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"title": "2020 IEEE 36th International Conference on Data Engineering (ICDE)",
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"article": {
"id": "1kaMzQnyYPm",
"doi": "10.1109/ICDE48307.2020.00178",
"title": "Predicting Origin-Destination Flow via Multi-Perspective Graph Convolutional Network",
"normalizedTitle": "Predicting Origin-Destination Flow via Multi-Perspective Graph Convolutional Network",
"abstract": "Predicting Origin-Destination (OD) flow is a crucial problem for intelligent transportation. However, it is extremely challenging because of three reasons: first, correlations exist between both origins and destinations; second, the correlations are dynamic across the time; at last, there are multiple correlations from different aspects. To the best of our knowledge, existing models for OD flow prediction cannot tackle all of these three issues simultaneously. We propose Multi-Perspective Graph Convolutional Networks (MPGCN) to capture the complex dependencies. Our proposed model first utilizes long short-term memory (LSTM) network to extract temporal features for each OD pair and then learns the spatial dependency of origins and destinations by a two-dimensional graph convolutional network. Furthermore, we design a dynamic graph together with two static graphs to capture the complicated spatial dependencies and use an average strategy to obtain the final predicted OD flow. We conduct extensive experiments on two large-scale and real-world datasets, which not only demonstrate our design philosophy but also validate the effectiveness of the proposed model.",
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"content": "Predicting Origin-Destination (OD) flow is a crucial problem for intelligent transportation. However, it is extremely challenging because of three reasons: first, correlations exist between both origins and destinations; second, the correlations are dynamic across the time; at last, there are multiple correlations from different aspects. To the best of our knowledge, existing models for OD flow prediction cannot tackle all of these three issues simultaneously. We propose Multi-Perspective Graph Convolutional Networks (MPGCN) to capture the complex dependencies. Our proposed model first utilizes long short-term memory (LSTM) network to extract temporal features for each OD pair and then learns the spatial dependency of origins and destinations by a two-dimensional graph convolutional network. Furthermore, we design a dynamic graph together with two static graphs to capture the complicated spatial dependencies and use an average strategy to obtain the final predicted OD flow. We conduct extensive experiments on two large-scale and real-world datasets, which not only demonstrate our design philosophy but also validate the effectiveness of the proposed model.",
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"normalizedAbstract": "Predicting Origin-Destination (OD) flow is a crucial problem for intelligent transportation. However, it is extremely challenging because of three reasons: first, correlations exist between both origins and destinations; second, the correlations are dynamic across the time; at last, there are multiple correlations from different aspects. To the best of our knowledge, existing models for OD flow prediction cannot tackle all of these three issues simultaneously. We propose Multi-Perspective Graph Convolutional Networks (MPGCN) to capture the complex dependencies. Our proposed model first utilizes long short-term memory (LSTM) network to extract temporal features for each OD pair and then learns the spatial dependency of origins and destinations by a two-dimensional graph convolutional network. Furthermore, we design a dynamic graph together with two static graphs to capture the complicated spatial dependencies and use an average strategy to obtain the final predicted OD flow. We conduct extensive experiments on two large-scale and real-world datasets, which not only demonstrate our design philosophy but also validate the effectiveness of the proposed model.",
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"Convolutional Neural Nets",
"Data Analysis",
"Feature Extraction",
"Graph Theory",
"Intelligent Transportation Systems",
"Learning Artificial Intelligence",
"Recurrent Neural Nets",
"Traffic Engineering Computing",
"Transportation",
"OD Flow Prediction",
"Long Short Term Memory Network",
"Dynamic Graph",
"Static Graphs",
"Origin Destination Flow",
"Multiperspective Graph Convolutional Network",
"Intelligent Transportation",
"Temporal Feature Extraction",
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"Traffic Data",
"Correlation",
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"Predictive Models",
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"Convolution",
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"Origin Destination Traffic Prediction",
"Graph Convolutional Network",
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{
"affiliation": "Tsinghua University,Beijing National Research Center for Information Science and Technology (BNRist), Department of Electronic Engineering,Beijing,China,100084",
"fullName": "Hongzhi Shi",
"givenName": "Hongzhi",
"surname": "Shi",
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"fullName": "Quanming Yao",
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{
"affiliation": "Sichuan University,College of Computer Science,Chengdu,China",
"fullName": "Qi Guo",
"givenName": "Qi",
"surname": "Guo",
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"affiliation": "Google Inc.,Mountain View,United States",
"fullName": "Yaguang Li",
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"affiliation": "AI Labs, Didi Chuxing,Beijing,China",
"fullName": "Lingyu Zhang",
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"affiliation": "AI Labs, Didi Chuxing,Beijing,China",
"fullName": "Jieping Ye",
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{
"affiliation": "Tsinghua University,Beijing National Research Center for Information Science and Technology (BNRist), Department of Electronic Engineering,Beijing,China,100084",
"fullName": "Yong Li",
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{
"affiliation": "University of Southern California,Department of Computer Science,Los Angeles,United States",
"fullName": "Yan Liu",
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"title": "Optimal viewpoint finding for space time cube to explore spatio-temporal characteristics of vehicle trajectories on crossroads",
