Publications

Building data analysis skills is part of modern elementary school curricula. Recent research has explored how to facilitate children's understanding of visual data representations through completion exercises which highlight links between concrete and abstract mappings. This approach scaffolds visualization activities by presenting a target visualization to children. But how can we engage children in more free-form visual data mapping exercises that are driven by their own mapping ideas? How can we scaffold a creative exploration of visualization techniques and mapping possibilities? We present Construct-A-Vis, a tablet-based tool designed to explore the feasibility of free-form and constructive visualization activities with elementary school children. Construct-A-Vis provides adjustable levels of scaffolding visual mapping processes. It can be used by children individually or as part of collaborative activities. Findings from a study with elementary school children using Construct-A-Vis individually and in pairs highlight the potential of this free-form constructive approach, as visible in children's diverse visualization outcomes and their critical engagement with the data and mapping processes. Based on our study findings we contribute insights into the design of free-form visualization tools for children, including the role of tool-based scaffolding mechanisms and shared interactions to guide visualization activities with children.

Programming assignments in computer science courses are often processed in pairs or groups of students. While working together, students face several shortcomings in today's software: The lack of real-time collaboration capabilities, the setup time of the development environment, and the use of different devices or operating systems can hamper students when working together on assignments. Text processing platforms like Google Docs solve these problems for the writing process of prose text, and computational notebooks like Google Colaboratory for data analysis tasks. However, none of these platforms allows users to implement interactive applications. We deployed a web-based literate programming system for three months during an introductory course on application development to explore how collaborative programming practices unfold and how the structure of computational notebooks affect the development. During the course, pairs of students solved weekly programming assignments. We analyzed data from weekly questionnaires, three focus groups with students and teaching assistants, and keystroke-level log data to facilitate the understanding of the subtleties of collaborative programming with computational notebooks. Findings reveal that there are distinct collaboration patterns; the preferred collaboration pattern varied between pairs and even varied within pairs over the course of three months. Recognizing these distinct collaboration patterns can help to design future computational notebooks for collaborative programming assignments.

As our field matures, evaluation of visualization techniques has extended from reporting runtime performance to studying user behavior. Consequently, many methodologies and best practices for user studies have evolved. While maintaining interactivity continues to be crucial for the exploration of large data sets, no similar methodological foundation for evaluating runtime performance has been developed. Our analysis of 50 recent visualization papers on new or improved techniques for rendering volumes or particles indicates that only a very limited set of parameters like different data sets, camera paths, viewport sizes, and GPUs are investigated, which make comparison with other techniques or generalization to other parameter ranges at least questionable. To derive a deeper understanding of qualitative runtime behavior and quantitative parameter dependencies, we developed a framework for the most exhaustive performance evaluation of volume and particle visualization techniques that we are aware of, including millions of measurements on ten different GPUs. This paper reports on our insights from statistical analysis of this data discussing independent and linear parameter behavior and non-obvious effects. We give recommendations for best practices when evaluating runtime performance of scientific visualization applications, which can serve as a starting point for more elaborate models of performance quantification.

Augmented Reality (AR) provides a unique opportunity to situate learning content in one's environment. In this work, we investigated how AR could be developed to provide an interactive context-based language learning experience. Specifically, we developed a novel handheld-AR app for learning case grammar by dynamically creating quizzes, based on real-life objects in the learner's surroundings. We compared this to the experience of learning with a non-contextual app that presented the same quizzes with static photographic images. Participants found AR suitable for use in their everyday lives and enjoyed the interactive experience of exploring grammatical relationships in their surroundings. Nonetheless, Bayesian tests provide substantial evidence that the interactive and context-embedded AR app did not improve case grammar skills, vocabulary retention, and usability over the experience with equivalent static images. Based on this, we propose how language learning apps could be designed to combine the benefits of contextual AR and traditional approaches.

Rapid Serial Visual Presentation (RSVP) has gained popular-ity as a method for presenting text on wearable devices with limited screen space. Nonetheless, it remains unclear how to calibrate RSVP display parameters, such as spatial alignments or presentation rates, to suit the reader’s information process-ing ability at high presentation speeds. Existing methods rely on comprehension and subjective workload scores, which are influenced by the user’s knowledge base and subjective percep-tion. Here, we use electroencephalography (EEG) to directly determine how individual information processing varies with changes in RSVP display parameters. Eighteen participants read text excerpts with RSVP in a repeated-measures design that manipulated the Text Alignment and Presentation Speed of text representation. We evaluated how predictive EEG metrics were of gains in reading speed, subjective workload, and text comprehension. We found significant correlations between EEG and increasing Presentation Speeds and propose how EEG can be used for dynamic selection of RSVP parameters.

Heatmaps are a popular visualization technique that encode 2D density distributions using color or brightness. Experimental studies have shown though that both of these visual variables are inaccurate when reading and comparing numeric data values. A potential remedy might be to use 3D heatmaps by introducing height as a third dimension to encode the data. Encoding abstract data in 3D, however, poses many problems, too. To better understand this tradeoff, we conducted an empirical study (N=48) to evaluate the user performance of 2D and 3D heatmaps for comparative analysis tasks. We test our conditions on a conventional 2D screen, but also in a virtual reality environment to allow for real stereoscopic vision. Our main results show that 3D heatmaps are superior in terms of error rate when reading and comparing single data items. However, for overview tasks, the well-established 2D heatmap performs better.

Subtitles play a crucial role in cross-lingual distribution of multimedia content and help communicate information where auditory content is not feasible (loud environments, hearing impairments, unknown languages). Established methods utilize text at the bottom of the screen, which may distract from the video. Alternative techniques place captions closer to related content (e.g., faces) but are not applicable to arbitrary videos such as documentations. Hence, we propose to leverage live gaze as indirect input method to adapt captions to individual viewing behavior. We implemented two gaze-adaptive methods and compared them in a user study (n=54) to traditional captions and audio-only videos. The results show that viewers with less experience with captions prefer our gaze-adaptive methods as they assist them in reading. Furthermore, gaze distributions resulting from our methods are closer to natural viewing behavior compared to the traditional approach. Based on these results, we provide design implications for gaze-adaptive captions.

We introduce AVAR, a prototypical implementation of an agile situated visualization (SV) toolkit targeting liveness, integration, and expressiveness. We report on results of an exploratory study with AVAR and seven expert users. In it, participants wore a Microsoft HoloLens device and used a Bluetooth keyboard to program a visualization script for a given dataset. To support our analysis, we (i) video recorded sessions, (ii) tracked users' interactions, and (iii) collected data of participants' impressions. Our prototype confirms that agile SV is feasible. That is, liveness boosted participants' engagement when programming an SV, and so, the sessions were highly interactive and participants were willing to spend much time using our toolkit (i.e., median ≥ 1.5 hours). Participants used our integrated toolkit to deal with data transformations, visual mappings, and view transformations without leaving the immersive environment. Finally, participants benefited from our expressive toolkit and employed multiple of the available features when programming an SV.

Even though load testing is an established technique to assess load-related quality properties of software systems, it is applied only seldom and with questionable results. Indeed, configuring, executing, and interpreting results of a load test require high effort and expertise. Since chatbots have shown promising results for interactively supporting complex tasks in various domains (including software engineering), we hypothesize that chatbots can provide developers suitable support for load testing. In this paper, we present PerformoBot, our chatbot for configuring and running load tests. In a natural language conversation, PerformoBot guides developers through the process of properly specifying the parameters of a load test, which is then automatically executed by PerformoBot using a state-of-the-art load testing tool. After the execution, PerformoBot provides developers a report that answers the respective concern. We report on results of a user study that involved 47 participants, in which we assessed our tool's acceptance and effectiveness. We found that participants in the study, particularly those with a lower level of expertise in performance engineering, had a mostly positive view of PerformoBot.

Rectangular treemaps visualize hierarchical numerical data by recursively partitioning an input rectangle into smaller rectangles whose areas match the data. Numerical data often has uncertainty associated with it. To visualize uncertainty in a rectangular treemap, we identify two conflicting key requirements: (i) to assess the data value of a node in the hierarchy, the area of its rectangle should directly match its data value, and (ii) to facilitate comparison between data and uncertainty, uncertainty should be encoded using the same visual variable as the data, that is, area. We present Uncertainty Treemaps, which meet both requirements simultaneously by introducing the concept of hierarchical uncertainty masks. First, we define a new cost function that measures the quality of Uncertainty Treemaps. Then, we show how to adapt existing treemapping algorithms to support uncertainty masks. Finally, we demonstrate the usefulness and quality of our technique through an expert review and a computational experiment on real-world datasets.

Background: Why do we eat? Our motives for eating are diverse, ranging from hunger and liking to social norms and affect regulation. Although eating motives can vary from eating event to eating event, which implies substantial moment-to-moment differences, current ways of measuring eating motives rely on single timepoint questionnaires that assess eating motives as situation-stable dispositions (traits). However, mobile technologies including smartphones allow eating events and motives to be captured in real time and real life, thus capturing experienced eating motives in-the-moment (states). Objective: This study aimed to examine differences between why people think they eat (trait motives) and why they eat in the moment of consumption (state motives) by comparing a dispositional (trait) and an in-the-moment (state) assessment of eating motives. Methods: A total of 15 basic eating motives included in The Eating Motivation Survey (ie, liking, habit, need and hunger, health, convenience, pleasure, traditional eating, natural concerns, sociability, price, visual appeal, weight control, affect regulation, social norms, and social image) were assessed in 35 participants using 2 methodological approaches: (1) a single timepoint dispositional assessment and (2) a smartphone-based ecological momentary assessment (EMA) across 8 days (N=888 meals) capturing eating motives in the moment of eating. Similarities between dispositional and in-the-moment eating motive profiles were assessed according to 4 different indices of profile similarity, that is, overall fit, shape, scatter, and elevation. Moreover, a visualized person × motive data matrix was created to visualize and analyze between- and within-person differences in trait and state eating motives. Results: Similarity analyses yielded a good overall fit between the trait and state eating motive profiles across participants, indicated by a double-entry intraclass correlation of 0.52 (P<.001). However, although trait and state motives revealed a comparable rank order (r=0.65; P<.001), trait motives overestimated 12 of 15 state motives (P<.001; d=1.97). Specifically, the participants assumed that 6 motives (need and hunger, price, habit, sociability, traditional eating, and natural concerns) are more essential for eating than they actually were in the moment (d>0.8). Furthermore, the visualized person × motive data matrix revealed substantial interindividual differences in intraindividual motive profiles. Conclusions: For a comprehensive understanding of why we eat what we eat, dispositional assessments need to be extended by in-the-moment assessments of eating motives. Smartphone-based EMAs reveal considerable intra- and interindividual differences in eating motives, which are not captured by single timepoint dispositional assessments. Targeting these differences between why people think they eat what they eat and why they actually eat in the moment may hold great promise for tailored mobile health interventions facilitating behavior changes.

Cross-device interaction with tablets is a popular topic in HCI research. Recent work has shown the benefits of including multiple devices into users’ workflows while various interaction techniques allow transferring content across devices. However, users are only reluctantly using multiple devices in combination. At the same time, research on cross-device interaction struggles to find a frame of reference to compare techniques or systems. In this paper, we try to address these challenges by studying the interplay of interaction techniques, device utilization, and task-specific activities in a user study with 24 participants from different but complementary angles of evaluation using an abstract task, a sensemaking task, and three interaction techniques. We found that different interaction techniques have a lower influence than expected, that work behaviors and device utilization depend on the task at hand, and that participants value specific aspects of cross-device interaction.

Automatic clustering techniques play a central role in Visual Analytics by helping analysts to discover interesting patterns in high-dimensional data. Evaluating these clustering techniques, however, is difficult due to the lack of universal ground truth. Instead, clustering approaches are usually evaluated based on a subjective visual judgment of low-dimensional scatterplots of different datasets. As clustering is an inherent human-in-the-loop task, we propose a more systematic way of evaluating clustering algorithms based on quantification of human perception of clusters in 2D scatterplots. The core question we are asking is in how far existing clustering techniques align with clusters perceived by humans. To do so, we build on a dataset from a previous study 1, in which 34 human subjects la-beled 1000 synthetic scatterplots in terms of whether they could see one or more than one cluster. Here, we use this dataset to benchmark state-of-the-art clustering techniques in terms of how far they agree with these human judgments. More specifically, we assess 1437 variants of K-means, Gaussian Mixture Models, CLIQUE, DBSCAN, and Agglomerative Clustering techniques on these benchmarks data. We get unexpected results. For instance, CLIQUE and DBSCAN are at best in slight agreement on this basic cluster counting task, while model-agnostic Agglomerative clustering can be up to a substantial agreement with human subjects depending on the variants. We discuss how to extend this perception-based clustering benchmark approach, and how it could lead to the design of perception-based clustering techniques that would better support more trustworthy and explainable models of cluster patterns.

Driving simulators are necessary for evaluating automotive technology for human users. While they can vary in terms of their fidelity, it is essential that users experience minimal simulator sickness and high presence in them. In this paper, we present two experiments that investigate how a virtual driving simulation system could be visually presented within a real vehicle, which moves on a test track but displays a virtual environment. Specifically, we contrasted display presentation of the simulation using either head-mounted displays (HMDs) or fixed displays in the vehicle itself. Overall, we find that fixed displays induced less simulator sickness than HMDs. Neither HMDs or fixed displays induced a stronger presence in our implementation, even when the field-of-view of the fixed display was extended. We discuss the implications of this, particular in the context of scenarios that could induce considerable motion sickness, such as testing non-driving activities in automated vehicles.

In this paper, we present a revised LFG account for Icelandic clause structure, factoring in new historical data from IcePaHC (Wallenberg et al., 2011).This builds on previous work by Sells (2001, 2005) and Booth et al. (2017), focusing more closely on the syntactic encoding of information structure.Based on findings from a series of corpus-based investigations, we argue thatthe functional category I was already obligatory in Old Icelandic, accounting for both V1 and V2 orders and the absence of V3/V-later orders. Moreover,we show that the basic c-structure skeleton persists throughout the diachrony; what changes is the way in which information structure is syntactically encoded, i.e. the association between c- and i-structure. Topics increasingly target SpecIP, which allows the finite verb in I to serve as a boundary between topic and comment. This goes hand in hand with certain discourse adverbs losing their function as a discourse partitioner in the midfield and ties in with other changes shown for Icelandic (Booth et al., 2017).

Foveal vision is located in the center of the field of view with a rich impression of detail and color, whereas peripheral visionoccurs on the side with more fuzzy and colorless perception. This visual acuity fall-off can be used to achieve higher frame ratesby adapting rendering quality to the human visual system. Volume raycasting has unique characteristics, preventing a directtransfer of many traditional foveated rendering techniques. We present an approach that utilizes the visual acuity fall-off toaccelerate volume rendering based on Linde-Buzo-Gray sampling and natural neighbor interpolation. First, we measure gazeusing a stationary 1200 Hz eye-tracking system. Then, we adapt our sampling and reconstruction strategy to that gaze. Finally,we apply a temporal smoothing filter to attenuate undersampling artifacts since peripheral vision is particularly sensitive tocontrast changes and movement. Our approach substantially improves rendering performance with barely perceptible changes invisual quality. We demonstrate the usefulness of our approach through performance measurements on various data se

We present a method for the spatio-temporal analysis of gaze data from multiple participants in the context of a video stimulus. For such data, an overview of the recorded patterns is important to identify common viewing behavior (such as attentional synchrony) and outliers. We adopt the approach of space-time cube visualization, which extends the spatial dimensions of the stimulus by time as the third dimension. Previous work mainly handled eye tracking data in the space-time cube as point cloud, providing no information about the stimulus context. This paper presents a novel visualization technique that combines gaze data, a dynamic stimulus, and optical flow with volume rendering to derive an overview of the data with contextual information. With specifically designed transfer functions, we emphasize different data aspects, making the visualization suitable for explorative analysis and for illustrative support of statistical findings alike.

We present an approach for the visualization and interactive analysis of dynamic graphs that contain a large number of time steps. A specific focus is put on the support of analyzing temporal aspects in the data. Central to our approach is a static, volumetric representation of the dynamic graph based on the concept of space-time cubes that we create by stacking the adjacency matrices of all time steps. The use of GPU-accelerated volume rendering techniques allows us to render this representation interactively. We identified four classes of analytics methods as being important for the analysis of large and complex graph data, which we discuss in detail: data views, aggregation and filtering, comparison, and evolution provenance. Implementations of the respective methods are presented in an integrated application, enabling interactive exploration and analysis of large graphs. We demonstrate the applicability, usefulness, and scalability of our approach by presenting two examples for analyzing dynamic graphs. Furthermore, we let visualization experts evaluate our analytics approach.

AgraphG=(V, E)isasupportof a hypergraphH=(V, S)if every hyperedge induces a connected subgraph inG. Supports are usedfor certain types of hypergraph visualizations. In this paper we considervisualizingspatialhypergraphs, where each vertex has a fixed location inthe plane. This is the case, e.g., when modeling set systems of geospatiallocations as hypergraphs. By applying established aesthetic quality cri-teria we are interested in finding supports that yield plane straight-linedrawings with minimum total edge length on the input point setV.Wefirst show, from a theoretical point of view, that the problem isNP-hardalready under rather mild conditions as well as a negative approxima-bility results. Therefore, the main focus of the paper lies on practicalheuristic algorithms as well as an exact, ILP-based approach for comput-ing short plane supports. We report results from computational exper-iments that investigate the effect of requiring planarity and acyclicityon the resulting support length. Further, we evaluate the performanceand trade-offs between solution quality and speed of several heuristicsrelative to each other and compared to optimal solutions.

The detection of interesting patterns in large high-dimensional datasets is difficult because of their dimensionality and pattern complexity. Therefore, analysts require automated support for the extraction of relevant patterns. In this paper, we present FDive, a visual active learning system that helps to create visually explorable relevance models, assisted by learning a pattern-based similarity. We use a small set of user-provided labels to rank similarity measures, consisting of feature descriptor and distance function combinations, by their ability to distinguish relevant from irrelevant data. Based on the best-ranked similarity measure, the system calculates an interactive Self-Organizing Map-based relevance model, which classifies data according to the cluster affiliation. It also automatically prompts further relevance feedback to improve its accuracy. Uncertain areas, especially near the decision boundaries, are highlighted and can be refined by the user. We evaluate our approach by comparison to state-of-the-art feature selection techniques and demonstrate the usefulness of our approach by a case study classifying electron microscopy images of brain cells. The results show that FDive enhances both the quality and understanding of relevance models and can thus lead to new insights for brain research.

We propose a technique to represent two-dimensional data using stipples. While stippling is often regarded as an illustrative method, we argue that it is worth investigating its suitability for the visualization domain. For this purpose, we generalize the Linde-Buzo-Gray stippling algorithm for information visualization purposes to encode continuous and discrete 2D data. Our proposed modifications provide more control over the resulting distribution of stipples for encoding additional information into the representation, such as contours. We show different approaches to depict contours in stipple drawings based on locally adjusting the stipple distribution. Combining stipple-based gradients and contours allows for simultaneous assessment of the overall structure of the data while preserving important local details. We discuss the applicability of our technique using datasets from different domains and conduct observation-validating studies to assess the perception of stippled representations

The recent availability of affordable and lightweight tracking sensors allows researchers to collect large and complex movement datasets. These datasets require applications that are capable of handling them whilst providing an environment that enables the analyst(s) to focus on the task of analysing the movement in the context of the geographic environment it occurred in. We present a framework for collaborative analysis of geospatial-temporal movement data with a use-case in collective behavior analysis. It supports the concurrent usage of several program instances, allowing to have different perspectives on the same data in collocated or remote setups. The implementation can be deployed in a variety of immersive environments, e.g. on a tiled display wall or mobile VR devices.

Star glyphs are a well-researched visualization technique to repre-sent multi-dimensional data. They are often used in small multiplesettings for a visual comparison of many data points. However, theiroverall visual appearance is strongly influenced by the ordering of di-mensions. To this end, two orthogonal categories of layout strategiesare proposed in the literature: order dimensions bysimilarityto gethomogeneously shaped glyphs vs. order bydissimilarityto empha-size spikes and salient shapes. While there is evidence that salientshapes support clustering tasks, evaluation, and direct comparisonof data-driven ordering strategies has not received much researchattention. We contribute an empirical user study to evaluate the effi-ciency, effectiveness, and user confidence in visual clustering tasksusing star glyphs. In comparison to similarity-based ordering, ourresults indicate that dissimilarity-based star glyph layouts supportusers better in clustering tasks, especially when clutter is present.

Handheld Augmented Reality (AR) displays offer a see-through option to create the illusion of virtual objects being integrated into the viewer’s physical environment. Some AR display technologies also allow for the deactivation of the see-through option, turning AR tablets into Virtual Reality (VR) devices that integrate the virtual objects into an exclusively virtual environment. Both display configurations are typically available on handheld devices, raising the question of their influence on users’ experience during collaborative activities. In two experiments, we studied how the different display configurations influence user experience, workload, and team performance of co-located and distributed collaborators during a spatial referencing task. A mixed-methods approach revealed that participants’ opinions were polarized towards the two display configurations, regardless of the spatial distribution of collaboration. Based on our findings, we identify critical aspects to be addressed in future research to better understand and support co-located and distributed collaboration using AR and VR displays.

