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"abstract": "Extensive research has been carried out to luxuries the human life using robotic and artificial intelligence technologies while only few of them focus on improvising the social life of children with Autism Spectrum Disorder (ASD). Understanding the serious effects of Autism to the posterity, United Nations General Assembly has declared 2nd April as World Autism Awareness Day on 21st January 2008 [1]. Utmost expectation of any technology is to help the mankind. Hence, this paper discusses the possibilities of using Robotic tool for treating physical rehabilitation, Learning and Social interaction of Children diagnosed with ASD. This paper also discusses the psychological, physiological and social interaction difficulties faced by the subject with ASD.",
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"abstract": "Autism Spectrum Disorder (ASD) is a neural development disorder characterized by specific patterns of behavioral and social difficulties. Beyond these core symptoms, additional problems such as absence of gender differences identification, interactional distortions of environmental and family responses are often present. Taking into account these emotional and behavioral problems researchers and clinicians are focusing on the design of innovative therapeutic approaches aimed to improve social capabilities of subjects with ASD. Thanks to the technological and scientific progresses of the last years, nowadays it is possible to create human-like robots with social and emotional capabilities. Furthermore it is also possible to analyze physiological signals inferring subjects' psycho-physiological state which can be compared with a behavioral analysis in order to obtain a deeper understanding of subjects reactions to treatments. In this work a preliminary evaluation of an innovative social robot-based treatment for subjects with ASD is described. The treatment consists in a complex stimulation and acquisition platform composed of a social robot, a multi-parametric acquisition system and a therapeutic protocol. During the preliminary tests of the treatment the subject's physiological signals and behavioral parameters have been recorded and used together with the therapists' annotations to infer the subjects' induced reactions. Physiological signals were analyzed and statistically evaluated demonstrating the possibility to correctly discern the two groups (ASD and normally developing subjects) with a classification percentage higher than 92%. Statistical analysis also highlighted the treatment capability to induce different affective states in subjects with ASDs more than in control subjects, demonstrating that the treatment is well designed and tuned on ASDs deficits and behavioral lacks.",
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"abstract": "The Kullback information criterion (KIC) (Cavanaugh, J.E., Statistics and Probability Letters, vol.42, p.333-43, 1999) and the bias corrected version, KICc, (Seghouane, A.-K. et al., Proc. ICASSP, p.145-8, 2003) are two methods for statistical model selection of regression variables and autoregressive models. Both criteria may be viewed as estimators of the Kullback symmetric divergence between the true model and the fitted approximating model. The bias of KIC and KICc is studied in the underfitting case, where none of the candidate models includes the true model. Here, only normal linear regression models are considered, where an exact expression of the bias is obtained for KIC and KICc. The bias of KICc is often smaller, in most cases drastically smaller, than KIC. A simulation study, in which the true model is of infinite order polynomial expansion, shows that, in small and moderate sample size, KICc provides a better model selection than KIC. Furthermore KICc outperforms the two well-known criteria, AIC and MDL.",
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"abstract": "Abstract: A major challenge for performing global query optimization in a multidatabase system (MDBS) is the lack of cost models for local database systems at the global level. The authors present a statistical procedure based on multiple regression analysis for building cost models for local database systems in an MDBS. Explanatory variables that can be included in a regression model are identified and a mixed forward and backward method for selecting significant explanatory variables is presented. Measures for developing useful regression cost models, such as removing outliers, eliminating multicollinearity, validating regression model assumptions, and checking significance of regression models, are discussed. Experimental results demonstrate that the presented statistical procedure can develop useful local cost models in an MDBS.",
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"content": "Abstract: A major challenge for performing global query optimization in a multidatabase system (MDBS) is the lack of cost models for local database systems at the global level. The authors present a statistical procedure based on multiple regression analysis for building cost models for local database systems in an MDBS. Explanatory variables that can be included in a regression model are identified and a mixed forward and backward method for selecting significant explanatory variables is presented. Measures for developing useful regression cost models, such as removing outliers, eliminating multicollinearity, validating regression model assumptions, and checking significance of regression models, are discussed. Experimental results demonstrate that the presented statistical procedure can develop useful local cost models in an MDBS.",
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"affiliation": "Dept. of Comput. & Inf. Sci., Michigan Univ., Dearborn, MI, USA",
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"title": "Lighting energy consumption estimation models for a library building with different lighting scenarios",
"normalizedTitle": "Lighting energy consumption estimation models for a library building with different lighting scenarios",
