increases the number of NNs that fixed compute resources can evaluate, thus giving NAS additional opportunity to find better NNs. We assess the effectiveness of our engine on 6,000 NNs across three diverse benchmark datasets and three state of the art NAS implementations using the Summit supercomputer. Augmenting these NAS implementations with <italic>PENGUIN</italic> can increase throughput by a factor of 1.6 to 7.1. Furthermore, walltime tests indicate that <italic>PENGUIN</italic> can reduce training time by a factor of 2.5 to 5.3.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Neural networks (NN) are used in high-performance computing and high-throughput analysis to extract knowledge from datasets. Neural architecture search (NAS) automates NN design by generating, training, and analyzing thousands of NNs. However, NAS requires massive computational power for NN training. To address challenges of efficiency and scalability, we propose <italic>PENGUIN</italic>, a decoupled fitness prediction engine that informs the search without interfering in it. <italic>PENGUIN</italic> uses parametric modeling to predict fitness of NNs. Existing NAS methods and parametric modeling functions can be plugged into <italic>PENGUIN</italic> to build flexible NAS workflows. Through this decoupling and flexible parametric modeling, <italic>PENGUIN</italic> reduces training costs: it predicts the fitness of NNs, enabling NAS to terminate training NNs early. Early termination increases the number of NNs that fixed compute resources can evaluate, thus giving NAS additional opportunity to find better NNs. We assess the effectiveness of our engine on 6,000 NNs across three diverse benchmark datasets and three state of the art NAS implementations using the Summit supercomputer. Augmenting these NAS implementations with <italic>PENGUIN</italic> can increase throughput by a factor of 1.6 to 7.1. Furthermore, walltime tests indicate that <italic>PENGUIN</italic> can reduce training time by a factor of 2.5 to 5.3.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Neural networks (NN) are used in high-performance computing and high-throughput analysis to extract knowledge from datasets. Neural architecture search (NAS) automates NN design by generating, training, and analyzing thousands of NNs. However, NAS requires massive computational power for NN training. To address challenges of efficiency and scalability, we propose PENGUIN, a decoupled fitness prediction engine that informs the search without interfering in it. PENGUIN uses parametric modeling to predict fitness of NNs. Existing NAS methods and parametric modeling functions can be plugged into PENGUIN to build flexible NAS workflows. Through this decoupling and flexible parametric modeling, PENGUIN reduces training costs: it predicts the fitness of NNs, enabling NAS to terminate training NNs early. Early termination increases the number of NNs that fixed compute resources can evaluate, thus giving NAS additional opportunity to find better NNs. We assess the effectiveness of our engine on 6,000 NNs across three diverse benchmark datasets and three state of the art NAS implementations using the Summit supercomputer. Augmenting these NAS implementations with PENGUIN can increase throughput by a factor of 1.6 to 7.1. Furthermore, walltime tests indicate that PENGUIN can reduce training time by a factor of 2.5 to 5.3.",
"title": "Building High-Throughput Neural Architecture Search Workflows via a Decoupled Fitness Prediction Engine",
"normalizedTitle": "Building High-Throughput Neural Architecture Search Workflows via a Decoupled Fitness Prediction Engine",
"fno": "09674227",
"hasPdf": true,
"idPrefix": "td",
"keywords": [
"Neural Net Architecture",
"Parallel Machines",
"High Throughput Neural Architecture Search",
"Decoupled Fitness Prediction Engine",
"Neural Networks",
"High Performance Computing",
"High Throughput Analysis",
"NN Design",
"Massive Computational Power",
"NN Training",
"PENGUIN",
"Parametric Modeling Functions",
"Flexible NAS Workflows",
"Flexible Parametric Modeling",
"Training Costs",
"Training N Ns",
"Fixed Compute Resources",
"NAS Additional Opportunity",
"Training Time",
"Summit Supercomputer",
"Training",
"Artificial Neural Networks",
"Predictive Models",
"Parametric Statistics",
"Engines",
"Search Problems",
"Data Models",
"Machine Learning",
"Artificial Intelligence",
"Performance Prediction",
"Neural Networks"
],
"authors": [
{
"givenName": "Ariel",
"surname": "Keller Rorabaugh",
"fullName": "Ariel Keller Rorabaugh",
"affiliation": "University of Tennessee at Knoxville, Knoxville, TN, USA",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Silvina",
"surname": "Caíno-Lores",
"fullName": "Silvina Caíno-Lores",
"affiliation": "University of Tennessee at Knoxville, Knoxville, TN, USA",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Travis",
"surname": "Johnston",
"fullName": "Travis Johnston",
"affiliation": "Striveworks, Austin, TX, USA",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Michela",
"surname": "Taufer",
"fullName": "Michela Taufer",
"affiliation": "University of Tennessee at Knoxville, Knoxville, TN, USA",
"__typename": "ArticleAuthorType"
}
],
"replicability": null,
"showBuyMe": false,
"showRecommendedArticles": true,
"isOpenAccess": true,
"issueNum": "11",
"pubDate": "2022-11-01 00:00:00",
"pubType": "trans",
"pages": "2913-2926",
"year": "2022",
"issn": "1045-9219",
"isbn": null,
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"recommendedArticles": [
{
"id": "proceedings/ecrts/2015/7570/0/7570a139",
"title": "Feasibility Analysis of Engine Control Tasks under EDF Scheduling",
"doi": null,
"abstractUrl": "/proceedings-article/ecrts/2015/7570a139/12OmNzX6cmA",
"parentPublication": {
"id": "proceedings/ecrts/2015/7570/0",
"title": "2015 27th Euromicro Conference on Real-Time Systems (ECRTS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tk/2022/11/09336245",
"title": "Estimating the Total Volume of Queries to a Search Engine",
"doi": null,
"abstractUrl": "/journal/tk/2022/11/09336245/1qHLoAfotuo",
"parentPublication": {
"id": "trans/tk",
"title": "IEEE Transactions on Knowledge & Data Engineering",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"adjacentArticles": {
"previous": {
"fno": "09714870",
"articleId": "1B2DimXLVYs",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "09665270",
"articleId": "1zJiRSAgWsg",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"webExtras": [
{
"id": "1DFdakIKWlO",
"name": "ttd202211-09674227s1-supp1-3140681.pdf",
"location": "https://www.computer.org/csdl/api/v1/extra/ttd202211-09674227s1-supp1-3140681.pdf",
"extension": "pdf",
"size": "111 kB",
"__typename": "WebExtraType"
}
],
"articleVideos": []
}
|
{
"issue": {
"id": "1yeDpGtSWLm",
"title": "Dec.",
"year": "2021",
"issueNum": "12",
"idPrefix": "tp",
"pubType": "journal",
"volume": "43",
"label": "Dec.",
"downloadables": {
"hasCover": false,
"__typename": "PeriodicalIssueDownloadablesType"
},
"__typename": "PeriodicalIssue"
},
"article": {
"id": "1k7oyvQ9LzO",
"doi": "10.1109/TPAMI.2020.2997007",
"abstract": "The ability of camera arrays to efficiently capture higher space-bandwidth product than single cameras has led to various multiscale and hybrid systems. These systems play vital roles in computational photography, including light field imaging, 360 VR camera, gigapixel videography, etc. One of the critical tasks in multiscale hybrid imaging is matching and fusing cross-resolution images from different cameras under perspective parallax. In this paper, we investigate the reference-based super-resolution (RefSR) problem associated with dual-camera or multi-camera systems. RefSR consists of super-resolving a low-resolution (LR) image given an external high-resolution (HR) reference image, where they suffer both a significant resolution gap (<inline-formula><tex-math notation=\"LaTeX\">Z_$8\\times$_Z</tex-math></inline-formula>) and large parallax (<inline-formula><tex-math notation=\"LaTeX\">Z_$\\sim 10\\%$_Z</tex-math></inline-formula> pixel displacement). We present CrossNet++, an end-to-end network containing novel two-stage cross-scale warping modules, image encoder and fusion decoder. The stage I learns to narrow down the parallax distinctively with the strong guidance of landmarks and intensity distribution consensus. Then the stage II operates more fine-grained alignment and aggregation in feature domain to synthesize the final super-resolved image. To further address the large parallax, new hybrid loss functions comprising warping loss, landmark loss and super-resolution loss are proposed to regularize training and enable better convergence. CrossNet++ significantly outperforms the state-of-art on light field datasets as well as real dual-camera data. We further demonstrate the generalization of our framework by transferring it to video super-resolution and video denoising.",
"abstracts": [
{
"abstractType": "Regular",
"content": "The ability of camera arrays to efficiently capture higher space-bandwidth product than single cameras has led to various multiscale and hybrid systems. These systems play vital roles in computational photography, including light field imaging, 360 VR camera, gigapixel videography, etc. One of the critical tasks in multiscale hybrid imaging is matching and fusing cross-resolution images from different cameras under perspective parallax. In this paper, we investigate the reference-based super-resolution (RefSR) problem associated with dual-camera or multi-camera systems. RefSR consists of super-resolving a low-resolution (LR) image given an external high-resolution (HR) reference image, where they suffer both a significant resolution gap (<inline-formula><tex-math notation=\"LaTeX\">$8\\times$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>8</mml:mn><mml:mo>×</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href=\"fang-ieq1-2997007.gif\"/></alternatives></inline-formula>) and large parallax (<inline-formula><tex-math notation=\"LaTeX\">$\\sim 10\\%$</tex-math><alternatives><mml:math><mml:mrow><mml:mo>∼</mml:mo><mml:mn>10</mml:mn><mml:mo>%</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href=\"fang-ieq2-2997007.gif\"/></alternatives></inline-formula> pixel displacement). We present CrossNet++, an end-to-end network containing novel two-stage cross-scale warping modules, image encoder and fusion decoder. The stage I learns to narrow down the parallax distinctively with the strong guidance of landmarks and intensity distribution consensus. Then the stage II operates more fine-grained alignment and aggregation in feature domain to synthesize the final super-resolved image. To further address the large parallax, new hybrid loss functions comprising warping loss, landmark loss and super-resolution loss are proposed to regularize training and enable better convergence. CrossNet++ significantly outperforms the state-of-art on light field datasets as well as real dual-camera data. We further demonstrate the generalization of our framework by transferring it to video super-resolution and video denoising.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "The ability of camera arrays to efficiently capture higher space-bandwidth product than single cameras has led to various multiscale and hybrid systems. These systems play vital roles in computational photography, including light field imaging, 360 VR camera, gigapixel videography, etc. One of the critical tasks in multiscale hybrid imaging is matching and fusing cross-resolution images from different cameras under perspective parallax. In this paper, we investigate the reference-based super-resolution (RefSR) problem associated with dual-camera or multi-camera systems. RefSR consists of super-resolving a low-resolution (LR) image given an external high-resolution (HR) reference image, where they suffer both a significant resolution gap (-) and large parallax (- pixel displacement). We present CrossNet++, an end-to-end network containing novel two-stage cross-scale warping modules, image encoder and fusion decoder. The stage I learns to narrow down the parallax distinctively with the strong guidance of landmarks and intensity distribution consensus. Then the stage II operates more fine-grained alignment and aggregation in feature domain to synthesize the final super-resolved image. To further address the large parallax, new hybrid loss functions comprising warping loss, landmark loss and super-resolution loss are proposed to regularize training and enable better convergence. CrossNet++ significantly outperforms the state-of-art on light field datasets as well as real dual-camera data. We further demonstrate the generalization of our framework by transferring it to video super-resolution and video denoising.",
"title": "CrossNet++: Cross-Scale Large-Parallax Warping for Reference-Based Super-Resolution",
"normalizedTitle": "CrossNet++: Cross-Scale Large-Parallax Warping for Reference-Based Super-Resolution",
"fno": "09099445",
"hasPdf": true,
"idPrefix": "tp",
"keywords": [
"Decoding",
"Image Coding",
"Image Denoising",
"Image Fusion",
"Image Reconstruction",
"Image Resolution",
"Image Sensors",
"Learning Artificial Intelligence",
"Neural Nets",
"Video Cameras",
"Video Signal Processing",
"Cross Scale Large Parallax Warping",
"360 VR Camera",
"Multiscale Hybrid Imaging",
"Reference Based Super Resolution Problem",
"Multicamera Systems",
"Two Stage Cross Scale Warping Modules",
"Super Resolution Loss",
"Video Super Resolution",
"Cross Net",
"Camera Arrays",
"Space Bandwidth Product",
"Single Cameras",
"Hybrid Systems",
"Computational Photography",
"Light Field Imaging",
"Gigapixel Videography",
"Cross Resolution Image Fusion",
"Ref SR Problem",
"LR Image",
"External High Resolution Reference Image",
"Image Encoder",
"Fusion Decoder",
"Intensity Distribution Consensus",
"Landmark Guidance",
"Landmark Loss",
"Warping Loss",
"Feature Domain",
"Hybrid Loss Functions",
"Real Dual Camera Data",
"Video Denoising",
"Cameras",
"Spatial Resolution",
"Signal Resolution",
"Superresolution",
"Light Fields",
"Training Data",
"Photography",
"Noise Reduction",
"Decoding",
"Reference Based Super Resolution",
"Camera Array",
"Light Field Imaging",
"Image Synthesis",
"Image Warping",
"Optical Flow"
],
"authors": [
{
"givenName": "Yang",
"surname": "Tan",
"fullName": "Yang Tan",
"affiliation": "Tsinghua University, Tsinghua-Berkeley Shenzhen Institite, Shenzhen, China",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Haitian",
"surname": "Zheng",
"fullName": "Haitian Zheng",
"affiliation": "Rochester University, Rochester, NY, USA",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Yinheng",
"surname": "Zhu",
"fullName": "Yinheng Zhu",
"affiliation": "Tsinghua University, Tsinghua-Berkeley Shenzhen Institite, Shenzhen, China",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Xiaoyun",
"surname": "Yuan",
"fullName": "Xiaoyun Yuan",
"affiliation": "Hong Kong University of Science and Technology, Hong Kong",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Xing",
"surname": "Lin",
"fullName": "Xing Lin",
"affiliation": "Beijing Innovation Center for Future Chip, Tsinghua University, Beijing, China",
"__typename": "ArticleAuthorType"
},
{
"givenName": "David",
"surname": "Brady",
"fullName": "David Brady",
"affiliation": "Duke University, Durham, NC, USA",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Lu",
"surname": "Fang",
"fullName": "Lu Fang",
"affiliation": "Tsinghua University, Tsinghua-Berkeley Shenzhen Institite, Shenzhen, China",
"__typename": "ArticleAuthorType"
}
],
"replicability": null,
"showBuyMe": true,
"showRecommendedArticles": true,
"isOpenAccess": false,
"issueNum": "12",
"pubDate": "2021-12-01 00:00:00",
"pubType": "trans",
"pages": "4291-4305",
"year": "2021",
"issn": "0162-8828",
"isbn": null,
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"recommendedArticles": [
{
"id": "proceedings/iccv/2021/2812/0/281200o4870",
"title": "LocalTrans: A Multiscale Local Transformer Network for Cross-Resolution Homography Estimation",
"doi": null,
"abstractUrl": "/proceedings-article/iccv/2021/281200o4870/1BmGwQnOCJy",
"parentPublication": {
"id": "proceedings/iccv/2021/2812/0",
"title": "2021 IEEE/CVF International Conference on Computer Vision (ICCV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/5555/01/09798876",
"title": "Deep Light Field Spatial Super-Resolution Using Heterogeneous Imaging",
"doi": null,
"abstractUrl": "/journal/tg/5555/01/09798876/1Eho8QXQucg",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvprw/2022/8739/0/873900a919",
"title": "SwiniPASSR: Swin Transformer based Parallax Attention Network for Stereo Image Super-Resolution",
"doi": null,
"abstractUrl": "/proceedings-article/cvprw/2022/873900a919/1G561QJhcbu",
"parentPublication": {
"id": "proceedings/cvprw/2022/8739/0",
"title": "2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2022/6946/0/694600r7621",
"title": "Deep Constrained Least Squares for Blind Image Super-Resolution",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2022/694600r7621/1H1lU7F9bwI",
"parentPublication": {
"id": "proceedings/cvpr/2022/6946/0",
"title": "2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tp/2021/12/09119166",
"title": "Deep Back-ProjectiNetworks for Single Image Super-Resolution",
"doi": null,
"abstractUrl": "/journal/tp/2021/12/09119166/1kHUHVxuNHi",
"parentPublication": {
"id": "trans/tp",
"title": "IEEE Transactions on Pattern Analysis & Machine Intelligence",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/2022/03/09186170",
"title": "Parallax Free Registration for Augmented Reality Optical See-Through Displays in the Peripersonal Space",
"doi": null,
"abstractUrl": "/journal/tg/2022/03/09186170/1mP2AYgyLQY",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tp/2022/09/09426428",
"title": "Liquid Warping GAN With Attention: A Unified Framework for Human Image Synthesis",
"doi": null,
"abstractUrl": "/journal/tp/2022/09/09426428/1tpwSZzVMZO",
"parentPublication": {
"id": "trans/tp",
"title": "IEEE Transactions on Pattern Analysis & Machine Intelligence",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tq/2022/06/09534710",
"title": "ExtendedSketch: Fusing Network Traffic for Super Host Identification With a Memory Efficient Sketch",
"doi": null,
"abstractUrl": "/journal/tq/2022/06/09534710/1wLbmcKmjRK",
"parentPublication": {
"id": "trans/tq",
"title": "IEEE Transactions on Dependable and Secure Computing",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvprw/2021/4899/0/489900a766",
"title": "Symmetric Parallax Attention for Stereo Image Super-Resolution",
"doi": null,
"abstractUrl": "/proceedings-article/cvprw/2021/489900a766/1yXsTsDbWrS",
"parentPublication": {
"id": "proceedings/cvprw/2021/4899/0",
"title": "2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tp/2022/12/09632408",
"title": "The Conditional Super Learner",
"doi": null,
"abstractUrl": "/journal/tp/2022/12/09632408/1yYPaL0KHrq",
"parentPublication": {
"id": "trans/tp",
"title": "IEEE Transactions on Pattern Analysis & Machine Intelligence",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"adjacentArticles": {
"previous": {
"fno": "09097964",
"articleId": "1k0L3hJG6u4",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "09121755",
"articleId": "1kMT53HO0es",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"webExtras": [],
"articleVideos": []
}
|
{
"issue": {
"id": "12OmNyGtjf4",
"title": "Aug.",
"year": "2018",
"issueNum": "08",
"idPrefix": "tp",
"pubType": "journal",
"volume": "40",
"label": "Aug.",
"downloadables": {
"hasCover": false,
"__typename": "PeriodicalIssueDownloadablesType"
},
"__typename": "PeriodicalIssue"
},
"article": {
"id": "13rRUEgaru7",
"doi": "10.1109/TPAMI.2017.2737425",
"abstract": "The article describes a pipeline that receives as input a sequence of stereo images, and outputs the camera motion and a Piecewise-Planar Reconstruction (PPR) of the scene. The pipeline, named Piecewise-Planar StereoScan (PPSS), works as follows: the planes in the scene are detected for each stereo view using semi-dense depth estimation; the relative pose is computed by a new closed-form minimal algorithm that only uses point correspondences whenever plane detections do not fully constrain the motion; the camera motion and the PPR are jointly refined by alternating between discrete optimization and continuous bundle adjustment; and, finally, the detected 3D planes are segmented in images using a new framework that handles low texture and visibility issues. PPSS is extensively validated in indoor and outdoor datasets, and benchmarked against two popular point-based SfM pipelines. The experiments confirm that plane-based visual odometry is resilient to situations of small image overlap, poor texture, specularity, and perceptual aliasing where the fast LIBVISO2 [1] pipeline fails. The comparison against VisualSfM+CMVS/PMVS [2], [3] shows that, for a similar computational complexity, PPSS is more accurate and provides much more compelling and visually pleasant 3D models. These results strongly suggest that plane primitives are an advantageous alternative to point correspondences for applications of SfM and 3D reconstruction in man-made environments.",
"abstracts": [
{
"abstractType": "Regular",
"content": "The article describes a pipeline that receives as input a sequence of stereo images, and outputs the camera motion and a Piecewise-Planar Reconstruction (PPR) of the scene. The pipeline, named Piecewise-Planar StereoScan (PPSS), works as follows: the planes in the scene are detected for each stereo view using semi-dense depth estimation; the relative pose is computed by a new closed-form minimal algorithm that only uses point correspondences whenever plane detections do not fully constrain the motion; the camera motion and the PPR are jointly refined by alternating between discrete optimization and continuous bundle adjustment; and, finally, the detected 3D planes are segmented in images using a new framework that handles low texture and visibility issues. PPSS is extensively validated in indoor and outdoor datasets, and benchmarked against two popular point-based SfM pipelines. The experiments confirm that plane-based visual odometry is resilient to situations of small image overlap, poor texture, specularity, and perceptual aliasing where the fast LIBVISO2 [1] pipeline fails. The comparison against VisualSfM+CMVS/PMVS [2], [3] shows that, for a similar computational complexity, PPSS is more accurate and provides much more compelling and visually pleasant 3D models. These results strongly suggest that plane primitives are an advantageous alternative to point correspondences for applications of SfM and 3D reconstruction in man-made environments.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "The article describes a pipeline that receives as input a sequence of stereo images, and outputs the camera motion and a Piecewise-Planar Reconstruction (PPR) of the scene. The pipeline, named Piecewise-Planar StereoScan (PPSS), works as follows: the planes in the scene are detected for each stereo view using semi-dense depth estimation; the relative pose is computed by a new closed-form minimal algorithm that only uses point correspondences whenever plane detections do not fully constrain the motion; the camera motion and the PPR are jointly refined by alternating between discrete optimization and continuous bundle adjustment; and, finally, the detected 3D planes are segmented in images using a new framework that handles low texture and visibility issues. PPSS is extensively validated in indoor and outdoor datasets, and benchmarked against two popular point-based SfM pipelines. The experiments confirm that plane-based visual odometry is resilient to situations of small image overlap, poor texture, specularity, and perceptual aliasing where the fast LIBVISO2 [1] pipeline fails. The comparison against VisualSfM+CMVS/PMVS [2], [3] shows that, for a similar computational complexity, PPSS is more accurate and provides much more compelling and visually pleasant 3D models. These results strongly suggest that plane primitives are an advantageous alternative to point correspondences for applications of SfM and 3D reconstruction in man-made environments.",
"title": "Piecewise-Planar StereoScan: Sequential Structure and Motion Using Plane Primitives",
"normalizedTitle": "Piecewise-Planar StereoScan: Sequential Structure and Motion Using Plane Primitives",
"fno": "08006257",
"hasPdf": true,
"idPrefix": "tp",
"keywords": [
"Computational Complexity",
"Image Reconstruction",
"Image Segmentation",
"Image Sequences",
"Image Texture",
"Minimisation",
"Motion Estimation",
"Object Detection",
"Pose Estimation",
"Stereo Image Processing",
"Plane Primitives",
"Camera Motion",
"PPR",
"PPSS",
"Semidense Depth Estimation",
"Closed Form Minimal Algorithm",
"Discrete Optimization",
"Continuous Bundle Adjustment",
"Indoor Datasets",
"Outdoor Datasets",
"Plane Based Visual Odometry",
"Image Overlap",
"Poor Texture",
"Computational Complexity",
"Piecewise Planar Reconstruction",
"Piecewise Planar Stereoscan",
"Sequential Structure And Motion",
"Stereo Images Sequence",
"Pose Estimation",
"Image Segmentation",
"Point Based Sf M Pipelines",
"3 D Reconstruction",
"Man Made Environments",
"3 D Plane Detection",
"Three Dimensional Displays",
"Pipelines",
"Labeling",
"Solid Modeling",
"Cameras",
"Image Reconstruction",
"Visualization",
"Structure And Motion",
"Piecewise Planar Reconstruction",
"Stereo Image Sequences",
"MRF"
],
"authors": [
{
"givenName": "Carolina",
"surname": "Raposo",
"fullName": "Carolina Raposo",
"affiliation": "Institute of Systems and Robotics, University of Coimbra, Coimbra, Portugal",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Michel",
"surname": "Antunes",
"fullName": "Michel Antunes",
"affiliation": "Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg, Esch-sur-Alzette, Luxembourg",
"__typename": "ArticleAuthorType"
},
{
"givenName": "João P.",
"surname": "Barreto",
"fullName": "João P. Barreto",
"affiliation": "Institute of Systems and Robotics, University of Coimbra, Coimbra, Portugal",
"__typename": "ArticleAuthorType"
}
],
"replicability": null,
"showBuyMe": true,
"showRecommendedArticles": true,
"isOpenAccess": false,
"issueNum": "08",
"pubDate": "2018-08-01 00:00:00",
"pubType": "trans",
"pages": "1918-1931",
"year": "2018",
"issn": "0162-8828",
"isbn": null,
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"recommendedArticles": [
{
"id": "proceedings/cvpr/2014/5118/0/5118a469",
"title": "Fast, Approximate Piecewise-Planar Modeling Based on Sparse Structure-from-Motion and Superpixels",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2014/5118a469/12OmNAtaRZ2",
"parentPublication": {
"id": "proceedings/cvpr/2014/5118/0",
"title": "2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccv/2015/8391/0/8391a801",
"title": "Optimizing the Viewing Graph for Structure-from-Motion",
"doi": null,
"abstractUrl": "/proceedings-article/iccv/2015/8391a801/12OmNBpVQcH",
"parentPublication": {
"id": "proceedings/iccv/2015/8391/0",
"title": "2015 IEEE International Conference on Computer Vision (ICCV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/wacv/2015/6683/0/6683a341",
"title": "A Sequential Online 3D Reconstruction System Using Dense Stereo Matching",
"doi": null,
"abstractUrl": "/proceedings-article/wacv/2015/6683a341/12OmNqH9hkt",
"parentPublication": {
"id": "proceedings/wacv/2015/6683/0",
"title": "2015 IEEE Winter Conference on Applications of Computer Vision (WACV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cis/2015/8660/0/8660a117",
"title": "An Effective Energy-Based Multi-view Piecewise Planar Stereo Method",
"doi": null,
"abstractUrl": "/proceedings-article/cis/2015/8660a117/12OmNro0Ii2",
"parentPublication": {
"id": "proceedings/cis/2015/8660/0",
"title": "2015 11th International Conference on Computational Intelligence and Security (CIS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2016/8851/0/8851d327",
"title": "Piecewise-Planar 3D Approximation from Wide-Baseline Stereo",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2016/8851d327/12OmNvA1heH",
"parentPublication": {
"id": "proceedings/cvpr/2016/8851/0",
"title": "2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccvw/2015/9711/0/5720b086",
"title": "Fast Structure from Motion for Sequential and Wide Area Motion Imagery",
"doi": null,
"abstractUrl": "/proceedings-article/iccvw/2015/5720b086/12OmNvmowTK",
"parentPublication": {
"id": "proceedings/iccvw/2015/9711/0",
"title": "2015 IEEE International Conference on Computer Vision Workshop (ICCVW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/3dv/2016/5407/0/5407a230",
"title": "Large Scale SfM with the Distributed Camera Model",
"doi": null,
"abstractUrl": "/proceedings-article/3dv/2016/5407a230/12OmNyQYty4",
"parentPublication": {
"id": "proceedings/3dv/2016/5407/0",
"title": "2016 Fourth International Conference on 3D Vision (3DV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2016/8851/0/07780814",
"title": "Structure-from-Motion Revisited",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2016/07780814/12OmNzBwGro",
"parentPublication": {
"id": "proceedings/cvpr/2016/8851/0",
"title": "2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/3dv/2018/8425/0/842500a616",
"title": "Multi-planar Monocular Reconstruction of Manhattan Indoor Scenes",
"doi": null,
"abstractUrl": "/proceedings-article/3dv/2018/842500a616/17D45XvMcbo",
"parentPublication": {
"id": "proceedings/3dv/2018/8425/0",
"title": "2018 International Conference on 3D Vision (3DV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2022/6946/0/694600i655",
"title": "PlaneMVS: 3D Plane Reconstruction from Multi-View Stereo",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2022/694600i655/1H0NwN7QUAU",
"parentPublication": {
"id": "proceedings/cvpr/2022/6946/0",
"title": "2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"adjacentArticles": {
"previous": {
"fno": "07995142",
"articleId": "13rRUy3xY3T",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "08014479",
"articleId": "13rRUyeTVjn",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"webExtras": [
{
"id": "17ShDTXFgHW",
"name": "ttp201808-08006257s1.zip",
"location": "https://www.computer.org/csdl/api/v1/extra/ttp201808-08006257s1.zip",
"extension": "zip",
"size": "2.06 MB",
"__typename": "WebExtraType"
}
],
"articleVideos": []
}
|
{
"issue": {
"id": "1uSOMRwZhKw",
"title": "Aug.",
"year": "2021",
"issueNum": "08",
"idPrefix": "tm",
"pubType": "journal",
"volume": "20",
"label": "Aug.",
"downloadables": {
"hasCover": false,
"__typename": "PeriodicalIssueDownloadablesType"
},
"__typename": "PeriodicalIssue"
},
"article": {
"id": "1iHr5YtaDvy",
"doi": "10.1109/TMC.2020.2984320",
"abstract": "We present DynamicSLAM: an indoor localization technique that eliminates the need for the daunting calibration step. DynamicSLAM is a novel Simultaneous Localization And Mapping (SLAM) framework that iteratively acquires the feature map of the environment while simultaneously localizing users relative to this map. Specifically, we employ the phone inertial sensors to keep track of the user's path. To compensate for the error accumulation due to the low-cost inertial sensors, DynamicSLAM leverages unique points in the environment (anchors) as observations to reduce the estimated location error. DynamicSLAM introduces the novel concept of mobile human anchors that are based on the encounters with other users in the environment, significantly increasing the number and ubiquity of anchors and boosting localization accuracy. We present different encounter models and show how they are incorporated in a unified probabilistic framework to reduce the ambiguity in the user location. Furthermore, we present a theoretical proof for system convergence and the human anchors ability to reset the accumulated error. Evaluation of DynamicSLAM using different Android phones shows that it can provide a localization accuracy with a median of 1.1m. This accuracy outperforms the state-of-the-art techniques by 55 percent, highlighting DynamicSLAM promise for ubiquitous indoor localization.",
"abstracts": [
{
"abstractType": "Regular",
"content": "We present DynamicSLAM: an indoor localization technique that eliminates the need for the daunting calibration step. DynamicSLAM is a novel Simultaneous Localization And Mapping (SLAM) framework that iteratively acquires the feature map of the environment while simultaneously localizing users relative to this map. Specifically, we employ the phone inertial sensors to keep track of the user's path. To compensate for the error accumulation due to the low-cost inertial sensors, DynamicSLAM leverages unique points in the environment (anchors) as observations to reduce the estimated location error. DynamicSLAM introduces the novel concept of mobile human anchors that are based on the encounters with other users in the environment, significantly increasing the number and ubiquity of anchors and boosting localization accuracy. We present different encounter models and show how they are incorporated in a unified probabilistic framework to reduce the ambiguity in the user location. Furthermore, we present a theoretical proof for system convergence and the human anchors ability to reset the accumulated error. Evaluation of DynamicSLAM using different Android phones shows that it can provide a localization accuracy with a median of 1.1m. This accuracy outperforms the state-of-the-art techniques by 55 percent, highlighting DynamicSLAM promise for ubiquitous indoor localization.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "We present DynamicSLAM: an indoor localization technique that eliminates the need for the daunting calibration step. DynamicSLAM is a novel Simultaneous Localization And Mapping (SLAM) framework that iteratively acquires the feature map of the environment while simultaneously localizing users relative to this map. Specifically, we employ the phone inertial sensors to keep track of the user's path. To compensate for the error accumulation due to the low-cost inertial sensors, DynamicSLAM leverages unique points in the environment (anchors) as observations to reduce the estimated location error. DynamicSLAM introduces the novel concept of mobile human anchors that are based on the encounters with other users in the environment, significantly increasing the number and ubiquity of anchors and boosting localization accuracy. We present different encounter models and show how they are incorporated in a unified probabilistic framework to reduce the ambiguity in the user location. Furthermore, we present a theoretical proof for system convergence and the human anchors ability to reset the accumulated error. Evaluation of DynamicSLAM using different Android phones shows that it can provide a localization accuracy with a median of 1.1m. This accuracy outperforms the state-of-the-art techniques by 55 percent, highlighting DynamicSLAM promise for ubiquitous indoor localization.",
"title": "DynamicSLAM: Leveraging Human Anchors for Ubiquitous Low-Overhead Indoor Localization",
"normalizedTitle": "DynamicSLAM: Leveraging Human Anchors for Ubiquitous Low-Overhead Indoor Localization",
"fno": "09055083",
"hasPdf": true,
"idPrefix": "tm",
"keywords": [
"Feature Extraction",
"Indoor Radio",
"Mobile Computing",
"Sensors",
"SLAM Robots",
"Smart Phones",
"Telecommunication Computing",
"Daunting Calibration Step",
"Simultaneous Localization And Mapping Framework",
"Feature Map",
"Phone Inertial Sensors",
"Error Accumulation",
"Low Cost Inertial Sensors",
"Environment",
"Estimated Location Error",
"Mobile Human Anchors",
"Boosting Localization Accuracy",
"User Location",
"Human Anchors Ability",
"Dynamic SLAM",
"Ubiquitous Low Overhead Indoor Localization",
"Simultaneous Localization And Mapping",
"Buildings",
"Mathematical Model",
"Mobile Computing",
"Unconventional Localization",
"SLAM",
"Indoor Localization",
"Unsupervised Localization"
],
"authors": [
{
"givenName": "Ahmed",
"surname": "Shokry",
"fullName": "Ahmed Shokry",
"affiliation": "Department of Computer Science, Alexandria University, Egypt",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Moustafa",
"surname": "Elhamshary",
"fullName": "Moustafa Elhamshary",
"affiliation": "Egypt-Japan University for Science and Technology (E-JUST), Alexandria, Egypt",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Moustafa",
"surname": "Youssef",
"fullName": "Moustafa Youssef",
"affiliation": "Egypt-Japan University for Science and Technology (E-JUST), Alexandria, Egypt",
"__typename": "ArticleAuthorType"
}
],
"replicability": null,
"showBuyMe": true,
"showRecommendedArticles": true,
"isOpenAccess": false,
"issueNum": "08",
"pubDate": "2021-08-01 00:00:00",
"pubType": "trans",
"pages": "2563-2575",
"year": "2021",
"issn": "1536-1233",
"isbn": null,
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"recommendedArticles": [
{
"id": "proceedings/ismarw/2016/3740/0/07836473",
"title": "Indoor Localisation and Navigation on Augmented Reality Devices",
"doi": null,
"abstractUrl": "/proceedings-article/ismarw/2016/07836473/12OmNwAKCLD",
"parentPublication": {
"id": "proceedings/ismarw/2016/3740/0",
"title": "2016 IEEE International Symposium on Mixed and Augmented Reality (ISMAR-Adjunct)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/percomw/2014/2736/0/06815191",
"title": "Fast-converging indoor mapping for wireless indoor localization",
"doi": null,
"abstractUrl": "/proceedings-article/percomw/2014/06815191/12OmNxd4tsz",
"parentPublication": {
"id": "proceedings/percomw/2014/2736/0",
"title": "2014 IEEE International Conference on Pervasive Computing and Communication Workshops (PERCOM WORKSHOPS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccvw/2011/0063/0/06130254",
"title": "Indoor SLAM using a range-augmented omnidirectional vision",
"doi": null,
"abstractUrl": "/proceedings-article/iccvw/2011/06130254/12OmNz5JCeO",
"parentPublication": {
"id": "proceedings/iccvw/2011/0063/0",
"title": "2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tm/2016/07/07265092",
"title": "SemanticSLAM: Using Environment Landmarks for Unsupervised Indoor Localization",
"doi": null,
"abstractUrl": "/journal/tm/2016/07/07265092/13rRUxAAT8n",
"parentPublication": {
"id": "trans/tm",
"title": "IEEE Transactions on Mobile Computing",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/5555/01/09797054",
"title": "Compact World Anchors: Registration Using Parametric Primitives as Scene Description",
"doi": null,
"abstractUrl": "/journal/tg/5555/01/09797054/1EexleVk9kk",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/mdm/2022/5176/0/517600a385",
"title": "A Framework for Indoor Localization Using the Magnetic Field",
"doi": null,
"abstractUrl": "/proceedings-article/mdm/2022/517600a385/1G89NTgI91u",
"parentPublication": {
"id": "proceedings/mdm/2022/5176/0",
"title": "2022 23rd IEEE International Conference on Mobile Data Management (MDM)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icghit/2019/0627/0/062700a007",
"title": "Indoor Positioning System using Sensor and Crowdsourcing Landmark Map Update",
"doi": null,
"abstractUrl": "/proceedings-article/icghit/2019/062700a007/1e5ZeaqFMK4",
"parentPublication": {
"id": "proceedings/icghit/2019/0627/0",
"title": "2019 International Conference on Green and Human Information Technology (ICGHIT)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/itca/2019/6494/0/09092481",
"title": "Sparse Visual Localization in GPS-Denied Indoor Environments",
"doi": null,
"abstractUrl": "/proceedings-article/itca/2019/09092481/1jPaWgTxwDC",
"parentPublication": {
"id": "proceedings/itca/2019/6494/0",
"title": "2019 International Conference on Information Technology and Computer Application (ITCA)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iv/2020/9134/0/913400a708",
"title": "Guido: Augmented Reality for Indoor Navigation Using Commodity Hardware",
"doi": null,
"abstractUrl": "/proceedings-article/iv/2020/913400a708/1rSR9YRSTpS",
"parentPublication": {
"id": "proceedings/iv/2020/9134/0",
"title": "2020 24th International Conference Information Visualisation (IV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icpr/2021/8808/0/09413258",
"title": "RISEdb: a Novel Indoor Localization Dataset",
"doi": null,
"abstractUrl": "/proceedings-article/icpr/2021/09413258/1tmidUG5Yqc",
"parentPublication": {
"id": "proceedings/icpr/2021/8808/0",
"title": "2020 25th International Conference on Pattern Recognition (ICPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"adjacentArticles": {
"previous": {
"fno": "09050836",
"articleId": "1iCrPQ7ZVMQ",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "09069304",
"articleId": "1j4G1lavXZ6",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"webExtras": [],
"articleVideos": []
}
|
{
"issue": {
"id": "12OmNwoxSj9",
"title": "April-June",
"year": "2020",
"issueNum": "02",
"idPrefix": "ta",
"pubType": "journal",
"volume": "11",
"label": "April-June",
"downloadables": {
"hasCover": false,
"__typename": "PeriodicalIssueDownloadablesType"
},
"__typename": "PeriodicalIssue"
},
"article": {
"id": "13rRUB7a1ea",
"doi": "10.1109/TAFFC.2018.2799593",
"abstract": "Social context plays an important role in everyday emotional interactions, and others' faces often provide contextual cues in social situations. Investigating this complex social process is a challenge that can be addressed with the use of computer-generated facial expressions. In the current research, we use synthesized facial expressions to investigate the influence of socioaffective inferential mechanisms on the recognition of social emotions. Participants judged blends of facial expressions of shame-sadness, or of anger-disgust, in a target avatar face presented at the center of a screen while a contextual avatar face expressed an emotion (disgust, contempt, and sadness) or remained neutral. The dynamics of the facial expressions and the head/gaze movements of the two avatars were manipulated in order to create an interaction in which the two avatars shared eye gaze only in the social interaction condition. Results of Experiment 1 revealed that when the avatars engaged in social interaction, target expression blends of shame and sadness were perceived as expressing more shame if the contextual face expressed disgust and more sadness when the contextual face expressed sadness. Interestingly, perceptions of shame were not enhanced when the contextual face expressed contempt. The latter finding is probably attributable to the low recognition rates for the expression of contempt observed in Experiment 2.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Social context plays an important role in everyday emotional interactions, and others' faces often provide contextual cues in social situations. Investigating this complex social process is a challenge that can be addressed with the use of computer-generated facial expressions. In the current research, we use synthesized facial expressions to investigate the influence of socioaffective inferential mechanisms on the recognition of social emotions. Participants judged blends of facial expressions of shame-sadness, or of anger-disgust, in a target avatar face presented at the center of a screen while a contextual avatar face expressed an emotion (disgust, contempt, and sadness) or remained neutral. The dynamics of the facial expressions and the head/gaze movements of the two avatars were manipulated in order to create an interaction in which the two avatars shared eye gaze only in the social interaction condition. Results of Experiment 1 revealed that when the avatars engaged in social interaction, target expression blends of shame and sadness were perceived as expressing more shame if the contextual face expressed disgust and more sadness when the contextual face expressed sadness. Interestingly, perceptions of shame were not enhanced when the contextual face expressed contempt. The latter finding is probably attributable to the low recognition rates for the expression of contempt observed in Experiment 2.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Social context plays an important role in everyday emotional interactions, and others' faces often provide contextual cues in social situations. Investigating this complex social process is a challenge that can be addressed with the use of computer-generated facial expressions. In the current research, we use synthesized facial expressions to investigate the influence of socioaffective inferential mechanisms on the recognition of social emotions. Participants judged blends of facial expressions of shame-sadness, or of anger-disgust, in a target avatar face presented at the center of a screen while a contextual avatar face expressed an emotion (disgust, contempt, and sadness) or remained neutral. The dynamics of the facial expressions and the head/gaze movements of the two avatars were manipulated in order to create an interaction in which the two avatars shared eye gaze only in the social interaction condition. Results of Experiment 1 revealed that when the avatars engaged in social interaction, target expression blends of shame and sadness were perceived as expressing more shame if the contextual face expressed disgust and more sadness when the contextual face expressed sadness. Interestingly, perceptions of shame were not enhanced when the contextual face expressed contempt. The latter finding is probably attributable to the low recognition rates for the expression of contempt observed in Experiment 2.",
"title": "Emotion Recognition in Simulated Social Interactions",
"normalizedTitle": "Emotion Recognition in Simulated Social Interactions",
"fno": "08319988",
"hasPdf": true,
"idPrefix": "ta",
"keywords": [
"Avatars",
"Emotion Recognition",
"Face Recognition",
"Emotion Recognition",
"Social Interactions",
"Emotional Interactions",
"Contextual Cues",
"Complex Social Process",
"Computer Generated Facial Expressions",
"Socioaffective Inferential Mechanisms",
"Social Emotions",
"Shame Sadness",
"Contextual Avatar Face",
"Social Interaction Condition",
"Contextual Face",
"Avatars",
"Emotion Recognition",
"Face Recognition",
"Cognitive Processes",
"Emotion Recognition",
"Cognitive Appraisal",
"Facial Expressions",
"Social Inferences",
"Social Interaction"
],
"authors": [
{
"givenName": "C.",
"surname": "Mumenthaler",
"fullName": "C. Mumenthaler",
"affiliation": "Laboratory for the Study of Emotion Elicitation and Expression (E3Lab), Department of Psychology, University of Geneva and the Swiss Center for Affective Sciences, University of Geneva, Geneva, GE, Switzerland",
"__typename": "ArticleAuthorType"
},
{
"givenName": "D.",
"surname": "Sander",
"fullName": "D. Sander",
"affiliation": "Laboratory for the Study of Emotion Elicitation and Expression (E3Lab), Department of Psychology, University of Geneva and the Swiss Center for Affective Sciences, University of Geneva, Geneva, GE, Switzerland",
"__typename": "ArticleAuthorType"
},
{
"givenName": "A. S. R.",
"surname": "Manstead",
"fullName": "A. S. R. Manstead",
"affiliation": "School of Psychology, Cardiff University, Cardiff, UK",
"__typename": "ArticleAuthorType"
}
],
"replicability": null,
"showBuyMe": false,
"showRecommendedArticles": true,
"isOpenAccess": true,
"issueNum": "02",
"pubDate": "2020-04-01 00:00:00",
"pubType": "trans",
"pages": "308-312",
"year": "2020",
"issn": "1949-3045",
"isbn": null,
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"recommendedArticles": [
{
"id": "proceedings/acii/2015/9953/0/07344640",
"title": "Emotion recognition from embedded bodily expressions and speech during dyadic interactions",
"doi": null,
"abstractUrl": "/proceedings-article/acii/2015/07344640/12OmNwfKjcD",
"parentPublication": {
"id": "proceedings/acii/2015/9953/0",
"title": "2015 International Conference on Affective Computing and Intelligent Interaction (ACII)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iscv/2017/4062/0/08054955",
"title": "A $P recognizer for automatic facial emotion recognition using Kinect sensor",
"doi": null,
"abstractUrl": "/proceedings-article/iscv/2017/08054955/12OmNx57HRY",
"parentPublication": {
"id": "proceedings/iscv/2017/4062/0",
"title": "2017 Intelligent Systems and Computer Vision (ISCV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ised/2014/6965/0/6965a217",
"title": "Emotion Recognition through Speech Signal for Human-Computer Interaction",
"doi": null,
"abstractUrl": "/proceedings-article/ised/2014/6965a217/12OmNzBwGJ5",
"parentPublication": {
"id": "proceedings/ised/2014/6965/0",
"title": "2014 Fifth International Symposium on Electronic System Design (ISED)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/pci/2009/3788/0/3788a102",
"title": "Comparison of Different Classifiers for Emotion Recognition",
"doi": null,
"abstractUrl": "/proceedings-article/pci/2009/3788a102/12OmNzsJ7BF",
"parentPublication": {
"id": "proceedings/pci/2009/3788/0",
"title": "2009 13th Panhellenic Conference on Informatics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/ta/2017/03/07451244",
"title": "BAUM-1: A Spontaneous Audio-Visual Face Database of Affective and Mental States",
"doi": null,
"abstractUrl": "/journal/ta/2017/03/07451244/13rRUxZzAg4",
"parentPublication": {
"id": "trans/ta",
"title": "IEEE Transactions on Affective Computing",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/dcabes/2021/2889/0/288900a108",
"title": "Classroom monitoring system based on facial expression recognition",
"doi": null,
"abstractUrl": "/proceedings-article/dcabes/2021/288900a108/1AqwrYlvDMI",
"parentPublication": {
"id": "proceedings/dcabes/2021/2889/0",
"title": "2021 20th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/csase/2022/2632/0/09759750",
"title": "Facial Emotion Classification of Multi-Type Datasets based on SVM Classifier",
"doi": null,
"abstractUrl": "/proceedings-article/csase/2022/09759750/1CRvUn98cso",
"parentPublication": {
"id": "proceedings/csase/2022/2632/0",
"title": "2022 International Conference on Computer Science and Software Engineering (CSASE)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/aipr/2021/2471/0/09762207",
"title": "Emotion Recognition Using Deep Learning",
"doi": null,
"abstractUrl": "/proceedings-article/aipr/2021/09762207/1CT99fsRrj2",
"parentPublication": {
"id": "proceedings/aipr/2021/2471/0",
"title": "2021 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/percom-workshops/2021/0424/0/09430999",
"title": "Effect of Facial Expression Categories and Calculation Methods on Automatic Emotion Recognition",
"doi": null,
"abstractUrl": "/proceedings-article/percom-workshops/2021/09430999/1tROOOouEzm",
"parentPublication": {
"id": "proceedings/percom-workshops/2021/0424/0",
"title": "2021 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccvw/2021/0191/0/019100d526",
"title": "FSER: Deep Convolutional Neural Networks for Speech Emotion Recognition",
"doi": null,
"abstractUrl": "/proceedings-article/iccvw/2021/019100d526/1yNhO91gFAQ",
"parentPublication": {
"id": "proceedings/iccvw/2021/0191/0",
"title": "2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"adjacentArticles": {
"previous": {
"fno": "08063916",
"articleId": "13rRUwI5Uem",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "08252762",
"articleId": "13rRUwhpBCr",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"webExtras": [
{
"id": "1kerShAi0Pm",
"name": "tta202002-08319988s1-taffc-mumenthaler-2799593-mm.zip",
"location": "https://www.computer.org/csdl/api/v1/extra/tta202002-08319988s1-taffc-mumenthaler-2799593-mm.zip",
"extension": "zip",
"size": "4.61 MB",
"__typename": "WebExtraType"
}
],
"articleVideos": []
}
|
{
"issue": {
"id": "12OmNBpEeNK",
"title": "Aug.",
"year": "2016",
"issueNum": "08",
"idPrefix": "tg",
"pubType": "journal",
"volume": "22",
"label": "Aug.",
"downloadables": {
"hasCover": false,
"__typename": "PeriodicalIssueDownloadablesType"
},
"__typename": "PeriodicalIssue"
},
"article": {
"id": "13rRUyv53Fv",
"doi": "10.1109/TVCG.2015.2498617",
"abstract": "Acquiring general material appearance with hand-held consumer RGB-D cameras is difficult for casual users, due to the inaccuracy in reconstructed camera poses and geometry, as well as the unknown lighting that is coupled with materials in measured color images. To tackle these challenges, we present a novel technique for estimating the spatially varying isotropic surface reflectance, solely from color and depth images captured with an RGB-D camera under unknown environment illumination. The core of our approach is a joint optimization, which alternates among solving for plausible camera poses, materials, the environment lighting and normals. To refine camera poses, we exploit the rich spatial and view-dependent variations of materials, treating the object as a localization-self-calibrating model. To recover the unknown lighting, measured color images along with the current estimate of materials are used in a global optimization, efficiently solved by exploiting the sparsity in the wavelet domain. We demonstrate the substantially improved quality of estimated appearance on a variety of daily objects.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Acquiring general material appearance with hand-held consumer RGB-D cameras is difficult for casual users, due to the inaccuracy in reconstructed camera poses and geometry, as well as the unknown lighting that is coupled with materials in measured color images. To tackle these challenges, we present a novel technique for estimating the spatially varying isotropic surface reflectance, solely from color and depth images captured with an RGB-D camera under unknown environment illumination. The core of our approach is a joint optimization, which alternates among solving for plausible camera poses, materials, the environment lighting and normals. To refine camera poses, we exploit the rich spatial and view-dependent variations of materials, treating the object as a localization-self-calibrating model. To recover the unknown lighting, measured color images along with the current estimate of materials are used in a global optimization, efficiently solved by exploiting the sparsity in the wavelet domain. We demonstrate the substantially improved quality of estimated appearance on a variety of daily objects.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Acquiring general material appearance with hand-held consumer RGB-D cameras is difficult for casual users, due to the inaccuracy in reconstructed camera poses and geometry, as well as the unknown lighting that is coupled with materials in measured color images. To tackle these challenges, we present a novel technique for estimating the spatially varying isotropic surface reflectance, solely from color and depth images captured with an RGB-D camera under unknown environment illumination. The core of our approach is a joint optimization, which alternates among solving for plausible camera poses, materials, the environment lighting and normals. To refine camera poses, we exploit the rich spatial and view-dependent variations of materials, treating the object as a localization-self-calibrating model. To recover the unknown lighting, measured color images along with the current estimate of materials are used in a global optimization, efficiently solved by exploiting the sparsity in the wavelet domain. We demonstrate the substantially improved quality of estimated appearance on a variety of daily objects.",
"title": "Simultaneous Localization and Appearance Estimation with a Consumer RGB-D Camera",
"normalizedTitle": "Simultaneous Localization and Appearance Estimation with a Consumer RGB-D Camera",
"fno": "07321825",
"hasPdf": true,
"idPrefix": "tg",
"keywords": [
"Lighting",
"Cameras",
"Optimization",
"Geometry",
"Joints",
"Image Reconstruction",
"Wavelet Domain",
"Joint Optimization",
"RGB D Camera",
"Spatially Varying BRDF"
],
"authors": [
{
"givenName": "Hongzhi",
"surname": "Wu",
"fullName": "Hongzhi Wu",
"affiliation": "State Key Lab of CAD & CG, Zhejiang University, Hangzhou, China",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Zhaotian",
"surname": "Wang",
"fullName": "Zhaotian Wang",
"affiliation": "State Key Lab of CAD & CG, Zhejiang University, Hangzhou, China",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Kun",
"surname": "Zhou",
"fullName": "Kun Zhou",
"affiliation": "State Key Lab of CAD & CG, Zhejiang University, Hangzhou, China",
"__typename": "ArticleAuthorType"
}
],
"replicability": null,
"showBuyMe": false,
"showRecommendedArticles": true,
"isOpenAccess": true,
"issueNum": "08",
"pubDate": "2016-08-01 00:00:00",
"pubType": "trans",
"pages": "2012-2023",
"year": "2016",
"issn": "1077-2626",
"isbn": null,
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"recommendedArticles": [
{
"id": "proceedings/cvpr/2007/1179/0/04270003",
"title": "Differential Camera Tracking through Linearizing the Local Appearance Manifold",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2007/04270003/12OmNBOCWm6",
"parentPublication": {
"id": "proceedings/cvpr/2007/1179/0",
"title": "2007 IEEE Conference on Computer Vision and Pattern Recognition",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2014/5118/0/5118c179",
"title": "What Camera Motion Reveals about Shape with Unknown BRDF",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2014/5118c179/12OmNBqMDzR",
"parentPublication": {
"id": "proceedings/cvpr/2014/5118/0",
"title": "2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccv/2017/1032/0/1032d133",
"title": "Intrinsic3D: High-Quality 3D Reconstruction by Joint Appearance and Geometry Optimization with Spatially-Varying Lighting",
"doi": null,
"abstractUrl": "/proceedings-article/iccv/2017/1032d133/12OmNC4eSyL",
"parentPublication": {
"id": "proceedings/iccv/2017/1032/0",
"title": "2017 IEEE International Conference on Computer Vision (ICCV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccv/2017/1032/0/1032f180",
"title": "What is Around the Camera?",
"doi": null,
"abstractUrl": "/proceedings-article/iccv/2017/1032f180/12OmNCdBDIs",
"parentPublication": {
"id": "proceedings/iccv/2017/1032/0",
"title": "2017 IEEE International Conference on Computer Vision (ICCV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icpr/2012/2216/0/06460768",
"title": "Face recognition in multi-camera surveillance videos",
"doi": null,
"abstractUrl": "/proceedings-article/icpr/2012/06460768/12OmNyo1nUU",
"parentPublication": {
"id": "proceedings/icpr/2012/2216/0",
"title": "2012 21st International Conference on Pattern Recognition (ICPR 2012)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tp/2005/12/i1845",
"title": "Autocalibration of a Projector-Camera System",
"doi": null,
"abstractUrl": "/journal/tp/2005/12/i1845/13rRUxASuiM",
"parentPublication": {
"id": "trans/tp",
"title": "IEEE Transactions on Pattern Analysis & Machine Intelligence",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccp/2022/5851/0/09887646",
"title": "Differentiable Appearance Acquisition from a Flash/No-flash RGB-D Pair",
"doi": null,
"abstractUrl": "/proceedings-article/iccp/2022/09887646/1GZixnSNfiM",
"parentPublication": {
"id": "proceedings/iccp/2022/5851/0",
"title": "2022 IEEE International Conference on Computational Photography (ICCP)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/wacv/2023/9346/0/934600d105",
"title": "High-Quality RGB-D Reconstruction via Multi-View Uncalibrated Photometric Stereo and Gradient-SDF",
"doi": null,
"abstractUrl": "/proceedings-article/wacv/2023/934600d105/1KxVaVLkeLS",
"parentPublication": {
"id": "proceedings/wacv/2023/9346/0",
"title": "2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/sitis/2022/6495/0/649500a281",
"title": "Lighting Spectral Power Distribution Estimation With RGB Camera",
"doi": null,
"abstractUrl": "/proceedings-article/sitis/2022/649500a281/1MeoGuSgRPO",
"parentPublication": {
"id": "proceedings/sitis/2022/6495/0",
"title": "2022 16th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/wacv/2020/6553/0/09093491",
"title": "Inferring Super-Resolution Depth from a Moving Light-Source Enhanced RGB-D Sensor: A Variational Approach",
"doi": null,
"abstractUrl": "/proceedings-article/wacv/2020/09093491/1jPbFHPBYnm",
"parentPublication": {
"id": "proceedings/wacv/2020/6553/0",
"title": "2020 IEEE Winter Conference on Applications of Computer Vision (WACV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"adjacentArticles": {
"previous": {
"fno": "07265099",
"articleId": "13rRUygBwhL",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "07226860",
"articleId": "13rRUILtJqS",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"webExtras": [],
"articleVideos": []
}
|
{
"issue": {
"id": "1soTDTPjWWk",
"title": "Jan.-March",
"year": "2021",
"issueNum": "01",
"idPrefix": "mu",
"pubType": "magazine",
"volume": "28",
"label": "Jan.-March",
"downloadables": {
"hasCover": true,
"__typename": "PeriodicalIssueDownloadablesType"
},
"__typename": "PeriodicalIssue"
},
"article": {
"id": "1pLFuFNNoxW",
"doi": "10.1109/MMUL.2020.3046491",
"abstract": "Classically, the style of the generated music by deep learning models is usually governed by the training dataset. In this article, we improved this by proposing the continuous style embedding <inline-formula><tex-math notation=\"LaTeX\">Z_${z}_{s}$_Z</tex-math></inline-formula> to the general formulation of variational autoencoder (VAE) to allow users to be able to condition on the style of the generated music. For this purpose, we explored and compared two different methods to integrate <inline-formula><tex-math notation=\"LaTeX\">Z_${z}_{s}$_Z</tex-math></inline-formula> into the VAE. In the literature of conditional generative modeling, disentanglement of attributes from the latent space is often associated with better generative performance. In our experiments, we find that this is not the case with our proposed model. Empirically and from a musical theory perspective, we show that our proposed model can generate better music samples than a baseline model that utilizes a discrete style label. The source code and generated samples are available at <ext-link ext-link-type=\"uri\" xlink:href=\"https://github.com/daQuincy/DeepMusicvStyle\">https://github.com/daQuincy/DeepMusicvStyle</ext-link>.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Classically, the style of the generated music by deep learning models is usually governed by the training dataset. In this article, we improved this by proposing the continuous style embedding <inline-formula><tex-math notation=\"LaTeX\">${z}_{s}$</tex-math><alternatives><mml:math><mml:msub><mml:mi>z</mml:mi><mml:mi>s</mml:mi></mml:msub></mml:math><inline-graphic xlink:href=\"chan-ieq1-3046491.gif\"/></alternatives></inline-formula> to the general formulation of variational autoencoder (VAE) to allow users to be able to condition on the style of the generated music. For this purpose, we explored and compared two different methods to integrate <inline-formula><tex-math notation=\"LaTeX\">${z}_{s}$</tex-math><alternatives><mml:math><mml:msub><mml:mi>z</mml:mi><mml:mi>s</mml:mi></mml:msub></mml:math><inline-graphic xlink:href=\"chan-ieq2-3046491.gif\"/></alternatives></inline-formula> into the VAE. In the literature of conditional generative modeling, disentanglement of attributes from the latent space is often associated with better generative performance. In our experiments, we find that this is not the case with our proposed model. Empirically and from a musical theory perspective, we show that our proposed model can generate better music samples than a baseline model that utilizes a discrete style label. The source code and generated samples are available at <ext-link ext-link-type=\"uri\" xlink:href=\"https://github.com/daQuincy/DeepMusicvStyle\">https://github.com/daQuincy/DeepMusicvStyle</ext-link>.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Classically, the style of the generated music by deep learning models is usually governed by the training dataset. In this article, we improved this by proposing the continuous style embedding - to the general formulation of variational autoencoder (VAE) to allow users to be able to condition on the style of the generated music. For this purpose, we explored and compared two different methods to integrate - into the VAE. In the literature of conditional generative modeling, disentanglement of attributes from the latent space is often associated with better generative performance. In our experiments, we find that this is not the case with our proposed model. Empirically and from a musical theory perspective, we show that our proposed model can generate better music samples than a baseline model that utilizes a discrete style label. The source code and generated samples are available at https://github.com/daQuincy/DeepMusicvStyle.",
"title": "ClaviNet: Generate Music With Different Musical Styles",
"normalizedTitle": "ClaviNet: Generate Music With Different Musical Styles",
"fno": "09302731",
"hasPdf": true,
"idPrefix": "mu",
"keywords": [
"Convolutional Neural Nets",
"Deep Learning Artificial Intelligence",
"Music",
"Music Samples",
"Musical Theory Perspective",
"Conditional Generative Modeling",
"VAE",
"Continuous Style Embedding",
"Deep Learning Models",
"Musical Styles",
"Music Generation",
"Music",
"Training",
"Computer Generated Music",
"Decoding",
"Task Analysis",
"Instruments",
"Context Modeling",
"Music Synthesis",
"Deep Learning",
"Style Transfer"
],
"authors": [
{
"givenName": "Yu-Quan",
"surname": "Lim",
"fullName": "Yu-Quan Lim",
"affiliation": "Center of Image and Signal Processing, Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Chee Seng",
"surname": "Chan",
"fullName": "Chee Seng Chan",
"affiliation": "Center of Image and Signal Processing, Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Fung Ying",
"surname": "Loo",
"fullName": "Fung Ying Loo",
"affiliation": "Department of Music, Cultural Centre, University of Malaya, Kuala Lumpur, Malaysia",
"__typename": "ArticleAuthorType"
}
],
"replicability": null,
"showBuyMe": true,
"showRecommendedArticles": true,
"isOpenAccess": false,
"issueNum": "01",
"pubDate": "2021-01-01 00:00:00",
"pubType": "mags",
"pages": "83-93",
"year": "2021",
"issn": "1070-986X",
"isbn": null,
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"recommendedArticles": [
{
"id": "trans/tb/2020/02/08624452",
"title": "A Reaction-Based Model of the State Space of Chemical Reaction Systems Enables Efficient Simulations",
"doi": null,
"abstractUrl": "/journal/tb/2020/02/08624452/17D45Xh13sh",
"parentPublication": {
"id": "trans/tb",
"title": "IEEE/ACM Transactions on Computational Biology and Bioinformatics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tp/2023/01/09681162",
"title": "Point Cloud Sampling via Graph Balancing and Gershgorin Disc Alignment",
"doi": null,
"abstractUrl": "/journal/tp/2023/01/09681162/1A8c6sY0Afe",
"parentPublication": {
"id": "trans/tp",
"title": "IEEE Transactions on Pattern Analysis & Machine Intelligence",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/td/2022/12/09729476",
"title": "Solving Consensus in True Partial Synchrony",
"doi": null,
"abstractUrl": "/journal/td/2022/12/09729476/1Byafq1ui4w",
"parentPublication": {
"id": "trans/td",
"title": "IEEE Transactions on Parallel & Distributed Systems",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/td/2023/01/09930647",
"title": "The High Faulty Tolerant Capability of the Alternating Group Graphs",
"doi": null,
"abstractUrl": "/journal/td/2023/01/09930647/1HMP3UkhGus",
"parentPublication": {
"id": "trans/td",
"title": "IEEE Transactions on Parallel & Distributed Systems",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tp/2023/06/09937157",
"title": "Adaptive Transfer Kernel Learning for Transfer Gaussian Process Regression",
"doi": null,
"abstractUrl": "/journal/tp/2023/06/09937157/1I05uIgMI1i",
"parentPublication": {
"id": "trans/tp",
"title": "IEEE Transactions on Pattern Analysis & Machine Intelligence",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tm/2021/11/09099372",
"title": "On Heterogeneous Sensing Capability for Distributed Rendezvous in Cognitive Radio Networks",
"doi": null,
"abstractUrl": "/journal/tm/2021/11/09099372/1k7oCRHzGAE",
"parentPublication": {
"id": "trans/tm",
"title": "IEEE Transactions on Mobile Computing",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tp/2022/01/09127807",
"title": "Average Top-k Aggregate Loss for Supervised Learning",
"doi": null,
"abstractUrl": "/journal/tp/2022/01/09127807/1l3uajhdTP2",
"parentPublication": {
"id": "trans/tp",
"title": "IEEE Transactions on Pattern Analysis & Machine Intelligence",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tq/2022/02/09141372",
"title": "Highly Efficient and Re-Executable Private Function Evaluation With Linear Complexity",
"doi": null,
"abstractUrl": "/journal/tq/2022/02/09141372/1lu2SzkDEpG",
"parentPublication": {
"id": "trans/tq",
"title": "IEEE Transactions on Dependable and Secure Computing",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tk/2022/09/09266749",
"title": "Easy-But-Effective Domain Sub-Similarity Learning for Transfer Regression",
"doi": null,
"abstractUrl": "/journal/tk/2022/09/09266749/1oZxqu9nYcg",
"parentPublication": {
"id": "trans/tk",
"title": "IEEE Transactions on Knowledge & Data Engineering",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tc/2022/10/09664360",
"title": "Hybrid Annealing Method Based on subQUBO Model Extraction With Multiple Solution Instances",
"doi": null,
"abstractUrl": "/journal/tc/2022/10/09664360/1zHDJ2qBROo",
"parentPublication": {
"id": "trans/tc",
"title": "IEEE Transactions on Computers",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"adjacentArticles": {
"previous": {
"fno": "09277584",
"articleId": "1petlCLKBsA",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "09310253",
"articleId": "1pXhM6VOq7C",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"webExtras": [],
"articleVideos": []
}
|
{
"issue": {
"id": "12OmNwMob9C",
"title": "April",
"year": "2018",
"issueNum": "04",
"idPrefix": "tg",
"pubType": "journal",
"volume": "24",
"label": "April",
"downloadables": {
"hasCover": false,
"__typename": "PeriodicalIssueDownloadablesType"
},
"__typename": "PeriodicalIssue"
},
"article": {
"id": "13rRUxly8T4",
"doi": "10.1109/TVCG.2018.2793561",
"abstract": "360° images and video have become extremely popular formats for immersive displays, due in large part to the technical ease of content production. While many experiences use a single camera viewpoint, an increasing number of experiences use multiple camera locations. In such multi-view 360° media (MV360M) systems, a visual effect is required when the user transitions from one camera location to another. This effect can take several forms, such as a cut or an image-based warp, and the choice of effect may impact many aspects of the experience, including issues related to enjoyment and scene understanding. To investigate the effect of transition types on immersive MV360M experiences, a repeated-measures experiment was conducted with 31 participants. Wearing a head-mounted display, participants explored four static scenes, for which multiple 360° images and a reconstructed 3D model were available. Three transition types were examined: teleport, a linear move through a 3D model of the scene, and an image-based transition using a Möbius transformation. The metrics investigated included spatial awareness, users' movement profiles, transition preference and the subjective feeling of moving through the space. Results indicate that there was no significant difference between transition types in terms of spatial awareness, while significant differences were found for users' movement profiles, with participants taking 1.6 seconds longer to select their next location following a teleport transition. The model and Möbius transitions were significantly better in terms of creating the feeling of moving through the space. Preference was also significantly different, with model and teleport transitions being preferred over Möbius transitions. Our results indicate that trade-offs between transitions will require content creators to think carefully about what aspects they consider to be most important when producing MV360M experiences.",
"abstracts": [
{
"abstractType": "Regular",
"content": "360° images and video have become extremely popular formats for immersive displays, due in large part to the technical ease of content production. While many experiences use a single camera viewpoint, an increasing number of experiences use multiple camera locations. In such multi-view 360° media (MV360M) systems, a visual effect is required when the user transitions from one camera location to another. This effect can take several forms, such as a cut or an image-based warp, and the choice of effect may impact many aspects of the experience, including issues related to enjoyment and scene understanding. To investigate the effect of transition types on immersive MV360M experiences, a repeated-measures experiment was conducted with 31 participants. Wearing a head-mounted display, participants explored four static scenes, for which multiple 360° images and a reconstructed 3D model were available. Three transition types were examined: teleport, a linear move through a 3D model of the scene, and an image-based transition using a Möbius transformation. The metrics investigated included spatial awareness, users' movement profiles, transition preference and the subjective feeling of moving through the space. Results indicate that there was no significant difference between transition types in terms of spatial awareness, while significant differences were found for users' movement profiles, with participants taking 1.6 seconds longer to select their next location following a teleport transition. The model and Möbius transitions were significantly better in terms of creating the feeling of moving through the space. Preference was also significantly different, with model and teleport transitions being preferred over Möbius transitions. Our results indicate that trade-offs between transitions will require content creators to think carefully about what aspects they consider to be most important when producing MV360M experiences.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "360° images and video have become extremely popular formats for immersive displays, due in large part to the technical ease of content production. While many experiences use a single camera viewpoint, an increasing number of experiences use multiple camera locations. In such multi-view 360° media (MV360M) systems, a visual effect is required when the user transitions from one camera location to another. This effect can take several forms, such as a cut or an image-based warp, and the choice of effect may impact many aspects of the experience, including issues related to enjoyment and scene understanding. To investigate the effect of transition types on immersive MV360M experiences, a repeated-measures experiment was conducted with 31 participants. Wearing a head-mounted display, participants explored four static scenes, for which multiple 360° images and a reconstructed 3D model were available. Three transition types were examined: teleport, a linear move through a 3D model of the scene, and an image-based transition using a Möbius transformation. The metrics investigated included spatial awareness, users' movement profiles, transition preference and the subjective feeling of moving through the space. Results indicate that there was no significant difference between transition types in terms of spatial awareness, while significant differences were found for users' movement profiles, with participants taking 1.6 seconds longer to select their next location following a teleport transition. The model and Möbius transitions were significantly better in terms of creating the feeling of moving through the space. Preference was also significantly different, with model and teleport transitions being preferred over Möbius transitions. Our results indicate that trade-offs between transitions will require content creators to think carefully about what aspects they consider to be most important when producing MV360M experiences.",
"title": "The Effect of Transition Type in Multi-View 360° Media",
"normalizedTitle": "The Effect of Transition Type in Multi-View 360° Media",
"fno": "08260946",
"hasPdf": true,
"idPrefix": "tg",
"keywords": [
"Cameras",
"Helmet Mounted Displays",
"Human Computer Interaction",
"Image Reconstruction",
"Stereo Image Processing",
"Three Dimensional Displays",
"Video Signal Processing",
"Transition Type",
"Immersive Displays",
"Single Camera Viewpoint",
"Multiple Camera Locations",
"Multiview 360 X 00 B 0 Media Systems",
"Visual Effect",
"Camera Location",
"Repeated Measures Experiment",
"Spatial Awareness",
"Transition Preference",
"Teleport Transition",
"Mo X 0308 Bius Transitions",
"Scene Understanding",
"Immersive MV 360 M Experiences",
"Reconstructed 3 D Model",
"Image Based Transition",
"Mo X 0308 Bius Transformation",
"Head Mounted Display",
"Time 1 6 S",
"Cameras",
"Media",
"Solid Modeling",
"Three Dimensional Displays",
"Navigation",
"Measurement",
"Resists",
