Publications and Awards

Abstract

Recently, neural network for scene flow estimation show impressive results on automotive data such as the KITTI benchmark. However, despite of using sophisticated rigidity assumptions and parametrizations, such networks are typically limited to only two frame pairs which does not allow them to exploit temporal information. In our paper we address this shortcoming by proposing a novel multi-frame approach that considers an additional preceding stereo pair. To this end, we proceed in two steps: Firstly, building upon the recent RAFT-3D approach, we develop an improved two-frame baseline by incorporating an advanced stereo method. Secondly, and even more importantly, exploiting the specific modeling concepts of RAFT-3D, we propose a U-Net architecture that performs a fusion of forward and backward flow estimates and hence allows to integrate temporal information on demand. Experiments on the KITTI benchmark do not only show that the advantages of the improved baseline and the temporal fusion approach complement each other, they also demonstrate that the computed scene flow is highly accurate. More precisely, our approach ranks second overall and first for the even more challenging foreground objects, in total outperforming the original RAFT-3D method by more than 16%. Code is available at https://github.com/cv-stuttgart/M-FUSE.

Abstract

We present an exploratory study on the accessibility of images in publications when viewed with color vision deficiencies (CVDs). The study is based on 1,710 images sampled from a visualization dataset (VIS30K) over five years. We simulated four CVDs on each image. First, four researchers (one with a CVD) identified existing issues and helpful aspects in a subset of the images. Based on the resulting labels, 200 crowdworkers provided  30,000 ratings on present CVD issues in the simulated images. We analyzed this data for correlations, clusters, trends, and free text comments to gain a first overview of paper figure accessibility. Overall, about 60 % of the images were rated accessible. Furthermore, our study indicates that accessibility issues are subjective and hard to detect. On a meta-level, we reflect on our study experience to point out challenges and opportunities of large-scale accessibility studies for future research directions.

Abstract

Over the last few years online platforms for running psychology experiments beyond simple questionnaires and surveys have become increasingly popular. This trend has especially increased after many laboratory facilities had to temporarily avoid in-person data collection following COVID-19-related lockdown regulations. Yet, while offering a valid alternative to in-person experiments in many cases, platforms for online experiments are still not a viable solution for a large part of human-based behavioral research. Two situations in particular pose challenges: First, when the research question requires design features or participant interaction which exceed the customization capability provided by the online platform; and second, when variation among hardware characteristics between participants results in an inadmissible confounding factor. To mitigate the effects of these limitations, we developed ReActLab (Remote Action Laboratory), a framework for programming remote, browser-based experiments using freely available and open-source JavaScript libraries. Since the experiment is run entirely within the browser, our framework allows for portability to any operating system and many devices. In our case, we tested our approach by running experiments using only a specific model of Android tablet. Using ReActLab with this standardized hardware allowed us to optimize our experimental design for our research questions, as well as collect data outside of laboratory facilities without introducing setup variation among participants. In this paper, we describe our framework and show examples of two different experiments carried out with it: one consisting of a visuomotor adaptation task, the other of a visual localization task. Through comparison with results obtained from similar tasks in in-person laboratory settings, we discuss the advantages and limitations for developing browser-based experiments using our framework.

Abstract

Modern machines continuously log status reports over long periods of time, which are valuable data to optimize working routines. Data visualization is a commonly used tool to gain insights into these data, mostly in retrospective, e.g. to determine causal dependencies between faults of different machines. We present an approach to bring such visual analyses to the shop floor to support reacting to faults in real time. This approach combines combines spatio-temporal analyses of time series using a handheld touch device with augmented reality for live monitoring. Important information augments machines directly in their real-world context and detailed logs of current and historical events are displayed on the handheld device. In collaboration with an industry partner, we designed and tested our approach on a live production line to obtain feedback from operators. We compare our approach for monitoring and analysis with existing solutions that are currently deployed.

Abstract

Spatially resolved transcriptomics is an emerging class of high-throughput technologies that enable biologists to systematically investigate the expression of genes along with spatial information. Upon data acquisition, one major hurdle is the subsequent interpretation and visualization of the datasets acquired. To address this challenge, VR-Cardiomicsis presented, which is a novel data visualization system with interactive functionalities designed to help biologists interpret spatially resolved transcriptomic datasets. By implementing the system in two separate immersive environments, fish tank virtual reality (FTVR) and head-mounted display virtual reality (HMD-VR), biologists can interact with the data in novel ways not previously possible, such as visually exploring the gene expression patterns of an organ, and comparing genes based on their 3D expression profiles. Further, a biologist-driven use-case is presented, in which immersive environments facilitate biologists to explore and compare the heart expression profiles of different genes.

Abstract

The visualization of results while the simulation is running is increasingly common in extreme scale computing environments. We present a novel approach for in situ generation of image databases to achieve cost savings on supercomputers. Our approach, a hybrid between traditional inline and in transit techniques, dynamically distributes visualization tasks between simulation nodes and visualization nodes, using probing as a basis to estimate rendering cost. Our hybrid design differs from previous works in that it creates opportunities to minimize idle time from four fundamental types of inefficiency: variability, limited scalability, overhead, and rightsizing. We demonstrate our results by comparing our method against both inline and in transit methods for a variety of configurations, including two simulation codes and a scaling study that goes above 19K cores. Our findings show that our approach is superior in many configurations. As in situ visualization becomes increasingly ubiquitous, we believe our technique could lead to significant amounts of reclaimed cycles on supercomputers.

Abstract

Virtual reality is increasingly used for tasks such as work and education. Thus, rendering scenarios that do not interfere with such goals and deplete user experience are becoming progressively more relevant. We present a physiologically adaptive system that optimizes the virtual environment based on physiological arousal, i.e., electrodermal activity. We investigated the usability of the adaptive system in a simulated social virtual reality scenario. Participants completed an n-back task (primary) and a visual detection (secondary) task. Here, we adapted the visual complexity of the secondary task in the form of the number of non-player characters of the secondary task to accomplish the primary task. We show that an adaptive virtual reality can improve users’ comfort by adapting to physiological arousal regarding the task complexity. Our findings suggest that physiologically adaptive virtual reality systems can improve users’ experience in a wide range of scenarios.

