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2020 IEEE Pacific Visualization Symposium (PacificVis)最新文献

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Uncertainty Treemaps 不确定性treemap
2020 IEEE Pacific Visualization Symposium (PacificVis) Pub Date : 2020-06-01 DOI: 10.1109/PacificVis48177.2020.7614
Max Sondag, Wouter Meulemans, C. Schulz, Kevin Verbeek, D. Weiskopf, B. Speckmann
{"title":"Uncertainty Treemaps","authors":"Max Sondag, Wouter Meulemans, C. Schulz, Kevin Verbeek, D. Weiskopf, B. Speckmann","doi":"10.1109/PacificVis48177.2020.7614","DOIUrl":"https://doi.org/10.1109/PacificVis48177.2020.7614","url":null,"abstract":"Rectangular treemaps visualize hierarchical numerical data by recursively partitioning an input rectangle into smaller rectangles whose areas match the data. Numerical data often has uncertainty associated with it. To visualize uncertainty in a rectangular treemap, we identify two conflicting key requirements: (i) to assess the data value of a node in the hierarchy, the area of its rectangle should directly match its data value, and (ii) to facilitate comparison between data and uncertainty, uncertainty should be encoded using the same visual variable as the data, that is, area. We present Uncertainty Treemaps, which meet both requirements simultaneously by introducing the concept of hierarchical uncertainty masks. First, we define a new cost function that measures the quality of Uncertainty Treemaps. Then, we show how to adapt existing treemapping algorithms to support uncertainty masks. Finally, we demonstrate the usefulness and quality of our technique through an expert review and a computational experiment on real-world datasets.","PeriodicalId":322092,"journal":{"name":"2020 IEEE Pacific Visualization Symposium (PacificVis)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122440849","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: 11
PacificVis 2020 Committees
2020 IEEE Pacific Visualization Symposium (PacificVis) Pub Date : 2020-06-01 DOI: 10.1109/pacificvis48177.2020.9086294
{"title":"PacificVis 2020 Committees","authors":"","doi":"10.1109/pacificvis48177.2020.9086294","DOIUrl":"https://doi.org/10.1109/pacificvis48177.2020.9086294","url":null,"abstract":"","PeriodicalId":322092,"journal":{"name":"2020 IEEE Pacific Visualization Symposium (PacificVis)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122062356","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: 0
PacificVis 2020 Executive Committee PacificVis 2020执行委员会
2020 IEEE Pacific Visualization Symposium (PacificVis) Pub Date : 2020-06-01 DOI: 10.1109/pacificvis48177.2020.9086191
{"title":"PacificVis 2020 Executive Committee","authors":"","doi":"10.1109/pacificvis48177.2020.9086191","DOIUrl":"https://doi.org/10.1109/pacificvis48177.2020.9086191","url":null,"abstract":"","PeriodicalId":322092,"journal":{"name":"2020 IEEE Pacific Visualization Symposium (PacificVis)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127789801","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: 0
Toward Feature-Preserving 2D and 3D Vector Field Compression 面向特征保持的二维和三维矢量场压缩
2020 IEEE Pacific Visualization Symposium (PacificVis) Pub Date : 2020-06-01 DOI: 10.1109/PacificVis48177.2020.6431
Xin Liang, Hanqi Guo, S. Di, F. Cappello, Mukund Raj, Chunhui Liu, K. Ono, Zizhong Chen, T. Peterka
{"title":"Toward Feature-Preserving 2D and 3D Vector Field Compression","authors":"Xin Liang, Hanqi Guo, S. Di, F. Cappello, Mukund Raj, Chunhui Liu, K. Ono, Zizhong Chen, T. Peterka","doi":"10.1109/PacificVis48177.2020.6431","DOIUrl":"https://doi.org/10.1109/PacificVis48177.2020.6431","url":null,"abstract":"The objective of this work is to develop error-bounded lossy compression methods to preserve topological features in 2D and 3D vector fields. Specifically, we explore the preservation of critical points in piecewise linear vector fields. We define the preservation of critical points as, without any false positive, false negative, or false type change in the decompressed data, (1) keeping each critical point in its original cell and (2) retaining the type of each critical point (e.g., saddle and attracting node). The key to our method is to adapt a vertex-wise error bound for each grid point and to compress input data together with the error bound field using a modified lossy compressor. Our compression algorithm can be also embarrassingly parallelized for large data handling and in situ processing. We benchmark our method by comparing it with existing lossy compressors in terms of false positive/negative/type rates, compression ratio, and various vector field visualizations with several scientific applications.","PeriodicalId":322092,"journal":{"name":"2020 IEEE Pacific Visualization Symposium (PacificVis)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125780418","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: 14
AutoCaption: An Approach to Generate Natural Language Description from Visualization Automatically 自动标题:一种从可视化中自动生成自然语言描述的方法
2020 IEEE Pacific Visualization Symposium (PacificVis) Pub Date : 2020-06-01 DOI: 10.1109/PacificVis48177.2020.1043
Can Liu, Liwenhan Xie, Yun Han, Datong Wei, Xiaoru Yuan