"H 5 1 Information Interfaces And Presentation Multimedia Information Systems Artificial Augmented And Virtual Realities"
],
"authors": [
{
"givenName": "Andrew",
"surname": "MacQuarrie",
"fullName": "Andrew MacQuarrie",
"affiliation": "University College London",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Anthony",
"surname": "Steed",
"fullName": "Anthony Steed",
"affiliation": "University College London",
"__typename": "ArticleAuthorType"
}
],
"replicability": null,
"showBuyMe": false,
"showRecommendedArticles": true,
"isOpenAccess": true,
"issueNum": "04",
"pubDate": "2018-04-01 00:00:00",
"pubType": "trans",
"pages": "1564-1573",
"year": "2018",
"issn": "1077-2626",
"isbn": null,
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"recommendedArticles": [
{
"id": "proceedings/vr/2017/6647/0/07892333",
"title": "Towards understanding scene transition techniques in immersive 360 movies and cinematic experiences",
"doi": null,
"abstractUrl": "/proceedings-article/vr/2017/07892333/12OmNAXxX2a",
"parentPublication": {
"id": "proceedings/vr/2017/6647/0",
"title": "2017 IEEE Virtual Reality (VR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icse/2018/5638/0/563801a598",
"title": "Launch-Mode-Aware Context-Sensitive Activity Transition Analysis",
"doi": null,
"abstractUrl": "/proceedings-article/icse/2018/563801a598/13l5NXlN9Qu",
"parentPublication": {
"id": "proceedings/icse/2018/5638/0",
"title": "2018 IEEE/ACM 40th International Conference on Software Engineering (ICSE)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/2019/05/08661657",
"title": "Motion parallax for 360° RGBD video",
"doi": null,
"abstractUrl": "/journal/tg/2019/05/08661657/18bmQqdj3Nu",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/5555/01/09779957",
"title": "Casual 6-DoF: free-viewpoint panorama using a handheld 360° camera",
"doi": null,
"abstractUrl": "/journal/tg/5555/01/09779957/1DBTD2uB4di",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/vr/2023/4815/0/481500a680",
"title": "Designing Viewpoint Transition Techniques in Multiscale Virtual Environments",
"doi": null,
"abstractUrl": "/proceedings-article/vr/2023/481500a680/1MNgp7L6LcY",
"parentPublication": {
"id": "proceedings/vr/2023/4815/0",
"title": "2023 IEEE Conference Virtual Reality and 3D User Interfaces (VR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/vr/2019/1377/0/08797876",
"title": "Live Stereoscopic 3D Image with Constant Capture Direction of 360° Cameras for High-Quality Visual Telepresence",
"doi": null,
"abstractUrl": "/proceedings-article/vr/2019/08797876/1cJ0HMTqjOU",
"parentPublication": {
"id": "proceedings/vr/2019/1377/0",
"title": "2019 IEEE Conference on Virtual Reality and 3D User Interfaces (VR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/vr/2019/1377/0/08797968",
"title": "Did You See What I Saw?: Comparing User Synchrony When Watching 360° Video In HMD Vs Flat Screen",
"doi": null,
"abstractUrl": "/proceedings-article/vr/2019/08797968/1cJ0X6CH9wk",
"parentPublication": {
"id": "proceedings/vr/2019/1377/0",
"title": "2019 IEEE Conference on Virtual Reality and 3D User Interfaces (VR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/3dv/2019/3131/0/313100a386",
"title": "Decoupled Hybrid 360° Panoramic Stereo Video",
"doi": null,
"abstractUrl": "/proceedings-article/3dv/2019/313100a386/1ezRBFr3eaA",
"parentPublication": {
"id": "proceedings/3dv/2019/3131/0",
"title": "2019 International Conference on 3D Vision (3DV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ismar/2020/8508/0/850800a185",
"title": "Transitioning360: Content-aware NFoV Virtual Camera Paths for 360° Video Playback",
"doi": null,
"abstractUrl": "/proceedings-article/ismar/2020/850800a185/1pysx79ZXcA",
"parentPublication": {
"id": "proceedings/ismar/2020/8508/0",
"title": "2020 IEEE International Symposium on Mixed and Augmented Reality (ISMAR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iiai-aai/2020/7397/0/739700a418",
"title": "Kansei Transition Analysis by Time-series Change of Media Content",
"doi": null,
"abstractUrl": "/proceedings-article/iiai-aai/2020/739700a418/1tGcwoZgSsg",
"parentPublication": {
"id": "proceedings/iiai-aai/2020/7397/0",
"title": "2020 9th International Congress on Advanced Applied Informatics (IIAI-AAI)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"adjacentArticles": {
"previous": {
"fno": "08260976",
"articleId": "13rRUxDqS8n",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "08260860",
"articleId": "13rRUwI5Ugi",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"webExtras": [
{
"id": "17ShDTXWRX6",
"name": "ttg201804-08260946s1.zip",
"location": "https://www.computer.org/csdl/api/v1/extra/ttg201804-08260946s1.zip",
"extension": "zip",
"size": "10.7 MB",
"__typename": "WebExtraType"
}
],
"articleVideos": []
}
|
{
"issue": {
"id": "12OmNvSbBJE",
"title": "September/October",
"year": "2005",
"issueNum": "05",
"idPrefix": "tg",
"pubType": "journal",
"volume": "11",
"label": "September/October",
"downloadables": {
"hasCover": false,
"__typename": "PeriodicalIssueDownloadablesType"
},
"__typename": "PeriodicalIssue"
},
"article": {
"id": "13rRUy0HYRf",
"doi": "10.1109/TVCG.2005.85",
"abstract": "The contribution of this paper is a novel framework for synthesizing nonphotorealistic animations from real video sequences. We demonstrate that, through automated mid-level analysis of the video sequence as a spatiotemporal volume—a block of frames with time as the third dimension—we are able to generate animations in a wide variety of artistic styles, exhibiting a uniquely high degree of temporal coherence. In addition to rotoscoping, matting, and novel temporal effects unique to our method, we demonstrate the extension of static nonphotorealistic rendering (NPR) styles to video, including painterly, sketchy, and cartoon shading. We demonstrate how this novel coherent shading framework may be combined with our earlier motion emphasis work to produce a comprehensive \"Video Paintbox” capable of rendering complete cartoon-styled animations from video clips.",
"abstracts": [
{
"abstractType": "Regular",
"content": "The contribution of this paper is a novel framework for synthesizing nonphotorealistic animations from real video sequences. We demonstrate that, through automated mid-level analysis of the video sequence as a spatiotemporal volume—a block of frames with time as the third dimension—we are able to generate animations in a wide variety of artistic styles, exhibiting a uniquely high degree of temporal coherence. In addition to rotoscoping, matting, and novel temporal effects unique to our method, we demonstrate the extension of static nonphotorealistic rendering (NPR) styles to video, including painterly, sketchy, and cartoon shading. We demonstrate how this novel coherent shading framework may be combined with our earlier motion emphasis work to produce a comprehensive \"Video Paintbox” capable of rendering complete cartoon-styled animations from video clips.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "The contribution of this paper is a novel framework for synthesizing nonphotorealistic animations from real video sequences. We demonstrate that, through automated mid-level analysis of the video sequence as a spatiotemporal volume—a block of frames with time as the third dimension—we are able to generate animations in a wide variety of artistic styles, exhibiting a uniquely high degree of temporal coherence. In addition to rotoscoping, matting, and novel temporal effects unique to our method, we demonstrate the extension of static nonphotorealistic rendering (NPR) styles to video, including painterly, sketchy, and cartoon shading. We demonstrate how this novel coherent shading framework may be combined with our earlier motion emphasis work to produce a comprehensive \"Video Paintbox” capable of rendering complete cartoon-styled animations from video clips.",
"title": "Stroke Surfaces: Temporally Coherent Artistic Animations from Video",
"normalizedTitle": "Stroke Surfaces: Temporally Coherent Artistic Animations from Video",
"fno": "v0540",
"hasPdf": true,
"idPrefix": "tg",
"keywords": [
"Index Terms Artistic Rendering",
"Video Based NPR",
"Stroke Surfaces",
"Video Paintbox",
"Rotoscoping"
],
"authors": [
{
"givenName": "John P.",
"surname": "Collomosse",
"fullName": "John P. Collomosse",
"affiliation": null,
"__typename": "ArticleAuthorType"
},
{
"givenName": "David",
"surname": "Rowntree",
"fullName": "David Rowntree",
"affiliation": null,
"__typename": "ArticleAuthorType"
},
{
"givenName": "Peter M.",
"surname": "Hall",
"fullName": "Peter M. Hall",
"affiliation": null,
"__typename": "ArticleAuthorType"
}
],
"replicability": null,
"showBuyMe": true,
"showRecommendedArticles": true,
"isOpenAccess": false,
"issueNum": "05",
"pubDate": "2005-09-01 00:00:00",
"pubType": "trans",
"pages": "540-549",
"year": "2005",
"issn": "1077-2626",
"isbn": null,
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"recommendedArticles": [
{
"id": "proceedings/das/2012/4661/0/4661a069",
"title": "A Fast Stroke-Based Method for Text Detection in Video",
"doi": null,
"abstractUrl": "/proceedings-article/das/2012/4661a069/12OmNro0IaR",
"parentPublication": {
"id": "proceedings/das/2012/4661/0",
"title": "Document Analysis Systems, IAPR International Workshop on",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2004/2158/1/01315053",
"title": "Space-time isosurface evolution for temporally coherent 3D reconstruction",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2004/01315053/12OmNwDSdMK",
"parentPublication": {
"id": "proceedings/cvpr/2004/2158/1",
"title": "Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004.",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icpr/2002/1695/2/169521090",
"title": "Creating Animations Combining Stochastic Paintbrush Transformation and Motion Detection",
"doi": null,
"abstractUrl": "/proceedings-article/icpr/2002/169521090/12OmNyuya6R",
"parentPublication": {
"id": "proceedings/icpr/2002/1695/2",
"title": "Pattern Recognition, International Conference on",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/5555/01/09774005",
"title": "Multi-scale Flow-based Occluding Effect and Content Separation for Cartoon Animations",
"doi": null,
"abstractUrl": "/journal/tg/5555/01/09774005/1DjDpHtWZfa",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/5555/01/10018537",
"title": "Regenerating Arbitrary Video Sequences with Distillation Path-Finding",
"doi": null,
"abstractUrl": "/journal/tg/5555/01/10018537/1K0DFSXIg5W",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"adjacentArticles": {
"previous": {
"fno": "v0529",
"articleId": "13rRUxYIN3Z",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "v0550",
"articleId": "13rRUxYrbUs",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"webExtras": [],
"articleVideos": []
}
|
{
"issue": {
"id": "12OmNwIHoDv",
"title": "January/February",
"year": "1988",
"issueNum": "01",
"idPrefix": "cg",
"pubType": "magazine",
"volume": "8",
"label": "January/February",
"downloadables": {
"hasCover": false,
"__typename": "PeriodicalIssueDownloadablesType"
},
"__typename": "PeriodicalIssue"
},
"article": {
"id": "13rRUwhpBPV",
"doi": "10.1109/38.487",
"abstract": "To demonstrate the feasibility and practicability of designing and building low-cost flight simulators, a prototype system was developed to model the performance of a new US Army remotely piloted missile system. The flight simulator displays a dynamic, three-dimensional, out-the-window view of the terrain in real time while responding to operator input from the command and control system. A description is given of the hardware, software, and database implementations. The system capabilities and limitations are examined. The total development cost is an order of magnitude less than that of the sophisticated systems currently in use.",
"abstracts": [
{
"abstractType": "Regular",
"content": "To demonstrate the feasibility and practicability of designing and building low-cost flight simulators, a prototype system was developed to model the performance of a new US Army remotely piloted missile system. The flight simulator displays a dynamic, three-dimensional, out-the-window view of the terrain in real time while responding to operator input from the command and control system. A description is given of the hardware, software, and database implementations. The system capabilities and limitations are examined. The total development cost is an order of magnitude less than that of the sophisticated systems currently in use.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "To demonstrate the feasibility and practicability of designing and building low-cost flight simulators, a prototype system was developed to model the performance of a new US Army remotely piloted missile system. The flight simulator displays a dynamic, three-dimensional, out-the-window view of the terrain in real time while responding to operator input from the command and control system. A description is given of the hardware, software, and database implementations. The system capabilities and limitations are examined. The total development cost is an order of magnitude less than that of the sophisticated systems currently in use.",
"title": "Flight Simulators for Under $100000",
"normalizedTitle": "Flight Simulators for Under $100000",
"fno": "mcg1988010019",
"hasPdf": true,
"idPrefix": "cg",
"keywords": [
"Aerospace Simulation",
"Buildings",
"Software Prototyping",
"Virtual Prototyping",
"Missiles",
"Three Dimensional Displays",
"Real Time Systems",
"Command And Control Systems",
"Hardware",
"Databases"
],
"authors": [
{
"givenName": "Michael J.",
"surname": "Zyda",
"fullName": "Michael J. Zyda",
"affiliation": "US Naval Postgraduate Sch., Monterey, CA, USA",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Robert B.",
"surname": "McGhee",
"fullName": "Robert B. McGhee",
"affiliation": "US Naval Postgraduate Sch., Monterey, CA, USA",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Ron S.",
"surname": "Ross",
"fullName": "Ron S. Ross",
"affiliation": "US Naval Postgraduate Sch., Monterey, CA, USA",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Douglas B.",
"surname": "Smith",
"fullName": "Douglas B. Smith",
"affiliation": "US Naval Postgraduate Sch., Monterey, CA, USA",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Dale G.",
"surname": "Streyle",
"fullName": "Dale G. Streyle",
"affiliation": "US Naval Postgraduate Sch., Monterey, CA, USA",
"__typename": "ArticleAuthorType"
}
],
"replicability": null,
"showBuyMe": true,
"showRecommendedArticles": false,
"isOpenAccess": false,
"issueNum": "01",
"pubDate": "1988-01-01 00:00:00",
"pubType": "mags",
"pages": "19-27",
"year": "1988",
"issn": "0272-1716",
"isbn": null,
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"recommendedArticles": [],
"adjacentArticles": {
"previous": {
"fno": "mcg1988010017",
"articleId": "13rRUwhpBPU",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "mcg1988010028",
"articleId": "13rRUy3gmZD",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"webExtras": [],
"articleVideos": []
}
|
{
"issue": {
"id": "12OmNyeWdCU",
"title": "May",
"year": "1999",
"issueNum": "05",
"idPrefix": "tp",
"pubType": "journal",
"volume": "21",
"label": "May",
"downloadables": {
"hasCover": false,
"__typename": "PeriodicalIssueDownloadablesType"
},
"__typename": "PeriodicalIssue"
},
"article": {
"id": "13rRUxbTMA3",
"doi": "10.1109/34.765657",
"abstract": "Abstract—This paper presents a unified method for detecting both reflection-symmetry and rotation-symmetry of 2D images based on an identical set of features, i.e., the first three nonzero generalized complex (GC) moments. This method is theoretically guaranteed to detect all the axes of symmetries of every 2D image, if more nonzero GC moments are used in the feature set. Furthermore, we establish the relationship between reflectional symmetry and rotational symmetry in an image, which can be used to check the correctness of symmetry detection. This method has been demonstrated experimentally using more than 200 images.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Abstract—This paper presents a unified method for detecting both reflection-symmetry and rotation-symmetry of 2D images based on an identical set of features, i.e., the first three nonzero generalized complex (GC) moments. This method is theoretically guaranteed to detect all the axes of symmetries of every 2D image, if more nonzero GC moments are used in the feature set. Furthermore, we establish the relationship between reflectional symmetry and rotational symmetry in an image, which can be used to check the correctness of symmetry detection. This method has been demonstrated experimentally using more than 200 images.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Abstract—This paper presents a unified method for detecting both reflection-symmetry and rotation-symmetry of 2D images based on an identical set of features, i.e., the first three nonzero generalized complex (GC) moments. This method is theoretically guaranteed to detect all the axes of symmetries of every 2D image, if more nonzero GC moments are used in the feature set. Furthermore, we establish the relationship between reflectional symmetry and rotational symmetry in an image, which can be used to check the correctness of symmetry detection. This method has been demonstrated experimentally using more than 200 images.",
"title": "Symmetry Detection by Generalized Complex (GC) Moments: A Close-Form Solution",
"normalizedTitle": "Symmetry Detection by Generalized Complex (GC) Moments: A Close-Form Solution",
"fno": "i0466",
"hasPdf": true,
"idPrefix": "tp",
"keywords": [
"Symmetry Detection",
"Reflectional And Rotational Symmetry",
"Symmetric Axis",
"Generalized Complex GC Moments",
"Fold Number",
"Fold Axes",
"Rotationally Symmetric Image",
"Reflection Symmetric Image"
],
"authors": [
{
"givenName": "Dinggang",
"surname": "Shen",
"fullName": "Dinggang Shen",
"affiliation": null,
"__typename": "ArticleAuthorType"
},
{
"givenName": "Horace H.S.",
"surname": "Ip",
"fullName": "Horace H.S. Ip",
"affiliation": null,
"__typename": "ArticleAuthorType"
},
{
"givenName": "Kent K.T.",
"surname": "Cheung",
"fullName": "Kent K.T. Cheung",
"affiliation": null,
"__typename": "ArticleAuthorType"
},
{
"givenName": "Eam Khwang",
"surname": "Teoh",
"fullName": "Eam Khwang Teoh",
"affiliation": null,
"__typename": "ArticleAuthorType"
}
],
"replicability": null,
"showBuyMe": true,
"showRecommendedArticles": false,
"isOpenAccess": false,
"issueNum": "05",
"pubDate": "1999-05-01 00:00:00",
"pubType": "trans",
"pages": "466-476",
"year": "1999",
"issn": "0162-8828",
"isbn": null,
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"recommendedArticles": [],
"adjacentArticles": {
"previous": {
"fno": "i0450",
"articleId": "13rRUxNmPES",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "i0476",
"articleId": "13rRUygBw83",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"webExtras": [],
"articleVideos": []
}
|
{
"issue": {
"id": "1Lk2nmMLR6M",
"title": "April",
"year": "2023",
"issueNum": "04",
"idPrefix": "tp",
"pubType": "journal",
"volume": "45",
"label": "April",
"downloadables": {
"hasCover": false,
"__typename": "PeriodicalIssueDownloadablesType"
},
"__typename": "PeriodicalIssue"
},
"article": {
"id": "1EzDOrzPeeI",
"doi": "10.1109/TPAMI.2022.3186876",
"abstract": "3D symmetry detection is a fundamental problem in computer vision and graphics. Most prior works detect symmetry when the object model is fully known, few studies symmetry detection on objects with partial observation, such as single RGB-D images. Recent work addresses the problem of detecting symmetries from incomplete data with a deep neural network by leveraging the dense and accurate symmetry annotations. However, due to the tedious labeling process, full symmetry annotations are not always practically available. In this work, we present a 3D symmetry detection approach to detect symmetry from single-view RGB-D images without using symmetry supervision. The key idea is to train the network in a weakly-supervised learning manner to complete the shape based on the predicted symmetry such that the completed shape be similar to existing plausible shapes. To achieve this, we first propose a discriminative variational autoencoder to learn the shape prior in order to determine whether a 3D shape is plausible or not. Based on the learned shape prior, a symmetry detection network is present to predict symmetries that produce shapes with high shape plausibility when completed based on those symmetries. Moreover, to facilitate end-to-end network training and multiple symmetry detection, we introduce a new symmetry parametrization for the learning-based symmetry estimation of both reflectional and rotational symmetry. The proposed approach, coupled symmetry detection with shape completion, essentially learns the symmetry-aware shape prior, facilitating more accurate and robust symmetry detection. Experiments demonstrate that the proposed method is capable of detecting reflectional and rotational symmetries accurately, and shows good generality in challenging scenarios, such as objects with heavy occlusion and scanning noise. Moreover, it achieves state-of-the-art performance, improving the F1-score over the existing supervised learning method by 2%-11% on the ShapeNet and ScanNet datasets.",
"abstracts": [
{
"abstractType": "Regular",
"content": "3D symmetry detection is a fundamental problem in computer vision and graphics. Most prior works detect symmetry when the object model is fully known, few studies symmetry detection on objects with partial observation, such as single RGB-D images. Recent work addresses the problem of detecting symmetries from incomplete data with a deep neural network by leveraging the dense and accurate symmetry annotations. However, due to the tedious labeling process, full symmetry annotations are not always practically available. In this work, we present a 3D symmetry detection approach to detect symmetry from single-view RGB-D images without using symmetry supervision. The key idea is to train the network in a weakly-supervised learning manner to complete the shape based on the predicted symmetry such that the completed shape be similar to existing plausible shapes. To achieve this, we first propose a discriminative variational autoencoder to learn the shape prior in order to determine whether a 3D shape is plausible or not. Based on the learned shape prior, a symmetry detection network is present to predict symmetries that produce shapes with high shape plausibility when completed based on those symmetries. Moreover, to facilitate end-to-end network training and multiple symmetry detection, we introduce a new symmetry parametrization for the learning-based symmetry estimation of both reflectional and rotational symmetry. The proposed approach, coupled symmetry detection with shape completion, essentially learns the symmetry-aware shape prior, facilitating more accurate and robust symmetry detection. Experiments demonstrate that the proposed method is capable of detecting reflectional and rotational symmetries accurately, and shows good generality in challenging scenarios, such as objects with heavy occlusion and scanning noise. Moreover, it achieves state-of-the-art performance, improving the F1-score over the existing supervised learning method by 2%-11% on the ShapeNet and ScanNet datasets.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "3D symmetry detection is a fundamental problem in computer vision and graphics. Most prior works detect symmetry when the object model is fully known, few studies symmetry detection on objects with partial observation, such as single RGB-D images. Recent work addresses the problem of detecting symmetries from incomplete data with a deep neural network by leveraging the dense and accurate symmetry annotations. However, due to the tedious labeling process, full symmetry annotations are not always practically available. In this work, we present a 3D symmetry detection approach to detect symmetry from single-view RGB-D images without using symmetry supervision. The key idea is to train the network in a weakly-supervised learning manner to complete the shape based on the predicted symmetry such that the completed shape be similar to existing plausible shapes. To achieve this, we first propose a discriminative variational autoencoder to learn the shape prior in order to determine whether a 3D shape is plausible or not. Based on the learned shape prior, a symmetry detection network is present to predict symmetries that produce shapes with high shape plausibility when completed based on those symmetries. Moreover, to facilitate end-to-end network training and multiple symmetry detection, we introduce a new symmetry parametrization for the learning-based symmetry estimation of both reflectional and rotational symmetry. The proposed approach, coupled symmetry detection with shape completion, essentially learns the symmetry-aware shape prior, facilitating more accurate and robust symmetry detection. Experiments demonstrate that the proposed method is capable of detecting reflectional and rotational symmetries accurately, and shows good generality in challenging scenarios, such as objects with heavy occlusion and scanning noise. Moreover, it achieves state-of-the-art performance, improving the F1-score over the existing supervised learning method by 2%-11% on the ShapeNet and ScanNet datasets.",
"title": "Learning to Detect 3D Symmetry From Single-View RGB-D Images With Weak Supervision",
"normalizedTitle": "Learning to Detect 3D Symmetry From Single-View RGB-D Images With Weak Supervision",
"fno": "09808406",
"hasPdf": true,
"idPrefix": "tp",
"keywords": [
"Computer Vision",
"Deep Learning Artificial Intelligence",
"Feature Extraction",
"Image Colour Analysis",
"Image Representation",
"Image Segmentation",
"Learning Artificial Intelligence",
"Object Detection",
"Supervised Learning",
"Accurate Symmetry Annotations",
"Accurate Symmetry Detection",
"Completed Shape",
"Coupled Symmetry Detection",
"Dense Symmetry Annotations",
"End To End Network Training",
"Existing Supervised Learning Method",
"High Shape Plausibility",
"Learned Shape",
"Learning Based Symmetry Estimation",
"Multiple Symmetry Detection",
"Plausible Shapes",
"Predicted Symmetry",
"Reflectional Symmetry",
"Robust Symmetry Detection",
"Rotational Symmetry",
"Shape Completion",
"Single RGB D Images",
"Single View RGB D Images",
"Symmetry Detection Network",
"Symmetry Parametrization",
"Symmetry Supervision",
"Symmetry Aware Shape",
"Weakly Supervised Learning Manner",
"Shape",
"Three Dimensional Displays",
"Annotations",
"Training",
"Solid Modeling",
"Transformers",
"Neural Networks",
"Symmetry Detection",
"Weakly Supervised Learning",
"RGB D Images",
"Deep Neural Networks"
],
"authors": [
{
"givenName": "Yifei",
"surname": "Shi",
"fullName": "Yifei Shi",
"affiliation": "College of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan, China",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Xin",
"surname": "Xu",
"fullName": "Xin Xu",
"affiliation": "College of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan, China",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Junhua",
"surname": "Xi",
"fullName": "Junhua Xi",
"affiliation": "College of Computer Science, National University of Defense Technology, Changsha, Hunan, China",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Xiaochang",
"surname": "Hu",
"fullName": "Xiaochang Hu",
"affiliation": "College of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan, China",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Dewen",
"surname": "Hu",
"fullName": "Dewen Hu",
"affiliation": "College of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan, China",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Kai",
"surname": "Xu",
"fullName": "Kai Xu",
"affiliation": "College of Computer Science, National University of Defense Technology, Changsha, Hunan, China",
"__typename": "ArticleAuthorType"
}
],
"replicability": null,
"showBuyMe": false,
"showRecommendedArticles": true,
"isOpenAccess": true,
"issueNum": "04",
"pubDate": "2023-04-01 00:00:00",
"pubType": "trans",
"pages": "4882-4896",
"year": "2023",
"issn": "0162-8828",
"isbn": null,
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"recommendedArticles": [
{
"id": "proceedings/icpr/1994/6265/1/00576336",
"title": "Symmetry of fuzzy data",
"doi": null,
"abstractUrl": "/proceedings-article/icpr/1994/00576336/12OmNBAqZKW",
"parentPublication": {
"id": "proceedings/icpr/1994/6265/1",
"title": "Proceedings of 12th International Conference on Pattern Recognition",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2014/5118/0/06909930",
"title": "Symmetry-Aware Nonrigid Matching of Incomplete 3D Surfaces",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2014/06909930/12OmNrJAdTB",
"parentPublication": {
"id": "proceedings/cvpr/2014/5118/0",
"title": "2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icpr/2000/0750/2/07502895",
"title": "Symmetry Based Indexing of Diatoms in an Image Database",
"doi": null,
"abstractUrl": "/proceedings-article/icpr/2000/07502895/12OmNzaQoBk",
"parentPublication": {
"id": "proceedings/icpr/2000/0750/2",
"title": "Pattern Recognition, International Conference on",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/1992/2855/0/00223196",
"title": "A measure of symmetry based on shape similarity",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/1992/00223196/12OmNzayN95",
"parentPublication": {
"id": "proceedings/cvpr/1992/2855/0",
"title": "Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccvw/2017/1034/0/1034b692",
"title": "2017 ICCV Challenge: Detecting Symmetry in the Wild",
"doi": null,
"abstractUrl": "/proceedings-article/iccvw/2017/1034b692/12OmNzmclDi",
"parentPublication": {
"id": "proceedings/iccvw/2017/1034/0",
"title": "2017 IEEE International Conference on Computer Vision Workshop (ICCVW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tp/1995/12/i1154",
"title": "Symmetry as a Continuous Feature",
"doi": null,
"abstractUrl": "/journal/tp/1995/12/i1154/13rRUNvyalV",
"parentPublication": {
"id": "trans/tp",
"title": "IEEE Transactions on Pattern Analysis & Machine Intelligence",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tp/1999/05/i0466",
"title": "Symmetry Detection by Generalized Complex (GC) Moments: A Close-Form Solution",
"doi": null,
"abstractUrl": "/journal/tp/1999/05/i0466/13rRUxbTMA3",
"parentPublication": {
"id": "trans/tp",
"title": "IEEE Transactions on Pattern Analysis & Machine Intelligence",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tp/2010/07/ttp2010071227",
"title": "Spectral Symmetry Analysis",
"doi": null,
"abstractUrl": "/journal/tp/2010/07/ttp2010071227/13rRUygT7gu",
"parentPublication": {
"id": "trans/tp",
"title": "IEEE Transactions on Pattern Analysis & Machine Intelligence",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/5555/01/09770416",
"title": "Learning-based Intrinsic Reflectional Symmetry Detection",
"doi": null,
"abstractUrl": "/journal/tg/5555/01/09770416/1D9G4zI0NIQ",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icvrv/2017/2636/0/263600a085",
"title": "A Hierarchical Symmetry Detection Algorithm Based on Voxelization",
"doi": null,
"abstractUrl": "/proceedings-article/icvrv/2017/263600a085/1ap5C6KjYze",
"parentPublication": {
"id": "proceedings/icvrv/2017/2636/0",
"title": "2017 International Conference on Virtual Reality and Visualization (ICVRV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"adjacentArticles": {
"previous": {
"fno": "09468390",
"articleId": "1uPuNjkjOnu",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "09811398",
"articleId": "1ECXB73xt6g",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"webExtras": [
{
"id": "1Lk2AsM31ra",
"name": "ttp202304-09808406s1-supp1-3186876.pdf",
"location": "https://www.computer.org/csdl/api/v1/extra/ttp202304-09808406s1-supp1-3186876.pdf",
"extension": "pdf",
"size": "168 kB",
"__typename": "WebExtraType"
}
],
"articleVideos": []
}
|
{
"issue": {
"id": "12OmNB9bvmx",
"title": "July-Aug.",
"year": "2017",
"issueNum": "04",
"idPrefix": "sc",
"pubType": "journal",
"volume": "10",
"label": "July-Aug.",
"downloadables": {
"hasCover": false,
"__typename": "PeriodicalIssueDownloadablesType"
},
"__typename": "PeriodicalIssue"
},
"article": {
"id": "13rRUxDqS5M",
"doi": "10.1109/TSC.2015.2499770",
"abstract": "Elasticity in the cloud is often achieved by on-demand autoscaling. In such context, the goal is to optimize the Quality of Service (QoS) and cost objectives for the cloud-based services. However, the difficulty lies in the facts that these objectives, e.g., throughput and cost, can be naturally conflicted; and the QoS of cloud-based services often interfere due to the shared infrastructure in cloud. Consequently, dynamic and effective trade-off decision making of autoscaling in the cloud is necessary, yet challenging. In particular, it is even harder to achieve well-compromised trade-offs, where the decision largely improves the majority of the objectives; while causing relatively small degradations to others. In this paper, we present a self-adaptive decision making approach for autoscaling in the cloud. It is capable to adaptively produce autoscaling decisions that lead to well-compromised trade-offs without heavy human intervention. We leverage on ant colony inspired multi-objective optimization for searching and optimizing the trade-offs decisions, the result is then filtered by compromise-dominance, a mechanism that extracts the decisions with balanced improvements in the trade-offs. We experimentally compare our approach to four state-of-the-arts autoscaling approaches: rule, heuristic, randomized and multi-objective genetic algorithm based solutions. The results reveal the effectiveness of our approach over the others, including better quality of trade-offs and significantly smaller violation of the requirements.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Elasticity in the cloud is often achieved by on-demand autoscaling. In such context, the goal is to optimize the Quality of Service (QoS) and cost objectives for the cloud-based services. However, the difficulty lies in the facts that these objectives, e.g., throughput and cost, can be naturally conflicted; and the QoS of cloud-based services often interfere due to the shared infrastructure in cloud. Consequently, dynamic and effective trade-off decision making of autoscaling in the cloud is necessary, yet challenging. In particular, it is even harder to achieve well-compromised trade-offs, where the decision largely improves the majority of the objectives; while causing relatively small degradations to others. In this paper, we present a self-adaptive decision making approach for autoscaling in the cloud. It is capable to adaptively produce autoscaling decisions that lead to well-compromised trade-offs without heavy human intervention. We leverage on ant colony inspired multi-objective optimization for searching and optimizing the trade-offs decisions, the result is then filtered by compromise-dominance, a mechanism that extracts the decisions with balanced improvements in the trade-offs. We experimentally compare our approach to four state-of-the-arts autoscaling approaches: rule, heuristic, randomized and multi-objective genetic algorithm based solutions. The results reveal the effectiveness of our approach over the others, including better quality of trade-offs and significantly smaller violation of the requirements.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Elasticity in the cloud is often achieved by on-demand autoscaling. In such context, the goal is to optimize the Quality of Service (QoS) and cost objectives for the cloud-based services. However, the difficulty lies in the facts that these objectives, e.g., throughput and cost, can be naturally conflicted; and the QoS of cloud-based services often interfere due to the shared infrastructure in cloud. Consequently, dynamic and effective trade-off decision making of autoscaling in the cloud is necessary, yet challenging. In particular, it is even harder to achieve well-compromised trade-offs, where the decision largely improves the majority of the objectives; while causing relatively small degradations to others. In this paper, we present a self-adaptive decision making approach for autoscaling in the cloud. It is capable to adaptively produce autoscaling decisions that lead to well-compromised trade-offs without heavy human intervention. We leverage on ant colony inspired multi-objective optimization for searching and optimizing the trade-offs decisions, the result is then filtered by compromise-dominance, a mechanism that extracts the decisions with balanced improvements in the trade-offs. We experimentally compare our approach to four state-of-the-arts autoscaling approaches: rule, heuristic, randomized and multi-objective genetic algorithm based solutions. The results reveal the effectiveness of our approach over the others, including better quality of trade-offs and significantly smaller violation of the requirements.",
"title": "Self-Adaptive Trade-off Decision Making for Autoscaling Cloud-Based Services",
"normalizedTitle": "Self-Adaptive Trade-off Decision Making for Autoscaling Cloud-Based Services",
"fno": "07327204",
"hasPdf": true,
"idPrefix": "sc",
"keywords": [
"Quality Of Service",
"Decision Making",
"Interference",
"Optimization",
"Cloud Computing",
"Throughput",
"Search Based Optimization Multi Objective Trade Offs Qo S Interference Cloud Computing"
],
"authors": [
{
"givenName": "Tao",
"surname": "Chen",
"fullName": "Tao Chen",
"affiliation": "School of Computer Science, University of Birmingham, Birmingham, United Kingdom",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Rami",
"surname": "Bahsoon",
"fullName": "Rami Bahsoon",
"affiliation": "School of Computer Science, University of Birmingham, Birmingham, United Kingdom",
"__typename": "ArticleAuthorType"
}
],
"replicability": null,
"showBuyMe": true,
"showRecommendedArticles": true,
"isOpenAccess": false,
"issueNum": "04",
"pubDate": "2017-10-01 00:00:00",
"pubType": "trans",
"pages": "618-632",
"year": "2017",
"issn": "1939-1374",
"isbn": null,
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"recommendedArticles": [
{
"id": "proceedings/seams/2015/5567/0/5567a134",
"title": "Minimizing Nasty Surprises with Better Informed Decision-Making in Self-Adaptive Systems",
"doi": null,
"abstractUrl": "/proceedings-article/seams/2015/5567a134/12OmNBZpHad",
"parentPublication": {
"id": "proceedings/seams/2015/5567/0",
"title": "2015 IEEE/ACM 10th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/compsac/2011/4439/0/4439a466",
"title": "A Semi-automated Decision Support Tool for Requirements Trade-Off Analysis",
"doi": null,
"abstractUrl": "/proceedings-article/compsac/2011/4439a466/12OmNyKJimV",
"parentPublication": {
"id": "proceedings/compsac/2011/4439/0",
"title": "2011 IEEE 35th Annual Computer Software and Applications Conference",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/sc2/2017/5862/0/5862a024",
"title": "Multilayered Cloud Applications Autoscaling Performance Estimation",
"doi": null,
"abstractUrl": "/proceedings-article/sc2/2017/5862a024/12OmNz61cWb",
"parentPublication": {
"id": "proceedings/sc2/2017/5862/0",
"title": "2017 IEEE 7th International Symposium on Cloud and Service Computing (SC2)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "mags/co/2015/09/mco2015090093",
"title": "Toward a Smarter Cloud: Self-Aware Autoscaling of Cloud Configurations and Resources",
"doi": null,
"abstractUrl": "/magazine/co/2015/09/mco2015090093/13rRUzp02js",
"parentPublication": {
"id": "mags/co",
"title": "Computer",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cloud/2018/7235/0/723501a954",
"title": "IaaS Reactive Autoscaling Performance Challenges",
"doi": null,
"abstractUrl": "/proceedings-article/cloud/2018/723501a954/13xI8AOXccL",
"parentPublication": {
"id": "proceedings/cloud/2018/7235/0",
"title": "2018 IEEE 11th International Conference on Cloud Computing (CLOUD)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/seams/2018/5715/0/571501a019",
"title": "RE-STORM: Mapping the Decision-Making Problem and Non-functional Requirements Trade-Off to Partially Observable Markov Decision Processes",
"doi": null,
"abstractUrl": "/proceedings-article/seams/2018/571501a019/17D45Xtvpdp",
"parentPublication": {
"id": "proceedings/seams/2018/5715/0",
"title": "2018 IEEE/ACM 13th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/5555/01/09768153",
"title": "Understanding How In-Visualization Provenance Can Support Trade-off Analysis",
"doi": null,
"abstractUrl": "/journal/tg/5555/01/09768153/1D6qPjvIP16",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icde/2022/0883/0/088300c762",
"title": "RobustScaler: QoS-Aware Autoscaling for Complex Workloads",
"doi": null,
"abstractUrl": "/proceedings-article/icde/2022/088300c762/1FwFpTafHhK",
"parentPublication": {
"id": "proceedings/icde/2022/0883/0",
"title": "2022 IEEE 38th International Conference on Data Engineering (ICDE)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icse-nier/2019/1758/0/175800a109",
"title": "Trade-Off-Oriented Development: Making Quality Attribute Trade-Offs First-Class",
"doi": null,
"abstractUrl": "/proceedings-article/icse-nier/2019/175800a109/1cI69s4jRw4",
"parentPublication": {
"id": "proceedings/icse-nier/2019/1758/0",
"title": "2019 IEEE/ACM 41st International Conference on Software Engineering: New Ideas and Emerging Results (ICSE-NIER)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/csci/2019/5584/0/558400b411",
"title": "Agnostic Approach for Microservices Autoscaling in Cloud Applications",
"doi": null,
"abstractUrl": "/proceedings-article/csci/2019/558400b411/1jdDVjyapCU",
"parentPublication": {
"id": "proceedings/csci/2019/5584/0",
"title": "2019 International Conference on Computational Science and Computational Intelligence (CSCI)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"adjacentArticles": {
"previous": {
"fno": "07332971",
"articleId": "13rRUxDIteI",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "07284705",
"articleId": "13rRUxbTMAO",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"webExtras": [],
"articleVideos": []
}
|
{
"issue": {
"id": "1J9y2mtpt3a",
"title": "Jan.",
"year": "2023",
"issueNum": "01",
"idPrefix": "tg",
"pubType": "journal",
"volume": "29",
"label": "Jan.",
"downloadables": {
"hasCover": false,
"__typename": "PeriodicalIssueDownloadablesType"
},
"__typename": "PeriodicalIssue"
},
"article": {
"id": "1GZonS2SkKs",
"doi": "10.1109/TVCG.2022.3209495",
"abstract": "Conducting data analysis tasks rarely occur in isolation. Especially in intelligence analysis scenarios where different experts contribute knowledge to a shared understanding, members must communicate how insights develop to establish common ground among collaborators. The use of provenance to communicate analytic sensemaking carries promise by describing the interactions and summarizing the steps taken to reach insights. Yet, no universal guidelines exist for communicating provenance in different settings. Our work focuses on the presentation of provenance information and the resulting conclusions reached and strategies used by new analysts. In an open-ended, 30-minute, textual exploration scenario, we qualitatively compare how adding different types of provenance information (specifically data coverage and interaction history) affects analysts' confidence in conclusions developed, propensity to repeat work, filtering of data, identification of relevant information, and typical investigation strategies. We see that data coverage (i.e., what was interacted with) provides provenance information without limiting individual investigation freedom. On the other hand, while interaction history (i.e., when something was interacted with) does not significantly encourage more mimicry, it does take more time to comfortably understand, as represented by less confident conclusions and less relevant information-gathering behaviors. Our results contribute empirical data towards understanding how provenance summarizations can influence analysis behaviors.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Conducting data analysis tasks rarely occur in isolation. Especially in intelligence analysis scenarios where different experts contribute knowledge to a shared understanding, members must communicate how insights develop to establish common ground among collaborators. The use of provenance to communicate analytic sensemaking carries promise by describing the interactions and summarizing the steps taken to reach insights. Yet, no universal guidelines exist for communicating provenance in different settings. Our work focuses on the presentation of provenance information and the resulting conclusions reached and strategies used by new analysts. In an open-ended, 30-minute, textual exploration scenario, we qualitatively compare how adding different types of provenance information (specifically data coverage and interaction history) affects analysts' confidence in conclusions developed, propensity to repeat work, filtering of data, identification of relevant information, and typical investigation strategies. We see that data coverage (i.e., what was interacted with) provides provenance information without limiting individual investigation freedom. On the other hand, while interaction history (i.e., when something was interacted with) does not significantly encourage more mimicry, it does take more time to comfortably understand, as represented by less confident conclusions and less relevant information-gathering behaviors. Our results contribute empirical data towards understanding how provenance summarizations can influence analysis behaviors.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Conducting data analysis tasks rarely occur in isolation. Especially in intelligence analysis scenarios where different experts contribute knowledge to a shared understanding, members must communicate how insights develop to establish common ground among collaborators. The use of provenance to communicate analytic sensemaking carries promise by describing the interactions and summarizing the steps taken to reach insights. Yet, no universal guidelines exist for communicating provenance in different settings. Our work focuses on the presentation of provenance information and the resulting conclusions reached and strategies used by new analysts. In an open-ended, 30-minute, textual exploration scenario, we qualitatively compare how adding different types of provenance information (specifically data coverage and interaction history) affects analysts' confidence in conclusions developed, propensity to repeat work, filtering of data, identification of relevant information, and typical investigation strategies. We see that data coverage (i.e., what was interacted with) provides provenance information without limiting individual investigation freedom. On the other hand, while interaction history (i.e., when something was interacted with) does not significantly encourage more mimicry, it does take more time to comfortably understand, as represented by less confident conclusions and less relevant information-gathering behaviors. Our results contribute empirical data towards understanding how provenance summarizations can influence analysis behaviors.",
"title": "The Influence of Visual Provenance Representations on Strategies in a Collaborative Hand-off Data Analysis Scenario",
"normalizedTitle": "The Influence of Visual Provenance Representations on Strategies in a Collaborative Hand-off Data Analysis Scenario",
"fno": "09903572",
"hasPdf": true,
"idPrefix": "tg",
"keywords": [
"Data Analysis",
"Data Integrity",
"Data Visualisation",
"Human Factors",
"Text Analysis",
"Collaborative Hand Off Data Analysis",
"Data Coverage",
"Data Interaction History",
"Influence Analysis Behaviors",
"Information Gathering Behaviors",
"Intelligence Analysis",
"Provenance Information",
"Provenance Summarizations",
"Textual Exploration",
"Visual Provenance Representations",
"Collaboration",
"Visualization",
"History",
"Behavioral Sciences",
"Data Visualization",
"Task Analysis",
"Data Analysis",
"Analytic Provenance",
"Sensemaking",
"Information Transfer",
"Visualization",
"Workflow Summarization",
"User Studies"
],
"authors": [
{
"givenName": "Jeremy E.",
"surname": "Block",
"fullName": "Jeremy E. Block",
"affiliation": "University of Florida, USA",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Shaghayegh",
"surname": "Esmaeili",
"fullName": "Shaghayegh Esmaeili",
"affiliation": "University of Florida, USA",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Eric D.",
"surname": "Ragan",
"fullName": "Eric D. Ragan",
"affiliation": "University of Florida, USA",
"__typename": "ArticleAuthorType"
},
{
"givenName": "John R.",
"surname": "Goodall",
"fullName": "John R. Goodall",
"affiliation": "Oak Ridge National Laboratory, USA",
"__typename": "ArticleAuthorType"
},
{
"givenName": "G. David",
"surname": "Richardson",
"fullName": "G. David Richardson",
"affiliation": "Oak Ridge National Laboratory, USA",
"__typename": "ArticleAuthorType"
}
],
"replicability": null,
"showBuyMe": true,
"showRecommendedArticles": true,
"isOpenAccess": false,
"issueNum": "01",
"pubDate": "2023-01-01 00:00:00",
"pubType": "trans",
"pages": "1113-1123",
"year": "2023",
"issn": "1077-2626",
"isbn": null,
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"recommendedArticles": [
{
"id": "proceedings/services/2009/3708/0/3708a291",
"title": "Using Mediation to Achieve Provenance Interoperability",
"doi": null,
"abstractUrl": "/proceedings-article/services/2009/3708a291/12OmNyfdOSv",
"parentPublication": {
"id": "proceedings/services/2009/3708/0",
"title": "2009 Congress on Services - I",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "mags/cg/2015/03/mcg2015030056",
"title": "Analytic Provenance for Sensemaking: A Research Agenda",
"doi": null,
"abstractUrl": "/magazine/cg/2015/03/mcg2015030056/13rRUB7a13F",
"parentPublication": {
"id": "mags/cg",
"title": "IEEE Computer Graphics and Applications",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/2013/12/ttg2013122198",
"title": "Identifying Redundancy and Exposing Provenance in Crowdsourced Data Analysis",
"doi": null,
"abstractUrl": "/journal/tg/2013/12/ttg2013122198/13rRUwInvsQ",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/2016/01/07192714",
"title": "Characterizing Provenance in Visualization and Data Analysis: An Organizational Framework of Provenance Types and Purposes",
"doi": null,
"abstractUrl": "/journal/tg/2016/01/07192714/13rRUxOdD2F",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/2013/12/ttg2013122139",
"title": "An Extensible Framework for Provenance in Human Terrain Visual Analytics",
"doi": null,
"abstractUrl": "/journal/tg/2013/12/ttg2013122139/13rRUyfbwqH",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/5555/01/09768153",
"title": "Understanding How In-Visualization Provenance Can Support Trade-off Analysis",
"doi": null,
"abstractUrl": "/journal/tg/5555/01/09768153/1D6qPjvIP16",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iv/2022/9007/0/900700a300",
"title": "Improving Cybersecurity Incident Analysis Workflow with Analytical Provenance",
"doi": null,
"abstractUrl": "/proceedings-article/iv/2022/900700a300/1KaH3Ocwicg",
"parentPublication": {
"id": "proceedings/iv/2022/9007/0",
"title": "2022 26th International Conference Information Visualisation (IV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/trustcom/2022/9425/0/942500a747",
"title": "Deepro: Provenance-based APT Campaigns Detection via GNN",
"doi": null,
"abstractUrl": "/proceedings-article/trustcom/2022/942500a747/1LFLUemOpt6",
"parentPublication": {
"id": "proceedings/trustcom/2022/9425/0",
"title": "2022 IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tq/5555/01/10120960",
"title": "T-Trace: Constructing the APTs Provenance Graphs Through Multiple Syslogs Correlation",
"doi": null,
"abstractUrl": "/journal/tq/5555/01/10120960/1MYO5vS3xfO",
"parentPublication": {
"id": "trans/tq",
"title": "IEEE Transactions on Dependable and Secure Computing",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "mags/cg/2019/06/08788592",
"title": "Analytic Provenance in Practice: The Role of Provenance in Real-World Visualization and Data Analysis Environments",
"doi": null,
"abstractUrl": "/magazine/cg/2019/06/08788592/1cfqCMPtgRy",
"parentPublication": {
"id": "mags/cg",
"title": "IEEE Computer Graphics and Applications",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"adjacentArticles": {
"previous": {
"fno": "09903281",
"articleId": "1GZolp3W1mE",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "09915529",
"articleId": "1HmgceWU2bK",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"webExtras": [],
"articleVideos": []
}
|
{
"issue": {
"id": "1qL5hsvvVkc",
"title": "Feb.",
"year": "2021",
"issueNum": "02",
"idPrefix": "tg",
"pubType": "journal",
"volume": "27",
"label": "Feb.",
"downloadables": {
"hasCover": false,
"__typename": "PeriodicalIssueDownloadablesType"
},
"__typename": "PeriodicalIssue"
},
"article": {
"id": "1nTqGZHBYM8",
"doi": "10.1109/TVCG.2020.3030408",
"abstract": "Many processes, from gene interaction in biology to computer networks to social media, can be modeled more precisely as temporal hypergraphs than by regular graphs. This is because hypergraphs generalize graphs by extending edges to connect any number of vertices, allowing complex relationships to be described more accurately and predict their behavior over time. However, the interactive exploration and seamless refinement of such hypergraph-based prediction models still pose a major challenge. We contribute Hyper-Matrix, a novel visual analytics technique that addresses this challenge through a tight coupling between machine-learning and interactive visualizations. In particular, the technique incorporates a geometric deep learning model as a blueprint for problem-specific models while integrating visualizations for graph-based and category-based data with a novel combination of interactions for an effective user-driven exploration of hypergraph models. To eliminate demanding context switches and ensure scalability, our matrix-based visualization provides drill-down capabilities across multiple levels of semantic zoom, from an overview of model predictions down to the content. We facilitate a focused analysis of relevant connections and groups based on interactive user-steering for filtering and search tasks, a dynamically modifiable partition hierarchy, various matrix reordering techniques, and interactive model feedback. We evaluate our technique in a case study and through formative evaluation with law enforcement experts using real-world internet forum communication data. The results show that our approach surpasses existing solutions in terms of scalability and applicability, enables the incorporation of domain knowledge, and allows for fast search-space traversal. With the proposed technique, we pave the way for the visual analytics of temporal hypergraphs in a wide variety of domains.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Many processes, from gene interaction in biology to computer networks to social media, can be modeled more precisely as temporal hypergraphs than by regular graphs. This is because hypergraphs generalize graphs by extending edges to connect any number of vertices, allowing complex relationships to be described more accurately and predict their behavior over time. However, the interactive exploration and seamless refinement of such hypergraph-based prediction models still pose a major challenge. We contribute Hyper-Matrix, a novel visual analytics technique that addresses this challenge through a tight coupling between machine-learning and interactive visualizations. In particular, the technique incorporates a geometric deep learning model as a blueprint for problem-specific models while integrating visualizations for graph-based and category-based data with a novel combination of interactions for an effective user-driven exploration of hypergraph models. To eliminate demanding context switches and ensure scalability, our matrix-based visualization provides drill-down capabilities across multiple levels of semantic zoom, from an overview of model predictions down to the content. We facilitate a focused analysis of relevant connections and groups based on interactive user-steering for filtering and search tasks, a dynamically modifiable partition hierarchy, various matrix reordering techniques, and interactive model feedback. We evaluate our technique in a case study and through formative evaluation with law enforcement experts using real-world internet forum communication data. The results show that our approach surpasses existing solutions in terms of scalability and applicability, enables the incorporation of domain knowledge, and allows for fast search-space traversal. With the proposed technique, we pave the way for the visual analytics of temporal hypergraphs in a wide variety of domains.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Many processes, from gene interaction in biology to computer networks to social media, can be modeled more precisely as temporal hypergraphs than by regular graphs. This is because hypergraphs generalize graphs by extending edges to connect any number of vertices, allowing complex relationships to be described more accurately and predict their behavior over time. However, the interactive exploration and seamless refinement of such hypergraph-based prediction models still pose a major challenge. We contribute Hyper-Matrix, a novel visual analytics technique that addresses this challenge through a tight coupling between machine-learning and interactive visualizations. In particular, the technique incorporates a geometric deep learning model as a blueprint for problem-specific models while integrating visualizations for graph-based and category-based data with a novel combination of interactions for an effective user-driven exploration of hypergraph models. To eliminate demanding context switches and ensure scalability, our matrix-based visualization provides drill-down capabilities across multiple levels of semantic zoom, from an overview of model predictions down to the content. We facilitate a focused analysis of relevant connections and groups based on interactive user-steering for filtering and search tasks, a dynamically modifiable partition hierarchy, various matrix reordering techniques, and interactive model feedback. We evaluate our technique in a case study and through formative evaluation with law enforcement experts using real-world internet forum communication data. The results show that our approach surpasses existing solutions in terms of scalability and applicability, enables the incorporation of domain knowledge, and allows for fast search-space traversal. With the proposed technique, we pave the way for the visual analytics of temporal hypergraphs in a wide variety of domains.",
"title": "Visual Analytics for Temporal Hypergraph Model Exploration",
"normalizedTitle": "Visual Analytics for Temporal Hypergraph Model Exploration",
"fno": "09222341",
"hasPdf": true,
"idPrefix": "tg",
"keywords": [
"Data Analysis",
"Data Visualisation",
"Deep Learning Artificial Intelligence",
"Geometry",
"Graph Theory",
"Interactive Systems",
"Internet",
"Law Administration",
"Matrix Algebra",
"Temporal Hypergraph Model Exploration",
"Hyper Matrix",
"Visual Analytics",
"Machine Learning",
"Interactive Visualizations",
"Geometric Deep Learning Model",
"Problem Specific Models",
"Category Based Data",
"User Driven Exploration",
"Matrix Based Visualization",
"Matrix Reordering Techniques",
"Interactive Model Feedback",
"Graph Based Data",
"Internet Forum Communication Data",
"Law Enforcement Experts",
"Visualization",
"Computational Modeling",
"Machine Learning",
"Predictive Models",
"Analytical Models",
"Semantics",
"Data Models",
"Hypergraph",
"Communication Analysis",
"Geometric Deep Learning",
"Semantic Zoom",
"Matrix Ordering",
"Visual Analytics"
],
"authors": [
{
"givenName": "Maximilian T.",
"surname": "Fischer",
"fullName": "Maximilian T. Fischer",
"affiliation": "University of Konstanz, Germany",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Devanshu",
"surname": "Arya",
"fullName": "Devanshu Arya",
"affiliation": "University of Amsterdam, The Netherlands",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Dirk",
"surname": "Streeb",
"fullName": "Dirk Streeb",
"affiliation": "University of Konstanz, Germany",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Daniel",
"surname": "Seebacher",
"fullName": "Daniel Seebacher",
"affiliation": "University of Konstanz, Germany",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Daniel A.",
"surname": "Keim",
"fullName": "Daniel A. Keim",
"affiliation": "University of Konstanz, Germany",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Marcel",
"surname": "Worring",
"fullName": "Marcel Worring",
"affiliation": "University of Amsterdam, The Netherlands",
"__typename": "ArticleAuthorType"
}
],
"replicability": null,
"showBuyMe": true,
"showRecommendedArticles": true,
"isOpenAccess": false,
"issueNum": "02",
"pubDate": "2021-02-01 00:00:00",
"pubType": "trans",
"pages": "550-560",
"year": "2021",
"issn": "1077-2626",
"isbn": null,
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"recommendedArticles": [
{
"id": "proceedings/icpr/2012/2216/0/06460777",
"title": "Hypergraph matching based on Marginalized Constrained Compatibility",
"doi": null,
"abstractUrl": "/proceedings-article/icpr/2012/06460777/12OmNyo1nKt",
"parentPublication": {
"id": "proceedings/icpr/2012/2216/0",
"title": "2012 21st International Conference on Pattern Recognition (ICPR 2012)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iiai-aai/2018/7447/0/744701a799",
"title": "A Hypergraph Based Formal Description Technique for Enterprise Architecture Representation",
"doi": null,
"abstractUrl": "/proceedings-article/iiai-aai/2018/744701a799/19m3H1W4wIU",
"parentPublication": {
"id": "proceedings/iiai-aai/2018/7447/0",
"title": "2018 7th International Congress on Advanced Applied Informatics (IIAI-AAI)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icdm/2021/2398/0/239800a091",
"title": "Hypergraph Ego-networks and Their Temporal Evolution",
"doi": null,
"abstractUrl": "/proceedings-article/icdm/2021/239800a091/1AqxnbOPIJ2",
"parentPublication": {
"id": "proceedings/icdm/2021/2398/0",
"title": "2021 IEEE International Conference on Data Mining (ICDM)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/hpca/2022/2027/0/202700a184",
"title": "Hardware-Accelerated Hypergraph Processing with Chain-Driven Scheduling",
"doi": null,
"abstractUrl": "/proceedings-article/hpca/2022/202700a184/1Ds0h32ECQg",
"parentPublication": {
"id": "proceedings/hpca/2022/2027/0",
"title": "2022 IEEE International Symposium on High-Performance Computer Architecture (HPCA)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cbd/2022/0745/0/074500a158",
"title": "An Optimization Method for Weight Computation in Hypergraph Transductive Learning Model",
"doi": null,
"abstractUrl": "/proceedings-article/cbd/2022/074500a158/1EVioxW9YbK",
"parentPublication": {
"id": "proceedings/cbd/2022/0745/0",
"title": "2021 Ninth International Conference on Advanced Cloud and Big Data (CBD)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ipdpsw/2022/9747/0/974700a275",
"title": "NWHy: A Framework for Hypergraph Analytics: Representations, Data structures, and Algorithms",
"doi": null,
"abstractUrl": "/proceedings-article/ipdpsw/2022/974700a275/1Fu9xVhUFyM",
"parentPublication": {
"id": "proceedings/ipdpsw/2022/9747/0",
"title": "2022 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icdm/2022/5099/0/509900a849",
"title": "THINK: Temporal Hypergraph Hyperbolic Network",
"doi": null,
"abstractUrl": "/proceedings-article/icdm/2022/509900a849/1KpCmdynhio",
"parentPublication": {
"id": "proceedings/icdm/2022/5099/0",
"title": "2022 IEEE International Conference on Data Mining (ICDM)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/2021/01/08789484",
"title": "Analyzing Dynamic Hypergraphs with Parallel Aggregated Ordered Hypergraph Visualization",
"doi": null,
"abstractUrl": "/journal/tg/2021/01/08789484/1ch5Lx3gcVO",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tk/2022/06/09169850",
"title": "Hypergraph Partitioning With Embeddings",
"doi": null,
"abstractUrl": "/journal/tk/2022/06/09169850/1mmOxMdvt1S",
"parentPublication": {
"id": "trans/tk",
"title": "IEEE Transactions on Knowledge & Data Engineering",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/vis/2021/3335/0/333500a081",
"title": "Towards a Survey on Static and Dynamic Hypergraph Visualizations",
"doi": null,
"abstractUrl": "/proceedings-article/vis/2021/333500a081/1yXukGBCJTq",
"parentPublication": {
"id": "proceedings/vis/2021/3335/0",
"title": "2021 IEEE Visualization Conference (VIS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"adjacentArticles": {
"previous": {
"fno": "09231268",
"articleId": "1o3nyGexOvu",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "09222271",
"articleId": "1nTrwyIRc1W",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"webExtras": [
{
"id": "1qLec71XEFq",
"name": "ttg202102-09222341s1-supp2-3030408.pdf",
"location": "https://www.computer.org/csdl/api/v1/extra/ttg202102-09222341s1-supp2-3030408.pdf",
"extension": "pdf",
"size": "88.3 kB",
"__typename": "WebExtraType"
},
{
"id": "1qLecvH4MmY",
"name": "ttg202102-09222341s1-supp1-3030408.mp4",
"location": "https://www.computer.org/csdl/api/v1/extra/ttg202102-09222341s1-supp1-3030408.mp4",
"extension": "mp4",
"size": "73.6 MB",
"__typename": "WebExtraType"
}
],
"articleVideos": []
}
|
{
"issue": {
"id": "12OmNx8fif0",
"title": "Sept.",
"year": "2017",
"issueNum": "09",
"idPrefix": "tg",
"pubType": "journal",
"volume": "23",
"label": "Sept.",
"downloadables": {
"hasCover": false,
"__typename": "PeriodicalIssueDownloadablesType"
},
"__typename": "PeriodicalIssue"
},
"article": {
"id": "13rRUxly95G",
"doi": "10.1109/TVCG.2016.2605092",
"abstract": "Biharmonic B-splines, proposed by Feng and Warren, are an elegant generalization of univariate B-splines to planar and curved domains with fully irregular knot configuration. Despite the theoretic breakthrough, certain technical difficulties are imperative, including the necessity of Voronoi tessellation, the lack of analytical formulation of bases on general manifolds, expensive basis re-computation during knot refinement/removal, being applicable for simple domains only (e.g., such as euclidean planes, spherical and cylindrical domains, and tori). To ameliorate, this paper articulates a new biharmonic B-spline computing paradigm with a simple formulation. We prove that biharmonic B-splines have an equivalent representation, which is solely based on a linear combination of Green’s functions of the bi-Laplacian operator. Consequently, without explicitly computing their bases, biharmonic B-splines can bypass the Voronoi partitioning and the discretization of bi-Laplacian, enable the computational utilities on any compact 2-manifold. The new representation also facilitates optimization-driven knot selection for constructing biharmonic B-splines on manifold triangle meshes. We develop algorithms for spline evaluation, data interpolation and hierarchical data decomposition. Our results demonstrate that biharmonic B-splines, as a new type of spline functions with theoretic and application appeal, afford progressive update of fully irregular knots, free of singularity, without the need of explicit parameterization, making it ideal for a host of graphics tasks on manifolds.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Biharmonic B-splines, proposed by Feng and Warren, are an elegant generalization of univariate B-splines to planar and curved domains with fully irregular knot configuration. Despite the theoretic breakthrough, certain technical difficulties are imperative, including the necessity of Voronoi tessellation, the lack of analytical formulation of bases on general manifolds, expensive basis re-computation during knot refinement/removal, being applicable for simple domains only (e.g., such as euclidean planes, spherical and cylindrical domains, and tori). To ameliorate, this paper articulates a new biharmonic B-spline computing paradigm with a simple formulation. We prove that biharmonic B-splines have an equivalent representation, which is solely based on a linear combination of Green’s functions of the bi-Laplacian operator. Consequently, without explicitly computing their bases, biharmonic B-splines can bypass the Voronoi partitioning and the discretization of bi-Laplacian, enable the computational utilities on any compact 2-manifold. The new representation also facilitates optimization-driven knot selection for constructing biharmonic B-splines on manifold triangle meshes. We develop algorithms for spline evaluation, data interpolation and hierarchical data decomposition. Our results demonstrate that biharmonic B-splines, as a new type of spline functions with theoretic and application appeal, afford progressive update of fully irregular knots, free of singularity, without the need of explicit parameterization, making it ideal for a host of graphics tasks on manifolds.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Biharmonic B-splines, proposed by Feng and Warren, are an elegant generalization of univariate B-splines to planar and curved domains with fully irregular knot configuration. Despite the theoretic breakthrough, certain technical difficulties are imperative, including the necessity of Voronoi tessellation, the lack of analytical formulation of bases on general manifolds, expensive basis re-computation during knot refinement/removal, being applicable for simple domains only (e.g., such as euclidean planes, spherical and cylindrical domains, and tori). To ameliorate, this paper articulates a new biharmonic B-spline computing paradigm with a simple formulation. We prove that biharmonic B-splines have an equivalent representation, which is solely based on a linear combination of Green’s functions of the bi-Laplacian operator. Consequently, without explicitly computing their bases, biharmonic B-splines can bypass the Voronoi partitioning and the discretization of bi-Laplacian, enable the computational utilities on any compact 2-manifold. The new representation also facilitates optimization-driven knot selection for constructing biharmonic B-splines on manifold triangle meshes. We develop algorithms for spline evaluation, data interpolation and hierarchical data decomposition. Our results demonstrate that biharmonic B-splines, as a new type of spline functions with theoretic and application appeal, afford progressive update of fully irregular knots, free of singularity, without the need of explicit parameterization, making it ideal for a host of graphics tasks on manifolds.",
"title": "Knot Optimization for Biharmonic B-splines on Manifold Triangle Meshes",
"normalizedTitle": "Knot Optimization for Biharmonic B-splines on Manifold Triangle Meshes",
"fno": "07558244",
"hasPdf": true,
"idPrefix": "tg",
"keywords": [
"Splines Mathematics",
"Manifolds",
"Greens Function Methods",
"Laplace Equations",
"Optimization",
"Interpolation",
"Geometry",
"Biharmonic B Splines",
"Greens Functions",
"Manifold Triangle Meshes",
"Implicit Representation",
"Knot Optimization"
],
"authors": [
{
"givenName": "Fei",
"surname": "Hou",
"fullName": "Fei Hou",
"affiliation": "School of Computer Science and Engineering, Nanyang Technological University, Singapore",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Ying",
"surname": "He",
"fullName": "Ying He",
"affiliation": "School of Computer Science and Engineering, Nanyang Technological University, Singapore",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Hong",
"surname": "Qin",
"fullName": "Hong Qin",
"affiliation": "Department of Computer Science, Stony Brook University, Stony Brook, NY",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Aimin",
"surname": "Hao",
"fullName": "Aimin Hao",
"affiliation": "State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China",
"__typename": "ArticleAuthorType"
}
],
"replicability": null,
"showBuyMe": true,
"showRecommendedArticles": true,
"isOpenAccess": false,
"issueNum": "09",
"pubDate": "2017-09-01 00:00:00",
"pubType": "trans",
"pages": "2082-2095",
"year": "2017",
"issn": "1077-2626",
"isbn": null,
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"recommendedArticles": [
{
"id": "proceedings/sccg/2001/1215/0/12150071",
"title": "Bivariate Simplex B-Splines: A New Paradigm",
"doi": null,
"abstractUrl": "/proceedings-article/sccg/2001/12150071/12OmNBQC88V",
"parentPublication": {
"id": "proceedings/sccg/2001/1215/0",
"title": "Proceedings Spring Conference on Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iv/2005/2397/0/23970419",
"title": "Curve Approximation with Quadratic B-Splines",
"doi": null,
"abstractUrl": "/proceedings-article/iv/2005/23970419/12OmNBuL1bt",
"parentPublication": {
"id": "proceedings/iv/2005/2397/0",
"title": "Ninth International Conference on Information Visualisation (IV'05)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iv/1997/8076/0/80760082",
"title": "Designing of curves and surfaces using cubic splines with geometric characterization",
"doi": null,
"abstractUrl": "/proceedings-article/iv/1997/80760082/12OmNvDZETM",
"parentPublication": {
"id": "proceedings/iv/1997/8076/0",
"title": "Proceedings. 1997 IEEE Conference on Information Visualization (Cat. No.97TB100165)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/3dpvt/2002/1521/0/15210372",
"title": "Surface Reconstruction from Multiple Views using Rational B-Splines and Knot Insertion",
"doi": null,
"abstractUrl": "/proceedings-article/3dpvt/2002/15210372/12OmNwG90iZ",
"parentPublication": {
"id": "proceedings/3dpvt/2002/1521/0",
"title": "3D Data Processing Visualization and Transmission, International Symposium on",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cadgraphics/2011/4497/0/4497a229",
"title": "Convergence of Geometric Interpolation Using Uniform B-splines",
"doi": null,
"abstractUrl": "/proceedings-article/cadgraphics/2011/4497a229/12OmNyNQSOW",
"parentPublication": {
"id": "proceedings/cadgraphics/2011/4497/0",
"title": "Computer-Aided Design and Computer Graphics, International Conference on",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cgi/1997/7825/0/78250131",
"title": "An Interpolation Subspline Scheme Related to B-Spline Techniques",
"doi": null,
"abstractUrl": "/proceedings-article/cgi/1997/78250131/12OmNzdoMp5",
"parentPublication": {
"id": "proceedings/cgi/1997/7825/0",
"title": "Computer Graphics International Conference",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/2012/05/ttg2012050703",
"title": "Restricted Trivariate Polycube Splines for Volumetric Data Modeling",
"doi": null,
"abstractUrl": "/journal/tg/2012/05/ttg2012050703/13rRUIM2VH1",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/1997/03/v0228",
"title": "Scattered Data Interpolation with Multilevel B-Splines",
"doi": null,
"abstractUrl": "/journal/tg/1997/03/v0228/13rRUxly9dH",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tp/2017/07/07533440",
"title": "Randomly Perturbed B-Splines for Nonrigid Image Registration",
"doi": null,
"abstractUrl": "/journal/tp/2017/07/07533440/13rRUyYSWmh",
"parentPublication": {
"id": "trans/tp",
"title": "IEEE Transactions on Pattern Analysis & Machine Intelligence",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/mcsi/2018/7500/0/750000a057",
"title": "On the Approximation by Local Complex-Valued Splines",
"doi": null,
"abstractUrl": "/proceedings-article/mcsi/2018/750000a057/1bXcRIWFmU0",
"parentPublication": {
"id": "proceedings/mcsi/2018/7500/0",
"title": "2018 5th International Conference on Mathematics and Computers in Sciences and Industry (MCSI)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"adjacentArticles": {
"previous": {
"fno": "07562447",
"articleId": "13rRUxly8T2",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "07565535",
"articleId": "13rRUwh80uE",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"webExtras": [],
"articleVideos": []
}
|
{
"issue": {
"id": "12OmNBsLPeW",
"title": "Jan./Feb.",
"year": "2018",
"issueNum": "01",
"idPrefix": "cg",
"pubType": "magazine",
"volume": "38",
"label": "Jan./Feb.",
"downloadables": {
"hasCover": true,
"__typename": "PeriodicalIssueDownloadablesType"
},
"__typename": "PeriodicalIssue"
},
"article": {
"id": "13rRUwciPhU",
"doi": "10.1109/MCG.2018.011461533",
"abstract": "In situ processing produces reduced size persistent representations of a simulations state while the simulation is running. The need for in situ visualization and data analysis is usually described in terms of supercomputer size and performance in relation to available storage size.",
"abstracts": [
{
"abstractType": "Regular",
