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International conference on data Science and Advanced Analytics (DSAA)

作者： Lorenzo De Stefani Leonhard F. Spiegelberg Eli Upfal Tim Kraska Department of Computer Science Brown University Providence United States of America CSAIL Massachusetts Institute of Technology Cambridge United States of America

Recently, there have been several proposals to develop visual recommendation systems. The most advanced systems aim to recommend visualizations, which help users to find new correlations or identify an interesting deviation based on the current context of the user's analysis. However, when recommending a visualization to a user, there is an inherent risk to visualize random fluctuations rather than solely true patterns: a problem largely ignored by current techniques. In this paper, we present VizCertify, a novel framework to improve the performance of visual recommendation systems by quantifying the statistical significance of recommended visualizations. The proposed methodology allows to control the probability of misleading visual recommendations using both classical statistical testing procedures and a novel application of the Vapnik Chervonenkis (VC) dimension towards visualization recommendation which results in an effective criterion to decide whether a recommendation corresponds to a true phenomenon or not.

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TranSeVis: A visual Analytics System for Transportation data Sensing and exploration 1

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15th International conference on Cooperative Design, visualization, and Engineering (CDVE)

作者： Gong, Rui Teng, Zhiyao Han, Mei Wei, Lirui Zhang, Yuwei Pu, Jiansu Univ Elect Sci & Technol China Big Data Res Ctr Web Sci Ctr CompleX Lab Chengdu 611731 Sichuan Peoples R China

ISBN: (数字)9783030005603

ISBN: (纸本)9783030005603;9783030005597

With increasing availability of location-acquisition technologies, huge volumes of data tracking transportation system have been collected. These data are highly valuable for unveiling human mobility patterns, transportation system utilization, and urban planning. However, it is still highly challenging to visualize and explore transportation data. In this paper, an interactive visual analytic system, TranSeVis has been proposed. It has two visualization modules, one named region view provides geographical information and effective temporal information comparison, the other named road view provides detailed visual analysis of mobility factors along routes or congestions spots. Besides, two case studies have been used to evaluate the visualization techniques and real-world taxi data sets have been used to demonstrate TranSeVis. Based on the results, TranSeVis offers transportation researchers an easy-to-use, efficient, and scalable platform to visualize and explore transportation data.

关键词： visual analysis Urban traffic Congestion analysis Spatio-temporal data

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Interactive visual data exploration with Subjective Feedback: An Information-Theoretic Approach 34

Interactive Visual Data Exploration with Subjective Feedback...

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34th IEEE International conference on data Engineering Workshops (ICDEW)

作者： Puolamaki, Kai Oikarinen, Emilia Kang, Bo Lijffijt, Jefrey De Bie, Tijl Aalto Univ Dept Comp Sci Espoo Finland Finnish Inst Occupat Hlth Helsinki Finland Univ Ghent IDLab Dept Elect & Informat Syst Ghent Belgium

ISBN: (纸本)9781538655207

The exploration of high-dimensional real-valued data is one of the fundamental exploratory data analysis (EDA) tasks. Existing methods use predefined criteria for the representation of data. There is a lack of methods eliciting the user's knowledge from the data and showing patterns the user does not know yet. We provide a theoretical model where the user can input the patterns she has learned as knowledge. The background knowledge is used to find a MaxEnt distribution of the data, and the user is shown maximally informative projections in which the MaxEnt distribution and the data differ the most. We provide an interactive open source EDA system, study its performance, and present use cases on real data.

关键词： Principal component analysis data visualization Task analysis Computational modeling Matrix decomposition data analysis data models

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19th International conference on Web Engineering, ICWE 2019

19th International Conference on Web Engineering, ICWE 2019

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19th International conference on Web Engineering, ICWE 2019

ISBN: (纸本)9783030192730

The proceedings contain 50 papers. The special focus in this conference is on Web Engineering. The topics include: Supervised group embedding for rumor detection in social media;fast incremental pagerank on dynamic networks;crowdsourced time-sync video recommendation via semantic-aware neural collaborative filtering;on Twitter bots behaving badly: Empirical study of code patterns on GitHub;CrowDIY: How to design and adapt collaborative crowdsourcing workflows under budget constraints;finding baby mothers on Twitter;an end-user pipeline for scraping and visualizing semi-structured data over the web;DotCHA: A 3D text-based scatter-type CAPTCHA;entropy and compression based analysis of web user interfaces;Augmenting LOD-based recommender systems using graph centrality measures;domain classifier: Compromised machines versus malicious registrations;the “game hack” scam;decentralized service registry and discovery in P2P networks using blockchain technology;amalgam: Hardware hacking for web developers with style (sheets);Jekyll RDF: Template-based linked data publication with minimized effort and maximum scalability;on the web platform cornucopia;Linked USDL extension for cloud services description;an automatic data service generation approach for cross-origin datasets;Merging intelligent API responses using a proportional representation approach;analyzing the evolution of linked vocabularies;ST-sem: A multimodal method for points-of-interest classification using street-level imagery;Comparison matrices of semantic RESTful APIs technologies;dragon: Decision tree learning for link discovery;catch & release: An approach to debugging distributed full-stack javascript applications;multi-device adaptation with liquid media queries;conversational data exploration;distributed intelligent client-centric personalisation;webifying heterogenous internet of things devices;VR-powered scenario-based testing for visual and acoustic web of things services.

