anchor.diff:  VAST,TVCG,Lessons Learned Developing a Visual Analytics Solution for Investigative Analysis of Scamming Activities,J),"Jay Koven, Cristian Felix, Hossein Siadati, Enrico Bertini, Markus Jakobsson",,,,,,,vimeo 289787515,10.1109/tvcg.2018.2865023,2018,2018,VAST,"Convention Hall 1, Section C",Thursday,"Security, Privacy, and Anomaly",25-Oct,10:20 AM,10:40 AM
   InfoVis,TVCG,Origin-Destination Flow Maps in Immersive Environments,J),"Yalong Yang, Tim Dwyer, Bernhard Jenny, Kim Marriott, Maxime Cordeil, Haohui Chen",https://ialab.it.monash.edu/~dwyer/papers/flow-maps-ia.pdf,"Immersive virtual- and augmented-reality headsets can overlay a flat image against any surface or hang virtual objects in the space around the user. The technology is rapidly improving and may, in the long term, replace traditional flat panel displays in many situations. When displays are no longer intrinsically flat, how should we use the space around the user for abstract data visualisation? In this paper, we ask this question with respect to origin-destination flow data in a global geographic context. We report on the findings of three studies exploring different spatial encodings for flow maps. The first experiment focuses on different 2D and 3D encodings for flows on flat maps. We find that participants are significantly more accurate with raised flow paths whose height is proportional to flow distance but fastest with traditional straight line 2D flows. In our second and third experiment we compared flat maps, 3D globes and a novel interactive design we call <i>MapsLink</i>, involving a pair of linked flat maps. We find that participants took significantly more time with MapsLink than other flow maps while the 3D globe with raised flows was the fastest, most accurate, and most preferred method. Our work suggests that <i>careful</i> use of the third spatial dimension can resolve visual clutter in complex flow maps.",,,,,vimeo 279605506,10.1109/tvcg.2018.2865192,2018,2018,InfoVis,"Convention Hall 1, Section D",Thursday,Immersive Analytics,25-Oct,9:00 AM,9:20 AM
   InfoVis,TVCG,FiberClay: Sculpting Three Dimensional Trajectories to Reveal Structural Insights,J),"Christophe Hurter, Nathalie Henry Riche, Steven Drucker, Maxime Cordeil, Richard Alligier, Romain Vuillemot",https://recherche.enac.fr/~hurter/FiberClay/FiberClay2018.pdf,"Visualizing 3D trajectories to extract insights about their similarities and spatial configuration is a critical task in several domains. Air traffic controllers for example deal with large quantities of aircrafts routes to optimize safety in airspace and neuroscientists attempt to understand neuronal pathways in the human brain by visualizing bundles of fibers from DTI images. Extracting insights from masses of 3D trajectories is challenging as the multiple three dimensional lines have complex geometries, may overlap, cross or even merge with each other, making it impossible to follow individual ones in dense areas. As trajectories are inherently spatial and three dimensional, we propose FiberClay: a system to display and interact with 3D trajectories in immersive environments. FiberClay renders a large quantity of trajectories in real time using GP-GPU techniques. FiberClay also introduces a new set of interactive techniques for composing complex queries in 3D space leveraging immersive environment controllers and user position. These techniques enable an analyst to select and compare sets of trajectories with specific geometries and data properties. We conclude by discussing insights found using FiberClay with domain experts in air traffic control and neurology.",,,,,vimeo 289784766,10.1109/tvcg.2018.2865191,2018,2018,InfoVis,"Convention Hall 1, Section D",Thursday,Immersive Analytics,25-Oct,9:20 AM,9:40 AM
  -InfoVis,TVCG,DXR: A Toolkit for Building Immersive Data Visualizations,J),"Ronell Sicat, Jiabao Li, JunYoung Choi, Maxime Cordeil, Won-Ki Jeong, Benjamin Bach, Hanspeter Pfister",,,,,,,youtube p4fB_OfoaZA,10.1109/tvcg.2018.2865152,2018,2018,InfoVis,"Convention Hall 1, Section D",Thursday,Immersive Analytics,25-Oct,9:40 AM,10:00 AM