"normalizedTitle": "Optimal viewpoint finding for space time cube to explore spatio-temporal characteristics of vehicle trajectories on crossroads",
"abstract": "Visualization combining space and time in a single display called “space time cube (STC)” is used for visualizing spatio-temporal movement data. An STC enables us to explore not only shapes and positions of vehicle trajectories but also their temporal distributions. However, it is difficult for users to manually find optimal viewpoints for understanding such characteristics of trajectories. In this paper, we propose an optimal viewpoints selection method for visualizing the spatio-temporal characteristics of vehicle trajectories on a large set of crossroads using an STC. For this purpose, we provide an algorithm based on viewpoint entropy weighted by angles of trajectories with a horizontal line as a measure of viewpoint quality on a projected 2D image. We then argue that our method can be adapted to crossroads with different trajectory shapes.",
"abstracts": [
{
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"content": "Visualization combining space and time in a single display called “space time cube (STC)” is used for visualizing spatio-temporal movement data. An STC enables us to explore not only shapes and positions of vehicle trajectories but also their temporal distributions. However, it is difficult for users to manually find optimal viewpoints for understanding such characteristics of trajectories. In this paper, we propose an optimal viewpoints selection method for visualizing the spatio-temporal characteristics of vehicle trajectories on a large set of crossroads using an STC. For this purpose, we provide an algorithm based on viewpoint entropy weighted by angles of trajectories with a horizontal line as a measure of viewpoint quality on a projected 2D image. We then argue that our method can be adapted to crossroads with different trajectory shapes.",
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"normalizedAbstract": "Visualization combining space and time in a single display called “space time cube (STC)” is used for visualizing spatio-temporal movement data. An STC enables us to explore not only shapes and positions of vehicle trajectories but also their temporal distributions. However, it is difficult for users to manually find optimal viewpoints for understanding such characteristics of trajectories. In this paper, we propose an optimal viewpoints selection method for visualizing the spatio-temporal characteristics of vehicle trajectories on a large set of crossroads using an STC. For this purpose, we provide an algorithm based on viewpoint entropy weighted by angles of trajectories with a horizontal line as a measure of viewpoint quality on a projected 2D image. We then argue that our method can be adapted to crossroads with different trajectory shapes.",
"fno": "08231856",
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"Shape",
"Entropy",
"Data Visualization",
"Two Dimensional Displays",
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"Big Data"
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"authors": [
{
"affiliation": "The University of Tokyo, and National Institute of Information and Communications Technology",
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"givenName": "Masahiko",
"surname": "Itoh",
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{
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{
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"proceeding": {
"id": "12OmNASrawz",
"title": "2009 IEEE Virtual Reality Conference",
"acronym": "vr",
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"article": {
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"doi": "10.1109/VR.2009.4811015",
"title": "Effective Presentation Technique of Scent Using Small Ejection Quantities of Odor",
"normalizedTitle": "Effective Presentation Technique of Scent Using Small Ejection Quantities of Odor",
"abstract": "Trials on the transmission of olfactory information together with audio/visual information are currently underway. However, a problem exists in that continuous emission of scent leaves scent in the air causing human olfactory adaptation. To resolve this problem, we aimed at minimizing the quantity of scent ejected using an ink-jet olfactory display developed. Following the development of a breath sensor for breath synchronization, we next developed an olfactory ejection system to present scent on each inspiration. We then measured human olfactory characteristics in order to determine the most suitable method for presenting scent on an inspiration. Experiments revealed that the intensity of scent perceived by the user was altered by differences in the presentation method even when the quantity of scent was unchanged. We present here a method of odor presentation that most effectively minimizes the ejection quantities.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Trials on the transmission of olfactory information together with audio/visual information are currently underway. However, a problem exists in that continuous emission of scent leaves scent in the air causing human olfactory adaptation. To resolve this problem, we aimed at minimizing the quantity of scent ejected using an ink-jet olfactory display developed. Following the development of a breath sensor for breath synchronization, we next developed an olfactory ejection system to present scent on each inspiration. We then measured human olfactory characteristics in order to determine the most suitable method for presenting scent on an inspiration. Experiments revealed that the intensity of scent perceived by the user was altered by differences in the presentation method even when the quantity of scent was unchanged. We present here a method of odor presentation that most effectively minimizes the ejection quantities.",
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"normalizedAbstract": "Trials on the transmission of olfactory information together with audio/visual information are currently underway. However, a problem exists in that continuous emission of scent leaves scent in the air causing human olfactory adaptation. To resolve this problem, we aimed at minimizing the quantity of scent ejected using an ink-jet olfactory display developed. Following the development of a breath sensor for breath synchronization, we next developed an olfactory ejection system to present scent on each inspiration. We then measured human olfactory characteristics in order to determine the most suitable method for presenting scent on an inspiration. Experiments revealed that the intensity of scent perceived by the user was altered by differences in the presentation method even when the quantity of scent was unchanged. We present here a method of odor presentation that most effectively minimizes the ejection quantities.",
