2021 IEEE Visualization Conference (VIS)最新文献

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“Why did my AI agent lose?”: Visual Analytics for Scaling Up After-Action Review “为什么我的人工智能代理输了?:用于扩大事后评估的可视化分析
2021 IEEE Visualization Conference (VIS) Pub Date : 2021-10-01 DOI: 10.1109/VIS49827.2021.9623268
Delyar Tabatabai, Anita Ruangrotsakun, Jed Irvine, Jonathan Dodge, Zeyad Shureih, Kin-Ho Lam, M. Burnett, Alan Fern, Minsuk Kahng
{"title":"“Why did my AI agent lose?”: Visual Analytics for Scaling Up After-Action Review","authors":"Delyar Tabatabai, Anita Ruangrotsakun, Jed Irvine, Jonathan Dodge, Zeyad Shureih, Kin-Ho Lam, M. Burnett, Alan Fern, Minsuk Kahng","doi":"10.1109/VIS49827.2021.9623268","DOIUrl":"https://doi.org/10.1109/VIS49827.2021.9623268","url":null,"abstract":"How can we help domain-knowledgeable users who do not have expertise in AI analyze why an AI agent failed? Our research team previously developed a new structured process for such users to assess AI, called After-Action Review for AI (AAR/AI), consisting of a series of steps a human takes to assess an AI agent and formalize their understanding. In this paper, we investigate how the AAR/AI process can scale up to support reinforcement learning (RL) agents that operate in complex environments. We augment the AAR/AI process to be performed at three levels—episode-level, decision-level, and explanation-level—and integrate it into our redesigned visual analytics interface. We illustrate our approach through a usage scenario of analyzing why a RL agent lost in a complex real-time strategy game built with the StarCraft 2 engine. We believe integrating structured processes like AAR/AI into visualization tools can help visualization play a more critical role in AI interpretability.","PeriodicalId":387572,"journal":{"name":"2021 IEEE Visualization Conference (VIS)","volume":"8 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":"116787073","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
Atlas: Grammar-based Procedural Generation of Data Visualizations Atlas:基于语法的数据可视化过程生成
2021 IEEE Visualization Conference (VIS) Pub Date : 2021-10-01 DOI: 10.1109/VIS49827.2021.9623315
Zhicheng Liu, Chen Chen, Francisco Morales, Yishan Zhao
{"title":"Atlas: Grammar-based Procedural Generation of Data Visualizations","authors":"Zhicheng Liu, Chen Chen, Francisco Morales, Yishan Zhao","doi":"10.1109/VIS49827.2021.9623315","DOIUrl":"https://doi.org/10.1109/VIS49827.2021.9623315","url":null,"abstract":"We present Atlas, a procedural grammar for constructing data visualizations. Unlike most visualization grammars which use declarative specifications to describe visualization components, Atlas exposes the generative process of a visualization through a set of concatenated high-level production rules. Each of these rules describes how an input graphical object is created, transformed, or joined with abstract data to derive an output object. The visualization state can thus be inspected throughout the generative process. We demonstrate Atlas’ expressivity through a catalog of visualization designs, and discuss the trade-offs in its design by comparing it to state-of-the-art grammars.","PeriodicalId":387572,"journal":{"name":"2021 IEEE Visualization Conference (VIS)","volume":"18 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":"127683880","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
VIS 2021 Program Committee VIS 2021项目委员会
2021 IEEE Visualization Conference (VIS) Pub Date : 2021-10-01 DOI: 10.1109/vis49827.2021.9623277
{"title":"VIS 2021 Program Committee","authors":"","doi":"10.1109/vis49827.2021.9623277","DOIUrl":"https://doi.org/10.1109/vis49827.2021.9623277","url":null,"abstract":"","PeriodicalId":387572,"journal":{"name":"2021 IEEE Visualization Conference (VIS)","volume":"260 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":"133496102","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
Where and Why is My Bot Failing? A Visual Analytics Approach for Investigating Failures in Chatbot Conversation Flows 我的Bot在哪里以及为什么会失败?一种用于调查聊天机器人会话流程失败的可视化分析方法
2021 IEEE Visualization Conference (VIS) Pub Date : 2021-10-01 DOI: 10.1109/VIS49827.2021.9623295
Avi Yaeli, Sergey Zeltyn