"content": "In situ processing produces reduced size persistent representations of a simulations state while the simulation is running. The need for in situ visualization and data analysis is usually described in terms of supercomputer size and performance in relation to available storage size.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "In situ processing produces reduced size persistent representations of a simulations state while the simulation is running. The need for in situ visualization and data analysis is usually described in terms of supercomputer size and performance in relation to available storage size.",
"title": "Optimizing Scientist Time through In Situ Visualization and Analysis",
"normalizedTitle": "Optimizing Scientist Time through In Situ Visualization and Analysis",
"fno": "mcg2018010119",
"hasPdf": true,
"idPrefix": "cg",
"keywords": [
"Data Visualisation",
"In Situ Visualization",
"Supercomputer Size",
"Data Analysis",
"Situ Processing",
"Data Visualization",
"Computational Modeling",
"Atmospheric Modeling",
"Solar System",
"Supercomputers",
"Data Models",
"Data Analysis",
"Visualization",
"In Situ",
"Data Analysis",
"Lossless Compression",
"Feature Extraction",
"Scientific Computing"
],
"authors": [
{
"givenName": "John",
"surname": "Patchett",
"fullName": "John Patchett",
"affiliation": "Los Alamos National Laboratory",
"__typename": "ArticleAuthorType"
},
{
"givenName": "James",
"surname": "Ahrens",
"fullName": "James Ahrens",
"affiliation": "Los Alamos National Laboratory",
"__typename": "ArticleAuthorType"
}
],
"replicability": null,
"showBuyMe": true,
"showRecommendedArticles": true,
"isOpenAccess": false,
"issueNum": "01",
"pubDate": "2018-01-01 00:00:00",
"pubType": "mags",
"pages": "119-127",
"year": "2018",
"issn": "0272-1716",
"isbn": null,
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"recommendedArticles": [
{
"id": "proceedings/sc/2016/8815/0/8815a276",
"title": "Performance Modeling of In Situ Rendering",
"doi": null,
"abstractUrl": "/proceedings-article/sc/2016/8815a276/12OmNyTfg9O",
"parentPublication": {
"id": "proceedings/sc/2016/8815/0",
"title": "SC16: International Conference for High Performance Computing, Networking, Storage and Analysis (SC)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/sc/2014/5500/0/5500a424",
"title": "An Image-Based Approach to Extreme Scale in Situ Visualization and Analysis",
"doi": null,
"abstractUrl": "/proceedings-article/sc/2014/5500a424/12OmNyYDDCh",
"parentPublication": {
"id": "proceedings/sc/2014/5500/0",
"title": "SC14: International Conference for High Performance Computing, Networking, Storage and Analysis",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cluster/2016/3653/0/3653a269",
"title": "Adaptive Performance-Constrained In Situ Visualization of Atmospheric Simulations",
"doi": null,
"abstractUrl": "/proceedings-article/cluster/2016/3653a269/12OmNz4SOxp",
"parentPublication": {
"id": "proceedings/cluster/2016/3653/0",
"title": "2016 IEEE International Conference on Cluster Computing (CLUSTER)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ipdps/2017/3914/0/07967188",
"title": "Characterizing and Modeling Power and Energy for Extreme-Scale In-Situ Visualization",
"doi": null,
"abstractUrl": "/proceedings-article/ipdps/2017/07967188/12OmNzV70lS",
"parentPublication": {
"id": "proceedings/ipdps/2017/3914/0",
"title": "2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "mags/cg/2016/02/mcg2016020005",
"title": "The Tensions of In Situ Visualization",
"doi": null,
"abstractUrl": "/magazine/cg/2016/02/mcg2016020005/13rRUILLkIJ",
"parentPublication": {
"id": "mags/cg",
"title": "IEEE Computer Graphics and Applications",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/e-science/2022/6124/0/612400a182",
"title": "SIM-SITU: A Framework for the Faithful Simulation of in situ Processing",
"doi": null,
"abstractUrl": "/proceedings-article/e-science/2022/612400a182/1J6hxD8UJYA",
"parentPublication": {
"id": "proceedings/e-science/2022/6124/0",
"title": "2022 IEEE 18th International Conference on e-Science (e-Science)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "mags/cg/2019/06/08891843",
"title": "<italic>In Situ</italic> Visualization for Computational Science",
"doi": null,
"abstractUrl": "/magazine/cg/2019/06/08891843/1eIcaz2vORq",
"parentPublication": {
"id": "mags/cg",
"title": "IEEE Computer Graphics and Applications",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ldav/2019/2605/0/08944265",
"title": "Low-Overhead In Situ Visualization Using Halo Replay",
"doi": null,
"abstractUrl": "/proceedings-article/ldav/2019/08944265/1grOFpiaovK",
"parentPublication": {
"id": "proceedings/ldav/2019/2605/0",
"title": "2019 IEEE 9th Symposium on Large Data Analysis and Visualization (LDAV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/pacificvis/2020/5697/0/09086237",
"title": "Distribution-based Particle Data Reduction for In-situ Analysis and Visualization of Large-scale N-body Cosmological Simulations",
"doi": null,
"abstractUrl": "/proceedings-article/pacificvis/2020/09086237/1kuHocdddi8",
"parentPublication": {
"id": "proceedings/pacificvis/2020/5697/0",
"title": "2020 IEEE Pacific Visualization Symposium (PacificVis)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "mags/cs/2021/01/09167432",
"title": "Feature Analysis, Tracking, and Data Reduction: An Application to Multiphase Reactor Simulation MFiX-Exa for <italic>In-Situ</italic> Use Case",
"doi": null,
"abstractUrl": "/magazine/cs/2021/01/09167432/1mhPII8wVeU",
"parentPublication": {
"id": "mags/cs",
"title": "Computing in Science & Engineering",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"adjacentArticles": {
"previous": {
"fno": "mcg2018010115",
"articleId": "13rRUxASuk5",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "mcg2018010128",
"articleId": "13rRUyuvRrk",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"webExtras": [],
"articleVideos": []
}
|
{
"issue": {
"id": "12OmNyKrHqw",
"title": "Aug.",
"year": "2019",
"issueNum": "04",
"idPrefix": "nt",
"pubType": "journal",
"volume": "27",
"label": "Aug.",
"downloadables": {
"hasCover": false,
"__typename": "PeriodicalIssueDownloadablesType"
},
"__typename": "PeriodicalIssue"
},
"article": {
"id": "1bemf5zUDV6",
"doi": "10.1109/TNET.2019.2923737",
"abstract": "Wireless sensor networks (WSNs) are widely used in environmental applications where the aim is to sense physical phenomena, such as temperature and air pollution. A careful deployment of sensors is necessary in order to get a better knowledge of these physical phenomena while ensuring the minimum deployment cost. In this paper, we focus on using WSN for air pollution mapping and tackle the optimization problem of sensor deployment. Unlike most of the existing deployment approaches that are either generic or assume that sensors have a given detection range, we define an appropriate coverage formulation based on an interpolation formula that is adapted to the characteristics of air pollution sensing. We derive, from this formulation, two deployment models for air pollution mapping using the integer linear programming while ensuring the connectivity of the network and taking into account the sensing error of nodes. We analyze the theoretical complexity of our models and propose the heuristic algorithms based on the linear programming relaxation and binary search. We perform extensive simulations on a dataset of the Lyon city, France, in order to assess the computational complexity of our proposal and evaluate the impact of the deployment requirements on the obtained results.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Wireless sensor networks (WSNs) are widely used in environmental applications where the aim is to sense physical phenomena, such as temperature and air pollution. A careful deployment of sensors is necessary in order to get a better knowledge of these physical phenomena while ensuring the minimum deployment cost. In this paper, we focus on using WSN for air pollution mapping and tackle the optimization problem of sensor deployment. Unlike most of the existing deployment approaches that are either generic or assume that sensors have a given detection range, we define an appropriate coverage formulation based on an interpolation formula that is adapted to the characteristics of air pollution sensing. We derive, from this formulation, two deployment models for air pollution mapping using the integer linear programming while ensuring the connectivity of the network and taking into account the sensing error of nodes. We analyze the theoretical complexity of our models and propose the heuristic algorithms based on the linear programming relaxation and binary search. We perform extensive simulations on a dataset of the Lyon city, France, in order to assess the computational complexity of our proposal and evaluate the impact of the deployment requirements on the obtained results.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Wireless sensor networks (WSNs) are widely used in environmental applications where the aim is to sense physical phenomena, such as temperature and air pollution. A careful deployment of sensors is necessary in order to get a better knowledge of these physical phenomena while ensuring the minimum deployment cost. In this paper, we focus on using WSN for air pollution mapping and tackle the optimization problem of sensor deployment. Unlike most of the existing deployment approaches that are either generic or assume that sensors have a given detection range, we define an appropriate coverage formulation based on an interpolation formula that is adapted to the characteristics of air pollution sensing. We derive, from this formulation, two deployment models for air pollution mapping using the integer linear programming while ensuring the connectivity of the network and taking into account the sensing error of nodes. We analyze the theoretical complexity of our models and propose the heuristic algorithms based on the linear programming relaxation and binary search. We perform extensive simulations on a dataset of the Lyon city, France, in order to assess the computational complexity of our proposal and evaluate the impact of the deployment requirements on the obtained results.",
"title": "On the Deployment of Wireless Sensor Networks for Air Quality Mapping: Optimization Models and Algorithms",
"normalizedTitle": "On the Deployment of Wireless Sensor Networks for Air Quality Mapping: Optimization Models and Algorithms",
"fno": "08750870",
"hasPdf": true,
"idPrefix": "nt",
"keywords": [
"Computational Complexity",
"Integer Programming",
"Interpolation",
"Linear Programming",
"Sensor Placement",
"Wireless Sensor Networks",
"Appropriate Coverage Formulation",
"Air Pollution Sensing",
"Deployment Models",
"Air Pollution Mapping",
"Deployment Requirements",
"Wireless Sensor Networks",
"Air Quality Mapping",
"Optimization Models",
"Environmental Applications",
"Physical Phenomena",
"Minimum Deployment Cost",
"Sensor Deployment",
"Detection Range",
"Interpolation Formula",
"Integer Linear Programming",
"Air Pollution",
"Wireless Sensor Networks",
"Atmospheric Modeling",
"Sensor Phenomena And Characterization",
"Monitoring",
"Wireless Sensor Networks WSN",
"Sensor Deployment",
"Air Pollution Mapping",
"Pollution Aware Coverage",
"Heterogeneous Connectivity"
],
"authors": [
{
"givenName": "Ahmed",
"surname": "Boubrima",
"fullName": "Ahmed Boubrima",
"affiliation": "Universit’e de Lyon, INRIA, INSA-Lyon, CITI-INRIA, Villeurbanne, France",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Walid",
"surname": "Bechkit",
"fullName": "Walid Bechkit",
"affiliation": "Universit’e de Lyon, INRIA, INSA-Lyon, CITI-INRIA, Villeurbanne, France",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Hervé",
"surname": "Rivano",
"fullName": "Hervé Rivano",
"affiliation": "Universit’e de Lyon, INRIA, INSA-Lyon, CITI-INRIA, Villeurbanne, France",
"__typename": "ArticleAuthorType"
}
],
"replicability": null,
"showBuyMe": true,
"showRecommendedArticles": true,
"isOpenAccess": false,
"issueNum": "04",
"pubDate": "2019-07-01 00:00:00",
"pubType": "trans",
"pages": "1629-1642",
"year": "2019",
"issn": "1063-6692",
"isbn": null,
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"recommendedArticles": [
{
"id": "proceedings/lcn/2016/2054/0/2054a380",
"title": "Error-Bounded Air Quality Mapping Using Wireless Sensor Networks",
"doi": null,
"abstractUrl": "/proceedings-article/lcn/2016/2054a380/12OmNC0y5GA",
"parentPublication": {
"id": "proceedings/lcn/2016/2054/0",
"title": "2016 IEEE 41st Conference on Local Computer Networks (LCN)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/dcoss/2016/1460/0/1460a102",
"title": "Optimal Deployment of Dense WSN for Error Bounded Air Pollution Mapping",
"doi": null,
"abstractUrl": "/proceedings-article/dcoss/2016/1460a102/12OmNvzJG9A",
"parentPublication": {
"id": "proceedings/dcoss/2016/1460/0",
"title": "2016 International Conference on Distributed Computing in Sensor Systems (DCOSS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ipsn/2017/4890/0/07944807",
"title": "Demo Abstract: IASA - Indoor Air Quality Sensing and Automation",
"doi": null,
"abstractUrl": "/proceedings-article/ipsn/2017/07944807/12OmNxecS1o",
"parentPublication": {
"id": "proceedings/ipsn/2017/4890/0",
"title": "2017 16th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "mags/cg/2017/05/mcg2017050098",
"title": "Visual Analytics for Spatial Clusters of Air-Quality Data",
"doi": null,
"abstractUrl": "/magazine/cg/2017/05/mcg2017050098/13rRUzpQPNU",
"parentPublication": {
"id": "mags/cg",
"title": "IEEE Computer Graphics and Applications",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icdmw/2018/9288/0/928800b507",
"title": "AirCalypse: Revealing Fine-Grained Air Quality from Social Media",
"doi": null,
"abstractUrl": "/proceedings-article/icdmw/2018/928800b507/18jXECbyUbm",
"parentPublication": {
"id": "proceedings/icdmw/2018/9288/0",
"title": "2018 IEEE International Conference on Data Mining Workshops (ICDMW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/5555/01/09765706",
"title": "Communicating Uncertainty and Risk in Air Quality Maps",
"doi": null,
"abstractUrl": "/journal/tg/5555/01/09765706/1CY3PcYVKaA",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ithings-greencom-cpscom-smartdata-cybermatics/2022/5417/0/541700a101",
"title": "Node Deployment and Confident Information Coverage for WSN-based Air Quality Monitoring",
"doi": null,
"abstractUrl": "/proceedings-article/ithings-greencom-cpscom-smartdata-cybermatics/2022/541700a101/1HcmO4wHf8I",
"parentPublication": {
"id": "proceedings/ithings-greencom-cpscom-smartdata-cybermatics/2022/5417/0",
"title": "2022 IEEE International Conferences on Internet of Things (iThings) and IEEE Green Computing & Communications (GreenCom) and IEEE Cyber, Physical & Social Computing (CPSCom) and IEEE Smart Data (SmartData) and IEEE Congress on Cybermatics (Cybermatics)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ithings-greencom-cpscom-smartdata/2019/2980/0/298000a967",
"title": "Evaluation of Precalibrated Electrochemical Gas Sensors for Air Quality Monitoring Systems",
"doi": null,
"abstractUrl": "/proceedings-article/ithings-greencom-cpscom-smartdata/2019/298000a967/1ehBHAmX1fi",
"parentPublication": {
"id": "proceedings/ithings-greencom-cpscom-smartdata/2019/2980/0",
"title": "2019 International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iotdi/2020/6602/0/660200a014",
"title": "MapTransfer : Urban Air Quality Map Generation for Downscaled Sensor Deployments",
"doi": null,
"abstractUrl": "/proceedings-article/iotdi/2020/660200a014/1k0P4IYqeZi",
"parentPublication": {
"id": "proceedings/iotdi/2020/6602/0",
"title": "2020 IEEE/ACM Fifth International Conference on Internet-of-Things Design and Implementation (IoTDI)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/smds/2020/8777/0/877700a105",
"title": "MY-AIR: A Personalized Air-quality Information Service",
"doi": null,
"abstractUrl": "/proceedings-article/smds/2020/877700a105/1pP3ODIhYYM",
"parentPublication": {
"id": "proceedings/smds/2020/8777/0",
"title": "2020 IEEE International Conference on Smart Data Services (SMDS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"adjacentArticles": {
"previous": {
"fno": "08789482",
"articleId": "1ch5vJhlzH2",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "08755338",
"articleId": "1bmEL5ct73O",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"webExtras": [],
"articleVideos": []
}
|
{
"issue": {
"id": "1vOUcBdw1IA",
"title": "July-Aug.",
"year": "2021",
"issueNum": "04",
"idPrefix": "sc",
"pubType": "journal",
"volume": "14",
"label": "July-Aug.",
"downloadables": {
"hasCover": false,
"__typename": "PeriodicalIssueDownloadablesType"
},
"__typename": "PeriodicalIssue"
},
"article": {
"id": "13rRUwcAqnF",
"doi": "10.1109/TSC.2018.2864147",
"abstract": "Community detection provides invaluable help for various network based services, such as marketing and product recommendation. A specific service usually requires a set of interested communities rather than all communities in the network. In this paper, we address the cases where some exemplar nodes are provided in advance and the set of interested communities is mined for some specific services. Providing sufficient and suitable priori exemplars is not an easy task in most cases. With inadequate priori knowledge, most of recent community detection methods may fail to capture the requirements of a service. We describe the service requirements' essence by a so-called interested attribute subspace with large importance weights on some focus attributes, and study the problem of detecting the set of interested communities based on the guidance of the most limited exemplar information, i.e., two exemplar nodes from any potential interested community. An Interested Subspace and Community Mining (ISCM) method is proposed. In ISCM, a priori knowledge extension technique is designed at first by utilizing the neighborhood of the two exemplar nodes to get more exemplar nodes. Then the interested subspace is inferred from the extension. Finally the set of interested communities are located and mined by the guidance of the interested subspace. Experiments on synthetic datasets demonstrate the effectiveness and efficiency of our method and applications on real-world datasets show its application values for network based services.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Community detection provides invaluable help for various network based services, such as marketing and product recommendation. A specific service usually requires a set of interested communities rather than all communities in the network. In this paper, we address the cases where some exemplar nodes are provided in advance and the set of interested communities is mined for some specific services. Providing sufficient and suitable priori exemplars is not an easy task in most cases. With inadequate priori knowledge, most of recent community detection methods may fail to capture the requirements of a service. We describe the service requirements' essence by a so-called interested attribute subspace with large importance weights on some focus attributes, and study the problem of detecting the set of interested communities based on the guidance of the most limited exemplar information, i.e., two exemplar nodes from any potential interested community. An Interested Subspace and Community Mining (ISCM) method is proposed. In ISCM, a priori knowledge extension technique is designed at first by utilizing the neighborhood of the two exemplar nodes to get more exemplar nodes. Then the interested subspace is inferred from the extension. Finally the set of interested communities are located and mined by the guidance of the interested subspace. Experiments on synthetic datasets demonstrate the effectiveness and efficiency of our method and applications on real-world datasets show its application values for network based services.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Community detection provides invaluable help for various network based services, such as marketing and product recommendation. A specific service usually requires a set of interested communities rather than all communities in the network. In this paper, we address the cases where some exemplar nodes are provided in advance and the set of interested communities is mined for some specific services. Providing sufficient and suitable priori exemplars is not an easy task in most cases. With inadequate priori knowledge, most of recent community detection methods may fail to capture the requirements of a service. We describe the service requirements' essence by a so-called interested attribute subspace with large importance weights on some focus attributes, and study the problem of detecting the set of interested communities based on the guidance of the most limited exemplar information, i.e., two exemplar nodes from any potential interested community. An Interested Subspace and Community Mining (ISCM) method is proposed. In ISCM, a priori knowledge extension technique is designed at first by utilizing the neighborhood of the two exemplar nodes to get more exemplar nodes. Then the interested subspace is inferred from the extension. Finally the set of interested communities are located and mined by the guidance of the interested subspace. Experiments on synthetic datasets demonstrate the effectiveness and efficiency of our method and applications on real-world datasets show its application values for network based services.",
"title": "Mining Set of Interested Communities with Limited Exemplar Nodes for Network Based Services",
"normalizedTitle": "Mining Set of Interested Communities with Limited Exemplar Nodes for Network Based Services",
"fno": "08428472",
"hasPdf": true,
"idPrefix": "sc",
"keywords": [
"Data Mining",
"Pattern Clustering",
"Supervised Learning",
"Unsupervised Learning",
"Web Services",
"Interested Communities",
"Community Detection",
"Service Requirements",
"Interested Attribute Subspace",
"Network Based Services",
"Limited Exemplar Nodes",
"Mining Set",
"Interested Subspace And Community Mining",
"ISCM",
"Knowledge Extension",
"Semisupervised Clustering",
"Google",
"Recommender Systems",
"Task Analysis",
"Social Network Services",
"Collaboration",
"Euclidean Distance",
"Attributed Network",
"Community Detection",
"Subspace Mining",
"Semi Supervised Clustering"
],
"authors": [
{
"givenName": "Peng",
"surname": "Wu",
"fullName": "Peng Wu",
"affiliation": "School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Li",
"surname": "Pan",
"fullName": "Li Pan",
"affiliation": "School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Conghui",
"surname": "Zheng",
"fullName": "Conghui Zheng",
"affiliation": "School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China",
"__typename": "ArticleAuthorType"
}
],
"replicability": null,
"showBuyMe": true,
"showRecommendedArticles": true,
"isOpenAccess": false,
"issueNum": "04",
"pubDate": "2021-07-01 00:00:00",
"pubType": "trans",
"pages": "1138-1151",
"year": "2021",
"issn": "1939-1374",
"isbn": null,
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"recommendedArticles": [
{
"id": "proceedings/icalt/2015/7334/0/7334a405",
"title": "Social Analytics Framework to Boost Recommendation in Online Learning Communities",
"doi": null,
"abstractUrl": "/proceedings-article/icalt/2015/7334a405/12OmNBO3KkN",
"parentPublication": {
"id": "proceedings/icalt/2015/7334/0",
"title": "2015 IEEE 15th International Conference on Advanced Learning Technologies (ICALT)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/hipcw/2016/5773/0/07837046",
"title": "A Hybrid Recommender System Using Weighted Ensemble Similarity Metrics and Digital Filters",
"doi": null,
"abstractUrl": "/proceedings-article/hipcw/2016/07837046/12OmNBTs7tq",
"parentPublication": {
"id": "proceedings/hipcw/2016/5773/0",
"title": "2016 IEEE 23rd International Conference on High-Performance Computing: Workshops (HiPCW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cts/2016/2300/0/07870959",
"title": "Idea Management in Social Networks: A Study of how to Tap into the Ideas of Facebook Communities",
"doi": null,
"abstractUrl": "/proceedings-article/cts/2016/07870959/12OmNBlFQWS",
"parentPublication": {
"id": "proceedings/cts/2016/2300/0",
"title": "2016 International Conference on Collaboration Technologies and Systems (CTS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icdmw/2015/8493/0/8493b001",
"title": "Defending Suspected Users by Exploiting Specific Distance Metric in Collaborative Filtering Recommender Systems",
"doi": null,
"abstractUrl": "/proceedings-article/icdmw/2015/8493b001/12OmNqHqSty",
"parentPublication": {
"id": "proceedings/icdmw/2015/8493/0",
"title": "2015 IEEE International Conference on Data Mining Workshop (ICDMW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/aina/2018/2195/0/219501a520",
"title": "Enhancing Money Saving Tips Recommendation System by Pairwise Preferences",
"doi": null,
"abstractUrl": "/proceedings-article/aina/2018/219501a520/12OmNvjgWKS",
"parentPublication": {
"id": "proceedings/aina/2018/2195/0",
"title": "2018 IEEE 32nd International Conference on Advanced Information Networking and Applications (AINA)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/aiccsa/2017/3581/0/3581a244",
"title": "A Personalized Hybrid Tourism Recommender System",
"doi": null,
"abstractUrl": "/proceedings-article/aiccsa/2017/3581a244/12OmNwLfMzO",
"parentPublication": {
"id": "proceedings/aiccsa/2017/3581/0",
"title": "2017 IEEE/ACS 14th International Conference on Computer Systems and Applications (AICCSA)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/big-data/2016/9005/0/07840789",
"title": "Improving item-based recommendation accuracy with user's preferences on Apache Mahout",
"doi": null,
"abstractUrl": "/proceedings-article/big-data/2016/07840789/12OmNxGSm09",
"parentPublication": {
"id": "proceedings/big-data/2016/9005/0",
"title": "2016 IEEE International Conference on Big Data (Big Data)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/compsacw/2012/4758/0/4758a284",
"title": "Performance Comparison of Combined Collaborative Filtering Algorithms for Recommender Systems",
"doi": null,
"abstractUrl": "/proceedings-article/compsacw/2012/4758a284/12OmNzvhvBd",
"parentPublication": {
"id": "proceedings/compsacw/2012/4758/0",
"title": "2012 IEEE 36th Annual Computer Software and Applications Conference Workshops",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tk/2007/03/k0355",
"title": "Random-Walk Computation of Similarities between Nodes of a Graph with Application to Collaborative Recommendation",
"doi": null,
"abstractUrl": "/journal/tk/2007/03/k0355/13rRUy0qnGD",
"parentPublication": {
"id": "trans/tk",
"title": "IEEE Transactions on Knowledge & Data Engineering",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/vr/2019/1377/0/08797841",
"title": "Virtual Reality for Deeper Learning: An Exemplar from High School Science",
"doi": null,
"abstractUrl": "/proceedings-article/vr/2019/08797841/1cJ13OMUD8Q",
"parentPublication": {
"id": "proceedings/vr/2019/1377/0",
"title": "2019 IEEE Conference on Virtual Reality and 3D User Interfaces (VR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"adjacentArticles": {
"previous": {
"fno": "08423192",
"articleId": "1vOUdZsJA1W",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "08428431",
"articleId": "13rRUIJcWue",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"webExtras": [],
"articleVideos": []
}
|
{
"issue": {
"id": "1CdAOlsos6Y",
"title": "May",
"year": "2022",
"issueNum": "05",
"idPrefix": "tp",
"pubType": "journal",
"volume": "44",
"label": "May",
"downloadables": {
"hasCover": false,
"__typename": "PeriodicalIssueDownloadablesType"
},
"__typename": "PeriodicalIssue"
},
"article": {
"id": "1osldrpucwg",
"doi": "10.1109/TPAMI.2020.3035599",
"abstract": "Finding a small set of representatives from an unlabeled dataset is a core problem in a broad range of applications such as dataset summarization and information extraction. Classical exemplar selection methods such as <inline-formula><tex-math notation=\"LaTeX\">Z_$k$_Z</tex-math></inline-formula>-medoids work under the assumption that the data points are close to a few cluster centroids, and cannot handle the case where data lie close to a union of subspaces. This paper proposes a new exemplar selection model that searches for a subset that best reconstructs all data points as measured by the <inline-formula><tex-math notation=\"LaTeX\">Z_$\\ell _1$_Z</tex-math></inline-formula> norm of the representation coefficients. Geometrically, this subset best covers all the data points as measured by the Minkowski functional of the subset. To solve our model efficiently, we introduce a farthest first search algorithm that iteratively selects the worst represented point as an exemplar. When the dataset is drawn from a union of independent subspaces, our method is able to select sufficiently many representatives from each subspace. We further develop an exemplar based subspace clustering method that is robust to imbalanced data and efficient for large scale data. Moreover, we show that a classifier trained on the selected exemplars (when they are labeled) can correctly classify the rest of the data points.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Finding a small set of representatives from an unlabeled dataset is a core problem in a broad range of applications such as dataset summarization and information extraction. Classical exemplar selection methods such as <inline-formula><tex-math notation=\"LaTeX\">$k$</tex-math><alternatives><mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href=\"you-ieq1-3035599.gif\"/></alternatives></inline-formula>-medoids work under the assumption that the data points are close to a few cluster centroids, and cannot handle the case where data lie close to a union of subspaces. This paper proposes a new exemplar selection model that searches for a subset that best reconstructs all data points as measured by the <inline-formula><tex-math notation=\"LaTeX\">$\\ell _1$</tex-math><alternatives><mml:math><mml:msub><mml:mi>ℓ</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:math><inline-graphic xlink:href=\"you-ieq2-3035599.gif\"/></alternatives></inline-formula> norm of the representation coefficients. Geometrically, this subset best covers all the data points as measured by the Minkowski functional of the subset. To solve our model efficiently, we introduce a farthest first search algorithm that iteratively selects the worst represented point as an exemplar. When the dataset is drawn from a union of independent subspaces, our method is able to select sufficiently many representatives from each subspace. We further develop an exemplar based subspace clustering method that is robust to imbalanced data and efficient for large scale data. Moreover, we show that a classifier trained on the selected exemplars (when they are labeled) can correctly classify the rest of the data points.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Finding a small set of representatives from an unlabeled dataset is a core problem in a broad range of applications such as dataset summarization and information extraction. Classical exemplar selection methods such as --medoids work under the assumption that the data points are close to a few cluster centroids, and cannot handle the case where data lie close to a union of subspaces. This paper proposes a new exemplar selection model that searches for a subset that best reconstructs all data points as measured by the - norm of the representation coefficients. Geometrically, this subset best covers all the data points as measured by the Minkowski functional of the subset. To solve our model efficiently, we introduce a farthest first search algorithm that iteratively selects the worst represented point as an exemplar. When the dataset is drawn from a union of independent subspaces, our method is able to select sufficiently many representatives from each subspace. We further develop an exemplar based subspace clustering method that is robust to imbalanced data and efficient for large scale data. Moreover, we show that a classifier trained on the selected exemplars (when they are labeled) can correctly classify the rest of the data points.",
"title": "Self-Representation Based Unsupervised Exemplar Selection in a Union of Subspaces",
"normalizedTitle": "Self-Representation Based Unsupervised Exemplar Selection in a Union of Subspaces",
"fno": "09247442",
"hasPdf": true,
"idPrefix": "tp",
"keywords": [
"Clustering Algorithms",
"Data Models",
"Databases",
"Clustering Methods",
"Computer Vision",
"Optimization",
"Image Reconstruction",
"Unsupervised Exemplar Selection",
"Imbalanced Data",
"Large Scale Data",
"Subspace Clustering"
],
"authors": [
{
"givenName": "Chong",
"surname": "You",
"fullName": "Chong You",
"affiliation": "Department of Electrical Engineering and Computer Science, University of California, Berkeley, CA, USA",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Chi",
"surname": "Li",
"fullName": "Chi Li",
"affiliation": "Apple Inc, Cupertino, CA, USA",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Daniel P.",
"surname": "Robinson",
"fullName": "Daniel P. Robinson",
"affiliation": "Department of Industrial and Systems Engineering, Lehigh University, Bethlehem, PA, USA",
"__typename": "ArticleAuthorType"
},
{
"givenName": "René",
"surname": "Vidal",
"fullName": "René Vidal",
"affiliation": "Department of Biomedical Engineering, The Johns Hopkins University, Baltimore, MD, USA",
"__typename": "ArticleAuthorType"
}
],
"replicability": null,
"showBuyMe": true,
"showRecommendedArticles": true,
"isOpenAccess": false,
"issueNum": "05",
"pubDate": "2022-05-01 00:00:00",
"pubType": "trans",
"pages": "2698-2711",
"year": "2022",
"issn": "0162-8828",
"isbn": null,
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"recommendedArticles": [
{
"id": "trans/tk/2023/05/09712197",
"title": "Fast LDP-MST: An Efficient Density-Peak-Based Clustering Method for Large-Size Datasets",
"doi": null,
"abstractUrl": "/journal/tk/2023/05/09712197/1AUkecqbRok",
"parentPublication": {
"id": "trans/tk",
"title": "IEEE Transactions on Knowledge & Data Engineering",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tp/2023/01/09712412",
"title": "Centerless Clustering",
"doi": null,
"abstractUrl": "/journal/tp/2023/01/09712412/1AZKZVM8b84",
"parentPublication": {
"id": "trans/tp",
"title": "IEEE Transactions on Pattern Analysis & Machine Intelligence",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tk/2023/05/09720081",
"title": "Accelerating Graph Similarity Search via Efficient GED Computation",
"doi": null,
"abstractUrl": "/journal/tk/2023/05/09720081/1Bef4rodeLK",
"parentPublication": {
"id": "trans/tk",
"title": "IEEE Transactions on Knowledge & Data Engineering",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/bd/2023/01/09784919",
"title": "Rethinking Embedded Unsupervised Feature Selection: A Simple Joint Approach",
"doi": null,
"abstractUrl": "/journal/bd/2023/01/09784919/1DQLFKdcpSo",
"parentPublication": {
"id": "trans/bd",
"title": "IEEE Transactions on Big Data",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tk/2021/12/09007415",
"title": "Flexible Aggregate Nearest Neighbor Queries and its Keyword-Aware Variant on Road Networks",
"doi": null,
"abstractUrl": "/journal/tk/2021/12/09007415/1hJKhc0u1So",
"parentPublication": {
"id": "trans/tk",
"title": "IEEE Transactions on Knowledge & Data Engineering",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tk/2022/04/09119142",
"title": "An Efficient Split-Merge Re-Start for the <inline-formula><tex-math notation=\"LaTeX\">Z_$K$_Z</tex-math></inline-formula>-Means Algorithm",
"doi": null,
"abstractUrl": "/journal/tk/2022/04/09119142/1kHUCLcBjDa",
"parentPublication": {
"id": "trans/tk",
"title": "IEEE Transactions on Knowledge & Data Engineering",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tq/2022/02/09187550",
"title": "Towards More Secure Constructions of Adjustable Join Schemes",
"doi": null,
"abstractUrl": "/journal/tq/2022/02/09187550/1mVFLWkSEQU",
"parentPublication": {
"id": "trans/tq",
"title": "IEEE Transactions on Dependable and Secure Computing",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tk/2022/08/09244575",
"title": "Distributed Density Peaks Clustering Revisited",
"doi": null,
"abstractUrl": "/journal/tk/2022/08/09244575/1ojYk1yEY1i",
"parentPublication": {
"id": "trans/tk",
"title": "IEEE Transactions on Knowledge & Data Engineering",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tp/2022/06/09309172",
"title": "Detecting Meaningful Clusters From High-Dimensional Data: A Strongly Consistent Sparse Center-Based Clustering Approach",
"doi": null,
"abstractUrl": "/journal/tp/2022/06/09309172/1pQEdzozLwY",
"parentPublication": {
"id": "trans/tp",
"title": "IEEE Transactions on Pattern Analysis & Machine Intelligence",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tp/2022/08/09369083",
"title": "Robust Low-Tubal-Rank Tensor Recovery From Binary Measurements",
"doi": null,
"abstractUrl": "/journal/tp/2022/08/09369083/1rFvS23KDAI",
"parentPublication": {
"id": "trans/tp",
"title": "IEEE Transactions on Pattern Analysis & Machine Intelligence",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"adjacentArticles": {
"previous": {
"fno": "09262085",
"articleId": "1oPzObSyqje",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "09259236",
"articleId": "1oIWmSvWXG8",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"webExtras": [],
"articleVideos": []
}
|
{
"issue": {
"id": "12OmNyGtjf0",
"title": "Nov.-Dec.",
"year": "2016",
"issueNum": "06",
"idPrefix": "ex",
"pubType": "magazine",
"volume": "31",
"label": "Nov.-Dec.",
"downloadables": {
"hasCover": true,
"__typename": "PeriodicalIssueDownloadablesType"
},
"__typename": "PeriodicalIssue"
},
"article": {
"id": "13rRUwgyOcc",
"doi": "10.1109/MIS.2016.94",
"abstract": "People share their opinions, stories, and reviews through online video sharing websites every day. The automatic analysis of these online opinion videos is bringing new or understudied research challenges to the field of computational linguistics and multimodal analysis. Among these challenges is the fundamental question of exploiting the dynamics between visual gestures and verbal messages to be able to better model sentiment. This article addresses this question in four ways: introducing the first multimodal dataset with opinion-level sentiment intensity annotations; studying the prototypical interaction patterns between facial gestures and spoken words when inferring sentiment intensity; proposing a new computational representation, called multimodal dictionary, based on a language-gesture study; and evaluating the authors' proposed approach in a speaker-independent paradigm for sentiment intensity prediction. The authors' study identifies four interaction types between facial gestures and verbal content: neutral, emphasizer, positive, and negative interactions. Experiments show statistically significant improvement when using multimodal dictionary representation over the conventional early fusion representation (that is, feature concatenation).",
"abstracts": [
{
"abstractType": "Regular",
"content": "People share their opinions, stories, and reviews through online video sharing websites every day. The automatic analysis of these online opinion videos is bringing new or understudied research challenges to the field of computational linguistics and multimodal analysis. Among these challenges is the fundamental question of exploiting the dynamics between visual gestures and verbal messages to be able to better model sentiment. This article addresses this question in four ways: introducing the first multimodal dataset with opinion-level sentiment intensity annotations; studying the prototypical interaction patterns between facial gestures and spoken words when inferring sentiment intensity; proposing a new computational representation, called multimodal dictionary, based on a language-gesture study; and evaluating the authors' proposed approach in a speaker-independent paradigm for sentiment intensity prediction. The authors' study identifies four interaction types between facial gestures and verbal content: neutral, emphasizer, positive, and negative interactions. Experiments show statistically significant improvement when using multimodal dictionary representation over the conventional early fusion representation (that is, feature concatenation).",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "People share their opinions, stories, and reviews through online video sharing websites every day. The automatic analysis of these online opinion videos is bringing new or understudied research challenges to the field of computational linguistics and multimodal analysis. Among these challenges is the fundamental question of exploiting the dynamics between visual gestures and verbal messages to be able to better model sentiment. This article addresses this question in four ways: introducing the first multimodal dataset with opinion-level sentiment intensity annotations; studying the prototypical interaction patterns between facial gestures and spoken words when inferring sentiment intensity; proposing a new computational representation, called multimodal dictionary, based on a language-gesture study; and evaluating the authors' proposed approach in a speaker-independent paradigm for sentiment intensity prediction. The authors' study identifies four interaction types between facial gestures and verbal content: neutral, emphasizer, positive, and negative interactions. Experiments show statistically significant improvement when using multimodal dictionary representation over the conventional early fusion representation (that is, feature concatenation).",
"title": "Multimodal Sentiment Intensity Analysis in Videos: Facial Gestures and Verbal Messages",
"normalizedTitle": "Multimodal Sentiment Intensity Analysis in Videos: Facial Gestures and Verbal Messages",
"fno": "mex2016060082",
"hasPdf": true,
"idPrefix": "ex",
"keywords": [
"Videos",
"Sentiment Analysis",
"Visualization",
"Motion Pictures",
"You Tube",
"Information Exchange",
"Feature Extraction",
"Intelligent Systems",
"Sentiment Analysis",
"Computational Linguistics",
"Natural Language Processing",
"Affective Computing",
"Machine Learning"
],
"authors": [
{
"givenName": "Amir",
"surname": "Zadeh",
"fullName": "Amir Zadeh",
"affiliation": "Carnegie Mellon University",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Rowan",
"surname": "Zellers",
"fullName": "Rowan Zellers",
"affiliation": "University of Washington",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Eli",
"surname": "Pincus",
"fullName": "Eli Pincus",
"affiliation": "University of Southern California",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Louis-Philippe",
"surname": "Morency",
"fullName": "Louis-Philippe Morency",
"affiliation": "Carnegie Mellon University",
"__typename": "ArticleAuthorType"
}
],
"replicability": null,
"showBuyMe": true,
"showRecommendedArticles": true,
"isOpenAccess": false,
"issueNum": "06",
"pubDate": "2016-11-01 00:00:00",
"pubType": "mags",
"pages": "82-88",
"year": "2016",
"issn": "1541-1672",
"isbn": null,
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"recommendedArticles": [
{
"id": "proceedings/ialp/2010/4288/0/4288a203",
"title": "Chinese Sentence-Level Sentiment Classification Based on Sentiment Morphemes",
"doi": null,
"abstractUrl": "/proceedings-article/ialp/2010/4288a203/12OmNx8fics",
"parentPublication": {
"id": "proceedings/ialp/2010/4288/0",
"title": "Asian Language Processing, International Conference on",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/intetain/2015/0061/0/07325507",
"title": "360-MAM-Affect: Sentiment analysis with the Google prediction API and EmoSenticNet",
"doi": null,
"abstractUrl": "/proceedings-article/intetain/2015/07325507/12OmNxcdFVs",
"parentPublication": {
"id": "proceedings/intetain/2015/0061/0",
"title": "2015 7th International Conference on Intelligent Technologies for Interactive Entertainment (INTETAIN)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icdm/2017/3835/0/3835b033",
"title": "Multi-level Multiple Attentions for Contextual Multimodal Sentiment Analysis",
"doi": null,
"abstractUrl": "/proceedings-article/icdm/2017/3835b033/12OmNxjjElj",
"parentPublication": {
"id": "proceedings/icdm/2017/3835/0",
"title": "2017 IEEE International Conference on Data Mining (ICDM)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/aciiw/2017/0680/0/08272618",
"title": "Sentiment analysis using image-based deep spectrum features",
"doi": null,
"abstractUrl": "/proceedings-article/aciiw/2017/08272618/12OmNy9Prlh",
"parentPublication": {
"id": "proceedings/aciiw/2017/0680/0",
"title": "2017 Seventh International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icme/2017/6067/0/08019301",
"title": "Select-additive learning: Improving generalization in multimodal sentiment analysis",
"doi": null,
"abstractUrl": "/proceedings-article/icme/2017/08019301/12OmNyuPKYX",
"parentPublication": {
"id": "proceedings/icme/2017/6067/0",
"title": "2017 IEEE International Conference on Multimedia and Expo (ICME)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/ta/2019/04/08039497",
"title": "A Methodology for the Automatic Extraction and Generation of Non-Verbal Signals Sequences Conveying Interpersonal Attitudes",
"doi": null,
"abstractUrl": "/journal/ta/2019/04/08039497/13rRUB7a0Zy",
"parentPublication": {
"id": "trans/ta",
"title": "IEEE Transactions on Affective Computing",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icme/2022/8563/0/09860014",
"title": "Utilizing BERT Intermediate Layers for Multimodal Sentiment Analysis",
"doi": null,
"abstractUrl": "/proceedings-article/icme/2022/09860014/1G9EKqcyxmU",
"parentPublication": {
"id": "proceedings/icme/2022/8563/0",
"title": "2022 IEEE International Conference on Multimedia and Expo (ICME)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/ta/5555/01/10003979",
"title": "TensorFormer: A Tensor-based Multimodal Transformer for Multimodal Sentiment Analysis and Depression Detection",
"doi": null,
"abstractUrl": "/journal/ta/5555/01/10003979/1JwLhn5bvnq",
"parentPublication": {
"id": "trans/ta",
"title": "IEEE Transactions on Affective Computing",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icmcce/2020/2314/0/231400c444",
"title": "Importance Evaluation of Movie Aspects: Aspect-Based Sentiment Analysis",
"doi": null,
"abstractUrl": "/proceedings-article/icmcce/2020/231400c444/1tzyGVUcMjC",
"parentPublication": {
"id": "proceedings/icmcce/2020/2314/0",
"title": "2020 5th International Conference on Mechanical, Control and Computer Engineering (ICMCCE)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/2022/01/09552921",
"title": "M2Lens: Visualizing and Explaining Multimodal Models for Sentiment Analysis",
"doi": null,
"abstractUrl": "/journal/tg/2022/01/09552921/1xic8w3ygrm",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"adjacentArticles": {
"previous": {
"fno": "mex2016060076",
"articleId": "13rRUxlgxVq",
"__typename": "AdjacentArticleType"
},
"next": null,
"__typename": "AdjacentArticlesType"
},
"webExtras": [],
"articleVideos": []
}
|
{
"issue": {
"id": "1DU9C1cnFPq",
"title": "July",
"year": "2022",
"issueNum": "07",
"idPrefix": "tp",
"pubType": "journal",
"volume": "44",
"label": "July",
"downloadables": {
"hasCover": false,
"__typename": "PeriodicalIssueDownloadablesType"
},
"__typename": "PeriodicalIssue"
},
"article": {
"id": "1rigXK0s5Ak",
"doi": "10.1109/TPAMI.2021.3059968",
"abstract": "Image segmentation is a key task in computer vision and image processing with important applications such as scene understanding, medical image analysis, robotic perception, video surveillance, augmented reality, and image compression, among others, and numerous segmentation algorithms are found in the literature. Against this backdrop, the broad success of deep learning (DL) has prompted the development of new image segmentation approaches leveraging DL models. We provide a comprehensive review of this recent literature, covering the spectrum of pioneering efforts in semantic and instance segmentation, including convolutional pixel-labeling networks, encoder-decoder architectures, multiscale and pyramid-based approaches, recurrent networks, visual attention models, and generative models in adversarial settings. We investigate the relationships, strengths, and challenges of these DL-based segmentation models, examine the widely used datasets, compare performances, and discuss promising research directions.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Image segmentation is a key task in computer vision and image processing with important applications such as scene understanding, medical image analysis, robotic perception, video surveillance, augmented reality, and image compression, among others, and numerous segmentation algorithms are found in the literature. Against this backdrop, the broad success of deep learning (DL) has prompted the development of new image segmentation approaches leveraging DL models. We provide a comprehensive review of this recent literature, covering the spectrum of pioneering efforts in semantic and instance segmentation, including convolutional pixel-labeling networks, encoder-decoder architectures, multiscale and pyramid-based approaches, recurrent networks, visual attention models, and generative models in adversarial settings. We investigate the relationships, strengths, and challenges of these DL-based segmentation models, examine the widely used datasets, compare performances, and discuss promising research directions.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Image segmentation is a key task in computer vision and image processing with important applications such as scene understanding, medical image analysis, robotic perception, video surveillance, augmented reality, and image compression, among others, and numerous segmentation algorithms are found in the literature. Against this backdrop, the broad success of deep learning (DL) has prompted the development of new image segmentation approaches leveraging DL models. We provide a comprehensive review of this recent literature, covering the spectrum of pioneering efforts in semantic and instance segmentation, including convolutional pixel-labeling networks, encoder-decoder architectures, multiscale and pyramid-based approaches, recurrent networks, visual attention models, and generative models in adversarial settings. We investigate the relationships, strengths, and challenges of these DL-based segmentation models, examine the widely used datasets, compare performances, and discuss promising research directions.",
"title": "Image Segmentation Using Deep Learning: A Survey",
"normalizedTitle": "Image Segmentation Using Deep Learning: A Survey",
"fno": "09356353",
"hasPdf": true,
"idPrefix": "tp",
"keywords": [
"Augmented Reality",
"Computer Vision",
"Data Compression",
"Image Coding",
"Image Processing",
"Image Segmentation",
"Learning Artificial Intelligence",
"Medical Image Processing",
"Video Surveillance",
"Image Processing",
"Medical Image Analysis",
"Image Compression",
"Numerous Segmentation Algorithms",
"Deep Learning",
"Image Segmentation",
"Semantic Instance Segmentation",
"DL Based Segmentation Models",
"Image Segmentation",
"Computer Architecture",
"Semantics",
"Deep Learning",
"Computational Modeling",
"Generative Adversarial Networks",
"Logic Gates",
"Image Segmentation",
"Deep Learning",
"Convolutional Neural Networks",
"Encoder Decoder Models",
"Recurrent Models",
"Generative Models",
"Semantic Segmentation",
"Instance Segmentation",
"Panoptic Segmentation",
"Medical Image Segmentation"
],
"authors": [
{
"givenName": "Shervin",
"surname": "Minaee",
"fullName": "Shervin Minaee",
"affiliation": "Snapchat Machine Learning Research, Venice, CA, USA",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Yuri",
"surname": "Boykov",
"fullName": "Yuri Boykov",
"affiliation": "University of Waterloo, Waterloo, ON, Canada",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Fatih",
"surname": "Porikli",
"fullName": "Fatih Porikli",
"affiliation": "Australian National University, Canberra, ACT, Australia",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Antonio",
"surname": "Plaza",
"fullName": "Antonio Plaza",
"affiliation": "University of Extremadura, Badajoz, Spain",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Nasser",
"surname": "Kehtarnavaz",
"fullName": "Nasser Kehtarnavaz",
"affiliation": "University of Texas at Dallas, Richardson, TX, USA",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Demetri",
"surname": "Terzopoulos",
"fullName": "Demetri Terzopoulos",
"affiliation": "University of California, Los Angeles, Los Angeles, CA, USA",
"__typename": "ArticleAuthorType"
}
],
"replicability": null,
"showBuyMe": true,
"showRecommendedArticles": true,
"isOpenAccess": false,
"issueNum": "07",
"pubDate": "2022-07-01 00:00:00",
"pubType": "trans",
"pages": "3523-3542",
"year": "2022",
"issn": "0162-8828",
"isbn": null,
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"recommendedArticles": [
{
"id": "proceedings/conf-spml/2021/1734/0/173400a278",
"title": "An Overview of Deep Learning Based Small Sample Medical Imaging Classification",
"doi": null,
"abstractUrl": "/proceedings-article/conf-spml/2021/173400a278/1B12gB3wdy0",
"parentPublication": {
"id": "proceedings/conf-spml/2021/1734/0",
"title": "2021 International Conference on Signal Processing and Machine Learning (CONF-SPML)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/wacv/2022/0915/0/091500a366",
"title": "Learning Foreground-Background Segmentation from Improved Layered GANs",
"doi": null,
"abstractUrl": "/proceedings-article/wacv/2022/091500a366/1B13wNAGYQE",
"parentPublication": {
"id": "proceedings/wacv/2022/0915/0",
"title": "2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iceitsa/2021/1300/0/130000a431",
"title": "Supervised Few-Shot Image Segmentation with Deep Metric Learning",
"doi": null,
"abstractUrl": "/proceedings-article/iceitsa/2021/130000a431/1B2HrDJXg5O",
"parentPublication": {
"id": "proceedings/iceitsa/2021/1300/0",
"title": "2021 International Conference on Electronic Information Technology and Smart Agriculture (ICEITSA)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccv/2021/2812/0/281200h304",
"title": "Unsupervised Segmentation incorporating Shape Prior via Generative Adversarial Networks",
"doi": null,
"abstractUrl": "/proceedings-article/iccv/2021/281200h304/1BmLktxXSW4",
"parentPublication": {
"id": "proceedings/iccv/2021/2812/0",
"title": "2021 IEEE/CVF International Conference on Computer Vision (ICCV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icipnp/2022/6405/0/640500a077",
"title": "A Study of Remote Sensing Image Ground Segmentation on Deep Learning",
"doi": null,
"abstractUrl": "/proceedings-article/icipnp/2022/640500a077/1LFLjVrK7Ly",
"parentPublication": {
"id": "proceedings/icipnp/2022/6405/0",
"title": "2022 International Conference on Information Processing and Network Provisioning (ICIPNP)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/isaiam/2022/8541/0/854100a135",
"title": "Multi-Attentional U-Net for Medical Image Segmentation",
"doi": null,
"abstractUrl": "/proceedings-article/isaiam/2022/854100a135/1MTTb3Muc7K",
"parentPublication": {
"id": "proceedings/isaiam/2022/8541/0",
"title": "2022 2nd International Symposium on Artificial Intelligence and its Application on Media (ISAIAM)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvprw/2019/2506/0/250600b065",
"title": "Cell Image Segmentation by Integrating Pix2pixs for Each Class",
"doi": null,
"abstractUrl": "/proceedings-article/cvprw/2019/250600b065/1iTvfMQjLTa",
"parentPublication": {
"id": "proceedings/cvprw/2019/2506/0",
"title": "2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/aiccsa/2020/8577/0/09316501",
"title": "3D Brain Image Segmentation Model using Deep Learning and Hidden Markov Random Fields",
"doi": null,
"abstractUrl": "/proceedings-article/aiccsa/2020/09316501/1qmfA6LCVUc",
"parentPublication": {
"id": "proceedings/aiccsa/2020/8577/0",
"title": "2020 IEEE/ACS 17th International Conference on Computer Systems and Applications (AICCSA)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bibe/2021/4261/0/09635576",
"title": "A Deep Learning-based cropping technique to improve segmentation of prostate's peripheral zone",
"doi": null,
"abstractUrl": "/proceedings-article/bibe/2021/09635576/1zmvrRIrmFO",
"parentPublication": {
"id": "proceedings/bibe/2021/4261/0",
"title": "2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ispa-bdcloud-socialcom-sustaincom/2021/3574/0/357400a522",
"title": "Synthesis and Segmentation Method of Cross-Staining Style Nuclei Pathology Image Based on Adversarial Learning",
"doi": null,
"abstractUrl": "/proceedings-article/ispa-bdcloud-socialcom-sustaincom/2021/357400a522/1zxL0vbtBoQ",
"parentPublication": {
"id": "proceedings/ispa-bdcloud-socialcom-sustaincom/2021/3574/0",
"title": "2021 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"adjacentArticles": {
"previous": {
"fno": "09340561",
"articleId": "1qMJNVemn7y",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "09350222",
"articleId": "1r3l4YANZyo",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"webExtras": [],
"articleVideos": []
}
|
{
"issue": {
"id": "12OmNwCsdFw",
"title": "PrePrints",
"year": "5555",
"issueNum": "01",
"idPrefix": "tk",
"pubType": "journal",
"volume": null,
"label": "PrePrints",
"downloadables": {
"hasCover": false,
"__typename": "PeriodicalIssueDownloadablesType"
},
"__typename": "PeriodicalIssue"
},
"article": {
"id": "1DU9toirrLG",
"doi": "10.1109/TKDE.2022.3179608",
"abstract": "It is challenging to detect 3D objects from noise point clouds by Graph Neural Networks (GNNs), though graph-based methods have shown promising results in 3D classifications. Since strong robustness against noise is offered by hypergraph, a relative paradigm named HyperGraph Construction-Compression-Conversion (HG3C) is proposed for detecting 3D objects from noise point clouds. Our method presents the capacity of reducing graph redundancy and capturing the variances from multiple features, by pre-encoding the graph, to improve the graph representations in point clouds. A fused graph neural network is further designed to predict the shape and category of the target in converted graphs. The experiments, on both the KITTI and Nuscene, show that the proposed approach achieves leading accuracy. Our results demonstrate the potential of using the hypergraph transformation to extract and compress point cloud information from noisy point clouds.",
"abstracts": [
{
"abstractType": "Regular",
"content": "It is challenging to detect 3D objects from noise point clouds by Graph Neural Networks (GNNs), though graph-based methods have shown promising results in 3D classifications. Since strong robustness against noise is offered by hypergraph, a relative paradigm named HyperGraph Construction-Compression-Conversion (HG3C) is proposed for detecting 3D objects from noise point clouds. Our method presents the capacity of reducing graph redundancy and capturing the variances from multiple features, by pre-encoding the graph, to improve the graph representations in point clouds. A fused graph neural network is further designed to predict the shape and category of the target in converted graphs. The experiments, on both the KITTI and Nuscene, show that the proposed approach achieves leading accuracy. Our results demonstrate the potential of using the hypergraph transformation to extract and compress point cloud information from noisy point clouds.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "It is challenging to detect 3D objects from noise point clouds by Graph Neural Networks (GNNs), though graph-based methods have shown promising results in 3D classifications. Since strong robustness against noise is offered by hypergraph, a relative paradigm named HyperGraph Construction-Compression-Conversion (HG3C) is proposed for detecting 3D objects from noise point clouds. Our method presents the capacity of reducing graph redundancy and capturing the variances from multiple features, by pre-encoding the graph, to improve the graph representations in point clouds. A fused graph neural network is further designed to predict the shape and category of the target in converted graphs. The experiments, on both the KITTI and Nuscene, show that the proposed approach achieves leading accuracy. Our results demonstrate the potential of using the hypergraph transformation to extract and compress point cloud information from noisy point clouds.",
"title": "Hypergraph Representation for Detecting 3D Objects from Noisy Point Clouds",
"normalizedTitle": "Hypergraph Representation for Detecting 3D Objects from Noisy Point Clouds",
"fno": "09788570",
"hasPdf": true,
"idPrefix": "tk",
"keywords": [
"Point Cloud Compression",
"Three Dimensional Displays",
"Noise Measurement",
"Task Analysis",
"Graph Neural Networks",
"Feature Extraction",
"Convolution",
"Noisy Point Clouds",
"3 D Detection",
"Hypergraph",
"Graph Neural Network"
],
"authors": [
{
"givenName": "Ping",
"surname": "Jiang",
"fullName": "Ping Jiang",
"affiliation": "School of Computer Science and Engineering, Central South University, Changsha, Hunan, China",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Xiaoheng",
"surname": "Deng",
"fullName": "Xiaoheng Deng",
"affiliation": "Shenzhen Research Institute, Central South University, Shenzhen, China",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Leilei",
"surname": "Wang",
"fullName": "Leilei Wang",
"affiliation": "School of Computer Science and Engineering, Central South University, Changsha, Hunan, China",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Zailiang",
"surname": "Chen",
"fullName": "Zailiang Chen",
"affiliation": "School of Computer Science and Engineering, Central South University, Changsha, Hunan, China",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Shichao",
"surname": "Zhang",
"fullName": "Shichao Zhang",
"affiliation": "School of Computer Science and Engineering, Central South University, Changsha, Hunan, China",
"__typename": "ArticleAuthorType"
}
],
"replicability": null,
"showBuyMe": true,
"showRecommendedArticles": true,
"isOpenAccess": false,
"issueNum": "01",
"pubDate": "2022-06-01 00:00:00",
"pubType": "trans",
"pages": "1-1",
"year": "5555",
"issn": "1041-4347",
"isbn": null,
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"recommendedArticles": [
{
"id": "proceedings/crv/2015/1986/0/1986a031",
"title": "Registration of Noisy Point Clouds Using Virtual Interest Points",
"doi": null,
"abstractUrl": "/proceedings-article/crv/2015/1986a031/12OmNApLGDA",
"parentPublication": {
"id": "proceedings/crv/2015/1986/0",
"title": "2015 12th Conference on Computer and Robot Vision (CRV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tp/2023/01/09693131",
"title": "Refine-Net: Normal Refinement Neural Network for Noisy Point Clouds",
"doi": null,
"abstractUrl": "/journal/tp/2023/01/09693131/1As6TjLcxmU",
"parentPublication": {
"id": "trans/tp",
"title": "IEEE Transactions on Pattern Analysis & Machine Intelligence",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccv/2021/2812/0/281200c773",
"title": "PR-GCN: A Deep Graph Convolutional Network with Point Refinement for 6D Pose Estimation",
"doi": null,
"abstractUrl": "/proceedings-article/iccv/2021/281200c773/1BmF4ZaLuz6",
"parentPublication": {
"id": "proceedings/iccv/2021/2812/0",
"title": "2021 IEEE/CVF International Conference on Computer Vision (ICCV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/5555/01/09756929",
"title": "Perceptual Quality Assessment of Colored 3D Point Clouds",
"doi": null,
"abstractUrl": "/journal/tg/5555/01/09756929/1Cxva6pb2iA",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tp/2023/06/09966842",
"title": "Robust Point Cloud Segmentation With Noisy Annotations",
"doi": null,
"abstractUrl": "/journal/tp/2023/06/09966842/1IIYhr3TOZq",
"parentPublication": {
"id": "trans/tp",
"title": "IEEE Transactions on Pattern Analysis & Machine Intelligence",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/5555/01/09998112",
"title": "From Noise Addition to Denoising: A Self-Variation Capture Network for Point Cloud Optimization",
"doi": null,
"abstractUrl": "/journal/tg/5555/01/09998112/1JlF32mS2SQ",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tp/5555/01/10024001",
"title": "AGConv: Adaptive Graph Convolution on 3D Point Clouds",
"doi": null,
"abstractUrl": "/journal/tp/5555/01/10024001/1K9spf0w0Ug",
"parentPublication": {
"id": "trans/tp",
"title": "IEEE Transactions on Pattern Analysis & Machine Intelligence",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/big-data/2022/8045/0/10020643",
"title": "SHCN: Self-supervised General Hypergraph Clustering Network",
"doi": null,
"abstractUrl": "/proceedings-article/big-data/2022/10020643/1KfRc1vo65y",
"parentPublication": {
"id": "proceedings/big-data/2022/8045/0",
"title": "2022 IEEE International Conference on Big Data (Big Data)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tk/2022/06/09169850",
"title": "Hypergraph Partitioning With Embeddings",
"doi": null,
"abstractUrl": "/journal/tk/2022/06/09169850/1mmOxMdvt1S",
"parentPublication": {
"id": "trans/tk",
"title": "IEEE Transactions on Knowledge & Data Engineering",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ictai/2021/0898/0/089800b480",
"title": "Geometric Invariant Representation Learning for 3D Point Cloud",
"doi": null,
"abstractUrl": "/proceedings-article/ictai/2021/089800b480/1zw6hJOIDcs",
"parentPublication": {
"id": "proceedings/ictai/2021/0898/0",
"title": "2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"adjacentArticles": {
"previous": {
"fno": "09787796",
"articleId": "1DU9te3baFi",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "09792226",
"articleId": "1E5LzdHFnna",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"webExtras": [],
"articleVideos": []
}
|
{
"issue": {
"id": "12OmNvDqsVW",
"title": "April",
"year": "2018",
"issueNum": "04",
"idPrefix": "tp",
"pubType": "journal",
"volume": "40",
"label": "April",
"downloadables": {
"hasCover": false,
"__typename": "PeriodicalIssueDownloadablesType"
},
"__typename": "PeriodicalIssue"
},
"article": {
"id": "13rRUxAAT8X",
"doi": "10.1109/TPAMI.2017.2701380",
"abstract": "Super resolving a low-resolution video, namely video super-resolution (SR), is usually handled by either single-image SR or multi-frame SR. Single-Image SR deals with each video frame independently, and ignores intrinsic temporal dependency of video frames which actually plays a very important role in video SR. Multi-Frame SR generally extracts motion information, e.g., optical flow, to model the temporal dependency, but often shows high computational cost. Considering that recurrent neural networks (RNNs) can model long-term temporal dependency of video sequences well, we propose a fully convolutional RNN named bidirectional recurrent convolutional network for efficient multi-frame SR. Different from vanilla RNNs, 1) the commonly-used full feedforward and recurrent connections are replaced with weight-sharing convolutional connections. So they can greatly reduce the large number of network parameters and well model the temporal dependency in a finer level, i.e., patch-based rather than frame-based, and 2) connections from input layers at previous timesteps to the current hidden layer are added by 3D feedforward convolutions, which aim to capture discriminate spatio-temporal patterns for short-term fast-varying motions in local adjacent frames. Due to the cheap convolutional operations, our model has a low computational complexity and runs orders of magnitude faster than other multi-frame SR methods. With the powerful temporal dependency modeling, our model can super resolve videos with complex motions and achieve well performance.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Super resolving a low-resolution video, namely video super-resolution (SR), is usually handled by either single-image SR or multi-frame SR. Single-Image SR deals with each video frame independently, and ignores intrinsic temporal dependency of video frames which actually plays a very important role in video SR. Multi-Frame SR generally extracts motion information, e.g., optical flow, to model the temporal dependency, but often shows high computational cost. Considering that recurrent neural networks (RNNs) can model long-term temporal dependency of video sequences well, we propose a fully convolutional RNN named bidirectional recurrent convolutional network for efficient multi-frame SR. Different from vanilla RNNs, 1) the commonly-used full feedforward and recurrent connections are replaced with weight-sharing convolutional connections. So they can greatly reduce the large number of network parameters and well model the temporal dependency in a finer level, i.e., patch-based rather than frame-based, and 2) connections from input layers at previous timesteps to the current hidden layer are added by 3D feedforward convolutions, which aim to capture discriminate spatio-temporal patterns for short-term fast-varying motions in local adjacent frames. Due to the cheap convolutional operations, our model has a low computational complexity and runs orders of magnitude faster than other multi-frame SR methods. With the powerful temporal dependency modeling, our model can super resolve videos with complex motions and achieve well performance.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Super resolving a low-resolution video, namely video super-resolution (SR), is usually handled by either single-image SR or multi-frame SR. Single-Image SR deals with each video frame independently, and ignores intrinsic temporal dependency of video frames which actually plays a very important role in video SR. Multi-Frame SR generally extracts motion information, e.g., optical flow, to model the temporal dependency, but often shows high computational cost. Considering that recurrent neural networks (RNNs) can model long-term temporal dependency of video sequences well, we propose a fully convolutional RNN named bidirectional recurrent convolutional network for efficient multi-frame SR. Different from vanilla RNNs, 1) the commonly-used full feedforward and recurrent connections are replaced with weight-sharing convolutional connections. So they can greatly reduce the large number of network parameters and well model the temporal dependency in a finer level, i.e., patch-based rather than frame-based, and 2) connections from input layers at previous timesteps to the current hidden layer are added by 3D feedforward convolutions, which aim to capture discriminate spatio-temporal patterns for short-term fast-varying motions in local adjacent frames. Due to the cheap convolutional operations, our model has a low computational complexity and runs orders of magnitude faster than other multi-frame SR methods. With the powerful temporal dependency modeling, our model can super resolve videos with complex motions and achieve well performance.",
"title": "Video Super-Resolution via Bidirectional Recurrent Convolutional Networks",
"normalizedTitle": "Video Super-Resolution via Bidirectional Recurrent Convolutional Networks",
"fno": "07919264",
"hasPdf": true,
"idPrefix": "tp",
"keywords": [
"Solid Modeling",
"Three Dimensional Displays",
"Feedforward Neural Networks",
"Computational Modeling",
"Motion Estimation",
"Recurrent Neural Networks",
"Visualization",
"Deep Learning",
"Recurrent Neural Networks",
"3 D Convolution",
"Video Super Resolution"
],
"authors": [
{
"givenName": "Yan",
"surname": "Huang",
"fullName": "Yan Huang",
"affiliation": "Center for Research on Intelligent Perception and Computing (CRIPAC), National Laboratory of Pattern Recognition (NLPR), Institute of Automation, Chinese Academy of Sciences (CASIA), University of Chinese Academy of Sciences (UCAS), Huairou, Beijing, China",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Wei",
"surname": "Wang",
"fullName": "Wei Wang",
"affiliation": "Center for Research on Intelligent Perception and Computing (CRIPAC), National Laboratory of Pattern Recognition (NLPR), Institute of Automation, Chinese Academy of Sciences (CASIA), University of Chinese Academy of Sciences (UCAS), Huairou, Beijing, China",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Liang",
"surname": "Wang",
"fullName": "Liang Wang",
"affiliation": "Center for Research on Intelligent Perception and Computing (CRIPAC), National Laboratory of Pattern Recognition (NLPR), Institute of Automation, Chinese Academy of Sciences (CASIA), University of Chinese Academy of Sciences (UCAS), Huairou, Beijing, China",
"__typename": "ArticleAuthorType"
}
],
"replicability": null,
"showBuyMe": true,
"showRecommendedArticles": true,
"isOpenAccess": false,
"issueNum": "04",
"pubDate": "2018-04-01 00:00:00",
"pubType": "trans",
"pages": "1015-1028",
"year": "2018",
"issn": "0162-8828",
"isbn": null,
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"recommendedArticles": [
{
"id": "proceedings/iccv/2015/8391/0/8391a531",
"title": "Video Super-Resolution via Deep Draft-Ensemble Learning",
"doi": null,
"abstractUrl": "/proceedings-article/iccv/2015/8391a531/12OmNCfSqLE",
"parentPublication": {
"id": "proceedings/iccv/2015/8391/0",
"title": "2015 IEEE International Conference on Computer Vision (ICCV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/fg/2018/2335/0/233501a746",
"title": "Convolutional Neural Network-Based Video Super-Resolution for Action Recognition",
"doi": null,
"abstractUrl": "/proceedings-article/fg/2018/233501a746/12OmNvjgWpE",
"parentPublication": {
"id": "proceedings/fg/2018/2335/0",
"title": "2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccv/2017/1032/0/1032c526",
"title": "Robust Video Super-Resolution with Learned Temporal Dynamics",
"doi": null,
"abstractUrl": "/proceedings-article/iccv/2017/1032c526/12OmNy4IF1x",
"parentPublication": {
"id": "proceedings/iccv/2017/1032/0",
"title": "2017 IEEE International Conference on Computer Vision (ICCV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2011/0394/0/05995360",
"title": "Space-time super-resolution from a single video",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2011/05995360/12OmNzXnNpd",
"parentPublication": {
"id": "proceedings/cvpr/2011/0394/0",
"title": "CVPR 2011",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2018/6420/0/642000b654",
"title": "Image Super-Resolution via Dual-State Recurrent Networks",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2018/642000b654/17D45WKWnIa",
"parentPublication": {
"id": "proceedings/cvpr/2018/6420/0",
"title": "2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ispa-bdcloud-socialcom-sustaincom/2022/6497/0/649700a531",
"title": "VRFormer: 360-Degree Video Streaming with FoV Combined Prediction and Super resolution",
"doi": null,
"abstractUrl": "/proceedings-article/ispa-bdcloud-socialcom-sustaincom/2022/649700a531/1LKwldiRY40",
"parentPublication": {
"id": "proceedings/ispa-bdcloud-socialcom-sustaincom/2022/6497/0",
"title": "2022 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvprw/2020/9360/0/09150882",
"title": "FBRNN: feedback recurrent neural network for extreme image super-resolution",
"doi": null,
"abstractUrl": "/proceedings-article/cvprw/2020/09150882/1lPHvdijXZC",
"parentPublication": {
"id": "proceedings/cvprw/2020/9360/0",
"title": "2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tp/2022/05/09279273",
"title": "A Progressive Fusion Generative Adversarial Network for Realistic and Consistent Video Super-Resolution",
"doi": null,
"abstractUrl": "/journal/tp/2022/05/09279273/1pg8t3V4Ico",
"parentPublication": {
"id": "trans/tp",
"title": "IEEE Transactions on Pattern Analysis & Machine Intelligence",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvprw/2021/4899/0/489900a166",
"title": "NTIRE 2021 Challenge on Video Super-Resolution",
"doi": null,
"abstractUrl": "/proceedings-article/cvprw/2021/489900a166/1yVzQIAkTDi",
"parentPublication": {
"id": "proceedings/cvprw/2021/4899/0",
"title": "2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ictai/2021/0898/0/089800a753",
"title": "FRAGAN-VSR: Frame-Recurrent Attention Generative Adversarial Network for Video Super-Resolution",
"doi": null,
"abstractUrl": "/proceedings-article/ictai/2021/089800a753/1zw6aV1LAmA",
"parentPublication": {
"id": "proceedings/ictai/2021/0898/0",
"title": "2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"adjacentArticles": {
"previous": {
"fno": "07917252",
"articleId": "13rRUxDqS5l",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "08306547",
"articleId": "17D45XreC6f",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"webExtras": [],
"articleVideos": []
}
|
{
"issue": {
"id": "1srMkVkkbT2",
"title": "May",
"year": "2021",
"issueNum": "05",
"idPrefix": "tk",
"pubType": "journal",
"volume": "33",
"label": "May",
"downloadables": {
"hasCover": false,
"__typename": "PeriodicalIssueDownloadablesType"