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Region of interest generation algorithms for eye tracking data 3

Region of interest generation algorithms for eye tracking da...

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conference on Eye Tracking and visualization (ETVIS)

作者： Fuhl, Wolfgang Kuebler, Thomas Brinkmann, Hanna Rosenberg, Raphael Rosenstiel, Wolfgang Kasneci, Enkelejda Univ Tubingen Tubingen Baden Wuerttemb Germany Univ Vienna Vienna Austria

ISBN: (纸本)9781450357876

Using human fixation behavior, we can interfere regions that require to be processed at high resolution and where stronger compression can be favored. Analyzing the visual scan path solely based on a predefined set of regions of interest (ROIs) limits the exploration room of the analysis. Insights can only be gained for those regions that the data analyst considered worthy of labeling. Furthermore, visual exploration is naturally time-dependent: A short initial overview phase may be followed by an in-depth analysis of regions that attracted the most attention. Therefore, the shape and size of regions of interest may change over time. Automatic ROI generation can help in automatically reshaping the ROIs to the data of a time slice. We developed three novel methods for automatic ROI generation and show their applicability to different eye tracking data sets. The methods are publicly available as part of the EyeTrace software at http://***/***

关键词： ROI generation AOI generation Eye tracking analysis visualization Expertise classification Abstract art

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Big data Landscapes: Improving the visualization of Machine Learning-based Clustering Algorithms 18

Big Data Landscapes: Improving the Visualization of Machine ...

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14th International conference on Advanced visual Interfaces (AVI)

作者： Kammer, Dietrich Keck, Mandy Gruender, Thomas Groh, Rainer Tech Univ Dresden D-01187 Dresden Germany

ISBN: (纸本)9781450356169

With the internet, massively heterogeneous data sources need to be understood and classified to provide suitable services to users such as content observation, data exploration, e-commerce, or adaptive learning environments. The key to providing these services is applying machine learning (ML) in order to generate structures via clustering and classification. Due to the intricate processes involved in ML, visual tools are needed to support designing and evaluating the ML pipelines. In this contribution, we propose a comprehensive tool that facilitates the analysis and design of ML-based clustering algorithms using multiple visualization features such as semantic zoom, glyphs, and histograms.

关键词： Big data Landscapes Machine Learning visualization Glyphs Clustering

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12th International conference on e-Learning and Games, Edutainment 2018

12th International Conference on e-Learning and Games, Eduta...

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12th International conference on e-Learning and Games, Edutainment 2018

ISBN: (纸本)9783030237110

The proceedings contain 56 papers. The special focus in this conference is on e-Learning and Games. The topics include: Naturally Interact with Mobile Virtual Reality by CAT;hand Pose Estimation Using Convolutional Neural Networks and Support Vector Regression;texture Image Segmentation Based on Stationary Directionlet Domain Probabilistic Graphical Model;using Face Recognition to Detect "Ghost Writer" Cheating in Examination;improved Modular Convolution Neural Network for Human Pose Estimation;humanoid Robot Control Based on Deep Learning;a Novel Feature-Based Pose Estimation Method for 3D Faces;A Combined Deep Learning and Semi-supervised Classification Algorithm for LS Area;static Gesture Recognition Method Based on 3D Human Hand Joints;a Plant Growing Game Based on Mobile Terminal and Embedded Technology;A WebRTC e-Learning System Based on Kurento Media Server;the Research on Serious Games in Social Skills Training for Children with Autism;hands on Work Game: Neuro-Pedagogical Method to Improve Math Fraction Teaching;analysis of College Students’ Employment, Unemployment and Enrollment with Self-Organizing Maps;a Study of Negative Emotion Regulation of College Students by Social Games Design;research on Mobile Learning System of Colleges and Universities;a Mobile Learning System with Multi-point Interaction;e-learning Rhythm Design: Case Study Using Fighting Games;developing an Augmented Reality Multiplayer Learning Game: Lessons Learned;Collecting visual Effect Linked data Using GWAP;interactive Web 3D Contents Development Framework Based on Linked data for Japanese History Education;the Dilemma and exploration of the Innovation of Internal Governance in Higher Education Institutions;a Motion-Driven System for Performing Art.

