DevLan/vispubs huggingface dataset:
TimeArcs: Visualizing Fluctuations in Dynamic Networks
Authors: Dang Tuan Nhon, N. Pendar, Angus Graeme Forbes
Published at EuroVis, 2016

In this paper we introduce TimeArcs, a novel visualization technique for representing dynamic relationships between entities in a network. Force‐directed layouts provide a way to highlight related entities by positioning them near to each other Entities are brought closer to each other (forming clusters) by forces applied on nodes and connections between nodes. In many application domains, relationships between entities are not temporally stable, which means that cluster structures and cluster memberships also may vary across time. Our approach merges multiple force‐directed layouts at different time points into a single comprehensive visualization that provides a big picture overview of the most significant clusters within a user‐defined period of time. TimeArcs also supports a range of interactive features, such as allowing users to drill‐down in order to see details about a particular cluster. To highlight the benefits of this technique, we demonstrate its application to various datasets, including the IMDB co‐star network, a dataset showing conflicting evidences within biomedical literature of protein interactions, and collocated popular phrases obtained from political blogs.
Towards Quantitative Visual Analytics with Structured Brushing and Linked Statistics
Authors: Sanjin Rados, Rainer Splechtna, Kresimir Matkovic, Mario Duras, M. Eduard Gröller, Helwig Hauser
Published at EuroVis, 2016

Until now a lot of visual analytics predominantly delivers qualitative results—based, for example, on a continuous color map or a detailed spatial encoding. Important target applications, however, such as medical diagnosis and decision making, clearly benefit from quantitative analysis results. In this paper we propose several specific extensions to the well‐established concept of linking&amp;brushing in order to make the analysis results more quantitative. We structure the brushing space in order to improve the reproducibility of the brushing operation, e.g., by introducing the percentile grid. We also enhance the linked visualization with overlaid descriptive statistics to enable a more quantitative reading of the resulting focus+context visualization. Additionally, we introduce two novel brushing techniques: the percentile brush and the Mahalanobis brush. Both use the underlying data to support statistically meaningful interactions with the data. We illustrate the use of the new techniques in the context of two case studies, one based on meteorological data and the other one focused on data from the automotive industry where we evaluate a shaft design in the context of mechanical power transmission in cars.
Using Visualization to Explore Original and Anonymized LBSN Data
Authors: Ebrahim Tarameshloo, M. Hosseinkhani Loorak, Philip W. L. Fong, Sheelagh Carpendale
Published at EuroVis, 2016

We present GSUVis, a visualization tool designed to provide better understanding of location‐based social network (LBSN) data. LBSN data is one of the most important sources of information for transportation, marketing, health, and public safety. LBSN data consumers are interested in accessing and analysing data that is as complete and as accurate as possible. However, LBSN data contains sensitive information about individuals. Consequently, data anonymization is of critical importance if this data is to be made available to consumers. However, anonymization commonly reduces the utility of information available. Working with privacy experts, we designed GSUVis a visual analytic tool to help experts better understand the effects of anonymization techniques on LBSN data utility. One of GSUVis's primary goals is to make it possible for people to use LBSN data, without requiring them to gain deep knowledge about data anonymization. To inform the design of GSUVis, we interviewed privacy experts, and collected their tasks and system requirements. Based on this understanding, we designed and implemented GSUVis. It applies two anonymization algorithms for social and location trajectory data to a real‐world LBSN dataset and visualizes the data both before and after anonymization. Through feedback from domain experts, we reflect on the effectiveness of GSUVis and the impact of anonymization using visualization.
Visibility Equalizer Cutaway Visualization of Mesoscopic Biological Models
Authors: Mathieu Le Muzic, Peter Mindek, Johannes Sorger, Ludovic Autin, David S. Goodsell, Ivan Viola
Published at EuroVis, 2016

