anchor.diff:  VAST,TVCG,Evaluating Multi-Dimensional Visualizations for Understanding Fuzzy Clusters,J),"Ying Zhao, Feng Luo, Minghui Chen, Yingchao Wang, Jiazhi Xia, Fangfang Zhou, Yunhai Wang, Yi Chen, Wei Chen",http://www.cad.zju.edu.cn/home/vagblog/VAG_Work/Fuzzy%20Clusters.pdf,"Fuzzy clustering assigns a probability of membership for a datum to a cluster, which veritably reflects real-world clustering scenarios but significantly increases the complexity of understanding fuzzy clusters. Many studies have demonstrated that visualization techniques for multi-dimensional data are beneficial to understand fuzzy clusters. However, no empirical evidence exists on the effectiveness and efficiency of these visualization techniques in solving analytical tasks featured by fuzzy clusters. In this paper, we conduct a controlled experiment to evaluate the ability of fuzzy clusters analysis to use four multi-dimensional visualization techniques, namely, parallel coordinate plot, scatterplot matrix, principal component analysis, and Radviz. First, we define the analytical tasks and their representative questions specific to fuzzy clusters analysis. Then, we design objective questionnaires to compare the accuracy, time, and satisfaction in using the four techniques to solve the questions. We also design subjective questionnaires to collect the experience of the volunteers with the four techniques in terms of ease of use, informativeness, and helpfulness. With a complete experiment process and a detailed result analysis, we test against four hypotheses that are formulated on the basis of our experience, and provide instructive guidance for analysts in selecting appropriate and efficient visualization techniques to analyze fuzzy clusters.",,,,,vimeo 289787891,10.1109/tvcg.2018.2865020,2018,2018,VAST,"Convention Hall 1, Section C",Tuesday,Evaluation and Theory,23-Oct,2:20 PM,2:40 PM
   VAST,VAST,The Effect of Proximity in Social Data Charts on Perceived Unity,C),"Marlen Promann, Sabine Brunswicker",,,,,,,vimeo 289787716,,2018,2018,VAST,"Convention Hall 1, Section C",Tuesday,Evaluation and Theory,23-Oct,2:40 PM,3:00 PM
   VAST,TVCG,Futzing and Moseying: Interviews with Professional Data Analysts on Exploration Practices,J),"Sara Alspaugh, Nava Zokaei, Andrea Liu, Cindy Jin, Marti Hearst",http://people.ischool.berkeley.edu/~hearst/papers/vast2018.pdf,"We report the results of interviewing thirty professional data analysts working in a range of industrial, academic, and regulatory environments. This study focuses on participants' descriptions of exploratory activities and tool usage in these activities. Highlights of the ndings include: distinctions between exploration as a precursor to more directed analysis versus truly open-ended exploration; conrmation that some analysts see “nding something interesting” as a valid goal of data exploration while others explicitly disavow this goal; conicting views about the role of intelligent tools in data exploration; and pervasive use of visualization for exploration, but with only a subset using direct manipulation interfaces. These ndings provide guidelines for future tool development, as well as a better understanding of the meaning of the term “data exploration” based on the words of practitioners “in the wild.”",,,,,vimeo 289787961,10.1109/TVCG.2018.2865040,2018,2018,VAST,"Convention Hall 1, Section C",Tuesday,Evaluation and Theory,23-Oct,3:00 PM,3:20 PM
  -VAST,VAST,The Effect of Semantic Interaction on Foraging in Text Analysis,C),"John Wenskovitch, Lauren Bradel, Michelle Dowling, Leanna House, Chris North",,,,,,,vimeo 289787752,,2018,2018,VAST,"Convention Hall 1, Section C",Tuesday,Evaluation and Theory,23-Oct,3:20 PM,3:40 PM