{"title":"AutoCaption: An Approach to Generate Natural Language Description from Visualization Automatically","authors":"Can Liu, Liwenhan Xie, Yun Han, Datong Wei, Xiaoru Yuan","doi":"10.1109/PacificVis48177.2020.1043","DOIUrl":"https://doi.org/10.1109/PacificVis48177.2020.1043","url":null,"abstract":"In this paper, we propose a novel approach to generate captions for visualization charts automatically. In the proposed method, visual marks and visual channels, together with the associated text information in the original charts, are first extracted and identified with a multilayer perceptron classifier. Meanwhile, data information can also be retrieved by parsing visual marks with extracted mapping relationships. Then a 1-D convolutional residual network is employed to analyze the relationship between visual elements, and recognize significant features of the visualization charts, with both data and visual information as input. In the final step, the full description of the visual charts can be generated through a template-based approach. The generated captions can effectively cover the main visual features of the visual charts and support major feature types in commons charts. We further demonstrate the effectiveness of our approach through several cases.","PeriodicalId":322092,"journal":{"name":"2020 IEEE Pacific Visualization Symposium (PacificVis)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125980729","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: 31
SCANViz: Interpreting the Symbol-Concept Association Captured by Deep Neural Networks through Visual Analytics SCANViz:通过视觉分析解释深度神经网络捕获的符号-概念关联
2020 IEEE Pacific Visualization Symposium (PacificVis) Pub Date : 2020-06-01 DOI: 10.1109/PacificVis48177.2020.3542
Junpeng Wang, Wei Zhang, Hao Yang
{"title":"SCANViz: Interpreting the Symbol-Concept Association Captured by Deep Neural Networks through Visual Analytics","authors":"Junpeng Wang, Wei Zhang, Hao Yang","doi":"10.1109/PacificVis48177.2020.3542","DOIUrl":"https://doi.org/10.1109/PacificVis48177.2020.3542","url":null,"abstract":"Two fundamental problems in machine learning are recognition and generation. Apart from the tremendous amount of research efforts devoted to these two problems individually, finding the association between them has attracted increasingly more attention recently. Symbol-Concept Association Network (SCAN) is one of the most popular models for this problem proposed by Google DeepMind lately, which integrates an unsupervised concept abstraction process and a supervised symbol-concept association process. Despite the outstanding performance of this deep neural network, interpreting and evaluating it remain challenging. Guided by the practical needs from deep learning experts, this paper proposes a visual analytics attempt, i.e., SCANViz, to address this challenge in the visual domain. Specifically, SCANViz evaluates the performance of SCAN through its power of recognition and generation, facilitates the exploration of the latent space derived from both the unsupervised extraction and supervised association processes, empowers interactive training of SCAN to interpret the model’s understanding on a particular visual concept. Through concrete case studies with multiple deep learning experts, we validate the effectiveness of SCANViz.","PeriodicalId":322092,"journal":{"name":"2020 IEEE Pacific Visualization Symposium (PacificVis)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130082818","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: 15
A Conference Paper Exploring System Based on Citing Motivation and Topic 基于引用动机和主题的会议论文检索系统
2020 IEEE Pacific Visualization Symposium (PacificVis) Pub Date : 2020-06-01 DOI: 10.1109/PacificVis48177.2020.1010
Taerin Yoon, Hyunwoo Han, Hyoji Ha, Juwon Hong, Kyungwon Lee
{"title":"A Conference Paper Exploring System Based on Citing Motivation and Topic","authors":"Taerin Yoon, Hyunwoo Han, Hyoji Ha, Juwon Hong, Kyungwon Lee","doi":"10.1109/PacificVis48177.2020.1010","DOIUrl":"https://doi.org/10.1109/PacificVis48177.2020.1010","url":null,"abstract":"Understanding and maintaining the intended meaning of original text used for citations is essential for unbiased and accurate scholarly work. To this end, this study aims to provide a visual system for exploring the citation relationships and motivations for citations within papers. For this purpose, papers from the IEEE Information Visualization Conference that introduce research on data visualization were collected, and based on the internal citation relationships, citation sentences were extracted and the text were analyzed. In addition, a visualization interface was provided to identify the citation relationships, citation pattern information, and citing motivation. Lastly, the pattern analysis of the citation relationships along with the citing motivation and topic was demonstrated through a case study. Our paper exploring system can confirm the purpose of specific papers being cited by other authors. Furthermore, the findings can help identify the characteristics of related studies based on the target papers.","PeriodicalId":322092,"journal":{"name":"2020 IEEE Pacific Visualization Symposium (PacificVis)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130704036","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: 5
PansyTree: Merging Multiple Hierarchies PansyTree:合并多个层次结构
2020 IEEE Pacific Visualization Symposium (PacificVis) Pub Date : 2020-06-01 DOI: 10.1109/PacificVis48177.2020.1007