"abstract": "Lighting is one of the major factors that accounts for the increase in global electricity consumption. One way to reduce electricity consumption is to implement energy efficient lighting usage. This paper presents a regression-based lighting energy consumption estimation models for a public library building in Sengkurong, Brunei Darussalam with artificial and mixed (artificial and natural) lightings. If this library adopts mixed lighting, about 14% of the mean lighting energy consumption and the electricity cost due to lighting could be saved in 2020 rather than employing artificial lighting. The most influential explanatory variable of the lighting energy consumption estimation models for a library with artificial and mixed lightings was the interaction variable between floor area and illuminance. For both lighting scenarios in a library building, the multiple non-linear regression (MNLR) model (R<sup>2</sup> = 0.97 and RMSE = 562.2kWh for artificial lighting and R<sup>2</sup> =0.94 and RMSE =910.0kWh for mixed lighting) performed better with a more accurate lighting energy consumption estimation than the multiple linear regression (MLR) model (R<sup>2</sup> =0.89 and RMSE =1,043.9kWh for artificial lighting and R<sup>2</sup> = 0.85 and RMSE = 1,279.4kWh for mixed lighting).",
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"abstract": "The state-of-the-art literature has largely accepted multivariate imputation techniques such as multiple imputation using chained equations (MICE) [1], [2]. The MICE model is primarily proposed to impute missing values in cross-sectional data. However, the time varying nature of longitudinal data has not been fully modeled in traditional imputation methods [3], [4]. We have speculated two issues with MICE model and proposed a new approach to address these issues. First, the chained equations used in MICE are linear regression models. The performance of non-linear regression models has not been well sought in the literature. Second, the regression models of MICE may yield estimates of missing values that are out of bounds or range of normalization. This phenomenon eventually affects the overall imputation performance. In this study, we propose three methods for imputing longitudinal data: 1) an unsupervised method using piece-wise interpolation, 2) supervised non-linear regression models, and 3) an integration of the proposed unsupervised and supervised methods in a hybrid imputation pipeline.",
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"content": "The state-of-the-art literature has largely accepted multivariate imputation techniques such as multiple imputation using chained equations (MICE) [1], [2]. The MICE model is primarily proposed to impute missing values in cross-sectional data. However, the time varying nature of longitudinal data has not been fully modeled in traditional imputation methods [3], [4]. We have speculated two issues with MICE model and proposed a new approach to address these issues. First, the chained equations used in MICE are linear regression models. The performance of non-linear regression models has not been well sought in the literature. Second, the regression models of MICE may yield estimates of missing values that are out of bounds or range of normalization. This phenomenon eventually affects the overall imputation performance. In this study, we propose three methods for imputing longitudinal data: 1) an unsupervised method using piece-wise interpolation, 2) supervised non-linear regression models, and 3) an integration of the proposed unsupervised and supervised methods in a hybrid imputation pipeline.",
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"title": "An Experimental Study of Financial Portfolio Selection with Visual Analytics for Decision Support",
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"abstract": "We investigate the decision process as applied to the practical task of choosing a financial portfolio. We developed PortfolioCompare, an interactive visual analytic decision support tool that helps the consumer quickly create, compare and choose among several portfolios consisting of different financial instruments. PortfolioCompare facilitates the analysis of risk and return aspects of each portfolio considered. We investigate behavior in this task using an economic experiment in which the user actively creates and compares portfolios from a set of funds. We elicit risk preferences using a separate task and find that subjects using PortfolioCompare make decisions that are closer to their risk tolerance as compared to subjects presented with similar information in textual form. This finding suggests that PortfolioCompare helps understand risk aspects of portfolios. Portfolio selections are improved during the course of the decision process, suggesting that this tool is valuable for decision support.",
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"content": "We investigate the decision process as applied to the practical task of choosing a financial portfolio. We developed PortfolioCompare, an interactive visual analytic decision support tool that helps the consumer quickly create, compare and choose among several portfolios consisting of different financial instruments. PortfolioCompare facilitates the analysis of risk and return aspects of each portfolio considered. We investigate behavior in this task using an economic experiment in which the user actively creates and compares portfolios from a set of funds. We elicit risk preferences using a separate task and find that subjects using PortfolioCompare make decisions that are closer to their risk tolerance as compared to subjects presented with similar information in textual form. This finding suggests that PortfolioCompare helps understand risk aspects of portfolios. Portfolio selections are improved during the course of the decision process, suggesting that this tool is valuable for decision support.",