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BRCA-Pathway: a structural integration and visualization system of TCGA breast cancer data on KEGG pathways

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BMC BIOINFORMATICS 2018年第SUPPL 1期19卷 63-72页

作者： Kim, Inyoung Choi, Saemi Kim, Sun Seoul Natl Univ Dept Comp Sci & Engn 1 Gwanak Ro Seoul 08826 South Korea Seoul Natl Univ Interdisciplinary Program Bioinformat 1 Gwanak Ro Seoul 08826 South Korea Seoul Natl Univ Bioinformat Inst 1 Gwanak Ro Seoul 08826 South Korea

Background: Bioinformatics research for finding biological mechanisms can be done by analysis of transcriptome data with pathway based interpretation. Therefore, researchers have tried to develop tools to analyze transcriptome data with pathway based interpretation. Over the years, the amount of omics data has become huge, e.g., TCGA, and the data types to be analyzed have come in many varieties, including mutations, copy number variations, and transcriptome. We also need to consider a complex relationship with regulators of genes, particularly Transcription Factors(TF). However, there has not been a system for pathway based exploration and analysis of TCGA multi-omics data. In this reason, We have developed a web based system BRCA-Pathway to fulfill the need for pathway based analysis of TCGA multi-omics data. Results: BRCA-Pathway is a structured integration and visual exploration system of TCGA breast cancer data on KEGG pathways. For data integration, a relational database is designed and used to integrate multi-omics data of TCGA-BRCA, KEGG pathway data, Hallmark gene sets, transcription factors, driver genes, and PAM50 subtypes. For data exploration, multi-omics data such as SNV, CNV and gene expression can be visualized simultaneously in KEGG pathway maps, together with transcription factors-target genes (TF-TG) correlation and relationships among cancer driver genes. In addition, 'Pathways summary' and 'Oncoprint' with mutual exclusivity sort can be generated dynamically with a request by the user. data in BRCA-Pathway can be downloaded by REST API for further analysis. Conclusions: BRCA-Pathway helps researchers navigate omics data towards potentially important genes, regulators, and discover complex patterns involving mutations, CNV, and gene expression data of various patient groups in the biological pathway context. In addition, mutually exclusive genomic alteration patterns in a specific pathway can be generated. BRCA-Pathway can provide an integrative

关键词： TCGA breast cancer KEGG-Pathway Gene expression Mutation Copy number variation

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GraphVR: A Virtual Reality Tool for the exploration of Graphs with HTC Vive System 22

GraphVR: A Virtual Reality Tool for the Exploration of Graph...

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22nd International conference Information visualisation (IV) - Biomedical visualization, visualisation on Built and Rural Environments and Geometric Modelling and Imaging

作者： Capece, Nicola Erra, Ugo Grippa, Jari Univ Basilicata Dipartimento Matemat Informat & Econ Potenza Italy

ISBN: (纸本)9781538672020

This work examines the use of virtual reality to visualize and interact with three-dimensional graphs. We developed the design to be as natural and intuitive as possible, through analysis, study, and use of several layout algorithms, which allow the nodes of a graph to be positioned to reduce entropy levels. We chose to use this schematic visualization of graphs because it can describe the graphical data synthetically and the links between the data while presenting the data in a visual format. The application was developed entirely using Unreal Engine version 4, and the visualization was performed using the head-mounted display HTC Vive.

关键词： Virtual Reality Graph exploration Human Computer Interaction

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visual Enhancement Techniques For Underwater Images

Visual Enhancement Techniques For Underwater Images

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International conference on Advanced Computing and Communication Systems (ICACCS)

作者： Jaya Lakshmi K. Prasanti S. Kalpana EIE V.R. Siddhartha Engineering College

In the recent days, wide range of research has been carried out on visual enhancement of under images in submarine and military operations to discover submerged structural designing and sea floor exploration. But, diving in the deep ocean for a long time has increased the difficulties for analysis of underwater images. Further, other factors such as scattering resulting from presence of particles inside the water and blurring effects reduce the quality of images being captured by underwater optic camera. There are several algorithms have been introduced to improve the visual quality of deep water images. Therefore, in this project, a novel algorithm based on bidirectional Empirical Mode Decomposition (BEMD) to enhance the visual quality of the underwater images will be implemented and comparison of data with conventional enhancement technique will be illustrated. The implementation will be done using MATLAB software.

关键词： Histograms Image color analysis visualization Lighting Scattering Communication systems Empirical mode decomposition

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