  +InfoVis,TVCG,DXR: A Toolkit for Building Immersive Data Visualizations,J),"Ronell Sicat, Jiabao Li, JunYoung Choi, Maxime Cordeil, Won-Ki Jeong, Benjamin Bach, Hanspeter Pfister",https://vcg.seas.harvard.edu/publications/dxr-a-toolkit-for-building-immersive-data-visualizations/paper,"This paper presents DXR, a toolkit for building immersive data visualizations based on the Unity development platform. Over the past years, immersive data visualizations in augmented and virtual reality (AR, VR) have been emerging as a promising medium for data sense-making beyond the desktop. However, creating immersive visualizations remains challenging, and often require complex low-level programming and tedious manual encoding of data attributes to geometric and visual properties. These can hinder the iterative idea-to-prototype process, especially for developers without experience in 3D graphics, AR, and VR programming. With DXR, developers can efficiently specify visualization designs using a concise declarative visualization grammar inspired by Vega-Lite. DXR further provides a GUI for easy and quick edits and previews of visualization designs in-situ, i.e., while immersed in the virtual world. DXR also provides reusable templates and customizable graphical marks, enabling unique and engaging visualizations. We demonstrate the flexibility of DXR through several examples spanning a wide range of applications.",,https://sites.google.com/view/dxr-vis/,,,youtube p4fB_OfoaZA,10.1109/tvcg.2018.2865152,2018,2018,InfoVis,"Convention Hall 1, Section D",Thursday,Immersive Analytics,25-Oct,9:40 AM,10:00 AM
   InfoVis,TVCG,Information Olfactation: Harnessing Scent to Convey Data,J),"Biswaksen Patnaik, Andrea Batch, Niklas Elmqvist",http://users.umiacs.umd.edu/~elm/projects/info-olfac/info-olfac.pdf,"Olfactory feedback for analytical tasks is a virtually unexplored area in spite of the advantages it offers for information recall, feature identification, and location detection. Here we introduce the concept of information olfactation as the fragrant sibling of information visualization, and discuss how scent can be used to convey data. Building on a review of the human olfactory system and mirroring common visualization practice, we propose olfactory marks, the substrate in which they exist, and their olfactory channels that are available to designers. To exemplify this idea, we present VISCENT: A six-scent stereo olfactory display capable of conveying olfactory glyphs of varying temperature and direction, as well as a corresponding software system that integrates the display with a traditional visualization display. Finally, we present three applications that make use of the viScent system: A 2D graph visualization, a 2D line and point chart, and an immersive analytics graph visualization in 3D virtual reality. We close the paper with a review of possible extensions of viScent and applications of information olfactation for general visualization beyond the examples in this paper.",,,,,vimeo 289784509,10.1109/tvcg.2018.2865237,2018,2018,InfoVis,"Convention Hall 1, Section D",Thursday,Immersive Analytics,25-Oct,10:00 AM,10:20 AM
   InfoVis,TVCG,Dynamic Composite Data Physicalization Using Wheeled Micro-Robots,J),"Mathieu Le Goc, Charles Perin, Sean Follmer, Jean-Daniel Fekete, Pierre Dragicevic",https://hal.inria.fr/hal-01848436/document,"This paper introduces dynamic composite physicalizations, a new class of physical visualizations that use collections of self-propelled objects to represent data. Dynamic composite physicalizations can be used both to give physical form to well-known interactive visualization techniques, and to explore new visualizations and interaction paradigms. We first propose a design space characterizing composite physicalizations based on previous work in the fields of Information Visualization and Human Computer Interaction. We illustrate dynamic composite physicalizations in two scenarios demonstrating potential benefits for collaboration and decision making, as well as new opportunities for physical interaction. We then describe our implementation using wheeled micro-robots capable of locating themselves and sensing user input, before discussing limitations and opportunities for future work.",,,,,vimeo 289784806,10.1109/tvcg.2018.2865159,2018,2018,InfoVis,"Convention Hall 1, Section D",Thursday,Immersive Analytics,25-Oct,10:20 AM,10:40 AM