  +VAST,VAST,The Effect of Semantic Interaction on Foraging in Text Analysis,C),"John Wenskovitch, Lauren Bradel, Michelle Dowling, Leanna House, Chris North",https://infovis.cs.vt.edu/sites/default/files/effect-semantic-interaction.pdf,"Completing text analysis tasks is a continuous sensemaking loop of foraging for information and incrementally synthesizing it into hypotheses. Past research has shown the advantages of using spatial workspaces as a means for synthesizing information through externalizing hypotheses and creating spatial schemas. However, spatializing the entirety of datasets becomes prohibitive as the number of documents available to the analysts grows, particularly when only a small subset are relevant to the task at hand. StarSPIRE is a visual analytics tool designed to explore collections of documents, leveraging users’ semantic interactions to steer (1) a synthesis model that aids in document layout, and (2) a foraging model to automatically retrieve new relevant information. In contrast to traditional keyword search foraging (KSF), “semantic interaction foraging” (SIF) occurs as a result of the user’s synthesis actions. To quantify the value of semantic interaction foraging, we use StarSPIRE to evaluate its utility for an intelligence analysis sensemaking task. Semantic interaction foraging accounted for 26% of useful documents found, and it also resulted in increased synthesis interactions and improved sensemaking task performance by users in comparison to only using keyword search.",,,,,vimeo 289787752,,2018,2018,VAST,"Convention Hall 1, Section C",Tuesday,Evaluation and Theory,23-Oct,3:20 PM,3:40 PM
   VAST,TVCG,Cost-benefit Analysis of Visualization in Virtual Environments,J),"Min Chen, Kelly Gaither, Nigel John, Brian McCann",,,,,,,vimeo 289787910,,2018,2018,VAST,"Convention Hall 1, Section C",Tuesday,Evaluation and Theory,23-Oct,3:40 PM,4:00 PM
   InfoVis,TVCG,Shape-preserving Star Coordinates,J),"Vladimir Molchanov, Lars Linsen",,,,,,,vimeo 289785145,10.1109/tvcg.2018.2865118,2018,2018,InfoVis,"Convention Hall 1, Section D",Tuesday,Multiple Dimensions,23-Oct,2:20 PM,2:40 PM
   TVCG,TVCG,Using Dashboard Networks to Visualize Multiple Patient Histories: A Design Study on Post-operative Prostate Cancer,T),"Jürgen Bernard, David Sessler, Jörn Kohlhammer, Roy A. Ruddle",http://eprints.whiterose.ac.uk/128739/1/bernard-ieee-tvcg-dashboard-networks.pdf,"In this design study, we present a visualization technique that segments patients' histories instead of treating them as raw event sequences, aggregates the segments using criteria such as the whole history or treatment combinations, and then visualizes the aggregated segments as static dashboards that are arranged in a dashboard network to show longitudinal changes. The static dashboards were developed in nine iterations, to show 15 important attributes from the patients' histories. The final design was evaluated with five non-experts, five visualization experts and four medical experts, who successfully used it to gain an overview of a 2,000 patient dataset, and to make observations about longitudinal changes and differences between two cohorts. The research represents a step-change in the detail of large-scale data that may be successfully visualized using dashboards, and provides guidance about how the approach may be generalized.",,,,,vimeo 289789053,10.1109/tvcg.2018.2803829,2018,2018,InfoVis,"Convention Hall 1, Section D",Tuesday,Multiple Dimensions,23-Oct,2:40 PM,3:00 PM
  
positive.code: CG&A,CGA,Name Profiler Toolkit,,"Feng Wang, Brett Hansen, Ryan Simmons, Ross Maciejewski",,,,,,,vimeo 289785766,10.1109/mcg.2017.3621224,,2018,Other,Room III,Wednesday,CG&A Session 1,24-Oct,12:20 PM,12:40 PM
  VAST,TVCG,"SIRIUS: Dual, Symmetric, Interactive Dimension Reductions ",J),"Michelle Dowling, John Wenskovitch, J.T. Fry, Leanna House, Scotland Leman, Chris North",,,,,,,vimeo 289787667,10.1109/tvcg.2018.2865047,2018,2018,VAST,"Convention Hall 1, Section C",Wednesday,High Dimensional Data,24-Oct,2:20 PM,2:40 PM
  TVCG,TVCG,A Perception-Driven Approach to Supervised Dimensionality Reduction for Visualization,T),"Yunhai Wang, Kang Feng, Xiaowei Chu, Jian Zhang, Chi-Wing Fu, Michael Sedlmair, Xiaohui Yu, Baoquan Chen",,,,,,,vimeo 289788938,10.1109/tvcg.2017.2701829,,2018,VAST,"Convention Hall 1, Section C",Wednesday,High Dimensional Data,24-Oct,2:40 PM,3:00 PM