},
"__typename": "PeriodicalIssue"
},
"article": {
"id": "1eX8mA3pSbm",
"doi": "10.1109/TKDE.2019.2951103",
"abstract": "Recurrent neural network(RNN) has achieved remarkable performances in complex reasoning on knowledge bases, which usually takes as inputs vector embeddings of relations along a path between an entity pair. However, it is insufficient to extract local correlations of a path due to RNN is better at capturing global sequential information of a path. In this paper, we take full advantages of convolutional neural network that can effectively extract local features, and propose a convolutional-based RNN architecture denoted as C-RNN to perform reasoning. C-RNN first utilizes CNN to extract local high-level correlation features of a path, and then feeds the correlation features into recurrent neural network to model the path representation. Our C-RNN architecture is adaptable to obtain not only local features but also global sequential features of a path. Based on C-RNN architecture, we devise two models, the unidirectional C-RNN and bidirectional C-RNN. We empirically evaluate them on a large-scale FreeBase+ClueWeb prediction task. Experimental results show that C-RNN models achieve state-of-the-art predictive performance.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Recurrent neural network(RNN) has achieved remarkable performances in complex reasoning on knowledge bases, which usually takes as inputs vector embeddings of relations along a path between an entity pair. However, it is insufficient to extract local correlations of a path due to RNN is better at capturing global sequential information of a path. In this paper, we take full advantages of convolutional neural network that can effectively extract local features, and propose a convolutional-based RNN architecture denoted as C-RNN to perform reasoning. C-RNN first utilizes CNN to extract local high-level correlation features of a path, and then feeds the correlation features into recurrent neural network to model the path representation. Our C-RNN architecture is adaptable to obtain not only local features but also global sequential features of a path. Based on C-RNN architecture, we devise two models, the unidirectional C-RNN and bidirectional C-RNN. We empirically evaluate them on a large-scale FreeBase+ClueWeb prediction task. Experimental results show that C-RNN models achieve state-of-the-art predictive performance.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Recurrent neural network(RNN) has achieved remarkable performances in complex reasoning on knowledge bases, which usually takes as inputs vector embeddings of relations along a path between an entity pair. However, it is insufficient to extract local correlations of a path due to RNN is better at capturing global sequential information of a path. In this paper, we take full advantages of convolutional neural network that can effectively extract local features, and propose a convolutional-based RNN architecture denoted as C-RNN to perform reasoning. C-RNN first utilizes CNN to extract local high-level correlation features of a path, and then feeds the correlation features into recurrent neural network to model the path representation. Our C-RNN architecture is adaptable to obtain not only local features but also global sequential features of a path. Based on C-RNN architecture, we devise two models, the unidirectional C-RNN and bidirectional C-RNN. We empirically evaluate them on a large-scale FreeBase+ClueWeb prediction task. Experimental results show that C-RNN models achieve state-of-the-art predictive performance.",
"title": "Knowledge Base Reasoning with Convolutional-Based Recurrent Neural Networks",
"normalizedTitle": "Knowledge Base Reasoning with Convolutional-Based Recurrent Neural Networks",
"fno": "08890615",
"hasPdf": true,
"idPrefix": "tk",
"keywords": [
"Case Based Reasoning",
"Feature Extraction",
"Graph Theory",
"Neural Nets",
"Recurrent Neural Nets",
"Convolutional Based RNN Architecture",
"Local High Level Correlation Features",
"Path Representation",
"C RNN Architecture",
"Local Features",
"Global Sequential Features",
"Unidirectional C RNN",
"C RNN Models",
"State Of The Art Predictive Performance",
"Knowledge Base Reasoning",
"Convolutional Based Recurrent Neural Networks",
"Remarkable Performances",
"Complex Reasoning",
"Knowledge Bases",
"Entity Pair",
"Local Correlations",
"Global Sequential Information",
"Convolutional Neural Network",
"Cognition",
"Recurrent Neural Networks",
"Knowledge Based Systems",
"Feature Extraction",
"Correlation",
"Computer Architecture",
"Convolutional Neural Nets",
"Knowledge Base Embedding",
"Knowledge Base Reasoning",
"Knowledge Representation Learning",
"Knowledge Graph Completion"
],
"authors": [
{
"givenName": "Qiannan",
"surname": "Zhu",
"fullName": "Qiannan Zhu",
"affiliation": "Institute of Information Engineering, >Chinese Academy of Sciences, Beijing, China",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Xiaofei",
"surname": "Zhou",
"fullName": "Xiaofei Zhou",
"affiliation": "Institute of Information Engineering, >Chinese Academy of Sciences, Beijing, China",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Jianlong",
"surname": "Tan",
"fullName": "Jianlong Tan",
"affiliation": "Institute of Information Engineering, >Chinese Academy of Sciences, Beijing, China",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Li",
"surname": "Guo",
"fullName": "Li Guo",
"affiliation": "Institute of Information Engineering, >Chinese Academy of Sciences, Beijing, China",
"__typename": "ArticleAuthorType"
}
],
"replicability": null,
"showBuyMe": true,
"showRecommendedArticles": true,
"isOpenAccess": false,
"issueNum": "05",
"pubDate": "2021-05-01 00:00:00",
"pubType": "trans",
"pages": "2015-2028",
"year": "2021",
"issn": "1041-4347",
"isbn": null,
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"recommendedArticles": [
{
"id": "proceedings/cvprw/2015/6759/0/07301268",
"title": "Convolutional recurrent neural networks: Learning spatial dependencies for image representation",
"doi": null,
"abstractUrl": "/proceedings-article/cvprw/2015/07301268/12OmNBAIASG",
"parentPublication": {
"id": "proceedings/cvprw/2015/6759/0",
"title": "2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvprw/2016/1437/0/1437a426",
"title": "ReSeg: A Recurrent Neural Network-Based Model for Semantic Segmentation",
"doi": null,
"abstractUrl": "/proceedings-article/cvprw/2016/1437a426/12OmNzBOhS6",
"parentPublication": {
"id": "proceedings/cvprw/2016/1437/0",
"title": "2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cbd/2018/8034/0/803400a311",
"title": "A Hybrid BLSTM-C Neural Network Proposed for Chinese Text Classification",
"doi": null,
"abstractUrl": "/proceedings-article/cbd/2018/803400a311/17D45WKWnK1",
"parentPublication": {
"id": "proceedings/cbd/2018/8034/0",
"title": "2018 Sixth International Conference on Advanced Cloud and Big Data (CBD)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/ai/2022/05/09762542",
"title": "A Novel Temporal Attentive-Pooling based Convolutional Recurrent Architecture for Acoustic Signal Enhancement",
"doi": null,
"abstractUrl": "/journal/ai/2022/05/09762542/1CRri74irnO",
"parentPublication": {
"id": "trans/ai",
"title": "IEEE Transactions on Artificial Intelligence",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cbd/2022/0745/0/074500a195",
"title": "Action recognition based on parallel convolutional recurrent neural networks",
"doi": null,
"abstractUrl": "/proceedings-article/cbd/2022/074500a195/1EVijA7T008",
"parentPublication": {
"id": "proceedings/cbd/2022/0745/0",
"title": "2021 Ninth International Conference on Advanced Cloud and Big Data (CBD)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ises/2022/9922/0/992200a225",
"title": "Isolated Word Recognition based on Convolutional Recurrent Neural Network",
"doi": null,
"abstractUrl": "/proceedings-article/ises/2022/992200a225/1KrgC5rfYRy",
"parentPublication": {
"id": "proceedings/ises/2022/9922/0",
"title": "2022 IEEE International Symposium on Smart Electronic Systems (iSES)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/apsec/2018/1970/0/197000a703",
"title": "Improving Bug Localization with Character-Level Convolutional Neural Network and Recurrent Neural Network",
"doi": null,
"abstractUrl": "/proceedings-article/apsec/2018/197000a703/1b66r2WTJok",
"parentPublication": {
"id": "proceedings/apsec/2018/1970/0",
"title": "2018 25th Asia-Pacific Software Engineering Conference (APSEC)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tb/2021/02/08747369",
"title": "Deep Convolutional and Recurrent Neural Networks for Cell Motility Discrimination and Prediction",
"doi": null,
"abstractUrl": "/journal/tb/2021/02/08747369/1bcHmwqQBa0",
"parentPublication": {
"id": "trans/tb",
"title": "IEEE/ACM Transactions on Computational Biology and Bioinformatics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icpr/2021/8808/0/09413071",
"title": "Multi-Scanning Based Recurrent Neural Network for Hyperspectral Image Classification",
"doi": null,
"abstractUrl": "/proceedings-article/icpr/2021/09413071/1tmjehVY4GQ",
"parentPublication": {
"id": "proceedings/icpr/2021/8808/0",
"title": "2020 25th International Conference on Pattern Recognition (ICPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/ts/2022/10/09543588",
"title": "RNN-Test: Towards Adversarial Testing for Recurrent Neural Network Systems",
"doi": null,
"abstractUrl": "/journal/ts/2022/10/09543588/1x4ULFvnmbC",
"parentPublication": {
"id": "trans/ts",
"title": "IEEE Transactions on Software Engineering",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"adjacentArticles": {
"previous": {
"fno": "08869780",
"articleId": "1e9gZkVORXy",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "08880522",
"articleId": "1emy27fTuzS",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"webExtras": [],
"articleVideos": []
}
|
{
"issue": {
"id": "12OmNAkEU3i",
"title": "Nov.",
"year": "2016",
"issueNum": "11",
"idPrefix": "tp",
"pubType": "journal",
"volume": "38",
"label": "Nov.",
"downloadables": {
"hasCover": false,
"__typename": "PeriodicalIssueDownloadablesType"
},
"__typename": "PeriodicalIssue"
},
"article": {
"id": "13rRUB6Sq1H",
"doi": "10.1109/TPAMI.2015.2511754",
"abstract": "Due to its causal semantics, Bayesian networks (BN) have been widely employed to discover the underlying data relationship in exploratory studies, such as brain research. Despite its success in modeling the probability distribution of variables, BN is naturally a generative model, which is not necessarily discriminative. This may cause the ignorance of subtle but critical network changes that are of investigation values across populations. In this paper, we propose to improve the discriminative power of BN models for continuous variables from two different perspectives. This brings two general discriminative learning frameworks for Gaussian Bayesian networks (GBN). In the first framework, we employ Fisher kernel to bridge the generative models of GBN and the discriminative classifiers of SVMs, and convert the GBN parameter learning to Fisher kernel learning via minimizing a generalization error bound of SVMs. In the second framework, we employ the max-margin criterion and build it directly upon GBN models to explicitly optimize the classification performance of the GBNs. The advantages and disadvantages of the two frameworks are discussed and experimentally compared. Both of them demonstrate strong power in learning discriminative parameters of GBNs for neuroimaging based brain network analysis, as well as maintaining reasonable representation capacity. The contributions of this paper also include a new Directed Acyclic Graph (DAG) constraint with theoretical guarantee to ensure the graph validity of GBN.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Due to its causal semantics, Bayesian networks (BN) have been widely employed to discover the underlying data relationship in exploratory studies, such as brain research. Despite its success in modeling the probability distribution of variables, BN is naturally a generative model, which is not necessarily discriminative. This may cause the ignorance of subtle but critical network changes that are of investigation values across populations. In this paper, we propose to improve the discriminative power of BN models for continuous variables from two different perspectives. This brings two general discriminative learning frameworks for Gaussian Bayesian networks (GBN). In the first framework, we employ Fisher kernel to bridge the generative models of GBN and the discriminative classifiers of SVMs, and convert the GBN parameter learning to Fisher kernel learning via minimizing a generalization error bound of SVMs. In the second framework, we employ the max-margin criterion and build it directly upon GBN models to explicitly optimize the classification performance of the GBNs. The advantages and disadvantages of the two frameworks are discussed and experimentally compared. Both of them demonstrate strong power in learning discriminative parameters of GBNs for neuroimaging based brain network analysis, as well as maintaining reasonable representation capacity. The contributions of this paper also include a new Directed Acyclic Graph (DAG) constraint with theoretical guarantee to ensure the graph validity of GBN.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Due to its causal semantics, Bayesian networks (BN) have been widely employed to discover the underlying data relationship in exploratory studies, such as brain research. Despite its success in modeling the probability distribution of variables, BN is naturally a generative model, which is not necessarily discriminative. This may cause the ignorance of subtle but critical network changes that are of investigation values across populations. In this paper, we propose to improve the discriminative power of BN models for continuous variables from two different perspectives. This brings two general discriminative learning frameworks for Gaussian Bayesian networks (GBN). In the first framework, we employ Fisher kernel to bridge the generative models of GBN and the discriminative classifiers of SVMs, and convert the GBN parameter learning to Fisher kernel learning via minimizing a generalization error bound of SVMs. In the second framework, we employ the max-margin criterion and build it directly upon GBN models to explicitly optimize the classification performance of the GBNs. The advantages and disadvantages of the two frameworks are discussed and experimentally compared. Both of them demonstrate strong power in learning discriminative parameters of GBNs for neuroimaging based brain network analysis, as well as maintaining reasonable representation capacity. The contributions of this paper also include a new Directed Acyclic Graph (DAG) constraint with theoretical guarantee to ensure the graph validity of GBN.",
"title": "Learning Discriminative Bayesian Networks from High-Dimensional Continuous Neuroimaging Data",
"normalizedTitle": "Learning Discriminative Bayesian Networks from High-Dimensional Continuous Neuroimaging Data",
"fno": "07364252",
"hasPdf": true,
"idPrefix": "tp",
"keywords": [
"Bayes Methods",
"Kernel",
"Brain Modeling",
"Diseases",
"Neuroimaging",
"Optimization",
"Probability Distribution",
"Brain Network",
"Bayesian Network",
"Discriminative Learning",
"Fisher Kernel Learning",
"Max Margin"
],
"authors": [
{
"givenName": "Luping",
"surname": "Zhou",
"fullName": "Luping Zhou",
"affiliation": "School of Computing and Information Technology, University of Wollongong, NSW, Australia",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Lei",
"surname": "Wang",
"fullName": "Lei Wang",
"affiliation": "School of Computing and Information Technology, University of Wollongong, NSW, Australia",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Lingqiao",
"surname": "Liu",
"fullName": "Lingqiao Liu",
"affiliation": "School of Computer Science, University of Adelaide, Australia",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Philip",
"surname": "Ogunbona",
"fullName": "Philip Ogunbona",
"affiliation": "School of Computing and Information Technology, University of Wollongong, NSW, Australia",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Dinggang",
"surname": "Shen",
"fullName": "Dinggang Shen",
"affiliation": "Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC",
"__typename": "ArticleAuthorType"
}
],
"replicability": null,
"showBuyMe": true,
"showRecommendedArticles": true,
"isOpenAccess": false,
"issueNum": "11",
"pubDate": "2016-11-01 00:00:00",
"pubType": "trans",
"pages": "2269-2283",
"year": "2016",
"issn": "0162-8828",
"isbn": null,
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"recommendedArticles": [
{
"id": "proceedings/cvpr/2013/4989/0/4989c243",
"title": "Discriminative Brain Effective Connectivity Analysis for Alzheimer's Disease: A Kernel Learning Approach upon Sparse Gaussian Bayesian Network",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2013/4989c243/12OmNBigFu5",
"parentPublication": {
"id": "proceedings/cvpr/2013/4989/0",
"title": "2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2017/0457/0/0457g846",
"title": "Multi-way Multi-level Kernel Modeling for Neuroimaging Classification",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2017/0457g846/12OmNC4eSw3",
"parentPublication": {
"id": "proceedings/cvpr/2017/0457/0",
"title": "2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccv/2017/1032/0/1032a182",
"title": "An Optimal Transportation Based Univariate Neuroimaging Index",
"doi": null,
"abstractUrl": "/proceedings-article/iccv/2017/1032a182/12OmNCdBDFb",
"parentPublication": {
"id": "proceedings/iccv/2017/1032/0",
"title": "2017 IEEE International Conference on Computer Vision (ICCV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ciis/2017/3886/0/3886a184",
"title": "Discretization of Continuous Variables in Bayesian Networks Based on Matrix Decomposition",
"doi": null,
"abstractUrl": "/proceedings-article/ciis/2017/3886a184/12OmNrYCY0f",
"parentPublication": {
"id": "proceedings/ciis/2017/3886/0",
"title": "2017 International Conference on Computing Intelligence and Information System (CIIS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/compsacw/2014/3578/0/3578a128",
"title": "Bayesian Model Averaging of Bayesian Network Classifiers for Intrusion Detection",
"doi": null,
"abstractUrl": "/proceedings-article/compsacw/2014/3578a128/12OmNvSbBGm",
"parentPublication": {
"id": "proceedings/compsacw/2014/3578/0",
"title": "2014 IEEE 38th International Computer Software and Applications Conference Workshops (COMPSACW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ictai/2003/2038/0/20380296",
"title": "Discriminative Parameter Learning of General Bayesian Network Classifiers",
"doi": null,
"abstractUrl": "/proceedings-article/ictai/2003/20380296/12OmNz3bdCT",
"parentPublication": {
"id": "proceedings/ictai/2003/2038/0",
"title": "Proceedings. 15th IEEE International Conference on Tools with Artificial Intelligence",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icpr/2014/5209/0/5209d185",
"title": "Pseudo-Marginal Bayesian Multiple-Class Multiple-Kernel Learning for Neuroimaging Data",
"doi": null,
"abstractUrl": "/proceedings-article/icpr/2014/5209d185/12OmNzWfoV2",
"parentPublication": {
"id": "proceedings/icpr/2014/5209/0",
"title": "2014 22nd International Conference on Pattern Recognition (ICPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tp/2016/12/07404063",
"title": "Discriminative Bayesian Dictionary Learning for Classification",
"doi": null,
"abstractUrl": "/journal/tp/2016/12/07404063/13rRUEgaru5",
"parentPublication": {
"id": "trans/tp",
"title": "IEEE Transactions on Pattern Analysis & Machine Intelligence",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tp/2013/02/ttp2013020286",
"title": "A Novel Bayesian Framework for Discriminative Feature Extraction in Brain-Computer Interfaces",
"doi": null,
"abstractUrl": "/journal/tp/2013/02/ttp2013020286/13rRUyuegib",
"parentPublication": {
"id": "trans/tp",
"title": "IEEE Transactions on Pattern Analysis & Machine Intelligence",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/asonam/2017/4993/0/09069145",
"title": "Using Modular Ontologies to Capture Causal Knowledge contained in Bayesian Networks",
"doi": null,
"abstractUrl": "/proceedings-article/asonam/2017/09069145/1j9xVlnr5lu",
"parentPublication": {
"id": "proceedings/asonam/2017/4993/0",
"title": "2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"adjacentArticles": {
"previous": {
"fno": "07368926",
"articleId": "13rRUNvgzb9",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "07378521",
"articleId": "13rRUx0gegD",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"webExtras": [
{
"id": "17ShDTXFgF9",
"name": "ttp201611-07364252s1.zip",
"location": "https://www.computer.org/csdl/api/v1/extra/ttp201611-07364252s1.zip",
"extension": "zip",
"size": "316 kB",
"__typename": "WebExtraType"
}
],
"articleVideos": []
}
|
{
"issue": {
"id": "12OmNviZlCQ",
"title": "June",
"year": "2014",
"issueNum": "06",
"idPrefix": "ts",
"pubType": "journal",
"volume": "40",
"label": "June",
"downloadables": {
"hasCover": false,
"__typename": "PeriodicalIssueDownloadablesType"
},
"__typename": "PeriodicalIssue"
},
"article": {
"id": "13rRUxASudc",
"doi": "10.1109/TSE.2014.2321179",
"abstract": "Recommendation systems in software engineering (SE) should be designed to integrate evidence into practitioners experience. Bayesian networks (BNs) provide a natural statistical framework for evidence-based decision-making by incorporating an integrated summary of the available evidence and associated uncertainty (of consequences). In this study, we follow the lead of computational biology and healthcare decision-making, and investigate the applications of BNs in SE in terms of 1) main software engineering challenges addressed, 2) techniques used to learn causal relationships among variables, 3) techniques used to infer the parameters, and 4) variable types used as BN nodes. We conduct a systematic mapping study to investigate each of these four facets and compare the current usage of BNs in SE with these two domains. Subsequently, we highlight the main limitations of the usage of BNs in SE and propose a Hybrid BN to improve evidence-based decision-making in SE. In two industrial cases, we build sample hybrid BNs and evaluate their performance. The results of our empirical analyses show that hybrid BNs are powerful frameworks that combine expert knowledge with quantitative data. As researchers in SE become more aware of the underlying dynamics of BNs, the proposed models will also advance and naturally contribute to evidence based-decision-making.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Recommendation systems in software engineering (SE) should be designed to integrate evidence into practitioners experience. Bayesian networks (BNs) provide a natural statistical framework for evidence-based decision-making by incorporating an integrated summary of the available evidence and associated uncertainty (of consequences). In this study, we follow the lead of computational biology and healthcare decision-making, and investigate the applications of BNs in SE in terms of 1) main software engineering challenges addressed, 2) techniques used to learn causal relationships among variables, 3) techniques used to infer the parameters, and 4) variable types used as BN nodes. We conduct a systematic mapping study to investigate each of these four facets and compare the current usage of BNs in SE with these two domains. Subsequently, we highlight the main limitations of the usage of BNs in SE and propose a Hybrid BN to improve evidence-based decision-making in SE. In two industrial cases, we build sample hybrid BNs and evaluate their performance. The results of our empirical analyses show that hybrid BNs are powerful frameworks that combine expert knowledge with quantitative data. As researchers in SE become more aware of the underlying dynamics of BNs, the proposed models will also advance and naturally contribute to evidence based-decision-making.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Recommendation systems in software engineering (SE) should be designed to integrate evidence into practitioners experience. Bayesian networks (BNs) provide a natural statistical framework for evidence-based decision-making by incorporating an integrated summary of the available evidence and associated uncertainty (of consequences). In this study, we follow the lead of computational biology and healthcare decision-making, and investigate the applications of BNs in SE in terms of 1) main software engineering challenges addressed, 2) techniques used to learn causal relationships among variables, 3) techniques used to infer the parameters, and 4) variable types used as BN nodes. We conduct a systematic mapping study to investigate each of these four facets and compare the current usage of BNs in SE with these two domains. Subsequently, we highlight the main limitations of the usage of BNs in SE and propose a Hybrid BN to improve evidence-based decision-making in SE. In two industrial cases, we build sample hybrid BNs and evaluate their performance. The results of our empirical analyses show that hybrid BNs are powerful frameworks that combine expert knowledge with quantitative data. As researchers in SE become more aware of the underlying dynamics of BNs, the proposed models will also advance and naturally contribute to evidence based-decision-making.",
"title": "Bayesian Networks For Evidence-Based Decision-Making in Software Engineering",
"normalizedTitle": "Bayesian Networks For Evidence-Based Decision-Making in Software Engineering",
"fno": "06808495",
"hasPdf": true,
"idPrefix": "ts",
"keywords": [
"Belief Networks",
"Decision Making",
"Software Metrics",
"Software Reliability",
"Bayesian Networks",
"Evidence Based Decision Making",
"Software Engineering",
"Recommendation Systems",
"SE",
"Natural Statistical Framework",
"Associated Uncertainty",
"Computational Biology",
"Health Care Decision Making",
"Systematic Mapping Study",
"Hybrid BN Node",
"Software Reliability",
"Software Metrics",
"Software Engineering",
"Decision Making",
"Bayes Methods",
"Software",
"Medical Services",
"Systematics",
"Buildings",
"Evidence Based Decision Making",
"Bayesian Networks",
"Bayesian Statistics",
"Software Reliability",
"Software Metrics",
"Post Release Defects"
],
"authors": [
{
"givenName": "Ayse Tosun",
"surname": "Misirli",
"fullName": "Ayse Tosun Misirli",
"affiliation": "Department of Information Processing Science, University of Oulu, Finland",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Ayse Basar",
"surname": "Bener",
"fullName": "Ayse Basar Bener",
"affiliation": "Mechanical and Industrial Engineering Department, Ryerson University, Toronto, CA",
"__typename": "ArticleAuthorType"
}
],
"replicability": null,
"showBuyMe": true,
"showRecommendedArticles": true,
"isOpenAccess": false,
"issueNum": "06",
"pubDate": "2014-06-01 00:00:00",
"pubType": "trans",
"pages": "533-554",
"year": "2014",
"issn": "0098-5589",
"isbn": null,
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"recommendedArticles": [
{
"id": "proceedings/seaa/2016/2820/0/2820a148",
"title": "Bayesian Synthesis for Knowledge Translation in Software Engineering: Method and Illustration",
"doi": null,
"abstractUrl": "/proceedings-article/seaa/2016/2820a148/12OmNA2cYBl",
"parentPublication": {
"id": "proceedings/seaa/2016/2820/0",
"title": "2016 42th Euromicro Conference on Software Engineering and Advanced Applications (SEAA)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccis/2010/4270/0/4270a685",
"title": "Group Decision-Making Model Based on Rough Set and Evidence Theory",
"doi": null,
"abstractUrl": "/proceedings-article/iccis/2010/4270a685/12OmNAlvHLk",
"parentPublication": {
"id": "proceedings/iccis/2010/4270/0",
"title": "2010 International Conference on Computational and Information Sciences",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iscsic/2017/2941/0/2941a113",
"title": "A Parallel Sampling Method for Bayesian Networks",
"doi": null,
"abstractUrl": "/proceedings-article/iscsic/2017/2941a113/12OmNBl6EGC",
"parentPublication": {
"id": "proceedings/iscsic/2017/2941/0",
"title": "2017 International Symposium on Computer Science and Intelligent Controls (ISCSIC)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ismvl/2015/1777/0/1777a176",
"title": "Belief Network Support via Decision Diagrams",
"doi": null,
"abstractUrl": "/proceedings-article/ismvl/2015/1777a176/12OmNrAdsCD",
"parentPublication": {
"id": "proceedings/ismvl/2015/1777/0",
"title": "2015 IEEE International Symposium on Multiple-Valued Logic (ISMVL)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icalt/2007/2916/0/29160783",
"title": "To improve Bayesian Network Learner Modelling thanks to Multinet",
"doi": null,
"abstractUrl": "/proceedings-article/icalt/2007/29160783/12OmNznkKbB",
"parentPublication": {
"id": "proceedings/icalt/2007/2916/0",
"title": "2007 International Conference on Advanced Learning Technologies",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/seams/2013/0344/0/06595498",
"title": "Dynamic decision networks for decision-making in self-adaptive systems: A case study",
"doi": null,
"abstractUrl": "/proceedings-article/seams/2013/06595498/12OmNzwZ6oa",
"parentPublication": {
"id": "proceedings/seams/2013/0344/0",
"title": "2013 8th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/5555/01/09754243",
"title": "Visual Assistance in Development and Validation of Bayesian Networks for Clinical Decision Support",
"doi": null,
"abstractUrl": "/journal/tg/5555/01/09754243/1CpcDU5uTsY",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cmbs/2022/6770/0/677000a400",
"title": "Towards Evidence-based Argumentation Graph for Clinical Decision Support",
"doi": null,
"abstractUrl": "/proceedings-article/cmbs/2022/677000a400/1GhW0K5Kabe",
"parentPublication": {
"id": "proceedings/cmbs/2022/6770/0",
"title": "2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/esem/2019/2968/0/08870144",
"title": "Software Engineering Research Community Viewpoints on Rapid Reviews",
"doi": null,
"abstractUrl": "/proceedings-article/esem/2019/08870144/1ecCOFOmP5K",
"parentPublication": {
"id": "proceedings/esem/2019/2968/0",
"title": "2019 ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icpr/2016/4847/0/07900204",
"title": "Bayesian approach to learn Bayesian networks using data and constraints",
"doi": null,
"abstractUrl": "/proceedings-article/icpr/2016/07900204/1gyspTpZNWE",
"parentPublication": {
"id": "proceedings/icpr/2016/4847/0",
"title": "2016 23rd International Conference on Pattern Recognition (ICPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"adjacentArticles": {
"previous": null,
"next": {
"fno": "06784505",
"articleId": "13rRUwkfASy",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"webExtras": [],
"articleVideos": []
}
|
{
"issue": {
"id": "12OmNqMPfRa",
"title": "October",
"year": "2007",
"issueNum": "10",
"idPrefix": "tk",
"pubType": "journal",
"volume": "19",
"label": "October",
"downloadables": {
"hasCover": false,
"__typename": "PeriodicalIssueDownloadablesType"
},
"__typename": "PeriodicalIssue"
},
"article": {
"id": "13rRUy0HYRQ",
"doi": "10.1109/TKDE.2007.1073",
"abstract": "Although Bayesian Nets (BNs) are increasingly being used to solve real world risk problems, their use is still constrained by the difficulty of constructing the node probability tables (NPTs). A key challenge is to construct relevant NPTs using the minimal amount of expert elicitation, recognising that it is rarely cost-effective to elicit complete sets of probability values. We describe a simple approach to defining NPTs for a large class of commonly occurring nodes (called ranked nodes). The approach is based on the doubly truncated Normal distribution with a central tendency that is invariably a type of weighted function of the parent nodes. In extensive real-world case studies we have found that this approach is sufficient for generating the NPTs of a very large class of nodes. We describe one such case study for validation purposes. The approach has been fully automated in a commercial tool, called AgenaRisk, and is thus accessible to all types of domain experts. We believe this work represents a useful contribution to BN research and technology since its application makes the difference between being able to build realistic BN models and not.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Although Bayesian Nets (BNs) are increasingly being used to solve real world risk problems, their use is still constrained by the difficulty of constructing the node probability tables (NPTs). A key challenge is to construct relevant NPTs using the minimal amount of expert elicitation, recognising that it is rarely cost-effective to elicit complete sets of probability values. We describe a simple approach to defining NPTs for a large class of commonly occurring nodes (called ranked nodes). The approach is based on the doubly truncated Normal distribution with a central tendency that is invariably a type of weighted function of the parent nodes. In extensive real-world case studies we have found that this approach is sufficient for generating the NPTs of a very large class of nodes. We describe one such case study for validation purposes. The approach has been fully automated in a commercial tool, called AgenaRisk, and is thus accessible to all types of domain experts. We believe this work represents a useful contribution to BN research and technology since its application makes the difference between being able to build realistic BN models and not.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Although Bayesian Nets (BNs) are increasingly being used to solve real world risk problems, their use is still constrained by the difficulty of constructing the node probability tables (NPTs). A key challenge is to construct relevant NPTs using the minimal amount of expert elicitation, recognising that it is rarely cost-effective to elicit complete sets of probability values. We describe a simple approach to defining NPTs for a large class of commonly occurring nodes (called ranked nodes). The approach is based on the doubly truncated Normal distribution with a central tendency that is invariably a type of weighted function of the parent nodes. In extensive real-world case studies we have found that this approach is sufficient for generating the NPTs of a very large class of nodes. We describe one such case study for validation purposes. The approach has been fully automated in a commercial tool, called AgenaRisk, and is thus accessible to all types of domain experts. We believe this work represents a useful contribution to BN research and technology since its application makes the difference between being able to build realistic BN models and not.",
"title": "Using Ranked Nodes to Model Qualitative Judgments in Bayesian Networks",
"normalizedTitle": "Using Ranked Nodes to Model Qualitative Judgments in Bayesian Networks",
"fno": "k1420",
"hasPdf": true,
"idPrefix": "tk",
"keywords": [
"Bayesian Networks",
"Node Probability Tables",
"Ranked Nodes",
"Probability Elicitation",
"Risk Analysis"
],
"authors": [
{
"givenName": "Norman E.",
"surname": "Fenton",
"fullName": "Norman E. Fenton",
"affiliation": null,
"__typename": "ArticleAuthorType"
},
{
"givenName": "Martin",
"surname": "Neil",
"fullName": "Martin Neil",
"affiliation": null,
"__typename": "ArticleAuthorType"
},
{
"givenName": "Jose Galan",
"surname": "Caballero",
"fullName": "Jose Galan Caballero",
"affiliation": null,
"__typename": "ArticleAuthorType"
}
],
"replicability": null,
"showBuyMe": true,
"showRecommendedArticles": true,
"isOpenAccess": false,
"issueNum": "10",
"pubDate": "2007-10-01 00:00:00",
"pubType": "trans",
"pages": "1420-1432",
"year": "2007",
"issn": "1041-4347",
"isbn": null,
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"recommendedArticles": [
{
"id": "proceedings/iscsic/2017/2941/0/2941a113",
"title": "A Parallel Sampling Method for Bayesian Networks",
"doi": null,
"abstractUrl": "/proceedings-article/iscsic/2017/2941a113/12OmNBl6EGC",
"parentPublication": {
"id": "proceedings/iscsic/2017/2941/0",
"title": "2017 International Symposium on Computer Science and Intelligent Controls (ISCSIC)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icpr/2008/2174/0/04761074",
"title": "Exploiting qualitative domain knowledge for learning Bayesian network parameters with incomplete data",
"doi": null,
"abstractUrl": "/proceedings-article/icpr/2008/04761074/12OmNBuL1lO",
"parentPublication": {
"id": "proceedings/icpr/2008/2174/0",
"title": "ICPR 2008 19th International Conference on Pattern Recognition",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/fbit/2007/2999/0/29990595",
"title": "Ontology-Based Semi-automatic Construction of Bayesian Network Models for Diagnosing Diseases in E-health Applications",
"doi": null,
"abstractUrl": "/proceedings-article/fbit/2007/29990595/12OmNqIQS56",
"parentPublication": {
"id": "proceedings/fbit/2007/2999/0",
"title": "2007 Frontiers in the Convergence of Bioscience and Information Technologies (FBIT '07)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ssiri/2009/3758/0/3758a447",
"title": "Simplifying Parametrization of Bayesian Networks in Prediction of System Quality",
"doi": null,
"abstractUrl": "/proceedings-article/ssiri/2009/3758a447/12OmNxGj9UT",
"parentPublication": {
"id": "proceedings/ssiri/2009/3758/0",
"title": "Secure System Integration and Reliability Improvement",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2008/2242/0/04587368",
"title": "Learning Bayesian Networks with qualitative constraints",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2008/04587368/12OmNyoiZ7d",
"parentPublication": {
"id": "proceedings/cvpr/2008/2242/0",
"title": "2008 IEEE Conference on Computer Vision and Pattern Recognition",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icalt/2007/2916/0/29160783",
"title": "To improve Bayesian Network Learner Modelling thanks to Multinet",
"doi": null,
"abstractUrl": "/proceedings-article/icalt/2007/29160783/12OmNznkKbB",
"parentPublication": {
"id": "proceedings/icalt/2007/2916/0",
"title": "2007 International Conference on Advanced Learning Technologies",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icassp/2009/2353/0/04960702",
"title": "A Bayesian NETWORKS approach for dialog modeling: The fusion BN",
"doi": null,
"abstractUrl": "/proceedings-article/icassp/2009/04960702/12OmNzxyiAZ",
"parentPublication": {
"id": "proceedings/icassp/2009/2353/0",
"title": "Acoustics, Speech, and Signal Processing, IEEE International Conference on",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tk/2012/07/ttk2012071306",
"title": "Optimizing the Calculation of Conditional Probability Tables in Hybrid Bayesian Networks Using Binary Factorization",
"doi": null,
"abstractUrl": "/journal/tk/2012/07/ttk2012071306/13rRUIM2VC2",
"parentPublication": {
"id": "trans/tk",
"title": "IEEE Transactions on Knowledge & Data Engineering",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/ts/2014/06/06808495",
"title": "Bayesian Networks For Evidence-Based Decision-Making in Software Engineering",
"doi": null,
"abstractUrl": "/journal/ts/2014/06/06808495/13rRUxASudc",
"parentPublication": {
"id": "trans/ts",
"title": "IEEE Transactions on Software Engineering",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icpr/2016/4847/0/07900204",
"title": "Bayesian approach to learn Bayesian networks using data and constraints",
"doi": null,
"abstractUrl": "/proceedings-article/icpr/2016/07900204/1gyspTpZNWE",
"parentPublication": {
"id": "proceedings/icpr/2016/4847/0",
"title": "2016 23rd International Conference on Pattern Recognition (ICPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"adjacentArticles": {
"previous": {
"fno": "k1404",
"articleId": "13rRUxEhFsY",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "k1433",
"articleId": "13rRUwvBy9d",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"webExtras": [],
"articleVideos": []
}
|
{
"issue": {
"id": "1FvJrIs02is",
"title": "Aug.",
"year": "2022",
"issueNum": "08",
"idPrefix": "co",
"pubType": "magazine",
"volume": "55",
"label": "Aug.",
"downloadables": {
"hasCover": true,
"__typename": "PeriodicalIssueDownloadablesType"
},
"__typename": "PeriodicalIssue"
},
"article": {
"id": "1FvJurM0IUw",
"doi": "10.1109/MC.2021.3138780",
"abstract": "Although the use of personal data from different contexts is essential to curbing the spread of COVID-19 in epidemic-handling systems (EHSs), it increases the chances of privacy breaches and personal data misuse. This article analyses the data lifecycle and proposes various technical requirements for privacy preservation in EHSs.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Although the use of personal data from different contexts is essential to curbing the spread of COVID-19 in epidemic-handling systems (EHSs), it increases the chances of privacy breaches and personal data misuse. This article analyses the data lifecycle and proposes various technical requirements for privacy preservation in EHSs.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Although the use of personal data from different contexts is essential to curbing the spread of COVID-19 in epidemic-handling systems (EHSs), it increases the chances of privacy breaches and personal data misuse. This article analyses the data lifecycle and proposes various technical requirements for privacy preservation in EHSs.",
"title": "A Privacy-Assured Data Lifecycle for Epidemic-Handling Systems",
"normalizedTitle": "A Privacy-Assured Data Lifecycle for Epidemic-Handling Systems",
"fno": "09847322",
"hasPdf": true,
"idPrefix": "co",
"keywords": [
"Data Privacy",
"Epidemics",
"Medical Administrative Data Processing",
"Privacy Assured Data Lifecycle",
"Epidemic Handling Systems",
"COVID 19",
"EH Ss",
"Privacy Breaches",
"Personal Data Misuse",
"Privacy Preservation",
"Epidemics",
"COVID 19",
"Data Privacy",
"Privacy Breach"
],
"authors": [
{
"givenName": "Abdul",
"surname": "Majeed",
"fullName": "Abdul Majeed",
"affiliation": "Gachon University",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Seong Oun",
"surname": "Hwang",
"fullName": "Seong Oun Hwang",
"affiliation": "Gachon University",
"__typename": "ArticleAuthorType"
}
],
"replicability": null,
"showBuyMe": true,
"showRecommendedArticles": true,
"isOpenAccess": false,
"issueNum": "08",
"pubDate": "2022-08-01 00:00:00",
"pubType": "mags",
"pages": "57-69",
"year": "2022",
"issn": "0018-9162",
"isbn": null,
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"recommendedArticles": [
{
"id": "proceedings/pdp/2022/6958/0/695800a275",
"title": "An approach to formal desription of the user notification scenarios in privacy policies",
"doi": null,
"abstractUrl": "/proceedings-article/pdp/2022/695800a275/1CFRZjpuwTK",
"parentPublication": {
"id": "proceedings/pdp/2022/6958/0",
"title": "2022 30th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tq/5555/01/09774940",
"title": "Privacy-Preserving Collaborative Data Collection and Analysis with Many Missing Values",
"doi": null,
"abstractUrl": "/journal/tq/5555/01/09774940/1Dlim2RcVEY",
"parentPublication": {
"id": "trans/tq",
"title": "IEEE Transactions on Dependable and Secure Computing",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/compsac/2022/8810/0/881000b555",
"title": "Trade-off between Privacy, Quality and Risk: Anonymization Strategy Evaluation for Data Warehouses",
"doi": null,
"abstractUrl": "/proceedings-article/compsac/2022/881000b555/1FJ5qO01mVy",
"parentPublication": {
"id": "proceedings/compsac/2022/8810/0",
"title": "2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/nana/2022/6131/0/613100a144",
"title": "Privacy risk estimation of online social networks",
"doi": null,
"abstractUrl": "/proceedings-article/nana/2022/613100a144/1JwPMuOaeOs",
"parentPublication": {
"id": "proceedings/nana/2022/6131/0",
"title": "2022 International Conference on Networking and Network Applications (NaNA)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icftic/2022/2195/0/10075291",
"title": "Design of Student Information Management System for Chinese University in Epidemic",
"doi": null,
"abstractUrl": "/proceedings-article/icftic/2022/10075291/1LRlbsX5r1e",
"parentPublication": {
"id": "proceedings/icftic/2022/2195/0",
"title": "2022 4th International Conference on Frontiers Technology of Information and Computer (ICFTIC)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cscloud-edgecom/2020/6550/0/09170992",
"title": "G-Model: A Novel Approach to Privacy-Preserving 1:M Microdata Publication",
"doi": null,
"abstractUrl": "/proceedings-article/cscloud-edgecom/2020/09170992/1mqcvGirq7u",
"parentPublication": {
"id": "proceedings/cscloud-edgecom/2020/6550/0",
"title": "2020 7th IEEE International Conference on Cyber Security and Cloud Computing (CSCloud)/2020 6th IEEE International Conference on Edge Computing and Scalable Cloud (EdgeCom)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/scc/2020/8789/0/878900a226",
"title": "PETA: Privacy Enabled Task Allocation",
"doi": null,
"abstractUrl": "/proceedings-article/scc/2020/878900a226/1pttWGPVMBy",
"parentPublication": {
"id": "proceedings/scc/2020/8789/0",
"title": "2020 IEEE International Conference on Services Computing (SCC)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/tps-isa/2020/8543/0/854300a134",
"title": "Privacy in the Era of 5G, IoT, Big Data and Machine Learning",
"doi": null,
"abstractUrl": "/proceedings-article/tps-isa/2020/854300a134/1qyxCWd78Y0",
"parentPublication": {
"id": "proceedings/tps-isa/2020/8543/0",
"title": "2020 Second IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "mags/sp/2021/05/09486476",
"title": "Privacy in a Time of COVID-19: How Concerned Are You?",
"doi": null,
"abstractUrl": "/magazine/sp/2021/05/09486476/1vg3ii8sLN6",
"parentPublication": {
"id": "mags/sp",
"title": "IEEE Security & Privacy",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/mass/2021/4935/0/493500a269",
"title": "Blockchain Meets COVID-19: A Framework for Contact Information Sharing and Risk Notification System",
"doi": null,
"abstractUrl": "/proceedings-article/mass/2021/493500a269/1ziOwlGmamQ",
"parentPublication": {
"id": "proceedings/mass/2021/4935/0",
"title": "2021 IEEE 18th International Conference on Mobile Ad Hoc and Smart Systems (MASS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"adjacentArticles": {
"previous": {
"fno": "09847318",
"articleId": "1FvJtK2Eisg",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "09847335",
"articleId": "1FvJvxlG4MM",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"webExtras": [],
"articleVideos": []
}
|
{
"issue": {
"id": "1zBamVZHyne",
"title": "Jan.",
"year": "2022",
"issueNum": "01",
"idPrefix": "tg",
"pubType": "journal",
"volume": "28",
"label": "Jan.",
"downloadables": {
"hasCover": false,
"__typename": "PeriodicalIssueDownloadablesType"
},
"__typename": "PeriodicalIssue"
},
"article": {
"id": "1xic0dHxM9a",
"doi": "10.1109/TVCG.2021.3114820",
"abstract": "There are a few prominent practices for conducting reviews of academic literature, including searching for specific keywords on Google Scholar or checking citations from some initial seed paper(s). These approaches serve a critical purpose for academic literature reviews, yet there remain challenges in identifying relevant literature when similar work may utilize different terminology (e.g., mixed-initiative visual analytics papers may not use the same terminology as papers on model-steering, yet the two topics are relevant to one another). In this paper, we introduce a system, VITALITY, intended to complement existing practices. In particular, VITALITY promotes serendipitous discovery of relevant literature using transformer language models, allowing users to find semantically similar papers in a word embedding space given (1) a list of input paper(s) or (2) a working abstract. VITALITY visualizes this document-level embedding space in an interactive 2-D scatterplot using dimension reduction. VITALITY also summarizes meta information about the document corpus or search query, including keywords and co-authors, and allows users to save and export papers for use in a literature review. We present qualitative findings from an evaluation of VITALITY, suggesting it can be a promising complementary technique for conducting academic literature reviews. Furthermore, we contribute data from 38 popular data visualization publication venues in VITALITY, and we provide scrapers for the open-source community to continue to grow the list of supported venues.",
"abstracts": [
{
"abstractType": "Regular",
"content": "There are a few prominent practices for conducting reviews of academic literature, including searching for specific keywords on Google Scholar or checking citations from some initial seed paper(s). These approaches serve a critical purpose for academic literature reviews, yet there remain challenges in identifying relevant literature when similar work may utilize different terminology (e.g., mixed-initiative visual analytics papers may not use the same terminology as papers on model-steering, yet the two topics are relevant to one another). In this paper, we introduce a system, VITALITY, intended to complement existing practices. In particular, VITALITY promotes serendipitous discovery of relevant literature using transformer language models, allowing users to find semantically similar papers in a word embedding space given (1) a list of input paper(s) or (2) a working abstract. VITALITY visualizes this document-level embedding space in an interactive 2-D scatterplot using dimension reduction. VITALITY also summarizes meta information about the document corpus or search query, including keywords and co-authors, and allows users to save and export papers for use in a literature review. We present qualitative findings from an evaluation of VITALITY, suggesting it can be a promising complementary technique for conducting academic literature reviews. Furthermore, we contribute data from 38 popular data visualization publication venues in VITALITY, and we provide scrapers for the open-source community to continue to grow the list of supported venues.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "There are a few prominent practices for conducting reviews of academic literature, including searching for specific keywords on Google Scholar or checking citations from some initial seed paper(s). These approaches serve a critical purpose for academic literature reviews, yet there remain challenges in identifying relevant literature when similar work may utilize different terminology (e.g., mixed-initiative visual analytics papers may not use the same terminology as papers on model-steering, yet the two topics are relevant to one another). In this paper, we introduce a system, VITALITY, intended to complement existing practices. In particular, VITALITY promotes serendipitous discovery of relevant literature using transformer language models, allowing users to find semantically similar papers in a word embedding space given (1) a list of input paper(s) or (2) a working abstract. VITALITY visualizes this document-level embedding space in an interactive 2-D scatterplot using dimension reduction. VITALITY also summarizes meta information about the document corpus or search query, including keywords and co-authors, and allows users to save and export papers for use in a literature review. We present qualitative findings from an evaluation of VITALITY, suggesting it can be a promising complementary technique for conducting academic literature reviews. Furthermore, we contribute data from 38 popular data visualization publication venues in VITALITY, and we provide scrapers for the open-source community to continue to grow the list of supported venues.",
"title": "VITALITY: Promoting Serendipitous Discovery of Academic Literature with Transformers & Visual Analytics",
"normalizedTitle": "VITALITY: Promoting Serendipitous Discovery of Academic Literature with Transformers & Visual Analytics",
"fno": "09552447",
"hasPdf": true,
"idPrefix": "tg",
"keywords": [
"Bibliographies",
"Data Visualization",
"Transformers",
"Keyword Search",
"Internet",
"Tools",
"Visual Analytics",
"Transformers",
"Word Embeddings",
"Literature Review",
"Web Scraper",
"Dataset",
"Visual Analytics"
],
"authors": [
{
"givenName": "Arpit",
"surname": "Narechania",
"fullName": "Arpit Narechania",
"affiliation": "Georgia Tech., United States",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Alireza",
"surname": "Karduni",
"fullName": "Alireza Karduni",
"affiliation": "UNC-Charlotte, United States",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Ryan",
"surname": "Wesslen",
"fullName": "Ryan Wesslen",
"affiliation": "UNC-Charlotte, United States",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Emily",
"surname": "Wall",
"fullName": "Emily Wall",
"affiliation": "Emory University, United States",
"__typename": "ArticleAuthorType"
}
],
"replicability": null,
"showBuyMe": true,
"showRecommendedArticles": true,
"isOpenAccess": false,
"issueNum": "01",
"pubDate": "2022-01-01 00:00:00",
"pubType": "trans",
"pages": "486-496",
"year": "2022",
"issn": "1077-2626",
"isbn": null,
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"recommendedArticles": [
{
"id": "proceedings/ecodim/2003/8590/0/01322643",
"title": "Electronics ecodesign research empirically studied",
"doi": null,
"abstractUrl": "/proceedings-article/ecodim/2003/01322643/12OmNyTwRhp",
"parentPublication": {
"id": "proceedings/ecodim/2003/8590/0",
"title": "2003. 3rd International Symposium on Environmentally Conscious Design and Inverse Manufacturing - EcoDesign'03",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icalt/2016/9041/0/9041a376",
"title": "The Effects of Motivation, Academic Emotions, and Self-Regulated Learning Strategies on Academic Achievements in Technology Enhanced Learning Environment",
"doi": null,
"abstractUrl": "/proceedings-article/icalt/2016/9041a376/12OmNzYeAYE",
"parentPublication": {
"id": "proceedings/icalt/2016/9041/0",
"title": "2016 IEEE 16th International Conference on Advanced Learning Technologies (ICALT)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "mags/cg/2018/05/mcg2018050038",
"title": "VitalVizor: A Visual Analytics System for Studying Urban Vitality",
"doi": null,
"abstractUrl": "/magazine/cg/2018/05/mcg2018050038/13WBGNxhc5X",
"parentPublication": {
"id": "mags/cg",
"title": "IEEE Computer Graphics and Applications",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/2013/07/ttg2013071076",
"title": "Guest Editors' Introduction: Special Section on the IEEE Conference on Visual Analytics Science and Technology (VAST)",
"doi": null,
"abstractUrl": "/journal/tg/2013/07/ttg2013071076/13rRUxOdD2D",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/eitt/2021/2757/0/275700a346",
"title": "The Application of Physiological Feedback to the Evaluation of Academic Emotion: A Literature Review",
"doi": null,
"abstractUrl": "/proceedings-article/eitt/2021/275700a346/1AFsp4BQj5e",
"parentPublication": {
"id": "proceedings/eitt/2021/2757/0",
"title": "2021 Tenth International Conference of Educational Innovation through Technology (EITT)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/fie/2022/6244/0/09962393",
"title": "Analysis of Academic Databases for Literature Review in the Computer Science Education Field",
"doi": null,
"abstractUrl": "/proceedings-article/fie/2022/09962393/1IHoj3gGzCM",
"parentPublication": {
"id": "proceedings/fie/2022/6244/0",
"title": "2022 IEEE Frontiers in Education Conference (FIE)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/2023/06/10081322",
"title": "How Does Attention Work in Vision Transformers? A Visual Analytics Attempt",
"doi": null,
"abstractUrl": "/journal/tg/2023/06/10081322/1LRbRtJhrG0",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/compsac/2020/7303/0/730300b097",
"title": "Google Scholar vs. Dblp vs. Microsoft Academic Search: An Indexing Comparison for Software Engineering Literature",
"doi": null,
"abstractUrl": "/proceedings-article/compsac/2020/730300b097/1nkDdU6ZiDe",
"parentPublication": {
"id": "proceedings/compsac/2020/7303/0",
"title": "2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/fie/2020/8961/0/09274264",
"title": "Literature Review on Work-Based Learning",
"doi": null,
"abstractUrl": "/proceedings-article/fie/2020/09274264/1phRM7OWAiA",
"parentPublication": {
"id": "proceedings/fie/2020/8961/0",
"title": "2020 IEEE Frontiers in Education Conference (FIE)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/2022/01/09552238",
"title": "What's the Situation with Situated Visualization? A Survey and Perspectives on Situatedness",
"doi": null,
"abstractUrl": "/journal/tg/2022/01/09552238/1xic77YygOk",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"adjacentArticles": {
"previous": {
"fno": "09552870",
"articleId": "1xic90zZWDu",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "09552840",
"articleId": "1xic2GL1FC0",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"webExtras": [
{
"id": "1zBaAJFMKZi",
"name": "ttg202201-09552447s1-supp1-3114820.mp4",
"location": "https://www.computer.org/csdl/api/v1/extra/ttg202201-09552447s1-supp1-3114820.mp4",
"extension": "mp4",
"size": "13.7 MB",
"__typename": "WebExtraType"
}
],
"articleVideos": []
}
|
{
"issue": {
"id": "12OmNqBKTZB",
"title": "December",
"year": "1994",
"issueNum": "06",
"idPrefix": "ex",
"pubType": "magazine",
"volume": "9",
"label": "December",
"downloadables": {
"hasCover": false,
"__typename": "PeriodicalIssueDownloadablesType"
},
"__typename": "PeriodicalIssue"
},
"article": {
"id": "13rRUyg2jPf",
"doi": "10.1109/64.363273",
"abstract": "In 1990, we started the Router project, a multistrategy-strategy adaptive navigation path planner. It assumes that a mission planner has generated a specific mission plan and identified specific path planning tasks. Given a specific path planning task, it uses a combination of model based and case based methods to solve it. New versions of the Router system view strategic metacontrol as a kind of design task that takes as input a specification of a problem solving task and gives as output the specification of a virtual architecture for addressing it. One version of the system operates in simulated navigation worlds and provides a simple natural language interface. Another version is embodied in Stimpy, an autonomous mobile robot. Stimpy addresses issues in spatial navigation beyond path planning, such as plan execution and monitoring. Our goal is to describe our general framework of multistrategy adaptive path planning, and the specific design of the Router system. To focus this discussion, we report on a series of experiments with Router in simulated navigation worlds.",
"abstracts": [
{
"abstractType": "Regular",
"content": "In 1990, we started the Router project, a multistrategy-strategy adaptive navigation path planner. It assumes that a mission planner has generated a specific mission plan and identified specific path planning tasks. Given a specific path planning task, it uses a combination of model based and case based methods to solve it. New versions of the Router system view strategic metacontrol as a kind of design task that takes as input a specification of a problem solving task and gives as output the specification of a virtual architecture for addressing it. One version of the system operates in simulated navigation worlds and provides a simple natural language interface. Another version is embodied in Stimpy, an autonomous mobile robot. Stimpy addresses issues in spatial navigation beyond path planning, such as plan execution and monitoring. Our goal is to describe our general framework of multistrategy adaptive path planning, and the specific design of the Router system. To focus this discussion, we report on a series of experiments with Router in simulated navigation worlds.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "In 1990, we started the Router project, a multistrategy-strategy adaptive navigation path planner. It assumes that a mission planner has generated a specific mission plan and identified specific path planning tasks. Given a specific path planning task, it uses a combination of model based and case based methods to solve it. New versions of the Router system view strategic metacontrol as a kind of design task that takes as input a specification of a problem solving task and gives as output the specification of a virtual architecture for addressing it. One version of the system operates in simulated navigation worlds and provides a simple natural language interface. Another version is embodied in Stimpy, an autonomous mobile robot. Stimpy addresses issues in spatial navigation beyond path planning, such as plan execution and monitoring. Our goal is to describe our general framework of multistrategy adaptive path planning, and the specific design of the Router system. To focus this discussion, we report on a series of experiments with Router in simulated navigation worlds.",
"title": "Multistrategy Adaptive Path Planning",
"normalizedTitle": "Multistrategy Adaptive Path Planning",
"fno": "x6057",
"hasPdf": true,
"idPrefix": "ex",
"keywords": [],
"authors": [
{
"givenName": "Ashok K.",
"surname": "Goel",
"fullName": "Ashok K. Goel",
"affiliation": null,
"__typename": "ArticleAuthorType"
},
{
"givenName": "Khaled S.",
"surname": "Ail",
"fullName": "Khaled S. Ail",
"affiliation": null,
"__typename": "ArticleAuthorType"
},
{
"givenName": "Michael W.",
"surname": "Donnellan",
"fullName": "Michael W. Donnellan",
"affiliation": null,
"__typename": "ArticleAuthorType"
},
{
"givenName": "Andres",
"surname": "Gomez de Silva Garza",
"fullName": "Andres Gomez de Silva Garza",
"affiliation": null,
"__typename": "ArticleAuthorType"
},
{
"givenName": "Todd J.",
"surname": "Callantine",
"fullName": "Todd J. Callantine",
"affiliation": null,
"__typename": "ArticleAuthorType"
}
],
"replicability": null,
"showBuyMe": true,
"showRecommendedArticles": false,
"isOpenAccess": false,
"issueNum": "06",
"pubDate": "1994-11-01 00:00:00",
"pubType": "mags",
"pages": "57-65",
"year": "1994",
"issn": "1541-1672",
"isbn": null,
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"recommendedArticles": [],
"adjacentArticles": {
"previous": {
"fno": "x6046",
"articleId": "13rRUytF45o",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "x6066",
"articleId": "13rRUwhpBK8",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"webExtras": [],
"articleVideos": []
}
|
{
"issue": {
"id": "12OmNBhpS2B",
"title": "April",
"year": "2014",
"issueNum": "04",
"idPrefix": "tg",
"pubType": "journal",
"volume": "20",
"label": "April",
"downloadables": {
"hasCover": false,
"__typename": "PeriodicalIssueDownloadablesType"
},
"__typename": "PeriodicalIssue"
},
"article": {
"id": "13rRUxOve9K",
"doi": "10.1109/TVCG.2014.18",
"abstract": "We investigated how the properties of interactive virtual reality systems affect user behavior in full-body embodied interactions. Our experiment compared four interactive virtual reality systems using different display types (CAVE vs. HMD) and modes of locomotion (walking vs. joystick). Participants performed a perceptual-motor coordination task, in which they had to choose among a series of opportunities to pass through a gate that cycled open and closed and then board a moving train. Mode of locomotion, but not type of display, affected how participants chose opportunities for action. Both mode of locomotion and display affected performance when participants acted on their choices. We conclude that technological properties of virtual reality system (both display and mode of locomotion) significantly affected opportunities for action available in the environment (affordances) and discuss implications for design and practical applications of immersive interactive systems.",
"abstracts": [
{
"abstractType": "Regular",
"content": "We investigated how the properties of interactive virtual reality systems affect user behavior in full-body embodied interactions. Our experiment compared four interactive virtual reality systems using different display types (CAVE vs. HMD) and modes of locomotion (walking vs. joystick). Participants performed a perceptual-motor coordination task, in which they had to choose among a series of opportunities to pass through a gate that cycled open and closed and then board a moving train. Mode of locomotion, but not type of display, affected how participants chose opportunities for action. Both mode of locomotion and display affected performance when participants acted on their choices. We conclude that technological properties of virtual reality system (both display and mode of locomotion) significantly affected opportunities for action available in the environment (affordances) and discuss implications for design and practical applications of immersive interactive systems.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "We investigated how the properties of interactive virtual reality systems affect user behavior in full-body embodied interactions. Our experiment compared four interactive virtual reality systems using different display types (CAVE vs. HMD) and modes of locomotion (walking vs. joystick). Participants performed a perceptual-motor coordination task, in which they had to choose among a series of opportunities to pass through a gate that cycled open and closed and then board a moving train. Mode of locomotion, but not type of display, affected how participants chose opportunities for action. Both mode of locomotion and display affected performance when participants acted on their choices. We conclude that technological properties of virtual reality system (both display and mode of locomotion) significantly affected opportunities for action available in the environment (affordances) and discuss implications for design and practical applications of immersive interactive systems.",
"title": "Dynamic Affordances in Embodied Interactive Systems: The Role of Display and Mode of Locomotion",
"normalizedTitle": "Dynamic Affordances in Embodied Interactive Systems: The Role of Display and Mode of Locomotion",
"fno": "ttg201404596",
"hasPdf": true,
"idPrefix": "tg",
"keywords": [
"Logic Gates",
"Legged Locomotion",
"Interactive Systems",
"Virtual Environments",
"Tracking",
"Psychology",
"Virtual Reality Embodied Interaction Affordances Perceptual Motor Coordination Display Type Interaction Technique Mode Of Locomotion"
],
"authors": [
{
"givenName": "Timofey Y.",
"surname": "Grechkin",
"fullName": "Timofey Y. Grechkin",
"affiliation": "Sch. of Interactive Arts + Technol., Simon Fraser Univ., Burnaby, BC, Canada",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Jodie M.",
"surname": "Plumert",
"fullName": "Jodie M. Plumert",
"affiliation": "Dept. of Psychol., Univ. of Iowa, Iowa City, IA, USA",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Joseph K.",
"surname": "Kearney",
"fullName": "Joseph K. Kearney",
"affiliation": "Dept. of Comput. Sci., Univ. of Iowa, Iowa City, IA, USA",
"__typename": "ArticleAuthorType"
}
],
"replicability": null,
"showBuyMe": true,
"showRecommendedArticles": true,
"isOpenAccess": false,
"issueNum": "04",
"pubDate": "2014-04-01 00:00:00",
"pubType": "trans",
"pages": "596-605",
"year": "2014",
"issn": "1077-2626",
"isbn": null,
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"recommendedArticles": [
{
"id": "proceedings/cira/1997/8138/0/81380094",
"title": "A Cerebellar Approach to Adaptive Locomotion for Legged Robots",
"doi": null,
"abstractUrl": "/proceedings-article/cira/1997/81380094/12OmNAOsMGB",
"parentPublication": {
"id": "proceedings/cira/1997/8138/0",
"title": "Computational Intelligence in Robotics and Automation, IEEE International Symposium on",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/vr/2014/2871/0/06802071",
"title": "A comparison of four different approaches to reducing unintended positional drift during walking-In-Place locomotion",
"doi": null,
"abstractUrl": "/proceedings-article/vr/2014/06802071/12OmNqzu6Ve",
"parentPublication": {
"id": "proceedings/vr/2014/2871/0",
"title": "2014 IEEE Virtual Reality (VR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/vr/2015/1727/0/07223389",
"title": "The effect of head mounted display weight and locomotion method on the perceived naturalness of virtual walking speeds",
"doi": null,
"abstractUrl": "/proceedings-article/vr/2015/07223389/12OmNwqft3l",
"parentPublication": {
"id": "proceedings/vr/2015/1727/0",
"title": "2015 IEEE Virtual Reality (VR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/vr/2005/8929/0/01492762",
"title": "Comparing VE locomotion interfaces",
"doi": null,
"abstractUrl": "/proceedings-article/vr/2005/01492762/12OmNx8fi8K",
"parentPublication": {
"id": "proceedings/vr/2005/8929/0",
"title": "IEEE Virtual Reality 2005",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/2018/07/07946183",
"title": "Walking with Virtual People: Evaluation of Locomotion Interfaces in Dynamic Environments",
"doi": null,
"abstractUrl": "/journal/tg/2018/07/07946183/13rRUEgs2C2",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/2022/05/09714054",
"title": "Remote research on locomotion interfaces for virtual reality: Replication of a lab-based study on teleporting interfaces",
"doi": null,
"abstractUrl": "/journal/tg/2022/05/09714054/1B0XZAXWaIg",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/5555/01/09737429",
"title": "Intentional Head-Motion Assisted Locomotion for Reducing Cybersickness",
"doi": null,
"abstractUrl": "/journal/tg/5555/01/09737429/1BQidPzNjBS",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/5555/01/09744001",
"title": "Influence of user posture and virtual exercise on impression of locomotion during VR observation",
"doi": null,
"abstractUrl": "/journal/tg/5555/01/09744001/1C8BFV420lq",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/vrw/2020/6532/0/09090608",
"title": "Towards an Affordance of Embodied Locomotion Interfaces in VR: How to Know How to Move?",
"doi": null,
"abstractUrl": "/proceedings-article/vrw/2020/09090608/1jIxnjPP9Ti",
"parentPublication": {
"id": "proceedings/vrw/2020/6532/0",
"title": "2020 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ismar/2020/8508/0/850800a452",
"title": "Studying the Inter-Relation Between Locomotion Techniques and Embodiment in Virtual Reality",
"doi": null,
"abstractUrl": "/proceedings-article/ismar/2020/850800a452/1pysvNRUnD2",
"parentPublication": {
"id": "proceedings/ismar/2020/8508/0",
"title": "2020 IEEE International Symposium on Mixed and Augmented Reality (ISMAR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"adjacentArticles": {
"previous": {
"fno": "ttg201404588",
"articleId": "13rRUyuegh9",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "ttg201404606",
"articleId": "13rRUwghd99",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"webExtras": [],
"articleVideos": []
}
|
{
"issue": {
"id": "12OmNvqEvRo",
"title": "PrePrints",
"year": "5555",
"issueNum": "01",
"idPrefix": "tg",
"pubType": "journal",
"volume": null,
"label": "PrePrints",
"downloadables": {
"hasCover": false,
"__typename": "PeriodicalIssueDownloadablesType"
},
"__typename": "PeriodicalIssue"
},
"article": {
"id": "1GIqrCx8RCE",
"doi": "10.1109/TVCG.2022.3207157",
"abstract": "Continuous locomotion in VR provides uninterrupted optical flow, which mimics real-world locomotion and supports path integration. However, optical flow limits the maximum speed and acceleration that can be effectively used without inducing cybersickness. In contrast, teleportation provides neither optical flow nor acceleration cues, and users can jump to any length without increasing cybersickness. However, teleportation cannot support continuous spatial updating and can increase disorientation. Thus, we designed ‘HyperJump’ in an attempt to merge benefits from continuous locomotion and teleportation. HyperJump adds iterative jumps every half a second on top of the continuous movement and was hypothesized to facilitate faster travel without compromising spatial awareness/orientation. In a user study, Participants travelled around a naturalistic virtual city with and without HyperJump (equivalent maximum speed). They followed waypoints to new landmarks, stopped near them and pointed back to all previously visited landmarks in random order. HyperJump was added to two continuous locomotion interfaces (controller- and leaning-based). Participants had better spatial awareness/orientation with leaning-based interfaces compared to controller-based (assessed via rapid pointing). With HyperJump, participants travelled significantly faster, while staying on the desired course without impairing their spatial knowledge. This provides evidence that optical flow can be effectively limited such that it facilitates faster travel without compromising spatial orientation. In future design iterations, we plan to utilize audio-visual effects to support jumping metaphors that help users better anticipate and interpret jumps, and use much larger virtual environments requiring faster speeds, where cybersickness will become increasingly prevalent and thus teleporting will become more important.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Continuous locomotion in VR provides uninterrupted optical flow, which mimics real-world locomotion and supports path integration. However, optical flow limits the maximum speed and acceleration that can be effectively used without inducing cybersickness. In contrast, teleportation provides neither optical flow nor acceleration cues, and users can jump to any length without increasing cybersickness. However, teleportation cannot support continuous spatial updating and can increase disorientation. Thus, we designed ‘HyperJump’ in an attempt to merge benefits from continuous locomotion and teleportation. HyperJump adds iterative jumps every half a second on top of the continuous movement and was hypothesized to facilitate faster travel without compromising spatial awareness/orientation. In a user study, Participants travelled around a naturalistic virtual city with and without HyperJump (equivalent maximum speed). They followed waypoints to new landmarks, stopped near them and pointed back to all previously visited landmarks in random order. HyperJump was added to two continuous locomotion interfaces (controller- and leaning-based). Participants had better spatial awareness/orientation with leaning-based interfaces compared to controller-based (assessed via rapid pointing). With HyperJump, participants travelled significantly faster, while staying on the desired course without impairing their spatial knowledge. This provides evidence that optical flow can be effectively limited such that it facilitates faster travel without compromising spatial orientation. In future design iterations, we plan to utilize audio-visual effects to support jumping metaphors that help users better anticipate and interpret jumps, and use much larger virtual environments requiring faster speeds, where cybersickness will become increasingly prevalent and thus teleporting will become more important.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Continuous locomotion in VR provides uninterrupted optical flow, which mimics real-world locomotion and supports path integration. However, optical flow limits the maximum speed and acceleration that can be effectively used without inducing cybersickness. In contrast, teleportation provides neither optical flow nor acceleration cues, and users can jump to any length without increasing cybersickness. However, teleportation cannot support continuous spatial updating and can increase disorientation. Thus, we designed ‘HyperJump’ in an attempt to merge benefits from continuous locomotion and teleportation. HyperJump adds iterative jumps every half a second on top of the continuous movement and was hypothesized to facilitate faster travel without compromising spatial awareness/orientation. In a user study, Participants travelled around a naturalistic virtual city with and without HyperJump (equivalent maximum speed). They followed waypoints to new landmarks, stopped near them and pointed back to all previously visited landmarks in random order. HyperJump was added to two continuous locomotion interfaces (controller- and leaning-based). Participants had better spatial awareness/orientation with leaning-based interfaces compared to controller-based (assessed via rapid pointing). With HyperJump, participants travelled significantly faster, while staying on the desired course without impairing their spatial knowledge. This provides evidence that optical flow can be effectively limited such that it facilitates faster travel without compromising spatial orientation. In future design iterations, we plan to utilize audio-visual effects to support jumping metaphors that help users better anticipate and interpret jumps, and use much larger virtual environments requiring faster speeds, where cybersickness will become increasingly prevalent and thus teleporting will become more important.",