   SciVis,TVCG,Robust and Fast Extraction of 3D Symmetric Tensor Field Topology,J),"Lawrence Roy, Prashant Kumar, Yue Zhang, Eugene Zhang",http://web.engr.oregonstate.edu/~zhange/images/3DTensorTopology_Detection.pdf,"3D symmetric tensor fields appear in many science and engineering domains, and topology-driven analysis is important in many of these application domains, such as solid mechanics and fluid dynamics. Degenerate curves and neutral surfaces are important topological features in 3D symmetric tensor fields. Existing methods to extract degenerate curves and neutral surfaces often miss parts of the curves and surfaces, respectively. Moreover, they are relatively expensive to extract. These issues are due to the lack of knowledge of their overall structures. In this paper, we provide theoretical analysis on the geometric and topological structures of degenerate curves and neutral surfaces of 3D linear tensor fields. These structures lead to parameterizations for degenerate curves and neutral surfaces that can not only provide more robust extraction of these features but also incur less computational cost. We demonstrate the benefits of our approach by applying our degenerate curve and neutral surface detection to solid mechanics simulation data sets.",,,,,vimeo 290325698,10.1109/tvcg.2018.2864768,2018,2018,SciVis,Estrel Hall A+B,Thursday,Tensors,25-Oct,9:00 AM,9:20 AM
  
positive.code: TVCG,TVCG,Bridging Text Visualization and Mining: A Task-Driven Survey,T),"Shixia Liu, Xiting Wang, Christopher Collins, Wenwen Dou, Fangxin Ouyang, Mennatallah El-Assady, Liu Jiang, Daniel Keim",,,,,,,vimeo 289789579,10.1109/tvcg.2018.2834341,2018,2018,VAST,Estrel Hall C,Wednesday,Text,24-Oct,9:00 AM,9:20 AM
  VAST,TVCG,Doccurate: A Curation-Based Approach for Clinical Text Visualization,J),Nicole Sultanum,,,,,,,vimeo 289787431,10.1109/TVCG.2018.2864905,2018,2018,VAST,Estrel Hall C,Wednesday,Text,24-Oct,9:20 AM,9:40 AM
  VAST,TVCG,VIS Author Profiles: Interactive Descriptions of Publication Records Combining Text and Visualization,J),"Shahid Latif, Fabian Beck, Devin Singh, Michael Brudno, Fanny Chevalier",https://www.vis.wiwi.uni-due.de/uploads/tx_itochairt3/publications/vap-preprint.pdf,"Publication records and collaboration networks are important for assessing the expertise and experience of researchers. Existing digital libraries show the raw publication lists in author profiles, whereas visualization techniques focus on specific subproblems. Instead, we look at publication records from various perspectives mixing low-level publication data with high-level abstractions and background information. This work presents VIS Author Profiles, a novel approach to generate integrated textual and visual descriptions to highlight patterns in publication records. We leverage template-based natural language generation to summarize notable publication statistics, evolution of research topics, and collaboration relationships. Seamlessly integrated visualizations augment the textual description and are interactively connected with each other and the text. The underlying publication data and detailed explanations of the analysis are available on demand. We compare our approach to existing systems by taking into account information needs of users and demonstrate its usefulness in two realistic application examples.",https://vis-tools.paluno.uni-due.de/vap/,,,,vimeo 289787504,10.1109/TVCG.2018.2865022,2018,2018,VAST,Estrel Hall C,Wednesday,Text,24-Oct,9:40 AM,10:00 AM