  VAST,VAST,SMARTexplore: Simplifying High-Dimensional Data Analysis through a Table-Based Visual Analytics Approach,C),"Michael Blumenschein, Michael Behrisch, Stefanie Schmid, Simon Butscher, Deborah R. Wahl, Karoline Villinger, Britta Renner, Harald Reiterer, Daniel Keim",,,,,,,vimeo 289787735,,2018,2018,VAST,"Convention Hall 1, Section C",Wednesday,High Dimensional Data,24-Oct,3:00 PM,3:20 PM
  TVCG,TVCG,ColorMapND: A Data-Driven Approach and Tool for Mapping Multivariate Data to Color,T),"Shenghui Cheng, Wei Xu, Klaus Mueller",,,,,,,vimeo 289788955,10.1109/tvcg.2018.2808489,,2018,VAST,"Convention Hall 1, Section C",Wednesday,High Dimensional Data,24-Oct,3:20 PM,3:40 PM
  VAST,VAST,EmbeddingVis: A Visual Analytics Approach to Comparative Network Embedding Inspection,C),"Quan Li, Kristanto Sean Njotoprawiro, Hammad Haleem, Qiaoan Chen, Chris YI, Xiaojuan Ma",,,,,,,vimeo 289788000,,2018,2018,VAST,"Convention Hall 1, Section C",Wednesday,High Dimensional Data,24-Oct,3:40 PM,4:00 PM
  InfoVis,TVCG,Evaluating 'Graphical Perception' with CNNs,,"Daniel Haehn, James Tompkin, Hanspeter Pfister",https://danielhaehn.com/papers/haehn2018evaluating.pdf,"Convolutional neural networks can successfully perform many computer vision tasks on images. For visualization, how do CNNs perform when applied to graphical perception tasks? We investigate this question by reproducing Cleveland and McGill's seminal 1984 experiments, which measured human perception efficiency of different visual encodings and defined elementary perceptual tasks for visualization. We measure the graphical perceptual capabilities of four network architectures on five different visualization tasks and compare to existing and new human performance baselines. While under limited circumstances CNNs are able to meet or outperform human task performance, we find that CNNs are not currently a good model for human graphical perception. We present the results of these experiments to foster the understanding of how CNNs succeed and fail when applied to data visualizations.",,https://github.com/rhoana/perception,,,vimeo 280506639,,2018,2018,InfoVis,"Convention Hall 1, Section D",Wednesday,Text & Communication,24-Oct,2:20 PM,2:40 PM
  InfoVis,TVCG,NLIZE: A Perturbation-Driven Visual Interrogation Tool for Analyzing and Interpreting Natural Language Inference Models,J),"Shusen Liu, Zhimin Li, Tao Li, Vivek Srikumar, Valerio Pascucci, Peer-Timo Bremer",http://www.sci.utah.edu/~shusenl/publications/paper_entailVis.pdf,"With the recent advances in deep learning, neural network models have obtained state-of-the-art performances for many linguistic tasks in natural language processing. However, this rapid progress also brings enormous challenges. The opaque nature of a neural network model leads to hard-to-debug-systems and difficult-to-interpret mechanisms. Here, we introduce a visualization system that, through a tight yet flexible integration between visualization elements and the underlying model, allows a user to interrogate the model by perturbing the input, internal state, and prediction while observing changes in other parts of the pipeline. We use the natural language inference problem as an example to illustrate how a perturbation-driven paradigm can help domain experts assess the potential limitation of a model, probe its inner states, and interpret and form hypotheses about fundamental model mechanisms such as attention.",,,,,vimeo 289784737,10.1109/tvcg.2018.2865230,2018,2018,InfoVis,"Convention Hall 1, Section D",Wednesday,Text & Communication,24-Oct,2:40 PM,3:00 PM