Parallel coordinates are a popular technique to visualize multidimensional data. However, they face a significant problem influencing the perception and interpretation of patterns. The distance between two parallel lines differs based on their slope. Vertical lines are rendered longer and closer to each other than horizontal lines. This problem is inherent in the technique and has two main consequences: (1) clusters which have a steep slope between two axes are visually more prominent than horizontal clusters. (2) Noise and clutter can be perceived as clusters, as a few parallel vertical lines visually emerge as a ghost cluster. Our paper makes two contributions: First, we formalize the problem and show its impact. Second, we present a novel technique to reduce the effects by rendering the polylines of the parallel coordinates based on their slope: horizontal lines are rendered with the default width, lines with a steep slope with a thinner line. Our technique avoids density distortions of clusters, can be computed in linear time, and can be added on top of most parallel coordinate variations. To demonstrate the usefulness, we show examples and compare them to the classical rendering.

The IEEE SciVis 2019 Contest targets the visual analysis of structure formation in the cosmic evolution of the universe from when the universe was five million years old up to now. In our submission, we analyze high-dimensional data to get an overview, then investigate the impact of Active Galactic Nuclei (AGNs) using various visualization techniques, for instance, an adapted filament filtering method for detailed analysis and particle flow in the vicinity of filaments. Based on feedback from domain scientists on these initial visualizations, we also analyzed X-ray emissions and star formation areas. The conversion of star-forming gas to stars and the resulting increasing molecular weight of the particles could be observed.

Language change is often assessed against a set of pre-determined time periods in order to be able to trace its diachronic trajectory. This is problematic, since a pre-determined periodization might obscure significant developments and lead to false assumptions about the data. Moreover, these time periods can be based on factors which are either arbitrary or non-linguistic, e.g., dividing the corpus data into equidistant stages or taking into account language-external events. Addressing this problem, in this paper we present a data-driven approach to periodization: `DiaHClust'. DiaHClust is based on iterative hierarchical clustering and offers a multi-layered perspective on change from text-level to broader time periods. We demonstrate the usefulness of DiaHClust via a case study investigating syntactic change in Icelandic, modelling the syntactic system of the language in terms of vectors of syntactic change.

Historical change typically is the result of complex interactions between several linguistic factors. Identifying the relevant factors and understanding how they interact across the temporal dimension is the core remit of historical linguistics. With respect to corpus work, this entails a separate annotation, extraction and painstaking pair-wise comparison of the relevant bits of information. This paper presents a significant extension of HistoBankVis, a multilayer visualization system which allows a fast and interactive exploration of complex linguistic data. Linguistic factors can be understood as data dimensions which show complex interrelationships. We model these relationships with the Parallel Sets technique. We demonstrate the powerful potential of this technique by applying the system to understanding the interaction of case, grammatical relations and word order in the history of Icelandic.

Selecting a good aspect ratio is crucial for effective 2D diagrams. There are several aspect ratio selection methods for function plots and line charts, but only few can handle general, discrete diagrams such as 2D scatter plots. However, these methods either lack a perceptual foundation or heavily rely on intermediate isoline representations, which depend on choosing the right isovalues and are time-consuming to compute. This paper introduces a general image-based approach for selecting aspect ratios for a wide variety of 2D diagrams, ranging from scatter plots and density function plots to line charts. Our approach is derived from Federer's co-area formula and a line integral representation that enable us to directly construct image-based versions of existing selection methods using density fields. In contrast to previous methods, our approach bypasses isoline computation, so it is faster to compute, while following the perceptual foundation to select aspect ratios. Furthermore, this approach is complemented by an anisotropic kernel density estimation to construct density fields, allowing us to more faithfully characterize data patterns, such as the subgroups in scatterplots or dense regions in time series. We demonstrate the effectiveness of our approach by quantitatively comparing to previous methods and revisiting a prior user study. Finally, we present extensions for ROI banking, multi-scale banking, and the application to image data.

In this paper, we examine the robustness of scagnostics through a series of theoretical and empirical studies. First, we investigate the sensitivity of scagnostics by employing perturbing operations on more than 60M synthetic and real-world scatterplots. We found that two scagnostic measures, Outlying and Clumpy, are overly sensitive to data binning. To understand how these measures align with human judgments of visual features, we conducted a study with 24 participants, which reveals that i) humans are not sensitive to small perturbations of the data that cause large changes in both measures, and ii) the perception of clumpiness heavily depends on per-cluster topologies and structures. Motivated by these results, we propose Robust Scagnostics (RScag) by combining adaptive binning with a hierarchy-based form of scagnostics. An analysis shows that RScag improves on the robustness of original scagnostics, aligns better with human judgments, and is equally fast as the traditional scagnostic measures.

We present a visualization approach for the analysis of CO 2 bubble-induced attenuation in porous rock formations. As a basis for this, we introduce customized techniques to extract CO 2 bubbles and their surrounding porous structure from X-ray computed tomography data (XCT) measurements. To understand how the structure of porous media influences the occurrence and the shape of formed bubbles, we automatically classify and relate them in terms of morphology and geometric features, and further directly support searching for promising porous structures. To allow for the meaningful direct visual comparison of bubbles and their structures, we propose a customized registration technique considering the bubble shape as well as its points of contact with the porous media surface. With our quantitative extraction of geometric bubble features, we further support the analysis as well as the creation of a physical model. We demonstrate that our approach was successfully used to answer several research questions in the domain, and discuss its high practical relevance to identify critical seismic characteristics of fluid-saturated rock that govern its capability to store CO 2.

In this paper, we propose a perceptually-guided visualization sharpening technique. We analyze the spectral behavior of an established comprehensive perceptual model to arrive at our approximated model based on an adapted weighting of the bandpass images from a Gaussian pyramid. The main benefit of this approximated model is its controllability and predictability for sharpening color-mapped visualizations. Our method can be integrated into any visualization tool as it adopts generic image-based post-processing, and it is intuitive and easy to use as viewing distance is the only parameter. Using highly diverse datasets, we show the usefulness of our method across a wide range of typical visualizations.

We present LOOM (Line-Ordering Optimized Maps), a fully automatic generator of geographically accurate transit maps. The input to LOOM is data about the lines of a given transit network, namely for each line, the sequence of stations it serves and the geographical course the vehicles of this line take. We parse this data from GTFS, the prevailing standard for public transit data. LOOM proceeds in three stages: (1) construct a so-called line graph, where edges correspond to segments of the network with the same set of lines following the same course; (2) construct an ILP that yields a line ordering for each edge which minimizes the total number of line crossings and line separations; (3) based on the line graph and the ILP solution, draw the map. As a naive ILP formulation is too demanding, we derive a new custom-tailored formulation which requires significantly fewer constraints. Furthermore, we present engineering techniques which use structural properties of the line graph to further reduce the ILP size. For the subway network of New York, we can reduce the number of constraints from 229,000 in the naive ILP formulation to about 3,700 with our techniques, enabling solution times of less than a second. Since our maps respect the geography of the transit network, they can be used for tiles and overlays in typical map services. Previous research work either did not take the geographical course of the lines into account, or was concerned with schematic maps without optimizing line crossings or line separations.

The visualization community has developed to date many intuitions and understandings of how to judge the quality of views in visualizing data. The computation of a visualization's quality and usefulness ranges from measuring clutter and overlap, up to the existence and perception of specific (visual) patterns. This survey attempts to report, categorize and unify the diverse understandings and aims to establish a common vocabulary that will enable a wide audience to understand their differences and subtleties. For this purpose, we present a commonly applicable quality metric formalization that should detail and relate all constituting parts of a quality metric. We organize our corpus of reviewed research papers along the data types established in the information visualization community: multi‐ and high‐dimensional, relational, sequential, geospatial and text data. For each data type, we select the visualization subdomains in which quality metrics are an active research field and report their findings, reason on the underlying concepts, describe goals and outline the constraints and requirements. One central goal of this survey is to provide guidance on future research opportunities for the field and outline how different visualization communities could benefit from each other by applying or transferring knowledge to their respective subdomain. Additionally, we aim to motivate the visualization community to compare computed measures to the perception of humans.

Tools for visual data exploration allow users to visually browse through and analyze datasets to possibly reveal interesting infor- mation hidden in the data that users are a priori unaware of. Such tools rely on both query recommendations to select data to be visualized and visualization recommendations for these data to best support users in their visual data exploration process. EVLIN ( e xploring v isually with lin eage) is a system that assists users in visually exploring relational data stored in a data ware- house. EVLIN implements novel techniques for recommending both queries and their result visualization in an integrated and interactive way 3 . Recommendations rely on provenance (aka lineage) that describes the production process of displayed data . The demonstration of EVLIN includes an introduction to its features and functionality through sample exploration sessions. Conference attendees will then have the opportunity to gain hands- on experience of provenance-based visual data exploration by performing their own exploration sessions. These sessions will explore real-world data from several domains. While exploration sessions use a Web-based visual interface, the demonstration also features a researcher console, where attendees may have a look behind the scenes to get a more in-depth understanding of the underlying recommendation algorithms. Fachgebiet (DDC): 004 Informatik

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 high-dimensional 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

Take-over requests (TORs) in highly automated vehicles are cues that prompt users to resume control. TORs however, are often evaluated in non-moving driving simulators. This ignores the role of motion, an important source of information for users who have their eyes off the road while engaged in non-driving related tasks. We ran a user study in a moving-base driving simulator to investigate the effect of motion on TOR responses. We found that with motion, user responses to TORs vary depending on the road context where TORs are issued. While previous work showed that participants are fast to respond to urgent cues, we show that this is true only when TORs are presented on straight roads. Urgent cues issued on curved roads elicit slower responses than non-urgent cues on curved roads. Our findings indicate that TORs should be designed to be aware of road context to accommodate natural user responses.

We present an approach for interactively analyzing large dynamic graphs consisting of several thousand time steps with a particular focus on temporal aspects. we employ a static representation of the time-varying graph based on the concept of space-time cubes, i.e., we create a volumetric representation of the graph by stacking the adjacency matrices of each of its time steps. To achieve an efficient analysis of complex data, we discuss three classes of analytics methods of particular importance in this context: data views, aggregation and filtering, and comparison. For these classes, we present a GPU-based implementation of respective analysis methods that enable the interactive analysis of large graphs. We demonstrate the utility as well as the scalability of our approach by presenting application examples for analyzing different time-varying data sets.

This special interest group addresses the status quo of HCI research with regards to research practices of transparency and openness. Specifically, it discusses whether current practices are in line with the standards applied to other fields (e.g., psychology, economics, medicine). It seeks to identify current practices that are more progressive and worth communicating to other disciplines, while evaluating whether practices in other disciplines are likely to apply to HCI research constructively. Potential outcomes include: (1) a review of current HCI research policies, (2) a report on recommended practices, and (3) a replication project of key findings in HCI research.

Analysis of functional magnetic resonance imaging (fMRI) plays a pivotal role in uncovering an understanding of the brain. fMRI data contain both spatial volume and temporal signal information, which provide a depiction of brain activity. The analysis pipeline, however, is hampered by numerous uncertainties in many of the steps; often seen as one of the last hurdles for the domain. In this review, we categorise fMRI research into three pipeline phases: (i) image acquisition and processing; (ii) image analysis; and (iii) visualisation and human interpretation, to explore the uncertainties that arise in each phase, including the compound effects due to the inter-dependence of steps. Attempts at mitigating uncertainties rely on providing interactive visual analytics that aid users in understanding the effects of the uncertainties and adjusting their analyses. This impetus for visual analytics comes in light of considerable research investigating uncertainty throughout the pipeline. However, to the best of our knowledge, there is yet to be a comprehensive review on the importance and utility of uncertainty visual analytics (UVA) in addressing fMRI concerns, which we term fMRI-UVA. Such techniques have been broadly implemented in related biomedical fields, and its potential for fMRI has recently been explored; however, these attempts are limited in their scope and utility, primarily focussing on addressing small parts of single pipeline phases. Our comprehensive review of the fMRI uncertainties from the perspective of visual analytics addresses the three identified phases in the pipeline. We also discuss the two interrelated approaches for future research opportunities for fMRI-UVA.

Background Despite the rigorous control of tap water quality, substantial price differences, and environmental concerns, bottled water consumption has increased in recent decades. To facilitate healthy and sustainable consumer choices, a deeper understanding of this "water consumption paradox" is needed. Therefore, the aim of the two present studies was to examine health-related beliefs and risk perceptions and their accuracy by implementing a combined product- and consumer-oriented approach. Methods An online survey (N = 578) and a blind taste test (N = 99) assessed perceptions and behaviors for tap and bottled water within primarily tap and bottled water consumers in a fully crossed design. The combined product- and consumer-oriented approach yielded significant consumer × product interaction effects. Results The two consumer groups showed “polarized” ratings regarding perceived quality/hygiene, health risks and taste for bottled and tap water, indicating that the two consumer groups substantially diverged in their beliefs. However, in the blind taste test, neither consumer group was able to distinguish tap from bottled water samples (consumer perspective). Moreover, tap or bottled water samples did not systemically vary in their ascribed health-risk or taste characteristics (product perspective). Conclusions Although the two consumer groups differ greatly in their beliefs, the perceived health risk and taste differences seem to reflect illusionary beliefs rather than actual experiences or product characteristics. Public health campaigns should address these illusions to promote healthy and sustainable consumer choices.

In the era of ubiquitous computing, people expect applications to work across different devices. To provide a seamless user experience it is therefore crucial that interfaces and interactions are consistent across different device types. In this paper, we present a method to create gesture sets that are consistent and easily transferable. Our proposed method entails 1) the gesture elicitation on each device type, 2) the consolidation of a unified gesture set, and 3) a final validation by calculating a transferability score. We tested our approach by eliciting a set of user-defined gestures for reading with Rapid Serial Visual Presentation (RSVP) of text for three device types: phone, watch, and glasses. We present the resulting, unified gesture set for RSVP reading and show the feasibility of our method to elicit gesture sets that are consistent across device types with different form factors.

We visualize contours for spatio‐temporal processes to indicate where and when non‐continuous changes occur or spatial bounds are encountered. All time steps are comprised densely in one visualization, with contours allowing to efficiently analyze processes in the data even in case of spatial or temporal overlap. Contours are determined on the basis of deep raycasting that collects samples across time and depth along each ray. For each sample along a ray, its closest neighbors from adjacent rays are identified, considering time, depth, and value in the process. Large distances are represented as contours in image space, using color to indicate temporal occurrence. This contour representation can easily be combined with volume rendering‐based techniques, providing both full spatial detail for individual time steps and an outline of the whole time series in one view. Our view‐dependent technique supports efficient progressive computation, and requires no prior assumptions regarding the shape or nature of processes in the data. We discuss and demonstrate the performance and utility of our approach via a variety of data sets, comparison and combination with an alternative technique, and feedback by a domain scientist.

We present an approach that dynamically adapts encoder settings for image tiles to yield the best possible quality for a given bandwidth. This reduces the overall size of the image while preserving details. Our application determines the encoding settings in two steps. In the first step, we predict the quality and size of the tiles for different encoding settings using a convolutional neural network. In the second step, we assign the optimal encoder setting to each tile, so that the overall size of the image is lower than a predetermined threshold. Commonly, for tiles that contain complicated structures, a high quality setting is used in order to prevent major information loss, while quality settings are lowered for others to keep the size below the threshold. We demonstrate that we can reduce the overall size of the image while preserving the details in areas of interest using the example of both particle and volume visualisation applications.

By orienting attention, auditory cues can improve the discrimination of spatially congruent visual targets. Looming sounds that increase in intensity are processed preferentially by the brain. Thus, we investigated whether auditory looming cues can orient visuo-spatial attention more effectively than static and receding sounds. Specifically, different auditory cues could redirect attention away from a continuous central visuo-motor tracking task to peripheral visual targets that appeared occasionally. To investigate the time course of crossmodal cuing, Experiment 1 presented visual targets at different time-points across a 500 ms auditory cue’s presentation. No benefits were found for simultaneous audio-visual cue-target presentation. The largest crossmodal benefit occurred at early cue-target asynchrony onsets (i.e., CTOA = 250 ms), regardless of auditory cue type, which diminished at CTOA = 500 ms for static and receding cues. However, auditory looming cues showed a late crossmodal cuing benefit at CTOA = 500 ms. Experiment 2 showed that this late auditory looming cue benefit was independent of the cue’s intensity when the visual target appeared. Thus, we conclude that the late crossmodal benefit throughout an auditory looming cue’s presentation is due to its increasing intensity profile. The neural basis for this benefit and its ecological implications are discussed.

Design recommendations for notifications are typically based on user performance and subjective feedback. In comparison, there has been surprisingly little research on how designed notifications might be processed by the brain for the information they convey. The current study uses EEG/ERP methods to evaluate auditory notifications that were designed to cue long-distance truck drivers for task-management and driving conditions, particularly for automated driving scenarios. Two experiments separately evaluated naive students and professional truck drivers for their behavioral and brain responses to auditory notifications, which were either auditory icons or verbal commands. Our EEG/ERP results suggest that verbal commands were more readily recognized by the brain as relevant targets, but that auditory icons were more likely to update contextual working memory. Both classes of notifications did not differ on behavioral measures. This suggests that auditory icons ought to be employed for communicating contextual information and verbal commands, for urgent requests.

Even if you have spent some time reading about machine learning, chances are that you have never heard of Gaussian processes. And if you have, rehearsing the basics is always a good way to refresh your memory. With this blog post we want to give an introduction to Gaussian processes and make the mathematical intuition behind them more approachable. Gaussian processes are a powerful tool in the machine learning toolbox. They allow us to make predictions about our data by incorporating prior knowledge. Their most obvious area of application is fitting a function to the data. This is called regression and is used, for example, in robotics or time series forecasting. But Gaussian processes are not limited to regression — they can also be extended to classification and clustering tasks. For a given set of training points, there are potentially infinitely many functions that fit the data. Gaussian processes offer an elegant solution to this problem by assigning a probability to each of these functions. The mean of this probability distribution then represents the most probable characterization of the data. Furthermore, using a probabilistic approach allows us to incorporate the confidence of the prediction into the regression result. We will first explore the mathematical foundation that Gaussian processes are built on — we invite you to follow along using the interactive figures and hands-on examples. They help to explain the impact of individual components, and show the flexibility of Gaussian processes. After following this article we hope that you will have a visual intuition on how Gaussian processes work and how you can configure them for different types of data.

We present a novel type of circular treemap, where we intentionally allocate extra space for additional visual variables. With this extended visual design space, we encode hierarchically structured data along with their uncertainties in a combined diagram. We introduce a hierarchical and force-based circle-packing algorithm to compute Bubble Treemaps, where each node is visualized using nested contour arcs. Bubble Treemaps do not require any color or shading, which offers additional design choices. We explore uncertainty visualization as an application of our treemaps using standard error and Monte Carlo-based statistical models. To this end, we discuss how uncertainty propagates within hierarchies. Furthermore, we show the effectiveness of our visualization using three different examples: the package structure of Flare, the S&P 500 index, and the US consumer expenditure survey

We propose a screening approach to find reliable and effectively expert crowd workers in image quality assessment (IQA). Our method measures the users' ability to identify image degradations by using test questions, together with several relaxed reliability checks. We conduct multiple experiments, obtaining reproducible results with a high agreement between the expertise-screened crowd and the freelance experts of 0.95 Spearman rank order correlation (SROCC), with one restriction on the image type. Our contributions include a reliability screening method for uninformative users, a new type of test questions that rely on our proposed database 1 of pristine and artificially distorted images, a group agreement extrapolation method and an analysis of the crowdsourcing experiments.

Recent research has demonstrated the benefits of mixedrealities for information visualisation. Often the focus lieson the visualisation itself, leaving interaction opportunitiesthrough different modalities largely unexplored. Yet, mixedreality in particular can benefit from a combination of differ-ent modalities. This work examines an existing mixed realityvisualisation which is combined with a large tabletop fortouch interaction. Although this allows for familiar operation,the approach comes with some limitations which we ad-dress by employing mobile devices, thus adding tangibilityand proxemics as input modalities

Fitness trackers not just provide easy means to acquire physiological data in real-world environments due to affordable sensing technologies, they further offer opportunities for physiology-aware applications and studies in HCI; however, their performance is not well understood. In this paper, we report findings on the quality of 3 sensing technologies: PPG-based wrist trackers (Apple Watch, Microsoft Band 2), an ECG-belt (Polar H7) and reference device with stick-on ECG electrodes (Nexus 10). We collected physiological (heart rate, electrodermal activity, skin temperature) and subjective data from 21 participants performing combinations of physical activity and stressful tasks. Our empirical research indicates that wrist devices provide a good sensing performance in stationary settings. However, they lack accuracy when participants are mobile or if tasks require physical activity. Based on our findings, we suggest a textitDesign Space for Wearables in Research Settings and reflected on the appropriateness of the investigated technologies in research contexts.

Ensuring sufficient quality of iris images acquired by handheld imaging devices in visible light poses many challenges to iris recognition systems. Many distortions affect the input iris images, and the source and types of these distortions are unknown in uncontrolled environments. We propose a fast no-reference image quality assessment measure for predicting iris image quality to handle severely degraded iris images. The proposed differential sign-magnitude statistics index (DSMI) is based on statistical features of the local difference sign-magnitude transform, which are computed by comparing the local mean with the central pixel of the patch and considering the noticeable variations. The experiments, conducted with a reference iris recognition system and three visible light datasets, showed that the quality of iris images strongly affects the recognition performance. Using the proposed method as a quality filtering step improved the performance of the iris recognition system by rejecting poor quality iris samples.