"fno": "04811015",
"keywords": [
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"Electronic Noses",
"User Interfaces",
"Presentation Technique",
"Odor Small Ejection Quantities",
"Olfactory Information",
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"Ink Jet Olfactory Display",
"Breath Sensor",
"Breath Synchronization",
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"Sensor Phenomena And Characterization",
"Sensor Systems",
"Anthropometry",
"Virtual Reality",
"Nose",
"Control Systems",
"Olfactory Information",
"Human Olfactory Characteristics",
"Olfactory Display",
"Ink Jet",
"Pulse Ejection",
"Breath Sensor",
"H 5 2 INFORMATION INTERFACES AND PRESENTATION User Interfaces User Centered Design",
"H 1 2 MODELS AND PRINCIPLES User Machine Systems Human Factors"
],
"authors": [
{
"affiliation": "Faculty of Science and Technology, Keio University e-mail: sato@mos.ics.keio.ac.jp",
"fullName": "Junta Sato",
"givenName": "Junta",
"surname": "Sato",
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{
"affiliation": "Faculty of Science and Technology, Keio University e-mail: ohtsu@mos.ics.keio.ac.jp",
"fullName": "Kaori Ohtsu",
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"affiliation": "Canon Inc. email: bannai.yuichi@canon.co.jp",
"fullName": "Yuichi Bannai",
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"affiliation": "Faculty of Science and Technology, Keio University e-mail: okada@mos.ics.keio.ac.jp",
"fullName": "Ken-ichi Okada",
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"title": "Acupuncture for Generalized Anxiety Disorder: A Systematic Review",
"normalizedTitle": "Acupuncture for Generalized Anxiety Disorder: A Systematic Review",
"abstract": "Objective Generalized anxiety disorder (GAD) is the most common anxiety disorder in primary care. The clinical studies on anxiety disorders have been commonly conducted, however the efficacy of acupuncture in treating GAD is uncertain. We therefore performed a systematic review to evaluate the evidence regarding acupuncture for GAD. Data Sources CBM, CAJ, CSJD, CNKI, WanFang, EMBASE, Pubmed/MEDLINE, OVID EBM Reviewers, Cochrane Library, Up-to-date, ACP journal club were searched through July 2016. Search terms included Condition = (Generalized Anxiety Disorder or Generalized Anxiety or GAD) And Intervention = (acupuncture* or acupressure or acupoint* or electro acupuncture* or electro-acupuncture*). Study Selection Included in this study were randomized controlled trials of participants have GAD diagnosed by operational criteria. Data Extraction Two review authors extracted data from each study independently. Information relating to study population, sample size, interventions, comparators, potential biases in the conduct of the trial, outcomes including adverse events, follow-up and methods of statistical analysis were abstracted from the original reports. Data Synthesis 3 RCTs (443 patients) were included. Overall the risk of bias in included studies was high. The included trials were extremely heterogeneous regarding acupuncture and control interventions. Therefore, pooling of data was not performed. We found equal benefits of acupuncture and medications for HAMA or SAS in 2 trials. In another trail no difference was found in the baseline analysis with HAMA and SAS measurement between groups at the2nd, 4thweek time-point. Adverse events associated with acupuncture are rarely reported. Conclusion Even though some methodological shortcomings influence the quality of the included studies, this review still suggests that acupuncture was an effective alternative therapy for generalized anxiety is safe.",
"abstracts": [
{
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"content": "Objective Generalized anxiety disorder (GAD) is the most common anxiety disorder in primary care. The clinical studies on anxiety disorders have been commonly conducted, however the efficacy of acupuncture in treating GAD is uncertain. We therefore performed a systematic review to evaluate the evidence regarding acupuncture for GAD. Data Sources CBM, CAJ, CSJD, CNKI, WanFang, EMBASE, Pubmed/MEDLINE, OVID EBM Reviewers, Cochrane Library, Up-to-date, ACP journal club were searched through July 2016. Search terms included Condition = (Generalized Anxiety Disorder or Generalized Anxiety or GAD) And Intervention = (acupuncture* or acupressure or acupoint* or electro acupuncture* or electro-acupuncture*). Study Selection Included in this study were randomized controlled trials of participants have GAD diagnosed by operational criteria. Data Extraction Two review authors extracted data from each study independently. Information relating to study population, sample size, interventions, comparators, potential biases in the conduct of the trial, outcomes including adverse events, follow-up and methods of statistical analysis were abstracted from the original reports. Data Synthesis 3 RCTs (443 patients) were included. Overall the risk of bias in included studies was high. The included trials were extremely heterogeneous regarding acupuncture and control interventions. Therefore, pooling of data was not performed. We found equal benefits of acupuncture and medications for HAMA or SAS in 2 trials. In another trail no difference was found in the baseline analysis with HAMA and SAS measurement between groups at the2nd, 4thweek time-point. Adverse events associated with acupuncture are rarely reported. Conclusion Even though some methodological shortcomings influence the quality of the included studies, this review still suggests that acupuncture was an effective alternative therapy for generalized anxiety is safe.",
"__typename": "ArticleAbstractType"
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],