  InfoVis,TVCG,Elastic Documents: Coupling Text and Tables through Contextual Visualizations for Enhanced Document Reading,J),"Sriram Karthik Badam, Zhicheng Liu, Niklas Elmqvist",http://users.umiacs.umd.edu/~elm/projects/elastic-documents/elastic-documents.pdf,"Today's data-rich documents are often complex datasets in themselves, consisting of information in different formats such as text, gures, and data tables. These additional media augment the textual narrative in the document. However, the static layout of a traditional for-print document often impedes deep understanding of its content because of the need to navigate to access content scattered throughout the text. In this paper, we seek to facilitate enhanced comprehension of such documents through a contextual visualization technique that couples text content with data tables contained in the document. We parse the text content and data tables, cross-link the components using a keyword-based matching algorithm, and generate on-demand visualizations based on the reader's current focus within a document. We evaluate this technique in a user study comparing our approach to a traditional reading experience. Results from our study show that (1) participants comprehend the content better with tighter coupling of text and data, (2) the contextual visualizations enable participants to develop better summaries that capture the main data-rich insights within the document, and (3) overall, our method enables participants to develop a more detailed understanding of the document content.",,,,,vimeo 289784966,,2018,2018,InfoVis,"Convention Hall 1, Section D",Wednesday,Text & Communication,24-Oct,3:00 PM,3:20 PM
  
negative.code: SciVis,SciVis,"TimeTubes: Automatic Extraction of Observable Blazar Features from Long-Term, Multi-Dimensional Datasets",,"Naoko Sawada, Masanori Nakayama, Makoto Uemura, Issei Fujishiro",,,,,,,vimeo 290328934,,2018,2018,SciVis Short Papers,Estrel Hall C,Thursday,"Flow, Astrophysics, and Computationally Intensive Data Visualization",25-Oct,5:22 PM,5:34 PM
  SciVis,SciVis,aflak: Pluggable Visual Programming Environment with Quick Feedback Loop Tuned for Astrophysical Observations,,"Malik Olivier Boussejra, Kazuya Matsubayashi, Yuriko Takeshima, Shunya Takekawa, Rikuo Uchiki, Makoto Uemura, Issei Fujishiro",,,,,,,vimeo 290328343,,2018,2018,SciVis Short Papers,Estrel Hall C,Thursday,"Flow, Astrophysics, and Computationally Intensive Data Visualization",25-Oct,5:35 PM,5:47 PM
  SciVis,SciVis,Biclusters based Visual Exploration of Multivariate Scientific Data,,"Xiangyang He, Yubo Tao, Qirui Wang, Hai Lin",,,,,,,vimeo 290328582,,2018,2018,SciVis Short Papers,Estrel Hall C,Thursday,"Flow, Astrophysics, and Computationally Intensive Data Visualization",25-Oct,5:47 PM,6:00 PM
  VAST,TVCG,MAQUI: Interweaving Queries and Pattern Mining for Recursive Event Sequence Exploration,J),"Po-Ming Law, Zhicheng Liu, Sana Malik, Rahul Basole",http://www.zcliu.org/maqui/MAQUI_vast18.pdf,"Exploring event sequences by defining queries alone or by using mining algorithms alone is often not sufficient to support analysis. Analysts often interweave querying and mining in a recursive manner during event sequence analysis: sequences extracted as query results are used for mining patterns, patterns generated are incorporated into a new query for segmenting the sequences, and the resulting segments are mined or queried again. To support flexible analysis, we propose a framework that describes the process of interwoven querying and mining. Based on this framework, we developed MAQUI, a Mining And Querying User Interface that enables recursive event sequence exploration. To understand the efficacy of MAQUI, we conducted two case studies with domain experts. The findings suggest that the capability of interweaving querying and mining helps the participants articulate their questions and gain novel insights from their data.",,https://github.com/terrancelaw/MAQUI,,,vimeo 289787863,10.1109/TVCG.2018.2864886,2018,2018,VAST,"Convention Hall 1, Section C",Friday,"Event, Sequence, and ML",26-Oct,9:00 AM,9:20 AM
