2020 IEEE Pacific Visualization Symposium (PacificVis)最新文献_第4页

A Radial Visualisation for Model Comparison and Feature Identification 用于模型比较和特征识别的径向可视化

2020 IEEE Pacific Visualization Symposium (PacificVis) Pub Date : 2020-06-01 DOI: 10.1109/PacificVis48177.2020.1028

Jianlong Zhou, Weidong Huang, Fang Chen

{"title":"A Radial Visualisation for Model Comparison and Feature Identification","authors":"Jianlong Zhou, Weidong Huang, Fang Chen","doi":"10.1109/PacificVis48177.2020.1028","DOIUrl":"https://doi.org/10.1109/PacificVis48177.2020.1028","url":null,"abstract":"Machine Learning (ML) plays a key role in various intelligent systems, and building an effective ML model for a data set is a difficult task involving various steps including data cleaning, feature definition and extraction, ML algorithms development, model training and evaluation as well as others. One of the most important steps in the process is to compare generated substantial amounts of ML models to find the optimal one for the deployment. It is challenging to compare such models with dynamic number of features. This paper proposes a novel visualisation approach based on a radial net to compare ML models trained with a different number of features of a given data set while revealing implicit dependent relations. In the proposed approach, ML models and features are represented by lines and arcs respectively. The dependence of ML models with dynamic number of features is encoded into the structure of visualisation, where ML models and their dependent features are directly revealed from related line connections. ML model performance information is encoded with colour and line width in the innovative visualisation. Together with the structure of visualization, feature importance can be directly discerned to help to understand ML models.","PeriodicalId":322092,"journal":{"name":"2020 IEEE Pacific Visualization Symposium (PacificVis)","volume":"138 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":"126164070","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

PacificVis 2020 Paper Reviewers PacificVis 2020论文审稿人

2020 IEEE Pacific Visualization Symposium (PacificVis) Pub Date : 2020-06-01 DOI: 10.1109/pacificvis48177.2020.9086232

引用次数: 0

Visualization of Unsteady Flow Using Heat Kernel Signatures 利用热核特征的非定常流场可视化

2020 IEEE Pacific Visualization Symposium (PacificVis) Pub Date : 2020-04-28 DOI: 10.1109/PacificVis48177.2020.1718

K. Jiang, M. Berger, J. Levine

{"title":"Visualization of Unsteady Flow Using Heat Kernel Signatures","authors":"K. Jiang, M. Berger, J. Levine","doi":"10.1109/PacificVis48177.2020.1718","DOIUrl":"https://doi.org/10.1109/PacificVis48177.2020.1718","url":null,"abstract":"We introduce a new technique to visualize complex flowing phenomena by using concepts from shape analysis. Our approach uses techniques that examine the intrinsic geometry of manifolds through their heat kernel, to obtain representations of such manifolds that are isometry-invariant and multi-scale. These representations permit us to compute heat kernel signatures of each point on that manifold, and we can use these signatures as features for classification and segmentation that identify points that have similar structural properties. Our approach adapts heat kernel signatures to unsteady flows by formulating a notion of shape where pathlines are observations of a manifold living in a high-dimensional space. We use this space to compute and visualize heat kernel signatures associated with each pathline. Besides being able to capture the structural features of a pathline, heat kernel signatures allow the comparison of pathlines from different flow datasets through a shape matching pipeline. We demonstrate the analytic power of heat kernel signatures by comparing both (1) different timesteps from the same unsteady flow as well as (2) flow datasets taken from ensemble simulations with varying simulation parameters. Our analysis only requires the pathlines themselves, and thus it does not utilize the underlying vector field directly. We make minimal assumptions on the pathlines: while we assume they are sampled from a continuous, unsteady flow, our computations can tolerate pathlines that have varying density and potential unknown boundaries. We evaluate our approach through visualizations of a variety of two-dimensional unsteady flows.","PeriodicalId":322092,"journal":{"name":"2020 IEEE Pacific Visualization Symposium (PacificVis)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117273893","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

BatchLayout: A Batch-Parallel Force-Directed Graph Layout Algorithm in Shared Memory BatchLayout:共享内存中的批并行力导向图布局算法

