2017 IEEE Conference on Visual Analytics Science and Technology (VAST)最新文献_第2页

Uncovering the Mistford Toxic Conspiracy 揭露米斯特福德毒气阴谋

2017 IEEE Conference on Visual Analytics Science and Technology (VAST) Pub Date : 2017-10-01 DOI: 10.1109/VAST.2017.8585661

Dirk Streeb, J. Buchmüller, U. Schlegel, Wolfgang Jentner, M. Behrisch, Bruno Schneider, Daniel Seebacher

引用次数: 0

QSAnglyzer: Visual Analytics for Prismatic Analysis of Question Answering System Evaluations QSAnglyzer:问答系统评估的棱镜分析的可视化分析

2017 IEEE Conference on Visual Analytics Science and Technology (VAST) Pub Date : 2017-10-01 DOI: 10.1109/VAST.2017.8585733

N. Chen, Been Kim

{"title":"QSAnglyzer: Visual Analytics for Prismatic Analysis of Question Answering System Evaluations","authors":"N. Chen, Been Kim","doi":"10.1109/VAST.2017.8585733","DOIUrl":"https://doi.org/10.1109/VAST.2017.8585733","url":null,"abstract":"Developing sophisticated artificial intelligence (AI) systems requires AI researchers to experiment with different designs and analyze results from evaluations (we refer this task as evaluation analysis). In this paper, we tackle the challenges of evaluation analysis in the domain of question-answering (QA) systems. Through in-depth studies with QA researchers, we identify tasks and goals of evaluation analysis and derive a set of design rationales, based on which we propose a novel approach termed prismatic analysis. Prismatic analysis examines data through multiple ways of categorization (referred as angles). Categories in each angle are measured by aggregate metrics to enable diverse comparison scenarios. To facilitate prismatic analysis of QA evaluations, we design and implement the Question Space Anglyzer (QSAnglyzer), a visual analytics (VA) tool. In QSAnglyzer, the high-dimensional space formed by questions is divided into categories based on several angles (e.g., topic and question type). Each category is aggregated by accuracy, the number of questions, and accuracy variance across evaluations. QSAnglyzer visualizes these angles so that QA researchers can examine and compare evaluations from various aspects both individually and collectively. Furthermore, QA researchers filter questions based on any angle by clicking to construct complex queries. We validate QSAnglyzer through controlled experiments and by expert reviews. The results indicate that when using QSAnglyzer, users perform analysis tasks faster $(p lt 0.01)$ and more accurately $(p lt 0.05)$, and are quick to gain new insight. We discuss how prismatic analysis and QSAnglyzer scaffold evaluation analysis, and provide directions for future research.","PeriodicalId":149607,"journal":{"name":"2017 IEEE Conference on Visual Analytics Science and Technology (VAST)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130413203","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

CRICTO: Supporting Sensemaking through Crowdsourced Information Schematization 克里斯托:通过众包信息图式支持意义生成

2017 IEEE Conference on Visual Analytics Science and Technology (VAST) Pub Date : 2017-10-01 DOI: 10.1109/VAST.2017.8585484

Haeyong Chung, Sai Prashanth Dasari, Santhosh Nandhakumar, Christopher Andrews

引用次数: 6

Visual Analysis for Multi-Spectral Images Comparisons 多光谱图像比较的可视化分析

2017 IEEE Conference on Visual Analytics Science and Technology (VAST) Pub Date : 2017-10-01 DOI: 10.1109/VAST.2017.8585456

Guozheng Li, Shuai Chen, Qiusheng Li, Zhibang Jiang, Yuening Shi, Qiangqiang Liu, Xi Liu, Xiaoru Yuan

引用次数: 0

A Visual Analytics System for Optimizing Communications in Massively Parallel Applications 在大规模并行应用中优化通信的可视化分析系统

2017 IEEE Conference on Visual Analytics Science and Technology (VAST) Pub Date : 2017-10-01 DOI: 10.1109/VAST.2017.8585646