  VAST,TVCG,iForest: Interpreting Random Forests via Visual Analytics,J),"Xun Zhao, Yanhong Wu, Dik Lun Lee, Weiwei Cui",http://zhaoxun.me/publication/iForest.pdf,"As an ensemble model that consists of many independent decision trees, random forests generate predictions by feeding the input to internal trees and summarizing their outputs. The ensemble nature of the model helps random forests outperform any individual decision tree. However, it also leads to a poor model interpretability, which significantly hinders the model from being used in fields that require transparent and explainable predictions, such as medical diagnosis and financial fraud detection. The interpretation challenges stem from the variety and complexity of the contained decision trees. Each decision tree has its unique structure and properties, such as the features used in the tree and the feature threshold in each tree node. Thus, a data input may lead to a variety of decision paths. To understand how a final prediction is achieved, it is desired to understand and compare all decision paths in the context of all tree structures, which is a huge challenge for any users. In this paper, we propose a visual analytic system aiming at interpreting random forest models and predictions. In addition to providing users with all the tree information, we summarize the decision paths in random forests, which eventually reflects the working mechanism of the model and reduces users’ mental burden of interpretation. To demonstrate the effectiveness of our system, two usage scenarios and a qualitative user study are conducted.",,,,,vimeo 289787165,10.1109/tvcg.2018.2864475,2018,2018,VAST,"Convention Hall 1, Section C",Friday,"Event, Sequence, and ML",26-Oct,9:20 AM,9:40 AM
  VAST,TVCG,Visual Progression Analysis of Event Sequence Data,J),"Shunan Guo, Zhuochen Jin, David Gotz, Fan Du, Hongyuan Zha, Nan Cao",http://gotz.web.unc.edu/files/2018/08/2018_VAST_EventThread_V2_Preprint.pdf,"Event sequence data is common to a broad range of application domains, from security to health care to scholarly communication. This form of data captures information about the progression of events for an individual entity (e.g., a computer network device; a patient; an author) in the form of a series of time-stamped observations. Moreover, each event is associated with an event type (e.g., a computer login attempt, or a hospital discharge). Analyses of event sequence data have been shown to help reveal important temporal patterns, such as clinical paths resulting in improved outcomes, or an understanding of common career trajectories for scholars. Moreover, recent research has demonstrated a variety of techniques designed to overcome methodological challenges such as large volumes of data and high dimensionality. However, the effective identication and analysis of latent stages of progression, which can allow for variation within different but similarly evolving event sequences, remain a signicant challenge with important real-world motivations. In this paper, we propose an unsupervised stage analysis algorithm to identify semantically meaningful progression stages as well as the critical events which help dene those stages. The algorithm follows three key steps: (1) event representation estimation, (2) event sequence warping and alignment, and (3) sequence segmentation. We also present a novel visualization system, ET2 , which interactively illustrates the results of the stage analysis algorithm to help reveal evolution patterns across stages. Finally, we report three forms of evaluation for ET2 : (1) case studies with two real-world datasets, (2) interviews with domain expert users, and (3) a performance evaluation on the progression analysis algorithm and the visualization design",,,,,vimeo 289787414,10.1109/TVCG.2018.2864885,2018,2018,VAST,"Convention Hall 1, Section C",Friday,"Event, Sequence, and ML",26-Oct,9:40 AM,10:00 AM
  TVCG,TVCG,StreamExplorer: A Multi-Stage System for Visually Exploring Events in Social Streams,T),"Yingcai Wu, Zhutian Chen, Guodao Sun, Xiao Xie, Nan Cao, Shixia Liu, Weiwei Cui",,,,,,,vimeo 289788908,10.1109/tvcg.2017.2764459,,2018,VAST,"Convention Hall 1, Section C",Friday,"Event, Sequence, and ML",26-Oct,10:00 AM,10:20 AM