Mastering fine motor tasks, such as playing the guitar, takes years of time-consuming practice. Commonly, expensive guidance by experts is essential for adjusting the training program to the student's proficiency. In our work, we showcase the suitability of Electromyography to detect fine-grained hand and finger postures in an exemplary guitar tutor scenario. We present EMGuitar, an interactive guitar tutoring system, that assists students by reporting on play correctness and adjusts playback tempi automatically. We report person-dependent classification utilizing a ring of electrodes around the forearm with an F1 score of up to 0.89 on recorded calibration data. Furthermore, our system was received well by neither diminishing ease of use nor being disruptive for the participants. Based on the received comments, we identified the need for detailed play accuracy feedback down to individual chords, for which we suggest an adapted visualization and an algorithmic approach.

The emerging field of Immersive Analytics investigates how novel display and interaction technologies can enable people to better explore and analyse data and complex information. Collaboration is a crucial aspect of Immersive Analytics. In this paper we present ContextuWall, a system for interactive local and remote collaboration using touch and mobile devices as well as displays of various sizes. The system enables groups of users located on different sites to share content to a jointly used virtual desktop which is accessible over a secured network. This virtual desktop can be shown on different large displays simultaneously, taking advantage of their high resolution. To enable users to intuitively share, arrange as well as annotate image content, a purpose-built client software has been developed and can easily be adapted with plug-ins for existing data analytics software. We show exemplary use cases and describe the system architecture and its implementation.

Entering text is one of the most common tasks when interacting with computing systems. Virtual Reality (VR) presents a challenge as neither the user's hands nor the physical input devices are directly visible. Hence, conventional desktop peripherals are very slow, imprecise, and cumbersome. We developed a apparatus that tracks the user's hands, and a physical keyboard, and visualize them in VR. In a text input study with 32 participants, we investigated the achievable text entry speed and the effect of hand representations and transparency on typing performance, workload, and presence. With our apparatus, experienced typists benefited from seeing their hands, and reach almost outside-VR performance. Inexperienced typists profited from semi-transparent hands, which enabled them to type just 5.6 WPM slower than with a regular desktop setup. We conclude that optimizing the visualization of hands in VR is important, especially for inexperienced typists, to enable a high typing performance.

Manual assembly at production is a mentally demanding task. With rapid prototyping and smaller production lot sizes, this results in frequent changes of assembly instructions that have to be memorized by workers. Assistive systems compensate this increase in mental workload by providing "just-in-time" assembly instructions through in-situ projections. The implementation of such systems and their benefits to reducing mental workload have previously been justified with self-perceived ratings. However, there is no evidence by objective measures if mental workload is reduced by in-situ assistance. In our work, we showcase electroencephalography (EEG) as a complementary evaluation tool to assess cognitive workload placed by two different assistive systems in an assembly task, namely paper instructions and in-situ projections. We identified the individual EEG bandwidth that varied with changes in working memory load. We show, that changes in the EEG bandwidth are found between paper instructions and in-situ projections, indicating that they reduce working memory compared to paper instructions. Our work contributes by demonstrating how design claims of cognitive demand can be validated. Moreover, it directly evaluates the use of assistive systems for delivering context-aware information. We analyze the characteristics of EEG as real-time assessment for cognitive workload to provide insights regarding the mental demand placed by assistive systems.

Within this paper we propose an end-to-end approach for classifying terrestrial images of building facades into five different utility classes (commercial, hybrid, residential, specialUse, underConstruction) by using Convolutional Neural Networks (CNNs). For our examples we use images provided by Google Street View. These images are automatically linked to a coarse city model, including the outlines of the buildings as well as their respective use classes. By these means an extensive dataset is available for training and evaluation of our Deep Learning pipeline. The paper describes the implemented end-to-end approach for classifying street-level images of building facades and discusses our experiments with various CNNs. In addition to the classification results, so-called Class Activation Maps (CAMs) are evaluated. These maps give further insights into decisive facade parts that are learned as features during the training process. Furthermore, they can be used for the generation of abstract presentations which facilitate the comprehension of semantic image content. The abstract representations are a result of the stippling method, an importance-based image rendering.

We present a real-time pipeline for large-scale 3D scenereconstruction from a single moving RGB-D camera to-gether with interactive visualization. Our approach com-bines a time and space efficient data structure capable ofrepresenting large scenes, a local variational update algo-rithm and a visualization system. The environment’s struc-ture is reconstructed by integrating the depth image of eachcamera view into a sparse volume representation using atruncated signed distance function, which is organized viaa hash table. Noise from real-world data is efficiently elim-inated by immediately performing local variational refine-ments on newly integrated data. The whole pipeline is ableto perform in real-time on consumer-available hardwareand allows for simultaneous inspection of the currently re-constructed scene.

Immersive Analytics is a new research initiative that aims to remove barriers between people, their data and the tools they use for analysis and decision making. Here the aims of immersive analytics research are clarified, its opportunities and historical context, as well as providing a broad research agenda for the field. In addition, it is reviewed how the term immersion has been used to refer to both technological and psychological immersion, both of which are central to immersive analytics research.

Pipeline approaches that interpolate and refine an initial set of point correspondenceshave recently shown a good performance in the field of optical flow estimation. However,so far, these methods are typically restricted to two frames which makes exploiting tem-poral information difficult. In this paper, we show how such pipeline approaches can beextended to the temporal domain and how directional constraints can be incorporated tofurther improve the estimation. To this end, we not only suggest to exploit temporal infor-mation in the prefiltering step, we also propose a trajectorial refinement method that liftssuccessful concepts of recent variational two-frame methods to the multi-frame domain.Experiments demonstrate the usefulness of our pipeline approach. They do not only showgood results in general, they also demonstrate the clear benefits of using multiple framesand of imposing directional constraints on the prefiltering step and the refinement.

Temporal coherence is a valuable source of information in the context of optical flow estimation. However, finding a suitable motion model to leverage this information is a non-trivial task. In this paper we propose an unsupervised online learning approach based on a convolutional neural network (CNN) that estimates such a motion model individually for each frame. By relating forward and backward motion these learned models not only allow to infer valuable motion information based on the backward flow, they also help to improve the performance at occlusions, where a reliable prediction is particularly useful. Moreover, our learned models are spatially variant and hence allow to estimate non-rigid motion per construction. This, in turns, allows to overcome the major limitation of recent rigidity-based approaches that seek to improve the estimation by incorporating additional stereo/SfM constraints. Experiments demonstrate the usefulness of our new approach. They not only show a consistent improvement of up to 27% for all major benchmarks (KITTI 2012, KITTI 2015, MPI Sintel) compared to a baseline without prediction, they also show top results for the MPI Sintel benchmark -- the one of the three benchmarks that contains the largest amount of non-rigid motion.

Many recent energy-based methods for optical flow estimation rely on a good initialization that is typically provided by some kind of feature matching. So far, however, these initial matching approaches are rather general: They do not incorporate any additional information that could help to improve the accuracy or the robustness of the estimation. In particular, they do not exploit potential cues on the camera poses and the thereby induced rigid motion of the scene. In the present paper, we tackle this problem. To this end, we propose a novel structure-from-motion-aware PatchMatch approach that, in contrast to existing matching techniques, combines two hierarchical feature matching methods: a recent two-frame PatchMatch approach for optical flow estimation (general motion) and a specifically tailored three-frame PatchMatch approach for rigid scene reconstruction (SfM). While the motion PatchMatch serves as baseline with good accuracy, the SfM counterpart takes over at occlusions and other regions with insufficient information. Experiments with our novel SfM-aware PatchMatch approach demonstrate its usefulness. They not only show excellent results for all major benchmarks (KITTI 2012/2015, MPI Sintel), but also improvements up to 50% compared to a PatchMatch approach without structure information.

Shape from shading (SfS) and stereo are two fundamentally different strategies for image-based 3-D reconstruction. While approaches for SfS infer the depth solely from pixel intensities, methods for stereo are based on a matching process that establishes correspondences across images. This difference in approaching the reconstruction problem yields complementary advantages that are worthwhile being combined. So far, however, most “joint” approaches are based on an initial stereo mesh that is subsequently refined using shading information. In this paper we follow a completely different approach. We propose a joint variational method that combines both cues within a single minimisation framework. To this end, we fuse a Lambertian SfS approach with a robust stereo model and supplement the resulting energy functional with a detail-preserving anisotropic second-order smoothness term. Moreover, we extend the resulting model in such a way that it jointly estimates depth, albedo and illumination. This in turn makes the approach applicable to objects with non-uniform albedo as well as to scenes with unknown illumination. Experiments for synthetic and real-world images demonstrate the benefits of our combined approach: They not only show that our method is capable of generating very detailed reconstructions, but also that joint approaches are feasible in practice.

One of the main challenges in no-reference video quality assessment is temporal variation in a video. Methods typically were designed and tested on videos with artificial distortions, without considering spatial and temporal variations simultaneously. We propose a no-reference spatiotemporal feature combination model which extracts spatiotemporal information from a video, and tested it on a database with authentic distortions. Comparing with other methods, our model gave satisfying performance for assessing the quality of natural videos.

Current neuroscientific models of bodily self-consciousness (BSC) argue that inaccurate integration of sensory signals leads to altered states of BSC. Indeed, using virtual reality technology, observers viewing a fake or virtual body while being exposed to tactile stimulation of the real body, can experience illusory ownership over–and mislocalization towards—the virtual body (Full-Body Illusion, FBI). Among the sensory inputs contributing to BSC, the vestibular system is believed to play a central role due to its importance in estimating self-motion and orientation. This theory is supported by clinical evidence that vestibular loss patients are more prone to altered BSC states, and by recent experimental evidence that visuo-vestibular conflicts can disrupt BSC in healthy individuals. Nevertheless, the contribution of vestibular information and self-motion perception to BSC remains largely unexplored. Here, we investigate the relationship between alterations of BSC and self-motion sensitivity in healthy individuals. Fifteen participants were exposed to visuo-vibrotactile conflicts designed to induce an FBI, and subsequently to visual rotations that evoked illusory self-motion (vection). We found that synchronous visuo-vibrotactile stimulation successfully induced the FBI, and further observed a relationship between the strength of the FBI and the time necessary for complete vection to arise. Specifically, higher self-reported FBI scores across synchronous and asynchronous conditions were associated to shorter vection latencies. Our findings are in agreement with clinical observations that vestibular loss patients have higher FBI susceptibility and lower vection latencies, and argue for increased visual over vestibular dependency during altered states of BSC.

Data provenance can support the understanding and debugging of complex data processing pipelines, which are for instance common in data integration scenarios. One task in data integration is entity resolution (ER), i.e., the identification of multiple representations of a same real world entity. This paper focuses of provenance modeling and capture for typical ER tasks. While our definition of ER provenance is independent of the actual language or technology used to define an ER task, the method we implement as a proof of concept instruments ER rules specified in HIL, a high-level data integration language.

Conventional dot plots use a constant dot size and are typically applied to show the frequency distribution of small data sets. Unfortunately, they are not designed for a high dynamic range of frequencies. We address this problem by introducing nonlinear dot plots. Adopting the idea of nonlinear scaling from logarithmic bar charts, our plots allow for dots of varying size so that columns with a large number of samples are reduced in height. For the construction of these diagrams, we introduce an efficient two-way sweep algorithm that leads to a dense and symmetrical layout. We compensate aliasing artifacts at high dot densities by a specifically designed low-pass filtering method. Examples of nonlinear dot plots are compared to conventional dot plots as well as linear and logarithmic histograms. Finally, we include feedback from an expert review.

The analysis of eye-tracking data can be very useful when evaluating controlled user studies. To support the analysis in a fast and easy fashion, we have developed a web-based framework for a visual inspection of eye-tracking data and a comparison of scanpaths based on filtering of fixations and similarity measures. Concerning the first part, we introduce a multiscale aggregation of fixations and saccades based on a spatial partitioning that reduces visual clutter of overlaid scanpaths without changing the overall impression of large-scale eye movements. The multiscale technique abstracts the individual scanpaths and allows an analyst to visually identify clusters or patterns inherent to the gaze data without the need for lengthy precomputations. For the second part, we introduce an approach where analysts can remove fixations from a pair of scanpaths in order to increase the similarity between them. This can be useful to discover and understand reasons for dissimilarity between scanpaths, data cleansing, and outlier detection. Our implementation uses the MultiMatch algorithm to predict similarities after the removal of individual fixations. Finally, we demonstrate the usefulness of our techniques in a use case with scanpaths that were recorded in a study with metro maps.

Clustering is a core building block for data analysis, aiming to extract otherwise hidden structures and relations from raw datasets, such as particular groups that can be effectively related, compared, and interpreted. A plethora of visual-interactive cluster analysis techniques has been proposed to date, however, arriving at useful clusterings often requires several rounds of user interactions to fine-tune the data preprocessing and algorithms. We present a multi-stage Visual Analytics (VA) approach for iterative cluster refinement together with an implementation (SOMFlow) that uses Self-Organizing Maps (SOM) to analyze time series data. It supports exploration by offering the analyst a visual platform to analyze intermediate results, adapt the underlying computations, iteratively partition the data, and to reflect previous analytical activities. The history of previous decisions is explicitly visualized within a flow graph, allowing to compare earlier cluster refinements and to explore relations. We further leverage quality and interestingness measures to guide the analyst in the discovery of useful patterns, relations, and data partitions. We conducted two pair analytics experiments together with a subject matter expert in speech intonation research to demonstrate that the approach is effective for interactive data analysis, supporting enhanced understanding of clustering results as well as the interactive process itself.

This study investigates the neural basis of inattentional deafness, which could result from task irrelevance in the auditory modality. Humans can fail to respond to auditory alarms under high workload situations. This failure, termed inattentional deafness, is often attributed to high workload in the visual modality, which reduces one’s capacity for information processing. Besides this, our capacity for processing auditory information could also be selectively diminished if there is no obvious task relevance in the auditory channel. This could be another contributing factor given the rarity of auditory warnings.

We present a technique that conveys the uncertainty in the secondary structure of proteins-an abstraction model based on atomic coordinates. While protein data inherently contains uncertainty due to the acquisition method or the simulation algorithm, we argue that it is also worth investigating uncertainty induced by analysis algorithms that precede visualization. Our technique helps researchers investigate differences between multiple secondary structure assignment methods. We modify established algorithms for fuzzy classification and introduce a discrepancy-based approach to project an ensemble of sequences to a single importance-weighted sequence. In 2D, we depict the aggregated secondary structure assignments based on the per-residue deviation in a collapsible sequence diagram. In 3D, we extend the ribbon diagram using visual variables such as transparency, wave form, frequency, or amplitude to facilitate qualitative analysis of uncertainty. We evaluated the effectiveness and acceptance of our technique through expert reviews using two example applications: the combined assignment against established algorithms and time-dependent structural changes originating from simulated protein dynamics.

We propose a tree visualization technique for comparison of structures and attributes across multiple hierarchies. Many software systems are structured hierarchically by design. For example, developers subdivide source code into libraries, modules, and functions. This design propagates to software configuration and business processes, rendering software hierarchies even more important. Often these structural elements are attributed with reference counts, code quality metrics, and the like. Throughout the entire software life cycle, these hierarchies are reviewed, integrated, debugged, and changed many times by different people so that the identity of a structural element and its attributes is not clearly traceable. We argue that pairwise comparison of similar trees is a tedious task due to the lack of overview, especially when applied to a large number of hierarchies. Therefore, we strive to visualize multiple similar trees as a whole by merging them into one supertree. To merge structures and combine attributes from different trees, we leverage the Jaccard similarity and solve a matching problem while keeping track of the origin of a structure element and its attributes. Our visualization approach allows users to inspect these supertrees using node-link diagrams and indented tree plots. The nodes in these plots depict aggregated attributes and, using word-sized line plots, detailed data. We demonstrate the usefulness of our method by exploring the evolution of software repositories and debugging data processing pipelines using provenance data.

Approaching a high degree of realism, android robots, and virtual humans may evoke uncomfortable feelings. Due to technologies that increase the realism of human replicas, this phenomenon, which is known as the uncanny valley, has been frequently highlighted in recent years by researchers from various fields. Although virtual animals play an important role in video games and entertainment, the question whether there is also an uncanny valley for virtual animals has been little investigated. This paper examines whether very realistic virtual pets tend to cause a similar aversion as humanlike characters. We conducted two empirical studies using cat renderings to investigate the effects of realism, stylization, and facial expressions of virtual cats on human perception. Through qualitative feedback, we gained deeper insight into the perception of realistic computer-generated animals. Our results indicate that depicting virtual animal-like characters at realism levels used in current video games causes negative reactions just as the uncanny valley predicts for humanlike characters. We conclude design implication to avoid that sensation and suggest that virtual animals should either be given a completely natural or a stylized appearance. We propose to further examine the uncanny valley by the inclusion of artificial animals.

The Icelandic case system presents an interesting linguistic puzzle. Languages tend to use either word order, case and/or agreement to signal grammatical relations (Kiparsky 1987, 1988, 1997). Icelandic is atypical in this respect as it has a rather rigid word order, but also retained a rich morphological case system over the centuries. Moreover, non-nominative subjects exist in the language, with in particular the synchronic existence of dative subjects being well-established (Andrews 1976, Zaenen et al. 1985). From a diachronic perspective, dative subjects have also attracted a good deal of research, specifically with respect to the question about whether dative subjects are a common Proto-Indo-European feature or whether they are a more recent historical innovation (see, e.g., Haspelmath 2001, Barðdal and Eythórsson 2009, Barðdal et al. 2012). In this thesis, I investigate factors conditioning the diachronic occurrence of dative subjects in the Icelandic Parsed Historical Corpus (IcePaHC, Wallenberg et al. 2011) to provide a window of understanding of the complex system licensing grammatical relations in the language, contributing to the discussion which evolved around the historical origin of dative subjects. As method of investigation, I utilize novel visualization techniques coming from the field of Visual Analytics (Keim et al. 2008). The investigations presented in this thesis show that dative subjects are part of a complex interlinked system in which case, word order, grammatical relations, lexical semantics and event structure interact in the mapping of arguments to grammatical relations. For one, I provide my findings with respect to the interaction between dative subjects, thematic roles, event structure and voice in IcePaHC, showing that the distribution of dative subjects has been changing in the history of Icelandic, in particular with respect to an increasingly systematic association between dative subjects and experiencer semantics. This correlates with an increasing use of verbs carrying middle morphology, which have been lexicalized as stative experiencer predicates with a dative subject over time. I furthermore present an investigation of the interaction between subject case and word order which examines the interrelation between dative case, subject positions, and verb placement in IcePaHC. This investigation provides evidence for the diachronic development of structure and the rise of positional licensing in the language (in line with Kiparsky 1997); developments in which dative subjects consistently lag behind. For the theoretical analysis of the historical developments observed in IcePaHC, I present a novel linking theory couched in the Lexical-Functional Grammar (LFG) framework in this thesis. My linking theory builds on the enhancements of LFG’s Lexical Mapping Theory by Zaenen (1993) and Kibort (2014) with respect to lexical semantic entailments and argument positions, separating out lexical semantics from structural positions. As core component of the linking system, I implement a reference frame in the form of Talmy’s (1978) figure-ground division, which functions as mediator between word order, lexical semantics, and event structure. Grammatical relations are linked to arguments via a set of lexical semantic entailments which follow from the event structure, the reference frame, and the sentience of arguments, associating grammatical relations with particular structural positions. Event structure is encoded in the linking system via the event participants assumed in Ramchand’s (2008) event-decompositional framework of the first-phase syntax and is taken to license case marking in Icelandic as has been suggested by Svenonius (2002). Overall, the linking analysis of the diachronic corpus data shows that the licensing conditions for case and grammatical relations have been changing over time, which questions the inheritance of a stable and monolithic dative subject construction from earlier language stages.

We present a comparison between autoencoders and variational autoencoders. For this, we describe their architecture and explain the respective advantages. To gain a deeper insight into the encoding and decoding process, we visualize the distribution of values in latent space for both models. While autoencoders are commonly used for compression, variational autoencoders typically act as generative models. We provide an interactive visualization to explore their differences. By manually modifying the latent activations, the user can directly observe the impact of different latent values on the generated output. In addition, we provide an information theoretic view on the compressive properties of autoencoders.

We present an empirical study that illustrates how individual users' decision making preferences and biases influence visualization design choices. Twenty-three participants, in a lab study, were shown two interactive financial portfolio optimization interfaces which allowed them to adjust the return for the portfolio and view how the risk changes. One interface showed the sensitivity of the risk to changes in the return and one did not have this feature. Our study highlights two classes of users. One which preferred the interface with the sensitivity feature and one group that does not prefer the sensitivity feature. We named these two groups the "risk fixers" and the "sweet spotters" due to the analysis method they used. The "risk fixers" selected a level of risk which they were comfortable with while the "sweet spotters" tried to find a point right before the risk increased greatly. Our study shows that exposing the sensitivity of investment parameters will impact the investment decision process and increase confidence for these "sweet spotters." We also discuss the implications for design.

We investigate priming and anchoring effects on perceptual tasks in visualization. Priming or anchoring effects depict the phenomena that a stimulus might influence subsequent human judgments on a perceptual level, or on a cognitive level by providing a frame of reference. Using visual class separability in scatterplots as an example task, we performed a set of five studies to investigate the potential existence of priming and anchoring effects. Our findings show that - under certain circumstances - such effects indeed exist. In other words, humans judge class separability of the same scatterplot differently depending on the scatterplot(s) they have seen before. These findings inform future work on better understanding and more accurately modeling human perception of visual patterns.

