2018 IEEE Pacific Visualization Symposium (PacificVis)最新文献_第3页

Visualizing Deep Neural Networks for Text Analytics 可视化文本分析的深度神经网络

2018 IEEE Pacific Visualization Symposium (PacificVis) Pub Date : 2018-04-10 DOI: 10.1109/PacificVis.2018.00031

Shaoliang Nie, C. Healey, Kalpesh Padia, Samuel P. Leeman-Munk, J. Benson, Dave Caira, Saratendu Sethi, Ravi Devarajan

{"title":"Visualizing Deep Neural Networks for Text Analytics","authors":"Shaoliang Nie, C. Healey, Kalpesh Padia, Samuel P. Leeman-Munk, J. Benson, Dave Caira, Saratendu Sethi, Ravi Devarajan","doi":"10.1109/PacificVis.2018.00031","DOIUrl":"https://doi.org/10.1109/PacificVis.2018.00031","url":null,"abstract":"Deep neural networks (DNNs) have made tremendous progress in many different areas in recent years. How these networks function internally, however, is often not well understood. Advances in under-standing DNNs will benefit and accelerate the development of the field. We present TNNVis, a visualization system that supports un-derstanding of deep neural networks specifically designed to analyze text. TNNVis focuses on DNNs composed of fully connected and convolutional layers. It integrates visual encodings and interaction techniques chosen specifically for our tasks. The tool allows users to: (1) visually explore DNN models with arbitrary input using a combination of node–link diagrams and matrix representation; (2) quickly identify activation values, weights, and feature map patterns within a network; (3) flexibly focus on visual information of interest with threshold, inspection, insight query, and tooltip operations; (4) discover network activation and training patterns through animation; and (5) compare differences between internal activation patterns for different inputs to the DNN. These functions allow neural network researchers to examine their DNN models from new perspectives, producing insights on how these models function. Clustering and summarization techniques are employed to support large convolutional and fully connected layers. Based on several part of speech models with different structure and size, we present multiple use cases where visualization facilitates an understanding of the models.","PeriodicalId":164616,"journal":{"name":"2018 IEEE Pacific Visualization Symposium (PacificVis)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132374129","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

A Visual Analytics Approach for Equipment Condition Monitoring in Smart Factories of Process Industry 过程工业智能工厂设备状态监测的可视化分析方法

2018 IEEE Pacific Visualization Symposium (PacificVis) Pub Date : 2018-04-10 DOI: 10.1109/PacificVis.2018.00026

Wenchao Wu, Yixian Zheng, Kaiyuan Chen, Xiangyu Wang, Nan Cao

{"title":"A Visual Analytics Approach for Equipment Condition Monitoring in Smart Factories of Process Industry","authors":"Wenchao Wu, Yixian Zheng, Kaiyuan Chen, Xiangyu Wang, Nan Cao","doi":"10.1109/PacificVis.2018.00026","DOIUrl":"https://doi.org/10.1109/PacificVis.2018.00026","url":null,"abstract":"Monitoring equipment conditions is of great value in manufacturing, which can not only reduce unplanned downtime by early detecting anomalies of equipment but also avoid unnecessary routine maintenance. With the coming era of Industry 4.0 (or industrial internet), more and more assets and machines in plants are equipped with various sensors and information systems, which brings an unprecedented opportunity to capture large-scale and fine-grained data for effective on-line equipment condition monitoring. However, due to the lack of systematic methods, analysts still find it challenging to carry out efficient analyses and extract valuable information from the mass volume of data collected, especially for process industry (e.g., a petrochemical plant) with complex manufacturing procedures. In this paper, we report the design and implementation of an interactive visual analytics system, which helps managers and operators at manufacturing sites leverage their domain knowledge and apply substantial human judgements to guide the automated analytical approaches, thus generating understandable and trustable results for real-world applications. Our system integrates advanced analytical algorithms (e.g., Gaussian mixture model with a Bayesian framework) and intuitive visualization designs to provide a comprehensive and adaptive semi-supervised solution to equipment condition monitoring. The example use cases based on a real-world manufacturing dataset and interviews with domain experts demonstrate the effectiveness of our system.","PeriodicalId":164616,"journal":{"name":"2018 IEEE Pacific Visualization Symposium (PacificVis)","volume":"156 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125596703","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: 41