In scientific illustrations and visualization, cutaway views are often employed as an effective technique for occlusion management in densely packed scenes. We propose a novel method for authoring cutaway illustrations of mesoscopic biological models. In contrast to the existing cutaway algorithms, we take advantage of the specific nature of the biological models. These models consist of thousands of instances with a comparably smaller number of different types. Our method constitutes a two stage process. In the first step, clipping objects are placed in the scene, creating a cutaway visualization of the model. During this process, a hierarchical list of stacked bars inform the user about the instance visibility distribution of each individual molecular type in the scene. In the second step, the visibility of each molecular type is fine‐tuned through these bars, which at this point act as interactive visibility equalizers. An evaluation of our technique with domain experts confirmed that our equalizer‐based approach for visibility specification is valuable and effective for both, scientific and educational purposes.
Visual Analysis of Biomolecular Cavities: State of the Art
Authors: Michael Krone, Barbora Kozlíková, Norbert Lindow, Marc Baaden, Daniel Baum, Július Parulek, Hans-Christian Hege, Ivan Viola
Published at EuroVis, 2016

In this report we review and structure the branch of molecular visualization that is concerned with the visual analysis of cavities in macromolecular protein structures. First the necessary background, the domain terminology, and the goals of analytical reasoning are introduced. Based on a comprehensive collection of relevant research works, we present a novel classification for cavity detection approaches and structure them into four distinct classes: grid‐based, Voronoi‐based, surface‐based, and probe‐based methods. The subclasses are then formed by their combinations. We match these approaches with corresponding visualization technologies starting with direct 3D visualization, followed with non‐spatial visualization techniques that for example abstract the interactions between structures into a relational graph, straighten the cavity of interest to see its profile in one view, or aggregate the time sequence into a single contour plot. We also discuss the current state of methods for the visual analysis of cavities in dynamic data such as molecular dynamics simulations. Finally, we give an overview of the most common tools that are actively developed and used in the structural biology and biochemistry research. Our report is concluded by an outlook on future challenges in the field.
Visual Analysis of Defects in Glass Fiber Reinforced Polymers for 4DCT Interrupted In situ Tests
Authors: Alexander Amirkhanov, Artem Amirkhanov, Dietmar Salaberger, Johann Kastner, M. Eduard Gröller, Christoph Heinzl
Published at EuroVis, 2016

Material engineers use interrupted in situ tensile testing to investigate the damage mechanisms in composite materials. For each subsequent scan, the load is incrementally increased until the specimen is completely fractured. During the interrupted in situ testing of glass fiber reinforced polymers (GFRPs) defects of four types are expected to appear: matrix fracture, fiber/matrix debonding, fiber pull‐out, and fiber fracture. There is a growing demand for the detection and analysis of these defects among the material engineers. In this paper, we present a novel workflow for the detection, classification, and visual analysis of defects in GFRPs using interrupted in situ tensile tests in combination with X‐ray Computed Tomography. The workflow is based on the automatic extraction of defects and fibers. We introduce the automatic Defect Classifier assigning the most suitable type to each defect based on its geometrical features. We present a visual analysis system that integrates four visualization methods: 1) the Defect Viewer highlights defects with visually encoded type in the context of the original CT image, 2) the Defect Density Maps provide an overview of the defect distributions according to type in 2D and 3D, 3) the Final Fracture Surface estimates the material fracture's location and displays it as a 3D surface, 4) the 3D Magic Lens enables interactive exploration by combining detailed visualizations in the region of interest with overview visualizations as context. In collaboration with material engineers, we evaluate our solution and demonstrate its practical applicability.
Visual Analysis of Governing Topological Structures in Excitable Network Dynamics
Authors: Quynh Quang Ngo, Marc-Thorsten Hütt, Lars Linsen
Published at EuroVis, 2016