"normalizedAbstract": "Objective Generalized anxiety disorder (GAD) is the most common anxiety disorder in primary care. The clinical studies on anxiety disorders have been commonly conducted, however the efficacy of acupuncture in treating GAD is uncertain. We therefore performed a systematic review to evaluate the evidence regarding acupuncture for GAD. Data Sources CBM, CAJ, CSJD, CNKI, WanFang, EMBASE, Pubmed/MEDLINE, OVID EBM Reviewers, Cochrane Library, Up-to-date, ACP journal club were searched through July 2016. Search terms included Condition = (Generalized Anxiety Disorder or Generalized Anxiety or GAD) And Intervention = (acupuncture* or acupressure or acupoint* or electro acupuncture* or electro-acupuncture*). Study Selection Included in this study were randomized controlled trials of participants have GAD diagnosed by operational criteria. Data Extraction Two review authors extracted data from each study independently. Information relating to study population, sample size, interventions, comparators, potential biases in the conduct of the trial, outcomes including adverse events, follow-up and methods of statistical analysis were abstracted from the original reports. Data Synthesis 3 RCTs (443 patients) were included. Overall the risk of bias in included studies was high. The included trials were extremely heterogeneous regarding acupuncture and control interventions. Therefore, pooling of data was not performed. We found equal benefits of acupuncture and medications for HAMA or SAS in 2 trials. In another trail no difference was found in the baseline analysis with HAMA and SAS measurement between groups at the2nd, 4thweek time-point. Adverse events associated with acupuncture are rarely reported. Conclusion Even though some methodological shortcomings influence the quality of the included studies, this review still suggests that acupuncture was an effective alternative therapy for generalized anxiety is safe.",
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"Medical Treatment",
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"fullName": "Rui Ma",
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"fullName": "Ding Luo",
"givenName": "Ding",
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"affiliation": null,
"fullName": "Yue Liu",
"givenName": "Yue",
"surname": "Liu",
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{
"affiliation": null,
"fullName": "Lu Lu",
"givenName": "Lu",
"surname": "Lu",
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{
"affiliation": null,
"fullName": "Shujun Xu",
"givenName": "Shujun",
"surname": "Xu",
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{
"affiliation": null,
"fullName": "Qian Wu",
"givenName": "Qian",
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"fullName": "Yefei Huang",
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"title": "The Implication of Boundary Adjustment in H/Alpha Plane on the Unsupervised Wishart Maximum Likelihood Segmentation for Fully Polarimetric SAR Data",
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"abstract": "In the practical work of the H/Alpha Wishart Maximum Likelihood segmentation, Cloude-Pottier's original boundary scheme would have often been a default setting. In this paper, the implication of boundary adjustment in H/Alpha plane on the unsupervised Wishart ML segmentation for fully polarimetric SAR data is researched. Four boundary adjustment schemes are designed, and the Wishart ML segmentation results are compared with that of the original boundary scheme. The analysis illustrates that boundary setting in H/Alpha plane is an important factor affecting the H/Alpha Wishart ML segmentation performance.",
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{
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"content": "In the practical work of the H/Alpha Wishart Maximum Likelihood segmentation, Cloude-Pottier's original boundary scheme would have often been a default setting. In this paper, the implication of boundary adjustment in H/Alpha plane on the unsupervised Wishart ML segmentation for fully polarimetric SAR data is researched. Four boundary adjustment schemes are designed, and the Wishart ML segmentation results are compared with that of the original boundary scheme. The analysis illustrates that boundary setting in H/Alpha plane is an important factor affecting the H/Alpha Wishart ML segmentation performance.",
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"normalizedAbstract": "In the practical work of the H/Alpha Wishart Maximum Likelihood segmentation, Cloude-Pottier's original boundary scheme would have often been a default setting. In this paper, the implication of boundary adjustment in H/Alpha plane on the unsupervised Wishart ML segmentation for fully polarimetric SAR data is researched. Four boundary adjustment schemes are designed, and the Wishart ML segmentation results are compared with that of the original boundary scheme. The analysis illustrates that boundary setting in H/Alpha plane is an important factor affecting the H/Alpha Wishart ML segmentation performance.",
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"fullName": "Li Liu",
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"doi": "10.1109/ICDMW.2012.88",
"title": "Interactive Grouping of Friends in OSN: Towards Online Context Management",
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"abstract": "In Online Social Networks (OSNs), it can be difficult to maintain the context of a conversation or action, i.e. to know what the situation is and how to act appropriately. The resulting uncertainties may lead to privacy issues. We focus on one issue Context Collision in this paper, and motivate that a first step to address this issue is to help users distinguish groups of contacts within their OSN accounts. We conducted a small user study to investigate the criteria of users grouping the people they know. We summarized our participants' strategy of labeling the groups and found that they perform the grouping mainly by their connections with others. We used these results in the design of FreeBu, a semi-automatic and interactive grouping tool, which is based on mining friend graph data for community detection and profile information for labeling.",
"abstracts": [
{
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"content": "In Online Social Networks (OSNs), it can be difficult to maintain the context of a conversation or action, i.e. to know what the situation is and how to act appropriately. The resulting uncertainties may lead to privacy issues. We focus on one issue Context Collision in this paper, and motivate that a first step to address this issue is to help users distinguish groups of contacts within their OSN accounts. We conducted a small user study to investigate the criteria of users grouping the people they know. We summarized our participants' strategy of labeling the groups and found that they perform the grouping mainly by their connections with others. We used these results in the design of FreeBu, a semi-automatic and interactive grouping tool, which is based on mining friend graph data for community detection and profile information for labeling.",
