DevLan/vispubs huggingface dataset:
A Shot at Visual Vulnerability Analysis
Authors: Ethan Kerzner, Lee A. Butler, Charles D. Hansen, Miriah D. Meyer
Published at EuroVis, 2015

Increasing the safety of vehicles is an important goal for vehicle manufacturers. These manufacturers often turn to simulations to understand how to improve a vehicle's design as real‐world safety tests are expensive and time consuming. Understanding the results of these simulations, however, is challenging due to the complexity of the data, which often includes both spatial and nonspatial data types. In this design study we collaborated with analysts who are trying to understand the vulnerability of military vehicles. From this design study we contribute a problem characterization, data abstraction, and task analysis for vehicle vulnerability analysis, as well as a validated and deployed tool called Shotviewer. Shotviewer links 3D spatial views with abstract 2D views to support a broad range of analysis needs. Furthermore, reflection on our design study process elucidates a strategy of view‐design parallelism for creating multiview visualizations, as well as four recommendations for conducting design studies in large organizations with sensitive data.
Adaptive Recommendations for Enhanced Non-linear Exploration of Annotated 3D Objects
Authors: Marcos Balsa Rodríguez, Marco Agus, Fabio Marton, Enrico Gobbetti
Published at EuroVis, 2015

We introduce a novel approach for letting casual viewers explore detailed 3D models integrated with structured spatially associated descriptive information organized in a graph. Each node associates a subset of the 3D surface seen from a particular viewpoint to the related descriptive annotation, together with its author‐defined importance. Graph edges describe, instead, the strength of the dependency relation between information nodes, allowing content authors to describe the preferred order of presentation of information. At run‐time, users navigate inside the 3D scene using a camera controller, while adaptively receiving unobtrusive guidance towards interesting viewpoints and history‐ and location‐dependent suggestions on important information, which is adaptively presented using 2D overlays displayed over the 3D scene. The capabilities of our approach are demonstrated in a real‐world cultural heritage application involving the public presentation of sculptural complex on a large projection‐based display. A user study has been performed in order to validate our approach.
An Evaluation of the Impact of Visual Embellishments in Bar Charts
Authors: Drew Skau, Lane Harrison, Robert Kosara
Published at EuroVis, 2015

As data visualization becomes further intertwined with the field of graphic design and information graphics, small graphical alterations are made to many common chart formats. Despite the growing prevalence of these embellishments, their effects on communication of the charts’ data is unknown. From an overview of the design space, we have outlined some of the common embellishments that are made to bar charts. We have studied the effects of these chart embellishments on the communication of the charts’ data through a series of user studies on Amazon's Mechanical Turk platform. The results of these studies lead to a better understanding of how each chart type is perceived, and help provide guiding principles for the graphic design of charts.
An Exploratory Study of Data Sketching for Visual Representation
Authors: Jagoda Walny, Samuel Huron, Sheelagh Carpendale
Published at EuroVis, 2015

Hand‐drawn sketching on napkins or whiteboards is a common, accessible method for generating visual representations. This practice is shared by experts and non‐experts and is probably one of the faster and more expressive ways to draft a visual representation of data. In order to better understand the types of and variations in what people produce when sketching data, we conducted a qualitative study. We asked people with varying degrees of visualization expertise, from novices to experts, to manually sketch representations of a small, easily understandable dataset using pencils and paper and to report on what they learned or found interesting about the data. From this study, we extract a data sketching representation continuum from numeracy to abstraction; a data report spectrum from individual data items to speculative data hypothesis; and show the correspondence between the representation types and the data reports from our results set. From these observations we discuss the participants’ representations in relation to their data reports, indicating implications for design and potentially fruitful directions for research.
Cell lineage visualisation
Authors: A. Johannes Pretorius, Imtiaz A. Khan, Rachel J. Errington
Published at EuroVis, 2015

Cell lineages describe the developmental history of Cell populations and are produced by combining time‐lapse imaging and image processing. Biomedical researchers study Cell lineages to understand fundamental processes such as Cell differentiation and the pharmacodynamic action of anticancer agents. Yet, the interpretation of Cell lineages is hindered by their complexity and insufficient capacity for visual analysis. We present a novel approach for interactive visualisation of Cell lineages. Based on an understanding of Cellular biology and live‐Cell imaging methodology, we identify three requirements: multimodality (Cell lineages combine spatial, temporal, and other properties), symmetry (related to lineage branching structure), and synchrony (related to temporal alignment of Cellular events). We address these by combining visual summaries of the spatiotemporal behaviour of an arbitrary number of lineages, including variation from average behaviour, with node‐link representations that emphasise the presence or absence of symmetry and synchrony. We illustrate the merit of our approach by presenting a real‐world case study where the cytotoxic action of the anticancer drug topotecan was determined.
Compressive Volume Rendering
Authors: Xiaoyang Liu, Usman R. Alim
Published at EuroVis, 2015