Exploring the Role of Sound in Augmenting Visualization to Enhance User Engagement 探索声音在增强可视化中的作用，以提高用户参与度

2018 IEEE Pacific Visualization Symposium (PacificVis) Pub Date : 2018-04-01 DOI: 10.1109/PacificVis.2018.00036

Meng Du, Jia-Kai Chou, Chen Ma, Senthil K. Chandrasegaran, K. Ma

{"title":"Exploring the Role of Sound in Augmenting Visualization to Enhance User Engagement","authors":"Meng Du, Jia-Kai Chou, Chen Ma, Senthil K. Chandrasegaran, K. Ma","doi":"10.1109/PacificVis.2018.00036","DOIUrl":"https://doi.org/10.1109/PacificVis.2018.00036","url":null,"abstract":"Studies on augmenting visualization with sound are typically based on the assumption that sound can be complementary and assist in data analysis tasks. While sound promotes a different sense of engagement than vision, we conjecture that by augmenting non-speech audio to a visualization can not only help enhance the users' perception of the data but also increase their engagement with the data exploration process. We have designed a preliminary user study to test users' performance and engagement while exploring in a data visualization system under two different settings: visual-only and audiovisual. For our study, we used basketball player movement data in a game and created an interactive visualization system with three linked views. We supplemented sound to the visualization to enhance the users' understanding of a team's offensive/defensive behavior. The results of our study suggest that we need to better understand the effect of sound choice and encoding before considering engagement. We also find that sound can be useful to draw novice users' attention to patterns or anomalies in the data. Finally, we propose follow-up studies with designs informed by the findings from this study.","PeriodicalId":164616,"journal":{"name":"2018 IEEE Pacific Visualization Symposium (PacificVis)","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117240840","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

Information Guided Data Sampling and Recovery Using Bitmap Indexing 使用位图索引的信息引导数据采样和恢复

2018 IEEE Pacific Visualization Symposium (PacificVis) Pub Date : 2018-04-01 DOI: 10.1109/PacificVis.2018.00016

Tzu-Hsuan Wei, Soumya Dutta, Han-Wei Shen

{"title":"Information Guided Data Sampling and Recovery Using Bitmap Indexing","authors":"Tzu-Hsuan Wei, Soumya Dutta, Han-Wei Shen","doi":"10.1109/PacificVis.2018.00016","DOIUrl":"https://doi.org/10.1109/PacificVis.2018.00016","url":null,"abstract":"Creating a data representation is a common approach for efficient and effective data management and exploration. The compressed bitmap indexing is one of the emerging data representation used for large-scale data exploration. Performing sampling on the bitmapindexing based data representation allows further reduction of storage overhead and be more flexible to meet the requirements of different applications. In this paper, we propose two approaches to solve two potential limitations when exploring and visualizing the data using sampling-based bitmap indexing data representation. First, we propose an adaptive sampling approach called information guided stratified sampling (IGStS) for creating compact sampled datasets that preserves the important characteristics of the raw data. Furthermore, we propose a novel data recovery approach to reconstruct the irregular subsampled dataset into a volume dataset with regular grid structure for qualitative post-hoc data exploration and visualization. The quantitative and visual efficacy of our proposed data sampling and recovery approaches are demonstrated through multiple experiments and applications.","PeriodicalId":164616,"journal":{"name":"2018 IEEE Pacific Visualization Symposium (PacificVis)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121394978","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: 18

In Situ Prediction Driven Feature Analysis in Jet Engine Simulations 喷气发动机仿真中的原位预测驱动特征分析

