anchor.diff:  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",,,,,,,vimeo 289787863,,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",,,,,,,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",,,,,,,vimeo 289787414,,2018,2018,VAST,"Convention Hall 1, Section C",Friday,"Event, Sequence, and ML",26-Oct,9:40 AM,10:00 AM
  +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",,,,,,,vimeo 289787850,,2018,2018,VAST,"Convention Hall 1, Section C",Friday,"Event, Sequence, and ML",26-Oct,10:20 AM,10:40 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
  
positive.code: 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,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
  
negative.code: SciVis,SciVis,Toward A Deep Understanding of What Makes a Scientific Visualization Memorable,,"Rui Li, Jian Chen",,,,,,,vimeo 290328799,,2018,2018,SciVis Short Papers,Estrel Hall C,Thursday,"Visual Abstractions, Perceptual Study and Immersive Visualization",25-Oct,3:22 PM,3:34 PM
  SciVis,SciVis,Ordering Perceptions about Perceptual Order,,"Roxana Bujack, Terece L Turton, David Rogers, James Ahrens",,,,,,,vimeo 290328692,,2018,2018,SciVis Short Papers,Estrel Hall C,Thursday,"Visual Abstractions, Perceptual Study and Immersive Visualization",25-Oct,3:35 PM,3:47 PM
  SciVis,SciVis,QuFlow: Visualizing Parameter Flow in Quantum Circuits for Understanding Quantum Computation,,"Siyuan Lin, Hao Jiang, Lingyun Sun",,,,,,,vimeo 290328716,,2018,2018,SciVis Short Papers,Estrel Hall C,Thursday,"Visual Abstractions, Perceptual Study and Immersive Visualization",25-Oct,3:47 PM,3:59 PM
  VAST,TVCG,RuleMatrix: Visualizing and Understanding Classifiers with Rules,J),"Yao Ming, Huamin Qu, Enrico Bertini",https://arxiv.org/pdf/1807.06228.pdf,"With the growing adoption of machine learning techniques, there is a surge of research interest towards making machine learning systems more transparent and interpretable. Various visualizations have been developed to help model developers understand, diagnose, and refine machine learning models. However, a large number of potential but neglected users are the domain experts with little knowledge of machine learning but are expected to work with machine learning systems. In this paper, we present an interactive visualization technique to help users with little expertise in machine learning to understand, explore and validate predictive models. By viewing the model as a black box, we extract a standardized rule-based knowledge representation from its input-output behavior. We design RuleMatrix, a matrix-based visualization of rules to help users navigate and verify the rules and the black-box model. We evaluate the effectiveness of RuleMatrix via two use cases and a usability study.",http://myaooo.com/projects/rule-matrix/,,,,vimeo 289787299,10.1109/TVCG.2018.2864812,2018,2018,VAST,"Convention Hall 1, Section C",Thursday,Explainable ML,25-Oct,4:20 PM,4:40 PM
  VAST,TVCG,Seq2Seq-Vis: A Visual Debugging Tool for Sequence to Sequence Models,J),"Hendrik Strobelt, Sebastian Gehrmann, Michael Behrisch, Adam Perer, Hanspeter Pfister, Alexander M. Rush",https://arxiv.org/pdf/1804.09299.pdf,"Neural sequence-to-sequence models have proven to be accurate and robust for many sequence prediction tasks, and have become the standard approach for automatic translation of text. The models work with a five-stage blackbox pipeline that begins with encoding a source sequence to a vector space and then decoding out to a new target sequence. This process is now standard, but like many deep learning methods remains quite difficult to understand or debug. In this work, we present a visual analysis tool that allows interaction and ”what if”-style exploration of trained sequence-to-sequence models through each stage of the translation process. The aim is to identify which patterns have been learned, to detect model errors, and to probe the model with counterfactual scenario. We demonstrate the utility of our tool through several real-world sequence-to-sequence use cases on large-scale models.",,https://github.com/HendrikStrobelt/Seq2Seq-Vis,,,vimeo 289787650,,2018,2018,VAST,"Convention Hall 1, Section C",Thursday,Explainable ML,25-Oct,4:40 PM,5:00 PM