  TVCG,TVCG,A Visual Analytics Framework for Identifying Topic Drivers in Media,T),"Yafeng Lu, Hong Wang, Steven Landis, Ross Maciejewski",,,,,,,vimeo 289788885,10.1109/TVCG.2017.2752166,2018,2018,VAST,Estrel Hall C,Wednesday,Text,24-Oct,10:00 AM,10:20 AM
  TVCG,TVCG,Visualizing a Thinker’s Life,T),"Patrick Riehmann, Dora Kiesel, Martin Kohlhaas, Bernd Fröhlich",,,,,,,vimeo 289789544,10.1109/TVCG.2018.2824822,2018,2018,VAST,Estrel Hall C,Wednesday,Text,24-Oct,10:20 AM,10:40 AM
  TVCG,TVCG,Commercial Visual Analytics Systems – Advances in the Big Data Analytics Field,T),"Michael Behrisch, Dirk Streeb, Florian Stoffel, Daniel Seebacher, Stefan Hagen Weber, Sebastian Mittelstaedt, Hanspeter Pfister, Daniel Keim",,,,,,,vimeo 289789498,10.1109/TVCG.2018.2859973,2018,2018,VAST,"Convention Hall 1, Section C",Wednesday,Applications,24-Oct,11:00 AM,11:20 AM
  VAST,TVCG,BitExTract: Interactive Visualization for Extracting Bitcoin Exchange Intelligence,J),"Xuanwu Yue, Xinhuan Shu, Xinyu ZHU, Xinnan Du, Zheqing Yu, Dimitrios Papadopoulos, Siyuan Liu",,,,,,,vimeo 289787312,10.1109/TVCG.2018.2864814,2018,2018,VAST,"Convention Hall 1, Section C",Wednesday,Applications,24-Oct,11:20 AM,11:40 AM
  TVCG,TVCG,KAVAGait: Knowledge-Assisted Visual Analytics for Clinical Gait Analysis,T),"Markus Wagner, Djordje Slijepcevic, Brian Horsak, Alexander Rind, Matthias Zeppelzauer, Wolfgang Aigner",,,,,,,vimeo 289788975,10.1109/tvcg.2017.2785271,2018,2018,VAST,"Convention Hall 1, Section C",Wednesday,Applications,24-Oct,11:40 AM,12:00 PM
  TVCG,TVCG,Precision Risk Analysis of Cancer Therapy with Interactive Nomograms and Survival Plots,T),"G.Elisabeta Marai, Chihua Ma, Andrew T. Burks, Filippo Pellolio, Guadalupe Canahuate, David M. Vock, Abdallah S.R. Mohamed, C. David Fuller",,,,,,,vimeo 289789079,10.1109/tvcg.2018.2817557,2018,2018,VAST,"Convention Hall 1, Section C",Wednesday,Applications,24-Oct,12:00 PM,12:20 PM
  VAST,VAST,VUSphere: Visual Analysis of Video Utilization in Online Distance Education,C),"Huan He, Qinghua Zheng, Bo Dong",,,,,,,vimeo 289788052,,2018,2018,VAST,"Convention Hall 1, Section C",Wednesday,Applications,24-Oct,12:20 PM,12:40 PM
  InfoVis,TVCG,Juniper: A Tree+Table Approach to Multivariate Graph Visualization,J),"Carolina Nobre, Marc Streit, Alexander Lex",http://sci.utah.edu/~vdl/papers/2018_infovis_juniper.pdf,"Analyzing large, multivariate graphs is an important problem in many domains, yet such graphs are challenging to visualize. In this paper, we introduce a novel, scalable, tree+table multivariate graph visualization technique, which makes many tasks related to multivariate graph analysis easier to achieve. The core principle we follow is to selectively query for nodes or subgraphs of interest and visualize these subgraphs as a spanning tree of the graph. The tree is laid out linearly, which enables us to juxtapose the nodes with a table visualization where diverse attributes can be shown. We also use this table as an adjacency matrix, so that the resulting technique is a hybrid node-link/adjacency matrix technique. We implement this concept in Juniper and complement it with a set of interaction techniques that enable analysts to dynamically grow, restructure, and aggregate the tree, as well as change the layout or show paths between nodes. We demonstrate the utility of our tool in usage scenarios for different multivariate networks: a bipartite network of scholars, papers, and citation metrics and a multitype network of story characters, places, books, etc.",,https://github.com/caleydo/lineage/tree/juniper,,,vimeo 289784894,10.1109/TVCG.2018.2865149,2018,2018,InfoVis,"Convention Hall 1, Section D",Wednesday,Graphs & Trees,24-Oct,11:00 AM,11:20 AM
  
