2019 IEEE Visualization Conference (VIS)最新文献_第4页

Toward Interface Defaults for Vague Modifiers in Natural Language Interfaces for Visual Analysis 面向可视化分析的自然语言界面中模糊修饰符的界面默认值

2019 IEEE Visualization Conference (VIS) Pub Date : 2019-10-01 DOI: 10.1109/VISUAL.2019.8933569

Marti A. Hearst, Melanie Tory, V. Setlur

引用次数: 20

Visual Inspection of DBS Efficacy DBS疗效的目视检查

2019 IEEE Visualization Conference (VIS) Pub Date : 2019-10-01 DOI: 10.1109/VISUAL.2019.8933720

B. Hollister, Gordon Duffley, C. Butson, Chris R. Johnson, P. Rosen

{"title":"Visual Inspection of DBS Efficacy","authors":"B. Hollister, Gordon Duffley, C. Butson, Chris R. Johnson, P. Rosen","doi":"10.1109/VISUAL.2019.8933720","DOIUrl":"https://doi.org/10.1109/VISUAL.2019.8933720","url":null,"abstract":"At present, approximately ten million people worldwide are afflicted by Parkinson’s Disease (PD). One of the most promising therapies for PD is Deep Brain Stimulation (DBS). DBS works via stimulation of targeted central brain regions (nuclei), whose dysfunction is implicated in PD. A key problem with DBS is determining optimal parameters for clinical outcome. While multiple parameters may influence outcomes in DBS, we explore spatial correlation of volume of tissue activated (VTA) to Unified Parkinson’s Disease Rating Scale (UPDRS) scores. Using the Neurostimulation Uncertainty Viewer (nuView), we investigate a number of cooperative visualizations for DBS inspection. Surface-to-surface Euclidean distance between VTA and selected brain nuclei are used in a linked 3D and parallel coordinates view of patient outcome. We then present a semivariogram-based approach to measure spatial correlation of patient outcomes with VTA. As a third component, nuView provides a unique visualization of an ensemble of electrode placements to reduce clutter and emphasize electrodes with spatially similar VTA. These methods corroborate a spatial aspect to DBS efficacy.","PeriodicalId":192801,"journal":{"name":"2019 IEEE Visualization Conference (VIS)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122880026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

RuleVis: Constructing Patterns and Rules for Rule-Based Models 规则:为基于规则的模型构建模式和规则

2019 IEEE Visualization Conference (VIS) Pub Date : 2019-10-01 DOI: 10.1109/VISUAL.2019.8933596

David Abramov, Jasmine Otto, Mahika Dubey, Cassia Artanegara, Pierre Boutillier, W. Fontana, A. Forbes

引用次数: 1

Interactive Bicluster Aggregation in Bipartite Graphs 二部图中的交互双聚类聚集

2019 IEEE Visualization Conference (VIS) Pub Date : 2019-10-01 DOI: 10.1109/VISUAL.2019.8933546

Maoyuan Sun, D. Koop, Jian Zhao, Chris North, Naren Ramakrishnan

引用次数: 6

MissBiN: Visual Analysis of Missing Links in Bipartite Networks 二部网络中缺失链接的视觉分析

2019 IEEE Visualization Conference (VIS) Pub Date : 2019-10-01 DOI: 10.1109/VISUAL.2019.8933639

Jian Zhao, Maoyuan Sun, Francine Chen, Patrick Chiu

引用次数: 11

Toward a Design Space for Mitigating Cognitive Bias in Vis 减轻视觉认知偏差的设计空间

2019 IEEE Visualization Conference (VIS) Pub Date : 2019-10-01 DOI: 10.1109/VISUAL.2019.8933611

Emily Wall, J. Stasko, A. Endert

引用次数: 28

Interpreting Distortions in Dimensionality Reduction by Superimposing Neighbourhood Graphs 用邻域图叠加解释降维中的畸变