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"abstract": "There are two alternative display metaphors for Augmented Reality (AR) screens: a see-through window or a magic mirror. Commonly used by task-support AR applications, the see-through display has not been compared with the mirror display in terms of user's task performance, even though the “mirror” hardware is more accessible to general users. We conducted a novel experiment to compare participants' performance when following object rotation cues with the two display metaphors. Results show that participants' overall performance under the mirror view was comparable to the see-through view, which indicates that the augmented mirror display may be a promising alternative to the window display for AR applications which guide moderately complex three-dimensional manipulations with physical objects.",
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"content": "There are two alternative display metaphors for Augmented Reality (AR) screens: a see-through window or a magic mirror. Commonly used by task-support AR applications, the see-through display has not been compared with the mirror display in terms of user's task performance, even though the “mirror” hardware is more accessible to general users. We conducted a novel experiment to compare participants' performance when following object rotation cues with the two display metaphors. Results show that participants' overall performance under the mirror view was comparable to the see-through view, which indicates that the augmented mirror display may be a promising alternative to the window display for AR applications which guide moderately complex three-dimensional manipulations with physical objects.",
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"abstract": "Visualization of multiple time series often suffers from overplotting, making it difficult to read the values of curves that are hidden by other curves. We present techniques for horizontally displacing the endpoints of line segments in discrete time series data that (1) enable the depiction of subsets of data and (2) reduce occlusion of endpoints. Because endpoints are not displaced vertically, their y values can still be read and compared. Unlike small multiples, our techniques do not move points with the same x or y values far from each other, making some comparison tasks easier. We present three novel techniques: compressed-superposed, compressed-juxtaposed, and shifted layers. One limitation of our techniques is that they work best when there is only a small number of x values being visualized, and additionally, one of them (compressed-superposed) modifies the slopes of curves in a way that makes the slopes incomparable. Our experimental comparison with three status quo techniques (conventional overlaid, vertically-stacked small multiples, and horizontally-stacked small multiples) shows that our proposed techniques are competitive with status quo techniques and in some cases superior.",
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"affiliation": "Croesus Finansoft, Canada",
"fullName": "Maxime Dumas",
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"abstract": "Disaster emergency response needs rapid estimation on disaster loss. In China it is of great importance to develop a loss index for rapidly assessing tropical cyclone (TC) disaster loss. In this paper, a new composite loss index for TC landing on China is proposed after reviewing past methods. Firstly, correlation analysis is made between historical TC direct loss and variables reflecting time, space and intensity of TC. Secondly, a rapid loss index is developed which is composed of mean translation speed, duration on the land, mean radius of wind exceeding Beaufort force eight and maximum of maximum sustained wind speed, four variables selected stepwise in the former step, as well as two parameters and four constants. Finally, the parameters and constants in the index are set based on data of 97 TCs from 1990 to 2009. The correlation coefficient of the index to loss value is 0.74 and passes confidence level of 99%, which means it is effective for rapid damage assessment.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Disaster emergency response needs rapid estimation on disaster loss. In China it is of great importance to develop a loss index for rapidly assessing tropical cyclone (TC) disaster loss. In this paper, a new composite loss index for TC landing on China is proposed after reviewing past methods. Firstly, correlation analysis is made between historical TC direct loss and variables reflecting time, space and intensity of TC. Secondly, a rapid loss index is developed which is composed of mean translation speed, duration on the land, mean radius of wind exceeding Beaufort force eight and maximum of maximum sustained wind speed, four variables selected stepwise in the former step, as well as two parameters and four constants. Finally, the parameters and constants in the index are set based on data of 97 TCs from 1990 to 2009. The correlation coefficient of the index to loss value is 0.74 and passes confidence level of 99%, which means it is effective for rapid damage assessment.",
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"normalizedAbstract": "Disaster emergency response needs rapid estimation on disaster loss. In China it is of great importance to develop a loss index for rapidly assessing tropical cyclone (TC) disaster loss. In this paper, a new composite loss index for TC landing on China is proposed after reviewing past methods. Firstly, correlation analysis is made between historical TC direct loss and variables reflecting time, space and intensity of TC. Secondly, a rapid loss index is developed which is composed of mean translation speed, duration on the land, mean radius of wind exceeding Beaufort force eight and maximum of maximum sustained wind speed, four variables selected stepwise in the former step, as well as two parameters and four constants. Finally, the parameters and constants in the index are set based on data of 97 TCs from 1990 to 2009. The correlation coefficient of the index to loss value is 0.74 and passes confidence level of 99%, which means it is effective for rapid damage assessment.",