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"normalizedAbstract": "Anchoring effect is the tendency to focus too heavily on one piece of information when making decisions. In this paper, we present a novel, systematic study and resulting analyses that investigate the effects of anchoring effect on human decision-making using visual analytic systems. Visual analytics interfaces typically contain multiple views that present various aspects of information such as spatial, temporal, and categorical. These views are designed to present complex, heterogeneous data in accessible forms that aid decision-making. However, human decision-making is often hindered by the use of heuristics, or cognitive biases, such as anchoring effect. Anchoring effect can be triggered by the order in which information is presented or the magnitude of information presented. Through carefully designed laboratory experiments, we present evidence of anchoring effect in analysis with visual analytics interfaces when users are primed by representation of different pieces of information. We also describe detailed analyses of users’ interaction logs which reveal the impact of anchoring bias on the visual representation preferred and paths of analysis. We discuss implications for future research to possibly detect and alleviate anchoring bias.",
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"abstract": "Data-driven decision making has been a common task in today's big data era, from simple choices such as finding a fast way to drive home, to complex decisions on medical treatment. It is often supported by visual analytics. For various reasons (e.g., system failure, interrupted network, intentional information hiding, or bias), visual analytics for sensemaking of data involves missingness (e.g., data loss and incomplete analysis), which impacts human decisions. For example, missing data can cost a business millions of dollars, and failing to recognize key evidence can put an innocent person in jail. Being aware of missingness is critical to avoid such catastrophes. To fulfill this, as an initial step, we consider missingness in visual analytics from two aspects: data-centric and human-centric. The former emphasizes missingness in three data-related categories: data composition, data relationship, and data usage. The latter focuses on the human-perceived missingness at three levels: observed-level, inferred-level, and ignored-level. Based on them, we discuss possible roles of visualizations for handling missingness, and conclude our discussion with future research opportunities.",
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"content": "Data-driven decision making has been a common task in today's big data era, from simple choices such as finding a fast way to drive home, to complex decisions on medical treatment. It is often supported by visual analytics. For various reasons (e.g., system failure, interrupted network, intentional information hiding, or bias), visual analytics for sensemaking of data involves missingness (e.g., data loss and incomplete analysis), which impacts human decisions. For example, missing data can cost a business millions of dollars, and failing to recognize key evidence can put an innocent person in jail. Being aware of missingness is critical to avoid such catastrophes. To fulfill this, as an initial step, we consider missingness in visual analytics from two aspects: data-centric and human-centric. The former emphasizes missingness in three data-related categories: data composition, data relationship, and data usage. The latter focuses on the human-perceived missingness at three levels: observed-level, inferred-level, and ignored-level. Based on them, we discuss possible roles of visualizations for handling missingness, and conclude our discussion with future research opportunities.",
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"affiliation": "Northern Illinois University",
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"affiliation": "Purdue University",
"fullName": "Tianyi Li",
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"affiliation": "University of Waterloo",
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"affiliation": "Northern Illinois University",
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"affiliation": "University of Illinois Chicago",
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"abstract": "In this paper, we focus on training a deep neural network to in-paint and restore the historical painting of Dunhuang Grottoes. Dunhuang Grottoes is more than 1000 years old and the wall-painting on the grottoes has suffered from various deterioration. The ground truth does not exist either. Furthermore, learning the style of the artists is not straight forward because the wall-paintings are created by thousands of artists over more 400-500 years. As the very first attempt to solve this problem, we propose an end-to-end image restoration model for Dunhuang wall-painting. The end-to-end image restoration model employ U-net with partially convoluational layers to construct, which is capable in restoring non-rigid deteriorated content given a loss content mask and a wall-painting image. To learn the various artists style from real data, the training set and validation set are collected by using a zooming-in-like and random cropping approach on the digital RGB images photographed on the healthy Grotto-painting. We also synthesize the deteriorated paintings from real data. To ensure the synthetic content in the masked region is consistent to the ground truths in term of texture, colors, artistic style and free of unnecessary noises, the loss function is in a hybrid form that comprises transition variation loss, content loss and style loss. Our contributions are of three folds: 1) proposed using partial convolutional U-net in restoring wall-paintings; 2) the method is tested in restoring highly non-rigid and irregular deteriorated regions; 3) two types of masks are designed for simulating deteriorations and experimental results are satisfactory.",
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"content": "In this paper, we focus on training a deep neural network to in-paint and restore the historical painting of Dunhuang Grottoes. Dunhuang Grottoes is more than 1000 years old and the wall-painting on the grottoes has suffered from various deterioration. The ground truth does not exist either. Furthermore, learning the style of the artists is not straight forward because the wall-paintings are created by thousands of artists over more 400-500 years. As the very first attempt to solve this problem, we propose an end-to-end image restoration model for Dunhuang wall-painting. The end-to-end image restoration model employ U-net with partially convoluational layers to construct, which is capable in restoring non-rigid deteriorated content given a loss content mask and a wall-painting image. To learn the various artists style from real data, the training set and validation set are collected by using a zooming-in-like and random cropping approach on the digital RGB images photographed on the healthy Grotto-painting. We also synthesize the deteriorated paintings from real data. To ensure the synthetic content in the masked region is consistent to the ground truths in term of texture, colors, artistic style and free of unnecessary noises, the loss function is in a hybrid form that comprises transition variation loss, content loss and style loss. Our contributions are of three folds: 1) proposed using partial convolutional U-net in restoring wall-paintings; 2) the method is tested in restoring highly non-rigid and irregular deteriorated regions; 3) two types of masks are designed for simulating deteriorations and experimental results are satisfactory.",