Compressive rendering refers to the process of reconstructing a full image from a small subset of the rendered pixels, thereby expediting the rendering task. In this paper, we empirically investigate three image order techniques for compressive rendering that are suitable for direct volume rendering. The first technique is based on the theory of compressed sensing and leverages the sparsity of the image gradient in the Fourier domain. The latter techniques exploit smoothness properties of the rendered image; the second technique recovers the missing pixels via a total variation minimization procedure while the third technique incorporates a smoothness prior in a variational reconstruction framework employing interpolating cubic B‐splines. We compare and contrast the three techniques in terms of quality, efficiency and sensitivity to the distribution of pixels. Our results show that smoothness‐based techniques significantly outperform techniques that are based on compressed sensing and are also robust in the presence of highly incomplete information. We achieve high quality recovery with as little as 20% of the pixels distributed uniformly in screen space.
Data-driven Evaluation of Visual Quality Measures
Authors: Michael Sedlmair, Michaël Aupetit 0001
Published at EuroVis, 2015

Visual quality measures seek to algorithmically imitate human judgments of patterns such as class separability, correlation, or outliers. In this paper, we propose a novel data‐driven framework for evaluating such measures. The basic idea is to take a large set of visually encoded data, such as scatterplots, with reliable human “ground truth” judgements, and to use this human‐labeled data to learn how well a measure would predict human judgements on previously unseen data. Measures can then be evaluated based on predictive performance—an approach that is crucial for generalizing across datasets but has gained little attention so far. To illustrate our framework, we use it to evaluate 15 state‐of‐the‐art class separation measures, using human ground truth data from 828 class separation judgments on color‐coded 2D scatterplots.
Detangler: Visual Analytics for Multiplex Networks
Authors: Benjamin Renoust, Guy Melançon, Tamara Munzner
Published at EuroVis, 2015

A multiplex network has links of different types, allowing it to express many overlapping types of relationships. A core task in network analysis is to evaluate and understand group cohesion; that is, to explain why groups of elements belong together based on the underlying structure of the network. We present Detangler, a system that supports visual analysis of group cohesion in multiplex networks through dual linked views. These views feature new data abstractions derived from the original multiplex network: the substrate network and the catalyst network. We contribute two novel techniques that allow the user to analyze the complex structure of the multiplex network without the extreme visual clutter that would result from simply showing it directly. The harmonized layout visual encoding technique provides spatial stability between the substrate and catalyst views. The pivot brushing interaction technique supports linked highlighting between the views based on computations in the underlying multiplex network to leapfrog between subsets of catalysts and substrates. We present results from the motivating application domain of annotated news documents with a usage scenario and preliminary expert feedback. A second usage scenario presents group cohesion analysis of the social network of the early American independence movement.
Dual Adjacency Matrix: Exploring Link Groups in Dense Networks
Authors: Kasper Dinkla, Nathalie Henry Riche, Michel A. Westenberg
Published at EuroVis, 2015

Node grouping is a common way of adding structure and information to networks that aids their interpretation. However, certain networks benefit from the grouping of links instead of nodes. Link communities, for example, are a form of link groups that describe high‐quality overlapping node communities. There is a conceptual gap between node groups and link groups that poses an interesting visualization challenge. We introduce the Dual Adjacency Matrix to bridge this gap. This matrix combines node and link group techniques via a generalization that also enables it to be coordinated with a node‐link‐contour diagram. These methods have been implemented in a prototype that we evaluated with an information scientist and neuroscientist via interviews and prototype walk‐throughs. We demonstrate this prototype with the analysis of a trade network and an fMRI correlation network.
Edge-Aware Volume Smoothing Using L0 Gradient Minimization
Authors: Qichao Wang, Yubo Tao, Hai Lin 0003
Published at EuroVis, 2015

In volume visualization, noise in regions of homogeneous material and at boundaries between different materials poses a great challenge in extracting, analyzing and rendering features of interest. In this paper, we present a novel volume denoising / smoothing method based on the L0 gradient minimization framework. This framework globally controls how many voxels with a non‐zero gradient are in the result in order to approximate important features’ structures in a sparse way. This procedure can be solved quickly by the alternating optimization strategy with half‐quadratic splitting. While the proposed L0 volume gradient minimization method can effectively remove noise in homogeneous materials, a blurring‐sharpening strategy is proposed to diminish noise or smooth local details on the boundaries. This generates salient features with smooth boundaries and visually pleasing structures. We compare our method with the bilateral filter and anisotropic diffusion, and demonstrate the effectiveness and efficiency of our method with several volumes in different modalities.
Efficient Contrast Effect Compensation with Personalized Perception Models
Authors: Sebastian Mittelstädt, Daniel A. Keim
Published at EuroVis, 2015