Yu Dong, A. Fauth, M. Huang, Yi Chen, Jie Liang
{"title":"PansyTree: Merging Multiple Hierarchies","authors":"Yu Dong, A. Fauth, M. Huang, Yi Chen, Jie Liang","doi":"10.1109/PacificVis48177.2020.1007","DOIUrl":"https://doi.org/10.1109/PacificVis48177.2020.1007","url":null,"abstract":"Hierarchical structures are very common in the real world for recording all kinds of relational data generated in our daily life and business procedures. A very popular visualization method for displaying such data structures is called \"Tree\". So far, there are a variety of Tree visualization methods that have been proposed and most of them can only visualize one hierarchical dataset at a time. Hence, it raises the difficulty of comparison between two or more hierarchical datasets.In this paper, we proposed Pansy Tree which used a tree metaphor to visualize merged hierarchies. We design a unique icon named pansy to represent each merged node in the structure. Each Pansy is encoded by three colors mapping data items from three different datasets in the same hierarchical position (or tree node). The petals and sepal on Pansy are designed for showing each attribute’s values and hierarchical information. We also redefine the links in force layout encoded by width and animation to better convey hierarchical information. We further apply Pansy Tree into CNCEE datasets and demonstrate two use cases to verify its effectiveness.The main contribution of this work is to merge three datasets into one tree that makes it much easier to explore and compare the structures, data items and data attributes with visual tools.","PeriodicalId":322092,"journal":{"name":"2020 IEEE Pacific Visualization Symposium (PacificVis)","volume":"226 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121259664","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: 7
ExplainExplore: Visual Exploration of Machine Learning Explanations ExplainExplore:机器学习解释的视觉探索
2020 IEEE Pacific Visualization Symposium (PacificVis) Pub Date : 2020-06-01 DOI: 10.1109/PacificVis48177.2020.7090
Dennis Collaris, J. V. Wijk
{"title":"ExplainExplore: Visual Exploration of Machine Learning Explanations","authors":"Dennis Collaris, J. V. Wijk","doi":"10.1109/PacificVis48177.2020.7090","DOIUrl":"https://doi.org/10.1109/PacificVis48177.2020.7090","url":null,"abstract":"Machine learning models often exhibit complex behavior that is difficult to understand. Recent research in explainable AI has produced promising techniques to explain the inner workings of such models using feature contribution vectors. These vectors are helpful in a wide variety of applications. However, there are many parameters involved in this process and determining which settings are best is difficult due to the subjective nature of evaluating interpretability. To this end, we introduce EXPLAINEXPLORE: an interactive explanation system to explore explanations that fit the subjective preference of data scientists. We leverage the domain knowledge of the data scientist to find optimal parameter settings and instance perturbations, and enable the discussion of the model and its explanation with domain experts. We present a use case on a real-world dataset to demonstrate the effectiveness of our approach for the exploration and tuning of machine learning explanations.","PeriodicalId":322092,"journal":{"name":"2020 IEEE Pacific Visualization Symposium (PacificVis)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126224540","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: 31
Interactive Attention Model Explorer for Natural Language Processing Tasks with Unbalanced Data Sizes 具有不平衡数据量的自然语言处理任务的交互式注意模型浏览器
2020 IEEE Pacific Visualization Symposium (PacificVis) Pub Date : 2020-06-01 DOI: 10.1109/PacificVis48177.2020.1031
Zhihang Dong, Tongshuang Sherry Wu, Sicheng Song, M. Zhang
{"title":"Interactive Attention Model Explorer for Natural Language Processing Tasks with Unbalanced Data Sizes","authors":"Zhihang Dong, Tongshuang Sherry Wu, Sicheng Song, M. Zhang","doi":"10.1109/PacificVis48177.2020.1031","DOIUrl":"https://doi.org/10.1109/PacificVis48177.2020.1031","url":null,"abstract":"Conventional attention visualization tools compromise either the readability or the information conveyed when documents are lengthy, especially when these documents have imbalanced sizes. Our work strives toward a more intuitive visualization for a subset of Natural Language Processing tasks, where attention is mapped between documents with imbalanced sizes. We extend the flow map visualization to enhance the readability of the attention-augmented documents. Through interaction, our design enables semantic filtering that helps users prioritize important tokens and meaningful matching for an in-depth exploration. Case studies and informal user studies in machine comprehension prove that our visualization effectively helps users gain initial understandings about what their models are \"paying attention to.\" We discuss how the work can be extended to other domains, as well as being plugged into more end-to-end systems for model error analysis.","PeriodicalId":322092,"journal":{"name":"2020 IEEE Pacific Visualization Symposium (PacificVis)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130912970","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: 8
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