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"Storms",
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"Indexes",
"Tropical Cyclones",
"Hurricanes",
"Wind Speed",
"Force",
"Economics",
"Typhoons",
"Tropical Cyclone",
"Disaster",
"Loss Index",
"Rapid Loss Assessment"
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"title": "Identification Method of Disaster-Bearing Body in Coastal Cities under Emergency Logistics Characteristics",
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"abstract": "In order to identify disaster-bearing bodies in coastal cities, this paper uses the mining association rule of Apriori algorithm to construct the identification method of damaged disaster-bearing bodies in coastal cities, takes the typhoon storm surge process and temperate storm surge process that caused disasters in China’s coastal cities from 1989 to 2018 as an example, and gives the minimum support threshold and the minimum confidence threshold of the Apriori association rule algorithm. The empirical results show that the damage victim identification method constructed in this paper is feasible and effective.",
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{
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"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)",
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"title": "Tropical Cyclone Maximum Wind Estimation from Infrared Satellite Data with Integrated Convolutional Neural Networks",
"normalizedTitle": "Tropical Cyclone Maximum Wind Estimation from Infrared Satellite Data with Integrated Convolutional Neural Networks",
"abstract": "Tropical cyclone (TC) maximum wind is an important parameter for estimating TC risks such as wind potential damage and storm surge. Previous work has shown that the estimation of TC maximum wind through a series of empirical rules based on the cloud characteristics shown in the satellite cloud image. Deep learning like convolutional neural networks (CNNs) has this ability of extracting and understanding these cloud features like the eye, the spiral rainbands that closely associated with its maximum wind. However, CNNs are used for object recognition and classification, CNS has less application in regression. We proposed an integrated architecture based on Convolutional Neural Network for the estimation of the TC maximum wind with higher accuracy. More specifically, it includes input layer, convolutional layers, activation functions and pooling layers for training and capturing non-linear relationships between cloud image and its wind, and a fully connection for the estimation task. We evaluate the state of the art for regression between infrared image and its TC maximum wind, discussing the necessity of different components. It demonstrates an improvement on the ability to estimate the TC intensity.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Tropical cyclone (TC) maximum wind is an important parameter for estimating TC risks such as wind potential damage and storm surge. Previous work has shown that the estimation of TC maximum wind through a series of empirical rules based on the cloud characteristics shown in the satellite cloud image. Deep learning like convolutional neural networks (CNNs) has this ability of extracting and understanding these cloud features like the eye, the spiral rainbands that closely associated with its maximum wind. However, CNNs are used for object recognition and classification, CNS has less application in regression. We proposed an integrated architecture based on Convolutional Neural Network for the estimation of the TC maximum wind with higher accuracy. More specifically, it includes input layer, convolutional layers, activation functions and pooling layers for training and capturing non-linear relationships between cloud image and its wind, and a fully connection for the estimation task. We evaluate the state of the art for regression between infrared image and its TC maximum wind, discussing the necessity of different components. It demonstrates an improvement on the ability to estimate the TC intensity.",
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"normalizedAbstract": "Tropical cyclone (TC) maximum wind is an important parameter for estimating TC risks such as wind potential damage and storm surge. Previous work has shown that the estimation of TC maximum wind through a series of empirical rules based on the cloud characteristics shown in the satellite cloud image. Deep learning like convolutional neural networks (CNNs) has this ability of extracting and understanding these cloud features like the eye, the spiral rainbands that closely associated with its maximum wind. However, CNNs are used for object recognition and classification, CNS has less application in regression. We proposed an integrated architecture based on Convolutional Neural Network for the estimation of the TC maximum wind with higher accuracy. More specifically, it includes input layer, convolutional layers, activation functions and pooling layers for training and capturing non-linear relationships between cloud image and its wind, and a fully connection for the estimation task. We evaluate the state of the art for regression between infrared image and its TC maximum wind, discussing the necessity of different components. It demonstrates an improvement on the ability to estimate the TC intensity.",
"fno": "298000a575",
"keywords": [
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"Infrared Imaging",
"Learning Artificial Intelligence",
"Neural Nets",
"Object Recognition",
"Storms",
"Wind",
"Wind Potential Damage",
"Storm Surge",
"TC Maximum Wind",
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"authors": [
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"affiliation": "Nanjing University of Information Science and Technology",
"fullName": "Wei Tian",
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{
"affiliation": "Nanjing University of Information Science and Technology",
"fullName": "Wei Huangwei",
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"affiliation": "Nanjing University of Information Science and Technology",
"fullName": "Chao Wang",