Color is one of the most effective visual variables and is frequently used to encode metric quantities. Contrast effects are considered harmful in data visualizations since they significantly bias our perception of colors. For instance, a gray patch appears brighter on a black background than on a white background. Accordingly, the perception of color‐encoded data items depends on the surround in the rendered visualization. A method that compensates for contrast effects has been presented previously, which significantly improves the users’ accuracy in reading and comparing color encoded data. The method utilizes established perception models to compensate for contrast effects, assuming an average human observer. In this paper, we provide experiments that show a significant difference in the perception of users. We introduce methods to personalize contrast effect compensation and show that this outperforms the original method with a user study. We, further, overcome the major limitation of the original method, which is a runtime of several minutes. With the use of efficient optimization and surrogate models, we are able to reduce runtime to milliseconds, making the method applicable in interactive visualizations.
Efficient Local Histogram Searching via Bitmap Indexing
Authors: Tzu-Hsuan Wei, Chun-Ming Chen, Ayan Biswas
Published at EuroVis, 2015

Representing features by local histograms is a proven technique in several volume analysis and visualization applications including feature tracking and transfer function design. The efficiency of these applications, however, is hampered by the high computational complexity of local histogram computation and matching. In this paper, we propose a novel algorithm to accelerate local histogram search by leveraging bitmap indexing. Our method avoids exhaustive searching of all voxels in the spatial domain by examining only the voxels whose values fall within the value range of user‐defined local features and their neighborhood. Based on the idea that the value range of local features is in general much smaller than the dynamic range of the entire dataset, we propose a local voting scheme to construct the local histograms so that only a small number of voxels need to be examined. Experimental results show that our method can reduce much computational workload compared to the conventional approaches. To demonstrate the utility of our method, an interactive interface was developed to assist users in defining target features as local histograms and identify the locations of these features in the dataset.
Evaluating 2D Flow Visualization Using Eye Tracking
Authors: Hsin Yang Ho, I-Cheng Yeh 0001, Yu-Chi Lai, Wen-Chieh Lin, Fu-Yin Cherng
Published at EuroVis, 2015

Flow visualization is recognized as an essential tool for many scientific research fields and different visualization approaches are proposed. Several studies are also conducted to evaluate their effectiveness but these studies rarely examine the performance from the perspective of visual perception. In this paper, we aim at exploring how users’ visual perception is influenced by different 2D flow visualization methods. An eye tracker is used to analyze users’ visual behaviors when they perform the free viewing, advection prediction, flow feature detection, and flow feature identification tasks on the flow field images generated by different visualizations methods. We evaluate the illustration capability of five representative visualization algorithms. Our results show that the eye‐tracking‐based evaluation provides more insights to quantitatively analyze the effectiveness of these visualization methods.
Exploring Traffic Dynamics in Urban Environments Using Vector-Valued Functions
Authors: Jorge Poco, Harish Doraiswamy, Huy T. Vo, João Luiz Dihl Comba, Juliana Freire, Cláudio T. Silva
Published at EuroVis, 2015

The traffic infrastructure greatly impacts the quality of life in urban environments. To optimize this infrastructure, engineers and decision makers need to explore traffic data. In doing so, they face two important challenges: the sparseness of speed sensors that cover only a limited number of road segments, and the complexity of traffic patterns they need to analyze. In this paper we take a first step at addressing these challenges. We use New York City (NYC) taxi trips as sensors to capture traffic information. While taxis provide substantial coverage of the city, the data captured about taxi trips contain neither the location of taxis at frequent intervals nor their routes. We propose an efficient traffic model to derive speed and direction information from these data, and show that it provides reliable estimates. Using these estimates, we define a time‐varying vector‐valued function on a directed graph representing the road network, and adapt techniques used for vector fields to visualize the traffic dynamics. We demonstrate the utility of our technique in several case studies that reveal interesting mobility patterns in NYC's traffic. These patterns were validated by experts from NYC's Department of Transportation and the NYC Taxi &amp; Limousine Commission, who also provided interesting insights into these results.
Feature-Driven Visual Analytics of Chaotic Parameter-Dependent Movement
Authors: Martin Luboschik, Martin Rohlig, Arne T. Bittig, Natalia V. Andrienko, Heidrun Schumann, Christian Tominski
Published at EuroVis, 2015