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"content": "Text labeling for classification is a time-consuming and unintuitive process. Given an unannotated text collection, it is difficult for users to determine what label to create and how to label the initial training set for classification. Thus, we present an interactive visual analytics system for incremental text classification based on a semi-supervised topic modeling method, modified Gibbs sampling maximum entropy discrimination latent Dirichlet allocation (Gibbs MedLDA). Given a text collection, Gibbs MedLDA generates topics as a summary of the text collection. We design a scatter plot to display documents and topics simultaneously to show the topic information, and this helps users explore the text collection structurally and find labels for creating. After labeling documents, Gibbs MedLDA is applied to the text collection with labels again, and it generates both the topic and classification information. We also provide a scatter plot with the classifier boundary and a matrix view to present weights of classifiers. Users can iteratively label documents to refine each classifier. We evaluate our system via a user study with a benchmark corpus for text classification and case studies with two unannotated text collections.",
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"abstract": "Microblogging is a new trend to communicate and to disseminate information. One microblog post could potentially reach millions of users. Millions of microblogs are generated on a daily basis on popular sites such as Twitter. The popularity of microblogging among programmers, software engineers, and software users has also led to their use of microblogs to communicate software engineering issues apart from using emails and other traditional communication channels. Understanding how millions of users use microblogs in software engineering related activities would shed light on ways we could leverage the fast evolving microblogging content to aid software development efforts. In this work, we perform a preliminary study on what the software engineering community microblogs about. We analyze the content of microblogs from Twitter and categorize the types of microblogs that are posted. We investigate the relative popularity of each category of microblogs. We also investigate what kinds of microblogs are diffused more widely in the Twitter network via the “retweet” feature. Our experiments show that microblogs commonly contain job openings, news, questions and answers, or links to download new tools and code. We find that microblogs concerning real-world events are more widely diffused in the Twitter network.",
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"abstract": "Information propagation in a microblog network aims to identify a set of seed users for propagating a target message to as many interested users as possible. This problem differs from the traditional influence maximization in two major ways: it has a content-rich target message for propagation and it treats each link in the network as communication on certain topics and emphasizes the topic relevance of such communication in propagating the target message. In realistic situations, however, the topics associated with a link are not explicitly expressed but are hidden in the microblogs previously exchanged through the link. In this paper, we present a topic-aware solution to information propagation in a microblog network. We first model the latent topic structure of the network using observed microblog messages published in the network. We then present two methods for estimating the propagation probability based on the topic relevance between a link and the target message. Once the propagation probability is estimated, we adopt the standard greedy algorithm for influence maximization to find seed users. This approach is topic-aware in that the target message finds its way of propagation according to its topic relevance to the latent topic structure in the network. Experiments conducted on real Twitter datasets suggest that the proposed methods are able to select right seed users.",
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"content": "Information propagation in a microblog network aims to identify a set of seed users for propagating a target message to as many interested users as possible. This problem differs from the traditional influence maximization in two major ways: it has a content-rich target message for propagation and it treats each link in the network as communication on certain topics and emphasizes the topic relevance of such communication in propagating the target message. In realistic situations, however, the topics associated with a link are not explicitly expressed but are hidden in the microblogs previously exchanged through the link. In this paper, we present a topic-aware solution to information propagation in a microblog network. We first model the latent topic structure of the network using observed microblog messages published in the network. We then present two methods for estimating the propagation probability based on the topic relevance between a link and the target message. Once the propagation probability is estimated, we adopt the standard greedy algorithm for influence maximization to find seed users. This approach is topic-aware in that the target message finds its way of propagation according to its topic relevance to the latent topic structure in the network. Experiments conducted on real Twitter datasets suggest that the proposed methods are able to select right seed users.",