Takanori Fujiwara, Preeti Malakar, K. Reda, V. Vishwanath, M. Papka, K. Ma

{"title":"A Visual Analytics System for Optimizing Communications in Massively Parallel Applications","authors":"Takanori Fujiwara, Preeti Malakar, K. Reda, V. Vishwanath, M. Papka, K. Ma","doi":"10.1109/VAST.2017.8585646","DOIUrl":"https://doi.org/10.1109/VAST.2017.8585646","url":null,"abstract":"Current and future supercomputers have tens of thousands of compute nodes interconnected with high-dimensional networks and complex network topologies for improved performance. Application developers are required to write scalable parallel programs in order to achieve high throughput on these machines. Application performance is largely determined by efficient inter-process communication. A common way to analyze and optimize performance is through profiling parallel codes to identify communication bottlenecks. However, understanding gigabytes of profiled at a is not a trivial task. In this paper, we present a visual analytics system for identifying the scalability bottlenecks and improving the communication efficiency of massively parallel applications. Visualization methods used in this system are designed to comprehend large-scale and varied communication patterns on thousands of nodes in complex networks such as the 5D torus and the dragonfly. We also present efficient rerouting and remapping algorithms that can be coupled with our interactive visual analytics design for performance optimization. We demonstrate the utility of our system with several case studies using three benchmark applications on two leading supercomputers. The mapping suggestion from our system led to 38% improvement in hop-bytes for Mini AMR application on 4,096 MPI processes.","PeriodicalId":149607,"journal":{"name":"2017 IEEE Conference on Visual Analytics Science and Technology (VAST)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133804162","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

ClockPetals: Interactive Sequential Analysis of Traffic Patterns VAST Challenge MC1 Award: Multi-Challenge Award for Aesthetic Design 巨大挑战MC1奖:多重挑战美学设计奖

2017 IEEE Conference on Visual Analytics Science and Technology (VAST) Pub Date : 2017-10-01 DOI: 10.1109/VAST.2017.8585620

Zheng Zhou, Sijin Wang, Wenjie Wu, Aijun Huang, Y. Niu, Hui Tang, Victor Y. Chen, Cheryl Z. Qian

引用次数: 0

Visual Integration of Meteorological and Sensor Data for Identifying Suspicious Company Behavior 用于识别可疑公司行为的气象和传感器数据的视觉集成

2017 IEEE Conference on Visual Analytics Science and Technology (VAST) Pub Date : 2017-10-01 DOI: 10.1109/VAST.2017.8585436

Daniel Seebacher, Bruno Schneider, M. Behrisch

引用次数: 0

Multilab: Multispectral image analysis in Matlab Multilab: Matlab中的多光谱图像分析

2017 IEEE Conference on Visual Analytics Science and Technology (VAST) Pub Date : 2017-10-01 DOI: 10.1109/VAST.2017.8585672

T. McGraw, Aijun Huang, Sijin Wang

引用次数: 0

Warning, Bias May Occur: A Proposed Approach to Detecting Cognitive Bias in Interactive Visual Analytics 警告，偏差可能会发生:一种在交互式视觉分析中检测认知偏差的建议方法

2017 IEEE Conference on Visual Analytics Science and Technology (VAST) Pub Date : 2017-10-01 DOI: 10.1109/VAST.2017.8585669

Emily Wall, L. Blaha, Lyndsey R. Franklin, A. Endert

引用次数: 94

MC2 - Mining Factory Pollution Data through a Spatial-Nonspatial Flow Approach (Honorable Mention for Clarity in Visual Communication) MC2 -通过空间-非空间流动方法挖掘工厂污染数据(视觉传达清晰度荣誉奖)

2017 IEEE Conference on Visual Analytics Science and Technology (VAST) Pub Date : 2017-10-01 DOI: 10.1109/VAST.2017.8585491

Joshua Castor, J. Borowicz, A. Burks, Manumol Thomas, T. Luciani, G. Marai

引用次数: 0