Analyzing movements in their spatial and temporal context is a complex task. We are additionally interested in understanding the movements’ dependency on parameters that govern the processes behind the movement. We propose a visual analytics approach combining analytic, visual, and interactive means to deal with the added complexity. The key idea is to perform an analytical extraction of features that capture distinct movement characteristics. Different parameter configurations and extracted features are then visualized in a compact fashion to facilitate an overview of the data. Interaction enables the user to access details about features, to compare features, and to relate features back to the original movement. We instantiate our approach with a repository of more than twenty accepted and novel features to help analysts in gaining insight into simulations of chaotic behavior of thousands of entities over thousands of data points. Domain experts applied our solution successfully to study dynamic groups in such movements in relation to thousands of parameter configurations.
Fiber Surfaces: Generalizing Isosurfaces to Bivariate Data
Authors: Hamish A. Carr, Zhao Geng, Julien Tierny, Amit Chattopadhyay, Aaron Knoll
Published at EuroVis, 2015

Scientific visualization has many effective methods for examining and exploring scalar and vector fields, but rather fewer for bivariate fields. We report the first general purpose approach for the interactive extraction of geometric separating surfaces in bivariate fields. This method is based on fiber surfaces: surfaces constructed from sets of fibers, the multivariate analogues of isolines. We show simple methods for fiber surface definition and extraction. In particular, we show a simple and efficient fiber surface extraction algorithm based on Marching Cubes. We also show how to construct fiber surfaces interactively with geometric primitives in the range of the function. We then extend this to build user interfaces that generate parameterized families of fiber surfaces with respect to arbitrary polygons. In the special case of isovalue‐gradient plots, fiber surfaces capture features geometrically for quantitative analysis that have previously only been analysed visually and qualitatively using multi‐dimensional transfer functions in volume rendering. We also demonstrate fiber surface extraction on a variety of bivariate data.
Finite-Time Mass Separation for Comparative Visualizations of Inertial Particles
Authors: Tobias Günther, Holger Theisel
Published at EuroVis, 2015

The visual analysis of flows with inertial particle trajectories is a challenging problem because time‐dependent particle trajectories additionally depend on mass, which gives rise to an infinite number of possible trajectories passing through every point in space‐time. This paper presents an approach to a comparative visualization of the inertial particles’ separation behavior. For this, we define the Finite‐Time Mass Separation (FTMS), a scalar field that measures at each point in the domain how quickly inertial particles separate that were released from the same location but with slightly different mass. Extracting and visualizing the mass that induces the largest separation provides a simplified view on the critical masses. By using complementary coordinated views, we additionally visualize corresponding inertial particle trajectories in space‐time by integral curves and surfaces. For a quantitative analysis, we plot Euclidean and arc length‐based distances to a reference particle over time, which allows to observe the temporal evolution of separation events. We demonstrate our approach on a number of analytic and one real‐world unsteady 2D field.
GraphUnit: Evaluating Interactive Graph Visualizations Using Crowdsourcing
Authors: Mershack Okoe, Radu Jianu
Published at EuroVis, 2015

We present GraphUnit, a framework and online service that automates the process of designing, running and analyzing results of controlled user studies of graph visualizations by leveraging crowdsourcing and a set of evaluation modules based on a graph task taxonomy. User studies play an important role in visualization research but conducting them requires expertise and is time consuming. GraphUnit simplifies the evaluation process by allowing visualization designers to easily configure user studies for their web‐based graph visualizations, deploy them online, use Mechanical Turk to attract participants, collect user responses and store them in a database, and analyze incoming results automatically using appropriate statistical tools and graphs. We demonstrate the effectiveness of GraphUnit by replicating two published evaluation studies on network visualization, and showing that these studies could be configured in less than an hour. Finally, we discuss how GraphUnit can facilitate quick evaluations of alternative graph designs and thus encourage the frequent use of user studies to evaluate design decisions in iterative development processes.
Guided Volume Editing based on Histogram Dissimilarity
Authors: Alexey Karimov, Gabriel Mistelbauer, Thomas Auzinger, Stefan Bruckner
Published at EuroVis, 2015

Segmentation of volumetric data is an important part of many analysis pipelines, but frequently requires manual inspection and correction. While plenty of volume editing techniques exist, it remains cumbersome and errorprone for the user to find and select appropriate regions for editing. We propose an approach to improve volume editing by detecting potential segmentation defects while considering the underlying structure of the object of interest. Our method is based on a novel histogram dissimilarity measure between individual regions, derived from structural information extracted from the initial segmentation. Based on this information, our interactive system guides the user towards potential defects, provides integrated tools for their inspection, and automatically generates suggestions for their resolution. We demonstrate that our approach can reduce interaction effort and supports the user in a comprehensive investigation for high‐quality segmentations.