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"affiliation": "College of Computer Science, Zhejiang University, China",
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"abstract": "Microblog plays an important role in the dissemination of information now, especially on some sensitive topics. We established the propagation model of the microblog was constructed in this paper. The weak ties are used in the microblog network to obtain independent communities. We analyzed degree centrality, betweenness centrality and closeness centrality of microblog network. Various messages disseminate from different nodes with various feature which can be preset. The characteristics of some nodes in the information dissemination process become clearer according to the comparison among results after message dissemination. We analyzed the impacts of some nodes with information entropy during the information dissemination, which is so important to guide public opinion and maintain social stability.",
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"normalizedAbstract": "The integration of technology into autistic classrooms has shown promising results, including an increase in attention span, creativity, and social skills. We have introduced a low-cost learning technology composed by electronic modules called eBlocks to an autistic middle-school classroom. The participants of this study had the opportunity to learn concepts in the design and implementation of electronic systems by using the eBlocks. Our initial findings show that the integration of hands-on real world based projects, centered on the design of systems for a Smart House estimulated peer-to-peer interaction and teamwork, while promoting spontaneous creative thinking. We present our experiences with six students, including summaries of our overall experiences, teacher's pre-and post-surveys, and the examination of students' work.",
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"affiliation": "Department of Electrical and Computer Engineering, University of Arizona",
"fullName": "Jennifer Drain",
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"affiliation": "Department of Electrical and Computer Engineering, University of Arizona",
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"abstract": "Presents a novel block segmentation method for document images which can be applied to various document formats. Some documents have complicated column structures, in which some figure areas have no surrounding rectangles and others cut across text areas. In the approach presented, in order to segment documents into text and figure areas, the text areas are analyzed first, and the figure areas are then detected by analyzing information on the text areas. The overall process is as follows. First, character strings are merged into text groups by analyzing regularity in the text areas. Next, border lines of columns are detected by linking the edges of the text groups. After that, the whole page is segmented into small blocks according to the border lines. The blocks are then unified by using the column information, and some unified blocks are detected. Finally, a projection profile method is applied to the unified blocks in order to detect text areas and figure areas. This method was applied to 61 pages of Japanese technical papers and magazines, and 93.3% of the text areas and 93.2% of the figure areas were detected correctly.<>",
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"title": "2015 Second International Conference on eDemocracy & eGovernment (ICEDEG)",
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"abstract": "Nowadays the advance of technology enables innovation for democracy, government and governance [2]. E-Governance takes advantage of it and uses the Information and Communication Technologies (ICTs) [11,8,6], that allow a centric governance (Meier, 2012; Saxena, 2005). The emergence of E-Governance allows E-Government, E-Democracy and E-Business, among other digital services [7]. The use of ICTs in EGovernment boost citizens to act and promote the enhancement of civil rights and the access of information and knowledge society [5] allowing a better governance and improving the public services [13]. E-Government simplifies the user's access to the information, providing full service by using government applications that reduce operational costs [9]. A basic example of E-Government is shown in [8], where payment of parking tickets is done online using electronic means rather than physical ones. Local governments have been one of the major users of Geographic Information System (GIS) [4]. One of the uses of digital spatial information is the treatment of urban services and the finding of related solutions [12]. The transition between digital cities into smart cities needs 3D geospatial framework, including the integration of GIS, Global Position System (GPS) and Remote Sensing [4]. All of this information is provided to the citizens in services like websites or portals that offer mapping geospatial services [10]. Today humanity has a wide range of issues; to solve them we use information and different levels of knowledge. No matter what the issue is in real life, to solve a problem some kind of spatial information is used. Based on what was said before, people such us: directors, technicians, customers or just citizens need to know and manage this spatial information. But, why spatial data is special? Spatial is special because whit the advantage of technology all feature or phenomena could be represented by spatial location. Spatial data brings us measures of the feature you are looking for and gives this spatial reference of it. The term feature could be taken as service, location, place of interest, hot spot in different fields, markets influence, etc. In other words, we can represent the real world and the human activity with spatial information and show it on maps, charts or tables. All this knowledge of spatial data makes sense when decision makers or just citizens are able to use spatial information to get new knowledge and take better decisions; one example could something as simple as choosing the best route to get home, or something complex like the construction of a new route for public transportation in the down town. In order to better understand the facilities that GIS could offer it is necessary to know basic concepts of components and functions of GIS and the type and structure of spatial information. An important issue of spatial information is the existence of two types of formats that are widely spread: vectorial and raster data formats [1]. The first format is represented by vectors (points, lines and polygons); the second one is represented by pixels present in aerial photos and satellite images. Nowadays in general, the information depicted in maps is represented by a mixture of both formats. In this talk, we use open source software that allows to access and use spatial information; the software used for the practices are QGIS and GvSIG. This software could be downloaded for free from the Web because they have open source licenses. Finally, to understand concepts and management of spatial data some practices will be done by using one of the above applications mentioned.",