Abstract

Adaptive visualization and interfaces pervade our everyday tasks to improve interaction from the point of view of user performance and experience. This approach allows using several user inputs, whether physiological, behavioral, qualitative, or multimodal combinations, to enhance the interaction. Due to the multitude of approaches, we outline the current research trends of inputs used to adapt visualizations and user interfaces. Moreover, we discuss methodological approaches used in mixed reality, physiological computing, visual analytics, and proficiency-aware systems. With this work, we provide an overview of the current research in adaptive systems.

Abstract

Dynamic balance is an essential skill for the human upright gait; therefore, regular balance training can improve postural control and reduce the risk of injury. Even slight variations in walking conditions like height or ground conditions can significantly impact walking performance. Virtual reality is used as a helpful tool to simulate such challenging situations. However, there is no agreement on design strategies for balance training in virtual reality under stressful environmental conditions such as height exposure. We investigate how two different training strategies, imitation learning, and gamified learning, can help dynamic balance control performance across different stress conditions. Moreover, we evaluate the stress response as indexed by peripheral physiological measures of stress, perceived workload, and user experience. Both approaches were tested against a baseline of no instructions and against each other. Thereby, we show that a learning-by-imitation approach immediately helps dynamic balance control, decreases stress, improves attention focus, and diminishes perceived workload. A gamified approach can lead to users being overwhelmed by the additional task. Finally, we discuss how our approaches could be adapted for balance training and applied to injury rehabilitation and prevention.

Abstract

Authoring situated visualizations in-situ is challenging due to the need of writing code in a mobile and highly dynamic fashion. To provide better support for that, we define requirements for text input methods that target situated visualization authoring. We identify wearable chorded keyboards as a potentially suitable method that fulfills some of these requirements. To further investigate this approach, we tailored a chorded keyboard device to visualization authoring, developed a learning application, and conducted a pilot user study. Our results confirm that learning a high number of chords is the main barrier for adoption, as in other application areas. Based on that, we discuss ideas on how chorded keyboards with a strongly reduced alphabet, hand gestures, and voice recognition might be used as a viable, multi-modal support for authoring situated visualizations in-situ.

Abstract

Augmented reality (AR) can create the illusion of being virtually co-located during remote collaboration, e.g., by visualizing remote co-workers as avatars. However, spatial awareness of each other's activities is limited as physical spaces, including the position of physical devices, are often incongruent. Therefore, alignment methods are needed to support activities on physical devices. In this paper, we present the concept of Re-locations, a method for enabling remote collaboration with augmented reality in incongruent spaces. The idea of the concept is to enrich remote collaboration activities on multiple physical devices with attributes of co-located collaboration such as spatial awareness and spatial referencing by locally relocating remote user representations to user-defined workspaces. We evaluated the Re-locations concept in an explorative user study with dyads using an authentic, collaborative task. Our findings indicate that Re-locations introduce attributes of co-located collaboration like spatial awareness and social presence. Based on our findings, we provide implications for future research and design of remote collaboration systems using AR.

Abstract

We present RagRug, an open-source toolkit for situated analytics. The abilities of RagRug go beyond previous immersive analytics toolkits by focusing on specific requirements emerging when using augmented reality (AR) rather than virtual reality. RagRug combines state of the art visual encoding capabilities with a comprehensive physical-virtual model, which lets application developers systematically describe the physical objects in the real world and their role in AR. We connect AR visualization with data streams from the Internet of Things using distributed dataflow. To this aim, we use reactive programming patterns so that visualizations become context-aware, i.e., they adapt to events coming in from the environment. The resulting authoring system is low-code; it emphasises describing the physical and the virtual world and the dataflow between the elements contained therein. We describe the technical design and implementation of RagRug, and report on five example applications illustrating the toolkit's abilities.

Abstract

This chapter presents a study of parameter adaptation in situ, exploring the resulting trade-offs in rendering quality and workload distribution. Four different use cases are analyzed with respect to configuration changes. First, the performance impact of load balancing and resource allocation variants on both simulation and visualization is investigated using the MegaMol framework. Its loose coupling scheme and architecture enable minimally invasive in situ operation without impacting the stability of the simulation with (potentially) experimental visualization code. Second, Volumetric Depth Images (VDIs) are considered: a compact, view-dependent intermediate representation that can efficiently be generated and used for post hoc exploration. A study of their inherent trade-offs regarding size, quality, and generation time provides the basis for parameter optimization. Third, streaming for remote visualization allows a user to monitor the progress of a simulation and to steer visualization parameters. Compression settings are adapted dynamically based on predictions via convolutional neural networks across different parts of images to achieve high frame rates for high-resolution displays like powerwalls. Fourth, different performance prediction models for volume rendering address offline scenarios (like hardware acquisition planning) as well as dynamic adaptation of parameters and load balancing. Finally, the chapter concludes by summarizing overarching approaches and challenges, discussing the potential role that adaptive approaches can play in increasing the efficiency of in situ visualization.

Abstract

Research data curation is the act of carefully preparing research data and artifacts for sharing and long-term preservation. Research data management is centrally implemented and formally defined in a data management plan to enable data curation. In tandem, data curation and management facilitate research repeatability. In contrast to other research fields, data curation and management in visualization are not yet part of the researcher’s compendium. In this position paper, we discuss the unique challenges visualization faces and propose how data curation can be practically realized. We share eight lessons learned in managing data in two large research consortia, outline the larger curation workflow, and define the typical roles. We complement our lessons with minimum criteria for selecting a suitable data repository and five challenging scenarios that occur in practice. We conclude with a vision of how the visualization research community can pave the way for new curation standards.

Abstract

The confusion matrix, a ubiquitous visualization for helping people evaluate machine learning models, is a tabular layout that compares predicted class labels against actual class labels over all data instances. We conduct formative research with machine learning practitioners at Apple and find that conventional confusion matrices do not support more complex data-structures found in modern-day applications, such as hierarchical and multi-output labels. To express such variations of confusion matrices, we design an algebra that models confusion matrices as probability distributions. Based on this algebra, we develop Neo, a visual analytics system that enables practitioners to flexibly author and interact with hierarchical and multi-output confusion matrices, visualize derived metrics, renormalize confusions, and share matrix specifications. Finally, we demonstrate Neo’s utility with three model evaluation scenarios that help people better understand model performance and reveal hidden confusions.