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"abstract": "Advances in compute power, sensor technology, and machine learning have facilitated a plethora of assistive and personal agents. These agents are poised to make our life more efficient, safer, feature rich, and more enjoyable. With so much activity in this area, there has been a lot of progress developing algorithms for localization, path planning, path guiding, and obstacle avoidance. Similarly, numerous frameworks for human computer interaction, obstacle recognition, object tracking, and advanced reasoning have been introduced. This research introduces a navigation stack written in Python using the Robot Operating System for modular indoor agent development. The localization system makes use of deep learning and particle filters and is easily trained to localize in new environments. The obstacle avoidance system can be changed to reflect the agents size, required safety margin, sensor properties and behavior. Different path planning algorithms can be substituted and used in the path guiding system. The created navigation stack was tested on an assistive technology wheelchair, exhibiting state of the art localization, collision avoidance, and navigation in complex scenarios.",
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"abstract": "Recent progress in 3D display technologies has raised the demand for stylized 3D digital content. Previous approaches either perform style transfer on stereoscopic image pairs or reconstruct 3D environment with multiple view images. In this paper, we propose a novel view stylization framework that can convert a single 2D image into multiple stylized views. It is a two-stage solution that contains view synthesis and neural style transfer. We estimate dense optical flow between the source and novel views so that the style transfer model can produce consistent results. Experimental results show that our method significantly improves the consistency among views compared to the baseline method.",
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"normalizedAbstract": "In this work, we propose a novel framework for synthesizing the stylization of text-based images. It is composed of several steps without supervision. Initially, the style image is segmented to foreground and background images. Then, the main color of foreground is accumulated and assigned to the stroke-based binarized geometric shape like text, symbols and icons to be content image. The foreground image is considered as the target style and transferred to the content image by image style transfer neural network. Meanwhile, the blank space in segmented background image is filled by image inpainting-based partial convolution neural network. Finally, the composition of the stylized geometric shape and the complete background image is accomplished by texture synthesis. The features of edge, color, texture, etc., are explored for the stylization of multiple geometric shapes. It explains the rationality of our proposed method on binarized geometric shape style transfer. The extensive experiments on various tasks, such as visual-textual presentation synthesis, icon/symbol rendering and structure-guided image inpainting, demonstrate the effectiveness of the proposed method.",
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"affiliation": "Wuhan University of Technology",
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"abstract": "Artistic style transfer aims at migrating the style from an example image to a content image. Currently, optimization-based methods have achieved great stylization quality, but expensive time cost restricts their practical applications. Meanwhile, feed-forward methods still fail to synthesize complex style, especially when holistic global and local patterns exist. Inspired by the common painting process of drawing a draft and revising the details, we introduce a novel feed-forward method named Laplacian Pyramid Network (LapStyle). LapStyle first transfers global style patterns in low-resolution via a Drafting Network. It then revises the local details in high-resolution via a Revision Network, which hallucinates a residual image according to the draft and the image textures extracted by Laplacian filtering. Higher resolution details can be easily generated by stacking Revision Networks with multiple Laplacian pyramid levels. The final stylized image is obtained by aggregating outputs of all pyramid levels. Experiments demonstrate that our method can synthesize high quality stylized images in real time, where holistic style patterns are properly transferred.",
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"content": "Artistic style transfer aims at migrating the style from an example image to a content image. Currently, optimization-based methods have achieved great stylization quality, but expensive time cost restricts their practical applications. Meanwhile, feed-forward methods still fail to synthesize complex style, especially when holistic global and local patterns exist. Inspired by the common painting process of drawing a draft and revising the details, we introduce a novel feed-forward method named Laplacian Pyramid Network (LapStyle). LapStyle first transfers global style patterns in low-resolution via a Drafting Network. It then revises the local details in high-resolution via a Revision Network, which hallucinates a residual image according to the draft and the image textures extracted by Laplacian filtering. Higher resolution details can be easily generated by stacking Revision Networks with multiple Laplacian pyramid levels. The final stylized image is obtained by aggregating outputs of all pyramid levels. Experiments demonstrate that our method can synthesize high quality stylized images in real time, where holistic style patterns are properly transferred.",
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"abstract": "We present an analytics-based framework for simultaneous visualization of large surface data collections arising in clinical neuroimaging studies. Termed Informatics Visualization for Neuroimaging (INVIZIAN), this framework allows the visualization of both cortical surfaces characteristics and feature relatedness in unison. It also uses dimension reduction methods to derive new coordinate systems using a Jensen-Shannon divergence metric for positioning cortical surfaces in a metric space such that the proximity in location is proportional to neuroanatomical similarity. Feature data such as thickness and volume are colored on the cortical surfaces and used to display both subject-specific feature values and global trends within the population. Additionally, a query-based framework allows the neuroscience researcher to investigate probable correlations between neuroanatomical and subject patient attribute values such as age and diagnosis.",