To understand how topology shapes the dynamics in excitable networks is one of the fundamental problems in network science when applied to computational systems biology and neuroscience. Recent advances in the field discovered the influential role of two macroscopic topological structures, namely hubs and modules. We propose a visual analytics approach that allows for a systematic exploration of the role of those macroscopic topological structures on the dynamics in excitable networks. Dynamical patterns are discovered using the dynamical features of excitation ratio and co‐activation. Our approach is based on the interactive analysis of the correlation of topological and dynamical features using coordinated views. We designed suitable visual encodings for both the topological and the dynamical features. A degree map and an adjacency matrix visualization allow for the interaction with hubs and modules, respectively. A barycentric‐coordinates layout and a multi‐dimensional scaling approach allow for the analysis of excitation ratio and co‐activation, respectively. We demonstrate how the interplay of the visual encodings allows us to quickly reconstruct recent findings in the field within an interactive analysis and even discovered new patterns. We apply our approach to network models of commonly investigated topologies as well as to the structural networks representing the connectomes of different species. We evaluate our approach with domain experts in terms of its intuitiveness, expressiveness, and usefulness.
Visual Analysis of Spatial Variability and Global Correlations in Ensembles of Iso-Contours
Authors: Florian Ferstl, Mathias Kanzler, Marc Rautenhaus, Rüdiger Westermann
Published at EuroVis, 2016

For an ensemble of iso‐contours in multi‐dimensional scalar fields, we present new methods to a) visualize their dominant spatial patterns of variability, and b) to compute the conditional probability of the occurrence of a contour at one location given the occurrence at some other location. We first show how to derive a statistical model describing the contour variability, by representing the contours implicitly via signed distance functions and clustering similar functions in a reduced order space. We show that the spatial patterns of the ensemble can then be derived by analytically transforming the boundaries of a confidence interval computed from each cluster into the spatial domain. Furthermore, we introduce a mathematical basis for computing correlations between the occurrences of iso‐contours at different locations. We show that the computation of these correlations can be posed in the reduced order space as an integration problem over a region bounded by four hyper‐planes. To visualize the derived statistical properties we employ a variant of variability plots for streamlines, now including the color coding of probabilities of joint contour occurrences. We demonstrate the use of the proposed techniques for ensemble exploration in a number of 2D and 3D examples, using artificial and meteorological data sets.
Visual Analysis of Tumor Control Models for Prediction of Radiotherapy Response
Authors: Renata Georgia Raidou, Oscar Casares-Magaz, Ludvig P. Muren, Uulke A. van der Heide, Jarle Rørvik, Marcel Breeuwer, Anna Vilanova Bartrolí
Published at EuroVis, 2016

In radiotherapy, tumors are irradiated with a high dose, while surrounding healthy tissues are spared. To quantify the probability that a tumor is effectively treated with a given dose, statistical models were built and employed in clinical research. These are called tumor control probability (TCP) models. Recently, TCP models started incorporating additional information from imaging modalities. In this way, patient‐specific properties of tumor tissues are included, improving the radiobiological accuracy of models. Yet, the employed imaging modalities are subject to uncertainties with significant impact on the modeling outcome, while the models are sensitive to a number of parameter assumptions. Currently, uncertainty and parameter sensitivity are not incorporated in the analysis, due to time and resource constraints. To this end, we propose a visual tool that enables clinical researchers working on TCP modeling, to explore the information provided by their models, to discover new knowledge and to confirm or generate hypotheses within their data. Our approach incorporates the following four main components: (1) It supports the exploration of uncertainty and its effect on TCP models; (2) It facilitates parameter sensitivity analysis to common assumptions; (3) It enables the identification of inter‐patient response variability; (4) It allows starting the analysis from the desired treatment outcome, to identify treatment strategies that achieve it. We conducted an evaluation with nine clinical researchers. All participants agreed that the proposed visual tool provides better understanding and new opportunities for the exploration and analysis of TCP modeling.
Visual Debugging Techniques for Reactive Data Visualization
Authors: Jane Hoffswell, Arvind Satyanarayan, Jeffrey Heer
Published at EuroVis, 2016