  VAST,TVCG,Manifold: A Model-Agnostic Framework for Interpretation and Diagnosis of Machine Learning Models,J),"Jiawei Zhang, Yang Wang, Piero Molino, Lezhi Li, David Ebert",https://arxiv.org/pdf/1808.00196.pdf,"Interpretation and diagnosis of machine learning models have gained renewed interest in recent years with breakthroughs in new approaches. We present Manifold, a framework that utilizes visual analysis techniques to support interpretation, debugging, and comparison of machine learning models in a more transparent and interactive manner. Conventional techniques usually focus on visualizing the internal logic of a specific model type (i.e., deep neural networks), lacking the ability to extend to a more complex scenario where different model types are integrated. To this end, Manifold is designed as a generic framework that does not rely on or access the internal logic of the model and solely observes the input (i.e., instances or features) and the output (i.e., the predicted result and probability distribution). We describe the workflow of Manifold as an iterative process consisting of three major phases that are commonly involved in the model development and diagnosis process: inspection (hypothesis), explanation (reasoning), and refinement (verification). The visual components supporting these tasks include a scatterplot-based visual summary that overviews the models’ outcome and a customizable tabular view that reveals feature discrimination. We demonstrate current applications of the framework on the classification and regression tasks and discuss other potential machine learning use scenarios where Manifold can be applied.",,,,,vimeo 289787203,10.1109/tvcg.2018.2864499,2018,2018,VAST,"Convention Hall 1, Section C",Thursday,Explainable ML,25-Oct,5:00 PM,5:20 PM
  VAST,TVCG,Visual Analytics for Topic Model Optimization based on User-Steerable Speculative Execution,J),"Mennatallah El-Assady, Fabian Sperrle, Oliver Deussen, Daniel Keim, Christopher Collins",http://graphics.uni-konstanz.de/publikationen/ElAssady2018VisualAnalytic/visualanalyticstopic.pdf,"To effectively assess the potential consequences of human interventions in model-driven analytics systems, we establish the concept of speculative execution as a visual analytics paradigm for creating user-steerable preview mechanisms. This paper presents an explainable, mixed-initiative topic modeling framework that integrates speculative execution into the algorithmic decisionmaking process. Our approach visualizes the model-space of our novel incremental hierarchical topic modeling algorithm, unveiling its inner-workings. We support the active incorporation of the user’s domain knowledge in every step through explicit model manipulation interactions. In addition, users can initialize the model with expected topic seeds, the backbone priors. For a more targeted optimization, the modeling process automatically triggers a speculative execution of various optimization strategies, and requests feedback whenever the measured model quality deteriorates. Users compare the proposed optimizations to the current model state and preview their effect on the next model iterations, before applying one of them. This supervised human-in-the-loop process targets maximum improvement for minimum feedback and has proven to be effective in three independent studies that confirm topic model quality improvements.",,,,,vimeo 289787264,10.1109/tvcg.2018.2864769,2018,2018,VAST,"Convention Hall 1, Section C",Thursday,Explainable ML,25-Oct,5:20 PM,5:40 PM
  VAST,TVCG,VIS4ML: An Ontology for Visual Analytics Assisted Machine Learning,J),"Dominik Sacha, Matthias Kraus, Daniel Keim, Min Chen",https://bib.dbvis.de/uploadedFiles/TVCG2864838.pdf,"While many VA workflows make use of machine-learned models to support analytical tasks, VA workflows have become increasingly important in understanding and improving Machine Learning (ML) processes. In this paper, we propose an ontology (VIS4ML) for a subarea of VA, namely “VA-assisted ML”. The purpose of VIS4ML is to describe and understand existing VA workflows used in ML as well as to detect gaps in ML processes and the potential of introducing advanced VA techniques to such processes. Ontologies have been widely used to map out the scope of a topic in biology, medicine, and many other disciplines. We adopt the scholarly methodologies for constructing VIS4ML, including the specification, conceptualization, formalization, implementation, and validation of ontologies. In particular, we reinterpret the traditional VA pipeline to encompass model-development workflows. We introduce necessary definitions, rules, syntaxes, and visual notations for formulating VIS4ML and make use of semantic web technologies for implementing it in the Web Ontology Language (OWL). VIS4ML captures the high-level knowledge about previous workflows where VA is used to assist in ML. It is consistent with the established VA concepts and will continue to evolve along with the future developments in VA and ML. While this ontology is an effort for building the theoretical foundation of VA, it can be used by practitioners in real-world applications to optimize model-development workflows by systematically examining the potential benefits that can be brought about by either machine or human capabilities. Meanwhile, VIS4ML is intended to be extensible and will continue to be updated to reflect future advancements in using VA for building high-quality data-analytical models or for building such models rapidly",,,,,vimeo 289787814,10.1109/TVCG.2018.2864838,2018,2018,VAST,"Convention Hall 1, Section C",Thursday,Explainable ML,25-Oct,5:40 PM,6:00 PM