"abstracts": [
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"content": "We present an analytics-based framework for simultaneous visualization of large surface data collections arising in clinical neuroimaging studies. Termed Informatics Visualization for Neuroimaging (INVIZIAN), this framework allows the visualization of both cortical surfaces characteristics and feature relatedness in unison. It also uses dimension reduction methods to derive new coordinate systems using a Jensen-Shannon divergence metric for positioning cortical surfaces in a metric space such that the proximity in location is proportional to neuroanatomical similarity. Feature data such as thickness and volume are colored on the cortical surfaces and used to display both subject-specific feature values and global trends within the population. Additionally, a query-based framework allows the neuroscience researcher to investigate probable correlations between neuroanatomical and subject patient attribute values such as age and diagnosis.",
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"abstract": "Head-worn devices with a narrow field of view are common commodity for Augmented Reality. However, their limited screen space makes view management difficult. Especially in dense information spaces this potentially leads to visual conflicts such as overlapping labels (occlusion) and visual clutter. In this paper, we look into the potential of using audio and vibrotactile feedback to guide search and information localization. Our results indicate users can be guided with high accuracy using audio-tactile feedback with maximum median deviations of only 2° on longitude, 3.6° on latitude and 0.07 meter in depth. Regarding the encoding of latitude we found a superior performance when using audio, resulting in an improvement of 61% and fastest search times. When interpreting localization cues the maximum median deviation was 9.9° on longitude and 18% of a selected distance to be encoded which could be reduced to 14% when using audio.",
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"abstract": "Selection of moving targets is a common, yet complex task in human-computer interaction (HCI) and virtual reality (VR). Predicting user intention may be beneficial to address the challenges inherent in interaction techniques for moving-target selection. This article extends previous models by integrating relative head-target and hand-target features to predict intended moving targets. The features are calculated in a time window ending at roughly two-thirds of the total target selection time and evaluated using decision trees. With two targets, this model is able to predict user choice with up to ∼ 72% accuracy on general moving-target selection tasks and up to ∼ 78% by also including task-related target properties.",
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"affiliation": "Iowa State University Arts et Metiers ParisTech",
"fullName": "Juan Sebastian Casallas",
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"abstract": "Ray-casting, i.e., a ray cast from a hand-held controller to select targets, is widely used in 3D environments. Inspired by the bubble cursor [12] which dynamically resizes its selection range on 2D surfaces, we investigate a bubble mechanism for ray-casting in virtual reality. Bubble mechanism identifies the target nearest to the ray, with which users do not have to accurately shoot through the target. We first design the criterion of selection and the visual feedback of the bubble. We then conduct two experiments to evaluate ray-casting techniques with bubble mechanism in both simple and complicated 3D target acquisition tasks. Results show the bubble mechanism significantly improves ray-casting on both performance and preference, and our Bubble Ray technique with angular distance definition is competitive compared with other target acquisition techniques. We also discuss potential improvements to show more practical implementations of ray-casting with bubble mechanism.",
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"affiliation": "Tsinghua University Key Laboratory of Pervasive Computing, Ministry of Education,Department of Computer Science and Technology,China",
"fullName": "Yiqin Lu",
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"affiliation": "Tsinghua University Key Laboratory of Pervasive Computing, Ministry of Education,Department of Computer Science and Technology,China",
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"affiliation": "Tsinghua University Key Laboratory of Pervasive Computing, Ministry of Education,Department of Computer Science and Technology,China",
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"abstract": "Online dense reconstruction is a major task of Augmented Reality (AR) applications, especially for realistic interactions like collisions and occlusions. Monocular cameras are most widely used on AR equipment, however existing monocular dense reconstruction methods have poor performance in practical applications due to the lack of true depth. This paper presents an online monocular dense reconstruction framework using learned depth, which overcomes the inherent difficulties of reconstruction for low-texture regions or pure rotational motions. Firstly, we design a depth prediction network combined with an adaptive loss, so that our network can be extended to train on mixed datasets with various intrinsic parameters. Then we loosely combine depth prediction with monocular SLAM and frame-wise point cloud fusion to build a dense 3D model of the scene. Experiments validate that our depth prediction from a single view reaches a state-of-the-art accuracy on different benchmarks, and the proposed framework can reconstruct smooth, surface-clear and dense models on various scenes with dedicated point cloud fusion scheme. Furthermore, collision and occlusion detection are tested using our dense model in an AR application, which demonstrates that the proposed framework is particularly suitable for AR scenarios. Our code will be publicly available along with our indoor RGB-D dataset at: https://github.com/NetEaseAI-CVLab/CNN-MonoFusion.",
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