This paper presents a blind image quality assessment (BIQA) method based on deep learning with convolutional neural networks (CNN). Our method is trained on full and arbitrarily sized images rather than small image patches or resized input images as usually done in CNNs for image classification and quality assessment. The resolution independence is achieved by pyramid pooling. This work is the first that applies a fine-tuned residual deep learning network (ResNet-101) to BIQA. The training is carried out on a new and very large, labeled dataset of 10, 073 images (KonIQ-10k) that contains quality rating histograms besides the mean opinion scores (MOS). In contrast to previous methods we do not train to approximate the MOS directly, but rather use the distributions of scores. Experiments were carried out on three benchmark image quality databases. The results showed clear improvements of the accuracy of the estimated MOS values, compared to current state-of-the-art algorithms. We also report on the quality of the estimation of the score distributions.

Dimensionality reduction (DR) is a common strategy for visual analysis of labeled high-dimensional data. Low-dimensional representations of the data help, for instance, to explore the class separability and the spatial distribution of the data. Widely-used unsupervised DR methods like PCA do not aim to maximize the class separation, while supervised DR methods like LDA often assume certain spatial distributions and do not take perceptual capabilities of humans into account. These issues make them ineffective for complicated class structures. Towards filling this gap, we present a perception-driven linear dimensionality reduction approach that maximizes the perceived class separation in projections. Our approach builds on recent developments in perception-based separation measures that have achieved good results in imitating human perception. We extend these measures to be density-aware and incorporate them into a customized simulated annealing algorithm, which can rapidly generate a near optimal DR projection. We demonstrate the effectiveness of our approach by comparing it to state-of-the-art DR methods on 93 datasets, using both quantitative measure and human judgments. We also provide case studies with class-imbalanced and unlabeled data.

We propose Graph Thumbnails, small icon-like visualisations of the high-level structure of network data. Graph Thumbnails are designed to be legible in small multiples to support rapid browsing within large graph corpora. Compared to existing graph-visualisation techniques our representation has several advantages: (1) the visualisation can be computed in linear time; (2) it is canonical in the sense that isomorphic graphs will always have identical thumbnails; and (3) it provides precise information about the graph structure. We report the results of two user studies. The first study compares Graph Thumbnails to node-link and matrix views for identifying similar graphs. The second study investigates the comprehensibility of the different representations. We demonstrate the usefulness of this representation for summarising the evolution of protein-protein interaction networks across a range of species.

Users' cognitive load while interacting with a system is a valuable metric for evaluations in HCI. We encourage the analysis of eye movements as an unobtrusive and widely available way to measure cognitive load. In this paper, we report initial findings from a user study with 26 participants working on three visual search tasks that represent different levels of difficulty. Also, we linearly increased the cognitive demand while solving the tasks. This allowed us to analyze the reaction of individual eye movements to different levels of task difficulty. Our results show how pupil dilation, blink rate, and the number of fixations and saccades per second individually react to changes in cognitive activity. We discuss how these measurements could be combined in future work to allow for a comprehensive investigation of cognitive load in interactive settings.

Background Pseudomonas aeruginosa often causes multidrug-resistant infections in immunocompromised patients, and polymyxins are often used as the last-line therapy. Alarmingly, resistance to polymyxins has been increasingly reported worldwide recently. To rescue this last-resort class of antibiotics, it is necessary to systematically understand how P. aeruginosa alters its metabolism in response to polymyxin treatment, thereby facilitating the development of effective therapies. To this end, a genome-scale metabolic model (GSMM) was used to analyze bacterial metabolic changes at the systems level. Findings A high-quality GSMM iPAO1 was constructed for P. aeruginosa PAO1 for antimicrobial pharmacological research. Model iPAO1 encompasses an additional periplasmic compartment and contains 3022 metabolites, 4265 reactions, and 1458 genes in total. Growth prediction on 190 carbon and 95 nitrogen sources achieved an accuracy of 89.1%, outperforming all reported P. aeruginosa models. Notably, prediction of the essential genes for growth achieved a high accuracy of 87.9%. Metabolic simulation showed that lipid A modifications associated with polymyxin resistance exert a limited impact on bacterial growth and metabolism but remarkably change the physiochemical properties of the outer membrane. Modeling with transcriptomics constraints revealed a broad range of metabolic responses to polymyxin treatment, including reduced biomass synthesis, upregulated amino acid catabolism, induced flux through the tricarboxylic acid cycle, and increased redox turnover. Conclusions Overall, iPAO1 represents the most comprehensive GSMM constructed to date for Pseudomonas. It provides a powerful systems pharmacology platform for the elucidation of complex killing mechanisms of antibiotics.

Our visual perception is limited to the abilities of our eyes, where we only perceive visible light. This limitation might influence how we perceive and react to our surroundings, however, this limitation might endanger us in certain scenarios e.g. firefighting. In this paper, we explore the potential of augmenting the visual sensing of the firefighters using depth and thermal imaging to increase their awareness about the environment. Additionally, we built and evaluated two form factors, hand held and head mounted display. To evaluate our built prototypes, we conducted two user studies in a simulated fire environment with real firefighters. In this workshop paper, we present our findings from the evaluation of the concept and prototypes with real firefighters.

In this study, we demonstrate the effects of anxiety and cognitive load on eye movement planning in an instrument flight task adhering to a single-sensor-single-indicator data visualisation design philosophy. The task was performed in neutral and anxiety conditions, while a low or high cognitive load, auditory n-back task was also performed. Cognitive load led to a reduction in the number of transitions between instruments, and impaired task performance. Changes in self-reported anxiety between the neutral and anxiety conditions positively correlated with changes in the randomness of eye movements between instruments, but only when cognitive load was high. Taken together, the results suggest that both cognitive load and anxiety impact gaze behavior, and that these effects should be explored when designing data visualization displays.

In the recent years JSON affirmed as a very popular dataformat for representing massive data collections. JSON datacollections are usually schemaless. While this ensures sev-eral advantages, the absence of schema information has im-portant negative consequences: the correctness of complexqueries and programs cannot be statically checked, userscannot rely on schema information to quickly figure out thestructural properties that could speed up the formulation ofcorrect queries, and many schema-based optimizations arenot possible.In this paper we deal with the problem of inferring aschema from massive JSON datasets. We first identify aJSON type language which is simple and, at the same time,expressive enough to capture irregularities and to give com-plete structural information about input data. We thenpresent our main contribution, which is the design of a schemainference algorithm, its theoretical study, and its implemen-tation based on Spark, enabling reasonable schema infer-ence time for massive collections. Finally, we report aboutan experimental analysis showing the effectiveness of our ap-proach in terms of execution time, precision, and concisenessof inferred schemas, and scalability.

Given a set of prioritized balls with fixed centers in ℝd whose radii grow linearly over time, we want to compute the elimination order of these balls assuming that when two balls touch, the one with lower priority is ‘crushed’. A straightforward algorithm has running time O(n2 log n) which we improve to expected O(Δdn(log n + Δd)) where Δ = rmax/rmin is the ratio between largest and smallest radius amongst the balls. For a natural application of this problem, namely drawing labels on the globe, we have Δ = O(1). An efficient implementation based on a spherical Delaunay triangulation allows to compute the elimination order for millions of labels on commodity Desktop hardware. Dealing with rounding error induced robustness issues turned out to be one of the major challenges in the implementation.

This work considers the inclusion detection problem of electrical impedance tomography with stochastic conductivities. It is shown that a conductivity anomaly with a random conductivity can be identified by applying the factorization method or the monotonicity method to the mean value of the corresponding Neumann-to-Dirichlet map provided that the anomaly has high enough contrast in the sense of expectation. The theoretical results are complemented by numerical examples in two spatial dimensions.

In this work we address the problem of retrieving potentially interesting matrix views to support the exploration of networks. We introduce Matrix Diagnostics (or Magnostics), following in spirit related approaches for rating and ranking other visualization techniques, such as Scagnostics for scatter plots. Our approach ranks matrix views according to the appearance of specific visual patterns, such as blocks and lines, indicating the existence of topological motifs in the data, such as clusters, bi-graphs, or central nodes. Magnostics can be used to analyze, query, or search for visually similar matrices in large collections, or to assess the quality of matrix reordering algorithms. While many feature descriptors for image analyzes exist, there is no evidence how they perform for detecting patterns in matrices. In order to make an informed choice of feature descriptors for matrix diagnostics, we evaluate 30 feature descriptors-27 existing ones and three new descriptors that we designed specifically for MAGNOSTICS-with respect to four criteria: pattern response, pattern variability, pattern sensibility, and pattern discrimination. We conclude with an informed set of six descriptors as most appropriate for Magnostics and demonstrate their application in two scenarios; exploring a large collection of matrices and analyzing temporal networks.

Visual data exploration allows users to analyze datasets based on visualizations of interesting data characteristics, to possibly discover interesting information about the data that users are a priori unaware of. In this context, both recommendations of queries selecting the data to be visualized and recommendations of visualizations that highlight interesting data characteristics support users in visual data exploration. So far, these two types of recommendations have been mostly considered in isolation of one another. We present a recommendation approach for visual data exploration that unifies query recommendation and visualization recommendation. The recommendations rely on two types of provenance, i.e., data provenance (aka lineage) and evolution provenance that tracks users' interactions with a data exploration system. This paper presents the provenance data model as well as the overall system architecture. We then provide details on our provenance-based recommendation algorithms. A preliminary experimental evaluation showcases the applicability of our solution in practice.

We present the results of research on two areas of Icelandic historical syntax: dative subjects and V1 word order. These strands of syntax had previously been examined independently, but were found to be intimately connected as part of a broader collaboration between theoretical and computational linguistics involving the Icelandic Parsed Historical Corpus (IcePaHC). The interaction we found between V1 declaratives and dative subjects provides evidence for: a) changes over time with respect to the association of dative arguments with the subject role (contra Barðdal and Eythórsson 2009); b) the gradual development of left peripheral structure and the rise of positional licensing (in line with Kiparsky 1995, 1997). We provide an analysis of positional licensing in LFG terms and account for the newly observed complex interaction between datives, subjects and word order presented in this paper.

We present an integrated approach for real-time performance prediction of volume raycasting that we employ for load balancing and sampling resolution tuning. In volume rendering, the usage of acceleration techniques such as empty space skipping and early ray termination, among others, can cause significant variations in rendering performance when users adjust the camera configuration or transfer function. These variations in rendering times may result in unpleasant effects such as jerky motions or abruptly reduced responsiveness during interactive exploration. To avoid those effects, we propose an integrated approach to adapt rendering parameters according to performance needs. We assess performance-relevant data on-the-fly, for which we propose a novel technique to estimate the impact of early ray termination. On the basis of this data, we introduce a hybrid model, to achieve accurate predictions with minimal computational footprint. Our hybrid model incorporates aspects from analytical performance modeling and machine learning, with the goal to combine their respective strengths. We show the applicability of our prediction model for two different use cases: (1) to dynamically steer the sampling density in object and/or image space and (2) to dynamically distribute the workload among several different parallel computing devices. Our approach allows to reliably meet performance requirements such as a user-defined frame rate, even in the case of sudden large changes to the transfer function or the camera orientation.

The visualization of dynamic graphs demands visually encoding at least three major data dimensions: vertices, edges, and time steps. Many of the state‐of‐the‐art techniques can show an overview of vertices and edges but lack a data‐scalable visual representation of the time aspect. In this paper, we address the problem of displaying dynamic graphs with a thousand or more time steps. Our proposed interleaved parallel edge splatting technique uses a time‐to‐space mapping and shows the complete dynamic graph in a static visualization. It provides an overview of all data dimensions, allowing for visually detecting time‐varying data patterns; hence, it serves as a starting point for further data exploration. By applying clustering and ordering techniques on the vertices, edge splatting on the links, and a dense time‐to‐space mapping, our approach becomes visually scalable in all three dynamic graph data dimensions. We illustrate the usefulness of our technique by applying it to call graphs and US domestic flight data with several hundred vertices, several thousand edges, and more than a thousand time steps.

In this study, we employ EEG methods to clarify why auditory notifications, which were designed for task management in highly automated trucks, resulted in different performance behavior, when deployed in two different test settings: (a) student volunteers in a lab environment, (b) professional truck drivers in a realistic vehicle simulator. Behavioral data showed that professional drivers were slower and less sensitive in identifying notifications compared to their counterparts. Such differences can be difficult to interpret and frustrates the deployment of implementations from the laboratory to more realistic settings. Our EEG recordings of brain activity reveal that these differences were not due to differences in the detection and recognition of the notifications. Instead, it was due to differences in EEG activity associated with response generation. Thus, we show how measuring brain activity can deliver insights into how notifications are processed, at a finer granularity than can be afforded by behavior alone.

Functional magnetic resonance imaging (fMRI) is a 4D medical imaging modality that depicts a proxy of neuronal activity in a series of temporal scans. Statistical processing of the modality shows promise in uncovering insights about the functioning of the brain, such as the default mode network, and characteristics of mental disorders. Current statistical processing generally summarises the temporal signals between brain regions into a single data point to represent the 'coactivation' of the regions. That is, how similar are their temporal patterns over the scans. However, the potential of such processing is limited by issues of possible data misrepresentation due to uncertainties, e.g. noise in the data. Moreover, it has been shown that brain signals are characterised by brief traces of coactivation, which are lost in the single value representations. To alleviate the issues, alternate statistical processes have been used, however creating effective techniques has proven difficult due to problems, e.g. issues with noise, which often require user input to uncover. Visual analytics, therefore, through its ability to interactively exploit human expertise, presents itself as an interesting approach of benefit to the domain. In this work, we present the conceptual design behind TemporalTracks, our visual analytics system for exploration of 4D fMRI time-series coactivation data, utilising a visual metaphor to effectively present coactivation data for easier understanding. We describe our design with a case study visually analysing Human Connectome Project data, demonstrating that TemporalTracks can uncover temporal events that would otherwise be hidden in standard analysis.

Prominent accounts of decision making state that decisions are made on the basis of an accumulationof sensory evidence, orchestrated by networks of prefrontal and parietal neural populations. Here weassess whether these findings generalize to decisions on self-motion.Participants were presented with whole body yaw rotations of different durations in a 2-Interval-Forced-Choice paradigm, and tasked to discriminate motions on the basis of their amplitude. The corticalhemodynamic response was recorded using functional near-infrared spectroscopy (fNIRS) while partic-ipants were performing the task.The imaging data was used to predict the specific response on individual experimental trials, and topredict whether the comparison stimulus would be judged larger than the reference. Classifier perfor-mance on the former variable was negligible. However, considerable performance was achieved for thelatter variable, specifically using parietal imaging data. The findings provide support for the notion thatactivity in the parietal cortex reflects modality independent decision variables that represent the strengthof the neural evidence in favor of a decision. The results are encouraging for the use of fNIRS as a methodto perform neuroimaging in moving individuals.

Data intensive scalable computing (DISC) systems, such asApache Hadoop or Spark, allow to process large amountsof heterogenous data. For varying provenance applications,emerging provenance solutions for DISC systems track allsource data items through each processing step, imposing ahigh space and time overhead during program execution.We introduce a provenance collection approach that re-duces the space overhead at runtime by sampling the inputdata based on the definition of equivalence classes. A pre-liminary empirical evaluation shows that this approach al-lows to satisfy many use cases of provenance applications indebugging and data exploration, indicating that provenancecollection for a fraction of the input data items often suf-fices for selected provenance applications. When additionalprovenance is required, we further outline a method to col-lect provenance at query time, reusing, when possible, par-tial provenance already collected during program execution

People’s alertness fluctuates across the day: at some times we are highly focused while at others we feel unable to concentrate. So far, extracting fluctuation patterns has been time and cost-intensive. Using an in-the-wild approach with 12 participants, we evaluated three cognitive tasks regarding their adequacy as a mobile and economical assessment tool of diurnal changes in mental performance. Participants completed the five-minute test battery on their smartphones multiple times a day for a period of 1-2 weeks. Our results show that people’s circadian rhythm can be obtained under unregulated non-laboratory conditions. Along with this validation study, we release our test battery as an open source library for future work towards cognition-aware systems as well as a tool for psychological and medical research. We discuss ways of integrating the toolkit and possibilities for implicitly measuring performance variations in common applications. The ability to detect systematic patterns in alertness levels will allow cognition-aware systems to provide in-situ assistance in accordance with users’ current cognitive capabilities and limitations.

Increasingly fast computing systems for simulations and high-accuracy measurement techniques drive the generation of time-dependent volumetric data sets with high resolution in both time and space. To gain insights from this spatio-temporal data, the computation and direct visualization of pairwise distances between time steps not only supports interactive user exploration, but also drives automatic analysis techniques like the generation of a meaningful static overview visualization, the identification of rare events, or the visual analysis of recurrent processes. However, the computation of pairwise differences between all time steps is prohibitively expensive for large-scale data not only due to the significant cost of computing expressive distance between high-resolution spatial data, but in particular owing to the large number of distance computations (O(|T|2)) , with |T| being the number of time steps). Addressing this issue, we present and evaluate different strategies for the progressive computation of similarity information in a time series, as well as an approach for estimating distance information that has not been determined so far. In particular, we investigate and analyze the utility of using neural networks for estimating pairwise distances. On this basis, our approach automatically determines the sampling strategy yielding the best result in combination with trained networks for estimation. We evaluate our approach with a variety of time-dependent 2D and 3D data from simulations and measurements as well as artificially generated data, and compare it against an alternative technique. Finally, we discuss prospects and limitations, and discuss different directions for improvement in future work.

We present a novel technique to generate transformations between arbitrary volumes, providing both expressive distances and smooth interpolates. In contrast to conventional morphing or warping approaches, our technique requires no user guidance, intermediate representations (like extracted features), or blending, and imposes no restrictions regarding shape or structure. Our technique operates directly on the volumetric data representation, and while linear programming approaches could solve the underlying problem optimally, their polynomial complexity makes them infeasible for high-resolution volumes. We therefore propose a progressive refinement approach designed for parallel execution that is able to quickly deliver approximate results that are iteratively improved toward the optimum. On this basis, we further present a new approach for the streaming selection of time steps in temporal data that allows for the reconstruction of the full sequence with a user-specified error bound. We finally demonstrate the utility of our technique for different applications, compare our approach against alternatives, and evaluate its characteristics with a variety of different data sets.

We present an approach to adaptively select time steps from time‐dependent volume data sets for an integrated and comprehensive visualization. This reduced set of time steps not only saves cost, but also allows to show both the spatial structure and temporal development in one combined rendering. Our selection optimizes the coverage of the complete data on the basis of a minimum‐cost flow‐based technique to determine meaningful distances between time steps. As both optimal solutions of the involved transport and selection problem are prohibitively expensive, we present new approaches that are significantly faster with only minor deviations. We further propose an adaptive scheme for the progressive incorporation of new time steps. An interactive volume raycaster produces an integrated rendering of the selected time steps, and their computed differences are visualized in a dedicated chart to provide additional temporal similarity information. We illustrate and discuss the utility of our approach by means of different data sets from measurements and simulation.

Durch die Digitalisierung der Photogrammetrie sind neue Auswertemethoden notwendig geworden, um das enorme Informationspotential der Bilder in allen Belangen auszuschöpfen. Dies erfordert auch ein Umdenken hinsichtlich der bisherigen Ansätze für die Erweiterung der Bündelblockausgleichung durch zusätzliche Parameter. Mittels exakt orientierten Bildern können dann die Methoden der dichten Bildzuordnung angewendet werden. Deren Punktwolken sind in 3D-CAD-Modelle zu überführen, die noch durch Bildtexturen angereichert werden können. Der folgende Beitrag beschreibt eine Neuauflage der Selbstkalibration, indem erstmalig eine exakte mathematische Begründung dafür gegeben wird und zwei Klassen von Parametersätzen eingeführt werden: Legendre- und Fourier-Parameter. Deren Leistungsfähigkeit ist anhand der Datensätze des DGPF-Kameratests über Vaihingen/Enz unter Beweis gestellt. Ferner wird ein Vergleich zu den über Jahrzehnte hinweg angewandten Parametersätzen von Brown, Ebner und Grün hergestellt. Der zweite Schwerpunkt demonstriert die Ableitung und Verarbeitung von dichten Punktwolken zu LoD3-Modellen, die durch eine Erweiterung der Methode des Semi-Global Matching mit der Software SURE erzeugt werden und mittels einer Fassadengrammatik die gewünschten Strukturinformationen liefern. Diese Modelle sind beispielsweise in Game Engines zu integrieren und u.a. in eindrucksvolle Augmented Reality Apps für mobile Geräte zu überführen.

The design of three-dimensional and four-dimensional Apps, running on the leading operating systems Android, iOS and Windows is the next challenge in Architecture, BIM, Civil Engineering, Digital Cultural Heritage (DCH) preservations and many more. Based on experiences developing Apps for archaeology and architecture, the paper introduces with general workflows for 3D data collection, using laser scanning, geometric computer vision and photogrammetry. The resulting point clouds have to be merged, using the most recent developments of laser scanning, computer vision, photogrammetry and statistical inference. 3D and 4D modeling is done using professional software from surveying and computer graphics, such as Leica’s Cyclone, Trimble’s SketchUp and Autodesk 3ds Max. The fourth dimension, time, is injected onto the 3D contemporary models using the texture of old photos. After homogenization of all 3D models in Autodesk 3ds Max these are exported to the game engine Unity to allow for the creation of the reference surface and finally the 3D urban model. The storyboard creates for the programmer an outline, which features and functions have to be fulfilled. Finally the Apps for Android, iOS and Windows are created and exported for the use on mobile devices.