  InfoVis,TVCG,Mitigating the Attraction Effect with Visualizations,J),"Evanthia Dimara, Gilles Bailly, Anastasia Bezerianos, Steven Franconeri",https://hal.inria.fr/hal-01845004v2/document,"Human decisions are prone to biases, and this is no less true for decisions made within data visualizations. Bias mitigation strategies often focus on the person, by educating people about their biases, typically with little success. We focus instead on the system, presenting the first evidence that altering the design of an interactive visualization tool can mitigate a strong bias � the attraction effect. Participants viewed 2D scatterplots where choices between superior alternatives were affected by the placement of other suboptimal points. We found that highlighting the superior alternatives weakened the bias, but did not eliminate it. We then tested an interactive approach where participants completely removed locally dominated points from the view, inspired by the elimination by aspects strategy in the decision-making literature. This approach strongly decreased the bias, leading to a counterintuitive suggestion: tools that allow removing inappropriately salient or distracting data from a view may help lead users to make more rational decisions.",,https://aviz.fr/deletion,https://aviz.fr/deletion,,vimeo 289784657,,2018,2018,InfoVis,"Convention Hall 1, Section D",Thursday,Perception & Cognition 2,25-Oct,4:20 PM,4:40 PM
  InfoVis,TVCG,Face to Face: Evaluating Visual Comparison,J),"Brian David Ondov, Nicole Jardine, Niklas Elmqvist, Steven Franconeri",http://users.umiacs.umd.edu/~elm/projects/face2face/face2face.pdf,"Data are often viewed as a single set of values, but those values frequently must be compared with another set. The existing evaluations of designs that facilitate these comparisons tend to be based on intuitive reasoning, rather than quantifiable measures. We build on this work with a series of crowdsourced experiments that use low-level perceptual comparison tasks that arise frequently in comparisons within data visualizations (e.g., which value changes the most between the two sets of data?). Participants completed these tasks across a variety of layouts: overlaid, two arrangements of juxtaposed small multiples, mirror-symmetric small multiples, and animated transitions. A staircase procedure sought the difficulty level (e.g., value change delta) that led to equivalent accuracy for each layout. Confirming prior intuition, we observe high levels of performance for overlaid versus standard small multiples. However, we also find performance improvements for both mirror symmetric small multiples and animated transitions. While some results are incongruent with common wisdom in data visualization, they align with previous work in perceptual psychology, and thus have potentially strong implications for visual comparison designs.",,,,,vimeo 289785372,10.1109/tvcg.2018.2864884,2018,2018,InfoVis,"Convention Hall 1, Section D",Thursday,Perception & Cognition 2,25-Oct,4:40 PM,5:00 PM
  TVCG,TVCG,Task-Based Effectiveness of Basic Visualizations,T),"Bahador Saket, Alex Endert, Çagatay Demiralp",https://arxiv.org/pdf/1709.08546.pdf,"Visualizations of tabular data are widely used; understanding their effectiveness in different task and data contexts is fundamental to scaling their impact. However, little is known about how basic tabular data visualizations perform across varying data analysis tasks. In this paper, we report results from a crowdsourced experiment to evaluate the effectiveness of five small scale (5-34 data points) two-dimensional visualization types�Table, Line Chart, Bar Chart, Scatterplot, and Pie Chart�across ten common data analysis tasks using two datasets. We find the effectiveness of these visualization types significantly varies across task, suggesting that visualization design would benefit from considering context-dependent effectiveness. Based on our findings, we derive recommendations on which visualizations to choose based on different tasks. We finally train a decision tree on the data we collected to drive a recommender, showcasing how to effectively engineer experimental user data into practical visualization systems.",,,https://github.com/gtvalab/ChartsEffectiveness/tree/master/Raw-Data,,vimeo 289789506,10.1109/tvcg.2018.2829750,2018,2018,InfoVis,"Convention Hall 1, Section D",Thursday,Perception & Cognition 2,25-Oct,5:00 PM,5:20 PM
  