Interaction is critical to effective visualization, but can be difficult to author and debug due to dependencies among input events, program state, and visual output. Recent advances leverage reactive semantics to support declarative design and avoid the “spaghetti code” of imperative event handlers. While reactive programming improves many aspects of development, textual specifications still fail to convey the complex runtime dynamics. In response, we contribute a set of visual debugging techniques to reveal the runtime behavior of reactive visualizations. A timeline view records input events and dynamic variable updates, allowing designers to replay and inspect the propagation of values step‐by‐step. On‐demand annotations overlay the output visualization to expose relevant state and scale mappings in‐situ. Dynamic tables visualize how backing datasets change over time. To evaluate the effectiveness of these techniques, we study how first‐time Vega users debug interactions in faulty, unfamiliar specifications; with no prior knowledge, participants were able to accurately trace errors through the specification.
Visualizing Co-occurrence of Events in Populations of Viral Genome Sequences
Authors: Alper Sarikaya, M. Correli, Joao M. Dinis, David H. O'Connor, Michael Gleicher
Published at EuroVis, 2016

Virologists are not only interested in point mutations in a genome, but also in relationships between mutations. In this work, we present a design study to support the discovery of correlated mutation events (called co‐occurrences) in populations of viral genomes. The key challenge is to identify potentially interesting pairs of events within the vast space of event combinations. In our work, we identify analyst requirements and develop a prototype through a participatory process. The key ideas of our approach are to use interest metrics to create dynamic filtering that guides the viewer to interesting and relevant correlations of genome mutations, and to provide visual encodings designed to fit scientists' mental map of the data, along with dynamic filtering techniques. We demonstrate the strength of our approach in virology‐situated case studies, and offer suggestions for extending our strategy to other sequence‐based domains.
Visualizing the Impact of Geographical Variations on Multivariate Clustering
Authors: Yifan Zhang 0007, Wei Luo, Elizabeth A. Mack, Ross Maciejewski
Published at EuroVis, 2016

Traditional multivariate clustering approaches are common in many geovisualization applications. These algorithms are used to define geodemographic profiles, ecosystems and various other land use patterns that are based on multivariate measures. Cluster labels are then projected onto a choropleth map to enable analysts to explore spatial dependencies and heterogeneity within the multivariate attributes. However, local variations in the data and choices of clustering parameters can greatly impact the resultant visualization. In this work, we develop a visual analytics framework for exploring and comparing the impact of geographical variations for multivariate clustering. Our framework employs a variety of graphical configurations and summary statistics to explore the spatial extents of clustering. It also allows users to discover patterns that can be concealed by traditional global clustering via several interactive visualization techniques including a novel drag &amp; drop clustering difference view. We demonstrate the applicability of our framework over a demographics dataset containing quick facts about counties in the continental United States and demonstrate the need for analytical tools that can enable users to explore and compare clustering results over varying geographical features and scales.
'A bit like British Weather, I suppose': Design and Evaluation of the Temperature Calendar
Authors: Enrico Costanza, Ben Bedwell, Michael O. Jewell, James A. Colley, Tom Rodden
Published at CHI, 2016

In this paper we present the design and evaluation of the Temperature Calendar -- a visualization of temperature variation within a workplace over the course of the past week. This highlights deviation from organizational temperature policy, and aims to bring staff "into the loop" of understanding and managing heating, and so reduce energy waste. The display was deployed for three weeks in five public libraries. Analysis of interaction logs, questionnaires and interviews shows that staff used the displays to understand heating in their buildings, and took action reflecting this new understanding. Bringing together our results, we discuss design implications for workplace displays, and an analysis of carbon emissions generated in constructing and operating our design. More in general, the findings helped us to reflect on the role of policy on energy consumption, and the potential for the HCI community to engage with its application, as well as its definition or modification.
A Comparative Evaluation on Online Learning Approaches using Parallel Coordinate Visualization
Authors: Bum Chul Kwon, Bongshin Lee
Published at CHI, 2016