We consider the classical line simplification problem subject to a given error bound ∊ but with additional topology constraints as they arise for example in the map rendering domain. While theoretically inapproximability has been proven for these problem variants, we show that in practice one can solve medium sized instances optimally using an integer linear programming approach and larger instances using an heuristic approach which for medium-sized real-world instances yields close-to-optimal results. Our approaches are evaluated on data sets which are synthetically generated, stem from the OpenStreetMap project, and the recent GISCup competition.

Current route planning services like Google Maps exhibit a clear-cut separation between the map rendering component and the route planning engine. While both rely on respective road network data, the route planning task is typically performed using state-of-the art data structures for speeding-up shortest/quickest path queries like Hub Labels, Arc Flags, or Transit Nodes, whereas the map rendering task usually involves a rendering framework like Mapnik or Kartograph. In this paper we show how to augment Contraction Hierarchies – another popular data structure for speeding-up shortest path queries – to also cater for the map rendering task. As a result we get a unified data structure (URAN) which lays the algorithmic foundation for novel map rendering and navigation systems. It also allows for customization of the map rendering, e.g. to accommodate different display devices (with varying resolution and hardware capabilities) or routing scenarios. At the heart of our approach lies a generalized graph simplification scheme derived from Contraction Hierarchies with a very lightweight augmentation for extracting (simplified) subgraphs. In a client-server scenario it additionally has the potential to shift the actual route computation to the client side, both relieving the server infrastructure as well as providing some degree of privacy when planning a route.

Analysis of software performance typically takes into account clock cycles and memory consumption at each sampling point in time. Although this is a valid strategy, we argue that it is also worth investigating data and control flow structures, as observed using memory traces and call stacks, because of their importance for performance engineering. In this work, we present a visual approach to memory profiling that supports analysis of memory layout, access patterns, and aliasing in correlation to program execution. Our method leverages language-agnostic dynamic code instrumentation to minimize the impact of tracing on performance, i.e., the application remains usable on commodity hardware. The profiled data is then clustered and visualized using a density-based scatter plot. If debug symbols are available, the scatter plot is augmented by a flame graph to ease linking to the high-level source code. Our visualization helps software engineers to identify runtime behavior by relating memory addresses to instruction execution. We demonstrate our approach using a set of examples revealing different memory access patterns and discuss their influence on software performance.

Power efficiency is one of the most important factors for the development of compute-intensive applications in the mobile domain. In this work, we evaluate and discuss the power consumption of a direct volume rendering app based on raycasting on a mobile system. For this, we investigate the influence of a broad set of algorithmic parameters, which are relevant for performance and rendering quality, on the energy usage of the system. Additionally, we compare an OpenCL implementation to a variant using OpenGL. By means of a variety of examples, we demonstrate that numerous factors can have a significant impact on power consumption. In particular, we also discuss the underlying reasons for the respective effects.

Provenance refers to any information describing the production process of an end product, which can be anything from a piece of digital data to a physical object. While this survey focuses on the former type of end product, this definition still leaves room for many different interpretations of and approaches to provenance. These are typically motivated by different application domains for provenance (e.g., accountability, reproducibility, process debugging) and varying technical requirements such as runtime, scalability, or privacy. As a result, we observe a wide variety of provenance types and provenance-generating methods. This survey provides an overview of the research field of provenance, focusing on what provenance is used for (what for?), what types of provenance have been defined and captured for the different applications (what form?), and which resources and system requirements impact the choice of deploying a particular provenance solution (what from?). For each of these three key questions, we provide a classification and review the state of the art for each class. We conclude with a summary and possible future research challenges.

Subjective video quality assessment (VQA) strongly depends on semantics, context, and the types of visual distortions. Currently, all existing VQA databases include only a small number of video sequences with artificial distortions. The development and evaluation of objective quality assessment methods would benefit from having larger datasets of real-world video sequences with corresponding subjective mean opinion scores (MOS), in particular for deep learning purposes. In addition, the training and validation of any VQA method intended to be `general purpose' requires a large dataset of video sequences that are representative of the whole spectrum of available video content and all types of distortions. We report our work on KoNViD-1k, a subjectively annotated VQA database consisting of 1,200 public-domain video sequences, fairly sampled from a large public video dataset, YFCC100m. We present the challenges and choices we have made in creating such a database aimed at `in the wild' authentic distortions, depicting a wide variety of content.

Light fields are a powerful concept in computational imaging and a mainstay in image-based rendering; however, so far their acquisition required either carefully designed and calibrated optical systems (micro-lens arrays), or multi-camera/multi-shot settings. Here, we show that fully calibrated light field data can be obtained from a single ordinary photograph taken through a partially wetted window. Each drop of water produces a distorted view on the scene, and the challenge of recovering the unknown mapping from pixel coordinates to refracted rays in space is a severely underconstrained problem. The key idea behind our solution is to combine ray tracing and low-level image analysis techniques (extraction of 2D drop contours and locations of scene features seen through drops) with state-of-the-art drop shape simulation and an iterative refinement scheme to enforce photo-consistency across features that are seen in multiple views. This novel approach not only recovers a dense pixel-to-ray mapping, but also the refractive geometry through which the scene is observed, to high accuracy. We therefore anticipate that our inherently self-calibrating scheme might also find applications in other fields, for instance in materials science where the wetting properties of liquids on surfaces are investigated.

This paper presents the results of the depth estimation challenge for dense light fields, which took place at the second workshop on Light Fields for Computer Vision (LF4CV) in conjunction with CVPR 2017. The challenge consisted of submission to a recent benchmark 7, which allows a thorough performance analysis. While individual results are readily available on the benchmark web page http://www.lightfield-analysis.net, we take this opportunity to give a detailed overview of the current participants. Based on the algorithms submitted to our challenge, we develop a taxonomy of light field disparity estimation algorithms and give a report on the current state-of-the-art. In addition, we include more comparative metrics, and discuss the relative strengths and weaknesses of the algorithms. Thus, we obtain a snapshot of where light field algorithm development stands at the moment and identify aspects with potential for further improvement.

Dimensionality reduction (DR) techniques aim to reduce the amount of considered dimensions, yet preserving as much information as possible. According to many visualization researchers, DR results lack interpretability, in particular for domain experts not familiar with machine learning or advanced statistics. Thus, interactive visual methods have been extensively researched for their ability to improve transparency and ease the interpretation of results. However, these methods have primarily been evaluated using case studies and interviews with experts trained in DR. In this paper, we describe a phenomenological analysis investigating if researchers with no or only limited training in machine learning or advanced statistics can interpret the depiction of a data projection and what their incentives are during interaction. We, therefore, developed an interactive system for DR, which unifies mixed data types as they appear in real-world data. Based on this system, we provided data analys ts of a Law Enforcement Agency (LEA) with dimensionally-reduced crime data and let them explore and analyze domain-relevant tasks without providing further conceptual information. Results of our study reveal that these untrained experts encounter few difficulties in interpreting the results and drawing conclusions given a domain relevant use case and their experience. We further discuss the results based on collected informal feedback and observations.

Subspace analysis methods have gained interest for identifying patterns in subspaces of high-dimensional data. Existing techniques allow to visualize and compare patterns in subspaces. However, many subspace analysis methods produce an abundant amount of patterns, which often remain redundant and are difficult to relate. Creating effective layouts for comparison of subspace patterns remains challenging. We introduce Pattern Trails, a novel approach for visually ordering and comparing subspace patterns. Central to our approach is the notion of pattern transitions as an interpretable structure imposed to order and compare patterns between subspaces. The basic idea is to visualize projections of subspaces side-by-side, and indicate changes between adjacent patterns in the subspaces by a linked representation, hence introducing pattern transitions. Our contributions comprise a systematization for how pairs of subspace patterns can be compared, and how changes can be interpreted in terms of pattern transitions. We also contribute a technique for visual subspace analysis based on a data-driven similarity measure between subspace representations. This measure is useful to order the patterns, and interactively group subspaces to reduce redundancy. We demonstrate the usefulness of our approach by application to several use cases, indicating that data can be meaningfully ordered and interpreted in terms of pattern transitions

We are often confronted with information interfaces designed in an unfamiliar language, especially in an increasingly globalized world, where the language barrier inhibits interaction with the system. In our work, we explore the design space for building interfaces that can detect the user's language proficiency. Specifically, we look at how a user's gaze properties can be used to detect whether the interface is presented in a language they understand. We report a study (N=21) where participants were presented with questions in multiple languages, whilst being recorded for gaze behavior. We identified fixation and blink durations to be effective indicators of the participants' language proficiencies. Based on these findings, we propose a classification scheme and technical guidelines for enabling language proficiency awareness on information displays using gaze data.

Head-mounted displays for virtual reality (VR) provide high-fidelity visual and auditory experiences. Other modalities are currently less supported. Current commercial devices typically deliver tactile feedback through controllers the user holds in the hands. Since both hands get occupied and tactile feedback can only be provided at a single position, research and industry proposed a range of approaches to provide richer tactile feedback. Approaches, such as tactile vests or electrical muscle stimulation, were proposed, but require additional body-worn devices. This limits comfort and restricts provided feedback to specific body parts. With this Interactivity installation, we propose quadcopters to provide tactile stimulation in VR. While the user is visually and acoustically immersed in VR, small quadcopters simulate bumblebees, arrows, and other objects hitting the user. The user wears a VR headset, mini-quadcopters, controlled by an optical marker tracking system, are used to provide tactile feedback.

The analysis of eye tracking data often requires the annotation of areas of interest (AOIs) to derive semantic interpretations of human viewing behavior during experiments. This annotation is typically the most time-consuming step of the analysis process. Especially for data from wearable eye tracking glasses, every independently recorded video has to be annotated individually and corresponding AOIs between videos have to be identified. We provide a novel visual analytics approach to ease this annotation process by image-based, automatic clustering of eye tracking data integrated in an interactive labeling and analysis system. The annotation and analysis are tightly coupled by multiple linked views that allow for a direct interpretation of the labeled data in the context of the recorded video stimuli. The components of our analytics environment were developed with a user-centered design approach in close cooperation with an eye tracking expert. We demonstrate our approach with eye tracking data from a real experiment and compare it to an analysis of the data by manual annotation of dynamic AOIs. Furthermore, we conducted an expert user study with 6 external eye tracking researchers to collect feedback and identify analysis strategies they used while working with our application.

The incorporation of subtitles in multimedia content plays an important role in communicating spoken content. For example, subtitles in the respective language are often preferred to expensive audio translation of foreign movies. The traditional representation of subtitles displays text centered at the bottom of the screen. This layout can lead to large distances between text and relevant image content, causing eye strain and even that we miss visual content. As a recent alternative, the technique of speaker-following subtitles places subtitle text in speech bubbles close to the current speaker. We conducted a controlled eye-tracking laboratory study (n = 40) to compare the regular approach (center-bottom subtitles) with content-sensitive, speaker-following subtitles. We compared different dialog-heavy video clips with the two layouts. Our results show that speaker-following subtitles lead to higher fixation counts on relevant image regions and reduce saccade length, which is an important factor for eye strain.

The depiction of motion in static representations has a long tradition in art and science alike. Often, motion is depicted by spatio-temporal summarizations that try to preserve as much information of the original dynamic content as possible. In our approach to depicting motion, we remove the spatial constraints and generate new content steered by the temporal changes in motion. Applying particle steering in combination with the dynamic color palette of the video content, we can create a wide range of different image styles. With recorded videos, or by live interaction with a webcam, one can influence the resulting image. We provide a set of intuitive parameters to affect the style of the result, the final image content depends on the video input. Based on a collection of results gathered from test users, we discuss example styles that can be achieved with FlowBrush. In general, our approach provides an open sandbox for creative people to generate aesthetic images from any video content they apply.

Touchscreens are the dominant input mechanism for a variety of devices. One of the main limitations of touchscreens is the latency to receive input, refresh, and respond. This latency is easily perceivable and reduces users' performance. Previous work proposed to reduce latency by extrapolating finger movements to identify future movements - albeit with limited success. In this paper, we propose PredicTouch, a system that improves this extrapolation using inertial measurement units (IMUs). We combine IMU data with users' touch trajectories to train a multi-layer feedforward neural network that predicts future trajectories. We found that this hybrid approach (software: prediction, and hardware: IMU) can significantly reduce the prediction error, reducing latency effects. We show that using a wrist-worn IMU increases the throughput by 15% for finger input and 17% for a stylus.

We present a reconstruction pipeline for a large-scale 3D environment viewed by a single moving RGB-D camera. Our approach combines advantages of fast and direct, regularization-free depth fusion and accurate, but costly variational schemes. The scene’s depth geometry is extracted from each camera view and efficiently integrated into a large, dense grid as a truncated signed distance function, which is organized in an octree. To account for noisy real-world input data, variational range image integration is performed in local regions of the volume directly on this octree structure. We focus on algorithms which are easily parallelizable on GPUs, allowing the pipeline to be used in real-time scenarios where the user can interactively view the reconstruction and adapt camera motion as required.

Variational approaches form an inherent part of most state-of-the-art pipeline approaches for optical flow computation. As the final step of the pipeline, the aim is to refine an initial flow field typically obtained by inpainting non-dense matches in order to provide highly accurate results. In this paper, we take advantage of recent improvements in variational optical flow estimation to construct an advanced variational model for this final refinement step. By combining an illumination aware data term with an order adaptive smoothness term, we obtain a highly flexible model that is able to cope well with a broad variety of different scenarios. Moreover, we propose the use of an additional reduced coarse-to-fine scheme instead of an exclusive initialisation scheme, which not only allows to refine the initialisation but also allows to correct larger erroneous displacements. Experiments on recent optical flow benchmarks show the advantages of the advanced variational refinement and the reduced coarse to fine scheme.

Recent approaches for variational motion estimation typically either rely on first or second order regularisation strategies. While first order strategies are more appropriate for scenes with fronto-parallel motion, second order constraints are superior if it comes to the estimation of affine flow fields. Since using the wrong regularisation order may lead to a significant deterioration of the results, it is surprising that there has not been much effort in the literature so far to determine this order automatically. In our work, we address the aforementioned problem in two ways. (i) First, we discuss two anisotropic smoothness terms of first and second order, respectively, that share important structural properties and that are thus particularly suited for being combined within an order-adaptive variational framework. (ii) Secondly, based on these two smoothness terms, we develop four different variational methods and with it four different strategies for adaptively selecting the regularisation order: a global and a local strategy based on half-quadratic regularisation, a non-local approach that relies on neighbourhood information, and a region based method using level sets. Experiments on recent benchmarks show the benefits of each of the strategies. Moreover, they demonstrate that adaptively combining different regularisation orders not only allows to outperform single-order strategies but also to obtain advantages beyond the ones of a frame-wise selection.

Many visualizations have proven to be effective in supporting various software related tasks. Although multiple media can be used to display a visualization, the standard computer screen is used the most. We hypothesize that the medium has a role in their effectiveness. We investigate our hypotheses by conducting a controlled user experiment. In the experiment we focus on the 3D city visualization technique used for software comprehension tasks. We deploy 3D city visualizations across a standard computer screen (SCS), an immersive 3D environment (I3D), and a physical 3D printed model (P3D). We asked twenty-seven participants (whom we divided in three groups for each medium) to visualize software systems of various sizes, solve a set of uniform comprehension tasks, and complete a questionnaire. We measured the effectiveness of visualizations in terms of performance, recollection, and user experience. We found that even though developers using P3D required the least time to identify outliers, they perceived the least difficulty when visualizing systems based on SCS. Moreover, developers using I3D obtained the highest recollection.

Whilemovingthroughtheenvironment,ourcentralnervoussystemaccumulatessensoryinformationovertimeto provideanestimateof ourself-motion,allowingforcompletingcrucialtaskssuchasmaintainingbalance.However,littleis knownonhowthedurationof themotionstimuliinfluencesourperformancesin a self-motiondiscriminationtask.Herewestudythehumanabilityto discriminateintensitiesof sinusoidal(0.5Hz)self-rotationsaroundtheverticalaxis(yaw)forfourdifferentstimulusdurations(1,2, 3 and5 s) in darkness.In a typicaltrial,par-ticipantsexperiencedtwoconsecutiverotationsof equaldurationanddifferentpeakamplitude,andreportedtheoneperceivedasstronger.Foreachstimulusduration,wedeterminedthesmallestdetectablechangein stimulusintensity(differentialthreshold)fora referencevelocityof 15deg/s.Resultsindicatethatdifferentialthresholdsdecreasewithstimulusdurationandasymptoticallyconvergeto a constant,positivevalue.Thissuggeststhatthecentralnervoussystemaccumulatessensoryinformationonself-motionovertime,resultingin improveddis-criminationperformances. Observedtrendsin differentialthresholdsareconsistentwithpre-dictionsbasedona driftdiffusionmodelwithleakyintegrationof sensoryevidence.

Visual search can be time-consuming, especially if the scene contains a large number of possibly relevant objects. An instance of this problem is present when using geographic or schematic maps with many different elements representing cities, streets, sights, and the like. Unless the map is well-known to the reader, the full map or at least large parts of it must be scanned to find the elements of interest. In this paper, we present a controlled eye-tracking study (30 participants) to compare four variants of map annotation with labels: within-image annotations, grid reference annotation, directional annotation, and miniature annotation. Within-image annotation places labels directly within the map without any further search support. Grid reference annotation corresponds to the traditional approach known from atlases. Directional annotation utilizes a label in combination with an arrow pointing in the direction of the label within the map. Miniature annotation shows a miniature grid to guide the reader to the area of the map in which the label is located. The study results show that within-image annotation is outperformed by all other annotation approaches. Best task completion times are achieved with miniature annotation. The analysis of eye-movement data reveals that participants applied significantly different visual task solution strategies for the different visual annotations.

We investigate task performance and reading characteristics for scatterplots (Cartesian coordinates) and parallel coordinates. In a controlled eye-tracking study, we asked 24 participants to assess the relative distance of points in multidimensional space, depending on the diagram type (parallel coordinates or a horizontal collection of scatterplots), the number of data dimensions (2, 4, 6, or 8), and the relative distance between points (15%, 20%, or 25%). For a given reference point and two target points, we instructed participants to choose the target point that was closer to the reference point in multidimensional space. We present a visual scanning model that describes different strategies to solve this retrieval task for both diagram types, and propose corresponding hypotheses that we test using task completion time, accuracy, and gaze positions as dependent variables. Our results show that scatterplots outperform parallel coordinates significantly in 2 dimensions, however, the task was solved more quickly and more accurately with parallel coordinates in 8 dimensions. The eye-tracking data further shows significant differences between Cartesian and parallel coordinates, as well as between different numbers of dimensions. For parallel coordinates, there is a clear trend toward shorter fixations and longer saccades with increasing number of dimensions. Using an area-of-interest (AOI) based approach, we identify different reading strategies for each diagram type: For parallel coordinates, the participants’ gaze frequently jumped back and forth between pairs of axes, while axes were rarely focused on when viewing Cartesian coordinates. We further found that participants’ attention is biased: toward the center of the whole plotfor parallel coordinates and skewed to the center/left side for Cartesian coordinates. We anticipate that these results may support the design of more effective visualizations for multidimensional data.

Recent advances in miniaturising sensor tags allow to obtain high-resolution bird trajectories, presenting an opportunity for immersive close-up observation of individual and group behaviour in mid-air. The combination of geographical, environmental, and movement data is well suited for investigation in immersive analytics environments. We explore the benefits and requirements of a wide range of such environments, and illustrate a multi-platform immersive analytics solution, based on a tiled 3D display wall and head-mounted displays (Google Cardboard, HTC Vive and Microsoft Hololens). Tailored to biologists studying bird movement data, the immersive environment provides a novel interactive mode to explore the geolocational time-series data. This paper aims to inform the 3D visualisation research community about design considerations obtained from a real world data set in different 3D immersive environments. This work also contributes to ongoing research efforts to promote better understanding of bird migration and the associated environmental factors at the planet-level scale, thereby capturing the public awareness of environmental issues.

Visualizing time series data with a spatial context is a problem that appears more and more often, since small and lightweight GPS devices allow us to enrich the time series data with position information. One example is the visualization of the energy output of power plants. We present a web-based application that aims to provide information about the energy production of a specified region, along with location information about the power plants. The application is intended to be used as a solid data basis for political discussions, nudging, and story telling about the German energy transition to renewables, called "Energiewende". It was therefore designed to be intuitive, easy to use, and provide information for a broad spectrum of users that do not need any domain-specific knowledge. Users are able to select different categories of power plants and look up their positions on an overview map. Glyphs indicate their exact positions and a selection mechanism allows users to compare the power output on different time scales using stacked area charts or ThemeRivers. As an evaluation of the application, we have collected web access statistics and conducted an online survey with respect to the intuitiveness, usability, and informativeness.