Abstract

Data used to train supervised machine learning models are commonly split into independent training, validation, and test sets. This paper illustrates that complex data leakage cases have occurred in the no-reference image and video quality assessment literature. Recently, papers in several journals reported performance results well above the best in the field. However, our analysis shows that information from the test set was inappropriately used in the training process in different ways and that the claimed performance results cannot be achieved. When correcting for the data leakage, the performances of the approaches drop even below the state-of-the-art by a large margin. Additionally, we investigate end-to-end variations to the discussed approaches, which do not improve upon the original.

Abstract

Augmented Reality (AR) embeds virtual content in physical spaces, including virtual agents that are known to exert a social presence on users. Existing design guidelines for AR rarely consider the social implications of an agent’s personal space (PS) and that it can impact user behavior and arousal. We report an experiment (N=54) where participants interacted with agents in an AR art gallery scenario. When participants approached six virtual agents (i.e., two males, two females, a humanoid robot, and a pillar) to ask for directions, we found that participants respected the agents’ PS and modulated interpersonal distances according to the human-like agents’ perceived gender. When participants were instructed to walk through the agents, we observed heightened skin-conductance levels that indicate physiological arousal. These results are discussed in terms of proxemic theory that result in design recommendations for implementing pervasive AR experiences with virtual agents.

Abstract

The nascent field of mixed reality is seeing an ever-increasing need for user studies and field evaluation, which are particularly challenging given device heterogeneity, diversity of use, and mobile deployment. Immersive analytics tools have recently emerged to support such analysis in situ, yet the complexity of the data also warrants an ex-situ analysis using more traditional non-immersive visual analytics setups. To bridge the gap between both approaches, we introduce ReLive: a mixed-immersion visual analytics framework for exploring and analyzing mixed reality user studies. ReLive combines an in-situ virtual reality view with a complementary ex-situ desktop view. While the virtual reality view allows users to relive interactive spatial recordings replicating the original study, the synchronized desktop view provides a familiar interface for analyzing aggregated data. We validated our concepts in a two-step evaluation consisting of a design walkthrough and an empirical expert user study.

Abstract

We present an extension of multidimensional scaling (MDS) to uncertain data, facilitating uncertainty visualization of multidimensional data. Our approach uses local projection operators that map high-dimensional random vectors to low-dimensional space to formulate a generalized stress. In this way, our generic model supports arbitrary distributions and various stress types. We use our uncertainty-aware multidimensional scaling (UAMDS) concept to derive a formulation for the case of normally distributed random vectors and a squared stress. The resulting minimization problem is numerically solved via gradient descent. We complement UAMDS by additional visualization techniques that address the sensitivity and trustworthiness of dimensionality reduction under uncertainty. With several examples, we demonstrate the usefulness of our approach and the importance of uncertainty-aware techniques.

Abstract

This paper provides a brief overview of uncertainty visualization along with some fundamental considerations on uncertainty propagation and modeling. Starting from the visualization pipeline, we discuss how the different stages along this pipeline can be affected by uncertainty and how they can deal with this and propagate uncertainty information to subsequent processing steps. We illustrate recent advances in the field with a number of examples from a wide range of applications: uncertainty visualization of hierarchical data, multivariate time series, stochastic partial differential equations, and data from linguistic annotation.

Abstract

Many classical and learning-based optical flow methods rely on hierarchical concepts to improve both accuracy and robustness. However, one of the currently most successful approaches -- RAFT -- hardly exploits such concepts. In this work, we show that multi-scale ideas are still valuable. More precisely, using RAFT as a baseline, we propose a novel multi-scale neural network that combines several hierarchical concepts within a single estimation framework. These concepts include (i) a partially shared coarse-to-fine architecture, (ii) multi-scale features, (iii) a hierarchical cost volume and (iv) a multi-scale multi-iteration loss. Experiments on MPI Sintel and KITTI clearly demonstrate the benefits of our approach. They show not only substantial improvements compared to RAFT, but also state-of-the-art results -- in particular in non-occluded regions.

Abstract

Creating comprehensible visualizations of highly overlapping set-typed data is a challenging task due to its complexity. To facilitate insights into set connectivity and to leverage semantic relations between intersections, we propose a fast two-step layout technique for Euler diagrams that are both well-matched and well-formed. Our method conforms to established form guidelines for Euler diagrams regarding semantics, aesthetics, and readability. First, we establish an initial ordering of the data, which we then use to incrementally create a planar, connected, and monotone dual graph representation. In the next step, the graph is transformed into a circular layout that maintains the semantics and yields simple Euler diagrams with smooth curves. When the data cannot be represented by simple diagrams, our algorithm always falls back to a solution that is not well-formed but still well-matched, whereas previous methods often fail to produce expected results. We show the usefulness of our method for visualizing set-typed data using examples from text analysis and infographics. Furthermore, we discuss the characteristics of our approach and evaluate our method against state-of-the-art methods.

Abstract

Immersive Analytics is concerned with the systematic examination of the benefits and challenges of using immersive environments for data analysis, and the development of corresponding designs that improve the quality and efficiency of the analysis process. While immersive technologies are now broadly available, practical solutions haven’t received broad acceptance in real-world applications outside of several core areas, and proper guidelines on the design of such solutions are still under development. Both fundamental research and applications bring together topics and questions from several fields, and open a wide range of directions regarding underlying theory, evidence from user studies, and practical solutions tailored towards the requirements of application areas. We give an overview on the concepts, topics, research questions, and challenges.

Abstract

Comparing mobile eye tracking data from multiple participants without information about areas of interest (AOIs) is challenging because of individual timing and coordinate systems. We present a technique, the gaze spiral, that visualizes individual recordings based on image content of the stimulus. The spiral layout of the slitscan visualization is used to create a compact representation of scanpaths. The visualization provides an overview of multiple recordings even for long time spans and helps identify and annotate recurring patterns within recordings. The gaze spirals can also serve as glyphs that can be projected to 2D space based on established scanpath metrics in order to interpret the metrics and identify groups of similar viewing behavior. We present examples based on two egocentric datasets to demonstrate the effectiveness of our approach for annotation and comparison tasks. Our examples show that the technique has the potential to let users compare even long-term recordings of pervasive scenarios without manual annotation.