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"content": "Nowadays the advance of technology enables innovation for democracy, government and governance [2]. E-Governance takes advantage of it and uses the Information and Communication Technologies (ICTs) [11,8,6], that allow a centric governance (Meier, 2012; Saxena, 2005). The emergence of E-Governance allows E-Government, E-Democracy and E-Business, among other digital services [7]. The use of ICTs in EGovernment boost citizens to act and promote the enhancement of civil rights and the access of information and knowledge society [5] allowing a better governance and improving the public services [13]. E-Government simplifies the user's access to the information, providing full service by using government applications that reduce operational costs [9]. A basic example of E-Government is shown in [8], where payment of parking tickets is done online using electronic means rather than physical ones. Local governments have been one of the major users of Geographic Information System (GIS) [4]. One of the uses of digital spatial information is the treatment of urban services and the finding of related solutions [12]. The transition between digital cities into smart cities needs 3D geospatial framework, including the integration of GIS, Global Position System (GPS) and Remote Sensing [4]. All of this information is provided to the citizens in services like websites or portals that offer mapping geospatial services [10]. Today humanity has a wide range of issues; to solve them we use information and different levels of knowledge. No matter what the issue is in real life, to solve a problem some kind of spatial information is used. Based on what was said before, people such us: directors, technicians, customers or just citizens need to know and manage this spatial information. But, why spatial data is special? Spatial is special because whit the advantage of technology all feature or phenomena could be represented by spatial location. Spatial data brings us measures of the feature you are looking for and gives this spatial reference of it. The term feature could be taken as service, location, place of interest, hot spot in different fields, markets influence, etc. In other words, we can represent the real world and the human activity with spatial information and show it on maps, charts or tables. All this knowledge of spatial data makes sense when decision makers or just citizens are able to use spatial information to get new knowledge and take better decisions; one example could something as simple as choosing the best route to get home, or something complex like the construction of a new route for public transportation in the down town. In order to better understand the facilities that GIS could offer it is necessary to know basic concepts of components and functions of GIS and the type and structure of spatial information. An important issue of spatial information is the existence of two types of formats that are widely spread: vectorial and raster data formats [1]. The first format is represented by vectors (points, lines and polygons); the second one is represented by pixels present in aerial photos and satellite images. Nowadays in general, the information depicted in maps is represented by a mixture of both formats. In this talk, we use open source software that allows to access and use spatial information; the software used for the practices are QGIS and GvSIG. This software could be downloaded for free from the Web because they have open source licenses. Finally, to understand concepts and management of spatial data some practices will be done by using one of the above applications mentioned.",
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"abstract": "Privacy protection is a vital issue for safe social interactions within social networking sites (SNS). Although SNSs such as MySpace and Facebook allow users to configure their privacy settings, it is not a simple task for normal users with hundreds of online friends. In this paper, we propose an intelligent semantics-based privacy configuration system, named SPAC, to automatically recommend privacy settings for SNS users. SPAC learns users' privacy configuration patterns and make predictions by utilizing machine learning techniques on users' profiles and privacy setting history. To increase the accuracy of the predicted privacy settings, especially in the context of heterogeneous user profiles, we enhance privacy configuration predictor by integrating it with structured semantic knowledge in the SNS. This, in turn, allows SPAC to make inferences based on additional source of knowledge, resulting in improved accuracy of privacy recommendation. Our experimental results have proven the effectiveness of our approach.",