We investigate the problem of analyzing word frequencies in multiple text sources with the aim to give an overview of word-based similarities in several texts as a starting point for further analysis. To reach this goal, we designed a visual analytics approach composed of typical stages and processes, combining algorithmic analysis, visualization techniques, the human users with their perceptual abilities, as well as interaction methods for both the data analysis and the visualization component. By our algorithmic analysis, we first generate a multivariate dataset where words build the cases and the individual text sources the attributes. Real-valued relevances express the significances of each word in each of the text sources. From the visualization perspective, we describe how this multivariate dataset can be visualized to generate, confirm, rebuild, refine, or reject hypotheses with the goal to derive meaning, knowledge, and insights from several text sources. We discuss benefits and drawbacks of the visualization approaches when analyzing word relevances in multiple texts.

Predicting the target of visual search from human gaze data is a challenging problem. In contrast to previous work that focused on predicting specific instances of search targets, we propose the first approach to predict a target's category and attributes. However, state-of-the-art models for categorical recognition require large amounts of training data, which is prohibitive for gaze data. We thus propose a novel Gaze Pooling Layer that integrates gaze information and CNN-based features by an attention mechanism - incorporating both spatial and temporal aspects of gaze behaviour. We show that our approach can leverage pre-trained CNN architectures, thus eliminating the need for expensive joint data collection of image and gaze data. We demonstrate the effectiveness of our method on a new 14 participant dataset, and indicate directions for future research in the gaze-based prediction of mental states.

We present a novel uncertain network visualization technique based on node-link diagrams. Nodes expand spatially in our probabilistic graph layout, depending on the underlying probability distributions of edges. The visualization is created by computing a two-dimensional graph embedding that combines samples from the probabilistic graph. A Monte Carlo process is used to decompose a probabilistic graph into its possible instances and to continue with our graph layout technique. Splatting and edge bundling are used to visualize point clouds and network topology. The results provide insights into probability distributions for the entire network-not only for individual nodes and edges. We validate our approach using three data sets that represent a wide range of network types: synthetic data, protein-protein interactions from the STRING database, and travel times extracted from Google Maps. Our approach reveals general limitations of the force-directed layout and allows the user to recognize that some nodes of the graph are at a specific position just by chance.

Analysis and visualization of eye movement data from eye-tracking studies typically take into account gazes, fixations, and saccades of both eyes filtered and fused into a combined eye. Although this is a valid strategy, we argue that it is also worth investigating low-level eye-tracking data prior to high-level analysis, because today’s eye-tracking systems measure and infer data from both eyes separately. In this work, we present an approach that supports visual analysis and cleansing of low-level time-varying data for eye-tracking experiments. The visualization helps researchers get insights into the quality of the data in terms of its uncertainty, or reliability. We discuss uncertainty originating from eye tracking, and how to reveal it for visualization, using a comparative approach for disagreement between plots, and a density-based approach for accuracy in volume rendering. Finally, we illustrate the usefulness of our approach by applying it to eye movement data recorded with two state-of-the-art eye trackers.

We present a visual analytics approach to support the workload management process for z/OS mainframes at IBM. This process typically requires the analysis of records consisting of 100 to 150 performance-related metrics, sampled over time. We aim at replacing the previous spreadsheet-based workflow with an easier, faster, and more scalable one regarding measurement periods and collected performance metrics. To achieve this goal, we collaborate with a developer embedded at IBM in a formative process. Based on that experience, we discuss the application background and formulate requirements to support decision making based on performance data for large-scale systems. Our visual approach helps analysts find outliers, patterns, and relations between performance metrics by data exploration through various visualizations. We demonstrate the usefulness and applicability of line plots, scatter plots, scatter plot matrices, parallel coordinates, and correlation matrices for workload management. Finally, we evaluate our approach in a qualitative user study with IBM domain experts.

Rendering the user's body in virtual reality increases immersion and presence the illusion of "being there". Recent technology enables determining the pose and position of the hands to render them accordingly while interacting within the virtual environment. Virtual reality applications often use realistic male or female hands, mimic robotic hands, or cartoon hands. However, it is unclear how users perceive different hand styles. We conducted a study with 14 male and 14 female participants in virtual reality to investigate the effect of gender on the perception of six different hands. Quantitative and qualitative results show that women perceive lower levels of presence while using male avatar hands and male perceive lower levels of presence using non-human avatar hands. While women dislike male hands, men accept and feel presence with avatar hands of both genders. Our results highlight the importance of considering the users' diversity when designing virtual reality experiences.

The hands of one's avatar are possibly the most visible aspect when interacting in virtual reality (VR). As video games in VR proliferate, it is important to understand how the appearance of avatar hands influence the user experience. Designers of video games often stylize hands and reduce the number of fingers of game characters. Previous work shows that the appearance of avatar hands has significant effects on the user's presence - the feeling of `being' and `acting' in VR. However, little is known about the effects of missing fingers of an avatar in VR. In this paper, we present a study (N=24) that investigated the effect of hand representations by parametrically varying the number of fingers of abstract and realistically rendered hands. We show that decreasing the number of fingers of realistic hands leads to significantly lower levels of presence, which is not the case for abstract hands. Qualitative feedback collected through think-aloud and video revealed potential reasons for the different assessment of realistic and abstract hands with fewer fingers in VR. We contribute design implications and recommend considering the human-likeness when a reduction of the number of fingers of avatar hands is desired.

In this paper, I present several corpus linguistic studies that show the continuity of the diachronic loss of the German genitive within novellas from the past two centuries. Moreover, I found that not all genitive constructions are diachronically receding and that e.g. the adnominal genitive is particularly stable along the analyzed time frame. Furthermore, some authors in Paul Heyse’s Deutscher Novellenschatz use genitives as stylistic device in order to relate their novellas to a specific register or an exalted stylistic level.

We present HistoBankVis, a novel visu-alization system designed for the inter-active analysis of complex, multidimen-sional data to facilitate historical linguisticwork. In this paper, we illustrate the vi-sualization’s efficacy and power by meansof a concrete case study investigating thediachronic interaction of word order andsubject case in Icelandic.

We investigate how the perceived abstraction quality of stipple illustrations is related to the number of points used to create them. Since it is difficult to find objective functions that quantify the visual quality of such illustrations, we gather comparative data by a crowdsourcing user study and employ a paired comparison model to deduce absolute quality values. Based on this study we show that it is possible to predict the perceived quality of stippled representations based on the properties of an input image. Our results are related to Weber-Fechner's law from psychophysics and indicate a logarithmic relation between numbers of points and perceived abstraction quality. We give guidance for the number of stipple points that is typically enough to represent an input image well.

Analysts in professional team sport regularly perform analysis to gain strategic and tactical insights into player and team behavior. Goals of team sport analysis regularly include identification of weaknesses of opposing teams, or assessing performance and improvement potential of a coached team. Current analysis workflows are typically based on the analysis of team videos. Also, analysts can rely on techniques from Information Visualization, to depict e.g., player or ball trajectories. However, video analysis is typically a time-consuming process, where the analyst needs to memorize and annotate scenes. In contrast, visualization typically relies on an abstract data model, often using abstract visual mappings, and is not directly linked to the observed movement context anymore. We propose a visual analytics system that tightly integrates team sport video recordings with abstract visualization of underlying trajectory data. We apply appropriate computer vision techniques to extract trajectory data from video input. Furthermore, we apply advanced trajectory and movement analysis techniques to derive relevant team sport analytic measures for region, event and player analysis in the case of soccer analysis. Our system seamlessly integrates video and visualization modalities, enabling analysts to draw on the advantages of both analysis forms. Several expert studies conducted with team sport analysts indicate the effectiveness of our integrated approach.

The integration of feature matches for handling large displacements is one of the key concepts of recent variational optical flow methods. In this context, many existing approaches rely on confidence measures to identify locations where a poor initial match can potentially be improved by adaptively integrating flow proposals. One very intuitive confidence measure to identify such locations is the matching cost of the data term. Problems arise, however, in the presence of illumination changes, since brightness constancy does not hold and invariant constancy assumptions typically discard too much information for an identification of poor matches. In this paper, we suggest a pipeline approach that addresses the aforementioned problem in two ways. First, we propose a novel confidence measure based on the illumination-compensated brightness constancy assumption. By estimating illumination changes from a pre-computed flow this measure allows us to reliably identify poor matches even in the presence of varying illumination. Secondly, in contrast to many existing pipeline approaches, we propose to integrate only feature matches that have been obtained from dense variational methods. This in turn not only provides robust matches due to the inherent regularization, it also demonstrates that in many cases sparse descriptor matches are not needed for large displacement optical flow. Experiments on the Sintel benchmark and on common large displacement sequences demonstrate the benefits of our strategy. They show a clear improvement over the baseline method and a comparable performance as similar methods from the literature based on sparse feature matches.

The optical flow within a scene can be an arbitrarily complex composition of motion patterns that typically differ regarding their scale. Hence, using a single algorithm with a single set of parameters is often not sufficient to capture the variety of these motion patterns. In particular, the estimation of large displacements of small objects poses a problem. In order to cope with this problem, many recent methods estimate the optical flow by a fusion of flow candidates obtained either from different algorithms or from the same algorithm using different parameters. This, however, typically results in a pipeline of methods for estimating and fusing the candidate flows, each requiring an individual model with a dedicated solution strategy. In this paper, we investigate what results can be achieved with a pure variational approach based on a standard coarse-to-fine optimization. To this end, we propose a novel variational method for the simultaneous estimation and fusion of flow candidates. By jointly using multiple smoothness weights within a single energy functional, we are able to capture different motion patterns and hence to estimate large displacements even without additional feature matches. In the same functional, an intrinsic model-based fusion allows to integrate all these candidates into a single flow field, combining sufficiently smooth overall motion with locally large displacements. Experiments on large displacement sequences and the Sintel benchmark demonstrate the feasibility of our approach and show improved results compared to a single-smoothness baseline method.

We present our data-driven, neural network-based approach to predicting the performance of a distributed GPU volume renderer for supporting cluster equipment acquisition. On the basis of timing measurements from a single cluster as well as from individual GPUs, we are able to predict the performance gain of upgrading an existing cluster with additional or faster GPUs, or even purchasing of a new cluster with a comparable network configuration. To achieve this, we employ neural networks to capture complex performance characteristics. However, merely relying on them for the prediction would require the collection of training data on multiple clusters with different hardware, which is impractical in most cases. Therefore, we propose a two-level approach to prediction, distinguishing between node and cluster level. On the node level, we generate performance histograms on individual nodes to capture local rendering performance. These performance histograms are then used to emulate the performance of different rendering hardware for cluster-level measurement runs. Crucially, this variety allows the neural network to capture the compositing performance of a cluster separately from the rendering performance on individual nodes. Therefore, we just need a performance histogram of the GPU of interest to generate a prediction. We demonstrate the utility of our approach using different cluster configurations as well as a range of image and volume resolutions.

Analysis of everyday human gaze behaviour has significant potential for ubiquitous computing, as evidenced by a large body of work in gaze-based human-computer interaction, attentive user interfaces, and eye-based user modelling. However, current mobile eye trackers are still obtrusive, which not only makes them uncomfortable to wear and socially unacceptable in daily life, but also prevents them from being widely adopted in the social and behavioural sciences. To address these challenges we present InvisibleEye, a novel approach for mobile eye tracking that uses millimetre-size RGB cameras that can be fully embedded into normal glasses frames. To compensate for the cameras’ low image resolution of only a few pixels, our approach uses multiple cameras to capture different views of the eye, as well as learning-based gaze estimation to directly regress from eye images to gaze directions. We prototypically implement our system and characterise its performance on three large-scale, increasingly realistic, and thus challenging datasets: 1) eye images synthesised using a recent computer graphics eye region model, 2) real eye images recorded of 17 participants under controlled lighting, and 3) eye images recorded of four participants over the course of four recording sessions in a mobile setting. We show that InvisibleEye achieves a top person-specific gaze estimation accuracy of 1.79° using four cameras with a resolution of only 5 × 5 pixels. Our evaluations not only demonstrate the feasibility of this novel approach but, more importantly, underline its significant potential for finally realising the vision of invisible mobile eye tracking and pervasive attentive user interfaces.

Recent years have shown a shift from pure geometric 3D city models to data with semantics. This is induced by new applications (e.g. Virtual/Augmented Reality) and also a requirement for concepts like Smart Cities. However, essential urban semantic data like building use categories is often not available. We present a first step in bridging this gap by proposing a pipeline to use crawled urban imagery and link it with ground truth cadastral data as an input for automatic building use classification. We aim to extract this city-relevant semantic information automatically from Street View (SV) imagery. Convolutional Neural Networks (CNNs) proved to be extremely successful for image interpretation, however, require a huge amount of training data. Main contribution of the paper is the automatic provision of such training datasets by linking semantic information as already available from databases provided from national mapping agencies or city administrations to the corresponding façade images extracted from SV. Finally, we present first investigations with a CNN and an alternative classifier as a proof of concept.

While the generation of geometric 3D virtual models has become feasible to a great extent, the enrichment of the resulting urban building models with semantics remains an open research question in the field of geoinformation and geovisualisation. This additional information is not only valuable for applications like Building Information Modeling (BIM) but also offers possibilities to enhance the visual insight for humans when interacting with that kind of data. Depending on the application, presenting users the highest level of detail of building models is often neither the most informative nor feasible way. For example when using mobile apps, resources and display sizes are quite limited. A concrete use case is the imparting of building use types in urban scenes to users. Within our preliminary work, user studies helped to identify important features for the human ability to associate a building with its correct usage type. In this work we now embed this knowledge into building category-specific grammars to automatically modify the geometry of a building to align its visual appearance to its underlying use type. If the building category for a model is not known beforehand, we investigate its feature space and try to derive its use type from there. Within the context of this work, we developed a Virtual Reality (VR) framework that gives the user the possibility to switch between different building representation types while moving in the VR world, thus enabling us in the future to evaluate the potential and effect of the grammar-enhanced building model in an immersive environment.

Given a set of rectangles embedded in the plane, we consider the problem of adjusting the layout to remove all overlap while preserving the orthogonal order of the rectangles. The objective is to minimize the displacement of the rectangles. We call this problem Minimum-Displacement Overlap Removal (mdor). Our interest in this problem is motivated by the application of displaying metadata of archaeological sites. Because most existing overlap removal algorithms are not designed to minimize displacement while preserving orthogonal order, we present and compare several approaches which are tailored to our particular usecase. We introduce a new overlap removal heuristic which we call reArrange. Although conceptually simple, it is very effective in removing the overlap while keeping the displacement small. Furthermore, we propose an additional procedure to repair the orthogonal order after every iteration, with which we extend both our new heuristic and PRISM, a widely used overlap removal algorithm. We compare the performance of both approaches with and without this order repair method. The experimental results indicate that reArrange is very effective for heterogeneous input data where the overlap is concentrated in few dense regions.

In recent years, research on cross-device interaction has become a popular topic in HCI leading to novel interaction techniques mutually interfering with new evolving theoretical paradigms. Building on previous research, we implemented an individual multi-device work environment for creative activities. In a study with 20 participants, we compared a traditional toolbar-based condition with two conditions facilitating spatially distributed tools on digital panels and on physical devices. We analyze participants' interactions with the tools, encountered problems and corresponding solutions, as well as subjective task load and user experience. Our findings show that the spatial distribution of tools indeed offers advantages, but also elicits new problems, that can partly be leveraged by the physical affordances of mobile devices.

People's ability to remember and recall spatial information can be harnessed to improve navigation and search performances in interactive systems. In this paper, we investigate how display size and input modality influence spatial memory, especially in relation to efficiency and user satisfaction. Based on an experiment with 28 participants, we analyze the effect of three input modalities (trackpad, direct touch, and gesture-based motion controller) and two display sizes (10.6" and 55") on people's ability to navigate to spatially spread items and recall their positions. Our findings show that the impact of input modality and display size on spatial memory is not straightforward, but characterized by trade-offs between spatial memory, efficiency, and user satisfaction.

Eye contact is an important non-verbal cue in social signal processing and promising as a measure of overt attention in human-object interactions and attentive user interfaces. However, robust detection of eye contact across different users, gaze targets, camera positions, and illumination conditions is notoriously challenging. We present a novel method for eye contact detection that combines a state-of-the-art appearance-based gaze estimator with a novel approach for unsupervised gaze target discovery, i.e. without the need for tedious and time-consuming manual data annotation. We evaluate our method in two real-world scenarios: detecting eye contact at the workplace, including on the main work display, from cameras mounted to target objects, as well as during everyday social interactions with the wearer of a head-mounted egocentric camera. We empirically evaluate the performance of our method in both scenarios and demonstrate its effectiveness for detecting eye contact independent of target object type and size, camera position, and user and recording environment.

Our goal is to accurately model human class separation judgements in color-coded scatterplots. Towards this goal, we propose a set of 2002 visual separation measures, by systematically combining 17 neighborhood graphs and 14 class purity functions, with different parameterizations. Using a Machine Learning framework, we evaluate these measures based on how well they predict human separation judgements. We found that more than 58% of the 2002 new measures outperform the best state-of-the-art Distance Consistency (DSC) measure. Among the 2002, the best measure is the average proportion of same-class neighbors among the 0.35-Observable Neighbors of each point of the target class (short GONG 0.35 DIR CPT), with a prediction accuracy of 92.9%, which is 11.7% better than DSC. We also discuss alternative, well-performing measures and give guidelines when to use which.

In this paper approximation methods for infinite-dimensional Lévy processes, also called (time-dependent) Lévy fields, are introduced. For square integrable fields beyond the Gaussian case, it is no longer given that the one-dimensional distributions in the spectral representation with respect to the covariance operator are independent. When simulated via a Karhunen–Loève expansion a set of dependent but uncorrelated one-dimensional Lévy processes has to be generated. The dependence structure among the one-dimensional processes ensures that the resulting field exhibits the correct point-wise marginal distributions. To approximate the respective (one-dimensional) Lévy-measures, a numerical method, called discrete Fourier inversion, is developed. For this method, Lp-convergence rates can be obtained and, under certain regularity assumptions, mean square and Lp-convergence of the approximated field is proved. Further, a class of (time-dependent) Lévy fields is introduced, where the point-wise marginal distributions are dependent but uncorrelated subordinated Wiener processes. For this specific class one may derive point-wise marginal distributions in closed form. Numerical examples, which include hyperbolic and normal-inverse Gaussian fields, demonstrate the efficiency of the approach.

Continuous sedimentation processes in a clarifier-thickener unit can be described by a scalar nonlinear conservation law whose flux density function is discontinuous with respect to the spatial position. In the applications of this model, which include mineral processing and wastewater treatment, the rate and composition of the feed flow cannot be given deterministically. Efficient numerical simulation is required to quantify the effect of uncertainty in these control parameters in terms of the response of the clarifier-thickener system. Thus, the problem at hand is one of uncertainty quantification for nonlinear hyperbolic problems with several random perturbations. The presented hybrid stochastic Galerkin method is devised so as to extend the polynomial chaos approximation by multiresolution discretization in the stochastic space. This approach leads to a deterministic hyperbolic system, which is partially decoupled and therefore suitable for efficient parallelisation. Stochastic adaptivity reduces the computational effort. Several numerical experiments are presented.

In this paper hyperbolic partial differential equations (PDEs) with random coefficients are discussed. We consider the challenging problem of flux functions with coefficients modeled by spatiotemporal random fields. Those fields are given by correlated Gaussian random fields in space and Ornstein--Uhlenbeck processes in time. The resulting system of equations consists of a stochastic differential equation for each random parameter coupled to the hyperbolic conservation law. We define an appropriate solution concept in this setting and analyze errors and convergence of discretization methods. A novel discretization framework, based on Monte Carlo finite volume methods, is presented for the robust computation of moments of solutions to those random hyperbolic PDEs. We showcase the approach on two examples which appear in applications---the magnetic induction equation and linear acoustics---both with a spatiotemporal random background velocity field.

Investigating user behavior involves abstracting low-level events to higher-level concepts. This requires an analyst to study individual user activities, assign codes which categorize behavior, and develop a consistent classification scheme. To better support this reasoning process of an analyst, we suggest a novel visual analytics approach which integrates rich user data including transcripts, videos, eye movement data, and interaction logs. Word-sized visualizations embedded into a tabular representation provide a space-efficient and detailed overview of user activities. An analyst assigns codes, grouped into code categories, as part of an interactive process. Filtering and searching helps to select specific activities and focus an analysis. A comparison visualization summarizes results of coding and reveals relationships between codes. Editing features support efficient assignment, refinement, and aggregation of codes. We demonstrate the practical applicability and usefulness of our approach in a case study and describe expert feedback.

We present an integrated approach for the real-time performance prediction and tuning of volume raycasting. The usage of empty space skipping and early ray termination, among others, can induce significant variations in performance when camera configuration and transfer functions are adjusted. For interactive exploration, this can result in various unpleasant effects like abruptly reduced responsiveness or jerky motions. To overcome those effects, we propose an integrated approach to accelerate the rendering and assess performance-relevant data on-the-fly, including a new technique to estimate the impact of early ray termination. On this basis, we introduce a hybrid model, to achieve accurate predictions with only minimal computational footprint. Our hybrid model incorporates both aspects from analytical performance modeling and machine learning, with the goal to combine their respective strengths. Using our model, we dynamically steer the sampling density along rays with our automatic tuning technique. This approach allows to reliably meet performance requirements like a fixed frame rate, even in the case of large sudden changes to the transfer function or the camera. We finally demonstrate the accuracy and utility of our approach by means of a variety of different volume data sets and interaction sequences.