  VAST,TVCG,Duet: Helping Data Analysis Novices Conduct Pairwise Comparisons by Minimal Specification,J),"Po-Ming Law, Rahul Basole, Yanhong Wu",https://terrancelaw.github.io/publications/duet_vast18.pdf,"Data analysis novices often encounter barriers in executing low-level operations for pairwise comparisons. They may also run into barriers in interpreting the artifacts (e.g., visualizations) created as a result of the operations. We developed Duet, a visual analysis system designed to help data analysis novices conduct pairwise comparisons by addressing execution and interpretation barriers. To reduce the barriers in executing low-level operations during pairwise comparison, Duet employs minimal specication: when one object group (i.e. a group of records in a data table) is specied, Duet recommends object groups that are similar to or different from the specied one; when two object groups are specied, Duet recommends similar and different attributes between them. To lower the barriers in interpreting its recommendations, Duet explains the recommended groups and attributes using both visualizations and textual descriptions. We conducted a qualitative evaluation with eight participants to understand the effectiveness of Duet. The results suggest that minimal specication is easy to use and Duet's explanations are helpful for interpreting the recommendations despite some usability issues.",,,,,vimeo 289787850,10.1109/TVCG.2018.2864526,2018,2018,VAST,"Convention Hall 1, Section C",Friday,"Event, Sequence, and ML",26-Oct,10:20 AM,10:40 AM
  InfoVis,TVCG,Visualizing Uncertain Tropical Cyclone Predictions using Representative Samples from Ensembles of Forecast Tracks,J),"Le Liu, Lace M. K. Padilla, Sarah Creem-Regehr, Donald House",http://lacepadilla.com/Downloads/publications/Liuetal_2018.pdf,"A common approach to sampling the space of a prediction is the generation of an ensemble of potential outcomes, where the ensemble's distribution reveals the statistical structure of the prediction space. For example, the US National Hurricane Center generates multiple day predictions for a storm's path, size, and wind speed, and then uses a Monte Carlo approach to sample this prediction into a large ensemble of potential storm outcomes. Various forms of summary visualizations are generated from such an ensemble, often using spatial spread to indicate its statistical characteristics. However, studies have shown that changes in the size of such summary glyphs, representing changes in the uncertainty of the prediction, are frequently confounded with other attributes of the phenomenon, such as its size or strength. In addition, simulation ensembles typically encode multivariate information, which can be difficult or confusing to include in a summary display. This problem can be overcome by directly displaying the ensemble as a set of annotated trajectories, however this solution will not be effective if ensembles are densely overdrawn or structurally disorganized. We propose to overcome these difficulties by selectively sampling the original ensemble, constructing a smaller representative and spatially well organized ensemble. This can be drawn directly as a set of paths that implicitly reveals the underlying spatial uncertainty distribution of the prediction. Since this approach does not use a visual channel to encode uncertainty, additional information can more easily be encoded in the display without leading to visual confusion. To demonstrate our argument, we describe the development of a visualization for ensembles of tropical cyclone forecast tracks, explaining how their spatial and temporal predictions, as well as other crucial storm characteristics such as size and intensity, can be clearly revealed. We verify the effectiveness of this visualization approach through a cognitive study exploring how storm damage estimates are affected by the density of tracks drawn, and by the presence or absence of annotating information on storm size and intensity.",,https://osf.io/hy3ba/,https://osf.io/hy3ba/,,vimeo 289784601,,2018,2018,InfoVis,"Convention Hall 1, Section D",Friday,Uncertainty & Error,26-Oct,9:00 AM,9:20 AM