"title": "Integrating Continuous and Teleporting VR Locomotion Into a Seamless ‘HyperJump’ Paradigm",
"normalizedTitle": "Integrating Continuous and Teleporting VR Locomotion Into a Seamless ‘HyperJump’ Paradigm",
"fno": "09894041",
"hasPdf": true,
"idPrefix": "tg",
"keywords": [
"Teleportation",
"Cybersickness",
"Optical Flow",
"Task Analysis",
"Navigation",
"Animation",
"Virtual Environments",
"Leaning",
"Locomotion",
"Semi Continuous Locomotion",
"Spatial Updating",
"Teleportation",
"Virtual Reality"
],
"authors": [
{
"givenName": "Ashu",
"surname": "Adhikari",
"fullName": "Ashu Adhikari",
"affiliation": "School of Interactive Arts & Technology, Simon Fraser University, Canada",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Daniel",
"surname": "Zielasko",
"fullName": "Daniel Zielasko",
"affiliation": "University of Trier, Germany",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Ivan",
"surname": "Aguilar",
"fullName": "Ivan Aguilar",
"affiliation": "School of Interactive Arts & Technology, Simon Fraser University, Canada",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Alexander",
"surname": "Bretin",
"fullName": "Alexander Bretin",
"affiliation": "School of Interactive Arts & Technology, Simon Fraser University, Canada",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Ernst",
"surname": "Kruijff",
"fullName": "Ernst Kruijff",
"affiliation": "Institute of Visual Computing, Bonn-Rhein-Sieg University of Applied Sciences, Germany",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Markus von der",
"surname": "Heyde",
"fullName": "Markus von der Heyde",
"affiliation": "vdH-IT and the School of Interactive Arts & Technology, Simon Fraser University, Canada",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Bernhard E.",
"surname": "Riecke",
"fullName": "Bernhard E. Riecke",
"affiliation": "School of Interactive Arts & Technology, Simon Fraser University, Canada",
"__typename": "ArticleAuthorType"
}
],
"replicability": null,
"showBuyMe": true,
"showRecommendedArticles": true,
"isOpenAccess": false,
"issueNum": "01",
"pubDate": "2022-09-01 00:00:00",
"pubType": "trans",
"pages": "1-17",
"year": "5555",
"issn": "1077-2626",
"isbn": null,
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"recommendedArticles": [
{
"id": "proceedings/vr/2018/3365/0/08446130",
"title": "Rapid, Continuous Movement Between Nodes as an Accessible Virtual Reality Locomotion Technique",
"doi": null,
"abstractUrl": "/proceedings-article/vr/2018/08446130/13bd1f3HvEx",
"parentPublication": {
"id": "proceedings/vr/2018/3365/0",
"title": "2018 IEEE Conference on Virtual Reality and 3D User Interfaces (VR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/vs-games/2018/7123/0/08493414",
"title": "Comparison of Teleportation and Fixed Track Driving in VR",
"doi": null,
"abstractUrl": "/proceedings-article/vs-games/2018/08493414/14tNJnrhcIw",
"parentPublication": {
"id": "proceedings/vs-games/2018/7123/0",
"title": "2018 10th International Conference on Virtual Worlds and Games for Serious Applications (VS-Games)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/5555/01/09737429",
"title": "Intentional Head-Motion Assisted Locomotion for Reducing Cybersickness",
"doi": null,
"abstractUrl": "/journal/tg/5555/01/09737429/1BQidPzNjBS",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/vr/2022/9617/0/961700a693",
"title": "Systematic Design Space Exploration of Discrete Virtual Rotations in VR",
"doi": null,
"abstractUrl": "/proceedings-article/vr/2022/961700a693/1CJbHGJZxeM",
"parentPublication": {
"id": "proceedings/vr/2022/9617/0",
"title": "2022 IEEE on Conference Virtual Reality and 3D User Interfaces (VR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ismar-adjunct/2022/5365/0/536500a794",
"title": "An Investigation on the Relationship between Cybersickness and Heart Rate Variability When Navigating a Virtual Environment",
"doi": null,
"abstractUrl": "/proceedings-article/ismar-adjunct/2022/536500a794/1J7We4du3FC",
"parentPublication": {
"id": "proceedings/ismar-adjunct/2022/5365/0",
"title": "2022 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ismar-adjunct/2022/5365/0/536500a501",
"title": "Exploring Three-Dimensional Locomotion Techniques in Virtual Reality",
"doi": null,
"abstractUrl": "/proceedings-article/ismar-adjunct/2022/536500a501/1J7WrBbMYEg",
"parentPublication": {
"id": "proceedings/ismar-adjunct/2022/5365/0",
"title": "2022 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/vrw/2020/6532/0/09090536",
"title": "Elastic-Move: Passive Haptic Device with Force Feedback for Virtual Reality Locomotion",
"doi": null,
"abstractUrl": "/proceedings-article/vrw/2020/09090536/1jIxqFQXvSE",
"parentPublication": {
"id": "proceedings/vrw/2020/6532/0",
"title": "2020 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/vrw/2021/4057/0/405700a373",
"title": "Continuous vs. Discontinuous (Teleport) Locomotion in VR: How Implications can Provide both Benefits and Disadvantages",
"doi": null,
"abstractUrl": "/proceedings-article/vrw/2021/405700a373/1tnWQrR1hAs",
"parentPublication": {
"id": "proceedings/vrw/2021/4057/0",
"title": "2021 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/vrw/2021/4057/0/405700a393",
"title": "Effects of a handlebar on standing VR locomotion",
"doi": null,
"abstractUrl": "/proceedings-article/vrw/2021/405700a393/1tnX2vv1TS8",
"parentPublication": {
"id": "proceedings/vrw/2021/4057/0",
"title": "2021 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/vrw/2021/4057/0/405700a383",
"title": "Combining Natural Techniques to Achieve Seamless Locomotion in Consumer VR Spaces",
"doi": null,
"abstractUrl": "/proceedings-article/vrw/2021/405700a383/1tnXJkWtJq8",
"parentPublication": {
"id": "proceedings/vrw/2021/4057/0",
"title": "2021 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"adjacentArticles": {
"previous": {
"fno": "09894103",
"articleId": "1GIqpPbyH7y",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "09894081",
"articleId": "1GIqtQDhf8I",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"webExtras": [
{
"id": "1GNpstTVfWM",
"name": "ttg555501-09894041s1-supp1-3207157.mp4",
"location": "https://www.computer.org/csdl/api/v1/extra/ttg555501-09894041s1-supp1-3207157.mp4",
"extension": "mp4",
"size": "53.2 MB",
"__typename": "WebExtraType"
}
],
"articleVideos": []
}
|
{
"issue": {
"id": "1KsRWKKVV7i",
"title": "March",
"year": "2023",
"issueNum": "03",
"idPrefix": "tp",
"pubType": "journal",
"volume": "45",
"label": "March",
"downloadables": {
"hasCover": false,
"__typename": "PeriodicalIssueDownloadablesType"
},
"__typename": "PeriodicalIssue"
},
"article": {
"id": "1Dqh2yvQWBi",
"doi": "10.1109/TPAMI.2022.3175371",
"abstract": "An effective 3D descriptor should be invariant to different geometric transformations, such as scale and rotation, robust to occlusions and clutter, and capable of generalising to different application domains. We present a simple yet effective method to learn general and distinctive 3D local descriptors that can be used to register point clouds that are captured in different domains. Point cloud patches are extracted, canonicalised with respect to their local reference frame, and encoded into scale and rotation-invariant compact descriptors by a deep neural network that is invariant to permutations of the input points. This design is what enables our descriptors to generalise across domains. We evaluate and compare our descriptors with alternative handcrafted and deep learning-based descriptors on several indoor and outdoor datasets that are reconstructed by using both RGBD sensors and laser scanners. Our descriptors outperform most recent descriptors by a large margin in terms of generalisation, and also become the state of the art in benchmarks where training and testing are performed in the same domain.",
"abstracts": [
{
"abstractType": "Regular",
"content": "An effective 3D descriptor should be invariant to different geometric transformations, such as scale and rotation, robust to occlusions and clutter, and capable of generalising to different application domains. We present a simple yet effective method to learn general and distinctive 3D local descriptors that can be used to register point clouds that are captured in different domains. Point cloud patches are extracted, canonicalised with respect to their local reference frame, and encoded into scale and rotation-invariant compact descriptors by a deep neural network that is invariant to permutations of the input points. This design is what enables our descriptors to generalise across domains. We evaluate and compare our descriptors with alternative handcrafted and deep learning-based descriptors on several indoor and outdoor datasets that are reconstructed by using both RGBD sensors and laser scanners. Our descriptors outperform most recent descriptors by a large margin in terms of generalisation, and also become the state of the art in benchmarks where training and testing are performed in the same domain.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "An effective 3D descriptor should be invariant to different geometric transformations, such as scale and rotation, robust to occlusions and clutter, and capable of generalising to different application domains. We present a simple yet effective method to learn general and distinctive 3D local descriptors that can be used to register point clouds that are captured in different domains. Point cloud patches are extracted, canonicalised with respect to their local reference frame, and encoded into scale and rotation-invariant compact descriptors by a deep neural network that is invariant to permutations of the input points. This design is what enables our descriptors to generalise across domains. We evaluate and compare our descriptors with alternative handcrafted and deep learning-based descriptors on several indoor and outdoor datasets that are reconstructed by using both RGBD sensors and laser scanners. Our descriptors outperform most recent descriptors by a large margin in terms of generalisation, and also become the state of the art in benchmarks where training and testing are performed in the same domain.",
"title": "Learning General and Distinctive 3D Local Deep Descriptors for Point Cloud Registration",
"normalizedTitle": "Learning General and Distinctive 3D Local Deep Descriptors for Point Cloud Registration",
"fno": "09775606",
"hasPdf": true,
"idPrefix": "tp",
"keywords": [
"Data Handling",
"Deep Learning Artificial Intelligence",
"Feature Extraction",
"Image Registration",
"Image Representation",
"Learning Artificial Intelligence",
"Alternative Handcrafted Learning Based Descriptors",
"Deep Learning Based Descriptors",
"Deep Neural Network",
"Different Application Domains",
"Different Geometric Transformations",
"Distinctive 3 D Local Descriptors",
"Effective 3 D Descriptor",
"General D Local Descriptors",
"Input Points",
"Local Reference Frame",
"Point Cloud Patches",
"Point Cloud Registration",
"Point Clouds",
"Recent Descriptors",
"Rotation Invariant Compact Descriptors",
"Simple Yet Effective Method",
"Three Dimensional Displays",
"Point Cloud Compression",
"Histograms",
"Electronics Packaging",
"Training",
"Covariance Matrices",
"Aggregates",
"Point Cloud Registration",
"Deep Learning Based Descriptors",
"Local Reference Frame",
"Contrastive Learning"
],
"authors": [
{
"givenName": "Fabio",
"surname": "Poiesi",
"fullName": "Fabio Poiesi",
"affiliation": "Technologies of Vision Lab, Fondazione Bruno Kessler, Trento, Italy",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Davide",
"surname": "Boscaini",
"fullName": "Davide Boscaini",
"affiliation": "Technologies of Vision Lab, Fondazione Bruno Kessler, Trento, Italy",
"__typename": "ArticleAuthorType"
}
],
"replicability": null,
"showBuyMe": true,
"showRecommendedArticles": true,
"isOpenAccess": false,
"issueNum": "03",
"pubDate": "2023-03-01 00:00:00",
"pubType": "trans",
"pages": "3979-3985",
"year": "2023",
"issn": "0162-8828",
"isbn": null,
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"recommendedArticles": [
{
"id": "proceedings/cvpr/2013/4989/0/4989c890",
"title": "Dense Segmentation-Aware Descriptors",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2013/4989c890/12OmNqC2uWu",
"parentPublication": {
"id": "proceedings/cvpr/2013/4989/0",
"title": "2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/fskd/2009/3735/1/3735a256",
"title": "Point Pattern Matching with Locality Preserving Descriptors",
"doi": null,
"abstractUrl": "/proceedings-article/fskd/2009/3735a256/12OmNqESufI",
"parentPublication": {
"id": "proceedings/fskd/2009/3735/1",
"title": "Fuzzy Systems and Knowledge Discovery, Fourth International Conference on",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icdmw/2013/3142/0/3143a032",
"title": "On Using SIFT Descriptors for Image Parameter Evaluation",
"doi": null,
"abstractUrl": "/proceedings-article/icdmw/2013/3143a032/12OmNyQph1g",
"parentPublication": {
"id": "proceedings/icdmw/2013/3142/0",
"title": "2013 IEEE 13th International Conference on Data Mining Workshops (ICDMW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icpr/2010/4109/0/4109b381",
"title": "Local Rotation Invariant Patch Descriptors for 3D Vector Fields",
"doi": null,
"abstractUrl": "/proceedings-article/icpr/2010/4109b381/12OmNzahbWd",
"parentPublication": {
"id": "proceedings/icpr/2010/4109/0",
"title": "Pattern Recognition, International Conference on",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tp/2012/10/ttp2012102031",
"title": "Rotationally Invariant Descriptors Using Intensity Order Pooling",
"doi": null,
"abstractUrl": "/journal/tp/2012/10/ttp2012102031/13rRUyYSWma",
"parentPublication": {
"id": "trans/tp",
"title": "IEEE Transactions on Pattern Analysis & Machine Intelligence",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tp/2023/03/09792207",
"title": "You Only Train Once: Learning General and Distinctive 3D Local Descriptors",
"doi": null,
"abstractUrl": "/journal/tp/2023/03/09792207/1E5LAGI5KXC",
"parentPublication": {
"id": "trans/tp",
"title": "IEEE Transactions on Pattern Analysis & Machine Intelligence",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tp/5555/01/10044259",
"title": "RoReg: Pairwise Point Cloud Registration with Oriented Descriptors and Local Rotations",
"doi": null,
"abstractUrl": "/journal/tp/5555/01/10044259/1KL6SJ4jOzS",
"parentPublication": {
"id": "trans/tp",
"title": "IEEE Transactions on Pattern Analysis & Machine Intelligence",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icpr/2021/8808/0/09411978",
"title": "Distinctive 3D local deep descriptors",
"doi": null,
"abstractUrl": "/proceedings-article/icpr/2021/09411978/1tmhxUd3Z6w",
"parentPublication": {
"id": "proceedings/icpr/2021/8808/0",
"title": "2020 25th International Conference on Pattern Recognition (ICPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/vrw/2021/4057/0/405700a516",
"title": "Matching 2D Image Patches and 3D Point Cloud Volumes by Learning Local Cross-domain Feature Descriptors",
"doi": null,
"abstractUrl": "/proceedings-article/vrw/2021/405700a516/1tnXntGFco8",
"parentPublication": {
"id": "proceedings/vrw/2021/4057/0",
"title": "2021 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tp/2022/12/09609696",
"title": "Unsupervised Learning of Local Equivariant Descriptors for Point Clouds",
"doi": null,
"abstractUrl": "/journal/tp/2022/12/09609696/1yoxG62W1La",
"parentPublication": {
"id": "trans/tp",
"title": "IEEE Transactions on Pattern Analysis & Machine Intelligence",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"adjacentArticles": {
"previous": {
"fno": "09793600",
"articleId": "1E5LBk8UQhi",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "09773979",
"articleId": "1DjDomoj3yg",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"webExtras": [],
"articleVideos": []
}
|
{
"issue": {
"id": "12OmNxvO04X",
"title": "PrePrints",
"year": "5555",
"issueNum": "01",
"idPrefix": "tp",
"pubType": "journal",
"volume": null,
"label": "PrePrints",
"downloadables": {
"hasCover": false,
"__typename": "PeriodicalIssueDownloadablesType"
},
"__typename": "PeriodicalIssue"
},
"article": {
"id": "1KL6SJ4jOzS",
"doi": "10.1109/TPAMI.2023.3244951",
"abstract": "We present RoReg, a novel point cloud registration framework that fully exploits oriented descriptors and estimated local rotations in the whole registration pipeline. Previous methods mainly focus on extracting rotation-invariant descriptors for registration but unanimously neglect the orientations of descriptors. In this paper, we show that the oriented descriptors and the estimated local rotations are very useful in the whole registration pipeline, including feature description, feature detection, feature matching, and transformation estimation. Consequently, we design a novel oriented descriptor RoReg-Desc and apply RoReg-Desc to estimate the local rotations. Such estimated local rotations enable us to develop a rotation-guided detector, a rotation coherence matcher, and a one-shot-estimation RANSAC, all of which greatly improve the registration performance. Extensive experiments demonstrate that RoReg achieves state-of-the-art performance on the widely-used 3DMatch and 3DLoMatch datasets, and also generalizes well to the outdoor ETH dataset. In particular, we also provide in-depth analysis on each component of RoReg, validating the improvements brought by oriented descriptors and the estimated local rotations. Source code and supplementary material are available at <uri>https://github.com/HpWang-whu/RoReg</uri>.",
"abstracts": [
{
"abstractType": "Regular",
"content": "We present RoReg, a novel point cloud registration framework that fully exploits oriented descriptors and estimated local rotations in the whole registration pipeline. Previous methods mainly focus on extracting rotation-invariant descriptors for registration but unanimously neglect the orientations of descriptors. In this paper, we show that the oriented descriptors and the estimated local rotations are very useful in the whole registration pipeline, including feature description, feature detection, feature matching, and transformation estimation. Consequently, we design a novel oriented descriptor RoReg-Desc and apply RoReg-Desc to estimate the local rotations. Such estimated local rotations enable us to develop a rotation-guided detector, a rotation coherence matcher, and a one-shot-estimation RANSAC, all of which greatly improve the registration performance. Extensive experiments demonstrate that RoReg achieves state-of-the-art performance on the widely-used 3DMatch and 3DLoMatch datasets, and also generalizes well to the outdoor ETH dataset. In particular, we also provide in-depth analysis on each component of RoReg, validating the improvements brought by oriented descriptors and the estimated local rotations. Source code and supplementary material are available at <uri>https://github.com/HpWang-whu/RoReg</uri>.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "We present RoReg, a novel point cloud registration framework that fully exploits oriented descriptors and estimated local rotations in the whole registration pipeline. Previous methods mainly focus on extracting rotation-invariant descriptors for registration but unanimously neglect the orientations of descriptors. In this paper, we show that the oriented descriptors and the estimated local rotations are very useful in the whole registration pipeline, including feature description, feature detection, feature matching, and transformation estimation. Consequently, we design a novel oriented descriptor RoReg-Desc and apply RoReg-Desc to estimate the local rotations. Such estimated local rotations enable us to develop a rotation-guided detector, a rotation coherence matcher, and a one-shot-estimation RANSAC, all of which greatly improve the registration performance. Extensive experiments demonstrate that RoReg achieves state-of-the-art performance on the widely-used 3DMatch and 3DLoMatch datasets, and also generalizes well to the outdoor ETH dataset. In particular, we also provide in-depth analysis on each component of RoReg, validating the improvements brought by oriented descriptors and the estimated local rotations. Source code and supplementary material are available at https://github.com/HpWang-whu/RoReg.",
"title": "RoReg: Pairwise Point Cloud Registration with Oriented Descriptors and Local Rotations",
"normalizedTitle": "RoReg: Pairwise Point Cloud Registration with Oriented Descriptors and Local Rotations",
"fno": "10044259",
"hasPdf": true,
"idPrefix": "tp",
"keywords": [
"Feature Extraction",
"Detectors",
"Point Cloud Compression",
"Training",
"Estimation",
"Pipelines",
"Feature Detection",
"3 D Registration",
"Point Cloud Registration",
"Learning Based Descriptors",
"Feature Detection",
"Feature Matching"
],
"authors": [
{
"givenName": "Haiping",
"surname": "Wang",
"fullName": "Haiping Wang",
"affiliation": "Department of State Key Laboratory of information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, China",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Yuan",
"surname": "Liu",
"fullName": "Yuan Liu",
"affiliation": "Computer Science Department, The University of Hong Kong, China",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Qingyong",
"surname": "Hu",
"fullName": "Qingyong Hu",
"affiliation": "Department of Computer Science, University of Oxford, U.K.",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Bing",
"surname": "Wang",
"fullName": "Bing Wang",
"affiliation": "Department of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University, China",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Jianguo",
"surname": "Chen",
"fullName": "Jianguo Chen",
"affiliation": "DiDi Chuxing, China",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Zhen",
"surname": "Dong",
"fullName": "Zhen Dong",
"affiliation": "Department of State Key Laboratory of information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, China",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Yulan",
"surname": "Guo",
"fullName": "Yulan Guo",
"affiliation": "School of Electronics and Communication Engineering, Shenzhen Campus of Sun Yat-sen University, China",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Wenping",
"surname": "Wang",
"fullName": "Wenping Wang",
"affiliation": "Department of Visualization, Texas A&M University, USA",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Bisheng",
"surname": "Yang",
"fullName": "Bisheng Yang",
"affiliation": "Department of State Key Laboratory of information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, China",
"__typename": "ArticleAuthorType"
}
],
"replicability": null,
"showBuyMe": true,
"showRecommendedArticles": true,
"isOpenAccess": false,
"issueNum": "01",
"pubDate": "2023-02-01 00:00:00",
"pubType": "trans",
"pages": "1-18",
"year": "5555",
"issn": "0162-8828",
"isbn": null,
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"recommendedArticles": [
{
"id": "proceedings/iccv/2017/1032/0/1032a891",
"title": "Local-to-Global Point Cloud Registration Using a Dictionary of Viewpoint Descriptors",
"doi": null,
"abstractUrl": "/proceedings-article/iccv/2017/1032a891/12OmNwp74DR",
"parentPublication": {
"id": "proceedings/iccv/2017/1032/0",
"title": "2017 IEEE International Conference on Computer Vision (ICCV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tp/2023/01/09705149",
"title": "STORM: Structure-Based Overlap Matching for Partial Point Cloud Registration",
"doi": null,
"abstractUrl": "/journal/tp/2023/01/09705149/1AII6wed0Bi",
"parentPublication": {
"id": "trans/tp",
"title": "IEEE Transactions on Pattern Analysis & Machine Intelligence",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/5555/01/09736452",
"title": "WSDesc: Weakly Supervised 3D Local Descriptor Learning for Point Cloud Registration",
"doi": null,
"abstractUrl": "/journal/tg/5555/01/09736452/1BN1Ujkoysg",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccv/2021/2812/0/281200p5994",
"title": "HRegNet: A Hierarchical Network for Large-scale Outdoor LiDAR Point Cloud Registration",
"doi": null,
"abstractUrl": "/proceedings-article/iccv/2021/281200p5994/1BmFeO4ChZC",
"parentPublication": {
"id": "proceedings/iccv/2021/2812/0",
"title": "2021 IEEE/CVF International Conference on Computer Vision (ICCV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccv/2021/2812/0/281200f510",
"title": "Feature Interactive Representation for Point Cloud Registration",
"doi": null,
"abstractUrl": "/proceedings-article/iccv/2021/281200f510/1BmFkzf6HuM",
"parentPublication": {
"id": "proceedings/iccv/2021/2812/0",
"title": "2021 IEEE/CVF International Conference on Computer Vision (ICCV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tp/2023/03/09775606",
"title": "Learning General and Distinctive 3D Local Deep Descriptors for Point Cloud Registration",
"doi": null,
"abstractUrl": "/journal/tp/2023/03/09775606/1Dqh2yvQWBi",
"parentPublication": {
"id": "trans/tp",
"title": "IEEE Transactions on Pattern Analysis & Machine Intelligence",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tp/2023/03/09792207",
"title": "You Only Train Once: Learning General and Distinctive 3D Local Descriptors",
"doi": null,
"abstractUrl": "/journal/tp/2023/03/09792207/1E5LAGI5KXC",
"parentPublication": {
"id": "trans/tp",
"title": "IEEE Transactions on Pattern Analysis & Machine Intelligence",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/ai/5555/01/09866792",
"title": "Partial Point Cloud Registration with Deep Local Feature",
"doi": null,
"abstractUrl": "/journal/ai/5555/01/09866792/1G7UlGeNaiA",
"parentPublication": {
"id": "trans/ai",
"title": "IEEE Transactions on Artificial Intelligence",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tp/5555/01/10097640",
"title": "Sparse-to-Dense Matching Network for Large-scale LiDAR Point Cloud Registration",
"doi": null,
"abstractUrl": "/journal/tp/5555/01/10097640/1M9lILSRgL6",
"parentPublication": {
"id": "trans/tp",
"title": "IEEE Transactions on Pattern Analysis & Machine Intelligence",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2020/7168/0/716800b916",
"title": "End-to-End Learning Local Multi-View Descriptors for 3D Point Clouds",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2020/716800b916/1m3nqr6FLmU",
"parentPublication": {
"id": "proceedings/cvpr/2020/7168/0",
"title": "2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"adjacentArticles": {
"previous": {
"fno": "10043749",
"articleId": "1KJs5SH0na8",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "10044160",
"articleId": "1KL6TgYfsLC",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"webExtras": [
{
"id": "1KMLSt16hz2",
"name": "ttp555501-010044259s1-supp1-3244951.pdf",
"location": "https://www.computer.org/csdl/api/v1/extra/ttp555501-010044259s1-supp1-3244951.pdf",
"extension": "pdf",
"size": "1.61 MB",
"__typename": "WebExtraType"
}
],
"articleVideos": []
}
|
{
"issue": {
"id": "12OmNwE9OmR",
"title": "July-Sept.",
"year": "2014",
"issueNum": "03",
"idPrefix": "cc",
"pubType": "journal",
"volume": "2",
"label": "July-Sept.",
"downloadables": {
"hasCover": false,
"__typename": "PeriodicalIssueDownloadablesType"
},
"__typename": "PeriodicalIssue"
},
"article": {
"id": "13rRUwdIOYO",
"doi": "10.1109/TCC.2014.2321168",
"abstract": "The popularity and rapid development of cloud computing in recent years has led to a huge amount of publications containing the achieved knowledge of this area of research. Due to the interdisciplinary nature and high relevance of cloud computing research, it becomes increasingly difficult or even impossible to understand the overall structure and development of this field without analytical approaches. While evaluating science has a long tradition in many fields, we identify a lack of a comprehensive scientometric study in the area of cloud computing. Based on a large bibliographic data base, this study applies scientometric means to empirically study the evolution and state of cloud computing research with a view from above the clouds. By this, we provide extensive insights into publication patterns, research impact and research productivity. Furthermore, we explore the interplay of related subtopics by analyzing keyword clusters. The results of this study provide a better understanding of patterns, trends and other important factors as a basis for directing research activities, sharing knowledge and collaborating in the area of cloud computing research.",
"abstracts": [
{
"abstractType": "Regular",
"content": "The popularity and rapid development of cloud computing in recent years has led to a huge amount of publications containing the achieved knowledge of this area of research. Due to the interdisciplinary nature and high relevance of cloud computing research, it becomes increasingly difficult or even impossible to understand the overall structure and development of this field without analytical approaches. While evaluating science has a long tradition in many fields, we identify a lack of a comprehensive scientometric study in the area of cloud computing. Based on a large bibliographic data base, this study applies scientometric means to empirically study the evolution and state of cloud computing research with a view from above the clouds. By this, we provide extensive insights into publication patterns, research impact and research productivity. Furthermore, we explore the interplay of related subtopics by analyzing keyword clusters. The results of this study provide a better understanding of patterns, trends and other important factors as a basis for directing research activities, sharing knowledge and collaborating in the area of cloud computing research.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "The popularity and rapid development of cloud computing in recent years has led to a huge amount of publications containing the achieved knowledge of this area of research. Due to the interdisciplinary nature and high relevance of cloud computing research, it becomes increasingly difficult or even impossible to understand the overall structure and development of this field without analytical approaches. While evaluating science has a long tradition in many fields, we identify a lack of a comprehensive scientometric study in the area of cloud computing. Based on a large bibliographic data base, this study applies scientometric means to empirically study the evolution and state of cloud computing research with a view from above the clouds. By this, we provide extensive insights into publication patterns, research impact and research productivity. Furthermore, we explore the interplay of related subtopics by analyzing keyword clusters. The results of this study provide a better understanding of patterns, trends and other important factors as a basis for directing research activities, sharing knowledge and collaborating in the area of cloud computing research.",
"title": "A Scientometric Analysis of Cloud Computing Literature",
"normalizedTitle": "A Scientometric Analysis of Cloud Computing Literature",
"fno": "06808484",
"hasPdf": true,
"idPrefix": "cc",
"keywords": [
"Cloud Computing",
"Bibliometrics",
"Productivity",
"Market Research",
"Indexes",
"Keyword Cluster Analysis",
"Cloud Computing",
"Cloud Computing Research",
"Scientometric Analysis",
"Scientometrics"
],
"authors": [
{
"givenName": "Leonard",
"surname": "Heilig",
"fullName": "Leonard Heilig",
"affiliation": "Institute of Information Systems, University of Hamburg, Von-Melle-Park 5, Hamburg, Germany",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Stefan",
"surname": "Vob",
"fullName": "Stefan Vob",
"affiliation": "Institute of Information Systems, University of Hamburg, Von-Melle-Park 5, Hamburg, Germany",
"__typename": "ArticleAuthorType"
}
],
"replicability": null,
"showBuyMe": true,
"showRecommendedArticles": true,
"isOpenAccess": false,
"issueNum": "03",
"pubDate": "2014-07-01 00:00:00",
"pubType": "trans",
"pages": "266-278",
"year": "2014",
"issn": "2168-7161",
"isbn": null,
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"recommendedArticles": [
{
"id": "proceedings/imis/2012/4684/0/4684a965",
"title": "Automatic Bibliometric Analysis of Research Literature in Adult Education",
"doi": null,
"abstractUrl": "/proceedings-article/imis/2012/4684a965/12OmNBSSV89",
"parentPublication": {
"id": "proceedings/imis/2012/4684/0",
"title": "Innovative Mobile and Internet Services in Ubiquitous Computing, International Conference on",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iiki/2014/8003/0/07064039",
"title": "Patented Network Analysis on Cloud Computing Technology in Internet of Things",
"doi": null,
"abstractUrl": "/proceedings-article/iiki/2014/07064039/12OmNyUFfWw",
"parentPublication": {
"id": "proceedings/iiki/2014/8003/0",
"title": "2014 International Conference on Identification, Information and Knowledge in the Internet of Things (IIKI)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/wism/2010/4224/2/05662439",
"title": "Scientometric Study of the Journal NeuroImage 1992-2009",
"doi": null,
"abstractUrl": "/proceedings-article/wism/2010/05662439/12OmNylbowg",
"parentPublication": {
"id": "proceedings/wism/2010/4224/2",
"title": "2010 International Conference on Web Information Systems and Mining (WISM 2010)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ic3/2015/7947/0/07346675",
"title": "A Scientometric analysis of computer science research in India",
"doi": null,
"abstractUrl": "/proceedings-article/ic3/2015/07346675/12OmNzcPAp9",
"parentPublication": {
"id": "proceedings/ic3/2015/7947/0",
"title": "2015 Eighth International Conference on Contemporary Computing (IC3)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ict4m/2018/7525/0/752500a278",
"title": "A Systematic Literature Review of Cloud Computing Adoption and Impacts among Small Medium Enterprises (SMEs)",
"doi": null,
"abstractUrl": "/proceedings-article/ict4m/2018/752500a278/17D45W1Oa5O",
"parentPublication": {
"id": "proceedings/ict4m/2018/7525/0",
"title": "2018 International Conference on Information and Communication Technology for the Muslim World (ICT4M)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/5555/01/09733942",
"title": "Scientometric Analysis of Interdisciplinary Collaboration and Gender Trends in 30 Years of IEEE VIS Publications",
"doi": null,
"abstractUrl": "/journal/tg/5555/01/09733942/1BJIbG1OGqc",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icise-ie/2021/3829/0/382900a931",
"title": "Digital Storytelling in Chinese Education (2005-2021): A Scientometric Analysis by Using CiteSpace",
"doi": null,
"abstractUrl": "/proceedings-article/icise-ie/2021/382900a931/1C8GpyDZst2",
"parentPublication": {
"id": "proceedings/icise-ie/2021/3829/0",
"title": "2021 2nd International Conference on Information Science and Education (ICISE-IE)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/dsc/2022/7480/0/748000a142",
"title": "Knowledge Graph of Artificial Intelligence in Medicine: A Scientometric Analysis",
"doi": null,
"abstractUrl": "/proceedings-article/dsc/2022/748000a142/1H44jLMDJHW",
"parentPublication": {
"id": "proceedings/dsc/2022/7480/0",
"title": "2022 7th IEEE International Conference on Data Science in Cyberspace (DSC)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/msieid/2020/1541/0/154100a264",
"title": "How Can Blockchain Shape Digital Transformation: A Scientometric Analysis and Review for Financial Services",
"doi": null,
"abstractUrl": "/proceedings-article/msieid/2020/154100a264/1scHFeEvuNy",
"parentPublication": {
"id": "proceedings/msieid/2020/1541/0",
"title": "2020 Management Science Informatization and Economic Innovation Development Conference (MSIEID)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icaie/2021/2492/0/249200a192",
"title": "The Implications of the Artificial Intelligence Capability for Language Industry Professionals: A Scientometric Analysis",
"doi": null,
"abstractUrl": "/proceedings-article/icaie/2021/249200a192/1wV1F3VSkms",
"parentPublication": {
"id": "proceedings/icaie/2021/2492/0",
"title": "2021 2nd International Conference on Artificial Intelligence and Education (ICAIE)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"adjacentArticles": {
"previous": {
"fno": "06811199",
"articleId": "13rRUxASu2P",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "06808479",
"articleId": "13rRUxASu5K",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"webExtras": [],
"articleVideos": []
}
|
{
"issue": {
"id": "1zBamVZHyne",
"title": "Jan.",
"year": "2022",
"issueNum": "01",
"idPrefix": "tg",
"pubType": "journal",
"volume": "28",
"label": "Jan.",
"downloadables": {
"hasCover": false,
"__typename": "PeriodicalIssueDownloadablesType"
},
"__typename": "PeriodicalIssue"
},
"article": {
"id": "1xic2GL1FC0",
"doi": "10.1109/TVCG.2021.3114787",
"abstract": "We present an exploratory analysis of gender representation among the authors, committee members, and award winners at the IEEE Visualization (VIS) conference over the last 30 years. Our goal is to provide descriptive data on which diversity discussions and efforts in the community can build. We look in particular at the gender of VIS authors as a proxy for the community at large. We consider measures of overall gender representation among authors, differences in careers, positions in author lists, and collaborations. We found that the proportion of female authors has increased from 9% in the first five years to 22% in the last five years of the conference. Over the years, we found the same representation of women in program committees and slightly more women in organizing committees. Women are less likely to appear in the last author position, but more in the middle positions. In terms of collaboration patterns, female authors tend to collaborate more than expected with other women in the community. All non-gender related data is available on https://osf.io/ydfj4/ and the gender-author matching can be accessed through https://nyu.databrary.org/volume/1301.",
"abstracts": [
{
"abstractType": "Regular",
"content": "We present an exploratory analysis of gender representation among the authors, committee members, and award winners at the IEEE Visualization (VIS) conference over the last 30 years. Our goal is to provide descriptive data on which diversity discussions and efforts in the community can build. We look in particular at the gender of VIS authors as a proxy for the community at large. We consider measures of overall gender representation among authors, differences in careers, positions in author lists, and collaborations. We found that the proportion of female authors has increased from 9% in the first five years to 22% in the last five years of the conference. Over the years, we found the same representation of women in program committees and slightly more women in organizing committees. Women are less likely to appear in the last author position, but more in the middle positions. In terms of collaboration patterns, female authors tend to collaborate more than expected with other women in the community. All non-gender related data is available on https://osf.io/ydfj4/ and the gender-author matching can be accessed through https://nyu.databrary.org/volume/1301.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "We present an exploratory analysis of gender representation among the authors, committee members, and award winners at the IEEE Visualization (VIS) conference over the last 30 years. Our goal is to provide descriptive data on which diversity discussions and efforts in the community can build. We look in particular at the gender of VIS authors as a proxy for the community at large. We consider measures of overall gender representation among authors, differences in careers, positions in author lists, and collaborations. We found that the proportion of female authors has increased from 9% in the first five years to 22% in the last five years of the conference. Over the years, we found the same representation of women in program committees and slightly more women in organizing committees. Women are less likely to appear in the last author position, but more in the middle positions. In terms of collaboration patterns, female authors tend to collaborate more than expected with other women in the community. All non-gender related data is available on https://osf.io/ydfj4/ and the gender-author matching can be accessed through https://nyu.databrary.org/volume/1301.",
"title": "Gender in 30 Years of IEEE Visualization",
"normalizedTitle": "Gender in 30 Years of IEEE Visualization",
"fno": "09552840",
"hasPdf": true,
"idPrefix": "tg",
"keywords": [
"Engineering Profession",
"Data Visualization",
"Manuals",
"Conferences",
"Collaboration",
"Cleaning",
"Gender Issues",
"Visualization",
"Gender",
"Diversity",
"Publication",
"Scientometry",
"Collaboration"
],
"authors": [
{
"givenName": "Natkamon",
"surname": "Tovanich",
"fullName": "Natkamon Tovanich",
"affiliation": "IRT SystemX, Paris-Saclay, Palaiseau, France",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Pierre",
"surname": "Dragicevic",
"fullName": "Pierre Dragicevic",
"affiliation": "Université Paris-Saclay, CNRS, Inria, LISN, Orsay, France",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Petra",
"surname": "Isenberg",
"fullName": "Petra Isenberg",
"affiliation": "Université Paris-Saclay, CNRS, Inria, LISN, Orsay, France",
"__typename": "ArticleAuthorType"
}
],
"replicability": null,
"showBuyMe": true,
"showRecommendedArticles": true,
"isOpenAccess": false,
"issueNum": "01",
"pubDate": "2022-01-01 00:00:00",
"pubType": "trans",
"pages": "497-507",
"year": "2022",
"issn": "1077-2626",
"isbn": null,
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"recommendedArticles": [
{
"id": "proceedings/fie/2006/0256/0/04117062",
"title": "\"You're all a bunch of fucking feminists:\" Addressing the perceived conflict between gender and professional identities using the Montreal Massacre",
"doi": null,
"abstractUrl": "/proceedings-article/fie/2006/04117062/12OmNzVoBAd",
"parentPublication": {
"id": "proceedings/fie/2006/0256/0",
"title": "Proceedings. Frontiers in Education. 36th Annual Conference",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "mags/ex/2016/04/mex2016040062",
"title": "What Men Say, What Women Hear: Finding Gender-Specific Meaning Shades",
"doi": null,
"abstractUrl": "/magazine/ex/2016/04/mex2016040062/13rRUxBJhj6",
"parentPublication": {
"id": "mags/ex",
"title": "IEEE Intelligent Systems",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/5555/01/09733942",
"title": "Scientometric Analysis of Interdisciplinary Collaboration and Gender Trends in 30 Years of IEEE VIS Publications",
"doi": null,
"abstractUrl": "/journal/tg/5555/01/09733942/1BJIbG1OGqc",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/geicse/2022/9294/0/929400a019",
"title": "Towards gender balance in modern hackathons: literature-based approaches for female inclusiveness",
"doi": null,
"abstractUrl": "/proceedings-article/geicse/2022/929400a019/1FRKuDW3Z3G",
"parentPublication": {
"id": "proceedings/geicse/2022/9294/0",
"title": "2022 IEEE/ACM 3rd International Workshop on Gender Equality, Diversity and Inclusion in Software Engineering (GEICSE)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/fie/2022/6244/0/09962687",
"title": "Gender differences in early careers of Finnish engineers",
"doi": null,
"abstractUrl": "/proceedings-article/fie/2022/09962687/1IHnLpE7zcA",
"parentPublication": {
"id": "proceedings/fie/2022/6244/0",
"title": "2022 IEEE Frontiers in Education Conference (FIE)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "mags/sp/2019/04/08755960",
"title": "Toward Gender-Equitable Privacy and Security in South Asia",
"doi": null,
"abstractUrl": "/magazine/sp/2019/04/08755960/1bojOWk4jMQ",
"parentPublication": {
"id": "mags/sp",
"title": "IEEE Security & Privacy",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "mags/so/2019/06/08880051",
"title": "Gender in Software Engineering",
"doi": null,
"abstractUrl": "/magazine/so/2019/06/08880051/1ekTayi7kt2",
"parentPublication": {
"id": "mags/so",
"title": "IEEE Software",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "mags/so/2021/02/09261329",
"title": "Gender Differences in Public Code Contributions: A 50-Year Perspective",
"doi": null,
"abstractUrl": "/magazine/so/2021/02/09261329/1oPzR4iA4nu",
"parentPublication": {
"id": "mags/so",
"title": "IEEE Software",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/isaiee/2020/5668/0/566800a087",
"title": "Feasibility of Gender Equality in Academic Research Based on STS Concept",
"doi": null,
"abstractUrl": "/proceedings-article/isaiee/2020/566800a087/1sQKh69qBG0",
"parentPublication": {
"id": "proceedings/isaiee/2020/5668/0",
"title": "2020 International Symposium on Advances in Informatics, Electronics and Education (ISAIEE)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/respect/2021/4905/0/09620659",
"title": "CS1 Students' Perspectives on the Computer Science Gender Gap: Achieving Equity Requires Awareness",
"doi": null,
"abstractUrl": "/proceedings-article/respect/2021/09620659/1yXuIb7pwKk",
"parentPublication": {
"id": "proceedings/respect/2021/4905/0",
"title": "2021 Conference on Research in Equitable and Sustained Participation in Engineering, Computing, and Technology (RESPECT)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"adjacentArticles": {
"previous": {
"fno": "09552447",
"articleId": "1xic0dHxM9a",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "09555490",
"articleId": "1xjR3LSQrLi",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"webExtras": [],
"articleVideos": []
}
|
{
"issue": {
"id": "1vDhWHXEYZW",
"title": "Sept.",
"year": "2021",
"issueNum": "09",
"idPrefix": "tg",
"pubType": "journal",
"volume": "27",
"label": "Sept.",
"downloadables": {
"hasCover": false,
"__typename": "PeriodicalIssueDownloadablesType"
},
"__typename": "PeriodicalIssue"
},
"article": {
"id": "1qnQCmbSzUA",
"doi": "10.1109/TVCG.2021.3051853",
"abstract": "Scatterplots with a model enable visual estimation of model-data fit. In Experiment 1 (N = 62) we quantified the influence of noise-level on subjective misfit and found a negatively accelerated relationship. Experiment 2 showed that decentering of noise only mildly reduced fit ratings. The results have consequences for model-evaluation.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Scatterplots with a model enable visual estimation of model-data fit. In Experiment 1 (N = 62) we quantified the influence of noise-level on subjective misfit and found a negatively accelerated relationship. Experiment 2 showed that decentering of noise only mildly reduced fit ratings. The results have consequences for model-evaluation.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Scatterplots with a model enable visual estimation of model-data fit. In Experiment 1 (N = 62) we quantified the influence of noise-level on subjective misfit and found a negatively accelerated relationship. Experiment 2 showed that decentering of noise only mildly reduced fit ratings. The results have consequences for model-evaluation.",
"title": "Visual Model Fit Estimation in Scatterplots: Influence of Amount and Decentering of Noise",
"normalizedTitle": "Visual Model Fit Estimation in Scatterplots: Influence of Amount and Decentering of Noise",
"fno": "09325067",
"hasPdf": true,
"idPrefix": "tg",
"keywords": [
"Data Visualisation",
"Estimation Theory",
"Noise",
"Model Data Fit",
"Subjective Misfit",
"Visual Model Fit Estimation",
"Scatterplots",
"Noise Decentering",
"Data Models",
"Visualization",
"Estimation",
"Noise Level",
"Correlation",
"Predictive Models",
"Computational Modeling",
"Information Visualization",
"Perception And Psychophysics",
"Theory And Models"
],
"authors": [
{
"givenName": "Daniel",
"surname": "Reimann",
"fullName": "Daniel Reimann",
"affiliation": "Department of Psychology, FernUniversität in Hagen, Hagen, Germany",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Christine",
"surname": "Blech",
"fullName": "Christine Blech",
"affiliation": "Department of Psychology, FernUniversität in Hagen, Hagen, Germany",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Nilam",
"surname": "Ram",
"fullName": "Nilam Ram",
"affiliation": "Departments of Communication and Psychology, Stanford University, Stanford, CA, USA",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Robert",
"surname": "Gaschler",
"fullName": "Robert Gaschler",
"affiliation": "Department of Psychology, FernUniversität in Hagen, Hagen, Germany",
"__typename": "ArticleAuthorType"
}
],
"replicability": null,
"showBuyMe": true,
"showRecommendedArticles": true,
"isOpenAccess": false,
"issueNum": "09",
"pubDate": "2021-09-01 00:00:00",
"pubType": "trans",
"pages": "3834-3838",
"year": "2021",
"issn": "1077-2626",
"isbn": null,
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"recommendedArticles": [
{
"id": "proceedings/iri/2014/5880/0/07051903",
"title": "The effect of noise level and distribution on classification of easy gene microarray data",
"doi": null,
"abstractUrl": "/proceedings-article/iri/2014/07051903/12OmNANkohr",
"parentPublication": {
"id": "proceedings/iri/2014/5880/0",
"title": "2014 IEEE International Conference on Information Reuse and Integration (IRI)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/simultech/2014/060/0/07095104",
"title": "Evaluate traffic noise level based on traffic microsimulation combined with a refined classic noise prediction method",
"doi": null,
"abstractUrl": "/proceedings-article/simultech/2014/07095104/12OmNAY799q",
"parentPublication": {
"id": "proceedings/simultech/2014/060/0",
"title": "2014 International Conference on Simulation and Modeling Methodologies, Technologies and Applications (SIMULTECH)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ewdts/2016/0693/0/07807707",
"title": "Influence of correlated additive non-Gaussian noise on accuracy of signal parameters measurement during continuous processing",
"doi": null,
"abstractUrl": "/proceedings-article/ewdts/2016/07807707/12OmNAYoKvu",
"parentPublication": {
"id": "proceedings/ewdts/2016/0693/0",
"title": "2016 IEEE East-West Design & Test Symposium (EWDTS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/imccc/2011/4519/0/4519a157",
"title": "The Influence of Reflection on Evaluating the Primary Noise Source",
"doi": null,
"abstractUrl": "/proceedings-article/imccc/2011/4519a157/12OmNApcuzp",
"parentPublication": {
"id": "proceedings/imccc/2011/4519/0",
"title": "Instrumentation, Measurement, Computer, Communication and Control, International Conference on",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/5555/01/09732236",
"title": "Lollipops Help Align Visual and Statistical Fit Estimates in Scatterplots with Nonlinear Models",
"doi": null,
"abstractUrl": "/journal/tg/5555/01/09732236/1BBtNDKgNFe",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/eiect/2022/9956/0/995600a074",
"title": "Influence analysis of noise on sensitivity and 1/f noise removal in a 220GHz radiometer",
"doi": null,
"abstractUrl": "/proceedings-article/eiect/2022/995600a074/1LHct41T2k8",
"parentPublication": {
"id": "proceedings/eiect/2022/9956/0",
"title": "2022 2nd International Conference on Electronic Information Engineering and Computer Technology (EIECT)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/2020/01/08794768",
"title": "Evaluating Perceptual Bias During Geometric Scaling of Scatterplots",
"doi": null,
"abstractUrl": "/journal/tg/2020/01/08794768/1cr2ZlCC2xG",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/2021/02/09222295",
"title": "Modeling the Influence of Visual Density on Cluster Perception in Scatterplots Using Topology",
"doi": null,
"abstractUrl": "/journal/tg/2021/02/09222295/1nTqtC45a12",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/big-data/2020/6251/0/09377939",
"title": "Real-Time Machine Learning for Air Quality and Environmental Noise Detection",
"doi": null,
"abstractUrl": "/proceedings-article/big-data/2020/09377939/1s64zV3Y4gw",
"parentPublication": {
"id": "proceedings/big-data/2020/6251/0",
"title": "2020 IEEE International Conference on Big Data (Big Data)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/2022/01/09556578",
"title": "The Weighted Average Illusion: Biases in Perceived Mean Position in Scatterplots",
"doi": null,
"abstractUrl": "/journal/tg/2022/01/09556578/1xlvYaEQTNC",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"adjacentArticles": {
"previous": {
"fno": "09337213",
"articleId": "1qJqUNl4cIo",
"__typename": "AdjacentArticleType"
},
"next": null,
"__typename": "AdjacentArticlesType"
},
"webExtras": [],
"articleVideos": []
}
|
{
"issue": {
"id": "1pERRCpDoGs",
"title": "Aug.",
"year": "5555",
"issueNum": "01",
"idPrefix": "ai",
"pubType": "journal",
"volume": "1",
"label": "Aug.",
"downloadables": {
"hasCover": false,
"__typename": "PeriodicalIssueDownloadablesType"
},
"__typename": "PeriodicalIssue"
},
"article": {
"id": "1G7UlGeNaiA",
"doi": "10.1109/TAI.2022.3201505",
"abstract": "How to accurately register partial point cloud still remains a challenging task, because of its irregular and unordered structure in a non-Euclidean space, noise, outliers, and other unfavorable factors. In this paper, an effective partial point cloud registration network is proposed, by devising a two-stage deep local feature extraction process and an outlier filtering strategy. To be specific, on the one hand, to effectively capture geometric interdependency in low-level space, a local attention feature extraction module is explored to extract local contextual attention features, by highlighting different attention weights on neighborhoods. On the other hand, in local feature aggregation module, two position encoding blocks are applied to increase the receptive field of each point in high-level space. Of these, an attentive pooling can automatically learn important local features to alleviate the possible information loss. Furthermore, to derive the weight of the putative correspondence, an outlier filtering module is designed by consisting of point context normalization block, differentiable pooling and differentiable unpooling layer. Moreover, in order to enhance robustness, a weighting point cloud registration model is formulated to alleviate outliers, by considering the contribution of each correspondence. Experiments on multiple datasets demonstrate that the proposed approach is competitive to several state-of-the-art algorithms. Code is publicly available at <uri>https://github.com/zhlSunLab/DLF</uri>.",
"abstracts": [
{
"abstractType": "Regular",
"content": "How to accurately register partial point cloud still remains a challenging task, because of its irregular and unordered structure in a non-Euclidean space, noise, outliers, and other unfavorable factors. In this paper, an effective partial point cloud registration network is proposed, by devising a two-stage deep local feature extraction process and an outlier filtering strategy. To be specific, on the one hand, to effectively capture geometric interdependency in low-level space, a local attention feature extraction module is explored to extract local contextual attention features, by highlighting different attention weights on neighborhoods. On the other hand, in local feature aggregation module, two position encoding blocks are applied to increase the receptive field of each point in high-level space. Of these, an attentive pooling can automatically learn important local features to alleviate the possible information loss. Furthermore, to derive the weight of the putative correspondence, an outlier filtering module is designed by consisting of point context normalization block, differentiable pooling and differentiable unpooling layer. Moreover, in order to enhance robustness, a weighting point cloud registration model is formulated to alleviate outliers, by considering the contribution of each correspondence. Experiments on multiple datasets demonstrate that the proposed approach is competitive to several state-of-the-art algorithms. Code is publicly available at <uri>https://github.com/zhlSunLab/DLF</uri>.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "How to accurately register partial point cloud still remains a challenging task, because of its irregular and unordered structure in a non-Euclidean space, noise, outliers, and other unfavorable factors. In this paper, an effective partial point cloud registration network is proposed, by devising a two-stage deep local feature extraction process and an outlier filtering strategy. To be specific, on the one hand, to effectively capture geometric interdependency in low-level space, a local attention feature extraction module is explored to extract local contextual attention features, by highlighting different attention weights on neighborhoods. On the other hand, in local feature aggregation module, two position encoding blocks are applied to increase the receptive field of each point in high-level space. Of these, an attentive pooling can automatically learn important local features to alleviate the possible information loss. Furthermore, to derive the weight of the putative correspondence, an outlier filtering module is designed by consisting of point context normalization block, differentiable pooling and differentiable unpooling layer. Moreover, in order to enhance robustness, a weighting point cloud registration model is formulated to alleviate outliers, by considering the contribution of each correspondence. Experiments on multiple datasets demonstrate that the proposed approach is competitive to several state-of-the-art algorithms. Code is publicly available at https://github.com/zhlSunLab/DLF.",
"title": "Partial Point Cloud Registration with Deep Local Feature",
"normalizedTitle": "Partial Point Cloud Registration with Deep Local Feature",
"fno": "09866792",
"hasPdf": true,
"idPrefix": "ai",
"keywords": [
"Feature Extraction",
"Point Cloud Compression",
"Three Dimensional Displays",
"Task Analysis",
"Encoding",
"Artificial Intelligence",
"Deep Learning",
"Partial Point Cloud",
"Registration",
"Deep Local Feature",
"Outlier Filtering",
"Deep Learning"
],
"authors": [
{
"givenName": "Yu-Xin",
"surname": "Zhang",
"fullName": "Yu-Xin Zhang",
"affiliation": "Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University, China",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Zhan-Li",
"surname": "Sun",
"fullName": "Zhan-Li Sun",
"affiliation": "Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University, China",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Zhi-Gang",
"surname": "Zeng",
"fullName": "Zhi-Gang Zeng",
"affiliation": "School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Kin-Man",
"surname": "Lam",
"fullName": "Kin-Man Lam",
"affiliation": "Department of Electronic and Information Engineering, The Hong Kong Polytechnic University, Hong Kong, China",
"__typename": "ArticleAuthorType"
}
],
"replicability": null,
"showBuyMe": true,
"showRecommendedArticles": true,
"isOpenAccess": false,
"issueNum": "01",
"pubDate": "2022-08-01 00:00:00",
"pubType": "trans",
"pages": "1-11",
"year": "5555",
"issn": "2691-4581",
"isbn": null,
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"recommendedArticles": [
{
"id": "trans/tp/2023/01/09705149",
"title": "STORM: Structure-Based Overlap Matching for Partial Point Cloud Registration",
"doi": null,
"abstractUrl": "/journal/tp/2023/01/09705149/1AII6wed0Bi",
"parentPublication": {
"id": "trans/tp",
"title": "IEEE Transactions on Pattern Analysis & Machine Intelligence",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/5555/01/09736452",
"title": "WSDesc: Weakly Supervised 3D Local Descriptor Learning for Point Cloud Registration",
"doi": null,
"abstractUrl": "/journal/tg/5555/01/09736452/1BN1Ujkoysg",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccv/2021/2812/0/281200f510",
"title": "Feature Interactive Representation for Point Cloud Registration",
"doi": null,
"abstractUrl": "/proceedings-article/iccv/2021/281200f510/1BmFkzf6HuM",
"parentPublication": {
"id": "proceedings/iccv/2021/2812/0",
"title": "2021 IEEE/CVF International Conference on Computer Vision (ICCV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccv/2021/2812/0/281200g108",
"title": "Sampling Network Guided Cross-Entropy Method for Unsupervised Point Cloud Registration",
"doi": null,
"abstractUrl": "/proceedings-article/iccv/2021/281200g108/1BmGp36SSvm",
"parentPublication": {
"id": "proceedings/iccv/2021/2812/0",
"title": "2021 IEEE/CVF International Conference on Computer Vision (ICCV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccv/2021/2812/0/281200d112",
"title": "OMNet: Learning Overlapping Mask for Partial-to-Partial Point Cloud Registration",
"doi": null,
"abstractUrl": "/proceedings-article/iccv/2021/281200d112/1BmH817i3jq",
"parentPublication": {
"id": "proceedings/iccv/2021/2812/0",
"title": "2021 IEEE/CVF International Conference on Computer Vision (ICCV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tp/2023/05/09878213",
"title": "Robust Point Cloud Registration Framework Based on Deep Graph Matching",
"doi": null,
"abstractUrl": "/journal/tp/2023/05/09878213/1GrP5OekHDy",
"parentPublication": {
"id": "trans/tp",
"title": "IEEE Transactions on Pattern Analysis & Machine Intelligence",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tp/5555/01/10044259",
"title": "RoReg: Pairwise Point Cloud Registration with Oriented Descriptors and Local Rotations",
"doi": null,
"abstractUrl": "/journal/tp/5555/01/10044259/1KL6SJ4jOzS",
"parentPublication": {
"id": "trans/tp",
"title": "IEEE Transactions on Pattern Analysis & Machine Intelligence",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ictai/2022/9744/0/974400a616",
"title": "RITNet: A Rotation Invariant Transformer based Network for Point Cloud Registration",
"doi": null,
"abstractUrl": "/proceedings-article/ictai/2022/974400a616/1MrFMu2m6E8",
"parentPublication": {
"id": "proceedings/ictai/2022/9744/0",
"title": "2022 IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ictai/2022/9744/0/974400a076",
"title": "Dual-Neighborhood Feature Aggregation Network for Point Cloud Semantic Segmentation",
"doi": null,
"abstractUrl": "/proceedings-article/ictai/2022/974400a076/1MrFYMfRVXa",
"parentPublication": {
"id": "proceedings/ictai/2022/9744/0",
"title": "2022 IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2021/4509/0/450900p5854",
"title": "PointDSC: Robust Point Cloud Registration using Deep Spatial Consistency",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2021/450900p5854/1yeHPco0aiY",
"parentPublication": {
"id": "proceedings/cvpr/2021/4509/0",
"title": "2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"adjacentArticles": {
"previous": {
"fno": "09863669",
"articleId": "1FXWWyZL8WY",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "09868120",
"articleId": "1G9WsP4GGfC",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"webExtras": [],
"articleVideos": []
}
|
{
"issue": {
"id": "12OmNwpGgK8",
"title": "Dec.",
"year": "2014",
"issueNum": "12",
"idPrefix": "tg",
"pubType": "journal",
"volume": "20",
"label": "Dec.",
"downloadables": {
"hasCover": false,
"__typename": "PeriodicalIssueDownloadablesType"
},
"__typename": "PeriodicalIssue"
},
"article": {
"id": "13rRUxlgy3J",
"doi": "10.1109/TVCG.2014.2346752",
"abstract": "Large scale data analysis is nowadays a crucial part of drug discovery. Biologists and chemists need to quickly explore and evaluate potentially effective yet safe compounds based on many datasets that are in relationship with each other. However, there is a lack of tools that support them in these processes. To remedy this, we developed ConTour, an interactive visual analytics technique that enables the exploration of these complex, multi-relational datasets. At its core ConTour lists all items of each dataset in a column. Relationships between the columns are revealed through interaction: selecting one or multiple items in one column highlights and re-sorts the items in other columns. Filters based on relationships enable drilling down into the large data space. To identify interesting items in the first place, ConTour employs advanced sorting strategies, including strategies based on connectivity strength and uniqueness, as well as sorting based on item attributes. ConTour also introduces interactive nesting of columns, a powerful method to show the related items of a child column for each item in the parent column. Within the columns, ConTour shows rich attribute data about the items as well as information about the connection strengths to other datasets. Finally, ConTour provides a number of detail views, which can show items from multiple datasets and their associated data at the same time. We demonstrate the utility of our system in case studies conducted with a team of chemical biologists, who investigate the effects of chemical compounds on cells and need to understand the underlying mechanisms.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Large scale data analysis is nowadays a crucial part of drug discovery. Biologists and chemists need to quickly explore and evaluate potentially effective yet safe compounds based on many datasets that are in relationship with each other. However, there is a lack of tools that support them in these processes. To remedy this, we developed ConTour, an interactive visual analytics technique that enables the exploration of these complex, multi-relational datasets. At its core ConTour lists all items of each dataset in a column. Relationships between the columns are revealed through interaction: selecting one or multiple items in one column highlights and re-sorts the items in other columns. Filters based on relationships enable drilling down into the large data space. To identify interesting items in the first place, ConTour employs advanced sorting strategies, including strategies based on connectivity strength and uniqueness, as well as sorting based on item attributes. ConTour also introduces interactive nesting of columns, a powerful method to show the related items of a child column for each item in the parent column. Within the columns, ConTour shows rich attribute data about the items as well as information about the connection strengths to other datasets. Finally, ConTour provides a number of detail views, which can show items from multiple datasets and their associated data at the same time. We demonstrate the utility of our system in case studies conducted with a team of chemical biologists, who investigate the effects of chemical compounds on cells and need to understand the underlying mechanisms.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Large scale data analysis is nowadays a crucial part of drug discovery. Biologists and chemists need to quickly explore and evaluate potentially effective yet safe compounds based on many datasets that are in relationship with each other. However, there is a lack of tools that support them in these processes. To remedy this, we developed ConTour, an interactive visual analytics technique that enables the exploration of these complex, multi-relational datasets. At its core ConTour lists all items of each dataset in a column. Relationships between the columns are revealed through interaction: selecting one or multiple items in one column highlights and re-sorts the items in other columns. Filters based on relationships enable drilling down into the large data space. To identify interesting items in the first place, ConTour employs advanced sorting strategies, including strategies based on connectivity strength and uniqueness, as well as sorting based on item attributes. ConTour also introduces interactive nesting of columns, a powerful method to show the related items of a child column for each item in the parent column. Within the columns, ConTour shows rich attribute data about the items as well as information about the connection strengths to other datasets. Finally, ConTour provides a number of detail views, which can show items from multiple datasets and their associated data at the same time. We demonstrate the utility of our system in case studies conducted with a team of chemical biologists, who investigate the effects of chemical compounds on cells and need to understand the underlying mechanisms.",
"title": "ConTour: Data-Driven Exploration of Multi-Relational Datasets for Drug Discovery",
"normalizedTitle": "ConTour: Data-Driven Exploration of Multi-Relational Datasets for Drug Discovery",
"fno": "06875994",
"hasPdf": true,
"idPrefix": "tg",
"keywords": [
"Biomedical Informatics",
"Drugs",
"Data Visualization",
"Proteins",
"Large Scale Systems",
"Visual Analytics",
"Drug Discovery",
"Multi Relational Data",
"Visual Data Analysis"
],
"authors": [
{
"givenName": "Christian",
"surname": "Partl",
"fullName": "Christian Partl",
"affiliation": ", Graz University of Technology",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Alexander",
"surname": "Lex",
"fullName": "Alexander Lex",
"affiliation": ", Harvard University",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Marc",
"surname": "Streit",
"fullName": "Marc Streit",
"affiliation": ", Johannes Kepler University Linz",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Hendrik",
"surname": "Strobelt",
"fullName": "Hendrik Strobelt",
"affiliation": ", Harvard University",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Anne-Mai",
"surname": "Wassermann",
"fullName": "Anne-Mai Wassermann",
"affiliation": ", Novartis Institutes for BicMedical Research",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Hanspeter",
"surname": "Pfister",
"fullName": "Hanspeter Pfister",
"affiliation": ", Harvard University",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Dieter",
"surname": "Schmalstieg",
"fullName": "Dieter Schmalstieg",
"affiliation": ", Graz University of Technology",
"__typename": "ArticleAuthorType"
}
],
"replicability": null,
"showBuyMe": true,
"showRecommendedArticles": true,
"isOpenAccess": false,
"issueNum": "12",
"pubDate": "2014-12-01 00:00:00",
"pubType": "trans",
"pages": "1883-1892",
"year": "2014",
"issn": "1077-2626",
"isbn": null,
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"recommendedArticles": [
{
"id": "proceedings/iv/2014/4103/0/4103a164",
"title": "A Visual Analytics of Geometric Distances between Amino Acids and Surface Pockets of Proteins",
"doi": null,
"abstractUrl": "/proceedings-article/iv/2014/4103a164/12OmNBOll3Y",
"parentPublication": {
"id": "proceedings/iv/2014/4103/0",
"title": "2014 18th International Conference on Information Visualisation (IV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/biovis/2013/1658/0/06664342",
"title": "COMBat: Visualizing co-occurrence of annotation terms",
"doi": null,
"abstractUrl": "/proceedings-article/biovis/2013/06664342/12OmNzAohRe",
"parentPublication": {
"id": "proceedings/biovis/2013/1658/0",
"title": "2013 IEEE Symposium on Biological Data Visualization (BioVis)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cw/2013/2246/0/2246a379",
"title": "A Scatterplot-Based Visual Analytics Tool for Protein Pocket Properties",
"doi": null,
"abstractUrl": "/proceedings-article/cw/2013/2246a379/12OmNzayNu1",
"parentPublication": {
"id": "proceedings/cw/2013/2246/0",
"title": "2013 International Conference on Cyberworlds (CW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icdm/2011/4408/0/4408a101",
"title": "SolarMap: Multifaceted Visual Analytics for Topic Exploration",
"doi": null,
"abstractUrl": "/proceedings-article/icdm/2011/4408a101/12OmNzw8j1t",
"parentPublication": {
"id": "proceedings/icdm/2011/4408/0",
"title": "2011 IEEE 11th International Conference on Data Mining",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "mags/cg/2014/02/mcg2014020048",
"title": "Visual Exploration of Parameter Influence on Phylogenetic Trees",
"doi": null,
"abstractUrl": "/magazine/cg/2014/02/mcg2014020048/13rRUIJuxxX",
"parentPublication": {
"id": "mags/cg",
"title": "IEEE Computer Graphics and Applications",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icde/2018/5520/0/552000b565",
"title": "MeDIAR: Multi-Drug Adverse Reactions Analytics",
"doi": null,
"abstractUrl": "/proceedings-article/icde/2018/552000b565/14Fq0V7LOyF",
"parentPublication": {
"id": "proceedings/icde/2018/5520/0",
"title": "2018 IEEE 34th International Conference on Data Engineering (ICDE)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bdva/2018/9194/0/08534025",
"title": "Towards Visual Exploration of Large Temporal Datasets",
"doi": null,
"abstractUrl": "/proceedings-article/bdva/2018/08534025/17D45XoXP4N",
"parentPublication": {
"id": "proceedings/bdva/2018/9194/0",
"title": "2018 International Symposium on Big Data Visual and Immersive Analytics (BDVA)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/5555/01/09729550",
"title": "Visual Exploration of Relationships and Structure in Low-Dimensional Embeddings",
"doi": null,
"abstractUrl": "/journal/tg/5555/01/09729550/1Bya8LDahDa",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ictai/2022/9744/0/974400b093",
"title": "Drug Side Effects Prediction via Heterogeneous Multi-Relational Graph Convolutional Networks",
"doi": null,
"abstractUrl": "/proceedings-article/ictai/2022/974400b093/1MrFMg4BxVm",
"parentPublication": {
"id": "proceedings/ictai/2022/9744/0",
"title": "2022 IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/vast/2018/6861/0/08802508",
"title": "Contourmap: Contour Based Visualization Of Water Chemical Data",
"doi": null,
"abstractUrl": "/proceedings-article/vast/2018/08802508/1cJ6Y0POhO0",
"parentPublication": {
"id": "proceedings/vast/2018/6861/0",
"title": "2018 IEEE Conference on Visual Analytics Science and Technology (VAST)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"adjacentArticles": {
"previous": {
"fno": "06875941",
"articleId": "13rRUxjQybU",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "06876007",
"articleId": "13rRUynHujb",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"webExtras": [
{
"id": "17ShDTXWRVc",
"name": "ttg201412-06875994s1.zip",
"location": "https://www.computer.org/csdl/api/v1/extra/ttg201412-06875994s1.zip",
"extension": "zip",
"size": "36.6 MB",
"__typename": "WebExtraType"
}
],
"articleVideos": []
}
|
{
"issue": {
"id": "12OmNCaLEju",
"title": "Jan.",
"year": "2018",
"issueNum": "01",
"idPrefix": "tg",
"pubType": "journal",
"volume": "24",
"label": "Jan.",
"downloadables": {
"hasCover": false,
"__typename": "PeriodicalIssueDownloadablesType"
},
"__typename": "PeriodicalIssue"
},
"article": {
"id": "13rRUzphDy1",
"doi": "10.1109/TVCG.2017.2745141",
"abstract": "Constructing distributed representations for words through neural language models and using the resulting vector spaces for analysis has become a crucial component of natural language processing (NLP). However, despite their widespread application, little is known about the structure and properties of these spaces. To gain insights into the relationship between words, the NLP community has begun to adapt high-dimensional visualization techniques. In particular, researchers commonly use t-distributed stochastic neighbor embeddings (t-SNE) and principal component analysis (PCA) to create two-dimensional embeddings for assessing the overall structure and exploring linear relationships (e.g., word analogies), respectively. Unfortunately, these techniques often produce mediocre or even misleading results and cannot address domain-specific visualization challenges that are crucial for understanding semantic relationships in word embeddings. Here, we introduce new embedding techniques for visualizing semantic and syntactic analogies, and the corresponding tests to determine whether the resulting views capture salient structures. Additionally, we introduce two novel views for a comprehensive study of analogy relationships. Finally, we augment t-SNE embeddings to convey uncertainty information in order to allow a reliable interpretation. Combined, the different views address a number of domain-specific tasks difficult to solve with existing tools.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Constructing distributed representations for words through neural language models and using the resulting vector spaces for analysis has become a crucial component of natural language processing (NLP). However, despite their widespread application, little is known about the structure and properties of these spaces. To gain insights into the relationship between words, the NLP community has begun to adapt high-dimensional visualization techniques. In particular, researchers commonly use t-distributed stochastic neighbor embeddings (t-SNE) and principal component analysis (PCA) to create two-dimensional embeddings for assessing the overall structure and exploring linear relationships (e.g., word analogies), respectively. Unfortunately, these techniques often produce mediocre or even misleading results and cannot address domain-specific visualization challenges that are crucial for understanding semantic relationships in word embeddings. Here, we introduce new embedding techniques for visualizing semantic and syntactic analogies, and the corresponding tests to determine whether the resulting views capture salient structures. Additionally, we introduce two novel views for a comprehensive study of analogy relationships. Finally, we augment t-SNE embeddings to convey uncertainty information in order to allow a reliable interpretation. Combined, the different views address a number of domain-specific tasks difficult to solve with existing tools.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Constructing distributed representations for words through neural language models and using the resulting vector spaces for analysis has become a crucial component of natural language processing (NLP). However, despite their widespread application, little is known about the structure and properties of these spaces. To gain insights into the relationship between words, the NLP community has begun to adapt high-dimensional visualization techniques. In particular, researchers commonly use t-distributed stochastic neighbor embeddings (t-SNE) and principal component analysis (PCA) to create two-dimensional embeddings for assessing the overall structure and exploring linear relationships (e.g., word analogies), respectively. Unfortunately, these techniques often produce mediocre or even misleading results and cannot address domain-specific visualization challenges that are crucial for understanding semantic relationships in word embeddings. Here, we introduce new embedding techniques for visualizing semantic and syntactic analogies, and the corresponding tests to determine whether the resulting views capture salient structures. Additionally, we introduce two novel views for a comprehensive study of analogy relationships. Finally, we augment t-SNE embeddings to convey uncertainty information in order to allow a reliable interpretation. Combined, the different views address a number of domain-specific tasks difficult to solve with existing tools.",