Abstract

In medicine, patients can obtain real benefits from a sham treatment. These benefits are known as the placebo effect. We report two experiments (Experiment I: N=369; Experiment II: N=100) demonstrating a placebo effect in adaptive interfaces. Participants were asked to solve word puzzles while being supported by no system or an adaptive AI interface. All participants experienced the same word puzzle difficulty and had no support from an AI throughout the experiments. Our results showed that the belief of receiving adaptive AI support increases expectations regarding the participant’s own task performance, sustained after interaction. These expectations were positively correlated to performance, as indicated by the number of solved word puzzles. We integrate our findings into technological acceptance theories and discuss implications for the future assessment of AI-based user interfaces and novel technologies. We argue that system descriptions can elicit placebo effects through user expectations biasing the results of user-centered studies.

Abstract

A large number of modern video background modeling algorithms deal with computational costly minimization problems that often need parameter adjustments. While in most cases spatial and temporal constraints are added artificially to the minimization process, our approach is to exploit Dynamic Mode Decomposition (DMD), a spectral decomposition technique that naturally extracts spatio-temporal patterns from data. Applied to video data, DMD can compute background models. However, the original DMD algorithm for background modeling is neither efficient nor robust. In this paper, we present an equivalent reformulation with constraints leading to a more suitable decomposition into fore- and background. Due to the reformulation, which uses sparse and low-dimensional structures, an efficient and robust algorithm is derived that computes accurate background models. Moreover, we show how our approach can be extended to RGB data, data with periodic parts, and streaming data enabling a versatile use.

Abstract

Redundancy matrices provide insights into the load carrying behavior of statically indeterminate structures. This information can be employed for the design and analysis of structures with regard to certain objectives, for example reliability, robustness, or adaptability. In this context, the structure is often iteratively examined with the help of slight adjustments. However, this procedure generally requires a high computational effort for the recalculation of the redundancy matrix due to the necessity of costly matrix operations. This paper addresses this problem by providing generic algebraic formulations for efficiently updating the redundancy matrix (and related matrices). The formulations include various modifications like adding, removing, and exchanging elements and are applicable to truss and frame structures. With several examples, we demonstrate the interaction between the formulas and their mechanical interpretation. Finally, a performance test for a scaleable structure is presented.

Abstract

Frequency-based decomposition of time series data is used in many visualization applications. Most of these decomposition methods (such as Fourier transform or singular spectrum analysis) only provide interaction via pre- and post-processing, but no means to influence the core algorithm. A method that also belongs to this class is Dynamic Mode Decomposition (DMD), a spectral decomposition method that extracts spatio-temporal patterns from data. In this paper, we incorporate frequency-based constraints into DMD for an adaptive decomposition that leads to user-controllable visualizations, allowing analysts to include their knowledge into the process. To accomplish this, we derive an equivalent reformulation of DMD that implicitly provides access to the eigenvalues (and therefore to the frequencies) identified by DMD. By utilizing a constrained minimization problem customized to DMD, we can guarantee the existence of desired frequencies by minimal changes to DMD. We complement this core approach by additional techniques for constrained DMD to facilitate explorative visualization and investigation of time series data. With several examples, we demonstrate the usefulness of constrained DMD and compare it to conventional frequency-based decomposition methods.

Abstract

The picturewise just noticeable difference (PJND) for a given image, compression scheme, and subject is the smallest distortion level that the subject can perceive when the image is compressed with this compression scheme. The PJND can be used to determine the compression level at which a given proportion of the population does not notice any distortion in the compressed image. To obtain accurate and diverse results, the PJND must be determined for a large number of subjects and images. This is particularly important when experimental PJND data are used to train deep learning models that can predict a probability distribution model of the PJND for a new image. To date, such subjective studies have been carried out in laboratory environments. However, the number of participants and images in all existing PJND studies is very small because of the challenges involved in setting up laboratory experiments. To address this limitation, we develop a framework to conduct PJND assessments via crowdsourcing. We use a new technique based on slider adjustment and a flicker test to determine the PJND. A pilot study demonstrated that our technique could decrease the study duration by 50% and double the perceptual sensitivity compared to the standard binary search approach that successively compares a test image side by side with its reference image. Our framework includes a robust and systematic scheme to ensure the reliability of the crowdsourced results. Using 1,008 source images and distorted versions obtained with JPEG and BPG compression, we apply our crowdsourcing framework to build the largest PJND dataset, KonJND-1k (Konstanz just noticeable difference 1k dataset). A total of 503 workers participated in the study, yielding 61,030 PJND samples that resulted in an average of 42 samples per source image. The KonJND-1k dataset is available at http://database.mmsp-kn.de/konjnd-1k-database.html

Abstract

Underwater image enhancement (UIE) is essential for a high-quality underwater optical imaging system. While a number of UIE algorithms have been proposed in recent years, there is little study on image quality assessment (IQA) of enhanced underwater images. In this paper, we conduct the first crowdsourced subjective IQA study on enhanced underwater images. We chose ten state-of-the-art UIE algorithms and applied them to yield enhanced images from an underwater image benchmark. Their latent quality scales were reconstructed from pair comparison. We demonstrate that the existing IQA metrics are not suitable for assessing the perceived quality of enhanced underwater images. In addition, the overall performance of 10 UIE algorithms on the benchmark is ranked by the newly proposed simulated pair comparison of the methods.

Abstract

Convolutional neural networks (CNNs) have significantly advanced computational modelling for saliency prediction. However, accurately simulating the mechanisms of visual attention in the human cortex remains an academic challenge. It is critical to integrate properties of human vision into the design of CNN architectures, leading to perceptually more relevant saliency prediction. Due to the inherent inductive biases of CNN architectures, there is a lack of sufficient long-range contextual encoding capacity. This hinders CNN-based saliency models from capturing properties that emulate viewing behaviour of humans. Transformers have shown great potential in encoding long-range information by leveraging the self-attention mechanism. In this paper, we propose a novel saliency model that integrates transformer components to CNNs to capture the long-range contextual visual information. Experimental results show that the transformers provide added value to saliency prediction, enhancing its perceptual relevance in the performance. Our proposed saliency model using transformers has achieved superior results on public benchmarks and competitions for saliency prediction models. The source code of our proposed saliency model TranSalNet is available at: https://github.com/LJOVO/TranSalNet.