  InfoVis,TVCG,Hypothetical Outcome Plots Help Untrained Observers Judge Trends in Ambiguous Data,J),"Alex Kale, Francis Nguyen, Matthew Kay, Jessica Hullman",http://users.eecs.northwestern.edu/~jhullman/hops_jobs_pfs.pdf,"Animated representations of outcomes drawn from distributions (hypothetical outcome plots, or HOPs) are used in the media and other public venues to communicate uncertainty. HOPs greatly improve multivariate probability estimation over conventional static uncertainty visualizations and leverage the ability of the visual system to quickly, accurately, and automatically process the summary statistical properties of ensembles. However, it is unclear how well HOPs support applied tasks resembling real world judgments posed in uncertainty communication. We identify and motivate an appropriate task to investigate realistic judgments of uncertainty in the public domain through a qualitative analysis of uncertainty visualizations in the news. We contribute two crowdsourced experiments comparing the effectiveness of HOPs, error bars, and line ensembles for supporting perceptual decision-making from visualized uncertainty. Participants infer which of two possible underlying trends is more likely to have produced a sample of time series data by referencing uncertainty visualizations which depict the two trends with variability due to sampling error. By modeling each participant's accuracy as a function of the level of evidence presented over many repeated judgments, we find that observers are able to correctly infer the underlying trend in samples conveying a lower level of evidence when using HOPs rather than static aggregate uncertainty visualizations as a decision aid. Modeling approaches like ours contribute theoretically grounded and richly descriptive accounts of user perceptions to visualization evaluation.",https://medium.com/@uwdata/hypothetical-outcome-plots-hops-help-users-separate-signal-from-noise-870d4e2b75d7,,https://github.com/kalealex/jobs-report-hops,https://osf.io/8cte3/registrations/,vimeo 289785258,10.1109/tvcg.2018.2864909,2018,2018,InfoVis,"Convention Hall 1, Section D",Friday,Uncertainty & Error,26-Oct,9:20 AM,9:40 AM
  InfoVis,TVCG,In Pursuit of Error: A Survey of Uncertainty Visualization Evaluation,J),"Jessica Hullman, Xiaoli Qiao, Michael Correll, Alex Kale, Matthew Kay",https://github.com/jhullman/uncertaintyVisEval/blob/master/uncertainty_vis_eval.pdf,"Understanding and accounting for uncertainty is critical to effectively reasoning about visualized data. However, evaluating the impact of an uncertainty visualization is complex due to the difficulties that people have interpreting uncertainty and the challenge of defining correct behavior with uncertainty information. Currently, evaluators of uncertainty visualization must rely on general purpose visualization evaluation frameworks which can be ill-equipped to provide guidance with the unique difficulties of assessing judgments under uncertainty. To help evaluators navigate these complexities, we present a taxonomy for characterizing decisions made in designing an evaluation of an uncertainty visualization. Our taxonomy differentiates six levels of decisions that comprise an uncertainty visualization evaluation: the behavioral targets of the study, expected effects from an uncertainty visualization, evaluation goals, measures, elicitation techniques, and analysis approaches. Applying our taxonomy to 86 user studies of uncertainty visualizations, we find that existing evaluation practice, particularly in visualization research, focuses on Performance and Satisfaction-based measures that assume more predictable and statistically-driven judgment behavior than is suggested by research on human judgment and decision making. We reflect on common themes in evaluation practice concerning the interpretation and semantics of uncertainty, the use of confidence reporting, and a bias toward evaluating performance as accuracy rather than decision quality. We conclude with a concrete set of recommendations for evaluators designed to reduce the mismatch between the conceptualization of uncertainty in visualization versus other fields.",,https://github.com/jhullman/uncertaintyVisEval,,,vimeo 289785271,10.1109/tvcg.2018.2864889,2018,2018,InfoVis,"Convention Hall 1, Section D",Friday,Uncertainty & Error,26-Oct,9:40 AM,10:00 AM
  