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2021 IEEE Visualization Conference (VIS)最新文献

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VIS 2021 Area Curation Committee VIS 2021区域策展委员会
2021 IEEE Visualization Conference (VIS) Pub Date : 2021-10-01 DOI: 10.1109/vis49827.2021.9623281
{"title":"VIS 2021 Area Curation Committee","authors":"","doi":"10.1109/vis49827.2021.9623281","DOIUrl":"https://doi.org/10.1109/vis49827.2021.9623281","url":null,"abstract":"","PeriodicalId":387572,"journal":{"name":"2021 IEEE Visualization Conference (VIS)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131544903","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
Title Page i 第1页
2021 IEEE Visualization Conference (VIS) Pub Date : 2021-10-01 DOI: 10.1109/vis49827.2021.9623309
{"title":"Title Page i","authors":"","doi":"10.1109/vis49827.2021.9623309","DOIUrl":"https://doi.org/10.1109/vis49827.2021.9623309","url":null,"abstract":"","PeriodicalId":387572,"journal":{"name":"2021 IEEE Visualization Conference (VIS)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114482049","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
Does the Layout Really Matter? A Study on Visual Model Accuracy Estimation 布局真的很重要吗?视觉模型精度估计的研究
2021 IEEE Visualization Conference (VIS) Pub Date : 2021-10-01 DOI: 10.1109/VIS49827.2021.9623326
N. Grossmann, J. Bernard, M. Sedlmair, Manuela Waldner
{"title":"Does the Layout Really Matter? A Study on Visual Model Accuracy Estimation","authors":"N. Grossmann, J. Bernard, M. Sedlmair, Manuela Waldner","doi":"10.1109/VIS49827.2021.9623326","DOIUrl":"https://doi.org/10.1109/VIS49827.2021.9623326","url":null,"abstract":"In visual interactive labeling, users iteratively assign labels to data items until the machine model reaches an acceptable accuracy. A crucial step of this process is to inspect the model’s accuracy and decide whether it is necessary to label additional elements. In scenarios with no or very little labeled data, visual inspection of the predictions is required. Similarity-preserving scatterplots created through a dimensionality reduction algorithm are a common visualization that is used in these cases. Previous studies investigated the effects of layout and image complexity on tasks like labeling. However, model evaluation has not been studied systematically. We present the results of an experiment studying the influence of image complexity and visual grouping of images on model accuracy estimation. We found that users outperform traditional automated approaches when estimating a model’s accuracy. Furthermore, while the complexity of images impacts the overall performance, the layout of the items in the plot has little to no effect on estimations.","PeriodicalId":387572,"journal":{"name":"2021 IEEE Visualization Conference (VIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131010899","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: 3
Semantic Resizing of Charts Through Generalization: A Case Study with Line Charts 通过泛化调整图表的语义大小:折线图的案例研究
2021 IEEE Visualization Conference (VIS) Pub Date : 2021-10-01 DOI: 10.1109/VIS49827.2021.9623306
V. Setlur, Haeyong Chung
{"title":"Semantic Resizing of Charts Through Generalization: A Case Study with Line Charts","authors":"V. Setlur, Haeyong Chung","doi":"10.1109/VIS49827.2021.9623306","DOIUrl":"https://doi.org/10.1109/VIS49827.2021.9623306","url":null,"abstract":"Inspired by cartographic generalization principles, we present a generalization technique for rendering line charts at different sizes, preserving the important semantics of the data at that display size. The algorithm automatically determines the generalization operators to be applied at that size based on spatial density, distance, and the semantic importance of the various visualization elements in the line chart. A qualitative evaluation of the prototype that implemented the algorithm indicates that the generalized line charts preserved the general data shape, while minimizing visual clutter. We identify future opportunities where generalization can be extended and applied to other chart types and visual analysis authoring tools.","PeriodicalId":387572,"journal":{"name":"2021 IEEE Visualization Conference (VIS)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131421676","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
VIS 2021 Executive Committee VIS 2021执行委员会
2021 IEEE Visualization Conference (VIS) Pub Date : 2021-10-01 DOI: 10.1109/vis49827.2021.9623276
{"title":"VIS 2021 Executive Committee","authors":"","doi":"10.1109/vis49827.2021.9623276","DOIUrl":"https://doi.org/10.1109/vis49827.2021.9623276","url":null,"abstract":"","PeriodicalId":387572,"journal":{"name":"2021 IEEE Visualization Conference (VIS)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124919894","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
CellProfiler Analyst Web (CPAW) - Exploration, analysis, and classification of biological images on the web CellProfiler Analyst Web (CPAW) -对网络上的生物图像进行探索、分析和分类
2021 IEEE Visualization Conference (VIS) Pub Date : 2021-10-01 DOI: 10.1109/VIS49827.2021.9623317
Bella Baidak, Yahiya Hussain, Emma Kelminson, T. Jones, Loraine Franke, D. Haehn
{"title":"CellProfiler Analyst Web (CPAW) - Exploration, analysis, and classification of biological images on the web","authors":"Bella Baidak, Yahiya Hussain, Emma Kelminson, T. Jones, Loraine Franke, D. Haehn","doi":"10.1109/VIS49827.2021.9623317","DOIUrl":"https://doi.org/10.1109/VIS49827.2021.9623317","url":null,"abstract":"CellProfiler Analyst (CPA) has enabled the scientific research community to explore image-based data and classify complex biological phenotypes through an interactive user interface since its release in 2008. This paper describes CellProfiler Analyst Web (CPAW), a newly redesigned and web-based version of the software, allowing for greater accessibility, quicker setup, and facilitating a simple workflow for users. Installation and managing new versions has been challenging and time-consuming, historically. CPAW is an alternative that ensures installation and future updates are not a hassle to the user. CPAW ports the core iteration loop of CPA to a pure server-less browser environment using modern web-development technologies, allowing computationally heavy activities, like machine learning, to occur without freezing the user interface (UI). With a setup as simple as navigating to a website, CPAW presents a clean UI to the user to refine their classifier and explore pheno-typic data easily. We evaluated both the old and the new version of the software in an extensive domain expert study. We found that users could complete the essential classification tasks in CPAW and CPA 3.0 with the same efficiency. Additionally, users completed the tasks 20 percent faster using CPAW compared to CPA 3.0. The code of CellProfiler Analyst Web is open-source and available at https://mpsych.github.io/CellProfilerAnalystWeb/.","PeriodicalId":387572,"journal":{"name":"2021 IEEE Visualization Conference (VIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124017716","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: 3