negative.code: 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.2017.2701829,2018,2018,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",https://bib.dbvis.de/uploadedFiles/BlumenscheinetalVAST2018SMARTexplore.pdf,"We present SMARTEXPLORE, a novel visual analytics technique that simplifies the identification and understanding of clusters, correlations, and complex patterns in high-dimensional data. The analysis is integrated into an interactive table-based visualization that maintains a consistent and familiar representation throughout the analysis. The visualization is tightly coupled with pattern matching, subspace analysis, reordering, and layout algorithms. To increase the analyst’s trust in the revealed patterns, SMARTEXPLORE automatically selects and computes statistical measures based on dimension and data properties. While existing approaches to analyzing highdimensional data (e.g., planar projections and Parallel coordinates) have proven effective, they typically have steep learning curves for non-visualization experts. Our evaluation, based on three expert case studies, confirms that non-visualization experts successfully reveal patterns in high-dimensional data when using SMARTEXPLORE.",,,,,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",https://www3.cs.stonybrook.edu/~shecheng/Paper/ColormapND.pdf,"A wide variety of color schemes have been devised for mapping scalar data to color. We address the challenge of color-mapping multivariate data. While a number of methods can map low-dimensional data to color, for example, using bilinear or barycentric interpolation for two or three variables, these methods do not scale to higher data dimensions. Likewise, schemes that take a more artistic approach through color mixing and the like also face limits when it comes to the number of variables they can encode. Our approach does not have these limitations. It is data driven in that it determines a proper and consistent color map from first embedding the data samples into a circular interactive multivariate color mapping display (ICD) and then fusing this display with a convex (CIE HCL) color space. The variables (data attributes) are arranged in terms of their similarity and mapped to the ICD’s boundary to control the embedding. Using this layout, the color of a multivariate data sample is then obtained via modified generalized barycentric coordinate interpolation of the map. The system we devised has facilities for contrast and feature enhancement, supports both regular and irregular grids, can deal with multi-field as well as multispectral data, and can produce heat maps, choropleth maps, and diagrams such as scatterplots.",,,,,vimeo 289788955,10.1109/tvcg.2018.2808489,2018,2018,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",https://arxiv.org/pdf/1808.09074.pdf,"Constructing latent vector representation for nodes in a network through embedding models has shown its practicality in many graph analysis applications, such as node classification, clustering, and link prediction. However, despite the high efficiency and accuracy of learning an embedding model, people have little clue of what information about the original network is preserved in the embedding vectors. The abstractness of low-dimensional vector representation, stochastic nature of the construction process, and non-transparent hyper-parameters all obscure understanding of network embedding results. Visualization techniques have been introduced to facilitate embedding vector inspection, usually by projecting the embedding space to a two-dimensional display. Although the existing visualization methods allow simple examination of the structure of embedding space, they cannot support in-depth exploration of the embedding vectors. In this paper, we design an exploratory visual analytics system that supports the comparative visual interpretation of embedding vectors at the cluster, instance, and structural levels. To be more specific, it facilitates comparison of what and how node metrics are preserved across different embedding models and investigation of relationships between node metrics and selected embedding vectors. Several case studies confirm the efficacy of our system. Experts’ feedback suggests that our approach indeed helps them better embrace the understanding of network embedding models.",,,,,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 280506639,10.1109/TVCG.2018.2865138,2018,2018,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
  