"title": "Visual Exploration of Semantic Relationships in Neural Word Embeddings",
"normalizedTitle": "Visual Exploration of Semantic Relationships in Neural Word Embeddings",
"fno": "08019864",
"hasPdf": true,
"idPrefix": "tg",
"keywords": [
"Data Visualisation",
"Natural Language Processing",
"Principal Component Analysis",
"Stochastic Processes",
"Neural Word Embeddings",
"Distributed Representations",
"Neural Language Models",
"Crucial Component",
"Natural Language Processing",
"NLP Community",
"High Dimensional Visualization Techniques",
"Stochastic Neighbor Embeddings",
"Two Dimensional Embeddings",
"Word Analogies",
"Semantic Relationships",
"Embedding Techniques",
"Semantic Analogies",
"Syntactic Analogies",
"Salient Structures",
"Analogy Relationships",
"Domain Specific Tasks",
"Visual Exploration",
"Vector Spaces",
"Linear Relationships",
"Domain Specific Visualization",
"T SNE Embeddings",
"Semantics",
"Principal Component Analysis",
"Visualization",
"Natural Language Processing",
"Tools",
"Data Visualization",
"Natural Language Processing",
"Word Embedding",
"High Dimensional Data"
],
"authors": [
{
"givenName": "Shusen",
"surname": "Liu",
"fullName": "Shusen Liu",
"affiliation": "Lawrence Livermore National Laboratory",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Peer-Timo",
"surname": "Bremer",
"fullName": "Peer-Timo Bremer",
"affiliation": "Lawrence Livermore National Laboratory",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Jayaraman J.",
"surname": "Thiagarajan",
"fullName": "Jayaraman J. Thiagarajan",
"affiliation": "Lawrence Livermore National Laboratory",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Vivek",
"surname": "Srikumar",
"fullName": "Vivek Srikumar",
"affiliation": "School of ComputingUniversity of Utah",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Bei",
"surname": "Wang",
"fullName": "Bei Wang",
"affiliation": "SCI InstituteUniversity of Utah",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Yarden",
"surname": "Livnat",
"fullName": "Yarden Livnat",
"affiliation": "SCI InstituteUniversity of Utah",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Valerio",
"surname": "Pascucci",
"fullName": "Valerio Pascucci",
"affiliation": "SCI InstituteUniversity of Utah",
"__typename": "ArticleAuthorType"
}
],
"replicability": null,
"showBuyMe": true,
"showRecommendedArticles": true,
"isOpenAccess": false,
"issueNum": "01",
"pubDate": "2018-01-01 00:00:00",
"pubType": "trans",
"pages": "553-562",
"year": "2018",
"issn": "1077-2626",
"isbn": null,
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"recommendedArticles": [
{
"id": "proceedings/bibm/2017/3050/0/08217836",
"title": "An exploration of semantic relations in neural word embeddings using extrinsic knowledge",
"doi": null,
"abstractUrl": "/proceedings-article/bibm/2017/08217836/12OmNCeaPUG",
"parentPublication": {
"id": "proceedings/bibm/2017/3050/0",
"title": "2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icme/2013/0015/0/06607550",
"title": "Nonlinear dimensionality reduction approaches applied to music and textural sounds",
"doi": null,
"abstractUrl": "/proceedings-article/icme/2013/06607550/12OmNrAdsvq",
"parentPublication": {
"id": "proceedings/icme/2013/0015/0",
"title": "2013 IEEE International Conference on Multimedia and Expo (ICME)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/big-data/2017/2715/0/08258516",
"title": "Network intrusion detection using word embeddings",
"doi": null,
"abstractUrl": "/proceedings-article/big-data/2017/08258516/17D45WgziNw",
"parentPublication": {
"id": "proceedings/big-data/2017/2715/0",
"title": "2017 IEEE International Conference on Big Data (Big Data)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2022/6946/0/694600a011",
"title": "CO-SNE: Dimensionality Reduction and Visualization for Hyperbolic Data",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2022/694600a011/1H0NVwymKFG",
"parentPublication": {
"id": "proceedings/cvpr/2022/6946/0",
"title": "2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/2023/01/09904619",
"title": "Predicting User Preferences of Dimensionality Reduction Embedding Quality",
"doi": null,
"abstractUrl": "/journal/tg/2023/01/09904619/1H1ggvuBvMc",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ichi/2019/9138/0/08904533",
"title": "Biomedical Semantic Embeddings: Using hybrid sentences to construct biomedical word embeddings and its applications",
"doi": null,
"abstractUrl": "/proceedings-article/ichi/2019/08904533/1f8Nd3p9PDq",
"parentPublication": {
"id": "proceedings/ichi/2019/9138/0",
"title": "2019 IEEE International Conference on Healthcare Informatics (ICHI)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/2020/08/09064929",
"title": "t-viSNE: Interactive Assessment and Interpretation of t-SNE Projections",
"doi": null,
"abstractUrl": "/journal/tg/2020/08/09064929/1iZGzFjpwPu",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2020/7168/0/716800g417",
"title": "Hyperbolic Image Embeddings",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2020/716800g417/1m3nMCN2sMM",
"parentPublication": {
"id": "proceedings/cvpr/2020/7168/0",
"title": "2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iri/2020/1054/0/09191515",
"title": "Semantic Embeddings for Medical Providers and Fraud Detection",
"doi": null,
"abstractUrl": "/proceedings-article/iri/2020/09191515/1n0Iw7KNSUw",
"parentPublication": {
"id": "proceedings/iri/2020/1054/0",
"title": "2020 IEEE 21st International Conference on Information Reuse and Integration for Data Science (IRI)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icdm/2020/8316/0/831600a292",
"title": "Semantics-Assisted Wasserstein Learning for Topic and Word Embeddings",
"doi": null,
"abstractUrl": "/proceedings-article/icdm/2020/831600a292/1r54wdk1O6I",
"parentPublication": {
"id": "proceedings/icdm/2020/8316/0",
"title": "2020 IEEE International Conference on Data Mining (ICDM)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"adjacentArticles": {
"previous": {
"fno": "08017649",
"articleId": "13rRUwdrdSC",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "08017597",
"articleId": "13rRUNvyaf6",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"webExtras": [
{
"id": "17ShDTXnFuN",
"name": "ttg201801-08019864s1.zip",
"location": "https://www.computer.org/csdl/api/v1/extra/ttg201801-08019864s1.zip",
"extension": "zip",
"size": "54.7 MB",
"__typename": "WebExtraType"
}
],
"articleVideos": []
}
|
{
"issue": {
"id": "12OmNvvc5OL",
"title": "April",
"year": "2013",
"issueNum": "04",
"idPrefix": "tg",
"pubType": "journal",
"volume": "19",
"label": "April",
"downloadables": {
"hasCover": false,
"__typename": "PeriodicalIssueDownloadablesType"
},
"__typename": "PeriodicalIssue"
},
"article": {
"id": "13rRUwwaKt7",
"doi": "10.1109/TVCG.2013.41",
"abstract": "In this paper, we investigate the validity of Mixed Reality (MR) Simulation by conducting an experiment studying the effects of the visual realism of the simulated environment on various search tasks in Augmented Reality (AR). MR Simulation is a practical approach to conducting controlled and repeatable user experiments in MR, including AR. This approach uses a high-fidelity Virtual Reality (VR) display system to simulate a wide range of equal or lower fidelity displays from the MR continuum, for the express purpose of conducting user experiments. For the experiment, we created three virtual models of a real-world location, each with a different perceived level of visual realism. We designed and executed an AR experiment using the real-world location and repeated the experiment within VR using the three virtual models we created. The experiment looked into how fast users could search for both physical and virtual information that was present in the scene. Our experiment demonstrates the usefulness of MR Simulation and provides early evidence for the validity of MR Simulation with respect to AR search tasks performed in immersive VR.",
"abstracts": [
{
"abstractType": "Regular",
"content": "In this paper, we investigate the validity of Mixed Reality (MR) Simulation by conducting an experiment studying the effects of the visual realism of the simulated environment on various search tasks in Augmented Reality (AR). MR Simulation is a practical approach to conducting controlled and repeatable user experiments in MR, including AR. This approach uses a high-fidelity Virtual Reality (VR) display system to simulate a wide range of equal or lower fidelity displays from the MR continuum, for the express purpose of conducting user experiments. For the experiment, we created three virtual models of a real-world location, each with a different perceived level of visual realism. We designed and executed an AR experiment using the real-world location and repeated the experiment within VR using the three virtual models we created. The experiment looked into how fast users could search for both physical and virtual information that was present in the scene. Our experiment demonstrates the usefulness of MR Simulation and provides early evidence for the validity of MR Simulation with respect to AR search tasks performed in immersive VR.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "In this paper, we investigate the validity of Mixed Reality (MR) Simulation by conducting an experiment studying the effects of the visual realism of the simulated environment on various search tasks in Augmented Reality (AR). MR Simulation is a practical approach to conducting controlled and repeatable user experiments in MR, including AR. This approach uses a high-fidelity Virtual Reality (VR) display system to simulate a wide range of equal or lower fidelity displays from the MR continuum, for the express purpose of conducting user experiments. For the experiment, we created three virtual models of a real-world location, each with a different perceived level of visual realism. We designed and executed an AR experiment using the real-world location and repeated the experiment within VR using the three virtual models we created. The experiment looked into how fast users could search for both physical and virtual information that was present in the scene. Our experiment demonstrates the usefulness of MR Simulation and provides early evidence for the validity of MR Simulation with respect to AR search tasks performed in immersive VR.",
"title": "The Effects of Visual Realism on Search Tasks in Mixed Reality Simulation",
"normalizedTitle": "The Effects of Visual Realism on Search Tasks in Mixed Reality Simulation",
"fno": "ttg2013040547",
"hasPdf": true,
"idPrefix": "tg",
"keywords": [
"Visualization",
"Solid Modeling",
"Lighting",
"Cameras",
"Geometry",
"Virtual Environments",
"Augmented Reality",
"MR Simulation",
"Visual Realism"
],
"authors": [
{
"givenName": null,
"surname": "Cha Lee",
"fullName": "Cha Lee",
"affiliation": "Univ. of California, Santa Barbara, Santa Barbara, CA, USA",
"__typename": "ArticleAuthorType"
},
{
"givenName": "G. A.",
"surname": "Rincon",
"fullName": "G. A. Rincon",
"affiliation": "Univ. of California, Santa Barbara, Santa Barbara, CA, USA",
"__typename": "ArticleAuthorType"
},
{
"givenName": "G.",
"surname": "Meyer",
"fullName": "G. Meyer",
"affiliation": "Univ. of California, Santa Barbara, Santa Barbara, CA, USA",
"__typename": "ArticleAuthorType"
},
{
"givenName": "T.",
"surname": "Hollerer",
"fullName": "T. Hollerer",
"affiliation": "Univ. of California, Santa Barbara, Santa Barbara, CA, USA",
"__typename": "ArticleAuthorType"
},
{
"givenName": "D. A.",
"surname": "Bowman",
"fullName": "D. A. Bowman",
"affiliation": "Center for Human-Comput. Interaction, Virginia Tech, Blacksburg, VA, USA",
"__typename": "ArticleAuthorType"
}
],
"replicability": null,
"showBuyMe": true,
"showRecommendedArticles": true,
"isOpenAccess": false,
"issueNum": "04",
"pubDate": "2013-04-01 00:00:00",
"pubType": "trans",
"pages": "547-556",
"year": "2013",
"issn": "1077-2626",
"isbn": null,
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"recommendedArticles": [
{
"id": "proceedings/isuvr/2017/3091/0/3091a038",
"title": "Empathic Mixed Reality: Sharing What You Feel and Interacting with What You See",
"doi": null,
"abstractUrl": "/proceedings-article/isuvr/2017/3091a038/12OmNBNM97G",
"parentPublication": {
"id": "proceedings/isuvr/2017/3091/0",
"title": "2017 International Symposium on Ubiquitous Virtual Reality (ISUVR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ismar/2016/3641/0/3641a037",
"title": "A Single Camera Image Based Approach for Glossy Reflections in Mixed Reality Applications",
"doi": null,
"abstractUrl": "/proceedings-article/ismar/2016/3641a037/12OmNrJAdMm",
"parentPublication": {
"id": "proceedings/ismar/2016/3641/0",
"title": "2016 IEEE International Symposium on Mixed and Augmented Reality (ISMAR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ismarw/2016/3740/0/07836491",
"title": "Using Visual Effects to Facilitate Depth Perception for Spatial Tasks in Virtual and Augmented Reality",
"doi": null,
"abstractUrl": "/proceedings-article/ismarw/2016/07836491/12OmNwdtw9P",
"parentPublication": {
"id": "proceedings/ismarw/2016/3740/0",
"title": "2016 IEEE International Symposium on Mixed and Augmented Reality (ISMAR-Adjunct)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cste/2022/8188/0/818800a082",
"title": "Integrating Inquiry-Based Pedagogy with Mixed Reality: Theories and Practices",
"doi": null,
"abstractUrl": "/proceedings-article/cste/2022/818800a082/1J7VZM9bxDi",
"parentPublication": {
"id": "proceedings/cste/2022/8188/0",
"title": "2022 4th International Conference on Computer Science and Technologies in Education (CSTE)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ismar-adjunct/2022/5365/0/536500a657",
"title": "Mixed Reality for Engineering Design Review Using Finite Element Analysis",
"doi": null,
"abstractUrl": "/proceedings-article/ismar-adjunct/2022/536500a657/1J7WwCL6CCQ",
"parentPublication": {
"id": "proceedings/ismar-adjunct/2022/5365/0",
"title": "2022 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/2023/05/10049710",
"title": "Exploring Plausibility and Presence in Mixed Reality Experiences",
"doi": null,
"abstractUrl": "/journal/tg/2023/05/10049710/1KYoplRZLWM",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/vr/2019/1377/0/08798101",
"title": "Mixed Reality in Art Education",
"doi": null,
"abstractUrl": "/proceedings-article/vr/2019/08798101/1cJ0RtUtRgk",
"parentPublication": {
"id": "proceedings/vr/2019/1377/0",
"title": "2019 IEEE Conference on Virtual Reality and 3D User Interfaces (VR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icisce/2019/5712/0/09107809",
"title": "Spatiotemporal Information System Using Mixed Reality",
"doi": null,
"abstractUrl": "/proceedings-article/icisce/2019/09107809/1koLCLg2qqY",
"parentPublication": {
"id": "proceedings/icisce/2019/5712/0",
"title": "2019 6th International Conference on Information Science and Control Engineering (ICISCE)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/2022/02/09144483",
"title": "Evaluating the Effects of Non-Isomorphic Rotation on 3D Manipulation Tasks in Mixed Reality Simulation",
"doi": null,
"abstractUrl": "/journal/tg/2022/02/09144483/1lClltCZfOg",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ismar-adjunct/2020/7675/0/767500a051",
"title": "Exploring Virtual Environments by Visually Impaired Using a Mixed Reality Cane Without Visual Feedback",
"doi": null,
"abstractUrl": "/proceedings-article/ismar-adjunct/2020/767500a051/1pBMgh7AbaU",
"parentPublication": {
"id": "proceedings/ismar-adjunct/2020/7675/0",
"title": "2020 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"adjacentArticles": {
"previous": {
"fno": "ttg2013040539",
"articleId": "13rRUwInvl1",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "ttg2013040557",
"articleId": "13rRUxly95x",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"webExtras": [],
"articleVideos": []
}
|
{
"issue": {
"id": "12OmNvqEvRo",
"title": "PrePrints",
"year": "5555",
"issueNum": "01",
"idPrefix": "tg",
"pubType": "journal",
"volume": null,
"label": "PrePrints",
"downloadables": {
"hasCover": false,
"__typename": "PeriodicalIssueDownloadablesType"
},
"__typename": "PeriodicalIssue"
},
"article": {
"id": "1Bya8dlokw0",
"doi": "10.1109/TVCG.2022.3157061",
"abstract": "We propose a self-supervised method for partial point set registration. Although recently proposed learning-based methods demonstrate impressive registration performance on full shape observations, these methods often suffer from performance degradation when dealing with partial shapes. To bridge the performance gap between partial and full point set registration, we propose to incorporate a shape completion network to benefit the registration process. To achieve this, we introduce a learnable latent code for each pair of shapes, which can be regarded as the geometric encoding of the target shape. By doing so, our model does not require an explicit feature embedding network to learn the feature encodings. More importantly, both our shape completion and point set registration networks take the shared latent codes as input, which are optimized simultaneously with the parameters of two decoder networks in the training process. Therefore, the point set registration process can benefit from the joint optimization process of latent codes, which are enforced to represent the information of full shapes instead of partial ones. In the inference stage, we fix the network parameters and optimize the latent codes to obtain the optimal shape completion and registration results. Our proposed method is purely unsupervised and does not require ground truth supervision. Experiments on the ModelNet40 dataset demonstrate the effectiveness of our model for partial point set registration.",
"abstracts": [
{
"abstractType": "Regular",
"content": "We propose a self-supervised method for partial point set registration. Although recently proposed learning-based methods demonstrate impressive registration performance on full shape observations, these methods often suffer from performance degradation when dealing with partial shapes. To bridge the performance gap between partial and full point set registration, we propose to incorporate a shape completion network to benefit the registration process. To achieve this, we introduce a learnable latent code for each pair of shapes, which can be regarded as the geometric encoding of the target shape. By doing so, our model does not require an explicit feature embedding network to learn the feature encodings. More importantly, both our shape completion and point set registration networks take the shared latent codes as input, which are optimized simultaneously with the parameters of two decoder networks in the training process. Therefore, the point set registration process can benefit from the joint optimization process of latent codes, which are enforced to represent the information of full shapes instead of partial ones. In the inference stage, we fix the network parameters and optimize the latent codes to obtain the optimal shape completion and registration results. Our proposed method is purely unsupervised and does not require ground truth supervision. Experiments on the ModelNet40 dataset demonstrate the effectiveness of our model for partial point set registration.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "We propose a self-supervised method for partial point set registration. Although recently proposed learning-based methods demonstrate impressive registration performance on full shape observations, these methods often suffer from performance degradation when dealing with partial shapes. To bridge the performance gap between partial and full point set registration, we propose to incorporate a shape completion network to benefit the registration process. To achieve this, we introduce a learnable latent code for each pair of shapes, which can be regarded as the geometric encoding of the target shape. By doing so, our model does not require an explicit feature embedding network to learn the feature encodings. More importantly, both our shape completion and point set registration networks take the shared latent codes as input, which are optimized simultaneously with the parameters of two decoder networks in the training process. Therefore, the point set registration process can benefit from the joint optimization process of latent codes, which are enforced to represent the information of full shapes instead of partial ones. In the inference stage, we fix the network parameters and optimize the latent codes to obtain the optimal shape completion and registration results. Our proposed method is purely unsupervised and does not require ground truth supervision. Experiments on the ModelNet40 dataset demonstrate the effectiveness of our model for partial point set registration.",
"title": "Unsupervised Category-Specific Partial Point Set Registration via Joint Shape Completion and Registration",
"normalizedTitle": "Unsupervised Category-Specific Partial Point Set Registration via Joint Shape Completion and Registration",
"fno": "09729524",
"hasPdf": true,
"idPrefix": "tg",
"keywords": [
"Shape",
"Three Dimensional Displays",
"Codes",
"Optimization",
"Task Analysis",
"Point Cloud Compression",
"Training",
"Point Set Registration",
"Partial Registration",
"Unsupervised Learning",
"Shape Completion"
],
"authors": [
{
"givenName": "Xiang",
"surname": "Li",
"fullName": "Xiang Li",
"affiliation": "Tandon School of Engineering, New York University, 5894 New York, New York, United States",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Lingjing",
"surname": "Wang",
"fullName": "Lingjing Wang",
"affiliation": "Electrical Engineering, New York University - Abu Dhabi Campus, 167632 Abu Dhabi, Abu Dhabi, United Arab Emirates",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Yi",
"surname": "Fang",
"fullName": "Yi Fang",
"affiliation": "Electrical and Computer Engineering, New York University Tandon School of Engineering, 34242 Brooklyn, New York, United States, 11201",
"__typename": "ArticleAuthorType"
}
],
"replicability": null,
"showBuyMe": true,
"showRecommendedArticles": true,
"isOpenAccess": false,
"issueNum": "01",
"pubDate": "2022-03-01 00:00:00",
"pubType": "trans",
"pages": "1-1",
"year": "5555",
"issn": "1077-2626",
"isbn": null,
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"recommendedArticles": [
{
"id": "trans/tp/2023/01/09705149",
"title": "STORM: Structure-Based Overlap Matching for Partial Point Cloud Registration",
"doi": null,
"abstractUrl": "/journal/tp/2023/01/09705149/1AII6wed0Bi",
"parentPublication": {
"id": "trans/tp",
"title": "IEEE Transactions on Pattern Analysis & Machine Intelligence",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccv/2021/2812/0/281200f663",
"title": "DeepPRO: Deep Partial Point Cloud Registration of Objects",
"doi": null,
"abstractUrl": "/proceedings-article/iccv/2021/281200f663/1BmGoTRdyCY",
"parentPublication": {
"id": "proceedings/iccv/2021/2812/0",
"title": "2021 IEEE/CVF International Conference on Computer Vision (ICCV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccv/2021/2812/0/281200d112",
"title": "OMNet: Learning Overlapping Mask for Partial-to-Partial Point Cloud Registration",
"doi": null,
"abstractUrl": "/proceedings-article/iccv/2021/281200d112/1BmH817i3jq",
"parentPublication": {
"id": "proceedings/iccv/2021/2812/0",
"title": "2021 IEEE/CVF International Conference on Computer Vision (ICCV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccv/2021/2812/0/281200f806",
"title": "3D Shape Generation and Completion through Point-Voxel Diffusion",
"doi": null,
"abstractUrl": "/proceedings-article/iccv/2021/281200f806/1BmHiEgI4q4",
"parentPublication": {
"id": "proceedings/iccv/2021/2812/0",
"title": "2021 IEEE/CVF International Conference on Computer Vision (ICCV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/ai/5555/01/09866792",
"title": "Partial Point Cloud Registration with Deep Local Feature",
"doi": null,
"abstractUrl": "/journal/ai/5555/01/09866792/1G7UlGeNaiA",
"parentPublication": {
"id": "trans/ai",
"title": "IEEE Transactions on Artificial Intelligence",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icme/2022/8563/0/09859918",
"title": "PDP-NET: Patch-Based Dual-Path Network for Point Cloud Completion",
"doi": null,
"abstractUrl": "/proceedings-article/icme/2022/09859918/1G9DLvbZp60",
"parentPublication": {
"id": "proceedings/icme/2022/8563/0",
"title": "2022 IEEE International Conference on Multimedia and Expo (ICME)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icme/2022/8563/0/09860002",
"title": "Partial-to-Partial Point Cloud Registration Based on Multi-Level Semantic-Structural Cognition",
"doi": null,
"abstractUrl": "/proceedings-article/icme/2022/09860002/1G9EKd6az6g",
"parentPublication": {
"id": "proceedings/icme/2022/8563/0",
"title": "2022 IEEE International Conference on Multimedia and Expo (ICME)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2022/6946/0/694600f533",
"title": "Learning a Structured Latent Space for Unsupervised Point Cloud Completion",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2022/694600f533/1H0KOsU2FZC",
"parentPublication": {
"id": "proceedings/cvpr/2022/6946/0",
"title": "2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/5555/01/10093999",
"title": "ANISE: Assembly-based Neural Implicit Surface rEconstruction",
"doi": null,
"abstractUrl": "/journal/tg/5555/01/10093999/1M80HueHnJS",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/3dv/2021/2688/0/268800a342",
"title": "DeepBBS: Deep Best Buddies for Point Cloud Registration",
"doi": null,
"abstractUrl": "/proceedings-article/3dv/2021/268800a342/1zWE3qPVGLe",
"parentPublication": {
"id": "proceedings/3dv/2021/2688/0",
"title": "2021 International Conference on 3D Vision (3DV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"adjacentArticles": {
"previous": {
"fno": "09729541",
"articleId": "1Bya7Ux2z2U",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "09729564",
"articleId": "1Bya8xf11Oo",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"webExtras": [],
"articleVideos": []
}
|
{
"issue": {
"id": "12OmNAWYKCh",
"title": "Sept.",
"year": "2017",
"issueNum": "09",
"idPrefix": "tp",
"pubType": "journal",
"volume": "39",
"label": "Sept.",
"downloadables": {
"hasCover": false,
"__typename": "PeriodicalIssueDownloadablesType"
},
"__typename": "PeriodicalIssue"
},
"article": {
"id": "13rRUwghdau",
"doi": "10.1109/TPAMI.2016.2614980",
"abstract": "The extension of conventional clustering to hypergraph clustering, which involves higher order similarities instead of pairwise similarities, is increasingly gaining attention in computer vision. This is due to the fact that many clustering problems require an affinity measure that must involve a subset of data of size more than two. In the context of hypergraph clustering, the calculation of such higher order similarities on data subsets gives rise to hyperedges. Almost all previous work on hypergraph clustering in computer vision, however, has considered the smallest possible hyperedge size, due to a lack of study into the potential benefits of large hyperedges and effective algorithms to generate them. In this paper, we show that large hyperedges are better from both a theoretical and an empirical standpoint. We then propose a novel guided sampling strategy for large hyperedges, based on the concept of random cluster models. Our method can generate large pure hyperedges that significantly improve grouping accuracy without exponential increases in sampling costs. We demonstrate the efficacy of our technique on various higher-order grouping problems. In particular, we show that our approach improves the accuracy and efficiency of motion segmentation from dense, long-term, trajectories.",
"abstracts": [
{
"abstractType": "Regular",
"content": "The extension of conventional clustering to hypergraph clustering, which involves higher order similarities instead of pairwise similarities, is increasingly gaining attention in computer vision. This is due to the fact that many clustering problems require an affinity measure that must involve a subset of data of size more than two. In the context of hypergraph clustering, the calculation of such higher order similarities on data subsets gives rise to hyperedges. Almost all previous work on hypergraph clustering in computer vision, however, has considered the smallest possible hyperedge size, due to a lack of study into the potential benefits of large hyperedges and effective algorithms to generate them. In this paper, we show that large hyperedges are better from both a theoretical and an empirical standpoint. We then propose a novel guided sampling strategy for large hyperedges, based on the concept of random cluster models. Our method can generate large pure hyperedges that significantly improve grouping accuracy without exponential increases in sampling costs. We demonstrate the efficacy of our technique on various higher-order grouping problems. In particular, we show that our approach improves the accuracy and efficiency of motion segmentation from dense, long-term, trajectories.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "The extension of conventional clustering to hypergraph clustering, which involves higher order similarities instead of pairwise similarities, is increasingly gaining attention in computer vision. This is due to the fact that many clustering problems require an affinity measure that must involve a subset of data of size more than two. In the context of hypergraph clustering, the calculation of such higher order similarities on data subsets gives rise to hyperedges. Almost all previous work on hypergraph clustering in computer vision, however, has considered the smallest possible hyperedge size, due to a lack of study into the potential benefits of large hyperedges and effective algorithms to generate them. In this paper, we show that large hyperedges are better from both a theoretical and an empirical standpoint. We then propose a novel guided sampling strategy for large hyperedges, based on the concept of random cluster models. Our method can generate large pure hyperedges that significantly improve grouping accuracy without exponential increases in sampling costs. We demonstrate the efficacy of our technique on various higher-order grouping problems. In particular, we show that our approach improves the accuracy and efficiency of motion segmentation from dense, long-term, trajectories.",
"title": "Clustering with Hypergraphs: The Case for Large Hyperedges",
"normalizedTitle": "Clustering with Hypergraphs: The Case for Large Hyperedges",
"fno": "07582510",
"hasPdf": true,
"idPrefix": "tp",
"keywords": [
"Motion Segmentation",
"Computer Vision",
"Clustering Algorithms",
"Image Segmentation",
"Computational Modeling",
"Tensile Stress",
"Sampling Methods",
"Higher Order Grouping",
"Hypergraph Clustering",
"Motion Segmentation"
],
"authors": [
{
"givenName": "Pulak",
"surname": "Purkait",
"fullName": "Pulak Purkait",
"affiliation": "School of Computer Science, University of Birmingham, Birmingham, United Kingdom",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Tat-Jun",
"surname": "Chin",
"fullName": "Tat-Jun Chin",
"affiliation": "School of Computer Science, University of Adelaide, Adelaide, SA, Australia",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Alireza",
"surname": "Sadri",
"fullName": "Alireza Sadri",
"affiliation": "Aerosp Mech & Manuf Eng, RMIT University, Melbourne, Vic, Australia",
"__typename": "ArticleAuthorType"
},
{
"givenName": "David",
"surname": "Suter",
"fullName": "David Suter",
"affiliation": "School of Computer Science, University of Adelaide, Adelaide, SA, Australia",
"__typename": "ArticleAuthorType"
}
],
"replicability": null,
"showBuyMe": true,
"showRecommendedArticles": true,
"isOpenAccess": false,
"issueNum": "09",
"pubDate": "2017-09-01 00:00:00",
"pubType": "trans",
"pages": "1697-1711",
"year": "2017",
"issn": "0162-8828",
"isbn": null,
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"recommendedArticles": [
{
"id": "proceedings/cvpr/2005/2372/2/237220838",
"title": "Beyond Pairwise Clustering",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2005/237220838/12OmNvjyxJy",
"parentPublication": {
"id": "proceedings/cvpr/2005/2372/2",
"title": "2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2012/1226/0/078P1B25",
"title": "Higher order motion models and spectral clustering",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2012/078P1B25/12OmNzayN8r",
"parentPublication": {
"id": "proceedings/cvpr/2012/1226/0",
"title": "2012 IEEE Conference on Computer Vision and Pattern Recognition",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tp/2016/10/07360186",
"title": "Exploiting Hierarchical Dense Structures on Hypergraphs for Multi-Object Tracking",
"doi": null,
"abstractUrl": "/journal/tp/2016/10/07360186/13rRUwvT9hu",
"parentPublication": {
"id": "trans/tp",
"title": "IEEE Transactions on Pattern Analysis & Machine Intelligence",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tk/2018/03/08103040",
"title": "ComClus: A Self-Grouping Framework for Multi-Network Clustering",
"doi": null,
"abstractUrl": "/journal/tk/2018/03/08103040/13rRUxYrbV6",
"parentPublication": {
"id": "trans/tk",
"title": "IEEE Transactions on Knowledge & Data Engineering",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tp/2011/06/ttp2011061266",
"title": "Unsupervised Image Categorization by Hypergraph Partition",
"doi": null,
"abstractUrl": "/journal/tp/2011/06/ttp2011061266/13rRUygT7ak",
"parentPublication": {
"id": "trans/tp",
"title": "IEEE Transactions on Pattern Analysis & Machine Intelligence",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tp/2019/03/08283797",
"title": "Searching for Representative Modes on Hypergraphs for Robust Geometric Model Fitting",
"doi": null,
"abstractUrl": "/journal/tp/2019/03/08283797/17D45WWzW3g",
"parentPublication": {
"id": "trans/tp",
"title": "IEEE Transactions on Pattern Analysis & Machine Intelligence",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icme/2022/8563/0/09859633",
"title": "Multi-View Clustering Through Hypergraphs Integration on Stiefel Manifold",
"doi": null,
"abstractUrl": "/proceedings-article/icme/2022/09859633/1G9EE10lbDG",
"parentPublication": {
"id": "proceedings/icme/2022/8563/0",
"title": "2022 IEEE International Conference on Multimedia and Expo (ICME)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bigcomp/2023/7578/0/757800a316",
"title": "Randomizing Hypergraphs Preserving Two-mode Clustering Coefficient",
"doi": null,
"abstractUrl": "/proceedings-article/bigcomp/2023/757800a316/1LFLGWG0wFO",
"parentPublication": {
"id": "proceedings/bigcomp/2023/7578/0",
"title": "2023 IEEE International Conference on Big Data and Smart Computing (BigComp)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/2021/01/08789484",
"title": "Analyzing Dynamic Hypergraphs with Parallel Aggregated Ordered Hypergraph Visualization",
"doi": null,
"abstractUrl": "/journal/tg/2021/01/08789484/1ch5Lx3gcVO",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/wacv/2021/0477/0/047700d911",
"title": "Hyperrealistic Image Inpainting with Hypergraphs",
"doi": null,
"abstractUrl": "/proceedings-article/wacv/2021/047700d911/1uqGkLna9DG",
"parentPublication": {
"id": "proceedings/wacv/2021/0477/0",
"title": "2021 IEEE Winter Conference on Applications of Computer Vision (WACV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"adjacentArticles": {
"previous": {
"fno": "08000780",
"articleId": "13rRUxYrbNE",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "07585122",
"articleId": "13rRUxjyX5l",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"webExtras": [],
"articleVideos": []
}
|
{
"issue": {
"id": "1MNboCLDDZC",
"title": "June",
"year": "2023",
"issueNum": "06",
"idPrefix": "tk",
"pubType": "journal",
"volume": "35",
"label": "June",
"downloadables": {
"hasCover": false,
"__typename": "PeriodicalIssueDownloadablesType"
},
"__typename": "PeriodicalIssue"
},
"article": {
"id": "1BVBKFEH4sw",
"doi": "10.1109/TKDE.2022.3160393",
"abstract": "Hypergraphs consist of vertices and hyperedges that can connect multiple vertices. Since hypergraphs can effectively simulate complex intergroup relationships between entities, they have a wide range of applications such as computer vision and bioinformatics. In this paper, we study the subhypergraph matching problem, which is one of the most challenging problems in the processing of the hypergraphs. We aim to extract all subhypergraph isomorphism embeddings of a query hypergraph <inline-formula><tex-math notation=\"LaTeX\">Z_$q$_Z</tex-math></inline-formula> in a large data hypergraph <inline-formula><tex-math notation=\"LaTeX\">Z_$D$_Z</tex-math></inline-formula>. The existing methods on subgraph matching are designed for the ordinary graphs, which typically achieve the goal by three phases, i.e., filtering candidate vertices, refining candidate sets, and then enumeration final results in some matching order. However, such a design cannot be trivially extended to efficiently handle hypergraphs due to the inherent difference between ordinary graphs and hypergraphs. This motivates us to enhance the performance by exploiting hyperedge features, such as the typical intersections and inclusion relations between hyperedges. In our work, we present an efficient subhypergraph matching solution with two novel techniques, maximum hyperedge candidate filtering and co-occurrence matrix candidate refinement strategy. Maximum hyperedge candidate filtering is a filtering method based on hyperedge features, which can provide powerful pruning capability. Co-occurrence matrix candidate refinement strategy considers the high-order relationship between vertices in the hypergraph and provides an effective candidate refinement scheme to further reduce the overall search space. In order to find more effective matching order, we design a new enumeration strategy, which calculates the pseudo-isomorphic mapping set and then performs hyperedge verification. On real and synthetic data sets, we conduct extensive experiments to show our method outperforms existing methods by up to 2 orders of magnitude.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Hypergraphs consist of vertices and hyperedges that can connect multiple vertices. Since hypergraphs can effectively simulate complex intergroup relationships between entities, they have a wide range of applications such as computer vision and bioinformatics. In this paper, we study the subhypergraph matching problem, which is one of the most challenging problems in the processing of the hypergraphs. We aim to extract all subhypergraph isomorphism embeddings of a query hypergraph <inline-formula><tex-math notation=\"LaTeX\">$q$</tex-math><alternatives><mml:math><mml:mi>q</mml:mi></mml:math><inline-graphic xlink:href=\"su-ieq1-3160393.gif\"/></alternatives></inline-formula> in a large data hypergraph <inline-formula><tex-math notation=\"LaTeX\">$D$</tex-math><alternatives><mml:math><mml:mi>D</mml:mi></mml:math><inline-graphic xlink:href=\"su-ieq2-3160393.gif\"/></alternatives></inline-formula>. The existing methods on subgraph matching are designed for the ordinary graphs, which typically achieve the goal by three phases, i.e., filtering candidate vertices, refining candidate sets, and then enumeration final results in some matching order. However, such a design cannot be trivially extended to efficiently handle hypergraphs due to the inherent difference between ordinary graphs and hypergraphs. This motivates us to enhance the performance by exploiting hyperedge features, such as the typical intersections and inclusion relations between hyperedges. In our work, we present an efficient subhypergraph matching solution with two novel techniques, maximum hyperedge candidate filtering and co-occurrence matrix candidate refinement strategy. Maximum hyperedge candidate filtering is a filtering method based on hyperedge features, which can provide powerful pruning capability. Co-occurrence matrix candidate refinement strategy considers the high-order relationship between vertices in the hypergraph and provides an effective candidate refinement scheme to further reduce the overall search space. In order to find more effective matching order, we design a new enumeration strategy, which calculates the pseudo-isomorphic mapping set and then performs hyperedge verification. On real and synthetic data sets, we conduct extensive experiments to show our method outperforms existing methods by up to 2 orders of magnitude.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Hypergraphs consist of vertices and hyperedges that can connect multiple vertices. Since hypergraphs can effectively simulate complex intergroup relationships between entities, they have a wide range of applications such as computer vision and bioinformatics. In this paper, we study the subhypergraph matching problem, which is one of the most challenging problems in the processing of the hypergraphs. We aim to extract all subhypergraph isomorphism embeddings of a query hypergraph - in a large data hypergraph -. The existing methods on subgraph matching are designed for the ordinary graphs, which typically achieve the goal by three phases, i.e., filtering candidate vertices, refining candidate sets, and then enumeration final results in some matching order. However, such a design cannot be trivially extended to efficiently handle hypergraphs due to the inherent difference between ordinary graphs and hypergraphs. This motivates us to enhance the performance by exploiting hyperedge features, such as the typical intersections and inclusion relations between hyperedges. In our work, we present an efficient subhypergraph matching solution with two novel techniques, maximum hyperedge candidate filtering and co-occurrence matrix candidate refinement strategy. Maximum hyperedge candidate filtering is a filtering method based on hyperedge features, which can provide powerful pruning capability. Co-occurrence matrix candidate refinement strategy considers the high-order relationship between vertices in the hypergraph and provides an effective candidate refinement scheme to further reduce the overall search space. In order to find more effective matching order, we design a new enumeration strategy, which calculates the pseudo-isomorphic mapping set and then performs hyperedge verification. On real and synthetic data sets, we conduct extensive experiments to show our method outperforms existing methods by up to 2 orders of magnitude.",
"title": "Efficient Subhypergraph Matching Based on Hyperedge Features",
"normalizedTitle": "Efficient Subhypergraph Matching Based on Hyperedge Features",
"fno": "09739081",
"hasPdf": true,
"idPrefix": "tk",
"keywords": [
"Proteins",
"Semantics",
"Optimization",
"Computational Modeling",
"Bipartite Graph",
"Search Problems",
"Knowledge Engineering",
"Hypergraphs",
"Subhypergraph Matching",
"Subgraph Matching",
"Maximum Hyperedge Candidate Filtering",
"Co Occurrence Matrix Candidate Refinement",
"Pseudoisomorphic Mapping"
],
"authors": [
{
"givenName": "Yuhang",
"surname": "Su",
"fullName": "Yuhang Su",
"affiliation": "Department of Computer Science, Northeastern University, Shenyang, Liaoning, China",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Yu",
"surname": "Gu",
"fullName": "Yu Gu",
"affiliation": "Department of Computer Science, Northeastern University, Shenyang, Liaoning, China",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Zhigang",
"surname": "Wang",
"fullName": "Zhigang Wang",
"affiliation": "College of Information Science and Engineering, Ocean University of China, Qingdao, Shandong, China",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Ying",
"surname": "Zhang",
"fullName": "Ying Zhang",
"affiliation": "Centre for Artificial Intelligence, University of Technology Sydney, Sydney, NSW, Australia",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Jianbin",
"surname": "Qin",
"fullName": "Jianbin Qin",
"affiliation": "College of Computer Science and Software Engineering, Shenzhen University of China, Shenzhen, Guangdong, China",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Ge",
"surname": "Yu",
"fullName": "Ge Yu",
"affiliation": "Department of Computer Science, Northeastern University, Shenyang, Liaoning, China",
"__typename": "ArticleAuthorType"
}
],
"replicability": null,
"showBuyMe": true,
"showRecommendedArticles": true,
"isOpenAccess": false,
"issueNum": "06",
"pubDate": "2023-06-01 00:00:00",
"pubType": "trans",
"pages": "5808-5822",
"year": "2023",
"issn": "1041-4347",
"isbn": null,
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"recommendedArticles": [
{
"id": "trans/tk/5555/01/10121476",
"title": "Efficient Maximal Biclique Enumeration on Large Uncertain Bipartite Graphs",
"doi": null,
"abstractUrl": "/journal/tk/5555/01/10121476/1MYNFoG81ws",
"parentPublication": {
"id": "trans/tk",
"title": "IEEE Transactions on Knowledge & Data Engineering",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tk/2022/01/09028268",
"title": "Cohesive Subgraph Search Using Keywords in Large Networks",
"doi": null,
"abstractUrl": "/journal/tk/2022/01/09028268/1i3ALWb6q6A",
"parentPublication": {
"id": "trans/tk",
"title": "IEEE Transactions on Knowledge & Data Engineering",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tk/2022/03/09093067",
"title": "Efficient Regular Expression Matching Based on Positional Inverted Index",
"doi": null,
"abstractUrl": "/journal/tk/2022/03/09093067/1jNtKSJKvVm",
"parentPublication": {
"id": "trans/tk",
"title": "IEEE Transactions on Knowledge & Data Engineering",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tk/2022/07/09187569",
"title": "Distributed Hypergraph Processing Using Intersection Graphs",
"doi": null,
"abstractUrl": "/journal/tk/2022/07/09187569/1mVFlr5j4Aw",
"parentPublication": {
"id": "trans/tk",
"title": "IEEE Transactions on Knowledge & Data Engineering",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tk/2022/08/09210126",
"title": "Truss-Based Structural Diversity Search in Large Graphs",
"doi": null,
"abstractUrl": "/journal/tk/2022/08/09210126/1nxQ8gROGQ0",
"parentPublication": {
"id": "trans/tk",
"title": "IEEE Transactions on Knowledge & Data Engineering",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tk/2022/09/09269479",
"title": "Efficient Radius-Bounded Community Search in Geo-Social Networks",
"doi": null,
"abstractUrl": "/journal/tk/2022/09/09269479/1p1c8tla0DK",
"parentPublication": {
"id": "trans/tk",
"title": "IEEE Transactions on Knowledge & Data Engineering",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tk/2023/01/09422157",
"title": "On Efficient Large Maximal Biplex Discovery",
"doi": null,
"abstractUrl": "/journal/tk/2023/01/09422157/1tiTooWy0gg",
"parentPublication": {
"id": "trans/tk",
"title": "IEEE Transactions on Knowledge & Data Engineering",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tc/2022/05/09423579",
"title": "Constructing Completely Independent Spanning Trees in a Family of Line-Graph-Based Data Center Networks",
"doi": null,
"abstractUrl": "/journal/tc/2022/05/09423579/1tkyeT8TOwg",
"parentPublication": {
"id": "trans/tc",
"title": "IEEE Transactions on Computers",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tk/2023/03/09534476",
"title": "Discovering Significant Communities on Bipartite Graphs: An Index-Based Approach",
"doi": null,
"abstractUrl": "/journal/tk/2023/03/09534476/1wLbitNpdle",
"parentPublication": {
"id": "trans/tk",
"title": "IEEE Transactions on Knowledge & Data Engineering",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tk/2023/04/09629291",
"title": "Efficient Influential Community Search in Large Uncertain Graphs",
"doi": null,
"abstractUrl": "/journal/tk/2023/04/09629291/1yXvGVkW7ra",
"parentPublication": {
"id": "trans/tk",
"title": "IEEE Transactions on Knowledge & Data Engineering",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"adjacentArticles": {
"previous": {
"fno": "09756854",
"articleId": "1Cxv5xOVQOs",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "09767630",
"articleId": "1D4HbHscPZK",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"webExtras": [],
"articleVideos": []
}
|
{
"issue": {
"id": "1JNmPQgzUwU",
"title": "March",
"year": "2023",
"issueNum": "03",
"idPrefix": "td",
"pubType": "journal",
"volume": "34",
"label": "March",
"downloadables": {
"hasCover": false,
"__typename": "PeriodicalIssueDownloadablesType"
},
"__typename": "PeriodicalIssue"
},
"article": {
"id": "1JU072eQJmU",
"doi": "10.1109/TPDS.2023.3236669",
"abstract": "Core maintenance for dynamic hypergraphs has been receiving an increasing attention. However, existing works mainly focus on the insertion/deletion of hyperedges. This article revisits the problem from the view of vertices change. We study core maintenance when the vertices are inserted/deleted into/from specific hyperedges in the hypergraph, which is a challenging task since the deletion of the vertex may increase the core numbers and the insertion of the vertex may decrease the core numbers. We discuss in detail the possible changes of core numbers in different situations. For the insertion/deletion of vertices contained by a single hyperedge, we design sequential algorithms to discover the vertices whose core numbers have changed. Compared with static recomputation (Leng et al. 2013) and LYCLC (Luo et al. 2021) algorithms, our sequential algorithms can accelerate more than 1,000× and 12× at most in the processing time, respectively. For the insertion/deletion of vertices contained by different hyperedges, we find that core numbers of all vertices change 1 at most if these hyperedges form a matching. We design parallel algorithms that divide a matching into different sets based on their core numbers and allot a thread to each set. Experiments show that our parallel algorithms have good stability, scalability, and parallelism. Compared with the parallel static algorithm (Gabert et al. 2021) and the parallel dynamic algorithm GPC (Gabert et al. 2021), our parallel algorithms with 32 threads can accelerate 33× and 22× at most in the processing time, respectively.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Core maintenance for dynamic hypergraphs has been receiving an increasing attention. However, existing works mainly focus on the insertion/deletion of hyperedges. This article revisits the problem from the view of vertices change. We study core maintenance when the vertices are inserted/deleted into/from specific hyperedges in the hypergraph, which is a challenging task since the deletion of the vertex may increase the core numbers and the insertion of the vertex may decrease the core numbers. We discuss in detail the possible changes of core numbers in different situations. For the insertion/deletion of vertices contained by a single hyperedge, we design sequential algorithms to discover the vertices whose core numbers have changed. Compared with static recomputation (Leng et al. 2013) and LYCLC (Luo et al. 2021) algorithms, our sequential algorithms can accelerate more than 1,000× and 12× at most in the processing time, respectively. For the insertion/deletion of vertices contained by different hyperedges, we find that core numbers of all vertices change 1 at most if these hyperedges form a matching. We design parallel algorithms that divide a matching into different sets based on their core numbers and allot a thread to each set. Experiments show that our parallel algorithms have good stability, scalability, and parallelism. Compared with the parallel static algorithm (Gabert et al. 2021) and the parallel dynamic algorithm GPC (Gabert et al. 2021), our parallel algorithms with 32 threads can accelerate 33× and 22× at most in the processing time, respectively.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Core maintenance for dynamic hypergraphs has been receiving an increasing attention. However, existing works mainly focus on the insertion/deletion of hyperedges. This article revisits the problem from the view of vertices change. We study core maintenance when the vertices are inserted/deleted into/from specific hyperedges in the hypergraph, which is a challenging task since the deletion of the vertex may increase the core numbers and the insertion of the vertex may decrease the core numbers. We discuss in detail the possible changes of core numbers in different situations. For the insertion/deletion of vertices contained by a single hyperedge, we design sequential algorithms to discover the vertices whose core numbers have changed. Compared with static recomputation (Leng et al. 2013) and LYCLC (Luo et al. 2021) algorithms, our sequential algorithms can accelerate more than 1,000× and 12× at most in the processing time, respectively. For the insertion/deletion of vertices contained by different hyperedges, we find that core numbers of all vertices change 1 at most if these hyperedges form a matching. We design parallel algorithms that divide a matching into different sets based on their core numbers and allot a thread to each set. Experiments show that our parallel algorithms have good stability, scalability, and parallelism. Compared with the parallel static algorithm (Gabert et al. 2021) and the parallel dynamic algorithm GPC (Gabert et al. 2021), our parallel algorithms with 32 threads can accelerate 33× and 22× at most in the processing time, respectively.",
"title": "Revisiting Core Maintenance for Dynamic Hypergraphs",
"normalizedTitle": "Revisiting Core Maintenance for Dynamic Hypergraphs",
"fno": "10016680",
"hasPdf": true,
"idPrefix": "td",
"keywords": [
"Graph Theory",
"Parallel Algorithms",
"Core Maintenance",
"Core Numbers",
"Dynamic Hypergraphs",
"GPC",
"Hyperedges",
"Parallel Algorithms",
"Sequential Algorithms",
"Vertices Insertion Deletion",
"Maintenance Engineering",
"Heuristic Algorithms",
"Parallel Algorithms",
"Task Analysis",
"Instruction Sets",
"Visualization",
"Stability Analysis",
"Core Maintenance",
"Dynamic Hypergraphs",
"Parallel Algorithm"
],
"authors": [
{
"givenName": "Qiang-Sheng",
"surname": "Hua",
"fullName": "Qiang-Sheng Hua",
"affiliation": "National Engineering Research Center–Big Data Technology and System Lab, Key Laboratory of Services Computing Technology and System, Key Laboratory of Cluster and Grid Computing, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Xiaohui",
"surname": "Zhang",
"fullName": "Xiaohui Zhang",
"affiliation": "National Engineering Research Center–Big Data Technology and System Lab, Key Laboratory of Services Computing Technology and System, Key Laboratory of Cluster and Grid Computing, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Hai",
"surname": "Jin",
"fullName": "Hai Jin",
"affiliation": "National Engineering Research Center–Big Data Technology and System Lab, Key Laboratory of Services Computing Technology and System, Key Laboratory of Cluster and Grid Computing, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Hong",
"surname": "Huang",
"fullName": "Hong Huang",
"affiliation": "National Engineering Research Center–Big Data Technology and System Lab, Key Laboratory of Services Computing Technology and System, Key Laboratory of Cluster and Grid Computing, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China",
"__typename": "ArticleAuthorType"
}
],
"replicability": null,
"showBuyMe": true,
"showRecommendedArticles": true,
"isOpenAccess": false,
"issueNum": "03",
"pubDate": "2023-03-01 00:00:00",
"pubType": "trans",
"pages": "981-994",
"year": "2023",
"issn": "1045-9219",
"isbn": null,
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"recommendedArticles": [
{
"id": "proceedings/icdcs/2017/1792/0/1792c366",
"title": "Parallel Algorithm for Core Maintenance in Dynamic Graphs",
"doi": null,
"abstractUrl": "/proceedings-article/icdcs/2017/1792c366/12OmNvT2oJa",
"parentPublication": {
"id": "proceedings/icdcs/2017/1792/0",
"title": "2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tk/2014/10/06613492",
"title": "Efficient Core Maintenance in Large Dynamic Graphs",
"doi": null,
"abstractUrl": "/journal/tk/2014/10/06613492/13rRUwbJD5f",
"parentPublication": {
"id": "trans/tk",
"title": "IEEE Transactions on Knowledge & Data Engineering",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/nt/2015/06/06880400",
"title": "Dynamic shortest path algorithms for hypergraphs",
"doi": null,
"abstractUrl": "/journal/nt/2015/06/06880400/13rRUwwaKpc",
"parentPublication": {
"id": "trans/nt",
"title": "IEEE/ACM Transactions on Networking",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/td/2018/11/08357916",
"title": "Core Maintenance in Dynamic Graphs: A Parallel Approach Based on Matching",
"doi": null,
"abstractUrl": "/journal/td/2018/11/08357916/14eRyAiWRdS",
"parentPublication": {
"id": "trans/td",
"title": "IEEE Transactions on Parallel & Distributed Systems",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/5555/01/09721603",
"title": "Topological Simplifications of Hypergraphs",
"doi": null,
"abstractUrl": "/journal/tg/5555/01/09721603/1BhzoNy6wWA",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ipdps/2022/8106/0/810600b195",
"title": "Shared-Memory Parallel Algorithms for Fully Dynamic Maintenance of 2-Connected Components",
"doi": null,
"abstractUrl": "/proceedings-article/ipdps/2022/810600b195/1F1W8wnrAo8",
"parentPublication": {
"id": "proceedings/ipdps/2022/8106/0",
"title": "2022 IEEE International Parallel and Distributed Processing Symposium (IPDPS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/2021/01/08789484",
"title": "Analyzing Dynamic Hypergraphs with Parallel Aggregated Ordered Hypergraph Visualization",
"doi": null,
"abstractUrl": "/journal/tg/2021/01/08789484/1ch5Lx3gcVO",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/td/2020/06/08935160",
"title": "Faster Parallel Core Maintenance Algorithms in Dynamic Graphs",
"doi": null,
"abstractUrl": "/journal/td/2020/06/08935160/1fPUnyElZjq",
"parentPublication": {
"id": "trans/td",
"title": "IEEE Transactions on Parallel & Distributed Systems",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/td/2022/01/09462402",
"title": "Efficient Distributed Approaches to Core Maintenance on Large Dynamic Graphs",
"doi": null,
"abstractUrl": "/journal/td/2022/01/09462402/1uDSBmVtEdi",
"parentPublication": {
"id": "trans/td",
"title": "IEEE Transactions on Parallel & Distributed Systems",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icde/2021/9184/0/918400c051",
"title": "Hypercore Maintenance in Dynamic Hypergraphs",
"doi": null,
"abstractUrl": "/proceedings-article/icde/2021/918400c051/1uGXh6kpZ2o",
"parentPublication": {
"id": "proceedings/icde/2021/9184/0",
"title": "2021 IEEE 37th International Conference on Data Engineering (ICDE)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"adjacentArticles": {
"previous": {
"fno": "10015693",
"articleId": "1JSl9fj9fB6",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "10018868",
"articleId": "1K0DIbfzBD2",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"webExtras": [
{
"id": "1KnSuZgpgQg",
"name": "ttd202303-010016680s1-supp1-3236669.pdf",
"location": "https://www.computer.org/csdl/api/v1/extra/ttd202303-010016680s1-supp1-3236669.pdf",
"extension": "pdf",
"size": "103 kB",
"__typename": "WebExtraType"
}
],
"articleVideos": []
}
|
{
"issue": {
"id": "1DU9tzfHy24",
"title": "July",
"year": "2022",
"issueNum": "07",
"idPrefix": "tk",
"pubType": "journal",
"volume": "34",
"label": "July",
"downloadables": {
"hasCover": false,
"__typename": "PeriodicalIssueDownloadablesType"
},
"__typename": "PeriodicalIssue"
},
"article": {
"id": "1mVFlr5j4Aw",
"doi": "10.1109/TKDE.2020.3022014",
"abstract": "The advent of online applications such as social networks has led to an unprecedented scale of data and complex relationships among data. Hypergraphs are introduced to represent complex relationships that may involve more than two entities. A hypergraph is a generalized form of a graph, where edges are generalized to hyperedges. Each hyperedge may consist of any number of vertices. The flexibility of hyperedges also brings challenges in distributed hypergraph processing. In particular, a hypergraph is more difficult to be partitioned and distributed among <inline-formula><tex-math notation=\"LaTeX\">Z_$k$_Z</tex-math> </inline-formula> workers with balanced partitions. In this paper, we propose to convert a hypergraph into an intersection graph before partitioning by leveraging the inherent shared relationships among hypergraphs. We explore the intersection graph construction method and the corresponding partition strategy which can achieve the goal of evenly distributing vertices and hyperedges across workers, while yielding a significant communication reduction. We also design a distributed processing framework named <inline-formula><tex-math notation=\"LaTeX\">Z_$Hyraph$_Z</tex-math> </inline-formula> that can directly run hypergraph analysis algorithms on our intersection graphs. Experimental results on real datasets confirm the effectiveness of our techniques and the efficiency of the <inline-formula><tex-math notation=\"LaTeX\">Z_$Hyraph$_Z</tex-math></inline-formula> framework.",
"abstracts": [
{
"abstractType": "Regular",
"content": "The advent of online applications such as social networks has led to an unprecedented scale of data and complex relationships among data. Hypergraphs are introduced to represent complex relationships that may involve more than two entities. A hypergraph is a generalized form of a graph, where edges are generalized to hyperedges. Each hyperedge may consist of any number of vertices. The flexibility of hyperedges also brings challenges in distributed hypergraph processing. In particular, a hypergraph is more difficult to be partitioned and distributed among <inline-formula><tex-math notation=\"LaTeX\">$k$</tex-math> <alternatives><mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href=\"gu-ieq1-3022014.gif\"/></alternatives></inline-formula> workers with balanced partitions. In this paper, we propose to convert a hypergraph into an intersection graph before partitioning by leveraging the inherent shared relationships among hypergraphs. We explore the intersection graph construction method and the corresponding partition strategy which can achieve the goal of evenly distributing vertices and hyperedges across workers, while yielding a significant communication reduction. We also design a distributed processing framework named <inline-formula><tex-math notation=\"LaTeX\">$Hyraph$</tex-math> <alternatives><mml:math><mml:mrow><mml:mi>H</mml:mi><mml:mi>y</mml:mi><mml:mi>r</mml:mi><mml:mi>a</mml:mi><mml:mi>p</mml:mi><mml:mi>h</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href=\"gu-ieq2-3022014.gif\"/></alternatives></inline-formula> that can directly run hypergraph analysis algorithms on our intersection graphs. Experimental results on real datasets confirm the effectiveness of our techniques and the efficiency of the <inline-formula><tex-math notation=\"LaTeX\">$Hyraph$</tex-math><alternatives><mml:math><mml:mrow><mml:mi>H</mml:mi><mml:mi>y</mml:mi><mml:mi>r</mml:mi><mml:mi>a</mml:mi><mml:mi>p</mml:mi><mml:mi>h</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href=\"gu-ieq3-3022014.gif\"/></alternatives></inline-formula> framework.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "The advent of online applications such as social networks has led to an unprecedented scale of data and complex relationships among data. Hypergraphs are introduced to represent complex relationships that may involve more than two entities. A hypergraph is a generalized form of a graph, where edges are generalized to hyperedges. Each hyperedge may consist of any number of vertices. The flexibility of hyperedges also brings challenges in distributed hypergraph processing. In particular, a hypergraph is more difficult to be partitioned and distributed among - workers with balanced partitions. In this paper, we propose to convert a hypergraph into an intersection graph before partitioning by leveraging the inherent shared relationships among hypergraphs. We explore the intersection graph construction method and the corresponding partition strategy which can achieve the goal of evenly distributing vertices and hyperedges across workers, while yielding a significant communication reduction. We also design a distributed processing framework named - that can directly run hypergraph analysis algorithms on our intersection graphs. Experimental results on real datasets confirm the effectiveness of our techniques and the efficiency of the - framework.",
"title": "Distributed Hypergraph Processing Using Intersection Graphs",
"normalizedTitle": "Distributed Hypergraph Processing Using Intersection Graphs",
"fno": "09187569",
"hasPdf": true,
"idPrefix": "tk",
"keywords": [
"Distributed Processing",
"Graph Theory",
"Distributed Hypergraph Processing",
"Intersection Graph Construction Method",
"Distributed Processing Framework",
"Hypergraph Analysis Algorithms",
"Social Networks",
"Bipartite Graph",
"Distributed Databases",
"Partitioning Algorithms",
"Electronic Mail",
"Heuristic Algorithms",
"Knowledge Engineering",
"Data Engineering",
"Hypergraphs",
"Shared Relationships",
"Intersection Graphs",
"Distributed Processing",
"Graph Processing"
],
"authors": [
{
"givenName": "Yu",
"surname": "Gu",
"fullName": "Yu Gu",
"affiliation": "Department of Computer Science, Northeastern University, Shenyang, P. R. China",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Kaiqiang",
"surname": "Yu",
"fullName": "Kaiqiang Yu",
"affiliation": "Department of Computer Science, Northeastern University, Shenyang, P. R. China",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Zhen",
"surname": "Song",
"fullName": "Zhen Song",
"affiliation": "Department of Computer Science, Northeastern University, Shenyang, P. R. China",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Jianzhong",
"surname": "Qi",
"fullName": "Jianzhong Qi",
"affiliation": "School of Computing and Information Systems, The University of Melbourne, Parkville, VIC, Australia",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Zhigang",
"surname": "Wang",
"fullName": "Zhigang Wang",
"affiliation": "College of Information Science and Engineering, Ocean University of China, Qingdao, Shandong, China",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Ge",
"surname": "Yu",
"fullName": "Ge Yu",
"affiliation": "Department of Computer Science, Northeastern University, Shenyang, P. R. China",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Rui",
"surname": "Zhang",
"fullName": "Rui Zhang",
"affiliation": "School of Computing and Information Systems, The University of Melbourne, Parkville, VIC, Australia",
"__typename": "ArticleAuthorType"
}
],
"replicability": null,
"showBuyMe": true,
"showRecommendedArticles": true,
"isOpenAccess": false,
"issueNum": "07",
"pubDate": "2022-07-01 00:00:00",
"pubType": "trans",
"pages": "3182-3195",
"year": "2022",
"issn": "1041-4347",
"isbn": null,
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"recommendedArticles": [
{
"id": "trans/tk/2023/06/09739081",
"title": "Efficient Subhypergraph Matching Based on Hyperedge Features",
"doi": null,
"abstractUrl": "/journal/tk/2023/06/09739081/1BVBKFEH4sw",
"parentPublication": {
"id": "trans/tk",
"title": "IEEE Transactions on Knowledge & Data Engineering",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tk/2022/04/09121771",
"title": "Continuous Monitoring of Maximum Clique Over Dynamic Graphs",
"doi": null,
"abstractUrl": "/journal/tk/2022/04/09121771/1kMT4CxqinC",
"parentPublication": {
"id": "trans/tk",
"title": "IEEE Transactions on Knowledge & Data Engineering",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tk/2022/08/09244575",
"title": "Distributed Density Peaks Clustering Revisited",
"doi": null,
"abstractUrl": "/journal/tk/2022/08/09244575/1ojYk1yEY1i",
"parentPublication": {
"id": "trans/tk",
"title": "IEEE Transactions on Knowledge & Data Engineering",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tk/2022/09/09250607",
"title": "Enumerating Maximum Cliques in Massive Graphs",
"doi": null,
"abstractUrl": "/journal/tk/2022/09/09250607/1oxjS6MBaA8",
"parentPublication": {
"id": "trans/tk",
"title": "IEEE Transactions on Knowledge & Data Engineering",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tk/2023/01/09422157",
"title": "On Efficient Large Maximal Biplex Discovery",
"doi": null,
"abstractUrl": "/journal/tk/2023/01/09422157/1tiTooWy0gg",
"parentPublication": {
"id": "trans/tk",
"title": "IEEE Transactions on Knowledge & Data Engineering",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tc/2022/05/09423579",
"title": "Constructing Completely Independent Spanning Trees in a Family of Line-Graph-Based Data Center Networks",
"doi": null,
"abstractUrl": "/journal/tc/2022/05/09423579/1tkyeT8TOwg",
"parentPublication": {
"id": "trans/tc",
"title": "IEEE Transactions on Computers",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tk/2023/01/09452800",
"title": "Core Decomposition on Uncertain Graphs Revisited",
"doi": null,
"abstractUrl": "/journal/tk/2023/01/09452800/1ulCu0Hdqs8",
"parentPublication": {
"id": "trans/tk",
"title": "IEEE Transactions on Knowledge & Data Engineering",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tk/2023/03/09534476",
"title": "Discovering Significant Communities on Bipartite Graphs: An Index-Based Approach",
"doi": null,
"abstractUrl": "/journal/tk/2023/03/09534476/1wLbitNpdle",
"parentPublication": {
"id": "trans/tk",
"title": "IEEE Transactions on Knowledge & Data Engineering",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tb/2022/06/09616390",
"title": "Checking Phylogenetics Decisiveness in Theory and in Practice",
"doi": null,
"abstractUrl": "/journal/tb/2022/06/09616390/1yA73pVevu0",
"parentPublication": {
"id": "trans/tb",
"title": "IEEE/ACM Transactions on Computational Biology and Bioinformatics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/td/2022/10/09665221",
"title": "An Efficient Index-Based Approach to Distributed Set Reachability on Small-World Graphs",
"doi": null,
"abstractUrl": "/journal/td/2022/10/09665221/1zJiQNKABEs",
"parentPublication": {
"id": "trans/td",
"title": "IEEE Transactions on Parallel & Distributed Systems",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"adjacentArticles": {
"previous": {
"fno": "09197722",
"articleId": "1n8WCSbSdxe",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "09180058",
"articleId": "1mF4jty4vPa",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"webExtras": [],
"articleVideos": []
}
|
{
"issue": {
"id": "12OmNzV70rO",
"title": "July",
"year": "2006",
"issueNum": "07",
"idPrefix": "tp",
"pubType": "journal",
"volume": "28",
"label": "July",
"downloadables": {
"hasCover": false,
"__typename": "PeriodicalIssueDownloadablesType"
},
"__typename": "PeriodicalIssue"
},
"article": {
"id": "13rRUILLkwr",
"doi": "10.1109/TPAMI.2006.147",
"abstract": "We describe an iterative stabilization method that can simultaneously recover camera motion and 3D shape from an image sequence captured under modest deviation from planar motion. This technique iteratively applies a factorization method based on planar motion and can approximate the observed image points to the 2D points projected under planar motion by stabilizing the camera motion. We apply the proposed method to aerial images acquired by a helicopter-borne camera and show better reconstruction of both motion and shape than Christy-Horaud's perspective factorization. Moreover, we confirm that the reprojection errors calculated from the recovered camera motion and 3D shape are very similar to the optimum results yielded by bundle adjustment.",
"abstracts": [
{
"abstractType": "Regular",
"content": "We describe an iterative stabilization method that can simultaneously recover camera motion and 3D shape from an image sequence captured under modest deviation from planar motion. This technique iteratively applies a factorization method based on planar motion and can approximate the observed image points to the 2D points projected under planar motion by stabilizing the camera motion. We apply the proposed method to aerial images acquired by a helicopter-borne camera and show better reconstruction of both motion and shape than Christy-Horaud's perspective factorization. Moreover, we confirm that the reprojection errors calculated from the recovered camera motion and 3D shape are very similar to the optimum results yielded by bundle adjustment.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "We describe an iterative stabilization method that can simultaneously recover camera motion and 3D shape from an image sequence captured under modest deviation from planar motion. This technique iteratively applies a factorization method based on planar motion and can approximate the observed image points to the 2D points projected under planar motion by stabilizing the camera motion. We apply the proposed method to aerial images acquired by a helicopter-borne camera and show better reconstruction of both motion and shape than Christy-Horaud's perspective factorization. Moreover, we confirm that the reprojection errors calculated from the recovered camera motion and 3D shape are very similar to the optimum results yielded by bundle adjustment.",
"title": "Motion and Shape Recovery Based on Iterative Stabilization for Modest Deviation from Planar Motion",
"normalizedTitle": "Motion and Shape Recovery Based on Iterative Stabilization for Modest Deviation from Planar Motion",
"fno": "i1176",
"hasPdf": true,
"idPrefix": "tp",
"keywords": [
"Shape",
"Cameras",
"Image Reconstruction",
"Iterative Methods",
"Computer Society",
"Image Sequences",
"Cities And Towns",
"Geographic Information Systems",
"Photography",
"Robustness",
"Aerial Image",
"Structure From Motion",
"Factorization Method",
"Planar Motion"
],
"authors": [
{
"givenName": "Isao",
"surname": "Miyagawa",
"fullName": "Isao Miyagawa",
"affiliation": "IEEE Computer Society",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Kenichi",
"surname": "Arakawa",
"fullName": "Kenichi Arakawa",
"affiliation": "IEEE Computer Society",
"__typename": "ArticleAuthorType"
}
],
"replicability": null,
"showBuyMe": true,
"showRecommendedArticles": true,
"isOpenAccess": false,
"issueNum": "07",
"pubDate": "2006-07-01 00:00:00",
"pubType": "trans",
"pages": "1176-1181",
"year": "2006",
"issn": "0162-8828",
"isbn": null,
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"recommendedArticles": [
{
"id": "proceedings/wacv-motion/2005/2271/2/227120154",
"title": "A Factorization Method for Structure from Planar Motion",
"doi": null,
"abstractUrl": "/proceedings-article/wacv-motion/2005/227120154/12OmNAKuoTo",
"parentPublication": {
"id": "proceedings/wacv-motion/2005/2271/2",
"title": "Applications of Computer Vision and the IEEE Workshop on Motion and Video Computing, IEEE Workshop on",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/1994/5825/0/00323866",
"title": "Recovery of ego-motion using image stabilization",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/1994/00323866/12OmNAoUTjD",
"parentPublication": {
"id": "proceedings/cvpr/1994/5825/0",
"title": "Proceedings of IEEE Conference on Computer Vision and Pattern Recognition",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccv/2001/1143/2/114320193",