Abstract

In this article, we discuss how Visualization (VIS) with Machine Learning (ML) could mutually benefit from each other. We do so through the lens of our own experience working at this intersection for the last decade. Particularly we focus on describing how VIS supports explaining ML models and aids ML-based Dimensionality Reduction techniques in solving tasks such as parameter space analysis. In the other direction, we discuss approaches showing how ML helps improve VIS, such as applying ML-based automation to improve visualization design. Based on the examples and our own perspective, we describe a number of open research challenges that we frequently encountered in our endeavors to combine ML and VIS.

Abstract

Computational models are often employed in systems biology to study the dynamic behaviours of complex systems. With the rise in the number of computational models, finding ways to improve the reusability of these models and their ability to reproduce virtual experiments becomes critical. Correct and effective model annotation in community-supported and standardised formats is necessary for this improvement. Here, we present recent efforts toward a common framework for annotated, accessible, reproducible and interoperable computational models in biology, and discuss key challenges of the field.

Abstract

In this work, we present and study a generalized family of differentiable renderers. We discuss from scratch which components are necessary for differentiable rendering and formalize the requirements for each component. We instantiate our general differentiable renderer, which generalizes existing differentiable renderers like SoftRas and DIB-R, with an array of different smoothing distributions to cover a large spectrum of reasonable settings. We evaluate an array of differentiable renderer instantiations on the popular ShapeNet 3D reconstruction benchmark and analyze the implications of our results. Surprisingly, the simple uniform distribution yields the best overall results when averaged over 13 classes; in general, however, the optimal choice of distribution heavily depends on the task.

Abstract

Reconstructing the 3D geometry of an object from an image is a major challenge in computer vision. Recently introduced differentiable renderers can be leveraged to learn the 3D geometry of objects from 2D images, but those approaches require additional supervision to enable the renderer to produce an output that can be compared to the input image. This can be scene information or constraints such as object silhouettes, uniform backgrounds, material, texture, and lighting. In this paper, we propose an approach that enables a differentiable rendering-based learning of 3D objects from images with backgrounds without the need for silhouette supervision. Instead of trying to render an image close to the input, we propose an adversarial style-transfer and domain adaptation pipeline that allows to translate the input image domain to the rendered image domain. This allows us to directly compare between a translated image and the differentiable rendering of a 3D object reconstruction in order to train the 3D object reconstruction network. We show that the approach learns 3D geometry from images with backgrounds and provides a better performance than constrained methods for single-view 3D object reconstruction on this task.

Abstract

Recent approaches for surgical activity localization rely on motion features derived from the optical flow (OF). However, although they consider state-of-the-art CNNs when computing the OF, they typically resort to pre-trained implementations which are domain-unaware. We address this problem in two ways: (i) Using the pre-trained OF-CNN of recent localization approach, we analyze the impact of video properties such as reflections, motion and blur on the quality of the OF from neurosurgical data. (ii) Based on this analysis, we design a specifically tailored synthetic training dataset which allows us to customize the pre-trained OF-CNN for surgical activity localization. Our evaluation clearly shows the benefit of this customization approach. It not only leads to an improved accuracy of the OF itself but, even more importantly, also to an improved performance for the actual localization task.

Abstract

We introduce relaxed dot plots as an improvement of nonlinear dot plots for unit visualization. Our plots produce more faithful data representations and reduce moire´ effects. Their contour is based on a customized kernel frequency estimation to match the shape of the distribution of underlying data values. Previous nonlinear layouts introduce column-centric nonlinear scaling of dot diameters for visualization of high-dynamic-range data with high peaks. We provide a mathematical approach to convert that column-centric scaling to our smooth envelope shape. This formalism allows us to use linear, root, and logarithmic scaling to find ideal dot sizes. Our method iteratively relaxes the dot layout for more correct and aesthetically pleasing results. To achieve this, we modified Lloyd's algorithm with additional constraints and heuristics. We evaluate the layouts of relaxed dot plots against a previously existing nonlinear variant and show that our algorithm produces less error regarding the underlying data while establishing the blue noise property that works against moire´ effects. Further, we analyze the readability of our relaxed plots in three crowd-sourced experiments. The results indicate that our proposed technique surpasses traditional dot plots.

Abstract

We propose a three-step concept and visual design for supporting the visual exploration of high-dimensional data in scatterplots through eye-tracking. First, we extract subsets in the underlying data using existing classifications, automated clustering algorithms, or eye-tracking. For the latter, we map gaze to the underlying data dimensions in the scatterplot. Clusters of data points that have been the focus of the viewers’ gaze are marked as clusters of interest (eye-mind hypothesis). In a second step, our concept extracts various properties from statistics and scagnostics from the clusters. The third step uses these measures to compare the current data clusters from the main scatterplot to the same data in other dimensions. The results enable analysts to retrieve similar or dissimilar views as guidance to explore the entire data set. We provide a proof-of-concept implementation as a test bench and describe a use case to show a practical application and initial results.

Abstract

Blind inpainting algorithms based on deep learning architectures have shown a remarkable performance in recent years, typically outperforming model-based methods both in terms of image quality and run time. However, neural network strategies typically lack a theoretical explanation, which contrasts with the well-understood theory underlying model-based methods. In this work, we leverage the advantages of both approaches by integrating theoretically founded concepts from transform domain methods and sparse approximations into a CNN-based approach for blind image inpainting. To this end, we present a novel strategy to learn convolutional kernels that applies a specifically designed filter dictionary whose elements are linearly combined with trainable weights. Numerical experiments demonstrate the competitiveness of this approach. Our results show not only an improved inpainting quality compared to conventional CNNs but also significantly faster network convergence within a lightweight network design. Our code is available at https://github.com/cv-stuttgart/SDPF_Blind-Inpainting.