2018 IEEE Pacific Visualization Symposium (PacificVis) Pub Date : 2018-04-01 DOI: 10.1109/PacificVis.2018.00017

Soumya Dutta, Han-Wei Shen, Jen‐Ping Chen

{"title":"In Situ Prediction Driven Feature Analysis in Jet Engine Simulations","authors":"Soumya Dutta, Han-Wei Shen, Jen‐Ping Chen","doi":"10.1109/PacificVis.2018.00017","DOIUrl":"https://doi.org/10.1109/PacificVis.2018.00017","url":null,"abstract":"Efficient feature exploration in large-scale data sets using traditional post-hoc analysis approaches is becoming prohibitive due to the bottleneck stemming from I/O and output data sizes. This problem becomes more challenging when an ensemble of simulations are required to run for studying the influence of input parameters on the model output. As a result, scientists are inclining more towards analyzing the data in situ while it resides in the memory. In situ analysis aims at minimizing expensive data movement while maximizing the resource utilization for extraction of important information from the data. In this work, we study the evolution of rotating stall in jet engines using data generated from a large-scale flow simulation under various input conditions. Since the features of interest lack a precise descriptor, we adopt a fuzzy rule-based machine learning algorithm for efficient and robust extraction of such features. For scalable exploration, we advocate for an off-line learning and in situ prediction driven strategy that facilitates in-depth study of the stall. Task-specific information estimated in situ is visualized interactively during the post-hoc analysis revealing important details about the inception and evolution of stall. We verify and validate our method through comprehensive expert evaluation demonstrating the efficacy of our approach.","PeriodicalId":164616,"journal":{"name":"2018 IEEE Pacific Visualization Symposium (PacificVis)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115399047","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

Image and Distribution Based Volume Rendering for Large Data Sets 基于图像和分布的大数据集体绘制

2018 IEEE Pacific Visualization Symposium (PacificVis) Pub Date : 2018-04-01 DOI: 10.1109/PacificVis.2018.00013

Ko-Chih Wang, N. Shareef, Han-Wei Shen

{"title":"Image and Distribution Based Volume Rendering for Large Data Sets","authors":"Ko-Chih Wang, N. Shareef, Han-Wei Shen","doi":"10.1109/PacificVis.2018.00013","DOIUrl":"https://doi.org/10.1109/PacificVis.2018.00013","url":null,"abstract":"Analyzing scientific datasets created from simulations on modern supercomputers is a daunting challenge due to the fast pace at which these datasets continue to grow. Low cost post analysis machines used by scientists to view and analyze these massive datasets are severely limited by their deficiencies in storage bandwidth, capacity, and computational power. Trying to simply move these datasets to these platforms is infeasible. Any approach to view and analyze these datasets on post analysis machines will have to effectively address the inevitable problem of data loss. Image based approaches are well suited for handling very large datasets on low cost platforms. Three challenges with these approaches are how to effectively represent the original data with minimal data loss, analyze the data in regards to transfer function exploration, which is a key analysis tool, and quantify the error from data loss during analysis. We present a novel image based approach using distributions to preserve data integrity. At each view sample, view dependent data is summarized at each pixel with distributions to define a compact proxy for the original dataset. We present this representation along with how to manipulate and render large scale datasets on post analysis machines. We show that our approach is a good trade off between rendering quality and interactive speed and provides uncertainty quantification for the information that is lost.","PeriodicalId":164616,"journal":{"name":"2018 IEEE Pacific Visualization Symposium (PacificVis)","volume":"132 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132296151","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: 12