{"title":"Where and Why is My Bot Failing? A Visual Analytics Approach for Investigating Failures in Chatbot Conversation Flows","authors":"Avi Yaeli, Sergey Zeltyn","doi":"10.1109/VIS49827.2021.9623295","DOIUrl":"https://doi.org/10.1109/VIS49827.2021.9623295","url":null,"abstract":"The ongoing coronavirus pandemic has accelerated the adoption of AI-powered task-oriented chatbots by businesses and healthcare organizations. Despite advancements in chatbot platforms, implementing a successful and effective bot is still challenging and requires a lot of manual work. There is a strong need for tools to help conversation analysts quickly identify problem areas and, consequently, introduce changes to chatbot design. We present a visual analytics approach and tool for conversation analysts to identify and assess common patterns of failure in conversation flows. We focus on two key capabilities: path flow analysis and root cause analysis. Interim evaluation results from applying our tool in real-world customer production projects are presented.","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":"124542638","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
CloudFindr: A Deep Learning Cloud Artifact Masker for Satellite DEM Data CloudFindr:一个用于卫星DEM数据的深度学习云伪影掩蔽器
2021 IEEE Visualization Conference (VIS) Pub Date : 2021-10-01 DOI: 10.1109/VIS49827.2021.9623327
Kalina Borkiewicz, Viraj Shah, J. Naiman, Chuanyue Shen, Stuart Levy, Jeff Carpenter
{"title":"CloudFindr: A Deep Learning Cloud Artifact Masker for Satellite DEM Data","authors":"Kalina Borkiewicz, Viraj Shah, J. Naiman, Chuanyue Shen, Stuart Levy, Jeff Carpenter","doi":"10.1109/VIS49827.2021.9623327","DOIUrl":"https://doi.org/10.1109/VIS49827.2021.9623327","url":null,"abstract":"Artifact removal is an integral component of cinematic scientific visualization, and is especially challenging with big datasets in which artifacts are difficult to define. In this paper, we describe a method for creating cloud artifact masks which can be used to remove artifacts from satellite imagery using a combination of traditional image processing together with deep learning based on U-Net. Compared to previous methods, our approach does not require multi-channel spectral imagery but performs successfully on single-channel Digital Elevation Models (DEMs). DEMs are a representation of the topography of the Earth and have a variety applications including planetary science, geology, flood modeling, and city planning.","PeriodicalId":387572,"journal":{"name":"2021 IEEE Visualization Conference (VIS)","volume":"268 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":"115968299","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
Conceptualizing Visual Analytic Interventions for Content Moderation 概念化视觉分析干预内容审核
2021 IEEE Visualization Conference (VIS) Pub Date : 2021-10-01 DOI: 10.1109/VIS49827.2021.9623288
Sahaj Vaidya, Jie Cai, Soumyadeep Basu, Azadeh Naderi, D. Y. Wohn, Aritra Dasgupta
{"title":"Conceptualizing Visual Analytic Interventions for Content Moderation","authors":"Sahaj Vaidya, Jie Cai, Soumyadeep Basu, Azadeh Naderi, D. Y. Wohn, Aritra Dasgupta","doi":"10.1109/VIS49827.2021.9623288","DOIUrl":"https://doi.org/10.1109/VIS49827.2021.9623288","url":null,"abstract":"Modern social media platforms like Twitch, YouTube, etc., embody an open space for content creation and consumption. However, an unintended consequence of such content democratization is the proliferation of toxicity and abuse that content creators get subjected to. Commercial and volunteer content moderators play an indispensable role in identifying bad actors and minimizing the scale and degree of harmful content. Moderation tasks are often laborious, complex, and even if semi-automated, they involve high-consequence human decisions that affect the safety and popular perception of the platforms. In this paper, through an interdisciplinary collaboration among researchers from social science, human-computer interaction, and visualization, we present a systematic understanding of how visual analytics can help in human-in-the-loop content moderation. We contribute a characterization of the data-driven problems and needs for proactive moderation and present a mapping between the needs and visual analytic tasks through a task abstraction framework. We discuss how the task abstraction framework can be used for transparent moderation, design interventions for moderators’ well-being, and ultimately, for creating futuristic human-machine interfaces for data-driven content moderation.","PeriodicalId":387572,"journal":{"name":"2021 IEEE Visualization Conference (VIS)","volume":"337 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":"116763827","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