"title": "Concentric Mosaic(s), Planar Motion and 1D Cameras",
"doi": null,
"abstractUrl": "/proceedings-article/iccv/2001/114320193/12OmNBTs7HO",
"parentPublication": {
"id": "proceedings/iccv/2001/1143/0",
"title": "Computer Vision, IEEE International Conference on",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/visapp/2014/8133/3/07295141",
"title": "Ego-motion recovery and robust tilt estimation for planar motion using several homographies",
"doi": null,
"abstractUrl": "/proceedings-article/visapp/2014/07295141/12OmNBpEeQL",
"parentPublication": {
"id": "proceedings/visapp/2014/8133/2",
"title": "2014 International Conference on Computer Vision Theory and Applications (VISAPP)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2008/2242/0/04587708",
"title": "A factorization approach to structure from motion with shape priors",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2008/04587708/12OmNCdk30d",
"parentPublication": {
"id": "proceedings/cvpr/2008/2242/0",
"title": "2008 IEEE Conference on Computer Vision and Pattern Recognition",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icpr/2004/2128/4/212840068",
"title": "Planar Motion of a Parabolic Catadioptric Camera",
"doi": null,
"abstractUrl": "/proceedings-article/icpr/2004/212840068/12OmNxwENnx",
"parentPublication": {
"id": "proceedings/icpr/2004/2128/0",
"title": "Pattern Recognition, International Conference on",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icpr/2000/0750/3/07503815",
"title": "A Novel Method for Camera Planar Motion Detection and Robust Estimation of the 1D Trifocal Tensor",
"doi": null,
"abstractUrl": "/proceedings-article/icpr/2000/07503815/12OmNxwncDf",
"parentPublication": {
"id": "proceedings/icpr/2000/0750/3",
"title": "Pattern Recognition, International Conference on",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/wacv-motion/2005/2271/2/227120160",
"title": "Planar Ego-Motion Without Correspondences",
"doi": null,
"abstractUrl": "/proceedings-article/wacv-motion/2005/227120160/12OmNy68EDI",
"parentPublication": {
"id": "proceedings/wacv-motion/2005/2271/2",
"title": "Applications of Computer Vision and the IEEE Workshop on Motion and Video Computing, IEEE Workshop on",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icpr/2000/0750/1/07501445",
"title": "Application of Planar Motion Segmentation for Scene Text Extraction",
"doi": null,
"abstractUrl": "/proceedings-article/icpr/2000/07501445/12OmNz61dhR",
"parentPublication": {
"id": "proceedings/icpr/2000/0750/1",
"title": "Pattern Recognition, International Conference on",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tp/2000/10/i1105",
"title": "Multi-Frame Estimation of Planar Motion",
"doi": null,
"abstractUrl": "/journal/tp/2000/10/i1105/13rRUwhpBEV",
"parentPublication": {
"id": "trans/tp",
"title": "IEEE Transactions on Pattern Analysis & Machine Intelligence",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"adjacentArticles": {
"previous": {
"fno": "i1170",
"articleId": "13rRUwh80vy",
"__typename": "AdjacentArticleType"
},
"next": null,
"__typename": "AdjacentArticlesType"
},
"webExtras": [],
"articleVideos": []
}
|
{
"issue": {
"id": "12OmNvGPE8n",
"title": "Jan.",
"year": "2016",
"issueNum": "01",
"idPrefix": "tg",
"pubType": "journal",
"volume": "22",
"label": "Jan.",
"downloadables": {
"hasCover": false,
"__typename": "PeriodicalIssueDownloadablesType"
},
"__typename": "PeriodicalIssue"
},
"article": {
"id": "13rRUxd2aZ3",
"doi": "10.1109/TVCG.2015.2467413",
"abstract": "We present a novel method to create planar visualizations of treelike structures (e.g., blood vessels and airway trees) where the shape of the object is well preserved, allowing for easy recognition by users familiar with the structures. Based on the extracted skeleton within the treelike object, a radial planar embedding is first obtained such that there are no self-intersections of the skeleton which would have resulted in occlusions in the final view. An optimization procedure which adjusts the angular positions of the skeleton nodes is then used to reconstruct the shape as closely as possible to the original, according to a specified view plane, which thus preserves the global geometric context of the object. Using this shape recovered embedded skeleton, the object surface is then flattened to the plane without occlusions using harmonic mapping. The boundary of the mesh is adjusted during the flattening step to account for regions where the mesh is stretched over concavities. This parameterized surface can then be used either as a map for guidance during endoluminal navigation or directly for interrogation and decision making. Depth cues are provided with a grayscale border to aid in shape understanding. Examples are presented using bronchial trees, cranial and lower limb blood vessels, and upper aorta datasets, and the results are evaluated quantitatively and with a user study.",
"abstracts": [
{
"abstractType": "Regular",
"content": "We present a novel method to create planar visualizations of treelike structures (e.g., blood vessels and airway trees) where the shape of the object is well preserved, allowing for easy recognition by users familiar with the structures. Based on the extracted skeleton within the treelike object, a radial planar embedding is first obtained such that there are no self-intersections of the skeleton which would have resulted in occlusions in the final view. An optimization procedure which adjusts the angular positions of the skeleton nodes is then used to reconstruct the shape as closely as possible to the original, according to a specified view plane, which thus preserves the global geometric context of the object. Using this shape recovered embedded skeleton, the object surface is then flattened to the plane without occlusions using harmonic mapping. The boundary of the mesh is adjusted during the flattening step to account for regions where the mesh is stretched over concavities. This parameterized surface can then be used either as a map for guidance during endoluminal navigation or directly for interrogation and decision making. Depth cues are provided with a grayscale border to aid in shape understanding. Examples are presented using bronchial trees, cranial and lower limb blood vessels, and upper aorta datasets, and the results are evaluated quantitatively and with a user study.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "We present a novel method to create planar visualizations of treelike structures (e.g., blood vessels and airway trees) where the shape of the object is well preserved, allowing for easy recognition by users familiar with the structures. Based on the extracted skeleton within the treelike object, a radial planar embedding is first obtained such that there are no self-intersections of the skeleton which would have resulted in occlusions in the final view. An optimization procedure which adjusts the angular positions of the skeleton nodes is then used to reconstruct the shape as closely as possible to the original, according to a specified view plane, which thus preserves the global geometric context of the object. Using this shape recovered embedded skeleton, the object surface is then flattened to the plane without occlusions using harmonic mapping. The boundary of the mesh is adjusted during the flattening step to account for regions where the mesh is stretched over concavities. This parameterized surface can then be used either as a map for guidance during endoluminal navigation or directly for interrogation and decision making. Depth cues are provided with a grayscale border to aid in shape understanding. Examples are presented using bronchial trees, cranial and lower limb blood vessels, and upper aorta datasets, and the results are evaluated quantitatively and with a user study.",
"title": "Planar Visualization of Treelike Structures",
"normalizedTitle": "Planar Visualization of Treelike Structures",
"fno": "07192698",
"hasPdf": true,
"idPrefix": "tg",
"keywords": [
"Skeleton",
"Shape",
"Three Dimensional Displays",
"Layout",
"Visualization",
"Blood Vessels",
"Context",
"Planar Embedding",
"Geometry Based Techniques",
"View Dependent Visualization",
"Medical Visualization",
"Planar Embedding",
"Geometry Based Techniques",
"View Dependent Visualization",
"Medical Visualization"
],
"authors": [
{
"givenName": "Joseph",
"surname": "Marino",
"fullName": "Joseph Marino",
"affiliation": "Computer Science Department, Stony Brook University",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Arie",
"surname": "Kaufman",
"fullName": "Arie Kaufman",
"affiliation": "Computer Science Department, Stony Brook University",
"__typename": "ArticleAuthorType"
}
],
"replicability": null,
"showBuyMe": true,
"showRecommendedArticles": true,
"isOpenAccess": false,
"issueNum": "01",
"pubDate": "2016-01-01 00:00:00",
"pubType": "trans",
"pages": "906-915",
"year": "2016",
"issn": "1077-2626",
"isbn": null,
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"recommendedArticles": [
{
"id": "proceedings/bia/1994/5802/0/00315853",
"title": "A geometric modeling tool for visualization of human anatomical structures",
"doi": null,
"abstractUrl": "/proceedings-article/bia/1994/00315853/12OmNApu5r0",
"parentPublication": {
"id": "proceedings/bia/1994/5802/0",
"title": "Proceedings of IEEE Workshop on Biomedical Image Analysis",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cgiv/2016/0811/0/0811a178",
"title": "Cloud Motion Estimation in Satellite Image Sequences by Tracking Skeleton Critical Points Using Lucas-Kanade Method",
"doi": null,
"abstractUrl": "/proceedings-article/cgiv/2016/0811a178/12OmNvy256I",
"parentPublication": {
"id": "proceedings/cgiv/2016/0811/0",
"title": "2016 13th International Conference on Computer Graphics, Imaging and Visualization (CGiV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/3dv/2015/8332/0/8332a545",
"title": "Towards Skeleton Based Reconstruction: From Projective Skeletonization to Canal Surface Estimation",
"doi": null,
"abstractUrl": "/proceedings-article/3dv/2015/8332a545/12OmNwNwzLH",
"parentPublication": {
"id": "proceedings/3dv/2015/8332/0",
"title": "2015 International Conference on 3D Vision (3DV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icpr/1992/2920/0/00201957",
"title": "Object recognition: the utopian method is dead; the time for combining simple methods has come",
"doi": null,
"abstractUrl": "/proceedings-article/icpr/1992/00201957/12OmNyYDDxO",
"parentPublication": {
"id": "proceedings/icpr/1992/2920/0",
"title": "11th IAPR International Conference on Pattern Recognition. Vol. III. Conference C: Image, Speech and Signal Analysis,",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cw/2013/2246/0/2246a318",
"title": "Skeleton-Based Anime Hair Modeling and Visualization",
"doi": null,
"abstractUrl": "/proceedings-article/cw/2013/2246a318/12OmNykCceO",
"parentPublication": {
"id": "proceedings/cw/2013/2246/0",
"title": "2013 International Conference on Cyberworlds (CW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tp/2015/09/07102776",
"title": "Semi-Continuity of Skeletons in Two-Manifold and Discrete Voronoi Approximation",
"doi": null,
"abstractUrl": "/journal/tp/2015/09/07102776/13rRUxASuHA",
"parentPublication": {
"id": "trans/tp",
"title": "IEEE Transactions on Pattern Analysis & Machine Intelligence",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tp/2016/01/07066924",
"title": "An Unified Multiscale Framework for Planar, Surface, and Curve Skeletonization",
"doi": null,
"abstractUrl": "/journal/tp/2016/01/07066924/13rRUxASuNO",
"parentPublication": {
"id": "trans/tp",
"title": "IEEE Transactions on Pattern Analysis & Machine Intelligence",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tp/2003/09/i1118",
"title": "Skeletonization of Ribbon-Like Shapes Based on a New Wavelet Function",
"doi": null,
"abstractUrl": "/journal/tp/2003/09/i1118/13rRUyY28Zr",
"parentPublication": {
"id": "trans/tp",
"title": "IEEE Transactions on Pattern Analysis & Machine Intelligence",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/5555/01/09721643",
"title": "Geometry-Aware Planar Embedding of Treelike Structures",
"doi": null,
"abstractUrl": "/journal/tg/5555/01/09721643/1BhzmWFFD9K",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccvw/2021/0191/0/019100c136",
"title": "Distance and Edge Transform for Skeleton Extraction",
"doi": null,
"abstractUrl": "/proceedings-article/iccvw/2021/019100c136/1yNi3UnSipG",
"parentPublication": {
"id": "proceedings/iccvw/2021/0191/0",
"title": "2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"adjacentArticles": {
"previous": {
"fno": "07192682",
"articleId": "13rRUwcS1CY",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "07192674",
"articleId": "13rRUxcsYLQ",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"webExtras": [],
"articleVideos": []
}
|
{
"issue": {
"id": "1qdSTDvknHa",
"title": "Feb.",
"year": "2021",
"issueNum": "02",
"idPrefix": "tp",
"pubType": "journal",
"volume": "43",
"label": "Feb.",
"downloadables": {
"hasCover": false,
"__typename": "PeriodicalIssueDownloadablesType"
},
"__typename": "PeriodicalIssue"
},
"article": {
"id": "1fFWEan02nS",
"doi": "10.1109/TPAMI.2019.2931577",
"abstract": "A novel method is proposed for the absolute pose estimation of a central 2D camera with respect to 3D depth data without the use of any dedicated calibration pattern or explicit point correspondences. The proposed method has no specific assumption about the data source: plain depth information is expected from the 3D sensing device and a central camera is used to capture the 2D images. Both the perspective and omnidirectional central cameras are handled within a single generic camera model. Pose estimation is formulated as a 2D-3D nonlinear shape registration task which is solved without point correspondences or complex similarity metrics. It relies on a set of corresponding planar regions, and the pose parameters are obtained by solving an overdetermined system of nonlinear equations. The efficiency and robustness of the proposed method were confirmed on both large scale synthetic data and on real data acquired from various types of sensors.",
"abstracts": [
{
"abstractType": "Regular",
"content": "A novel method is proposed for the absolute pose estimation of a central 2D camera with respect to 3D depth data without the use of any dedicated calibration pattern or explicit point correspondences. The proposed method has no specific assumption about the data source: plain depth information is expected from the 3D sensing device and a central camera is used to capture the 2D images. Both the perspective and omnidirectional central cameras are handled within a single generic camera model. Pose estimation is formulated as a 2D-3D nonlinear shape registration task which is solved without point correspondences or complex similarity metrics. It relies on a set of corresponding planar regions, and the pose parameters are obtained by solving an overdetermined system of nonlinear equations. The efficiency and robustness of the proposed method were confirmed on both large scale synthetic data and on real data acquired from various types of sensors.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "A novel method is proposed for the absolute pose estimation of a central 2D camera with respect to 3D depth data without the use of any dedicated calibration pattern or explicit point correspondences. The proposed method has no specific assumption about the data source: plain depth information is expected from the 3D sensing device and a central camera is used to capture the 2D images. Both the perspective and omnidirectional central cameras are handled within a single generic camera model. Pose estimation is formulated as a 2D-3D nonlinear shape registration task which is solved without point correspondences or complex similarity metrics. It relies on a set of corresponding planar regions, and the pose parameters are obtained by solving an overdetermined system of nonlinear equations. The efficiency and robustness of the proposed method were confirmed on both large scale synthetic data and on real data acquired from various types of sensors.",
"title": "Absolute Pose Estimation of Central Cameras Using Planar Regions",
"normalizedTitle": "Absolute Pose Estimation of Central Cameras Using Planar Regions",
"fno": "08778782",
"hasPdf": true,
"idPrefix": "tp",
"keywords": [
"Calibration",
"Cameras",
"Image Registration",
"Image Sensors",
"Nonlinear Equations",
"Nonlinear Estimation",
"Optical Radar",
"Pose Estimation",
"Radar Imaging",
"Omnidirectional Central Cameras",
"Central Camera",
"3 D Sensing Device",
"Plain Depth Information",
"Data Source",
"Specific Assumption",
"Explicit Point Correspondences",
"Dedicated Calibration Pattern",
"3 D Depth Data",
"Central 2 D Camera",
"Absolute Pose Estimation",
"Scale Synthetic Data",
"Corresponding Planar Regions",
"2 D 3 D Nonlinear Shape Registration Task",
"Single Generic Camera Model",
"Cameras",
"Calibration",
"Three Dimensional Displays",
"Pose Estimation",
"Two Dimensional Displays",
"Mathematical Model",
"Laser Radar",
"Pose Estimation",
"Calibration",
"Data Fusion",
"Registration",
"Lidar",
"Omnidirectional Camera"
],
"authors": [
{
"givenName": "Robert",
"surname": "Frohlich",
"fullName": "Robert Frohlich",
"affiliation": "Department of Image Processing and Computer Graphics, University of Szeged, Szeged, Hungary",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Levente",
"surname": "Tamas",
"fullName": "Levente Tamas",
"affiliation": "Automation Department, Technical University of Cluj-Napoca, Cluj-Napoca, Romania",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Zoltan",
"surname": "Kato",
"fullName": "Zoltan Kato",
"affiliation": "Department of Image Processing and Computer Graphics, University of Szeged, Szeged, Hungary",
"__typename": "ArticleAuthorType"
}
],
"replicability": null,
"showBuyMe": true,
"showRecommendedArticles": true,
"isOpenAccess": false,
"issueNum": "02",
"pubDate": "2021-02-01 00:00:00",
"pubType": "trans",
"pages": "377-391",
"year": "2021",
"issn": "0162-8828",
"isbn": null,
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"recommendedArticles": [
{
"id": "proceedings/avss/2016/3811/0/07738081",
"title": "A visual SLAM-based approach for calibration of distributed camera networks",
"doi": null,
"abstractUrl": "/proceedings-article/avss/2016/07738081/12OmNC3Xhhg",
"parentPublication": {
"id": "proceedings/avss/2016/3811/0",
"title": "2016 13th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccvw/2017/1034/0/1034c472",
"title": "Multiview Absolute Pose Using 3D – 2D Perspective Line Correspondences and Vertical Direction",
"doi": null,
"abstractUrl": "/proceedings-article/iccvw/2017/1034c472/12OmNyUnELg",
"parentPublication": {
"id": "proceedings/iccvw/2017/1034/0",
"title": "2017 IEEE International Conference on Computer Vision Workshop (ICCVW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tp/2020/02/08388302",
"title": "Globally-Optimal Inlier Set Maximisation for Camera Pose and Correspondence Estimation",
"doi": null,
"abstractUrl": "/journal/tp/2020/02/08388302/13rRUx0gegI",
"parentPublication": {
"id": "trans/tp",
"title": "IEEE Transactions on Pattern Analysis & Machine Intelligence",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvprw/2018/6100/0/610000b856",
"title": "Human Pose as Calibration Pattern: 3D Human Pose Estimation with Multiple Unsynchronized and Uncalibrated Cameras",
"doi": null,
"abstractUrl": "/proceedings-article/cvprw/2018/610000b856/17D45XzbnKx",
"parentPublication": {
"id": "proceedings/cvprw/2018/6100/0",
"title": "2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/wacv/2023/9346/0/934600c881",
"title": "Partially calibrated semi-generalized pose from hybrid point correspondences",
"doi": null,
"abstractUrl": "/proceedings-article/wacv/2023/934600c881/1KxUyylVBzG",
"parentPublication": {
"id": "proceedings/wacv/2023/9346/0",
"title": "2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2019/3293/0/329300l1788",
"title": "The Alignment of the Spheres: Globally-Optimal Spherical Mixture Alignment for Camera Pose Estimation",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2019/329300l1788/1gyscEL6ng4",
"parentPublication": {
"id": "proceedings/cvpr/2019/3293/0",
"title": "2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccv/2019/4803/0/4.803E44",
"title": "Calibration of Axial Fisheye Cameras Through Generic Virtual Central Models",
"doi": null,
"abstractUrl": "/proceedings-article/iccv/2019/4.803E44/1hQqpDxzYbe",
"parentPublication": {
"id": "proceedings/iccv/2019/4803/0",
"title": "2019 IEEE/CVF International Conference on Computer Vision (ICCV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccvw/2019/5023/0/502300a895",
"title": "Robust Absolute and Relative Pose Estimation of a Central Camera System from 2D-3D Line Correspondences",
"doi": null,
"abstractUrl": "/proceedings-article/iccvw/2019/502300a895/1i5mxbBns0o",
"parentPublication": {
"id": "proceedings/iccvw/2019/5023/0",
"title": "2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/wacv/2020/6553/0/09093539",
"title": "Estimating 3D Camera Pose from 2D Pedestrian Trajectories",
"doi": null,
"abstractUrl": "/proceedings-article/wacv/2020/09093539/1jPbm2kBcgo",
"parentPublication": {
"id": "proceedings/wacv/2020/6553/0",
"title": "2020 IEEE Winter Conference on Applications of Computer Vision (WACV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2021/4509/0/450900l1881",
"title": "Multi-View Multi-Person 3D Pose Estimation with Plane Sweep Stereo",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2021/450900l1881/1yeJb6FUA4E",
"parentPublication": {
"id": "proceedings/cvpr/2021/4509/0",
"title": "2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"adjacentArticles": {
"previous": {
"fno": "09319600",
"articleId": "1qiRD7JMwEw",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "08787881",
"articleId": "1oFCqZUsdCo",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"webExtras": [],
"articleVideos": []
}
|
{
"issue": {
"id": "12OmNyp9Mqu",
"title": "Jan.",
"year": "2014",
"issueNum": "01",
"idPrefix": "tp",
"pubType": "journal",
"volume": "36",
"label": "Jan.",
"downloadables": {
"hasCover": false,
"__typename": "PeriodicalIssueDownloadablesType"
},
"__typename": "PeriodicalIssue"
},
"article": {
"id": "13rRUx0ger4",
"doi": "10.1109/TPAMI.2013.135",
"abstract": "In this work, we show that using the eigen-decomposition of the adjacency matrix, we can consistently estimate latent positions for random dot product graphs provided the latent positions are i.i.d. from some distribution. If class labels are observed for a number of vertices tending to infinity, then we show that the remaining vertices can be classified with error converging to Bayes optimal using the Z_$(k)$_Z-nearest-neighbors classification rule. We evaluate the proposed methods on simulated data and a graph derived from .",
"abstracts": [
{
"abstractType": "Regular",
"content": "In this work, we show that using the eigen-decomposition of the adjacency matrix, we can consistently estimate latent positions for random dot product graphs provided the latent positions are i.i.d. from some distribution. If class labels are observed for a number of vertices tending to infinity, then we show that the remaining vertices can be classified with error converging to Bayes optimal using the $(k)$-nearest-neighbors classification rule. We evaluate the proposed methods on simulated data and a graph derived from .",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "In this work, we show that using the eigen-decomposition of the adjacency matrix, we can consistently estimate latent positions for random dot product graphs provided the latent positions are i.i.d. from some distribution. If class labels are observed for a number of vertices tending to infinity, then we show that the remaining vertices can be classified with error converging to Bayes optimal using the --nearest-neighbors classification rule. We evaluate the proposed methods on simulated data and a graph derived from .",
"title": "Consistent Latent Position Estimation and Vertex Classification for Random Dot Product Graphs",
"normalizedTitle": "Consistent Latent Position Estimation and Vertex Classification for Random Dot Product Graphs",
"fno": "ttp2014010048",
"hasPdf": true,
"idPrefix": "tp",
"keywords": [
"Vectors",
"Stochastic Processes",
"Estimation",
"Internet",
"Random Variables",
"Pattern Recognition",
"Encyclopedias",
"Universal Consistency",
"Random Graph",
"K Nearest Neighbor",
"Latent Space Model"
],
"authors": [
{
"givenName": "Daniel L.",
"surname": "Sussman",
"fullName": "Daniel L. Sussman",
"affiliation": "Johns Hopkins Univ., Baltimore, MD, USA",
"__typename": "ArticleAuthorType"
},
{
"givenName": null,
"surname": "Minh Tang",
"fullName": "Minh Tang",
"affiliation": "Johns Hopkins Univ., Baltimore, MD, USA",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Carey E.",
"surname": "Priebe",
"fullName": "Carey E. Priebe",
"affiliation": "Johns Hopkins Univ., Baltimore, MD, USA",
"__typename": "ArticleAuthorType"
}
],
"replicability": null,
"showBuyMe": true,
"showRecommendedArticles": true,
"isOpenAccess": false,
"issueNum": "01",
"pubDate": "2014-01-01 00:00:00",
"pubType": "trans",
"pages": "48-57",
"year": "2014",
"issn": "0162-8828",
"isbn": null,
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"recommendedArticles": [
{
"id": "proceedings/sitis/2014/7978/0/7978a339",
"title": "Modeling Multi-topic Information Diffusion in Social Networks Using Latent Dirichlet Allocation and Hawkes Processes",
"doi": null,
"abstractUrl": "/proceedings-article/sitis/2014/7978a339/12OmNAY79eh",
"parentPublication": {
"id": "proceedings/sitis/2014/7978/0",
"title": "2014 Tenth International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/infcom/2001/7016/2/00916301",
"title": "A new analysis framework for discrete time queueing systems with general stochastic sources",
"doi": null,
"abstractUrl": "/proceedings-article/infcom/2001/00916301/12OmNBDQbeE",
"parentPublication": {
"id": "proceedings/infcom/2001/7016/2",
"title": "Proceedings IEEE INFOCOM 2001. Conference on Computer Communications. Twentieth Annual Joint Conference of the IEEE Computer and Communications Society",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/chinacom/2012/2698/0/06417483",
"title": "Finding community structure in complex network based on latent variables",
"doi": null,
"abstractUrl": "/proceedings-article/chinacom/2012/06417483/12OmNBQC86W",
"parentPublication": {
"id": "proceedings/chinacom/2012/2698/0",
"title": "7th International Conference on Communications and Networking in China",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/robot/1992/2720/0/00220086",
"title": "A note on optimal order of M machines in tandem with blocking",
"doi": null,
"abstractUrl": "/proceedings-article/robot/1992/00220086/12OmNC17hTt",
"parentPublication": {
"id": "proceedings/robot/1992/2720/0",
"title": "Proceedings 1992 IEEE International Conference on Robotics and Automation",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iiaiaai/2014/4174/0/06913408",
"title": "Visualizing Basic Words Chosen by Latent Dirichlet Allocation for Serendipitous Recommendation",
"doi": null,
"abstractUrl": "/proceedings-article/iiaiaai/2014/06913408/12OmNqESud3",
"parentPublication": {
"id": "proceedings/iiaiaai/2014/4174/0",
"title": "2014 IIAI 3rd International Conference on Advanced Applied Informatics (IIAIAAI)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icdm/2015/9504/0/9504a697",
"title": "Are You Going to the Party: Depends, Who Else is Coming?: [Learning Hidden Group Dynamics via Conditional Latent Tree Models]",
"doi": null,
"abstractUrl": "/proceedings-article/icdm/2015/9504a697/12OmNvlg8rF",
"parentPublication": {
"id": "proceedings/icdm/2015/9504/0",
"title": "2015 IEEE International Conference on Data Mining (ICDM)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/sibgrapi/2016/3568/0/3568a378",
"title": "Complexity-Aware Assignment of Latent Values in Discriminative Models for Accurate Gesture Recognition",
"doi": null,
"abstractUrl": "/proceedings-article/sibgrapi/2016/3568a378/12OmNwMXnoF",
"parentPublication": {
"id": "proceedings/sibgrapi/2016/3568/0",
"title": "2016 29th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tp/2016/03/07159084",
"title": "Robust Vertex Classification",
"doi": null,
"abstractUrl": "/journal/tp/2016/03/07159084/13rRUxly96L",
"parentPublication": {
"id": "trans/tp",
"title": "IEEE Transactions on Pattern Analysis & Machine Intelligence",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/big-data/2017/2715/0/08258280",
"title": "Analyzing regional characteristics of living activities of elderly people from large survey data with probabilistic latent spatial semantic structure modeling",
"doi": null,
"abstractUrl": "/proceedings-article/big-data/2017/08258280/17D45Xq6dD9",
"parentPublication": {
"id": "proceedings/big-data/2017/2715/0",
"title": "2017 IEEE International Conference on Big Data (Big Data)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icdmw/2018/9288/0/928800b086",
"title": "Mining Connections Between Domains through Latent Space Mapping",
"doi": null,
"abstractUrl": "/proceedings-article/icdmw/2018/928800b086/18jXz12ZCFy",
"parentPublication": {
"id": "proceedings/icdmw/2018/9288/0",
"title": "2018 IEEE International Conference on Data Mining Workshops (ICDMW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"adjacentArticles": {
"previous": {
"fno": "ttp2014010033",
"articleId": "13rRUwh80I2",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "ttp2014010058",
"articleId": "13rRUwbJD65",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"webExtras": [],
"articleVideos": []
}
|
{
"issue": {
"id": "12OmNvqEvRo",
"title": "PrePrints",
"year": "5555",
"issueNum": "01",
"idPrefix": "tg",
"pubType": "journal",
"volume": null,
"label": "PrePrints",
"downloadables": {
"hasCover": false,
"__typename": "PeriodicalIssueDownloadablesType"
},
"__typename": "PeriodicalIssue"
},
"article": {
"id": "1IIYlkz8kkE",
"doi": "10.1109/TVCG.2022.3225554",
"abstract": "DNGs are diverse network graphs with texts and different styles of nodes and edges, including mind maps, modeling graphs, and flowcharts. They are high-level visualizations that are easy for humans to understand but difficult for machines. Inspired by the process of human perception of graphs, we propose a method called GraphDecoder to extract data from raster images. Given a raster image, we extract the content based on a neural network. We built a semantic segmentation network based on U-Net. We increase the attention mechanism module, simplify the network model, and design a specific loss function to improve the model's ability to extract graph data. After this semantic segmentation network, we can extract the data of all nodes and edges. We then combine these data to obtain the topological relationship of the entire DNG. We also provide an interactive interface for users to redesign the DNGs. We verify the effectiveness of our method by evaluations and user studies on datasets collected on the Internet and generated datasets.",
"abstracts": [
{
"abstractType": "Regular",
"content": "DNGs are diverse network graphs with texts and different styles of nodes and edges, including mind maps, modeling graphs, and flowcharts. They are high-level visualizations that are easy for humans to understand but difficult for machines. Inspired by the process of human perception of graphs, we propose a method called GraphDecoder to extract data from raster images. Given a raster image, we extract the content based on a neural network. We built a semantic segmentation network based on U-Net. We increase the attention mechanism module, simplify the network model, and design a specific loss function to improve the model's ability to extract graph data. After this semantic segmentation network, we can extract the data of all nodes and edges. We then combine these data to obtain the topological relationship of the entire DNG. We also provide an interactive interface for users to redesign the DNGs. We verify the effectiveness of our method by evaluations and user studies on datasets collected on the Internet and generated datasets.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "DNGs are diverse network graphs with texts and different styles of nodes and edges, including mind maps, modeling graphs, and flowcharts. They are high-level visualizations that are easy for humans to understand but difficult for machines. Inspired by the process of human perception of graphs, we propose a method called GraphDecoder to extract data from raster images. Given a raster image, we extract the content based on a neural network. We built a semantic segmentation network based on U-Net. We increase the attention mechanism module, simplify the network model, and design a specific loss function to improve the model's ability to extract graph data. After this semantic segmentation network, we can extract the data of all nodes and edges. We then combine these data to obtain the topological relationship of the entire DNG. We also provide an interactive interface for users to redesign the DNGs. We verify the effectiveness of our method by evaluations and user studies on datasets collected on the Internet and generated datasets.",
"title": "GraphDecoder: Recovering Diverse Network Graphs from Visualization Images via Attention-Aware Learning",
"normalizedTitle": "GraphDecoder: Recovering Diverse Network Graphs from Visualization Images via Attention-Aware Learning",
"fno": "09966829",
"hasPdf": true,
"idPrefix": "tg",
"keywords": [
"Data Mining",
"Data Visualization",
"Visualization",
"Bars",
"Image Edge Detection",
"Task Analysis",
"Solids",
"Information Visualization",
"Chart Mining",
"Semantic Segmentation",
"Network Graph",
"Attention Mechanism"
],
"authors": [
{
"givenName": "Sicheng",
"surname": "Song",
"fullName": "Sicheng Song",
"affiliation": "School of Computer Science and Technology, East China Normal University, Shanghai, China",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Chenhui",
"surname": "Li",
"fullName": "Chenhui Li",
"affiliation": "School of Computer Science and Technology, East China Normal University, Shanghai, China",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Dong",
"surname": "Li",
"fullName": "Dong Li",
"affiliation": "School of Computer Science and Technology, East China Normal University, Shanghai, China",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Juntong",
"surname": "Chen",
"fullName": "Juntong Chen",
"affiliation": "School of Computer Science and Technology, East China Normal University, Shanghai, China",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Changbo",
"surname": "Wang",
"fullName": "Changbo Wang",
"affiliation": "School of Computer Science and Technology, East China Normal University, Shanghai, China",
"__typename": "ArticleAuthorType"
}
],
"replicability": null,
"showBuyMe": true,
"showRecommendedArticles": true,
"isOpenAccess": false,
"issueNum": "01",
"pubDate": "2022-11-01 00:00:00",
"pubType": "trans",
"pages": "1-17",
"year": "5555",
"issn": "1077-2626",
"isbn": null,
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"recommendedArticles": [
{
"id": "proceedings/weit/2013/3057/0/3057a135",
"title": "N-GraphML: Language and Formal Grammar for Proof-Graphs",
"doi": null,
"abstractUrl": "/proceedings-article/weit/2013/3057a135/12OmNAlvHAm",
"parentPublication": {
"id": "proceedings/weit/2013/3057/0",
"title": "2013 2nd Workshop-School on Theoretical Computer Science (WEIT)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/jcdl/2016/4229/0/07559622",
"title": "Curve separation for line graphs in scholarly documents",
"doi": null,
"abstractUrl": "/proceedings-article/jcdl/2016/07559622/12OmNscOUib",
"parentPublication": {
"id": "proceedings/jcdl/2016/4229/0",
"title": "2016 IEEE/ACM Joint Conference on Digital Libraries (JCDL)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iv/2015/7568/0/7568a211",
"title": "Adjasankey: Visualization of Huge Hierarchical Weighted and Directed Graphs",
"doi": null,
"abstractUrl": "/proceedings-article/iv/2015/7568a211/12OmNxR5UM5",
"parentPublication": {
"id": "proceedings/iv/2015/7568/0",
"title": "2015 19th International Conference on Information Visualisation (iV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/vlhcc/2012/0852/0/06344514",
"title": "Rapid Serial Visual Presentation in dynamic graph visualization",
"doi": null,
"abstractUrl": "/proceedings-article/vlhcc/2012/06344514/12OmNxYL5fe",
"parentPublication": {
"id": "proceedings/vlhcc/2012/0852/0",
"title": "2012 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC 2012)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "mags/cg/2017/05/mcg2017050018",
"title": "Typology of Uncertainty in Static Geolocated Graphs for Visualization",
"doi": null,
"abstractUrl": "/magazine/cg/2017/05/mcg2017050018/13rRUIJuxxZ",
"parentPublication": {
"id": "mags/cg",
"title": "IEEE Computer Graphics and Applications",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icbk/2018/9125/0/912500a139",
"title": "Snapshot Visualization of Complex Graphs with Force-Directed Algorithms",
"doi": null,
"abstractUrl": "/proceedings-article/icbk/2018/912500a139/17D45VsBU1x",
"parentPublication": {
"id": "proceedings/icbk/2018/9125/0",
"title": "2018 IEEE International Conference on Big Knowledge (ICBK)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iv/2018/7202/0/720200a159",
"title": "Improving Perception Accuracy in Bar Charts with Internal Contrast and Framing Enhancements",
"doi": null,
"abstractUrl": "/proceedings-article/iv/2018/720200a159/17D45WnnFWc",
"parentPublication": {
"id": "proceedings/iv/2018/7202/0",
"title": "2018 22nd International Conference Information Visualisation (IV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/5555/01/09720180",
"title": "VividGraph: Learning to Extract and Redesign Network Graphs from Visualization Images",
"doi": null,
"abstractUrl": "/journal/tg/5555/01/09720180/1Befc7QugjS",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icci*cc/2019/1419/0/09146098",
"title": "Visualizing the Temporal Similarity Between Clusters of Dynamic Graphs",
"doi": null,
"abstractUrl": "/proceedings-article/icci*cc/2019/09146098/1lFJdiVGPio",
"parentPublication": {
"id": "proceedings/icci*cc/2019/1419/0",
"title": "2019 IEEE 18th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/2023/03/09622132",
"title": "Scalable Comparative Visualization of Ensembles of Call Graphs",
"doi": null,
"abstractUrl": "/journal/tg/2023/03/09622132/1yEUqT5fBwQ",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"adjacentArticles": {
"previous": {
"fno": "09965773",
"articleId": "1IHMR48xnyM",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "09968044",
"articleId": "1IKDeaftuUM",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"webExtras": [
{
"id": "1IRiLXfqSru",
"name": "ttg555501-09966829s1-supp1-3225554.mp4",
"location": "https://www.computer.org/csdl/api/v1/extra/ttg555501-09966829s1-supp1-3225554.mp4",
"extension": "mp4",
"size": "23.7 MB",
"__typename": "WebExtraType"
},
{
"id": "1IRiLNVvzC8",
"name": "ttg555501-09966829s1-supp2-3225554.pdf",
"location": "https://www.computer.org/csdl/api/v1/extra/ttg555501-09966829s1-supp2-3225554.pdf",
"extension": "pdf",
"size": "4.13 MB",
"__typename": "WebExtraType"
}
],
"articleVideos": []
}
|
{
"issue": {
"id": "12OmNAWYKCh",
"title": "Sept.",
"year": "2017",
"issueNum": "09",
"idPrefix": "tp",
"pubType": "journal",
"volume": "39",
"label": "Sept.",
"downloadables": {
"hasCover": false,
"__typename": "PeriodicalIssueDownloadablesType"
},
"__typename": "PeriodicalIssue"
},
"article": {
"id": "13rRUxYIMWv",
"doi": "10.1109/TPAMI.2016.2613862",
"abstract": "Photometric stereo is widely used for 3D reconstruction. However, its use in scattering media such as water, biological tissue and fog has been limited until now, because of forward scattered light from both the source and object, as well as light scattered back from the medium (backscatter). Here we make three contributions to address the key modes of light propagation, under the common single scattering assumption for dilute media. First, we show through extensive simulations that single-scattered light from a source can be approximated by a point light source with a single direction. This alleviates the need to handle light source blur explicitly. Next, we model the blur due to scattering of light from the object. We measure the object point-spread function and introduce a simple deconvolution method. Finally, we show how imaging fluorescence emission where available, eliminates the backscatter component and increases the signal-to-noise ratio. Experimental results in a water tank, with different concentrations of scattering media added, show that deconvolution produces higher-quality 3D reconstructions than previous techniques, and that when combined with fluorescence, can produce results similar to that in clear water even for highly turbid media.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Photometric stereo is widely used for 3D reconstruction. However, its use in scattering media such as water, biological tissue and fog has been limited until now, because of forward scattered light from both the source and object, as well as light scattered back from the medium (backscatter). Here we make three contributions to address the key modes of light propagation, under the common single scattering assumption for dilute media. First, we show through extensive simulations that single-scattered light from a source can be approximated by a point light source with a single direction. This alleviates the need to handle light source blur explicitly. Next, we model the blur due to scattering of light from the object. We measure the object point-spread function and introduce a simple deconvolution method. Finally, we show how imaging fluorescence emission where available, eliminates the backscatter component and increases the signal-to-noise ratio. Experimental results in a water tank, with different concentrations of scattering media added, show that deconvolution produces higher-quality 3D reconstructions than previous techniques, and that when combined with fluorescence, can produce results similar to that in clear water even for highly turbid media.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Photometric stereo is widely used for 3D reconstruction. However, its use in scattering media such as water, biological tissue and fog has been limited until now, because of forward scattered light from both the source and object, as well as light scattered back from the medium (backscatter). Here we make three contributions to address the key modes of light propagation, under the common single scattering assumption for dilute media. First, we show through extensive simulations that single-scattered light from a source can be approximated by a point light source with a single direction. This alleviates the need to handle light source blur explicitly. Next, we model the blur due to scattering of light from the object. We measure the object point-spread function and introduce a simple deconvolution method. Finally, we show how imaging fluorescence emission where available, eliminates the backscatter component and increases the signal-to-noise ratio. Experimental results in a water tank, with different concentrations of scattering media added, show that deconvolution produces higher-quality 3D reconstructions than previous techniques, and that when combined with fluorescence, can produce results similar to that in clear water even for highly turbid media.",
"title": "Photometric Stereo in a Scattering Medium",
"normalizedTitle": "Photometric Stereo in a Scattering Medium",
"fno": "07577857",
"hasPdf": true,
"idPrefix": "tp",
"keywords": [
"Scattering",
"Backscatter",
"Cameras",
"Light Sources",
"Surface Reconstruction",
"Three Dimensional Displays",
"Media",
"Photometric Stereo",
"Scattering Medium",
"Fluorescence"
],
"authors": [
{
"givenName": "Zak",
"surname": "Murez",
"fullName": "Zak Murez",
"affiliation": "Department of Computer Science and Engineering, University of California, San Diego, CA",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Tali",
"surname": "Treibitz",
"fullName": "Tali Treibitz",
"affiliation": "Department for Marine Technologies, Charney School of Marine Sciences, University of Haifa, Haifa, Isreal",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Ravi",
"surname": "Ramamoorthi",
"fullName": "Ravi Ramamoorthi",
"affiliation": "Department of Computer Science and Engineering, University of California, San Diego, CA",
"__typename": "ArticleAuthorType"
},
{
"givenName": "David J.",
"surname": "Kriegman",
"fullName": "David J. Kriegman",
"affiliation": "Department of Computer Science and Engineering, University of California, San Diego, CA",
"__typename": "ArticleAuthorType"
}
],
"replicability": null,
"showBuyMe": true,
"showRecommendedArticles": true,
"isOpenAccess": false,
"issueNum": "09",
"pubDate": "2017-09-01 00:00:00",
"pubType": "trans",
"pages": "1880-1891",
"year": "2017",
"issn": "0162-8828",
"isbn": null,
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"recommendedArticles": [
{
"id": "proceedings/cvpr/2014/5118/0/5118c259",
"title": "Backscatter Compensated Photometric Stereo with 3 Sources",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2014/5118c259/12OmNBPtJCx",
"parentPublication": {
"id": "proceedings/cvpr/2014/5118/0",
"title": "2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccv/2015/8391/0/8391d415",
"title": "Photometric Stereo in a Scattering Medium",
"doi": null,
"abstractUrl": "/proceedings-article/iccv/2015/8391d415/12OmNBV9Igo",
"parentPublication": {
"id": "proceedings/iccv/2015/8391/0",
"title": "2015 IEEE International Conference on Computer Vision (ICCV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icpr/2014/5209/0/5209e382",
"title": "Light Transport Refocusing for Unknown Scattering Medium",
"doi": null,
"abstractUrl": "/proceedings-article/icpr/2014/5209e382/12OmNqzu6Nb",
"parentPublication": {
"id": "proceedings/icpr/2014/5209/0",
"title": "2014 22nd International Conference on Pattern Recognition (ICPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccv/2017/1032/0/1032c420",
"title": "Depth and Image Restoration from Light Field in a Scattering Medium",
"doi": null,
"abstractUrl": "/proceedings-article/iccv/2017/1032c420/12OmNxjjEm8",
"parentPublication": {
"id": "proceedings/iccv/2017/1032/0",
"title": "2017 IEEE International Conference on Computer Vision (ICCV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/eqec/2005/8973/0/01567441",
"title": "Quantum optics in multiple scattering random media",
"doi": null,
"abstractUrl": "/proceedings-article/eqec/2005/01567441/12OmNyGtjoY",
"parentPublication": {
"id": "proceedings/eqec/2005/8973/0",
"title": "2005 European Quantum Electronics Conference",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/sitis/2013/3211/0/3211a093",
"title": "Multiple-Scattering Optical Tomography with Layered Material",
"doi": null,
"abstractUrl": "/proceedings-article/sitis/2013/3211a093/12OmNzRZpYR",
"parentPublication": {
"id": "proceedings/sitis/2013/3211/0",
"title": "2013 International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cerma/2010/4204/0/4204a721",
"title": "FPGA Implementation of a 16-Channel Lock-In Laser Light Scattering System",
"doi": null,
"abstractUrl": "/proceedings-article/cerma/2010/4204a721/12OmNzhna82",
"parentPublication": {
"id": "proceedings/cerma/2010/4204/0",
"title": "Electronics, Robotics and Automotive Mechanics Conference",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/2020/07/08600345",
"title": "Precomputed Multiple Scattering for Rapid Light Simulation in Participating Media",
"doi": null,
"abstractUrl": "/journal/tg/2020/07/08600345/17D45Xh13tH",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2018/6420/0/642000h445",
"title": "Photometric Stereo in Participating Media Considering Shape-Dependent Forward Scatter",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2018/642000h445/17D45Xh13wm",
"parentPublication": {
"id": "proceedings/cvpr/2018/6420/0",
"title": "2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/5555/01/09715049",
"title": "Collimated Whole Volume Light Scattering in Homogeneous Finite Media",
"doi": null,
"abstractUrl": "/journal/tg/5555/01/09715049/1B2DbhImWwE",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"adjacentArticles": {
"previous": {
"fno": "07588057",
"articleId": "13rRUx0xPV4",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "07567535",
"articleId": "13rRUyYjK3Y",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"webExtras": [],
"articleVideos": []
}
|
{
"issue": {
"id": "12OmNBCZnUr",
"title": "July",
"year": "2019",
"issueNum": "07",
"idPrefix": "tp",
"pubType": "journal",
"volume": "41",
"label": "July",
"downloadables": {
"hasCover": false,
"__typename": "PeriodicalIssueDownloadablesType"
},
"__typename": "PeriodicalIssue"
},
"article": {
"id": "13rRUypp58W",
"doi": "10.1109/TPAMI.2018.2840724",
"abstract": "We study the problem of photo cropping, which aims to find a cropping window of an input image to preserve as much as possible its important parts while being aesthetically pleasant. Seeking a deep learning-based solution, we design a neural network that has two branches for attention box prediction (ABP) and aesthetics assessment (AA), respectively. Given the input image, the ABP network predicts an attention bounding box as an initial minimum cropping window, around which a set of cropping candidates are generated with little loss of important information. Then, the AA network is employed to select the final cropping window with the best aesthetic quality among the candidates. The two sub-networks are designed to share the same full-image convolutional feature map, and thus are computationally efficient. By leveraging attention prediction and aesthetics assessment, the cropping model produces high-quality cropping results, even with the limited availability of training data for photo cropping. The experimental results on benchmark datasets clearly validate the effectiveness of the proposed approach. In addition, our approach runs at 5 fps, outperforming most previous solutions. The code and results are available at: https://github.com/shenjianbing/DeepCropping.",
"abstracts": [
{
"abstractType": "Regular",
"content": "We study the problem of photo cropping, which aims to find a cropping window of an input image to preserve as much as possible its important parts while being aesthetically pleasant. Seeking a deep learning-based solution, we design a neural network that has two branches for attention box prediction (ABP) and aesthetics assessment (AA), respectively. Given the input image, the ABP network predicts an attention bounding box as an initial minimum cropping window, around which a set of cropping candidates are generated with little loss of important information. Then, the AA network is employed to select the final cropping window with the best aesthetic quality among the candidates. The two sub-networks are designed to share the same full-image convolutional feature map, and thus are computationally efficient. By leveraging attention prediction and aesthetics assessment, the cropping model produces high-quality cropping results, even with the limited availability of training data for photo cropping. The experimental results on benchmark datasets clearly validate the effectiveness of the proposed approach. In addition, our approach runs at 5 fps, outperforming most previous solutions. The code and results are available at: https://github.com/shenjianbing/DeepCropping.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "We study the problem of photo cropping, which aims to find a cropping window of an input image to preserve as much as possible its important parts while being aesthetically pleasant. Seeking a deep learning-based solution, we design a neural network that has two branches for attention box prediction (ABP) and aesthetics assessment (AA), respectively. Given the input image, the ABP network predicts an attention bounding box as an initial minimum cropping window, around which a set of cropping candidates are generated with little loss of important information. Then, the AA network is employed to select the final cropping window with the best aesthetic quality among the candidates. The two sub-networks are designed to share the same full-image convolutional feature map, and thus are computationally efficient. By leveraging attention prediction and aesthetics assessment, the cropping model produces high-quality cropping results, even with the limited availability of training data for photo cropping. The experimental results on benchmark datasets clearly validate the effectiveness of the proposed approach. In addition, our approach runs at 5 fps, outperforming most previous solutions. The code and results are available at: https://github.com/shenjianbing/DeepCropping.",
"title": "A Deep Network Solution for Attention and Aesthetics Aware Photo Cropping",
"normalizedTitle": "A Deep Network Solution for Attention and Aesthetics Aware Photo Cropping",
"fno": "08365844",
"hasPdf": true,
"idPrefix": "tp",
"keywords": [
"Convolution",
"Learning Artificial Intelligence",
"Neural Nets",
"Deep Network Solution",
"Aesthetics Aware Photo Cropping",
"Deep Learning Based Solution",
"Neural Network",
"Aesthetics Assessment",
"ABP Network",
"Attention Bounding Box",
"Initial Minimum Cropping Window",
"Cropping Candidates",
"AA Network",
"Final Cropping Window",
"Aesthetic Quality",
"Full Image Convolutional Feature Map",
"Leveraging Attention Prediction",
"Cropping Model",
"High Quality Cropping Results",
"Visualization",
"Microsoft Windows",
"Machine Learning",
"Task Analysis",
"Prediction Algorithms",
"Feature Extraction",
"Computational Modeling",
"Photo Cropping",
"Attention Box Prediction",
"Aesthetics Assessment",
"Deep Learning"
],
"authors": [
{
"givenName": "Wenguan",
"surname": "Wang",
"fullName": "Wenguan Wang",
"affiliation": "Beijing Laboratory of Intelligent Information Technology, School of Computer Science, Beijing Institute of Technology, Beijing, China",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Jianbing",
"surname": "Shen",
"fullName": "Jianbing Shen",
"affiliation": "Beijing Laboratory of Intelligent Information Technology, School of Computer Science, Beijing Institute of Technology, Beijing, China",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Haibin",
"surname": "Ling",
"fullName": "Haibin Ling",
"affiliation": "Department of Computer and Information Sciences, Temple University, Philadelphia, PA",
"__typename": "ArticleAuthorType"
}
],
"replicability": null,
"showBuyMe": true,
"showRecommendedArticles": true,
"isOpenAccess": false,
"issueNum": "07",
"pubDate": "2019-07-01 00:00:00",
"pubType": "trans",
"pages": "1531-1544",
"year": "2019",
"issn": "0162-8828",
"isbn": null,
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"recommendedArticles": [
{
"id": "proceedings/wacv/2015/6683/0/6683a448",
"title": "Learning an Aesthetic Photo Cropping Cascade",
"doi": null,
"abstractUrl": "/proceedings-article/wacv/2015/6683a448/12OmNvSKNRd",
"parentPublication": {
"id": "proceedings/wacv/2015/6683/0",
"title": "2015 IEEE Winter Conference on Applications of Computer Vision (WACV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2017/0457/0/0457a722",
"title": "A-Lamp: Adaptive Layout-Aware Multi-patch Deep Convolutional Neural Network for Photo Aesthetic Assessment",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2017/0457a722/12OmNviHKkz",
"parentPublication": {
"id": "proceedings/cvpr/2017/0457/0",
"title": "2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/wacv/2017/4822/0/07926615",
"title": "Quantitative Analysis of Automatic Image Cropping Algorithms: A Dataset and Comparative Study",
"doi": null,
"abstractUrl": "/proceedings-article/wacv/2017/07926615/12OmNwc3wAt",
"parentPublication": {
"id": "proceedings/wacv/2017/4822/0",
"title": "2017 IEEE Winter Conference on Applications of Computer Vision (WACV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2016/8851/0/8851a497",
"title": "Composition-Preserving Deep Photo Aesthetics Assessment",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2016/8851a497/12OmNx4gUlB",
"parentPublication": {
"id": "proceedings/cvpr/2016/8851/0",
"title": "2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccv/2017/1032/0/1032c205",
"title": "Deep Cropping via Attention Box Prediction and Aesthetics Assessment",
"doi": null,
"abstractUrl": "/proceedings-article/iccv/2017/1032c205/12OmNyqiaOb",
"parentPublication": {
"id": "proceedings/iccv/2017/1032/0",
"title": "2017 IEEE International Conference on Computer Vision (ICCV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/ts/1985/08/01702080",
"title": "An Aspect of Aesthetics in Human-Computer Communications: Pretty Windows",
"doi": null,
"abstractUrl": "/journal/ts/1985/08/01702080/13rRUB7a12x",
"parentPublication": {
"id": "trans/ts",
"title": "IEEE Transactions on Software Engineering",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvpr/2018/6420/0/642000i193",
"title": "A2-RL: Aesthetics Aware Reinforcement Learning for Image Cropping",
"doi": null,
"abstractUrl": "/proceedings-article/cvpr/2018/642000i193/17D45WIXbNF",
"parentPublication": {
"id": "proceedings/cvpr/2018/6420/0",
"title": "2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bigmm/2019/5527/0/552700a252",
"title": "Photo Filter Recommendation Through Analyzing Objects, Scenes and Aesthetics",
"doi": null,
"abstractUrl": "/proceedings-article/bigmm/2019/552700a252/1fHjJ38f1n2",
"parentPublication": {
"id": "proceedings/bigmm/2019/5527/0",
"title": "2019 IEEE Fifth International Conference on Multimedia Big Data (BigMM)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvprw/2020/9360/0/09150886",
"title": "Density Map Guided Object Detection in Aerial Images",
"doi": null,
"abstractUrl": "/proceedings-article/cvprw/2020/09150886/1lPHvj6Oxby",
"parentPublication": {
"id": "proceedings/cvprw/2020/9360/0",
"title": "2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/mipr/2020/4272/0/427200a360",
"title": "Aesthetics-Assisted Multi-task Learning with Attention for Image Memorability Prediction",
"doi": null,
"abstractUrl": "/proceedings-article/mipr/2020/427200a360/1mA9XM4Jbjy",
"parentPublication": {
"id": "proceedings/mipr/2020/4272/0",
"title": "2020 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"adjacentArticles": {
"previous": {
"fno": "08730450",
"articleId": "1aAwOIy0cYE",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "08362956",
"articleId": "13rRUILc8gB",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"webExtras": [],
"articleVideos": []
}
|
{
"issue": {
"id": "12OmNCaLEju",
"title": "Jan.",
"year": "2018",
"issueNum": "01",
"idPrefix": "tg",
"pubType": "journal",
"volume": "24",
"label": "Jan.",
"downloadables": {
"hasCover": false,
"__typename": "PeriodicalIssueDownloadablesType"
},
"__typename": "PeriodicalIssue"
},
"article": {
"id": "13rRUwgQpqN",
"doi": "10.1109/TVCG.2017.2744059",
"abstract": "We propose a dynamically load-balanced algorithm for parallel particle tracing, which periodically attempts to evenly redistribute particles across processes based on k-d tree decomposition. Each process is assigned with (1) a statically partitioned, axis-aligned data block that partially overlaps with neighboring blocks in other processes and (2) a dynamically determined k-d tree leaf node that bounds the active particles for computation; the bounds of the k-d tree nodes are constrained by the geometries of data blocks. Given a certain degree of overlap between blocks, our method can balance the number of particles as much as possible. Compared with other load-balancing algorithms for parallel particle tracing, the proposed method does not require any preanalysis, does not use any heuristics based on flow features, does not make any assumptions about seed distribution, does not move any data blocks during the run, and does not need any master process for work redistribution. Based on a comprehensive performance study up to 8K processes on a Blue Gene/Q system, the proposed algorithm outperforms baseline approaches in both load balance and scalability on various flow visualization and analysis problems.",
"abstracts": [
{
"abstractType": "Regular",
"content": "We propose a dynamically load-balanced algorithm for parallel particle tracing, which periodically attempts to evenly redistribute particles across processes based on k-d tree decomposition. Each process is assigned with (1) a statically partitioned, axis-aligned data block that partially overlaps with neighboring blocks in other processes and (2) a dynamically determined k-d tree leaf node that bounds the active particles for computation; the bounds of the k-d tree nodes are constrained by the geometries of data blocks. Given a certain degree of overlap between blocks, our method can balance the number of particles as much as possible. Compared with other load-balancing algorithms for parallel particle tracing, the proposed method does not require any preanalysis, does not use any heuristics based on flow features, does not make any assumptions about seed distribution, does not move any data blocks during the run, and does not need any master process for work redistribution. Based on a comprehensive performance study up to 8K processes on a Blue Gene/Q system, the proposed algorithm outperforms baseline approaches in both load balance and scalability on various flow visualization and analysis problems.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "We propose a dynamically load-balanced algorithm for parallel particle tracing, which periodically attempts to evenly redistribute particles across processes based on k-d tree decomposition. Each process is assigned with (1) a statically partitioned, axis-aligned data block that partially overlaps with neighboring blocks in other processes and (2) a dynamically determined k-d tree leaf node that bounds the active particles for computation; the bounds of the k-d tree nodes are constrained by the geometries of data blocks. Given a certain degree of overlap between blocks, our method can balance the number of particles as much as possible. Compared with other load-balancing algorithms for parallel particle tracing, the proposed method does not require any preanalysis, does not use any heuristics based on flow features, does not make any assumptions about seed distribution, does not move any data blocks during the run, and does not need any master process for work redistribution. Based on a comprehensive performance study up to 8K processes on a Blue Gene/Q system, the proposed algorithm outperforms baseline approaches in both load balance and scalability on various flow visualization and analysis problems.",
"title": "Dynamic Load Balancing Based on Constrained K-D Tree Decomposition for Parallel Particle Tracing",
"normalizedTitle": "Dynamic Load Balancing Based on Constrained K-D Tree Decomposition for Parallel Particle Tracing",
"fno": "08017633",
"hasPdf": true,
"idPrefix": "tg",
"keywords": [
"Heuristic Algorithms",
"Load Management",
"Partitioning Algorithms",
"Algorithm Design And Analysis",
"Data Visualization",
"Scalability",
"Load Modeling",
"Parallel Particle Tracing",
"Dynamic Load Balancing",
"K D Trees",
"Performance Analysis"
],
"authors": [
{
"givenName": "Jiang",
"surname": "Zhang",
"fullName": "Jiang Zhang",
"affiliation": "Ministry of EducationKey Laboratory of Machine PerceptionSchool of EECSPeking University",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Hanqi",
"surname": "Guo",
"fullName": "Hanqi Guo",
"affiliation": "Mathematics and Computer Science Division, Argonne National Laboratory, Lemont, IL, USA",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Fan",
"surname": "Hong",
"fullName": "Fan Hong",
"affiliation": "Ministry of EducationKey Laboratory of Machine PerceptionSchool of EECSPeking University",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Xiaoru",
"surname": "Yuan",
"fullName": "Xiaoru Yuan",
"affiliation": "Ministry of EducationKey Laboratory of Machine PerceptionSchool of EECSPeking University",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Tom",
"surname": "Peterka",
"fullName": "Tom Peterka",
"affiliation": "Mathematics and Computer Science Division, Argonne National Laboratory, Lemont, IL, USA",
"__typename": "ArticleAuthorType"
}
],
"replicability": null,
"showBuyMe": true,
"showRecommendedArticles": true,
"isOpenAccess": false,
"issueNum": "01",
"pubDate": "2018-01-01 00:00:00",
"pubType": "trans",
"pages": "954-963",
"year": "2018",
"issn": "1077-2626",
"isbn": null,
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"recommendedArticles": [
{
"id": "proceedings/pacificvis/2018/1424/0/142401a086",
"title": "Dynamic Data Repartitioning for Load-Balanced Parallel Particle Tracing",
"doi": null,
"abstractUrl": "/proceedings-article/pacificvis/2018/142401a086/12OmNANBZoz",
"parentPublication": {
"id": "proceedings/pacificvis/2018/1424/0",
"title": "2018 IEEE Pacific Visualization Symposium (PacificVis)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icppw/2014/5615/0/5615a027",
"title": "Work-Efficient Load Balancing",
"doi": null,
"abstractUrl": "/proceedings-article/icppw/2014/5615a027/12OmNBa2izx",
"parentPublication": {
"id": "proceedings/icppw/2014/5615/0",
"title": "2014 43nd International Conference on Parallel Processing Workshops (ICCPW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/hipc/2016/5411/0/07839674",
"title": "Load Balancing for Molecular Dynamics Simulations on Heterogeneous Architectures",
"doi": null,
"abstractUrl": "/proceedings-article/hipc/2016/07839674/12OmNqI04Di",
"parentPublication": {
"id": "proceedings/hipc/2016/5411/0",
"title": "2016 IEEE 23rd International Conference on High-Performance Computing (HiPC)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/trustcom/2013/5022/0/5022b107",
"title": "Low-Cost Load Balancing for Parallel Particle-in-Cell Simulations with Thick Overlapping Layers",
"doi": null,
"abstractUrl": "/proceedings-article/trustcom/2013/5022b107/12OmNvonII3",
"parentPublication": {
"id": "proceedings/trustcom/2013/5022/0",
"title": "2013 12th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/scivis/2015/9785/0/07429492",
"title": "CPU ray tracing large particle data with balanced P-k-d trees",
"doi": null,
"abstractUrl": "/proceedings-article/scivis/2015/07429492/12OmNxbW4O5",
"parentPublication": {
"id": "proceedings/scivis/2015/9785/0",
"title": "2015 IEEE Scientific Visualization Conference (SciVis)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/clusterwksp/2010/8396/0/05613105",
"title": "Parallel sorting algorithms for optimizing particle simulations",
"doi": null,
"abstractUrl": "/proceedings-article/clusterwksp/2010/05613105/12OmNyS6RHw",
"parentPublication": {
"id": "proceedings/clusterwksp/2010/8396/0",
"title": "2010 IEEE International Conference On Cluster Computing Workshops and Posters (CLUSTER WORKSHOPS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ipdpsw/2012/4676/0/4676b661",
"title": "Dynamic Load Balancing for Unstructured Meshes on Space-Filling Curves",
"doi": null,
"abstractUrl": "/proceedings-article/ipdpsw/2012/4676b661/12OmNzC5SED",
"parentPublication": {
"id": "proceedings/ipdpsw/2012/4676/0",
"title": "2012 IEEE 26th International Parallel and Distributed Processing Symposium Workshops & PhD Forum",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icde/2016/2020/0/07498273",
"title": "When two choices are not enough: Balancing at scale in Distributed Stream Processing",
"doi": null,
"abstractUrl": "/proceedings-article/icde/2016/07498273/12OmNzhnaaE",
"parentPublication": {
"id": "proceedings/icde/2016/2020/0",
"title": "2016 IEEE 32nd International Conference on Data Engineering (ICDE)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/2023/06/09706326",
"title": "Reinforcement Learning for Load-Balanced Parallel Particle Tracing",
"doi": null,
"abstractUrl": "/journal/tg/2023/06/09706326/1AO2j4ICNLa",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/pacificvis/2020/5697/0/09086288",
"title": "LBVis: Interactive Dynamic Load Balancing Visualization for Parallel Particle Tracing",
"doi": null,
"abstractUrl": "/proceedings-article/pacificvis/2020/09086288/1kuHlZvEurK",
"parentPublication": {
"id": "proceedings/pacificvis/2020/5697/0",
"title": "2020 IEEE Pacific Visualization Symposium (PacificVis)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"adjacentArticles": {
"previous": {
"fno": "08017605",
"articleId": "13rRUxly9dZ",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "08019819",
"articleId": "13rRUwInvBc",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"webExtras": [],
"articleVideos": []
}
|
{
"issue": {
"id": "1wJkVQpr5Pq",
"title": "July-Sept.",
"year": "2021",
"issueNum": "03",
"idPrefix": "su",
"pubType": "journal",
"volume": "6",
"label": "July-Sept.",
"downloadables": {
"hasCover": false,
"__typename": "PeriodicalIssueDownloadablesType"
},
"__typename": "PeriodicalIssue"
},
"article": {
"id": "1j4FFN5tvHi",
"doi": "10.1109/TSUSC.2019.2910533",
"abstract": "The majority of today's power-hungry datacenters are physically co-located with office rooms in mixed-use buildings (MUBs). The heating, ventilation, and air conditioning (HVAC) system within each MUB is often shared or partially-shared between datacenter rooms and office zones, for removing the heat generated by computing equipment and maintaining desired room temperature for building tenants. To effectively reduce the total energy cost of MUBs, it is important to leverage the scheduling flexibility in both the HVAC system and the datacenter workload. In this work, we formulate both HVAC control and datacenter workload scheduling as a Markov decision process (MDP), and propose a deep reinforcement learning (DRL) based algorithm for minimizing the total energy cost while maintaining desired room temperature and meeting datacenter workload deadline constraints. Moreover, we also develop a heuristic DRL-based algorithm to enable interactive workload allocation among geographically distributed MUBs for further energy reduction. The experiment results demonstrate that our regular DRL-based algorithm can achieve up to 26.9 percent cost reduction for a single MUB, when compared with a baseline strategy. Our heuristic DRL-based algorithm can reduce the total energy cost by an additional 5.5 percent, when intelligently allocating interactive workload for multiple geographically distributed MUBs.",
"abstracts": [
{
"abstractType": "Regular",
"content": "The majority of today's power-hungry datacenters are physically co-located with office rooms in mixed-use buildings (MUBs). The heating, ventilation, and air conditioning (HVAC) system within each MUB is often shared or partially-shared between datacenter rooms and office zones, for removing the heat generated by computing equipment and maintaining desired room temperature for building tenants. To effectively reduce the total energy cost of MUBs, it is important to leverage the scheduling flexibility in both the HVAC system and the datacenter workload. In this work, we formulate both HVAC control and datacenter workload scheduling as a Markov decision process (MDP), and propose a deep reinforcement learning (DRL) based algorithm for minimizing the total energy cost while maintaining desired room temperature and meeting datacenter workload deadline constraints. Moreover, we also develop a heuristic DRL-based algorithm to enable interactive workload allocation among geographically distributed MUBs for further energy reduction. The experiment results demonstrate that our regular DRL-based algorithm can achieve up to 26.9 percent cost reduction for a single MUB, when compared with a baseline strategy. Our heuristic DRL-based algorithm can reduce the total energy cost by an additional 5.5 percent, when intelligently allocating interactive workload for multiple geographically distributed MUBs.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "The majority of today's power-hungry datacenters are physically co-located with office rooms in mixed-use buildings (MUBs). The heating, ventilation, and air conditioning (HVAC) system within each MUB is often shared or partially-shared between datacenter rooms and office zones, for removing the heat generated by computing equipment and maintaining desired room temperature for building tenants. To effectively reduce the total energy cost of MUBs, it is important to leverage the scheduling flexibility in both the HVAC system and the datacenter workload. In this work, we formulate both HVAC control and datacenter workload scheduling as a Markov decision process (MDP), and propose a deep reinforcement learning (DRL) based algorithm for minimizing the total energy cost while maintaining desired room temperature and meeting datacenter workload deadline constraints. Moreover, we also develop a heuristic DRL-based algorithm to enable interactive workload allocation among geographically distributed MUBs for further energy reduction. The experiment results demonstrate that our regular DRL-based algorithm can achieve up to 26.9 percent cost reduction for a single MUB, when compared with a baseline strategy. Our heuristic DRL-based algorithm can reduce the total energy cost by an additional 5.5 percent, when intelligently allocating interactive workload for multiple geographically distributed MUBs.",
"title": "Deep Reinforcement Learning for Joint Datacenter and HVAC Load Control in Distributed Mixed-Use Buildings",
"normalizedTitle": "Deep Reinforcement Learning for Joint Datacenter and HVAC Load Control in Distributed Mixed-Use Buildings",
"fno": "08691496",
"hasPdf": true,
"idPrefix": "su",
"keywords": [
"Building Management Systems",
"Computer Centres",
"Control Engineering Computing",
"Deep Learning Artificial Intelligence",
"HVAC",
"Markov Processes",
"Power Aware Computing",
"Scheduling",
"Datacenter Workload Deadline Constraints",
"Single MUB",
"Energy Reduction",
"Interactive Workload Allocation",
"Heuristic DRL Based Algorithm",
"Deep Reinforcement Learning Based Algorithm",
"Markov Decision Process",
"Scheduling Flexibility",
"Total Energy Cost",
"Building Tenants",
"Room Temperature",
"Office Zones",
"Datacenter Rooms",
"Air Conditioning System",
"Office Rooms",
"Power Hungry Datacenters",
"Distributed Mixed Use Buildings",
"HVAC Load Control",
"Joint Datacenter",
"HVAC",
"Buildings",
"Heuristic Algorithms",
"Load Modeling",
"Processor Scheduling",
"Energy Consumption",
"Servers",
"Deep Reinforcement Learning",
"Mixed Use Buildings",
"HVAC",
"Datacenter",
"Geographically Distributed"
],
"authors": [
{
"givenName": "Tianshu",
"surname": "Wei",
"fullName": "Tianshu Wei",
"affiliation": "Department of Electrical and Computer Engineering, University of California at Riverside, Riverside, CA, USA",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Shaolei",
"surname": "Ren",
"fullName": "Shaolei Ren",
"affiliation": "Department of Electrical and Computer Engineering, University of California at Riverside, Riverside, CA, USA",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Qi",
"surname": "Zhu",
"fullName": "Qi Zhu",
"affiliation": "Department of Electrical Engineering and Computer Science, Northwestern University, Evanston, IL, USA",
"__typename": "ArticleAuthorType"
}
],
"replicability": null,
"showBuyMe": true,
"showRecommendedArticles": true,
"isOpenAccess": false,
"issueNum": "03",
"pubDate": "2021-07-01 00:00:00",
"pubType": "trans",
"pages": "370-384",
"year": "2021",
"issn": "2377-3782",
"isbn": null,
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"recommendedArticles": [
{
"id": "proceedings/igsc/2016/5117/0/07892609",
"title": "Co-scheduling of datacenter and HVAC loads in mixed-use buildings",
"doi": null,
"abstractUrl": "/proceedings-article/igsc/2016/07892609/12OmNxYtuaP",
"parentPublication": {
"id": "proceedings/igsc/2016/5117/0",
"title": "2016 Seventh International Green and Sustainable Computing Conference (IGSC)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/su/2022/04/09748006",
"title": "Safe Building HVAC Control via Batch Reinforcement Learning",
"doi": null,
"abstractUrl": "/journal/su/2022/04/09748006/1CdACdGDfDa",
"parentPublication": {
"id": "trans/su",
"title": "IEEE Transactions on Sustainable Computing",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccps/2022/0967/0/096700a181",
"title": "Safe HVAC Control via Batch Reinforcement Learning",
"doi": null,
"abstractUrl": "/proceedings-article/iccps/2022/096700a181/1Et6jOcX66c",
"parentPublication": {
"id": "proceedings/iccps/2022/0967/0",
"title": "2022 ACM/IEEE 13th International Conference on Cyber-Physical Systems (ICCPS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/su/2022/03/08847368",
"title": "An RC-Network Approach for HVAC Precooling Optimization in Buildings",
"doi": null,
"abstractUrl": "/journal/su/2022/03/08847368/1Gv8RwTFPAk",