Semantic Explanation of Interactive Dimensionality Reduction 交互降维的语义解释
2021 IEEE Visualization Conference (VIS) Pub Date : 2021-10-01 DOI: 10.1109/VIS49827.2021.9623322
Yail Bian, Chris North, Eric Krokos, Sarah Joseph
{"title":"Semantic Explanation of Interactive Dimensionality Reduction","authors":"Yail Bian, Chris North, Eric Krokos, Sarah Joseph","doi":"10.1109/VIS49827.2021.9623322","DOIUrl":"https://doi.org/10.1109/VIS49827.2021.9623322","url":null,"abstract":"Interactive dimensionality reduction helps analysts explore the high-dimensional data based on their personal needs and domain-specific problems. Recently, expressive nonlinear models are employed to support these tasks. However, the interpretation of these human-steered nonlinear models during human-in-the-loop analysis has not been explored. To address this problem, we present a new visual explanation design called semantic explanation. Semantic explanation visualizes model behaviors in a manner that is similar to users’ direct projection manipulations. This design conforms to the spatial analytic process and enables analysts better understand the updated model in response to their interactions. We propose a pipeline to empower interactive dimensionality reduction with semantic explanation using counterfactuals. Based on the pipeline, we implement a visual text analytics system with nonlinear dimensionality reduction powered by deep learning via the BERT model. We demonstrate the efficacy of semantic explanation with two case studies of academic article exploration and intelligence analysis.","PeriodicalId":387572,"journal":{"name":"2021 IEEE Visualization Conference (VIS)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121151497","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: 3
GeoSneakPique: Visual Autocompletion for Geospatial Queries GeoSneakPique:地理空间查询的可视化自动补全
2021 IEEE Visualization Conference (VIS) Pub Date : 2021-10-01 DOI: 10.1109/VIS49827.2021.9623324
V. Setlur, S. Battersby, Tracy Wong
{"title":"GeoSneakPique: Visual Autocompletion for Geospatial Queries","authors":"V. Setlur, S. Battersby, Tracy Wong","doi":"10.1109/VIS49827.2021.9623324","DOIUrl":"https://doi.org/10.1109/VIS49827.2021.9623324","url":null,"abstract":"How many crimes occurred in the city center? And exactly which part of town is the “city center”? While location is at the heart of many data questions, geographic location can be difficult to specify in natural language (NL) queries. This is especially true when working with fuzzy cognitive regions or regions that may be defined based on data distributions instead of absolute administrative location (e.g., state, country). GeoSneakPique presents a novel method for using a mapping widget to support the NL query process, allowing users to specify location via direct manipulation with data-driven guidance on spatial distributions to help select the area of interest. Users receive feedback to help them evaluate and refine their spatial selection interactively and can save spatial definitions for re-use in subsequent queries. We conduct a qualitative evaluation of the GeoSneakPique that indicates the usefulness of the interface as well as opportunities for better supporting geospatial workflows in visual analysis tasks employing cognitive regions.","PeriodicalId":387572,"journal":{"name":"2021 IEEE Visualization Conference (VIS)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122250848","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: 3
Text Visualization and Close Reading for Journalism with Storifier 使用Storifier进行新闻文本可视化和细读
2021 IEEE Visualization Conference (VIS) Pub Date : 2021-10-01 DOI: 10.1109/VIS49827.2021.9623264
N. Sultanum, A. Bezerianos, Fanny Chevalier
{"title":"Text Visualization and Close Reading for Journalism with Storifier","authors":"N. Sultanum, A. Bezerianos, Fanny Chevalier","doi":"10.1109/VIS49827.2021.9623264","DOIUrl":"https://doi.org/10.1109/VIS49827.2021.9623264","url":null,"abstract":"Journalistic inquiry often requires analysis and close study of large text collections around a particular topic. We argue that this practice could benefit from a more text- and reading-centered approach to journalistic text analysis, one that allows for a fluid transition between overview of entities of interest, the context of these entities in the text, down to the detailed documents they are extracted from. In this context, we present the design and development of Storifier, a text visualization tool created in close collaboration with a large francophone news office. We also discuss a case study on how our tool was used to analyze a text collection and helped publish a story.","PeriodicalId":387572,"journal":{"name":"2021 IEEE Visualization Conference (VIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115037219","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: 2
VIS 2021 Conference Committee VIS 2021会议委员会
2021 IEEE Visualization Conference (VIS) Pub Date : 2021-10-01 DOI: 10.1109/vis49827.2021.9623283
{"title":"VIS 2021 Conference Committee","authors":"","doi":"10.1109/vis49827.2021.9623283","DOIUrl":"https://doi.org/10.1109/vis49827.2021.9623283","url":null,"abstract":"","PeriodicalId":387572,"journal":{"name":"2021 IEEE Visualization Conference (VIS)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123761032","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
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