Abstract

Current adversarial attacks for motion estimation (optical flow) optimize small per-pixel perturbations, which are unlikely to appear in the real world. In contrast, we exploit a real-world weather phenomenon for a novel attack with adversarially optimized snow. At the core of our attack is a differentiable renderer that consistently integrates photorealistic snowflakes with realistic motion into the 3D scene. Through optimization we obtain adversarial snow that significantly impacts the optical flow while being indistinguishable from ordinary snow. Surprisingly, the impact of our novel attack is largest on methods that previously showed a high robustness to small L_p perturbations.

Abstract

Recent optical flow methods are almost exclusively judged in terms of accuracy, while their robustness is often neglected. Although adversarial attacks offer a useful tool to perform such an analysis, current attacks on optical flow methods focus on real-world attacking scenarios rather than a worst case robustness assessment. Hence, in this work, we propose a novel adversarial attack - the Perturbation-Constrained Flow Attack (PCFA) - that emphasizes destructivity over applicability as a real-world attack. PCFA is a global attack that optimizes adversarial perturbations to shift the predicted flow towards a specified target flow, while keeping the L2 norm of the perturbation below a chosen bound. Our experiments demonstrate PCFA's applicability in white- and black-box settings, and show it finds stronger adversarial samples than previous attacks. Based on these strong samples, we provide the first joint ranking of optical flow methods considering both prediction quality and adversarial robustness, which reveals state-of-the-art methods to be particularly vulnerable.

Abstract

The ubiquity of mobile devices in peoples’ everyday life makes them a feasible tool for language learning. Learning anytime and anywhere creates great flexibility but comes with the inherent risk of infrequent learning and learning in interruption-prone environments. No matter the length of the learning break, it can negatively affect knowledge consolidation and recall. This work presents the design and implementation of memory cues to support task resumption in mobile language learning applications and two evaluations to assess their impact on user experience. An initial laboratory experiment (N=15) revealed that while the presentation of the cues had no significant effect on objective performance measures (task completion time and error rate), the users still perceived the cues as helpful and would appreciate them in a mobile learning app. A follow-up study (N=16) investigated revised cue designs in a real-world field setting and found that users particularly appreciated our interactive test cue design. We discuss strengths and limitations of our concept and implications for the application of task resumption cues beyond the scope of mobile language learning.

Abstract

We instrument Group Diagrams (GDs) to reduce clutter in sets of eye-tracking scanpaths. Group Diagrams consist of trajectory subsets that cover, or represent, the whole set of trajectories with respect to some distance measure and an adjustable distance threshold. The original GDs allow for an application of various distance measures. We implement the GD framework and evaluate it on scanpaths that were collected by a former user study on public transit maps. We find that the Fréchet distance is the most appropriate measure to get meaningful results, yet it is flexible enough to cover outliers.We discuss several implementation-specific challenges and improve the scalability of the algorithm.

Abstract

Computer vision models for image quality assessment (IQA) predict the subjective effect of generic image degradation, such as artefacts, blurs, bad exposure, or colors. The scarcity of face images in existing IQA datasets (below 10\%) is limiting the precision of IQA required for accurately filtering low-quality face images or guiding CV models for face image processing, such as super-resolution, image enhancement, and generation. In this paper, we first introduce the largest annotated IQA database to date that contains 20,000 human faces (an order of magnitude larger than all existing rated datasets of faces), of diverse individuals, in highly varied circumstances, quality levels, and distortion types. Based on the database, we further propose a novel deep learning model, which re-purposes generative prior features for predicting subjective face quality. By exploiting rich statistics encoded in well-trained generative models, we obtain generative prior information of the images and serve them as latent references to facilitate the blind IQA task. Experimental results demonstrate the superior prediction accuracy of the proposed model on the face IQA task.

Abstract

Evaluation of rendering performance is crucial when selecting or developing algorithms, but challenging as performance can largely differ across a set of selected scenarios. Despite this, performance metrics are often reported and compared in a highly aggregated way. In this paper we suggest a more fine-grained approach for the evaluation of rendering performance, taking into account multiple perspectives on the scenario: camera position and orientation along different paths, rendering algorithms, image resolution, and hardware. The approach comprises a visual analysis system that shows and contrasts the data from these perspectives. The users can explore combinations of perspectives and gain insight into the performance characteristics of several rendering algorithms. A stylized representation of the camera path provides a base layout for arranging the multivariate performance data as radar charts, each comparing the same set of rendering algorithms while linking the performance data with the rendered images. To showcase our approach, we analyze two types of scientific visualization benchmarks.

Abstract

Gaze-based analysis of areas of interest (AOI) is widely used in information visualisation research to understand how people explore visualisations or assess the quality of visualisations concerning key characteristics such as memorability. However, nearby AOIs in visualisations amplify the uncertainty caused by the gaze estimation error, which strongly influences the mapping between gaze samples or fixations and different AOIs. We contribute a novel investigation into gaze uncertainty and quantify its impact on AOI-based analysis on visualisations using two novel metrics: the Flipping Candidate Rate (FCR) and Hit Any AOI Rate (HAAR). Our analysis of 40 real-world visualisations, including human gaze and AOI annotations, shows that uncertainty commonly appears in visualisations, which significantly impacts the analysis conducted in AOI-based studies. Moreover, we analysed four visualisation types and found that bar and scatter plots are commonly designed in a way that causes more uncertainty than line and pie plots in gaze-based analysis.

Abstract

Despite its importance for assessing the effectiveness of communicating information visually, fine-grained recallability of information visualisations has not been studied quantitatively so far. In this work, we propose a question-answering paradigm to study visualisation recallability and present VisRecall - a novel dataset consisting of 200 visualisations that are annotated with crowd-sourced human (N = 305) recallability scores obtained from 1,000 questions of five question types. Furthermore, we present the first computational method to predict recallability of different visualisation elements, such as the title or specific data values. We report detailed analyses of our method on VisRecall and demonstrate that it outperforms several baselines in overall recallability and FE-, F-, RV-, and U-question recallability. Our work makes fundamental contributions towards a new generation of methods to assist designers in optimising visualisations.