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"abstract": "With the rapid increase of scientific data volumes, interactive tools that enable effective visual representation for scientists are needed. This is critical when scientists are manipulating voluminous datasets and especially when they need to explore datasets interactively to develop their hypotheses. In this paper, we present an interactive visual analytics framework, GeoLens. GeoLens provides fast and expressive interactions with voluminous geo-spatial datasets. We provide an expressive visual query evaluation scheme to support advanced interactive visual analytics technique, such as brushing and linking. To achieve this, we designed and developed the Geohash based image tile generation algorithm that automatically adjusts the range of data to access based on the minimum acceptable size of the image tile. In addition, we have also designed an autonomous histogram generation algorithm that generates histograms of user-defined data subsets that do not have pre-computed data properties. Using our approach, applications can generate histograms of datasets containing millions of data points with sub-second latency. The work builds on our visual query coordinating scheme that evaluates geo-spatial query and orchestrates data aggregation in a distributed storage environment while preserving data locality and minimizing data movements. This paper includes empirical benchmarks of our framework encompassing a billion-file dataset published by the National Climactic Data Center.",
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"title": "A Simplex Hypergraph Clustering Method for Detecting Higher-order Modules in Microbial Network",
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"abstract": "Microbial interactions are of great importance for maintaining ecological balance and regulating human health. Most of the previous studies focus on the paired relationships and pay less attention to the higher-order interaction relationships in the microbial communities. The hypergraph was applied to establish higher-order interaction networks among microbes in microbial communities and the result of hypergraph clustering depends on hyperedge weights. So, we adopt simplex and take advantage of its volume for reconstructing each hyperedge weight to improve hypergraph clustering. We proposed a novel hypergraph clustering algorithm based on simplex (HCBS) here to detect the higher-order interaction modules in the network in a manner of clustering. The HCBS algorithm achieves the hyperedge weight from a unique higher-order relationship by calculating the joint contribution of all nodes in each hyperedge. The maximum modularity was utilized to optimize the clustering number of the hypergraph in the paper. The experimental results illustrate that the HCBS algorithm emphasis the differences of hyperedge weights and it is very effective in detecting microbial higher-order modules.",
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"content": "Microbial interactions are of great importance for maintaining ecological balance and regulating human health. Most of the previous studies focus on the paired relationships and pay less attention to the higher-order interaction relationships in the microbial communities. The hypergraph was applied to establish higher-order interaction networks among microbes in microbial communities and the result of hypergraph clustering depends on hyperedge weights. So, we adopt simplex and take advantage of its volume for reconstructing each hyperedge weight to improve hypergraph clustering. We proposed a novel hypergraph clustering algorithm based on simplex (HCBS) here to detect the higher-order interaction modules in the network in a manner of clustering. The HCBS algorithm achieves the hyperedge weight from a unique higher-order relationship by calculating the joint contribution of all nodes in each hyperedge. The maximum modularity was utilized to optimize the clustering number of the hypergraph in the paper. The experimental results illustrate that the HCBS algorithm emphasis the differences of hyperedge weights and it is very effective in detecting microbial higher-order modules.",
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"abstract": "The wide adoption of the RDF data model, as well as the Linked Open Data initiative, have made available large linked datasets that have the potential to offer invaluable knowledge. Accessing, evaluating and understanding these datasets as published, though, requires extensive training and experience in the field of the Semantic Web, making these valuable sources of information inaccessible to a wider audience. In the recent years, there have been many efforts to create systems that allow the visualization and exploration of this information. Some of there systems rely on techniques that allow them to limit the volume of the displayed information, by providing aggregated, filtered or summarized access to the datasets while others initialize the exploration of the dataset based on actions performed by the users, such as keyword searches and queries. The underlying technique is key for the sustainability of the system, the definition of the requirements that the input must comply with, the datasets that can be visualized as well as the visualization types provided. We present here a survey on these techniques, their strengths and weaknesses as well as the datasets that they can support. The survey will provide the reader with a deep understanding of the challenges regarding the visualization of large linked datasets, a categorization of the developed techniques to resolve them as well as an overview of the available systems and their functionalities.",
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"normalizedAbstract": "The wide adoption of the RDF data model, as well as the Linked Open Data initiative, have made available large linked datasets that have the potential to offer invaluable knowledge. Accessing, evaluating and understanding these datasets as published, though, requires extensive training and experience in the field of the Semantic Web, making these valuable sources of information inaccessible to a wider audience. In the recent years, there have been many efforts to create systems that allow the visualization and exploration of this information. Some of there systems rely on techniques that allow them to limit the volume of the displayed information, by providing aggregated, filtered or summarized access to the datasets while others initialize the exploration of the dataset based on actions performed by the users, such as keyword searches and queries. The underlying technique is key for the sustainability of the system, the definition of the requirements that the input must comply with, the datasets that can be visualized as well as the visualization types provided. We present here a survey on these techniques, their strengths and weaknesses as well as the datasets that they can support. The survey will provide the reader with a deep understanding of the challenges regarding the visualization of large linked datasets, a categorization of the developed techniques to resolve them as well as an overview of the available systems and their functionalities.",