2020 IEEE Pacific Visualization Symposium (PacificVis) Pub Date : 2020-02-11 DOI: 10.1109/PacificVis48177.2020.3756

Md. Khaledur Rahman, Majedul Haque Sujon, A. Azad

引用次数: 9

A Visual Analytics Framework for Reviewing Streaming Performance Data 用于审查流性能数据的可视化分析框架

2020 IEEE Pacific Visualization Symposium (PacificVis) Pub Date : 2020-01-26 DOI: 10.1109/PacificVis48177.2020.9280

Suraj P. Kesavan, Takanori Fujiwara, J. Li, Caitlin J. Ross, M. Mubarak, C. Carothers, R. Ross, K. Ma

{"title":"A Visual Analytics Framework for Reviewing Streaming Performance Data","authors":"Suraj P. Kesavan, Takanori Fujiwara, J. Li, Caitlin J. Ross, M. Mubarak, C. Carothers, R. Ross, K. Ma","doi":"10.1109/PacificVis48177.2020.9280","DOIUrl":"https://doi.org/10.1109/PacificVis48177.2020.9280","url":null,"abstract":"Understanding and tuning the performance of extreme-scale parallel computing systems demands a streaming approach due to the computational cost of applying offline algorithms to vast amounts of performance log data. Analyzing large streaming data is challenging because the rate of receiving data and limited time to comprehend data make it difficult for the analysts to sufficiently examine the data without missing important changes or patterns. To support streaming data analysis, we introduce a visual analytic framework comprising of three modules: data management, analysis, and interactive visualization. The data management module collects various computing and communication performance metrics from the monitored system using streaming data processing techniques and feeds the data to the other two modules. The analysis module automatically identifies important changes and patterns at the required latency. In particular, we introduce a set of online and progressive analysis methods for not only controlling the computational costs but also helping analysts better follow the critical aspects of the analysis results. Finally, the interactive visualization module provides the analysts with a coherent view of the changes and patterns in the continuously captured performance data. Through a multi-faceted case study on performance analysis of parallel discrete-event simulation, we demonstrate the effectiveness of our framework for identifying bottlenecks and locating outliers.","PeriodicalId":322092,"journal":{"name":"2020 IEEE Pacific Visualization Symposium (PacificVis)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123732614","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

A Study of Mental Maps in Immersive Network Visualization 沉浸式网络可视化中的心理地图研究

2020 IEEE Pacific Visualization Symposium (PacificVis) Pub Date : 2020-01-17 DOI: 10.1109/PacificVis48177.2020.4722

Joseph Kotlarek, Oh-Hyun Kwon, K. Ma, P. Eades, A. Kerren, Karsten Klein, F. Schreiber

引用次数: 22

Leveraging Peer Feedback to Improve Visualization Education 利用同伴反馈改善可视化教育

2020 IEEE Pacific Visualization Symposium (PacificVis) Pub Date : 2020-01-12 DOI: 10.1109/PacificVis48177.2020.1261

Zachariah J. Beasley, A. Friedman, L. Piegl, Paul Rosen

{"title":"Leveraging Peer Feedback to Improve Visualization Education","authors":"Zachariah J. Beasley, A. Friedman, L. Piegl, Paul Rosen","doi":"10.1109/PacificVis48177.2020.1261","DOIUrl":"https://doi.org/10.1109/PacificVis48177.2020.1261","url":null,"abstract":"Peer review is a widely utilized pedagogical feedback mechanism for engaging students, which has been shown to improve educational outcomes. However, we find limited discussion and empirical measurement of peer review in visualization coursework. In addition to engagement, peer review provides direct and diverse feedback and reinforces recently-learned course concepts through critical evaluation of others’ work. In this paper, we discuss the construction and application of peer review in a computer science visualization course, including: projects that reuse code and visualizations in a feedback-guided, continual improvement process and a peer review rubric to reinforce key course concepts. To measure the effectiveness of the approach, we evaluate student projects, peer review text, and a post-course questionnaire from 3 semesters of mixed undergraduate and graduate courses. The results indicate that course concepts are reinforced with peer review—82% reported learning more because of peer review, and 75% of students recommended continuing it. Finally, we provide a road-map for adapting peer review to other visualization courses to produce more highly engaged students.","PeriodicalId":322092,"journal":{"name":"2020 IEEE Pacific Visualization Symposium (PacificVis)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129154902","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