"parentPublication": {
"id": "trans/su",
"title": "IEEE Transactions on Sustainable Computing",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/su/2022/03/08781847",
"title": "An Edge Based Data-Driven Chiller Sequencing Framework for HVAC Electricity Consumption Reduction in Commercial Buildings",
"doi": null,
"abstractUrl": "/journal/su/2022/03/08781847/1Gv8UrQFEAg",
"parentPublication": {
"id": "trans/su",
"title": "IEEE Transactions on Sustainable Computing",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/su/5555/01/10057050",
"title": "Fast Human-in-the-loop Control for HVAC Systems via Meta-learning and Model-based Offline Reinforcement Learning",
"doi": null,
"abstractUrl": "/journal/su/5555/01/10057050/1La0vnZtLLq",
"parentPublication": {
"id": "trans/su",
"title": "IEEE Transactions on Sustainable Computing",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/igsc/2019/5416/0/08957177",
"title": "Smart Room-by-Room HVAC Scheduling for Residential Savings and Comfort",
"doi": null,
"abstractUrl": "/proceedings-article/igsc/2019/08957177/1gAuduLUaxG",
"parentPublication": {
"id": "proceedings/igsc/2019/5416/0",
"title": "2019 Tenth International Green and Sustainable Computing Conference (IGSC)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ispa-bdcloud-socialcom-sustaincom/2019/4328/0/09047280",
"title": "Fault Detection and Diagnosis in HVAC Systems Using Diagnostic Multi-Query Graphs",
"doi": null,
"abstractUrl": "/proceedings-article/ispa-bdcloud-socialcom-sustaincom/2019/09047280/1iC6C8pje1i",
"parentPublication": {
"id": "proceedings/ispa-bdcloud-socialcom-sustaincom/2019/4328/0",
"title": "2019 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/sges/2020/8550/0/855000a934",
"title": "Nonconvex Thermal Modelling and Energy Optimization for Multizone Commercial Buildings with VAV Type HVAC Units",
"doi": null,
"abstractUrl": "/proceedings-article/sges/2020/855000a934/1rITID9rnSU",
"parentPublication": {
"id": "proceedings/sges/2020/8550/0",
"title": "2020 International Conference on Smart Grids and Energy Systems (SGES)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/big-data/2020/6251/0/09377735",
"title": "Methodology for Interpretable Reinforcement Learning Model for HVAC Energy Control",
"doi": null,
"abstractUrl": "/proceedings-article/big-data/2020/09377735/1s64wgunu5G",
"parentPublication": {
"id": "proceedings/big-data/2020/6251/0",
"title": "2020 IEEE International Conference on Big Data (Big Data)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"adjacentArticles": {
"previous": {
"fno": "08540056",
"articleId": "1wJkWA0BRm0",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "08624377",
"articleId": "1wJkXlX7ftC",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"webExtras": [],
"articleVideos": []
}
|
{
"issue": {
"id": "1I6Nvxq2hxe",
"title": "Dec.",
"year": "2022",
"issueNum": "12",
"idPrefix": "tp",
"pubType": "journal",
"volume": "44",
"label": "Dec.",
"downloadables": {
"hasCover": false,
"__typename": "PeriodicalIssueDownloadablesType"
},
"__typename": "PeriodicalIssue"
},
"article": {
"id": "1zc6yjofBSM",
"doi": "10.1109/TPAMI.2021.3133717",
"abstract": "Deep reinforcement learning (RL) agents are becoming increasingly proficient in a range of complex control tasks. However, the agent’s behavior is usually difficult to interpret due to the introduction of black-box function, making it difficult to acquire the trust of users. Although there have been some interesting interpretation methods for vision-based RL, most of them cannot uncover temporal causal information, raising questions about their reliability. To address this problem, we present a temporal-spatial causal interpretation (TSCI) model to understand the agent’s long-term behavior, which is essential for sequential decision-making. TSCI model builds on the formulation of temporal causality, which reflects the temporal causal relations between sequential observations and decisions of RL agent. Then a separate causal discovery network is employed to identify temporal-spatial causal features, which are constrained to satisfy the temporal causality. TSCI model is applicable to recurrent agents and can be used to discover causal features with high efficiency once trained. The empirical results show that TSCI model can produce high-resolution and sharp attention masks to highlight task-relevant temporal-spatial information that constitutes most evidence about how vision-based RL agents make sequential decisions. In addition, we further demonstrate that our method is able to provide valuable causal interpretations for vision-based RL agents from the temporal perspective.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Deep reinforcement learning (RL) agents are becoming increasingly proficient in a range of complex control tasks. However, the agent’s behavior is usually difficult to interpret due to the introduction of black-box function, making it difficult to acquire the trust of users. Although there have been some interesting interpretation methods for vision-based RL, most of them cannot uncover temporal causal information, raising questions about their reliability. To address this problem, we present a temporal-spatial causal interpretation (TSCI) model to understand the agent’s long-term behavior, which is essential for sequential decision-making. TSCI model builds on the formulation of temporal causality, which reflects the temporal causal relations between sequential observations and decisions of RL agent. Then a separate causal discovery network is employed to identify temporal-spatial causal features, which are constrained to satisfy the temporal causality. TSCI model is applicable to recurrent agents and can be used to discover causal features with high efficiency once trained. The empirical results show that TSCI model can produce high-resolution and sharp attention masks to highlight task-relevant temporal-spatial information that constitutes most evidence about how vision-based RL agents make sequential decisions. In addition, we further demonstrate that our method is able to provide valuable causal interpretations for vision-based RL agents from the temporal perspective.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Deep reinforcement learning (RL) agents are becoming increasingly proficient in a range of complex control tasks. However, the agent’s behavior is usually difficult to interpret due to the introduction of black-box function, making it difficult to acquire the trust of users. Although there have been some interesting interpretation methods for vision-based RL, most of them cannot uncover temporal causal information, raising questions about their reliability. To address this problem, we present a temporal-spatial causal interpretation (TSCI) model to understand the agent’s long-term behavior, which is essential for sequential decision-making. TSCI model builds on the formulation of temporal causality, which reflects the temporal causal relations between sequential observations and decisions of RL agent. Then a separate causal discovery network is employed to identify temporal-spatial causal features, which are constrained to satisfy the temporal causality. TSCI model is applicable to recurrent agents and can be used to discover causal features with high efficiency once trained. The empirical results show that TSCI model can produce high-resolution and sharp attention masks to highlight task-relevant temporal-spatial information that constitutes most evidence about how vision-based RL agents make sequential decisions. In addition, we further demonstrate that our method is able to provide valuable causal interpretations for vision-based RL agents from the temporal perspective.",
"title": "Temporal-Spatial Causal Interpretations for Vision-Based Reinforcement Learning",
"normalizedTitle": "Temporal-Spatial Causal Interpretations for Vision-Based Reinforcement Learning",
"fno": "09645253",
"hasPdf": true,
"idPrefix": "tp",
"keywords": [
"Causality",
"Computer Vision",
"Decision Making",
"Deep Learning Artificial Intelligence",
"Multi Agent Systems",
"Reinforcement Learning",
"Agent Long Term Behavior",
"Attention Masks",
"Black Box Function",
"Causal Discovery Network",
"Deep Reinforcement Learning Agents",
"Recurrent Agents",
"Sequential Decision Making",
"Sequential Observations",
"Task Relevant Temporal Spatial Information",
"Temporal Causal Relations",
"Temporal Causality",
"Temporal Spatial Causal Interpretations",
"TSCI",
"Vision Based Reinforcement Learning",
"Vision Based RL Agents",
"Adaptation Models",
"Reliability",
"Decision Making",
"Perturbation Methods",
"Visualization",
"Task Analysis",
"Feature Extraction",
"Reinforcement Learning",
"Markov Decision Process",
"Interpretability",
"Attention Map",
"Temporal Causality"
],
"authors": [
{
"givenName": "Wenjie",
"surname": "Shi",
"fullName": "Wenjie Shi",
"affiliation": "Department of Automation / Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, China",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Gao",
"surname": "Huang",
"fullName": "Gao Huang",
"affiliation": "Department of Automation / Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, China",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Shiji",
"surname": "Song",
"fullName": "Shiji Song",
"affiliation": "Department of Automation / Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, China",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Cheng",
"surname": "Wu",
"fullName": "Cheng Wu",
"affiliation": "Department of Automation / Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, China",
"__typename": "ArticleAuthorType"
}
],
"replicability": null,
"showBuyMe": true,
"showRecommendedArticles": true,
"isOpenAccess": false,
"issueNum": "12",
"pubDate": "2022-12-01 00:00:00",
"pubType": "trans",
"pages": "10222-10235",
"year": "2022",
"issn": "0162-8828",
"isbn": null,
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"recommendedArticles": [
{
"id": "proceedings/time/1996/7528/0/75280014",
"title": "Time in a causal theory",
"doi": null,
"abstractUrl": "/proceedings-article/time/1996/75280014/12OmNvjyxvo",
"parentPublication": {
"id": "proceedings/time/1996/7528/0",
"title": "Temporal Representation and Reasoning, International Syposium on",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iceccs/1996/7614/0/76140458",
"title": "Temporal Causal Networks for Simulation and Diagnosis",
"doi": null,
"abstractUrl": "/proceedings-article/iceccs/1996/76140458/12OmNwcCIM5",
"parentPublication": {
"id": "proceedings/iceccs/1996/7614/0",
"title": "Engineering of Complex Computer Systems, IEEE International Conference on",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/isai/2016/1585/0/1585a332",
"title": "Using Emotions as Intrinsic Motivation to Accelerate Classic Reinforcement Learning",
"doi": null,
"abstractUrl": "/proceedings-article/isai/2016/1585a332/12OmNxFsmxe",
"parentPublication": {
"id": "proceedings/isai/2016/1585/0",
"title": "2016 International Conference on Information System and Artificial Intelligence (ISAI)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "mags/ic/2022/01/09706608",
"title": "CausalKG: Causal Knowledge Graph Explainability Using Interventional and Counterfactual Reasoning",
"doi": null,
"abstractUrl": "/magazine/ic/2022/01/09706608/1APlAUMvn4A",
"parentPublication": {
"id": "mags/ic",
"title": "IEEE Internet Computing",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cain/2022/9275/0/927500a043",
"title": "Structural Causal Models as Boundary Objects in AI System Development",
"doi": null,
"abstractUrl": "/proceedings-article/cain/2022/927500a043/1Ehsjt2rGRq",
"parentPublication": {
"id": "proceedings/cain/2022/9275/0",
"title": "2022 IEEE/ACM 1st International Conference on AI Engineering – Software Engineering for AI (CAIN)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/acii/2022/5908/0/09953887",
"title": "Play with Emotion: Affect-Driven Reinforcement Learning",
"doi": null,
"abstractUrl": "/proceedings-article/acii/2022/09953887/1IAJVnhGyIM",
"parentPublication": {
"id": "proceedings/acii/2022/5908/0",
"title": "2022 10th International Conference on Affective Computing and Intelligent Interaction (ACII)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/apsec/2022/5537/0/553700a031",
"title": "Efficient Reinforcement Learning with Generalized-Reactivity Specifications",
"doi": null,
"abstractUrl": "/proceedings-article/apsec/2022/553700a031/1KOv8uIlID6",
"parentPublication": {
"id": "proceedings/apsec/2022/5537/0",
"title": "2022 29th Asia-Pacific Software Engineering Conference (APSEC)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/dsn-w/2020/7263/0/09151835",
"title": "Blackbox Attacks on Reinforcement Learning Agents Using Approximated Temporal Information",
"doi": null,
"abstractUrl": "/proceedings-article/dsn-w/2020/09151835/1lRm2fSRYMo",
"parentPublication": {
"id": "proceedings/dsn-w/2020/7263/0",
"title": "2020 50th Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/td/2021/03/09203981",
"title": "ADRL: A Hybrid Anomaly-Aware Deep Reinforcement Learning-Based Resource Scaling in Clouds",
"doi": null,
"abstractUrl": "/journal/td/2021/03/09203981/1nkz1c0oVDG",
"parentPublication": {
"id": "trans/td",
"title": "IEEE Transactions on Parallel & Distributed Systems",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tp/2022/05/09259236",
"title": "Self-Supervised Discovering of Interpretable Features for Reinforcement Learning",
"doi": null,
"abstractUrl": "/journal/tp/2022/05/09259236/1oIWmSvWXG8",
"parentPublication": {
"id": "trans/tp",
"title": "IEEE Transactions on Pattern Analysis & Machine Intelligence",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"adjacentArticles": {
"previous": {
"fno": "09613748",
"articleId": "1ythZkR4guc",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "09594662",
"articleId": "1y5Z7w8oCCQ",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"webExtras": [
{
"id": "1I6ND6dHZBu",
"name": "ttp202212-09645253s1-supp1-3133717.pdf",
"location": "https://www.computer.org/csdl/api/v1/extra/ttp202212-09645253s1-supp1-3133717.pdf",
"extension": "pdf",
"size": "392 kB",
"__typename": "WebExtraType"
}
],
"articleVideos": []
}
|
{
"issue": {
"id": "1KsRzJZl0ly",
"title": "March",
"year": "2023",
"issueNum": "03",
"idPrefix": "tk",
"pubType": "journal",
"volume": "35",
"label": "March",
"downloadables": {
"hasCover": false,
"__typename": "PeriodicalIssueDownloadablesType"
},
"__typename": "PeriodicalIssue"
},
"article": {
"id": "1wWCbkQuLrG",
"doi": "10.1109/TKDE.2021.3112746",
"abstract": "Graph Neural Networks (GNNs) are emerging machine learning models on graphs. Although sufficiently deep GNNs are shown theoretically capable of fully preserving graph structures, most existing GNN models in practice are shallow and essentially feature-centric. We show empirically and analytically that the existing shallow GNNs cannot preserve graph structures well. To overcome this fundamental challenge, we propose Eigen-GNN, a simple yet effective and general plug-in module to boost GNNs ability in preserving graph structures. Specifically, we integrate the eigenspace of graph structures with GNNs by treating GNNs as a type of dimensionality reduction and expanding the initial dimensionality reduction bases. Without needing to increase depths, Eigen-GNN possesses more flexibilities in handling both feature-driven and structure-driven tasks since the initial bases contain both node features and graph structures. We present extensive experimental results to demonstrate the effectiveness of Eigen-GNN for tasks including node classification, link prediction, and graph isomorphism tests.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Graph Neural Networks (GNNs) are emerging machine learning models on graphs. Although sufficiently deep GNNs are shown theoretically capable of fully preserving graph structures, most existing GNN models in practice are shallow and essentially feature-centric. We show empirically and analytically that the existing shallow GNNs cannot preserve graph structures well. To overcome this fundamental challenge, we propose Eigen-GNN, a simple yet effective and general plug-in module to boost GNNs ability in preserving graph structures. Specifically, we integrate the eigenspace of graph structures with GNNs by treating GNNs as a type of dimensionality reduction and expanding the initial dimensionality reduction bases. Without needing to increase depths, Eigen-GNN possesses more flexibilities in handling both feature-driven and structure-driven tasks since the initial bases contain both node features and graph structures. We present extensive experimental results to demonstrate the effectiveness of Eigen-GNN for tasks including node classification, link prediction, and graph isomorphism tests.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Graph Neural Networks (GNNs) are emerging machine learning models on graphs. Although sufficiently deep GNNs are shown theoretically capable of fully preserving graph structures, most existing GNN models in practice are shallow and essentially feature-centric. We show empirically and analytically that the existing shallow GNNs cannot preserve graph structures well. To overcome this fundamental challenge, we propose Eigen-GNN, a simple yet effective and general plug-in module to boost GNNs ability in preserving graph structures. Specifically, we integrate the eigenspace of graph structures with GNNs by treating GNNs as a type of dimensionality reduction and expanding the initial dimensionality reduction bases. Without needing to increase depths, Eigen-GNN possesses more flexibilities in handling both feature-driven and structure-driven tasks since the initial bases contain both node features and graph structures. We present extensive experimental results to demonstrate the effectiveness of Eigen-GNN for tasks including node classification, link prediction, and graph isomorphism tests.",
"title": "Eigen-GNN: A Graph Structure Preserving Plug-in for GNNs",
"normalizedTitle": "Eigen-GNN: A Graph Structure Preserving Plug-in for GNNs",
"fno": "09540311",
"hasPdf": true,
"idPrefix": "tk",
"keywords": [
"Eigenvalues And Eigenfunctions",
"Graph Neural Networks",
"Learning Artificial Intelligence",
"Deep GN Ns",
"Dimensionality Reduction",
"Eigen GNN",
"Graph Isomorphism Tests",
"Graph Neural Networks",
"Graph Structure Preserving Plug In Module",
"Link Prediction",
"Machine Learning Models",
"Node Classification",
"Shallow GN Ns",
"Task Analysis",
"Training",
"Laplace Equations",
"Convolutional Codes",
"Convolution",
"Social Networking Online",
"Smoothing Methods",
"Graph Neural Networks",
"Eigenvector",
"Graph Structure",
"Dimensionality Reduction"
],
"authors": [
{
"givenName": "Ziwei",
"surname": "Zhang",
"fullName": "Ziwei Zhang",
"affiliation": "Department of Computer Science and Technology, Tsinghua University, Beijing, China",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Peng",
"surname": "Cui",
"fullName": "Peng Cui",
"affiliation": "Department of Computer Science and Technology, Tsinghua University, Beijing, China",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Jian",
"surname": "Pei",
"fullName": "Jian Pei",
"affiliation": "School of Computing Science, Simon Fraser University, Burnaby, BC, Canada",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Xin",
"surname": "Wang",
"fullName": "Xin Wang",
"affiliation": "Department of Computer Science and Technology, Tsinghua University, Beijing, China",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Wenwu",
"surname": "Zhu",
"fullName": "Wenwu Zhu",
"affiliation": "Department of Computer Science and Technology, Tsinghua University, Beijing, China",
"__typename": "ArticleAuthorType"
}
],
"replicability": null,
"showBuyMe": true,
"showRecommendedArticles": true,
"isOpenAccess": false,
"issueNum": "03",
"pubDate": "2023-03-01 00:00:00",
"pubType": "trans",
"pages": "2544-2555",
"year": "2023",
"issn": "1041-4347",
"isbn": null,
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"recommendedArticles": [
{
"id": "proceedings/icdm/2021/2398/0/239800b421",
"title": "Adapting Membership Inference Attacks to GNN for Graph Classification: Approaches and Implications",
"doi": null,
"abstractUrl": "/proceedings-article/icdm/2021/239800b421/1Aqx6oOoaLm",
"parentPublication": {
"id": "proceedings/icdm/2021/2398/0",
"title": "2021 IEEE International Conference on Data Mining (ICDM)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bigcomp/2022/2197/0/219700a108",
"title": "Graph Neural Networks with Stability and Discernability",
"doi": null,
"abstractUrl": "/proceedings-article/bigcomp/2022/219700a108/1BYIEhM2D6w",
"parentPublication": {
"id": "proceedings/bigcomp/2022/2197/0",
"title": "2022 IEEE International Conference on Big Data and Smart Computing (BigComp)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tk/5555/01/09839432",
"title": "OOD-GNN: Out-of-Distribution Generalized Graph Neural Network",
"doi": null,
"abstractUrl": "/journal/tk/5555/01/09839432/1Fisq5OcXeg",
"parentPublication": {
"id": "trans/tk",
"title": "IEEE Transactions on Knowledge & Data Engineering",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icde/2022/0883/0/088300d012",
"title": "BA-GNN: On Learning Bias-Aware Graph Neural Network",
"doi": null,
"abstractUrl": "/proceedings-article/icde/2022/088300d012/1FwFIuf82iI",
"parentPublication": {
"id": "proceedings/icde/2022/0883/0",
"title": "2022 IEEE 38th International Conference on Data Engineering (ICDE)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tq/5555/01/09910382",
"title": "Cure-GNN: A Robust Curvature-Enhanced Graph Neural Network against Adversarial Attacks",
"doi": null,
"abstractUrl": "/journal/tq/5555/01/09910382/1Hcjw6oR19S",
"parentPublication": {
"id": "trans/tq",
"title": "IEEE Transactions on Dependable and Secure Computing",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/sc/2022/5444/0/544400b038",
"title": "HGL: Accelerating Heterogeneous GNN Training with Holistic Representation and Optimization",
"doi": null,
"abstractUrl": "/proceedings-article/sc/2022/544400b038/1I0bTaqibAc",
"parentPublication": {
"id": "proceedings/sc/2022/5444/0/",
"title": "SC22: International Conference for High Performance Computing, Networking, Storage and Analysis",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icdm/2022/5099/0/509900a231",
"title": "CC-GNN: A Community and Contraction-based Graph Neural Network",
"doi": null,
"abstractUrl": "/proceedings-article/icdm/2022/509900a231/1KpCGX7nMmk",
"parentPublication": {
"id": "proceedings/icdm/2022/5099/0",
"title": "2022 IEEE International Conference on Data Mining (ICDM)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icdm/2022/5099/0/509900a723",
"title": "Feature-Oriented Sampling for Fast and Scalable GNN Training",
"doi": null,
"abstractUrl": "/proceedings-article/icdm/2022/509900a723/1KpCJS3Op68",
"parentPublication": {
"id": "proceedings/icdm/2022/5099/0",
"title": "2022 IEEE International Conference on Data Mining (ICDM)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/sc/2022/5444/0/544400b038",
"title": "HGL: Accelerating Heterogeneous GNN Training with Holistic Representation and Optimization",
"doi": null,
"abstractUrl": "/proceedings-article/sc/2022/544400b038/1L07nNodM4g",
"parentPublication": {
"id": "proceedings/sc/2022/5444/0/",
"title": "SC22: International Conference for High Performance Computing, Networking, Storage and Analysis",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/trustcom/2022/9425/0/942500a258",
"title": "CGDF-GNN: Cascaded GNN fraud detector with dual features facing imbalanced graphs with camouflaged fraudsters",
"doi": null,
"abstractUrl": "/proceedings-article/trustcom/2022/942500a258/1LFM2lJzBAs",
"parentPublication": {
"id": "proceedings/trustcom/2022/9425/0",
"title": "2022 IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"adjacentArticles": {
"previous": {
"fno": "09563222",
"articleId": "1xvtfEgKoQU",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "09560019",
"articleId": "1xtOkJ4uJDG",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"webExtras": [
{
"id": "1KsRMf1VgZi",
"name": "ttk202303-09540311s1-supp1-3112746.pdf",
"location": "https://www.computer.org/csdl/api/v1/extra/ttk202303-09540311s1-supp1-3112746.pdf",
"extension": "pdf",
"size": "260 kB",
"__typename": "WebExtraType"
}
],
"articleVideos": []
}
|
{
"issue": {
"id": "12OmNyQphgV",
"title": "May",
"year": "2013",
"issueNum": "05",
"idPrefix": "co",
"pubType": "magazine",
"volume": "46",
"label": "May",
"downloadables": {
"hasCover": false,
"__typename": "PeriodicalIssueDownloadablesType"
},
"__typename": "PeriodicalIssue"
},
"article": {
"id": "13rRUx0xQ3b",
"doi": "10.1109/MC.2013.36",
"abstract": "Presentation—specifically, its use of elements from storytelling—is the next logical step in visualization research and should be a focus of at least equal importance with exploration and analysis.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Presentation—specifically, its use of elements from storytelling—is the next logical step in visualization research and should be a focus of at least equal importance with exploration and analysis.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Presentation—specifically, its use of elements from storytelling—is the next logical step in visualization research and should be a focus of at least equal importance with exploration and analysis.",
"title": "Storytelling: The Next Step for Visualization",
"normalizedTitle": "Storytelling: The Next Step for Visualization",
"fno": "mco2013050044",
"hasPdf": true,
"idPrefix": "co",
"keywords": [
"Data Visualization",
"Visual Databases",
"Visual Effects",
"Collaboration",
"Storytelling",
"Visualization",
"Visual Communication",
"Narratives"
],
"authors": [
{
"givenName": "Robert",
"surname": "Kosara",
"fullName": "Robert Kosara",
"affiliation": "Tableau Software, Seattle",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Jock",
"surname": "Mackinlay",
"fullName": "Jock Mackinlay",
"affiliation": "Tableau Software, Seattle",
"__typename": "ArticleAuthorType"
}
],
"replicability": null,
"showBuyMe": true,
"showRecommendedArticles": true,
"isOpenAccess": false,
"issueNum": "05",
"pubDate": "2013-05-01 00:00:00",
"pubType": "mags",
"pages": "44-50",
"year": "2013",
"issn": "0018-9162",
"isbn": null,
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"recommendedArticles": [
{
"id": "proceedings/wmute/2012/4662/0/4662a009",
"title": "Mobile Digital Storytelling for Promoting Creative Collaborative Learning",
"doi": null,
"abstractUrl": "/proceedings-article/wmute/2012/4662a009/12OmNBSBkkt",
"parentPublication": {
"id": "proceedings/wmute/2012/4662/0",
"title": "IEEE International Conference on Wireless, Mobile, and Ubiquitous Technology in Education",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/vs-games/2017/5812/0/08056610",
"title": "Guidelines for interactive digital storytelling presentations of cultural heritage",
"doi": null,
"abstractUrl": "/proceedings-article/vs-games/2017/08056610/12OmNC3FG5s",
"parentPublication": {
"id": "proceedings/vs-games/2017/5812/0",
"title": "2017 9th International Conference on Virtual Worlds and Games for Serious Applications (VS-Games)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cw/2011/4467/0/4467a144",
"title": "Transmedia Storytelling and Online Representations -- Issues of Trust on the Internet",
"doi": null,
"abstractUrl": "/proceedings-article/cw/2011/4467a144/12OmNCbCrKL",
"parentPublication": {
"id": "proceedings/cw/2011/4467/0",
"title": "2011 International Conference on Cyberworlds",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/digitel/2012/4663/0/4663a084",
"title": "Micro Culture: Interactive Storytelling and Learning in the Museum",
"doi": null,
"abstractUrl": "/proceedings-article/digitel/2012/4663a084/12OmNwe2Iu8",
"parentPublication": {
"id": "proceedings/digitel/2012/4663/0",
"title": "Digital Game and Intelligent Toy Enhanced Learning, IEEE International Workshop on",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icme/2016/7258/0/07552974",
"title": "Automatic suggestion of presentation image for storytelling",
"doi": null,
"abstractUrl": "/proceedings-article/icme/2016/07552974/12OmNy4r3OB",
"parentPublication": {
"id": "proceedings/icme/2016/7258/0",
"title": "2016 IEEE International Conference on Multimedia and Expo (ICME)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ct/1997/8084/0/80840098",
"title": "Storytelling systems: constructing the innerface of the interface",
"doi": null,
"abstractUrl": "/proceedings-article/ct/1997/80840098/12OmNy87QzC",
"parentPublication": {
"id": "proceedings/ct/1997/8084/0",
"title": "Cognitive Technology, International Conference on",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/2010/06/ttg2010061139",
"title": "Narrative Visualization: Telling Stories with Data",
"doi": null,
"abstractUrl": "/journal/tg/2010/06/ttg2010061139/13rRUxAAST1",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "mags/cg/2012/01/mcg2012010012",
"title": "Scientific Storytelling Using Visualization",
"doi": null,
"abstractUrl": "/magazine/cg/2012/01/mcg2012010012/13rRUyuegjn",
"parentPublication": {
"id": "mags/cg",
"title": "IEEE Computer Graphics and Applications",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/2023/06/09695173",
"title": "Roslingifier: Semi-Automated Storytelling for Animated Scatterplots",
"doi": null,
"abstractUrl": "/journal/tg/2023/06/09695173/1AvqJqAJOKY",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "mags/cg/5555/01/10107759",
"title": "The Stories We Tell About Data: Surveying Data-Driven Storytelling Using Visualization",
"doi": null,
"abstractUrl": "/magazine/cg/5555/01/10107759/1MDGmTM8oOA",
"parentPublication": {
"id": "mags/cg",
"title": "IEEE Computer Graphics and Applications",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"adjacentArticles": {
"previous": {
"fno": "mco2013050034",
"articleId": "13rRUxk89f2",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "mco2013050051",
"articleId": "13rRUy0ZzW0",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"webExtras": [],
"articleVideos": []
}
|
{
"issue": {
"id": "1zKXryr0JDG",
"title": "Feb.",
"year": "2022",
"issueNum": "02",
"idPrefix": "tg",
"pubType": "journal",
"volume": "28",
"label": "Feb.",
"downloadables": {
"hasCover": false,
"__typename": "PeriodicalIssueDownloadablesType"
},
"__typename": "PeriodicalIssue"
},
"article": {
"id": "1lxmsQXZ36U",
"doi": "10.1109/TVCG.2020.3009949",
"abstract": "In the animation industry, the colorization of raw sketch images is a vitally important but very time-consuming task. This article focuses on providing a novel solution that semiautomatically colorizes a set of images using a single colorized reference image. Our method is able to provide coherent colors for regions that have similar semantics to those in the reference image. An active-learning-based framework is used to match local regions, followed by mixed-integer quadratic programming (MIQP) which considers the spatial contexts to further refine the matching results. We efficiently utilize user interactions to achieve high accuracy in the final colorized images. Experiments show that our method outperforms the current state-of-the-art deep learning based colorization method in terms of color coherency with the reference image. The region matching framework could potentially be applied to other applications, such as color transfer.",
"abstracts": [
{
"abstractType": "Regular",
"content": "In the animation industry, the colorization of raw sketch images is a vitally important but very time-consuming task. This article focuses on providing a novel solution that semiautomatically colorizes a set of images using a single colorized reference image. Our method is able to provide coherent colors for regions that have similar semantics to those in the reference image. An active-learning-based framework is used to match local regions, followed by mixed-integer quadratic programming (MIQP) which considers the spatial contexts to further refine the matching results. We efficiently utilize user interactions to achieve high accuracy in the final colorized images. Experiments show that our method outperforms the current state-of-the-art deep learning based colorization method in terms of color coherency with the reference image. The region matching framework could potentially be applied to other applications, such as color transfer.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "In the animation industry, the colorization of raw sketch images is a vitally important but very time-consuming task. This article focuses on providing a novel solution that semiautomatically colorizes a set of images using a single colorized reference image. Our method is able to provide coherent colors for regions that have similar semantics to those in the reference image. An active-learning-based framework is used to match local regions, followed by mixed-integer quadratic programming (MIQP) which considers the spatial contexts to further refine the matching results. We efficiently utilize user interactions to achieve high accuracy in the final colorized images. Experiments show that our method outperforms the current state-of-the-art deep learning based colorization method in terms of color coherency with the reference image. The region matching framework could potentially be applied to other applications, such as color transfer.",
"title": "Active Colorization for Cartoon Line Drawings",
"normalizedTitle": "Active Colorization for Cartoon Line Drawings",
"fno": "09143503",
"hasPdf": true,
"idPrefix": "tg",
"keywords": [
"Image Colour Analysis",
"Image Matching",
"Integer Programming",
"Learning Artificial Intelligence",
"Quadratic Programming",
"Active Colorization",
"Cartoon Line Drawings",
"Animation Industry",
"Raw Sketch Images",
"Single Colorized Reference Image",
"Coherent Colors",
"Similar Semantics",
"Active Learning Based Framework",
"Local Regions",
"Mixed Integer Quadratic Programming",
"Matching Results",
"Final Colorized Images",
"Current State Of The Art Deep Learning Based Colorization Method",
"Color Coherency",
"Region Matching Framework",
"Color Transfer",
"Image Color Analysis",
"Image Segmentation",
"Semantics",
"Feature Extraction",
"Shape",
"Machine Learning",
"Animation",
"Active Learning",
"Line Drawing Colorization",
"Region Matching"
],
"authors": [
{
"givenName": "Shu-Yu",
"surname": "Chen",
"fullName": "Shu-Yu Chen",
"affiliation": "Beijing Key Laboratory of Mobile Computing and Pervasive Device, Chinese Academy of Sciences, Institute of Computing Technology, Beijing, China",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Jia-Qi",
"surname": "Zhang",
"fullName": "Jia-Qi Zhang",
"affiliation": "University of Chinese Academy of Sciences, Beijing, China",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Lin",
"surname": "Gao",
"fullName": "Lin Gao",
"affiliation": "Beijing Key Laboratory of Mobile Computing and Pervasive Device, Chinese Academy of Sciences, Institute of Computing Technology, Beijing, China",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Yue",
"surname": "He",
"fullName": "Yue He",
"affiliation": "Beijing Key Laboratory of Mobile Computing and Pervasive Device, Chinese Academy of Sciences, Institute of Computing Technology, Beijing, China",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Shihong",
"surname": "Xia",
"fullName": "Shihong Xia",
"affiliation": "Beijing Key Laboratory of Mobile Computing and Pervasive Device, Chinese Academy of Sciences, Institute of Computing Technology, Beijing, China",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Min",
"surname": "Shi",
"fullName": "Min Shi",
"affiliation": "University of Chinese Academy of Sciences, Beijing, China",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Fang-Lue",
"surname": "Zhang",
"fullName": "Fang-Lue Zhang",
"affiliation": "North China Electric Power University, Beijing, China",
"__typename": "ArticleAuthorType"
}
],
"replicability": null,
"showBuyMe": true,
"showRecommendedArticles": true,
"isOpenAccess": false,
"issueNum": "02",
"pubDate": "2022-02-01 00:00:00",
"pubType": "trans",
"pages": "1198-1208",
"year": "2022",
"issn": "1077-2626",
"isbn": null,
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"recommendedArticles": [
{
"id": "proceedings/icdar/2017/3586/3/3586d072",
"title": "cGAN-Based Manga Colorization Using a Single Training Image",
"doi": null,
"abstractUrl": "/proceedings-article/icdar/2017/3586d072/12OmNAkWvpy",
"parentPublication": {
"id": "icdar/2017/3586/3",
"title": "2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccv/2015/8391/0/8391d460",
"title": "Depth Map Estimation and Colorization of Anaglyph Images Using Local Color Prior and Reverse Intensity Distribution",
"doi": null,
"abstractUrl": "/proceedings-article/iccv/2015/8391d460/12OmNB1wkHc",
"parentPublication": {
"id": "proceedings/iccv/2015/8391/0",
"title": "2015 IEEE International Conference on Computer Vision (ICCV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icig/2013/5050/0/5050a089",
"title": "An Interactive Framework for Video Colorization",
"doi": null,
"abstractUrl": "/proceedings-article/icig/2013/5050a089/12OmNBLdKIJ",
"parentPublication": {
"id": "proceedings/icig/2013/5050/0",
"title": "2013 Seventh International Conference on Image and Graphics (ICIG)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icpr/2012/2216/0/06460810",
"title": "Patch-based image colorization",
"doi": null,
"abstractUrl": "/proceedings-article/icpr/2012/06460810/12OmNBigFy1",
"parentPublication": {
"id": "proceedings/icpr/2012/2216/0",
"title": "2012 21st International Conference on Pattern Recognition (ICPR 2012)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iccv/2015/8391/0/8391a567",
"title": "Learning Large-Scale Automatic Image Colorization",
"doi": null,
"abstractUrl": "/proceedings-article/iccv/2015/8391a567/12OmNzgwmSr",
"parentPublication": {
"id": "proceedings/iccv/2015/8391/0",
"title": "2015 IEEE International Conference on Computer Vision (ICCV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/wacv/2023/9346/0/934600b787",
"title": "iColoriT: Towards Propagating Local Hints to the Right Region in Interactive Colorization by Leveraging Vision Transformer",
"doi": null,
"abstractUrl": "/proceedings-article/wacv/2023/934600b787/1KxUuahpqJG",
"parentPublication": {
"id": "proceedings/wacv/2023/9346/0",
"title": "2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bcd/2019/0886/0/08885391",
"title": "Do You Like the Sclera?: Sclera-Region Detection in Line Drawings for Automated Colorization",
"doi": null,
"abstractUrl": "/proceedings-article/bcd/2019/08885391/1ezRZdJ15kI",
"parentPublication": {
"id": "proceedings/bcd/2019/0886/0",
"title": "2019 IEEE International Conference on Big Data, Cloud Computing, Data Science & Engineering (BCD)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icdh/2020/9234/0/923400a001",
"title": "Automatic Image Colorization via Weighted Sparse Representation Learning",
"doi": null,
"abstractUrl": "/proceedings-article/icdh/2020/923400a001/1uGXZvzfk4w",
"parentPublication": {
"id": "proceedings/icdh/2020/9234/0",
"title": "2020 8th International Conference on Digital Home (ICDH)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/wacv/2021/0477/0/047700d871",
"title": "Line Art Correlation Matching Feature Transfer Network for Automatic Animation Colorization",
"doi": null,
"abstractUrl": "/proceedings-article/wacv/2021/047700d871/1uqGPvnkBUI",
"parentPublication": {
"id": "proceedings/wacv/2021/0477/0",
"title": "2021 IEEE Winter Conference on Applications of Computer Vision (WACV)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cvprw/2021/4899/0/489900d941",
"title": "Line Art Colorization with Concatenated Spatial Attention",
"doi": null,
"abstractUrl": "/proceedings-article/cvprw/2021/489900d941/1yVzYqkncJy",
"parentPublication": {
"id": "proceedings/cvprw/2021/4899/0",
"title": "2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"adjacentArticles": {
"previous": null,
"next": {
"fno": "09451590",
"articleId": "1ujXLK9Vgac",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"webExtras": [
{
"id": "1zKXsawWWYw",
"name": "ttg202202-09143503s1-supp1-3009949.mp4",
"location": "https://www.computer.org/csdl/api/v1/extra/ttg202202-09143503s1-supp1-3009949.mp4",
"extension": "mp4",
"size": "24.2 MB",
"__typename": "WebExtraType"
}
],
"articleVideos": []
}
|
{
"issue": {
"id": "1J9y2mtpt3a",
"title": "Jan.",
"year": "2023",
"issueNum": "01",
"idPrefix": "tg",
"pubType": "journal",
"volume": "29",
"label": "Jan.",
"downloadables": {
"hasCover": false,
"__typename": "PeriodicalIssueDownloadablesType"
},
"__typename": "PeriodicalIssue"
},
"article": {
"id": "1H1gq4Y9HZm",
"doi": "10.1109/TVCG.2022.3209470",
"abstract": "Data workers usually seek to understand the semantics of data wrangling scripts in various scenarios, such as code debugging, reusing, and maintaining. However, the understanding is challenging for novice data workers due to the variety of programming languages, functions, and parameters. Based on the observation that differences between input and output tables highly relate to the type of data transformation, we outline a design space including 103 characteristics to describe table differences. Then, we develop C<sc>omantics</sc>, a three-step pipeline that automatically detects the semantics of data transformation scripts. The first step focuses on the detection of table differences for each line of wrangling code. Second, we incorporate a characteristic-based component and a Siamese convolutional neural network-based component for the detection of transformation types. Third, we derive the parameters of each data transformation by employing a “slot filling” strategy. We design experiments to evaluate the performance of C<sc>omantics</sc>. Further, we assess its flexibility using three example applications in different domains.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Data workers usually seek to understand the semantics of data wrangling scripts in various scenarios, such as code debugging, reusing, and maintaining. However, the understanding is challenging for novice data workers due to the variety of programming languages, functions, and parameters. Based on the observation that differences between input and output tables highly relate to the type of data transformation, we outline a design space including 103 characteristics to describe table differences. Then, we develop C<sc>omantics</sc>, a three-step pipeline that automatically detects the semantics of data transformation scripts. The first step focuses on the detection of table differences for each line of wrangling code. Second, we incorporate a characteristic-based component and a Siamese convolutional neural network-based component for the detection of transformation types. Third, we derive the parameters of each data transformation by employing a “slot filling” strategy. We design experiments to evaluate the performance of C<sc>omantics</sc>. Further, we assess its flexibility using three example applications in different domains.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Data workers usually seek to understand the semantics of data wrangling scripts in various scenarios, such as code debugging, reusing, and maintaining. However, the understanding is challenging for novice data workers due to the variety of programming languages, functions, and parameters. Based on the observation that differences between input and output tables highly relate to the type of data transformation, we outline a design space including 103 characteristics to describe table differences. Then, we develop Comantics, a three-step pipeline that automatically detects the semantics of data transformation scripts. The first step focuses on the detection of table differences for each line of wrangling code. Second, we incorporate a characteristic-based component and a Siamese convolutional neural network-based component for the detection of transformation types. Third, we derive the parameters of each data transformation by employing a “slot filling” strategy. We design experiments to evaluate the performance of Comantics. Further, we assess its flexibility using three example applications in different domains.",
"title": "Revealing the Semantics of Data Wrangling Scripts With Comantics",
"normalizedTitle": "Revealing the Semantics of Data Wrangling Scripts With Comantics",
"fno": "09904459",
"hasPdf": true,
"idPrefix": "tg",
"keywords": [
"Convolutional Neural Nets",
"Data Analysis",
"Data Visualisation",
"Programming Language Semantics",
"Characteristic Based Component",
"Comantics",
"Data Transformation",
"Data Workers",
"Data Wrangling Scripts",
"Input Tables",
"Output Tables",
"Programming Languages",
"Siamese Convolutional Neural Network",
"Slot Filling",
"Table Differences",
"Wrangling Code",
"Codes",
"Semantics",
"Pipelines",
"Data Visualization",
"Programming",
"Debugging",
"Task Analysis",
"Data Transformation",
"Semantic Inference",
"Program Understanding",
"Table Comparison"
],
"authors": [
{
"givenName": "Kai",
"surname": "Xiong",
"fullName": "Kai Xiong",
"affiliation": "State Key Lab of CAD&CG, Zhejiang University, Hangzhou, China",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Zhongsu",
"surname": "Luo",
"fullName": "Zhongsu Luo",
"affiliation": "Zhejiang University of Technology, Hangzhou, China",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Siwei",
"surname": "Fu",
"fullName": "Siwei Fu",
"affiliation": "Zhejiang Lab, Hangzhou, China",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Yongheng",
"surname": "Wang",
"fullName": "Yongheng Wang",
"affiliation": "Zhejiang Lab, Hangzhou, China",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Mingliang",
"surname": "Xu",
"fullName": "Mingliang Xu",
"affiliation": "School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou, China",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Yingcai",
"surname": "Wu",
"fullName": "Yingcai Wu",
"affiliation": "State Key Lab of CAD&CG, Zhejiang University, Hangzhou, China",
"__typename": "ArticleAuthorType"
}
],
"replicability": null,
"showBuyMe": true,
"showRecommendedArticles": true,
"isOpenAccess": false,
"issueNum": "01",
"pubDate": "2023-01-01 00:00:00",
"pubType": "trans",
"pages": "117-127",
"year": "2023",
"issn": "1077-2626",
"isbn": null,
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"recommendedArticles": [
{
"id": "proceedings/big-data/2017/2715/0/08258015",
"title": "Data context informed data wrangling",
"doi": null,
"abstractUrl": "/proceedings-article/big-data/2017/08258015/17D45WGGoLr",
"parentPublication": {
"id": "proceedings/big-data/2017/2715/0",
"title": "2017 IEEE International Conference on Big Data (Big Data)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/bd/2021/01/08691825",
"title": "Incorporating Data Context to Cost-Effectively Automate End-to-End Data Wrangling",
"doi": null,
"abstractUrl": "/journal/bd/2021/01/08691825/19fxlYsck0g",
"parentPublication": {
"id": "trans/bd",
"title": "IEEE Transactions on Big Data",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/ase/2021/0337/0/033700a304",
"title": "Subtle Bugs Everywhere: Generating Documentation for Data Wrangling Code",
"doi": null,
"abstractUrl": "/proceedings-article/ase/2021/033700a304/1AjThH8TZEQ",
"parentPublication": {
"id": "proceedings/ase/2021/0337/0",
"title": "2021 36th IEEE/ACM International Conference on Automated Software Engineering (ASE)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/2023/06/09693232",
"title": "Visualizing the Scripts of Data Wrangling With <sc>Somnus</sc>",
"doi": null,
"abstractUrl": "/journal/tg/2023/06/09693232/1As79CUmeZO",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tk/5555/01/09953543",
"title": "AI Assistants: A Framework for Semi-Automated Data Wrangling",
"doi": null,
"abstractUrl": "/journal/tk/5555/01/09953543/1IlJyZsPxwQ",
"parentPublication": {
"id": "trans/tk",
"title": "IEEE Transactions on Knowledge & Data Engineering",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/vds/2022/5721/0/572100a037",
"title": "Interactive Visualization for Data Science Scripts",
"doi": null,
"abstractUrl": "/proceedings-article/vds/2022/572100a037/1JezKXQ9OQ8",
"parentPublication": {
"id": "proceedings/vds/2022/5721/0",
"title": "2022 IEEE Visualization in Data Science (VDS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/ts/2023/04/10002189",
"title": "Learning Approximate Execution Semantics From Traces for Binary Function Similarity",
"doi": null,
"abstractUrl": "/journal/ts/2023/04/10002189/1JtvI9RigDK",
"parentPublication": {
"id": "trans/ts",
"title": "IEEE Transactions on Software Engineering",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/vis/2019/4941/0/08933542",
"title": "Uncovering Data Landscapes through Data Reconnaissance and Task Wrangling",
"doi": null,
"abstractUrl": "/proceedings-article/vis/2019/08933542/1fTgGbNyvi8",
"parentPublication": {
"id": "proceedings/vis/2019/4941/0",
"title": "2019 IEEE Visualization Conference (VIS)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/iri/2020/1054/0/09191630",
"title": "Fairness in Data Wrangling",
"doi": null,
"abstractUrl": "/proceedings-article/iri/2020/09191630/1n0Iv5fBnjy",
"parentPublication": {
"id": "proceedings/iri/2020/1054/0",
"title": "2020 IEEE 21st International Conference on Information Reuse and Integration for Data Science (IRI)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tg/2021/02/09229515",
"title": "Table Scraps: An Actionable Framework for Multi-Table Data Wrangling From An Artifact Study of Computational Journalism",
"doi": null,
"abstractUrl": "/journal/tg/2021/02/09229515/1o3nxS8lm7u",
"parentPublication": {
"id": "trans/tg",
"title": "IEEE Transactions on Visualization & Computer Graphics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"adjacentArticles": {
"previous": {
"fno": "09904770",
"articleId": "1H2loP2yJpe",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "09908529",
"articleId": "1HbasXGQDEk",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"webExtras": [
{
"id": "1J9z2SILKMw",
"name": "ttg202301-09904459s1-tvcg-3209470-mm.zip",
"location": "https://www.computer.org/csdl/api/v1/extra/ttg202301-09904459s1-tvcg-3209470-mm.zip",
"extension": "zip",
"size": "27.3 MB",
"__typename": "WebExtraType"
}
],
"articleVideos": []
}
|
{
"issue": {
"id": "12OmNxGAKWJ",
"title": "Oct.-Dec.",
"year": "2017",
"issueNum": "04",
"idPrefix": "th",
"pubType": "journal",
"volume": "10",
"label": "Oct.-Dec.",
"downloadables": {
"hasCover": false,
"__typename": "PeriodicalIssueDownloadablesType"
},
"__typename": "PeriodicalIssue"
},
"article": {
"id": "13rRUILLkvB",
"doi": "10.1109/TOH.2017.2679000",
"abstract": "Despite the fact that conventional haptic interfaces and rendering algorithms commonly approximate interactions with force only, the dynamic effects of even simple tasks, e.g., writing on a paper, involve both forces and torques. To extend previous algorithms as well as to investigate the effects of torque feedback on human roughness perception, we deployed a novel haptic platform with two probes, fingertip and penhandle. Three torque conditions were examined: 1) Slope Torque, which orients the probe perpendicular to the surface, 2) No Torque, where no active torque is provided by the device, and 3) Stiff Torque, where torque feedback is provided to keep the probe upright. A conventional magnitude estimation experiment was performed. The results indicated that both the torque signals and grasp type mediate human perception of virtual textures. Slope Torque led to greater perceived roughness when the fingertip was used, and the fingertip led to higher roughness ratings than the penhandle with the Slope Torque condition. The Slope Torque algorithm appears to be advantageous for generating rougher surfaces compared to the force-based algorithms which are typically limited by the system stability and actuator saturation.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Despite the fact that conventional haptic interfaces and rendering algorithms commonly approximate interactions with force only, the dynamic effects of even simple tasks, e.g., writing on a paper, involve both forces and torques. To extend previous algorithms as well as to investigate the effects of torque feedback on human roughness perception, we deployed a novel haptic platform with two probes, fingertip and penhandle. Three torque conditions were examined: 1) Slope Torque, which orients the probe perpendicular to the surface, 2) No Torque, where no active torque is provided by the device, and 3) Stiff Torque, where torque feedback is provided to keep the probe upright. A conventional magnitude estimation experiment was performed. The results indicated that both the torque signals and grasp type mediate human perception of virtual textures. Slope Torque led to greater perceived roughness when the fingertip was used, and the fingertip led to higher roughness ratings than the penhandle with the Slope Torque condition. The Slope Torque algorithm appears to be advantageous for generating rougher surfaces compared to the force-based algorithms which are typically limited by the system stability and actuator saturation.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Despite the fact that conventional haptic interfaces and rendering algorithms commonly approximate interactions with force only, the dynamic effects of even simple tasks, e.g., writing on a paper, involve both forces and torques. To extend previous algorithms as well as to investigate the effects of torque feedback on human roughness perception, we deployed a novel haptic platform with two probes, fingertip and penhandle. Three torque conditions were examined: 1) Slope Torque, which orients the probe perpendicular to the surface, 2) No Torque, where no active torque is provided by the device, and 3) Stiff Torque, where torque feedback is provided to keep the probe upright. A conventional magnitude estimation experiment was performed. The results indicated that both the torque signals and grasp type mediate human perception of virtual textures. Slope Torque led to greater perceived roughness when the fingertip was used, and the fingertip led to higher roughness ratings than the penhandle with the Slope Torque condition. The Slope Torque algorithm appears to be advantageous for generating rougher surfaces compared to the force-based algorithms which are typically limited by the system stability and actuator saturation.",
"title": "Torque Contribution to Haptic Rendering of Virtual Textures",
"normalizedTitle": "Torque Contribution to Haptic Rendering of Virtual Textures",
"fno": "07873287",
"hasPdf": true,
"idPrefix": "th",
"keywords": [
"Torque",
"Haptic Interfaces",
"Force",
"Probes",
"Rough Surfaces",
"Surface Roughness",
"Magnetic Levitation",
"Haptics",
"Feedback",
"Torque",
"Force",
"Roughness Perception",
"Virtual Textures",
"Magnetic Levitation Haptic Interface"
],
"authors": [
{
"givenName": "Sahba Aghajani",
"surname": "Pedram",
"fullName": "Sahba Aghajani Pedram",
"affiliation": "Department of Mechanical and Aerospace Engineering, University of California at Los Angeles, Los Angeles, CA",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Roberta L.",
"surname": "Klatzky",
"fullName": "Roberta L. Klatzky",
"affiliation": "Department of Psychology, Carnegie Mellon University, Pittsburgh, PA",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Peter",
"surname": "Berkelman",
"fullName": "Peter Berkelman",
"affiliation": "Department of Mechanical Engineering, University of Hawaii at Manoa, Honolulu, HI",
"__typename": "ArticleAuthorType"
}
],
"replicability": null,
"showBuyMe": true,
"showRecommendedArticles": true,
"isOpenAccess": false,
"issueNum": "04",
"pubDate": "2017-10-01 00:00:00",
"pubType": "trans",
"pages": "567-579",
"year": "2017",
"issn": "1939-1412",
"isbn": null,
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"recommendedArticles": [
{
"id": "proceedings/whc/2007/2738/0/04145143",
"title": "JND Analysis of Texture Roughness Perception using a Magnetic Levitation Haptic Device",
"doi": null,
"abstractUrl": "/proceedings-article/whc/2007/04145143/12OmNAMbZFn",
"parentPublication": {
"id": "proceedings/whc/2007/2738/0",
"title": "2007 2nd Joint EuroHaptics Conference and Symposium on Haptic Interfaces for Virtual Environments and Teleoperator Systems",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/vr/2010/6237/0/05444754",
"title": "Design and evaluation of a haptic tactile actuator to simulate rough textures",
"doi": null,
"abstractUrl": "/proceedings-article/vr/2010/05444754/12OmNAS9zy0",
"parentPublication": {
"id": "proceedings/vr/2010/6237/0",
"title": "2010 IEEE Virtual Reality Conference (VR)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cca/2000/6562/0/00897525",
"title": "Torque feedback control of dry friction clutches for a dissipative passive haptic interface",
"doi": null,
"abstractUrl": "/proceedings-article/cca/2000/00897525/12OmNBbaH5N",
"parentPublication": {
"id": "proceedings/cca/2000/6562/0",
"title": "Proceedings of the 2000 IEEE International Conference on Control Applications",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/cw/2010/4215/0/4215a025",
"title": "Haptic Rendering Algorithm for Biomolecular Docking with Torque Force",
"doi": null,
"abstractUrl": "/proceedings-article/cw/2010/4215a025/12OmNrMHOlc",
"parentPublication": {
"id": "proceedings/cw/2010/4215/0",
"title": "2010 International Conference on Cyberworlds",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/haptics/2008/2005/0/04479905",
"title": "The Geometric Model for Perceived Roughness Applies to Virtual Textures",
"doi": null,
"abstractUrl": "/proceedings-article/haptics/2008/04479905/12OmNwt5sjl",
"parentPublication": {
"id": "proceedings/haptics/2008/2005/0",
"title": "IEEE Haptics Symposium 2008",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/whc/2007/2738/0/04145153",
"title": "Texture Gradients and Perceptual Constancy under Haptic Exploration",
"doi": null,
"abstractUrl": "/proceedings-article/whc/2007/04145153/12OmNyUnEDQ",
"parentPublication": {
"id": "proceedings/whc/2007/2738/0",
"title": "2007 2nd Joint EuroHaptics Conference and Symposium on Haptic Interfaces for Virtual Environments and Teleoperator Systems",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/icie/2010/4080/1/05570945",
"title": "Impact of Planting Grass on Loessial Earthen Road with Different Slope Gradients to Avoid Runoff and Erosion",
"doi": null,
"abstractUrl": "/proceedings-article/icie/2010/05570945/13bd1sv5Nya",
"parentPublication": {
"id": "proceedings/icie/2010/4080/1",
"title": "Information Engineering, International Conference on",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/th/2013/01/tth2013010081",
"title": "Psychophysical Dimensions of Tactile Perception of Textures",
"doi": null,
"abstractUrl": "/journal/th/2013/01/tth2013010081/13rRUx0xPTW",
"parentPublication": {
"id": "trans/th",
"title": "IEEE Transactions on Haptics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/th/2011/02/tth2011020122",
"title": "Roughness Perception in Virtual Textures",
"doi": null,
"abstractUrl": "/journal/th/2011/02/tth2011020122/13rRUxYINfp",
"parentPublication": {
"id": "trans/th",
"title": "IEEE Transactions on Haptics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/th/2015/02/07060666",
"title": "Surface-Roughness-Based Virtual Textiles: Evaluation Using a Multi-Contactor Display",
"doi": null,
"abstractUrl": "/journal/th/2015/02/07060666/13rRUxly95L",
"parentPublication": {
"id": "trans/th",
"title": "IEEE Transactions on Haptics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"adjacentArticles": {
"previous": {
"fno": "07876800",
"articleId": "13rRUx0xPTX",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "07922602",
"articleId": "13rRUy3gn7I",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"webExtras": [],
"articleVideos": []
}
|
{
"issue": {
"id": "12OmNC0PGNt",
"title": "May",
"year": "1999",
"issueNum": "05",
"idPrefix": "tc",
"pubType": "journal",
"volume": "48",
"label": "May",
"downloadables": {
"hasCover": false,
"__typename": "PeriodicalIssueDownloadablesType"
},
"__typename": "PeriodicalIssue"
},
"article": {
"id": "13rRUygT7m4",
"doi": "10.1109/12.769436",
"abstract": "Abstract—This paper introduces a new network topology, called Multi-Mesh (MM), which uses multiple meshes as the basic building blocks interconnected in a suitable manner. The proposed network consists of Z_$n^4$_Z processors and is 4-regular with a diameter of Z_$2n$_Z. The network also contains a Hamiltonian cycle. Simple routing algorithms for point-to-point communication, one-to-all broadcast, and multicast have been described for this network. It is shown that a simple Z_$n^2\\times n^2$_Z mesh can also be emulated on this network in O(1) time. Several application examples have been discussed for which this network is found to be more efficient with regard to computational time than the corresponding mesh with the same number of processors. As examples, OZ_$(n)$_Z time algorithms for finding the sum, average, minimum, and maximum of Z_$n^4$_Z data values, located at Z_$n^4$_Z different processors have been discussed. Time-efficient implementations of algorithms for solving nontrivial problems, e.g., Lagrange's interpolation, matrix transposition, matrix multiplication, and Discrete Fourier Transform (DFT) computation have also been discussed. The time complexity of Lagrange's interpolation on this network is OZ_$(n)$_Z for Z_$n^2$_Z data points compared to O(Z_$n^2$_Z) time on mesh of the same size. Matrix transpose requires OZ_$(n^{0.5}$_Z) time for an Z_$n \\times n$_Z matrix. The time for multiplying two Z_$n\\times n$_Z matrices is OZ_$(n^{0.6})$_Z with an AT-cost of OZ_$(n^3)$_Z. DFT of Z_$n$_Z sample points can be computed in OZ_$(n^{0.6})$_Z time on this network. Papers [6], [7] show that Z_$n^4$_Z data elements can be sorted on this network in Z_$O(n)$_Z time.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Abstract—This paper introduces a new network topology, called Multi-Mesh (MM), which uses multiple meshes as the basic building blocks interconnected in a suitable manner. The proposed network consists of $n^4$ processors and is 4-regular with a diameter of $2n$. The network also contains a Hamiltonian cycle. Simple routing algorithms for point-to-point communication, one-to-all broadcast, and multicast have been described for this network. It is shown that a simple $n^2\\times n^2$ mesh can also be emulated on this network in O(1) time. Several application examples have been discussed for which this network is found to be more efficient with regard to computational time than the corresponding mesh with the same number of processors. As examples, O$(n)$ time algorithms for finding the sum, average, minimum, and maximum of $n^4$ data values, located at $n^4$ different processors have been discussed. Time-efficient implementations of algorithms for solving nontrivial problems, e.g., Lagrange's interpolation, matrix transposition, matrix multiplication, and Discrete Fourier Transform (DFT) computation have also been discussed. The time complexity of Lagrange's interpolation on this network is O$(n)$ for $n^2$ data points compared to O($n^2$) time on mesh of the same size. Matrix transpose requires O$(n^{0.5}$) time for an $n \\times n$ matrix. The time for multiplying two $n\\times n$ matrices is O$(n^{0.6})$ with an AT-cost of O$(n^3)$. DFT of $n$ sample points can be computed in O$(n^{0.6})$ time on this network. Papers [6], [7] show that $n^4$ data elements can be sorted on this network in $O(n)$ time.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Abstract—This paper introduces a new network topology, called Multi-Mesh (MM), which uses multiple meshes as the basic building blocks interconnected in a suitable manner. The proposed network consists of - processors and is 4-regular with a diameter of -. The network also contains a Hamiltonian cycle. Simple routing algorithms for point-to-point communication, one-to-all broadcast, and multicast have been described for this network. It is shown that a simple - mesh can also be emulated on this network in O(1) time. Several application examples have been discussed for which this network is found to be more efficient with regard to computational time than the corresponding mesh with the same number of processors. As examples, O- time algorithms for finding the sum, average, minimum, and maximum of - data values, located at - different processors have been discussed. Time-efficient implementations of algorithms for solving nontrivial problems, e.g., Lagrange's interpolation, matrix transposition, matrix multiplication, and Discrete Fourier Transform (DFT) computation have also been discussed. The time complexity of Lagrange's interpolation on this network is O- for - data points compared to O(-) time on mesh of the same size. Matrix transpose requires O-) time for an - matrix. The time for multiplying two - matrices is O- with an AT-cost of O-. DFT of - sample points can be computed in O- time on this network. Papers [6], [7] show that - data elements can be sorted on this network in - time.",
"title": "A New Network Topology with Multiple Meshes",
"normalizedTitle": "A New Network Topology with Multiple Meshes",
"fno": "t0536",
"hasPdf": true,
"idPrefix": "tc",
"keywords": [
"Mesh",
"Multimesh",
"Diameter",
"Hamiltonian Cycle",
"Point To Point Communication",
"One To All Broadcast",
"Multicast",
"Fault Diameter",
"Lagranges Interpolation",
"Matrix Transpose",
"Matrix Multiplication",
"DFT"
],
"authors": [
{
"givenName": "Debasish",
"surname": "Das",
"fullName": "Debasish Das",
"affiliation": null,
"__typename": "ArticleAuthorType"
},
{
"givenName": "Mallika",
"surname": "De",
"fullName": "Mallika De",
"affiliation": null,
"__typename": "ArticleAuthorType"
},
{
"givenName": "Bhabani P.",
"surname": "Sinha",
"fullName": "Bhabani P. Sinha",
"affiliation": null,
"__typename": "ArticleAuthorType"
}
],
"replicability": null,
"showBuyMe": true,
"showRecommendedArticles": false,
"isOpenAccess": false,
"issueNum": "05",
"pubDate": "1999-05-01 00:00:00",
"pubType": "trans",
"pages": "536-551",
"year": "1999",
"issn": "0018-9340",
"isbn": null,
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"recommendedArticles": [],
"adjacentArticles": {
"previous": {
"fno": "t0528",
"articleId": "13rRUILc8eq",
"__typename": "AdjacentArticleType"
},
"next": null,
"__typename": "AdjacentArticlesType"
},
"webExtras": [],
"articleVideos": []
}
|
{
"issue": {
"id": "12OmNzw8iTa",
"title": "Jan.-Feb.",
"year": "2020",
"issueNum": "01",
"idPrefix": "tb",
"pubType": "journal",
"volume": "17",
"label": "Jan.-Feb.",
"downloadables": {
"hasCover": false,
"__typename": "PeriodicalIssueDownloadablesType"
},
"__typename": "PeriodicalIssue"
},
"article": {
"id": "13rRUNvyadr",
"doi": "10.1109/TCBB.2018.2859952",
"abstract": "Essential proteins are indispensable units for living organisms. Removing those leads to disruption of protein complexes and causing lethality. Recently, theoretical methods have been presented to detect essential proteins in protein interaction network. In these methods, an essential protein is predicted as a high-degree vertex of protein interaction network. However, interaction data are usually incomplete and an essential protein cannot have high-connection due to data deficiency. Then, it is critical to design informative networks from other biological data sources. In this paper, we defined a minimal set of proteins to disrupt the maximum number of protein complexes. We constructed a weighted graph using a set of given complexes. We proposed a more appropriate method based on betweenness values to diagnose a minimal set of proteins whose removal would generate the disruption of protein complexes. The effectiveness of the proposed method was benchmarked using given dataset of complexes. The results of our method were compared to the results of other methods in terms of the number of disrupted complexes. Also, results indicated significant superiority of the minimal set of proteins in the massive disruption of complexes. Finally, we investigated the performance of our method for yeast and human datasets and analyzed biological properties of the selected proteins. Our algorithm and some example are freely available from http://bs.ipm.ac.ir/softwares/DPC/DPC.zip.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Essential proteins are indispensable units for living organisms. Removing those leads to disruption of protein complexes and causing lethality. Recently, theoretical methods have been presented to detect essential proteins in protein interaction network. In these methods, an essential protein is predicted as a high-degree vertex of protein interaction network. However, interaction data are usually incomplete and an essential protein cannot have high-connection due to data deficiency. Then, it is critical to design informative networks from other biological data sources. In this paper, we defined a minimal set of proteins to disrupt the maximum number of protein complexes. We constructed a weighted graph using a set of given complexes. We proposed a more appropriate method based on betweenness values to diagnose a minimal set of proteins whose removal would generate the disruption of protein complexes. The effectiveness of the proposed method was benchmarked using given dataset of complexes. The results of our method were compared to the results of other methods in terms of the number of disrupted complexes. Also, results indicated significant superiority of the minimal set of proteins in the massive disruption of complexes. Finally, we investigated the performance of our method for yeast and human datasets and analyzed biological properties of the selected proteins. Our algorithm and some example are freely available from http://bs.ipm.ac.ir/softwares/DPC/DPC.zip.",
"__typename": "ArticleAbstractType"
}
],
"normalizedAbstract": "Essential proteins are indispensable units for living organisms. Removing those leads to disruption of protein complexes and causing lethality. Recently, theoretical methods have been presented to detect essential proteins in protein interaction network. In these methods, an essential protein is predicted as a high-degree vertex of protein interaction network. However, interaction data are usually incomplete and an essential protein cannot have high-connection due to data deficiency. Then, it is critical to design informative networks from other biological data sources. In this paper, we defined a minimal set of proteins to disrupt the maximum number of protein complexes. We constructed a weighted graph using a set of given complexes. We proposed a more appropriate method based on betweenness values to diagnose a minimal set of proteins whose removal would generate the disruption of protein complexes. The effectiveness of the proposed method was benchmarked using given dataset of complexes. The results of our method were compared to the results of other methods in terms of the number of disrupted complexes. Also, results indicated significant superiority of the minimal set of proteins in the massive disruption of complexes. Finally, we investigated the performance of our method for yeast and human datasets and analyzed biological properties of the selected proteins. Our algorithm and some example are freely available from http://bs.ipm.ac.ir/softwares/DPC/DPC.zip.",
"title": "Disruption of Protein Complexes from Weighted Complex Networks",
"normalizedTitle": "Disruption of Protein Complexes from Weighted Complex Networks",
"fno": "08419278",
"hasPdf": true,
"idPrefix": "tb",
"keywords": [
"Biology Computing",
"Complex Networks",
"Data Handling",
"Graph Theory",
"Molecular Biophysics",
"Network Theory Graphs",
"Proteins",
"Protein Complexes",
"Weighted Complex Networks",
"Essential Protein",
"Lethality",
"Protein Interaction Network",
"Disrupted Complexes",
"Living Organisms",
"Interaction Data",
"Data Deficiency",
"Weighted Graph",
"Biological Properties",
"Proteins",
"Protein Engineering",
"Complex Networks",
"Biological Processes",
"Organisms",
"Drugs",
"Weighed Complex Network",
"Essential Protein",
"Betweenness",
"Complex Disruption"
],
"authors": [
{
"givenName": "Mahnaz",
"surname": "Habibi",
"fullName": "Mahnaz Habibi",
"affiliation": "Department of Mathematics, Qazvin Branch, Islamic Azad University, Qazvin, Iran",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Pegah",
"surname": "Khosravi",
"fullName": "Pegah Khosravi",
"affiliation": "School of Biological Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran",
"__typename": "ArticleAuthorType"
}
],
"replicability": null,
"showBuyMe": true,
"showRecommendedArticles": true,
"isOpenAccess": false,
"issueNum": "01",
"pubDate": "2020-01-01 00:00:00",
"pubType": "trans",
"pages": "102-109",
"year": "2020",
"issn": "1545-5963",
"isbn": null,
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
"recommendedArticles": [
{
"id": "proceedings/bibm/2012/2559/0/06392693",
"title": "A random walk based approach for improving protein-protein interaction network and protein complex prediction",
"doi": null,
"abstractUrl": "/proceedings-article/bibm/2012/06392693/12OmNBpmDK9",
"parentPublication": {
"id": "proceedings/bibm/2012/2559/0",
"title": "2012 IEEE International Conference on Bioinformatics and Biomedicine",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bibm/2016/1611/0/07822611",
"title": "Mining protein complexes based on topology potential from weighted dynamic PPI network",
"doi": null,
"abstractUrl": "/proceedings-article/bibm/2016/07822611/12OmNCzKlMI",
"parentPublication": {
"id": "proceedings/bibm/2016/1611/0",
"title": "2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bibm/2014/5669/0/06999204",
"title": "Essential protein identification based on essential protein-protein interaction prediction by integrated edge weights",
"doi": null,
"abstractUrl": "/proceedings-article/bibm/2014/06999204/12OmNrY3Lxn",
"parentPublication": {
"id": "proceedings/bibm/2014/5669/0",
"title": "2014 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bibmw/2012/2746/0/06470299",
"title": "Mining hub-based protein complexes in massive biological networks",
"doi": null,
"abstractUrl": "/proceedings-article/bibmw/2012/06470299/12OmNs0TKTA",
"parentPublication": {
"id": "proceedings/bibmw/2012/2746/0",
"title": "2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bibm/2011/1799/0/06120420",
"title": "An Improved Graph Entropy-based Method for Identifying Protein Complexes",
"doi": null,
"abstractUrl": "/proceedings-article/bibm/2011/06120420/12OmNwCsdAM",
"parentPublication": {
"id": "proceedings/bibm/2011/1799/0",
"title": "2011 IEEE International Conference on Bioinformatics and Biomedicine",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/mcsi/2014/4324/0/4324a048",
"title": "Characterization of Nested Complexes in Protein Interaction Networks",
"doi": null,
"abstractUrl": "/proceedings-article/mcsi/2014/4324a048/12OmNwtWfHP",
"parentPublication": {
"id": "proceedings/mcsi/2014/4324/0",
"title": "2014 International Conference on Mathematics and Computers in Sciences and in Industry (MCSI)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bibm/2014/5669/0/06999291",
"title": "Identification of protein complexes and functional modules in integrated PPI networks",
"doi": null,
"abstractUrl": "/proceedings-article/bibm/2014/06999291/12OmNynsbzG",
"parentPublication": {
"id": "proceedings/bibm/2014/5669/0",
"title": "2014 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bibm/2010/8306/0/05706571",
"title": "Semi-supervised learning protein complexes from protein interaction networks",
"doi": null,
"abstractUrl": "/proceedings-article/bibm/2010/05706571/12OmNyuPKVV",
"parentPublication": {
"id": "proceedings/bibm/2010/8306/0",
"title": "2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "proceedings/bibmw/2012/2746/0/06470303",
"title": "Relevance judgment algorithm for detecting protein complexes from protein interaction networks",
"doi": null,
"abstractUrl": "/proceedings-article/bibmw/2012/06470303/12OmNzVoBxT",
"parentPublication": {
"id": "proceedings/bibmw/2012/2746/0",
"title": "2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
},
{
"id": "trans/tb/2012/03/06138846",
"title": "Protein Complexes Discovery Based on Protein-Protein Interaction Data via a Regularized Sparse Generative Network Model",
"doi": null,
"abstractUrl": "/journal/tb/2012/03/06138846/13rRUwdrdRd",
"parentPublication": {
"id": "trans/tb",
"title": "IEEE/ACM Transactions on Computational Biology and Bioinformatics",
"__typename": "ParentPublication"
},
"__typename": "RecommendedArticleType"
}
],
"adjacentArticles": {
"previous": {
"fno": "08417906",
"articleId": "13rRUwI5Uez",
"__typename": "AdjacentArticleType"
},
"next": {
"fno": "08395000",
"articleId": "13rRUyeCk8F",
"__typename": "AdjacentArticleType"
},
"__typename": "AdjacentArticlesType"
},
"webExtras": [],
"articleVideos": []
}
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.