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"abstract": "With the huge amount of data collected from the web, it is hard to manually analyze and extract useful incites from tables, matrices, or rows of data. Therefore, we need a way to represent these data (maps or graphs) to analyze/interpret them. Data visualization makes it easier to identify trends, patterns, and outliers with large datasets. Data Analytics is used to analyze raw data to make conclusions about the information. In this paper, we tackled web scrapping strategies to get COVID-19 data from the web, and then we went through some data analysis and exploration using EDA.",
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{
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"content": "With the huge amount of data collected from the web, it is hard to manually analyze and extract useful incites from tables, matrices, or rows of data. Therefore, we need a way to represent these data (maps or graphs) to analyze/interpret them. Data visualization makes it easier to identify trends, patterns, and outliers with large datasets. Data Analytics is used to analyze raw data to make conclusions about the information. In this paper, we tackled web scrapping strategies to get COVID-19 data from the web, and then we went through some data analysis and exploration using EDA.",
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"Data Visualisation",
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"Analysing COVID 19 Datasets",
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"Data Visualization",
"Data Analytics",
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"COVID 19 Data",
"Data Analysis",
"COVID 19",
"Knowledge Engineering",
"Data Analysis",
"Correlation",
"Decision Making",
"Data Visualization",
"Market Research",
"Object Tracking",
"Data Visualization",
"Patterns",
"Web Scraping",
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"title": "2011 IEEE Symposium on Biological Data Visualization (BioVis).",
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"title": "A visual analysis system for metabolomics data",
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"abstract": "When analyzing metabolomics data, cancer care researchers are searching for differences between known healthy samples and unhealthy samples. By analyzing and understanding these differences, researchers hope to identify cancer biomarkers. In this work we present a novel system that enables interactive comparative visualization and analysis of metabolomics data obtained by two-dimensional gas chromatography-mass spectrometry (GCxGC-MS). Our system allows the user to produce, and interactively explore, visualizations of multiple GCxGC-MS data sets, thereby allowing a user to discover differences and features in real time. Our system provides statistical support in the form of mean and standard deviation calculations to aid users in identifying meaningful differences between sample groups. We combine these with multiform, linked visualizations in order to provide researchers with a powerful new tool for GCxGC-MS exploration and bio-marker discovery.",
"abstracts": [
{
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"content": "When analyzing metabolomics data, cancer care researchers are searching for differences between known healthy samples and unhealthy samples. By analyzing and understanding these differences, researchers hope to identify cancer biomarkers. In this work we present a novel system that enables interactive comparative visualization and analysis of metabolomics data obtained by two-dimensional gas chromatography-mass spectrometry (GCxGC-MS). Our system allows the user to produce, and interactively explore, visualizations of multiple GCxGC-MS data sets, thereby allowing a user to discover differences and features in real time. Our system provides statistical support in the form of mean and standard deviation calculations to aid users in identifying meaningful differences between sample groups. We combine these with multiform, linked visualizations in order to provide researchers with a powerful new tool for GCxGC-MS exploration and bio-marker discovery.",
"__typename": "ArticleAbstractType"
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"normalizedAbstract": "When analyzing metabolomics data, cancer care researchers are searching for differences between known healthy samples and unhealthy samples. By analyzing and understanding these differences, researchers hope to identify cancer biomarkers. In this work we present a novel system that enables interactive comparative visualization and analysis of metabolomics data obtained by two-dimensional gas chromatography-mass spectrometry (GCxGC-MS). Our system allows the user to produce, and interactively explore, visualizations of multiple GCxGC-MS data sets, thereby allowing a user to discover differences and features in real time. Our system provides statistical support in the form of mean and standard deviation calculations to aid users in identifying meaningful differences between sample groups. We combine these with multiform, linked visualizations in order to provide researchers with a powerful new tool for GCxGC-MS exploration and bio-marker discovery.",
"fno": "071078livengoo",
"keywords": [
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"Biology Computing",
"Cancer",
"Chromatography",
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"Data Visualisation",
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"Cancer Care Researchers",
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"Mass Spectrometry",
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"G Cx GC MS Exploration",
"Image Color Analysis",
"Data Visualization",
"Lenses",
"Cancer",
"Visualization",
"Rendering Computer Graphics",
"Biomarkers",
"GCXGC MS",
"Metabolomics",
"Visual Analysis",
"TIC"
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"fullName": "P. Livengood",
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{
"affiliation": "Arizona State Univ., Tempe, AZ, USA",
"fullName": "R. Maciejewski",
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