Abstract

We propose Unified Model of Saliency and Scanpaths (UMSS) -- a model that learns to predict visual saliency and scanpaths (i.e. sequences of eye fixations) on information visualisations. Although scanpaths provide rich information about the importance of different visualisation elements during the visual exploration process, prior work has been limited to predicting aggregated attention statistics, such as visual saliency. We present in-depth analyses of gaze behaviour for different information visualisation elements (e.g. Title, Label, Data) on the popular MASSVIS dataset. We show that while, overall, gaze patterns are surprisingly consistent across visualisations and viewers, there are also structural differences in gaze dynamics for different elements. Informed by our analyses, UMSS first predicts multi-duration element-level saliency maps, then probabilistically samples scanpaths from them. Extensive experiments on MASSVIS show that our method consistently outperforms state-of-the-art methods with respect to several, widely used scanpath and saliency evaluation metrics. Our method achieves a relative improvement in sequence score of 11.5% for scanpath prediction, and a relative improvement in Pearson correlation coefficient of up to 23.6% for saliency prediction. These results are auspicious and point towards richer user models and simulations of visual attention on visualisations without the need for any eye tracking equipment.

Abstract

This paper provides an overview of uncertainty visualization in general, along with specific examples of applications in bioinformatics. Starting from a processing and interaction pipeline of visualization, components are discussed that are relevant for handling and visualizing uncertainty introduced with the original data and at later stages in the pipeline, which shows the importance of making the stages of the pipeline aware of uncertainty and allowing them to propagate uncertainty. We detail concepts and methods for visual mappings of uncertainty, distinguishing between explicit and implict representations of distributions, different ways to show summary statistics, and combined or hybrid visualizations. The basic concepts are illustrated for several examples of graph visualization under uncertainty. Finally, this review paper discusses implications for the visualization of biological data and future research directions.

Abstract

With increasing complexity in visual computing tasks, a single device may not be sufficient to adequately support the user’s workflow. Here, we can employ multi-device ecologies such as cross-device interaction, where a workflow can be split across multiple devices, each dedicated to a specific role. But what makes these multi-device ecologies compelling? Based on insights from our research, each device or interface component must contribute a complementary characteristic to increase the quality of interaction and further support users in their current activity. We establish the term complementary interfaces for such meaningful combinations of devices and modalities and provide an initial set of challenges. In addition, we demonstrate the value of complementarity with examples from within our own research.

Abstract

We design general-purpose algorithms for addressing fairness issues and mode collapse in generative modeling. More precisely, to design fair algorithms for as many sensitive variables as possible, including variables we might not be aware of, we assume no prior knowledge of sensitive variables: our algorithms use unsupervised fairness only, meaning no information related to the sensitive variables is used for our fairness-improving methods. All images of faces (even generated ones) have been removed to mitigate legal risks.

Abstract

The analysis of movement trajectories plays a central role in many application areas, such as traffic management, sports analysis, and collective behavior research, where large and complex trajectory data sets are routinely collected these days. While automated analysis methods are available to extract characteristics of trajectories such as statistics on the geometry, movement patterns, and locations that might be associated with important events, human inspection is still required to interpret the results, derive parameters for the analysis, compare trajectories and patterns, and to further interpret the impact factors that influence trajectory shapes and their underlying movement processes. Every step in the acquisition and analysis pipeline might introduce artifacts or alterate trajectory features, which might bias the human interpretation or confound the automated analysis. Thus, visualization methods as well as the visualizations themselves need to take into account the corresponding factors in order to allow sound interpretation without adding or removing important trajectory features or putting a large strain on the analyst. In this paper, we provide an overview of the challenges arising in robust trajectory visualization tasks. We then discuss several methods that contribute to improved visualizations. In particular, we present practical algorithms for simplifying trajectory sets that take semantic and uncertainty information directly into account. Furthermore, we describe a complementary approach that allows to visualize the uncertainty along with the trajectories.

Abstract

Large metabolic models, including genome-scale metabolic models, are nowadays common in systems biology, biotechnology and pharmacology. They typically contain thousands of metabolites and reactions and therefore methods for their automatic visualization and interactive exploration can facilitate a better understanding of these models.We developed a novel method for the visual exploration of large metabolic models and implemented it in LMME (Large Metabolic Model Explorer), an add-on for the biological network analysis tool VANTED. The underlying idea of our method is to analyze a large model as follows. Starting from a decomposition into several subsystems, relationships between these subsystems are identified and an overview is computed and visualized. From this overview, detailed subviews may be constructed and visualized in order to explore subsystems and relationships in greater detail. Decompositions may either be predefined or computed, using built-in or self-implemented methods. Realized as add-on for VANTED, LMME is embedded in a domain-specific environment, allowing for further related analysis at any stage during the exploration. We describe the method, provide a use case and discuss the strengths and weaknesses of different decomposition methods.The methods and algorithms presented here are implemented in LMME, an open-source add-on for VANTED. LMME can be downloaded from www.cls.uni-konstanz.de/software/lmme and VANTED can be downloaded from www.vanted.org. The source code of LMME is available from GitHub, at https://github.com/LSI-UniKonstanz/lmme.

Abstract

Observers typically present a strong bias in estimating the orientation of a visual bar when their body is tilted >60° in the roll plane and in the absence of visual background information. Known as the A-effect, this phenomenon likely results from the under-compensation of body tilt. Static visual cues can reduce such bias in the perceived vertical. Yet, it is unknown whether dynamic visual cues would be also effective. Here we presented projectile motions of a visual target along parabolic trajectories with different orientations relative to physical gravity. The aim of the experiment was twofold: First, we assessed whether the projectile motions could bias the estimation of the perceived orientation of a visual bar, measured with a classical subjective visual vertical (SVV) task. Second, we evaluated whether the ability to estimate time-to-contact of the visual target in an interception task was influenced by the orientation of these parabolic trajectories. Two groups of participants performed the experiment, either with their head and body tilted 90° along the roll plane or in an upright position. We found that the perceived orientation of the visual bar in the SVV task was affected by the orientation of the parabolic trajectories. This result was present in the tilted but not in the upright participants. In the interception task, the timing error increased linearly as a function of the orientation of the parabola. These results support the hypothesis that a gravity vector estimated from dynamic visual stimuli contributes to the subjective visual vertical.