As visualizations are increasingly used as a storytelling medium for the general public, it becomes important to help people learn how to understand visualizations. Prior studies indicate that interactive multimedia learning environments can increase the effectiveness of learning [11]. To investigate the efficacy of the multimedia learning environments for data visualization education, we compared four online learning approaches 1) baseline (i.e., no tutorial), 2) static tutorial, 3) video tutorial, and 4) interactive tutorial-through a crowdsourced user study. We measured participants' learning outcomes in using parallel coordinates with 18 tasks. Results show that participants with the interactive condition achieved higher scores than those with the static and baseline conditions, and reported that they had a more engaging experience than those with the static condition.
A Comparison of Cooperative and Competitive Visualizations for Co-located Collaboration
Authors: Sarah Tausch, Stephanie Ta, Heinrich Hussmann
Published at CHI, 2016

We present a study that investigates the influence of different types of visualizations on collaboration. The visualizations present the group's performance either in a more cooperative or more competitive way. Decades of research suggest that cooperation leads to greater productivity than competition. However, most of the existing group mirror visualizations achieve an increase in productivity and better self-regulation by enabling a direct comparison of performance within the group. We conducted a repeated measures study with 12 groups that were supported by visualizations that displayed the number of ideas of a brainstorming session (1) per person (competitive condition) (2) per group (cooperative condition), (3) per person and per group (mixed condition) and (4) without visualization (baseline). Results indicate that groups that see a combination of individual and group performance (mixed condition) are more productive, more satisfied with their results and participate in a more balanced way.
AniSAM & AniAvatar: Animated Visualizations of Affective States
Authors: Andreas Sonderegger, Klaus Heyden, Alain Chavaillaz, Jürgen S. Sauer
Published at CHI, 2016

Tools that provide visual feedback about emotions to the user in the form of an avatar or an emoticon have become increasingly important. While a great deal of effort has already been put into the reliable and accurate automatic detection of emotions, only very little is known about how this information about affective states should be displayed in a comprehensible way to the user. In the present study, three newly developed feedback tools were evaluated. The tools were developed on the basis of an existing non-verbal questionnaire to represent two dimensions of emotion (i.e. valence and arousal) based on the circumplex model of affect. A total number of 826 participants were tested, using different vignettes that describe situations with specific affective content. Employing three newly developed affective feedback tools (AniSAM, AniAvatar and MergedSAM), the ratings obtained were compared to ratings using the original SAM instrument, a well-established questionnaire to measure affect. Results indicated that the animated feedback increased the accuracy of the arousal representation. Furthermore, valence feedback was more accurate when provided with an animated manikin-based tool rather than an avatar-based tool. This provided first evidence for the usefulness of animated tools offering visual feedback on user emotion. All instruments need to undergo further development. AniSAM and AniAvatar can be downloaded for purposes of practical applications and further research.
Augmenting the Field-of-View of Head-Mounted Displays with Sparse Peripheral Displays
Authors: Robert Xiao, Hrvoje Benko
Published at CHI, 2016

In this paper, we explore the concept of a sparse peripheral display, which augments the field-of-view of a head-mounted display with a lightweight, low-resolution, inexpensively produced array of LEDs surrounding the central high-resolution display. We show that sparse peripheral displays expand the available field-of-view up to 190º horizontal, nearly filling the human field-of-view. We prototyped two proof-of-concept implementations of sparse peripheral displays: a virtual reality headset, dubbed SparseLightVR, and an augmented reality headset, called SparseLightAR. Using SparseLightVR, we conducted a user study to evaluate the utility of our implementation, and a second user study to assess different visualization schemes in the periphery and their effect on simulator sickness. Our findings show that sparse peripheral displays are useful in conveying peripheral information and improving situational awareness, are generally preferred, and can help reduce motion sickness in nausea-susceptible people.