Metro maps can be regarded as a particular version of information visualization. The goal is to produce readable and effective map designs. In this paper, we combine the expertise of design experts and visualization researchers to achieve this goal. The aesthetic design of the maps should play a major role as the intention of the designer is to make them attractive for the human viewer in order to use the designs in a way that is the most efficient. The designs should invoke accurate actions by the user—in the case of a metro map, the user would be making journeys. We provide two views on metro map designs: one from a designer point of view and one from a visualization expert point of view. The focus of this work is to find a combination of both worlds from which the designer as well as the visualizer can benefit. To reach this goal we first describe the designer’s work when designing metro maps, then we take a look at how a visualizer measures performance from an end user perspective by tracking people’s eyes when working with the formerly designed maps while answering a route finding task.

Distortion-based visualization techniques allow users to examine focused regions of a multiscale space at high scales but preserve their contextual information. However, the distortion can come at the coast of confusion, disorientation and impairment of the users' spatial memory. Yet, how distortions influence users' ability to build up spatial memory, while taking into account human skills of perception, interpretation and comprehension, remains underexplored. This note reports findings of an experimental comparison between a distortion-based focus+context interface and an undistorted overview+detail interface. The focus+context technique follows guidelines for the design of comprehensible distortions: make use of real-world metaphors, visual clues like shading, smooth transitions and scaled-only focus regions. The results show that the focus+context technique designed following these guidelines help to keep track of the position within the multiscale space and does not impair users' spatial memory.

While smartwatches have become common for mobile interaction, one of their main limitation is the limited screen size. To facilitate reading activities despite these limitations, reading with Rapid Serial Visual Presentation (RSVP) has been shown to be feasible. However, when text is presented in rapid sequence, single words are easily missed due to blinking or briefly glancing up from the screen. This gets worse the more the reader is engaged in a secondary task, such as walking. To give implicit control over the reading flow we combined an RSVP reading application on a smartwatch with a head-worn eye tracker. When the reading flow is briefly interrupted, the text presentation automatically pauses or backtracks. In a user study with 15 participants we show that using eye tracking in combination with RSVP increases users' comprehension compared to a touch-based UI to control the text presentation. We argue that eye tracking will be a valuable extension for future smartwatch interaction.

Unrestricted gaze tracking that allows for head and body movements can enable us to understand interactive gaze behavior with large-scale visualizations. Approaches that support this, by simultaneously recording eye- and user-movements, can either be based on geometric or data-driven regression models. A data-driven approach can be implemented more flexibly but its performance can suffer with poor quality training data. In this paper, we introduce a pre-processing procedure to remove training data for periods when the gaze is not fixating the presented target stimuli. Our procedure is based on a velocity-based filter for rapid eye-movements (i.e., saccades). Our results show that this additional procedure improved the accuracy of our unrestricted gaze-tracking model by as much as 56 %. Future improvements to data-driven approaches for unrestricted gaze-tracking are proposed, in order to allow for more complex dynamic visualizations.

Advances in high-accuracy measurement techniques and parallel computing systems for simulations lead to a widening gapbetween the rate at which data is generated and the rate at which it can be transferred and stored. In situ visualization directly tackles thisissue by processing—and with this reducing—data as soon as it is generated. This allows to create, transmit and store visualizations at amuch higher resolution than what would be possible otherwise with traditional approaches. So-called hybrid in situ visualization is apopular variant that transforms data into an intermediate visualization representation of reduced size. These intermediate representationscondense the original data by applying visualization techniques, but in contrast to the traditional result of a rendered image, they stillpreserve some degrees of freedom for live and a posteriori exploration and analysis. However, the configuration of the involvedprocessing steps requires careful configuration under the consideration of achieved quality and preserved degrees of freedom againstbandwidth and storage resources.To optimize the generation of intermediate representations for hybrid in situ visualization, we present our approach to (1) analyze andquantify the impact of input parameters, and (2) to auto-tune them on this basis under the consideration of different constraints. Wedemonstrate its application and evaluate respective results at the example of Volumetric Depth Images (VDIs), a view-dependentrepresentation for volumetric data. VDIs can quickly and flexibly be generated via a modified volume raycasting procedure that partitionsand partially composits samples along view rays. In particular, we study the impact of respective input parameters on this process w.r.t.the involved quality-space trade-off. We quantify rendering quality via image quality metrics and space requirements via the compressedsize of the intermediate representation. On this basis, we then automatically determine the parameter settings that yield the best qualityunder different constraints. We demonstrate the utility of our approach by means of a variety of different data sets, and show that weoptimize the achieved results without having to rely on tedious and time-consuming manual tweaking.

For a directed graph G with vertex set V, we call a subset C⊆V a k-(All-)Path Cover if C contains a node from any simple path in G consisting of k nodes. This paper considers the problem of constructing small k-Path Covers in the context of road networks with millions of nodes and edges. In many application scenarios, the set C and its induced overlay graph constitute a very compact synopsis of G, which is the basis for the currently fastest data structure for personalized shortest path queries, visually pleasing overlays of subsampled paths, and efficient reporting, retrieval and aggregation of associated data in spatial network databases. Apart from a theoretic investigation of the problem, we provide efficient algorithms that produce very small k-Path Covers for large real-world road networks (with a posteriori guarantees via instance-based lower bounds). We also apply our algorithms to other (social, collaboration, web, etc.) networks and can improve in several instances upon previous approaches.

Given a set of prioritized disks with fixed centers in R2 whose radii grow linearly over time, we are interested in computing an elimination order of these disks assuming that when two disks touch, the one with lower priority is ‘crushed’. A straightforward algorithm has running time O(n2logn) which we improve to expected O(n(log6n+Δ2log2n+Δ4logn)) where Δ is the ratio between largest and smallest radii amongst the disks. For a very natural application of this problem in the map rendering domain, we have Δ=O(1).

In everyday life, people regularly make decisions based on uncertain data, e.g., when using a navigation device or looking at the weather forecast. In our work, we compare four representations that communicate different amounts of uncertainty information to the user. We compared them in a study by publishing a web-based game on Facebook. In total, 44 users played 991 turns. We analyzed the turns by logging game metrics such as the gain per turn and included a survey element. The results show that abundance of uncertainty information leads to taking unnecessary risks. However, representations with aggregated detailed uncertainty provide a good trade-off between being understandable by the players and encouraging medium risks with high gains. Absence of uncertainty information reduces the risk taking and leads to more won turns, but with the lowest money gain.

Collecting and processing provenance, i.e., information describing the production process of some end product, is important in various applications, e.g., to assess quality, to ensure reproducibility, or to reinforce trust in the end product. In the past, different types of provenance meta-data have been proposed, each with a different scope. The first part of the proposed tutorial provides an overview and comparison of these different types of provenance. To put provenance to good use, it is essential to be able to interact with and present provenance data in a user-friendly way. Often, users interested in provenance are not necessarily experts in databases or query languages, as they are typically domain experts of the product and production process for which provenance is collected (biologists, journalists, etc.). Furthermore, in some scenarios, it is difficult to use solely queries for analyzing and exploring provenance data. The second part of this tutorial therefore focuses on enabling users to leverage provenance through adapted visualizations. To this end, we will present some fundamental concepts of visualization before we discuss possible visualizations for provenance.

The visualization of graphs using classical node-link diagrams works well upto the point where the number of nodes exceeds the capacity of the display.To overcome this limitation Zinsmaier et al. 5 proposed a rendering techniquewhich aggregates nodes based on their spatial distribution, thereby allowing forvisual exploration of large graphs. Since the rendering is done on the graphicsprocessing unit (GPU) this process is reasonably fast. However, the connectionbetween input graph and visual image is partially lost, which makes it harder,for instance, to process weights and labels of the input graph. We reproduce their approach with the goal of establishing a flexible structureto improve the connection between input data and visualization. Additionally,we control the layout features in a more direct way. For example, contour linesare explicitly drawn in order to remove fuzziness of the density visualization.Though the proposed CPU-based approach cannot render at interactive rates, itcan be computed as a preprocessing step and then interactively explored givensome predefined resolution constraints.

Saliency-driven image coding is well worth pursuing. Previous studies on JPEG and JPEG2000 have suggested that region-of-interest coding brings little overall benefit compared to the standard implementation. We show that our saliency-driven variable quantization JPEG coding method significantly improves perceived image quality. To validate our findings, we performed large crowdsourcing experiments involving several hundred contributors, on 44 representative images. To quantify the level of improvement, we devised an approach to equate Likert-type opinions to bitrate differences. Our saliency-driven coding showed 11% bpp average benefit over the standard JPEG.

We study the use of crowdsourcing for self-reported attention in image quality assessment (IQA) tasks. We present the results from two crowdsourcing campaigns: one where participants indicated via mouse clicks the image locations that influenced their rating of quality, and another where participants chose locations they looked at in a free-viewing setting. The results are compared to in-lab eye tracking experiments. Our analysis shows a strong connection between the in-lab and self-reported IQA locations. This suggests that crowdsourced studies are an affordable and valid alternative to eye tracking for IQA tasks.

Medical doctors and researchers in bio-medicine are increasingly confronted with complex patient data, posing new and difficult analysis challenges. These data are often comprising high-dimensional descriptions of patient conditions and measurements on the success of certain therapies. An important analysis question in such data is to compare and correlate patient conditions and therapy results along with combinations of dimensions. As the number of dimensions is often very large, one needs to map them to a smaller number of relevant dimensions to be more amenable for expert analysis. This is because irrelevant, redundant, and conflicting dimensions can negatively affect effectiveness and efficiency of the analytic process (the so-called curse of dimensionality). However, the possible mappings from high- to low-dimensional spaces are ambiguous. For example, the similarity between patients may change by considering different combinations of relevant dimensions (subspaces). We demonstrate the potential of subspace analysis for the interpretation of high-dimensional medical data. Specifically, we present SubVIS, an interactive tool to visually explore subspace clusters from different perspectives, introduce a novel analysis workflow, and discuss future directions for high-dimensional (medical) data analysis and its visual exploration. We apply the presented workflow to a real-world dataset from the medical domain and show its usefulness with a domain expert evaluation.

The quality assessment of results of clustering algorithms is challenging as different cluster methodologies lead to different cluster characteristics and topologies. A further complication is that in high-dimensional data, subspace clustering adds to the complexity by detecting clusters in multiple different lower-dimensional projections. The quality assessment for (subspace) clustering is especially difficult if no benchmark data is available to compare the clustering results. In this research paper, we present SubEval, a novel subspace evaluation framework, which provides visual support for comparing quality criteria of subspace clusterings. We identify important aspects for evaluation of subspace clustering results and show how our system helps to derive quality assessments. SubEval allows assessing subspace cluster quality at three different granularity levels: (1) A global overview of similarity of clusters and estimated redundancy in cluster members and subspace dimensions. (2) A view of a selection of multiple clusters supports in-depth analysis of object distributions and potential cluster overlap. (3) The detail analysis of characteristics of individual clusters helps to understand the (non-)validity of a cluster. We demonstrate the usefulness of SubEval in two case studies focusing on the targeted algorithm- and domain scientists and show how the generated insights lead to a justified selection of an appropriate clustering algorithm and an improved parameter setting. Likewise, SubEval can be used for the understanding and improvement of newly developed subspace clustering algorithms. SubEval is part of SubVA, a novel open-source web-based framework for the visual analysis of different subspace analysis techniques.

Humans are inherently skilled at using subtle physiological cues from other persons, for example gaze direction in a conversation. Personal computers have yet to explore this implicit input modality. In a study with 14 participants, we investigate how a user's gaze can be leveraged in adaptive computer systems. In particular, we examine the impact of different languages on eye movements by presenting simple questions in multiple languages to our participants. We found that fixation duration is sufficient to ascertain if a user is highly proficient in a given language. We propose how these findings could be used to implement adaptive visualizations that react implicitly on the user's gaze.

We facilitate the comparative visual analysis of eye tracking data from multiple participants with a visualization that represents the temporal changes of viewing behavior. Common approaches to visually analyze eye tracking data either occlude or ignore the underlying visual stimulus, impairing the interpretation of displayed measures. We introduce fixation-image charts: a new technique to display the temporal changes of fixations in the context of the stimulus without visual overlap between participants. Fixation durations, the distance and direction of saccades between consecutive fixations, as well as the stimulus context can be interpreted in one visual representation. Our technique is not limited to static stimuli, but can be applied to dynamic stimuli as well. Using fixation metrics and the visual similarity of stimulus regions, we complement our visualization technique with an interactive filter concept that allows for the identification of interesting fixation sequences without the time-consuming annotation of areas of interest. We demonstrate how our technique can be applied to different types of stimuli to perform a range of analysis tasks. Furthermore, we discuss advantages and shortcomings derived from a preliminary user study.

We present a new visualization approach for displaying eye tracking data from multiple participants. We aim to show the spatio-temporal data of the gaze points in the context of the underlying image or video stimulus without occlusion. Our technique, denoted as gaze stripes, does not require the explicit definition of areas of interest but directly uses the image data around the gaze points, similar to thumbnails for images. A gaze stripe consists of a sequence of such gaze point images, oriented along a horizontal timeline. By displaying multiple aligned gaze stripes, it is possible to analyze and compare the viewing behavior of the participants over time. Since the analysis is carried out directly on the image data, expensive post-processing or manual annotation are not required. Therefore, not only patterns and outliers in the participants' scanpaths can be detected, but the context of the stimulus is available as well. Furthermore, our approach is especially well suited for dynamic stimuli due to the non-aggregated temporal mapping. Complementary views, i.e., markers, notes, screenshots, histograms, and results from automatic clustering, can be added to the visualization to display analysis results. We illustrate the usefulness of our technique on static and dynamic stimuli. Furthermore, we discuss the limitations and scalability of our approach in comparison to established visualization techniques.

The application of eye tracking for the evaluation of humans’ viewing behavior is a common approach in psy-chological research. So far, the use of this technique for the evaluation of visual analytics and visualization isless prominent. We investigate recent scientific publications from the main visualization and visual analyticsconferences and journals, as well as related research fields that include an evaluation by eye tracking.Furthermore, we provide an overview of evaluation goals that can be achieved by eye tracking and state-of-the-art analysis techniques for eye tracking data. Ideally, visual analytics leads to a mixed-initiative cognitivesystem where the mechanism of distribution is the interaction of the user with the visualization environment.Therefore, we also include a discussion of cognitive approaches and models to include the user in the evalua-tion process. Based on our review of the current use of eye tracking evaluation in our field and the cognitivetheory, we propose directions for future research on evaluation methodology, leading to the grand challengeof developing an evaluation approach to the mixed-initiative cognitive system of visual analytics.

Size and resolution of computer screens are constantly increasing. Individual screens can easily be combined to wall-sized displays. This enables computer displays that are folded, straight, bow shaped or even spread. As possibilities for arranging the screens are manifold, it is unclear what arrangements are appropriate. Moreover, it is unclear how content and applications should be arranged on such large displays. To determine guidelines for the arrangement of multiple screens and for content and application layouts, we conducted a design study. In the study, we asked 16 participants to arrange a large screen setup as well as to create layouts of multiple common application windows. Based on the results we provide a classification for screen arrangements and interaction areas. We identified, that screen space should be divided into a central area for interactive applications and peripheral areas, mainly for displaying additional content.

Display space in offices constantly increased in the last decades. We believe that this trend will continue and ultimately result in the use of wall-sized displays in the future office. One of the most challenging tasks while interacting with large high-resolution displays is target acquisition. The most important challenges reported in previous work are the long distances that need to be traveled with the pointer while still enabling precise selection as well as seeking for the pointer on the large display. In this paper, we investigate if MAGIC-Pointing, controlling the pointer through eye gaze, can help overcome both challenges. We implemented MAGIC-Pointing for a 2.85m x 1.13m large display. Using this system we conducted a target selection study. The results show that using MAGIC-Pointing for selecting targets on wall-sized displays decreases the task completion time significantly and it also decreases the users' task load. We therefore argue that MAGIC-Pointing can help to make interaction with wall-sized displays usable.

In collaborative activities, collaborators can use physical objects in their shared environment as spatial cues to guide each other's attention. Collaborative mixed reality environments (MREs) include both, physical and digital objects. To study how virtual objects influence collaboration and whether they are used as spatial cues, we conducted a controlled lab experiment with 16 dyads. Results of our study show that collaborators favored the digital objects as spatial cues over the physical environment and the physical objects: Collaborators used significantly less deictic gestures in favor of more disambiguous verbal references and a decreased subjective workload when virtual objects were present. This suggests adding additional virtual objects as spatial cues to MREs to improve user experience during collaborative mixed reality tasks.

Attention maps-often in the form of heatmaps-are a common visualization approach to obtaining an overview of the spatial distribution of gaze data from eye tracking experiments. However, attention maps are not designed to let us easily analyze the temporal information of gaze data: they completely ignore temporal information by aggregating over time, or they use animation to build a sequence of attention maps. To overcome this issue, we introduce Hilbert attention maps: a 2D static visualization of the spatiotemporal distribution of gaze points. The visualization is based on the projection of the 2D spatial domain onto a space-filling Hilbert curve that is used as one axis of our new attention map; the other axis represents time. We visualize Hilbert attention maps either as dot displays or heatmaps. This 2D visualization works for data from individual participants or large groups of participants, it supports static and dynamic stimuli alike, and it does not require any preprocessing or definition of areas of interest. We demonstrate how our visualization allows analysts to identify spatiotemporal patterns of visual reading behavior, including attentional synchrony and smooth pursuit.

In this short paper, we present a lightweight application for the interactive annotation of eye tracking data for both static and dynamic stimuli. The main functionality is the annotation of fixations that takes into account the scanpath and stimulus. Our visual interface allows the annotator to work through a sequence of fixations, while it shows the context of the scanpath in the form of previous and subsequent fixations. The context of the stimulus is included as visual overlay. Our application supports the automatic initial labeling according to areas of interest (AOIs), but is not dependent on AOIs. The software is easily configurable, supports user-defined annotation schemes, and fits in existing workflows of eye tracking experiments and the evaluation thereof by providing import and export functionalities for data files.

We conducted a controlled empirical eye tracking study with 40 participants using schematic metro maps. The study focused on two aspects: determining different reading strategies and assessing user performance. We considered the following factors: color encoding (color vs. gray-scale), map complexity (three levels), and task difficulty (three levels). There was one type of task: find a route from a start to a target location and state the number of transfers that have to be performed. To identify reading strategies, we annotated fixations of scanpaths, computed a transition matrix of each annotated scanpath, and used these matrices as input to cluster scanpaths into groups of similar behavior. We show how these reading strategies relate to the geodesic structure of the scanpaths' fixations projected onto the geodesic line that connects start and target locations. The analysis of the eye tracking data is complemented by statistical inference working on two eye tracking metrics (average fixation duration and saccade length). User performance was evaluated with a statistical analysis of task correctness and completion time. Our study shows that the design factors have a significant impact on user task performance. Also, we were able to identify typical reading strategies like directly finding a path from start to target location. Often, participants check the correctness of their result multiple times by moving back and forth between start and target. Our findings also indicate that the choice of reading strategies does not depend on whether color or gray-scale encoding is used.

Small-world networks have characteristically low pairwise shortest-path distances, causing distance-based layout methods to generate hairball drawings. Recent approaches thus aim at finding a sparser representation of the graph to amplify variations in pairwise distances. Since the effect of sparsification on the layout is difficult to describe analytically, the incorporated filtering parameters of these approaches typically have to be selected manually and individually for each input instance. We here propose the use of graph invariants to determine suitable parameters automatically. This allows us to perform adaptive filtering to obtain drawings in which the cluster structure is most prominent. The approach is based on an empirical relationship between input and output characteristics that is derived from real and synthetic networks. Experimental evaluation shows the effectiveness of our approach and suggests that it can be used by default to increase the robustness of force-directed layout methods.

In this paper, we present a proof-of-concept approach to estimating mental workload by measuring the user's pupil diameter under various controlled lighting conditions. Knowing the user's mental workload is desirable for many application scenarios, ranging from driving a car, to adaptive workplace setups. Typically, physiological sensors allow inferring mental workload, but these sensors might be rather uncomfortable to wear. Measuring pupil diameter through remote eye-tracking instead is an unobtrusive method. However, a practical eye-tracking-based system must also account for pupil changes due to variable lighting conditions. Based on the results of a study with tasks of varying mental demand and six different lighting conditions, we built a simple model that is able to infer the workload independently of the lighting condition in 75% of the tested conditions.

We carried out crowdsourced video quality as-sessments using paired comparisons and converting the resultsto differential mean opinion scores (DMOS). A previous lab-based study had provided corresponding MOS-values for absolutecategory ratings. Using a simple linear transformation to fit thecrowdsourcing-based DMOS values to the lab-based MOS values,we compared the results in terms of correlation coefficients andvisually checked the relationship on scatter plots. The comparisonresult is surprisingly good with correlation coefficients more than0.96, although (1) the original video sequences had to be croppedand downscaled in the crowdsourcing-based experiments, (2) thecontrol of the experimental setup for the crowdsourcing casewas much less and (3) it was widely believed that data fromcrowdsourcing workers are less reliable. Our result suggestscrowdsourcing workers can actually be used to collect reliableVQA data in some applications.