2019 IEEE Visualization Conference (VIS) Pub Date : 2019-09-20 DOI: 10.1109/VISUAL.2019.8933568

Benoît Colange, L. Vuillon, S. Lespinats, D. Dutykh

{"title":"Interpreting Distortions in Dimensionality Reduction by Superimposing Neighbourhood Graphs","authors":"Benoît Colange, L. Vuillon, S. Lespinats, D. Dutykh","doi":"10.1109/VISUAL.2019.8933568","DOIUrl":"https://doi.org/10.1109/VISUAL.2019.8933568","url":null,"abstract":"To perform visual data exploration, many dimensionality reduction methods have been developed. These tools allow data analysts to represent multidimensional data in a 2D or 3D space, while preserving as much relevant information as possible. Yet, they cannot preserve all structures simultaneously and they induce some unavoidable distortions. Hence, many criteria have been introduced to evaluate a map’s overall quality, mostly based on the preservation of neighbourhoods. Such global indicators are currently used to compare several maps, which helps to choose the most appropriate mapping method and its hyperparameters. However, those aggregated indicators tend to hide the local repartition of distortions. Thereby, they need to be supplemented by local evaluation to ensure correct interpretation of maps.In this paper, we describe a new method, called MING, for \"Map Interpretation using Neighbourhood Graphs\". It offers a graphical interpretation of pairs of map quality indicators, as well as local evaluation of the distortions. This is done by displaying on the map the nearest neighbours graphs computed in the data space and in the embedding. Shared and unshared edges exhibit reliable and unreliable neighbourhood information conveyed by the mapping. By this mean, analysts may determine whether proximity (or remoteness) of points on the map faithfully represents similarity (or dissimilarity) of original data, within the meaning of a chosen map quality criteria. We apply this approach to two pairs of widespread indicators: precision/recall and trustworthiness/continuity, chosen for their wide use in the community, which will allow an easy handling by users.","PeriodicalId":192801,"journal":{"name":"2019 IEEE Visualization Conference (VIS)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121258930","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

SANVis: Visual Analytics for Understanding Self-Attention Networks SANVis:理解自我注意网络的可视化分析

2019 IEEE Visualization Conference (VIS) Pub Date : 2019-09-13 DOI: 10.1109/VISUAL.2019.8933677

Cheonbok Park, Inyoup Na, Yongjang Jo, Sungbok Shin, J. Yoo, B. Kwon, Jian Zhao, Hyungjong Noh, Yeonsoo Lee, J. Choo

{"title":"SANVis: Visual Analytics for Understanding Self-Attention Networks","authors":"Cheonbok Park, Inyoup Na, Yongjang Jo, Sungbok Shin, J. Yoo, B. Kwon, Jian Zhao, Hyungjong Noh, Yeonsoo Lee, J. Choo","doi":"10.1109/VISUAL.2019.8933677","DOIUrl":"https://doi.org/10.1109/VISUAL.2019.8933677","url":null,"abstract":"Attention networks, a deep neural network architecture inspired by humans’ attention mechanism, have seen significant success in image captioning, machine translation, and many other applications. Recently, they have been further evolved into an advanced approach called multi-head self-attention networks, which can encode a set of input vectors, e.g., word vectors in a sentence, into another set of vectors. Such encoding aims at simultaneously capturing diverse syntactic and semantic features within a set, each of which corresponds to a particular attention head, forming altogether multi-head attention. Meanwhile, the increased model complexity prevents users from easily understanding and manipulating the inner workings of models. To tackle the challenges, we present a visual analytics system called SANVis, which helps users understand the behaviors and the characteristics of multi-head self-attention networks. Using a state-of-the-art self-attention model called Transformer, we demonstrate usage scenarios of SANVis in machine translation tasks. Our system is available at http://short.sanvis.org.","PeriodicalId":192801,"journal":{"name":"2019 IEEE Visualization Conference (VIS)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121964640","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 25

Sociotechnical Considerations for Accessible Visualization Design 无障碍可视化设计的社会技术考虑