VIS 2021 Steering Committee VIS 2021指导委员会
2021 IEEE Visualization Conference (VIS) Pub Date : 2021-10-01 DOI: 10.1109/vis49827.2021.9623279
{"title":"VIS 2021 Steering Committee","authors":"","doi":"10.1109/vis49827.2021.9623279","DOIUrl":"https://doi.org/10.1109/vis49827.2021.9623279","url":null,"abstract":"","PeriodicalId":387572,"journal":{"name":"2021 IEEE Visualization Conference (VIS)","volume":"31 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":"114973121","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
Ray-traced Shell Traversal of Tetrahedral Meshes for Direct Volume Visualization 面向直接体可视化的四面体网格射线追踪壳遍历
2021 IEEE Visualization Conference (VIS) Pub Date : 2021-10-01 DOI: 10.1109/VIS49827.2021.9623298
Alper Sahistan, S. Demirci, N. Morrical, Stefan Zellmann, Aytek Aman, I. Wald, U. Güdükbay
{"title":"Ray-traced Shell Traversal of Tetrahedral Meshes for Direct Volume Visualization","authors":"Alper Sahistan, S. Demirci, N. Morrical, Stefan Zellmann, Aytek Aman, I. Wald, U. Güdükbay","doi":"10.1109/VIS49827.2021.9623298","DOIUrl":"https://doi.org/10.1109/VIS49827.2021.9623298","url":null,"abstract":"A well-known method for rendering unstructured volumetric data is tetrahedral marching (tet marching), where rays are marched through a series of tetrahedral elements. Rowever, existing tet marching techniques do not easily generalize to rays with arbitrary origin and direction required for advanced shading effects or non-convex meshes. Additionally, the memory footprint of these methods may exceed GPU memory limits. Interactive performance and high image quality are opposing goals. Our approach significantly lowers the burden to render unstructured datasets with high image fidelity while maintaining real-time and interactive performance even for large datasets. To this end, we leverage hardware-accelerated ray tracing to find entry and exit faces for a given ray into a volume and utilize a compact mesh representation to enable the efficient marching of arbitrary rays, thus allowing for advanced shading effects that ultimately yields more convincing and grounded images. Our approach is also robust, supporting both convex and non-convex unstructured meshes. We show that our method achieves interactive rates even with moderately-sized datasets while secondary effects are applied.","PeriodicalId":387572,"journal":{"name":"2021 IEEE Visualization Conference (VIS)","volume":"61 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":"133243833","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
Fast & Accurate Gaussian Kernel Density Estimation 快速准确的高斯核密度估计
2021 IEEE Visualization Conference (VIS) Pub Date : 2021-10-01 DOI: 10.1109/VIS49827.2021.9623323
Jeffrey Heer, ExtBox Deriche
{"title":"Fast & Accurate Gaussian Kernel Density Estimation","authors":"Jeffrey Heer, ExtBox Deriche","doi":"10.1109/VIS49827.2021.9623323","DOIUrl":"https://doi.org/10.1109/VIS49827.2021.9623323","url":null,"abstract":"Kernel density estimation (KDE) models a discrete sample of data as a continuous distribution, supporting the construction of visualizations such as violin plots, heatmaps, and contour plots. This paper draws on the statistics and image processing literature to survey efficient and scalable density estimation techniques for the common case of Gaussian kernel functions. We evaluate the accuracy and running time of these methods across multiple visualization contexts and find that the combination of linear binning and a recursive filter approximation by Deriche efficiently produces pixel-perfect estimates across a compelling range of kernel bandwidths.","PeriodicalId":387572,"journal":{"name":"2021 IEEE Visualization Conference (VIS)","volume":"130 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":"129366873","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
Message from the VIS 2021 General Chairs 2021年VIS大会主席致辞
2021 IEEE Visualization Conference (VIS) Pub Date : 2021-10-01 DOI: 10.1109/vis49827.2021.9623292
{"title":"Message from the VIS 2021 General Chairs","authors":"","doi":"10.1109/vis49827.2021.9623292","DOIUrl":"https://doi.org/10.1109/vis49827.2021.9623292","url":null,"abstract":"","PeriodicalId":387572,"journal":{"name":"2021 IEEE Visualization Conference (VIS)","volume":"37 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":"130491275","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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