The current study investigates the demands that steering places on mental resources. Instead of a conventional dual-task paradigm, participants of this study were only required to perform a steering task while task-irrelevant auditory distractor probes (environmental sounds and beep tones) were intermittently presented. The event-related potentials (ERPs), which were generated by these probes, were analyzed for their sensitivity to the steering task’s demands. The steering task required participants to counteract unpredictable roll disturbances and difficulty was manipulated either by adjusting the bandwidth of the roll disturbance or by varying the complexity of the control dynamics. A mass univariate analysis revealed that steering selectively diminishes the amplitudes of early P3, late P3, and the re-orientation negativity (RON) to task-irrelevant environmental sounds but not to beep tones. Our findings are in line with a three-stage distraction model, which interprets these ERPs to reflect the post-sensory detection of the task-irrelevant stimulus, engagement, and re-orientation back to the steering task. This interpretation is consistent with our manipulations for steering difficulty. More participants showed diminished amplitudes for these ERPs in the “hard” steering condition relative to the “easy” condition. To sum up, the current work identifies the spatiotemporal ERP components of task-irrelevant auditory probes that are sensitive to steering demands on mental resources. This provides a non-intrusive method for evaluating mental workload in novel steering environments.

We argue that there is a need for substantially more research on the use of generative data models in the validation and evaluation of visualization techniques. For example, user studies will require the display of representative and uncon-founded visual stimuli, while algorithms will need functional coverage and assessable benchmarks. However, data is often collected in a semi-automatic fashion or entirely hand-picked, which obscures the view of generality, impairs availability, and potentially violates privacy. There are some sub-domains of visualization that use synthetic data in the sense of generative data models, whereas others work with real-world-based data sets and simulations. Depending on the visualization domain, many generative data models are "side projects" as part of an ad-hoc validation of a techniques paper and thus neither reusable nor general-purpose. We review existing work on popular data collections and generative data models in visualization to discuss the opportunities and consequences for technique validation, evaluation, and experiment design. We distill handling and future directions, and discuss how we can engineer generative data models and how visualization research could benefit from more and better use of generative data models.

This paper presents a visualization tool for the analysis of diachronic multidimensional language data. Our tool was developed withrespect to a corpus study of dative subjects in Icelandic based on the Icelandic Parsed Historical Corpus (Wallenberg et al., 2011) whichinvestigates determining factors for the appearance of dative subjects in the history of Icelandic. The visualization provides an interactiveaccess to the underlying multidimensional data and significantly facilitates the analysis of the complex diachronic interactions of factorsat hand. We were able to identify various interactions of conditioning factors for dative subjects in Icelandic via the visualization tooland showed that dative subjects are increasingly associated with experiencer arguments in Icelandic across time. We also found that therise of dative subjects with experiencer arguments is correlated with an increasing use of middle voice. This lexical semantic changeargues against dative subjects as a Proto Indo-European inheritance. Moreover, the visualization helped us to draw conclusions aboutuncertainties and problems of our lexical semantic data annotation which will be revised for future work.

Virtual 3D cities are becoming increasingly important as a means of visually communicating diverse urban-related information. To get a deeper understanding of a human’s cognitive experience of virtual 3D cities, this paper presents a user study on the human ability to perceive building categories (e.g. residential home, office building, building with shops etc.) from geometric 3D building representations. The study reveals various dependencies between geometric properties of the 3D representations and the perceptibility of the building categories. Knowledge about which geometries are relevant, helpful or obstructive for perceiving a specific building category is derived. The importance and usability of such knowledge is demonstrated based on a perception-guided 3D building abstraction process.

Virtual 3D cities are becoming increas- ingly important as a means of visually communicating diverse urban-related information. Since humans are the direct recipients of this information transfer, it is vital that the 3D city representations account for the humans' spatial cognition. Thus, our long-term goal is providing a model for the effective perception-aware visual communication of urban- or building-related semantic information via geometric 3D building representations which induce a maximum degree of perceptual insight in the user's mind. A first step towards this goal is to get a deeper understanding of a human's cognitive expe- rience of virtual 3D cities. In this context, the paper presents a user study on the human ability to perceive building categories, e.g.residential home, office building, building with shops etc., from geometric 3D building representations. The study reveals various dependencies between geometric properties of the 3D representations and the perceptibility of the building categories. Knowledge about which geometries are relevant, helpful or obstructive for perceiving a specific building category is derived. The importance and usability of such knowledge is demonstrated based on a perception-guided 3D building abstraction process. Zusammenfassung: Eine Studieüber die menschliche Wahrnehmung von Gebäudekategorien auf Basis unterschiedlicher 3D-Gebäuderepräsentationen. Virtuelle 3D-Städte werden zunehmend wichtig, um unterschiedlichste stadtrelevante Informationen visuell zu vermitteln. Da Menschen die direkten Empfänger dieses Informationstransfers sind, ist es unerlässlich, dass 3D-Stadtreprä-sentationen die räumliche Wahrnehmung von uns Menschen berücksichtigen. Unser längerfristiges Ziel ist es daher, ein Modell zur wahrnehmungsbe- wussten visuellen Kommunikation von städteoder gebäud espezifischen semantischen Informationen zu entwickeln, welchesüber geometrische 3D-Gebäuderepräsentationen dem Nutzer ein Maximum an Erkenntnisgewinn ermöglicht. Ein erster Schritt dorthin ist, sich ein besseres Verständnis der menschlichen Wahrnehmung von virtuellen 3D-Städten zu verschaffen. In diesem Zusammenhang präsentiert der Beitrag einen Nutzertestüber die menschliche Fähigkeit, Gebäudekategorien (z. B. Wohngebäude, Büros, Gebäude mit Läden usw.) anhand geometrischer 3D-Gebäuderepräsentationen zu erkennen. Die Studie zeigt zahlreiche Abhängigkeiten zwischen geometrischen Eigenschaften der 3D-Repräsentationen und der Wahrnehmbarkeit der Gebäudekategorien auf. Wissen darüber, welche geometrischen Eigenschaften relevant, hilfreich oder hinderlich sind, um eine bestimmte Gebäudekategorie zu erkennen, wird aus den Ergebnissen der Studie abgeleitet. Die Wichtigkeit und der Nutzen dieser Erkenntnisse werden anhand einer wahrnehmungsgesteuerten Abstraktion von 3D-Gebäudemodellen aufgezeigt.

Notifications are a core mechanism of current smart devices. They inform about a variety of events including messages, social network comments, and application updates. While users appreciate the awareness that notifications provide, notifications cause distraction, higher cognitive load, and task interruptions. With the increasing importance of smart environments, the number of sensors that could trigger notifications will increase dramatically. A flower with a moisture sensor, for example, could create a notification whenever the flower needs water. We assume that current notification mechanisms will not scale with the increasing number of notifications. We therefore explore notification mechanisms for smart homes. Notifications are shown on smartphones, on displays in the environment, next to the sending objects, or on the user's body. In an online survey, we compare the four locations in four scenarios. While different aspects influence the perceived suitability of each notification location, the smartphone generally is rated the best.

Latency between a user's movement and visual feedback is inevitable in every Virtual Reality application, as signal transmission and processing take time. Unfortunately, a high end-to-end latency impairs perception and motor performance. While it is possible to reduce feedback delay to tens of milliseconds, these delays will never completely vanish. Currently, there is a gap in literature regarding the impact of feedback delays on perception and motor performance as well as on their interplay in virtual environments employing full-body avatars. With the present study at hand, we address this gap by performing a systematic investigation of different levels of delay across a variety of perceptual and motor tasks during full-body action inside a Cave Automatic Virtual Environment. We presented participants with their virtual mirror image, which responded to their actions with feedback delays ranging from 45 to 350 ms. We measured the impact of these delays on motor performance, sense of agency, sense of body ownership and simultaneity perception by means of psychophysical procedures. Furthermore, we looked at interaction effects between these aspects to identify possible dependencies. The results show that motor performance and simultaneity perception are affected by latencies above 75 ms. Although sense of agency and body ownership only decline at a latency higher than 125 ms, and deteriorate for a latency greater than 300 ms, they do not break down completely even at the highest tested delay. Interestingly, participants perceptually infer the presence of delays more from their motor error in the task than from the actual level of delay. Whether or not participants notice a delay in a virtual environment might therefore depend on the motor task and their performance rather than on the actual delay.

This book discusses research, methods, and recent developments in the interdisciplinary field that spans research in visualization, eye tracking, human-computer interaction, and psychology. It presents extended versions of papers from the First Workshop on Eye Tracking and Visualization (ETVIS), which was organized as a workshop of the IEEE VIS Conference 2015. Topics include visualization and visual analytics of eye-tracking data, metrics and cognitive models, eye-tracking experiments in the context of visualization interfaces, and eye tracking in 3D and immersive environments. The extended ETVIS papers are complemented by a chapter offering an overview of visualization approaches for analyzing eye-tracking data and a chapter that discusses electrooculography (EOG) as an alternative of acquiring information about eye movements. Covering scientific visualization, information visualization, and visual analytics, this book is a valuable resource for eye-tracking researchers within the visualization community.

Learning-based methods for appearance-based gaze estimation achieve state-of-the-art performance in challenging real-world settings but require large amounts of labelled training data. Learning-by-synthesis was proposed as a promising solution to this problem but current methods are limited with respect to speed, appearance variability, and the head pose and gaze angle distribution they can synthesize. We present UnityEyes, a novel method to rapidly synthesize large amounts of variable eye region images as training data. Our method combines a novel generative 3D model of the human eye region with a real-time rendering framework. The model is based on high-resolution 3D face scans and uses real-time approximations for complex eyeball materials and structures as well as anatomically inspired procedural geometry methods for eyelid animation. We show that these synthesized images can be used to estimate gaze in difficult in-the-wild scenarios, even for extreme gaze angles or in cases in which the pupil is fully occluded. We also demonstrate competitive gaze estimation results on a benchmark in-the-wild dataset, despite only using a light-weight nearest-neighbor algorithm. We are making our UnityEyes synthesis framework available online for the benefit of the research community.

Morphable face models are a powerful tool, but have previously failed to model the eye accurately due to complexities in its material and motion. We present a new multi-part model of the eye that includes a morphable model of the facial eye region, as well as an anatomy-based eyeball model. It is the first morphable model that accurately captures eye region shape, since it was built from high-quality head scans. It is also the first to allow independent eyeball movement, since we treat it as a separate part. To showcase our model we present a new method for illumination- and head-pose–invariant gaze estimation from a single RGB image. We fit our model to an image through analysis-by-synthesis, solving for eye region shape, texture, eyeball pose, and illumination simultaneously. The fitted eyeball pose parameters are then used to estimate gaze direction. Through evaluation on two standard datasets we show that our method generalizes to both webcam and high-quality camera images, and outperforms a state-of-the-art CNN method achieving a gaze estimation accuracy of 9.44∘ in a challenging user-independent scenario.

We present a computational model to predict users' spatio-temporal visual attention on WIMP-style (windows, icons, menus, pointer) graphical user interfaces. Like existing models of bottom-up visual attention in computer vision, our model does not require any eye tracking equipment. Instead, it predicts attention solely using information available to the interface, specifically users' mouse and keyboard input as well as the UI components they interact with. To study our model in a principled way, we further introduce a method to synthesize user interface layouts that are functionally equivalent to real-world interfaces, such as from Gmail, Facebook, or GitHub. We first quantitatively analyze attention allocation and its correlation with user input and UI components using ground-truth gaze, mouse, and keyboard data of 18 participants performing a text editing task. We then show that our model predicts attention maps more accurately than state-of-the-art methods. Our results underline the significant potential of spatio-temporal attention modeling for user interface evaluation, optimization, or even simulation.

Cross-device collaboration with tablets is an increasingly popular topic in HCI. Previous work has shown that tablet-only collaboration can be improved by an additional shared workspace on an interactive tabletop. However, large tabletops are costly and need space, raising the question to what extent the physical size of shared horizontal surfaces really pays off. In order to analyse the suitability of smaller-than-tabletop devices (e.g. tablets) as a low-cost alternative, we studied the effect of the size of a shared horizontal interactive workspace on users' attention, awareness, and efficiency during cross-device collaboration. In our study, 15 groups of two users executed a sensemaking task with two personal tablets (9.7") and a horizontal shared display of varying sizes (10.6", 27", and 55"). Our findings show that different sizes lead to differences in participants' interaction with the tabletop and in the groups' communication styles. To our own surprise we found that larger tabletops do not necessarily improve collaboration or sensemaking results, because they can divert users' attention away from their collaborators and towards the shared display.

In this position paper we encourage the use of eye tracking measurements to investigate users' cognitive load while interacting with a system. We start with an overview of how eye movements can be interpreted to provide insight about cognitive processes and present a descriptive model representing the relations of eye movements and cognitive load. Then, we discuss how specific characteristics of human-computer interaction (HCI) interfere with the model and impede the application of eye tracking data to measure cognitive load in visual computing. As a result, we present a refined model, embedding the characteristics of HCI into the relation of eye tracking data and cognitive load. Based on this, we argue that eye tracking should be considered as a valuable instrument to analyze cognitive processes in visual computing and suggest future research directions to tackle outstanding issues.

We develop an approach for the detection of ruins of livestock enclosures (LEs) in alpine areas captured by high-resolution remotely sensed images. These structures are usually of approximately rectangular shape and appear in images as faint fragmented contours in complex background. We address this problem by introducing a rectangularity feature that quantifies the degree of alignment of an optimal subset of extracted linear segments with a contour of rectangular shape. The rectangularity feature has high values not only for perfectly regular enclosures but also for ruined ones with distorted angles, fragmented walls, or even a completely missing wall. Furthermore, it has a zero value for spurious structures with less than three sides of a perceivable rectangle. We show how the detection performance can be improved by learning a linear combination of the rectangularity and size features from just a few available representative examples and a large number of negatives. Our approach allowed detection of enclosures in the Silvretta Alps that were previously unknown. A comparative performance analysis is provided. Among other features, our comparison includes the state-of-the-art features that were generated by pretrained deep convolutional neural networks (CNNs). The deep CNN features, although learned from a very different type of images, provided the basic ability to capture the visual concept of the LEs. However, our handcrafted rectangularity-size features showed considerably higher performance.

Immersive Analytics is an emerging research thrust investigating how new interaction and display technologies can be used to support analytical reasoning and decision making. The aim is to provide multi-sensory interfaces that support collaboration and allow users to immerse themselves in their data in a way that supports real-world analytics tasks. Immersive Analytics builds on technologies such as large touch surfaces, immersive virtual and augmented reality environments, sensor devices and other, rapidly evolving, natural user interface devices. While there is a great deal of past and current work on improving the display technologies themselves, our focus in this position paper is on bringing attention to the higher-level usability and design issues in creating effective user interfaces for data analytics in immersive environments.

Gaze-tracking technology is used increasingly to determinehow and which information is accessed and processed ina given interface environment, such as in-vehicle informa-tion systems in automobiles. Typically, fixations on regionsof interest (e.g., windshield, GPS) are treated as an indica-tion that the underlying information has been attended toand is, thus, vital to the task. Therefore, decisions such asoptimal instrument placement are often made on the basisof the distribution of recorded fixations. In this paper, webriefly introduce gaze-tracking methods for in-vehicle moni-toring, followed by a discussion on the relationship betweengaze and user-attention. We posit that gaze-tracking datacan yield stronger insights on the utility of novel regions-of-interests if they are considered in terms of their devia-tion from basic gaze patterns. In addition, we suggest howEEG recordings could complement gaze-tracking data andraise outstanding challenges in its implementation. It is con-tended that gaze-tracking is a powerful tool for understand-ing how visual information is processed in a given environ-ment, provided it is understood in the context of a modelthat first specifies the task that has to be carried o

A control schema for a human-machine system allows the human operator to be integrated as a mathematical description in a closed-loop control system, i.e., a pilot in an aircraft. Such an approach typically assumes that error feedback is perfectly communicated to the pilot who is responsible for tracking a single flight variable. However, this is unlikely to be true in a flight simulator or a real flight environment. This paper discusses different aspects that pertain to error visualization and the pilot’s ability in seeking out relevant information across a range of flight variables.

Eye-movements are typically measured with video cameras and image recognition algorithms. Unfortunately, these systems are susceptible to changes in illumination during measurements. Electrooculography (EOG) is another approach for measuring eye-movements that does not suffer from the same weakness. Here, we introduce and compare two methods that allow us to extract the dwells of our participants from EOG signals under presentation conditions that are too difficult for optical eye tracking. The first method is unsupervised and utilizes density-based clustering. The second method combines the optical eye-tracker’s methods to determine fixations and saccades with unsupervised clustering. Our results show that EOG can serve as a sufficiently precise and robust substitute for optical eye tracking, especially in studies with changing lighting conditions. Moreover, EOG can be recorded alongside electroencephalography (EEG) without additional effort.

We present a novel approach for handling sampling and compression in remote visualization in an integrative fashion. As adaptive sampling and compression share the same underlying concepts and criteria, the times spent for visualization and transfer can be balanced directly to optimize the image quality that can be achieved within a prescribed time window. Our dynamic adjustments regarding adaptive sampling, compression, and balancing, employ regression analysis-based error estimation which is carried out individually for each image block of a visualization frame. Our approach is tuned for high parallel efficiency in GPU-based remote visualization. We demonstrate its utility within a prototypical remote volume visualization pipeline by means of different datasets and configurations.

Computing the similarity between objects is a central task for many applications in the field of information retrieval and data mining. For finding k-nearest neighbors, typically a ranking is computed based on a predetermined set of data dimensions and a distance function, constant over all possible queries. However, many high-dimensional feature spaces contain a large number of dimensions, many of which may contain noise, irrelevant, redundant, or contradicting information. More specifically, the relevance of dimensions may depend on the query object itself, and in general, different dimension sets (subspaces) may be appropriate for a query. Approaches for feature selection or -weighting typically provide a global subspace selection, which may not be suitable for all possibly queries. In this position paper, we frame a new research problem, called subspace nearest neighbor search, aiming at multiple query-dependent subspaces for nearest neighbor search. We describe relevant problem characteristics, relate to existing approaches, and outline potential research directions.

The authors describe the creation of a tridimensional fly-through animation across the largest map of galaxies to date. This project represented a challenge: creating a scientifically accurate representation of the galaxy distribution that was aesthetically pleasing. The animation shows almost half a million galaxies as the viewer travels through the vast intergalactic regions, giving a glimpse of the sheer size of the universe.

We can observe a continuous trend for using larger screens with higher resolutions and greater pixel density. With advances in hard- and software technology, wall-sized displays for daily office work are already on the horizon. We assume that there will be no hard paradigm change in interaction techniques in the near future. Therefore, new concepts for wall-sized displays will be included in existing products. Designing interaction concepts for wall-sized displays in an office environment is a challenging task. Most crucial is designing appropriate input techniques. Moving the mouse pointer from one corner to another over a longer distance is cumbersome. However, pointing with a mouse is precise and commonplace. We propose using mid-air gestures to support input with mouse and keyboard on large displays. In particular, we designed a gesture set for manipulating regular windows.

Due to advances in technology large displays with very high resolution started to become affordable for daily work. Today it is possible to build display walls with a pixel density that is comparable to standard office screens. Previous work indicates that physical navigation enables a deeper engagement with the data set. In particular, the visibility of detailed data subsets on large screens supports the user's work and understanding of large data. In contrast to previous work we explore how users' performance scales with an increasing amount of large display space when working with text documents. In a controlled experiment, we determine participants' performance when searching for titles and images in large text documents using one to six 50" 4K monitors. Our results show that the users' visual search performance does not linearly increase with an increasing amount of display space.

Large screen displays are part of many future visions, such as i-LAND that describes the possible workspace of the future. Research showed that wall-sized screens provide clear benefits for data exploration, collaboration and organizing work in office environments. With the increase of computational power and falling display prices wall-sized screens currently make the step out of research labs and specific settings into office environments and private life. Today, there is no standard set of interaction techniques for interacting with wall-sized displays and it is even unclear if a single mode of input is suitable for all potential applications. In this workshop, we will bring together researchers from academia and industry who work on large screens. Together, we will survey current research directions, review promising interaction techniques, and identify the underlying fundamental research challenges.

Analysis and visualization of eye movement data from eye tracking studies typically take into account gazes, fixations,and saccades of both eyes filtered and fused into a combined eye. Although this is a valid strategy, we argue that it is also worthinvestigating low-level eye tracking data prior to high-level analysis, since today’s eye tracking systems measure and infer data fromboth eyes separately. In this work, we present an approach that supports visual analysis and cleansing of low-level time-varying datafor a wide range of eye tracking experiments. The visualization helps researchers get insights into the quality in terms of uncertainty—not only for both eyes in combination but each eye individually. Furthermore, we discuss uncertainty originating from eye tracking,how to reveal it for visualization and illustrate its usefulness using our approach by applying it to eye movement data formerly recordedwith a Tobii T60XL stationary eye tracker using a prototypical implementation.

The Uncanny Valley hypothesis describes the negative emotional response of human observers that is evoked by artificial figures or prostheses with a human-like appearance. Many studies have pointed out the meaning of facial features, but did not further investigate the importance of eye contact and its role in decision making about artificial faces. In this study we recorded the number and duration of fixations of participants (N = 53) and recorded gaze movements and fixations on different areas of interest, as well as the response time when a participant judged a face as non-human. In a subsequent questionnaire, we grasped subjective ratings. In our analysis we found correlations between the likeability and the duration of eye fixations on the eye area. The gaze sequences show that artificial faces were visually processed similar to the real ones and mostly remained not assessed as artificial as long as the eye regions were not considered.

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.