2019 IEEE Visualization Conference (VIS) Pub Date : 2019-09-11 DOI: 10.1109/VISUAL.2019.8933762

Alan Lundgard, Crystal Lee, Arvind Satyanarayan

{"title":"Sociotechnical Considerations for Accessible Visualization Design","authors":"Alan Lundgard, Crystal Lee, Arvind Satyanarayan","doi":"10.1109/VISUAL.2019.8933762","DOIUrl":"https://doi.org/10.1109/VISUAL.2019.8933762","url":null,"abstract":"Accessibility—the process of designing for people with disabilities (PWD)—is an important but under-explored challenge in the visualization research community. Without careful attention, and if PWD are not included as equal participants throughout the process, there is a danger of perpetuating a vision-first approach to accessible design that marginalizes the lived experience of disability (e.g., by creating overly simplistic \"sensory translations\" that map visual to non-visual modalities in a one-to-one fashion). In this paper, we present a set of sociotechnical considerations for research in accessible visualization design, drawing on literature in disability studies, tactile information systems, and participatory methods. We identify that using state-of-the-art technologies may introduce more barriers to access than they remove, and that expectations of research novelty may not produce outcomes well-aligned with the needs of disability communities. Instead, to promote a more inclusive design process, we emphasize the importance of clearly communicating goals, following existing accessibility guidelines, and treating PWD as equal participants who are compensated for their specialized skills. To illustrate how these considerations can be applied in practice, we discuss a case study of an inclusive design workshop held in collaboration with the Perkins School for the Blind.","PeriodicalId":192801,"journal":{"name":"2019 IEEE Visualization Conference (VIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122139433","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 46

Evaluating Alignment Approaches in Superimposed Time-Series and Temporal Event-Sequence Visualizations 评价叠加时间序列和时间事件序列可视化中的对齐方法

2019 IEEE Visualization Conference (VIS) Pub Date : 2019-08-20 DOI: 10.1109/VISUAL.2019.8933584

Yixuan Zhang, Sara Di Bartolomeo, Fangfang Sheng, H. Jimison, Cody Dunne

{"title":"Evaluating Alignment Approaches in Superimposed Time-Series and Temporal Event-Sequence Visualizations","authors":"Yixuan Zhang, Sara Di Bartolomeo, Fangfang Sheng, H. Jimison, Cody Dunne","doi":"10.1109/VISUAL.2019.8933584","DOIUrl":"https://doi.org/10.1109/VISUAL.2019.8933584","url":null,"abstract":"Composite temporal event sequence visualizations have included sentinel event alignment techniques to cope with data volume and variety. Prior work has demonstrated the utility of using single-event alignment for understanding the precursor, co-occurring, and aftereffect events surrounding a sentinel event. However, the usefulness of single-event alignment has not been sufficiently evaluated in composite visualizations. Furthermore, recently proposed dual-event alignment techniques have not been empirically evaluated. In this work, we designed tasks around temporal event sequence and timing analysis and conducted a controlled experiment on Amazon Mechanical Turk to examine four sentinel event alignment approaches: no sentinel event alignment (NoAlign), single-event alignment (SingleAlign), dual-event alignment with left justification (DualLeft), and dual-event alignment with stretch justification (DualStretch). Differences between approaches were most pronounced with more rows of data. For understanding intermediate events between two sentinel events, dual-event alignment was the clear winner for correctness—71% vs. 18% for NoAlign and SingleAlign. For understanding the duration between two sentinel events, NoAlign was the clear winner: correctness—88% vs. 36% for DualStretch— completion time—55 seconds vs. 101 seconds for DualLeft—and error—1.5% vs. 8.4% for DualStretch. For understanding precursor and aftereffect events, there was no significant difference among approaches. A free copy of this paper, the evaluation stimuli and data, and source code are available at osf.io/78fs5","PeriodicalId":192801,"journal":{"name":"2019 IEEE Visualization Conference (VIS)","volume":"13 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126083430","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6