Topologically Controlled Lossy Compression 拓扑控制有损压缩

2018 IEEE Pacific Visualization Symposium (PacificVis) Pub Date : 2018-02-08 DOI: 10.1109/PacificVis.2018.00015

Maxime Soler, Mélanie Plainchault, B. Conche, Julien Tierny

{"title":"Topologically Controlled Lossy Compression","authors":"Maxime Soler, Mélanie Plainchault, B. Conche, Julien Tierny","doi":"10.1109/PacificVis.2018.00015","DOIUrl":"https://doi.org/10.1109/PacificVis.2018.00015","url":null,"abstract":"This paper presents a new algorithm for the lossy compression of scalar data defined on 2D or 3D regular grids, with topological control. Certain techniques allow users to control the pointwise error induced by the compression. However, in many scenarios it is desirable to control in a similar way the preservation of higher-level notions, such as topological features, in order to provide guarantees on the outcome of post-hoc data analyses. This paper presents the first compression technique for scalar data which supports a strictly controlled loss of topological features. It provides users with specific guarantees both on the preservation of the important features and on the size of the smaller features destroyed during compression. In particular, we present a simple compression strategy based on a topologically adaptive quantization of the range. Our algorithm provides strong guarantees on the bottleneck distance between persistence diagrams of the input and decompressed data, specifically those associated with extrema. A simple extension of our strategy additionally enables a control on the pointwise error. We also show how to combine our approach with state-of-the-art compressors, to further improve the geometrical reconstruction. Extensive experiments, for comparable compression rates, demonstrate the superiority of our algorithm in terms of the preservation of topological features. We show the utility of our approach by illustrating the compatibility between the output of post-hoc topological data analysis pipelines, executed on the input and decompressed data, for simulated or acquired data sets. We also provide a lightweight VTK-based C++ implementation of our approach for reproduction purposes.","PeriodicalId":164616,"journal":{"name":"2018 IEEE Pacific Visualization Symposium (PacificVis)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121550072","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: 27

Visual Detection of Structural Changes in Time-Varying Graphs Using Persistent Homology 基于持久同调的时变图结构变化的视觉检测

2018 IEEE Pacific Visualization Symposium (PacificVis) Pub Date : 2017-07-20 DOI: 10.1109/PacificVis.2018.00024

Mustafa Hajij, Bei Wang, C. Scheidegger, P. Rosen

引用次数: 50

Visual Analytics for Networked-Guarantee Loans Risk Management 网络担保贷款风险管理的可视化分析

2018 IEEE Pacific Visualization Symposium (PacificVis) Pub Date : 2017-04-06 DOI: 10.1109/PacificVis.2018.00028

Zhibin Niu, Dawei Cheng, Liqing Zhang, Jiawan Zhang

{"title":"Visual Analytics for Networked-Guarantee Loans Risk Management","authors":"Zhibin Niu, Dawei Cheng, Liqing Zhang, Jiawan Zhang","doi":"10.1109/PacificVis.2018.00028","DOIUrl":"https://doi.org/10.1109/PacificVis.2018.00028","url":null,"abstract":"Groups of enterprises can guarantee each other and form complex networks in order to try to obtain loans from banks. Monitoring the financial status of a network, and preventing or reducing systematic risk in case of a crisis, is an area of great concern for the regulatory commission and for the banks. We set the ultimate goal of developing a visual analytic approach and tool for risk dissolving and decision-making. We have consolidated four main analysis tasks conducted by financial experts: i) Multi-faceted Default Risk Visualization, whereby a hybrid representation is devised to predict the default risk and an interface developed to visualize key indicators; ii) Risk Guarantee Patterns Discovery. We follow the Shneiderman mantra guidance for designing interactive visualization applications, whereby an interactive risk guarantee community detection and a motif detection based risk guarantee pattern discovery approach are described; iii) Network Evolution and Retrospective, whereby animation is used to help users to understand the guarantee dynamic; iv) Risk Communication Analysis. The temporal diffusion path analysis can be useful for the government and banks to monitor the spread of the default status. It also provides insight for taking precautionary measures to prevent and dissolve systematic financial risk. We implement the system with case studies using real-world bank loan data. Two financial experts are consulted to endorse the developed tool. To the best of our knowledge, this is the first visual analytics tool developed to explore networked-guarantee loan risks in a systematic manner.","PeriodicalId":164616,"journal":{"name":"2018 IEEE Pacific Visualization Symposium (PacificVis)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130743763","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: 22