Abstract

In interactive visual data exploration, users rely on recommendations on what data to explore next. EVLIN is a system that recommends queries to retrieve these data for the next exploration step, paired with suited visualizations. This paper extends EVLIN by combining its content-based recommendations with recommendations leveraging collaborative filtering to improve the effectiveness of recommendation-based visual data exploration. The recommendations rely on evolution provenance, which tracks users’ interactions during interactive visual data exploration. As more users explore a dataset, the evolution provenance of individual user explorations is incrementally integrated into a multi-user graph, for which we present match and merge algorithms. To compute collaborative-filtering recommendations, we present a search algorithm and optimizations to efficiently search queries similar to a current user’s query in the multi-user graph and give preference to queries that have been previously explored in an exploration step succeeding those similar queries. Our experimental evaluation studies the efficiency and effectiveness of the solutions proposed in this paper and demonstrates that using the full system with both content-based and collaborative-filtering recommendations enabled allows for effective interactive visual data exploration.

Abstract

Class separation is an important concept in machine learning and visual analytics. We address the visual analysis of class separation measures for both high-dimensional data and its corresponding projections into 2D through dimensionality reduction (DR) methods. Although a plethora of separation measures have been proposed, it is difficult to compare class separation between multiple datasets with different characteristics, multiple separation measures, and multiple DR methods. We present ProSeCo, an interactive visualization approach to support comparison between up to 20 class separation measures and up to 4 DR methods, with respect to any of 7 dataset characteristics: dataset size, dataset dimensions, class counts, class size variability, class size skewness, outlieriness, and real-world vs. synthetically generated data. ProSeCo supports (1) comparing across measures, (2) comparing high-dimensional to dimensionally-reduced 2D data across measures, (3) comparing between different DR methods across measures, (4) partitioning with respect to a dataset characteristic, (5) comparing partitions for a selected characteristic across measures, and (6) inspecting individual datasets in detail. We demonstrate the utility of ProSeCo in two usage scenarios, using datasets 1 posted at https://osf.io/epcf9/.

Abstract

Strategies for selecting the next data instance to label, in service of generating labeled data for machine learning, have been considered separately in the machine learning literature on active learning and in the visual analytics literature on human-centered approaches. We propose a unified design space for instance selection strategies to support detailed and fine-grained analysis covering both of these perspectives. We identify a concise set of 15 properties, namely measureable characteristics of datasets or of machine learning models applied to them, that cover most of the strategies in these literatures. To quantify these properties, we introduce Property Measures (PM) as fine-grained building blocks that can be used to formalize instance selection strategies. In addition, we present a taxonomy of PMs to support the description, evaluation, and generation of PMs across four dimensions: machine learning (ML) Model Output, Instance Relations, Measure Functionality, and Measure Valence. We also create computational infrastructure to support qualitative visual data analysis: a visual analytics explainer for PMs built around an implementation of PMs using cascades of eight atomic functions. It supports eight analysis tasks, covering the analysis of datasets and ML models using visual comparison within and between PMs and groups of PMs, and over time during the interactive labeling process. We iteratively refined the PM taxonomy, the explainer, and the task abstraction in parallel with each other during a two-year formative process, and show evidence of their utility through a summative evaluation with the same infrastructure. This research builds a formal baseline for the better understanding of the commonalities and differences of instance selection strategies, which can serve as the stepping stone for the synthesis of novel strategies in future work.

Abstract

Detecting emotions while driving remains a challenge in Human-Computer Interaction. Current methods to estimate the driver’s experienced emotions use physiological sensing (e.g., skin-conductance, electroencephalography), speech, or facial expressions. However, drivers need to use wearable devices, perform explicit voice interaction, or require robust facial expressiveness. We present VEmotion (Virtual Emotion Sensor), a novel method to predict driver emotions in an unobtrusive way using contextual smartphone data. VEmotion analyzes information including traffic dynamics, environmental factors, in-vehicle context, and road characteristics to implicitly classify driver emotions. We demonstrate the applicability in a real-world driving study (N = 12) to evaluate the emotion prediction performance. Our results show that VEmotion outperforms facial expressions by 29% in a person-dependent classification and by 8.5% in a person-independent classification. We discuss how VEmotion enables empathic car interfaces to sense the driver’s emotions and will provide in-situ interface adaptations on-the-go.

Abstract

We propose a visualization method to understand the effect of multidimensional projection on local subspaces, using implicit function differentiation. Here, we understand the local subspace as the multidimensional local neighborhood of data points. Existing methods focus on the projection of multidimensional data points, and the neighborhood information is ignored. Our method is able to analyze the shape and directional information of the local subspace to gain more insights into the global structure of the data through the perception of local structures. Local subspaces are fitted by multidimensional ellipses that are spanned by basis vectors. An accurate and efficient vector transformation method is proposed based on analytical differentiation of multidimensional projections formulated as implicit functions. The results are visualized as glyphs and analyzed using a full set of specifically-designed interactions supported in our efficient web-based visualization tool. The usefulness of our method is demonstrated using various multi- and high-dimensional benchmark datasets. Our implicit differentiation vector transformation is evaluated through numerical comparisons; the overall method is evaluated through exploration examples and use cases.

Abstract

The occurrence of V1 declaratives in Icelandic has attracted much attention in the generative literature (e.g. Sigurðsson 1990, Franco 2008), and such structures are known to be more frequent in earlier stages compared to the modern language. In this paper, we provide an account for the diachrony of V1 and V2 in Icelandic where the decreasing frequency of V1 is argued to be related to an ongoing change concerning the preferred structural position for subject topics. Our claims are supported by corpus evidence from IcePaHC (Wallenberg, Ingason, Sigurðsson & Rögnvaldsson 2011) and the formal analysis is conducted within Lexical Functional Grammar, which allows us to neatly capture the changing associations between clause structure and information structure. As we show, this overall change can also be linked to wider diachronic developments in Icelandic involving Stylistic Fronting and expletives.

Abstract

In this paper, we propose SineStream, a new variant of streamgraphs that improves their readability by minimizing sine illusion effects. Such effects reflect the tendency of humans to take the orthogonal rather than the vertical distance between two curves as their distance. In SineStream, we connect the readability of streamgraphs with minimizing sine illusions and by doing so provide a perceptual foundation for their design. As the geometry of a streamgraph is controlled by its baseline (the bottom-most curve) and the ordering of the layers, we re-interpret baseline computation and layer ordering algorithms in terms of reducing sine illusion effects. For baseline computation, we improve previous methods by introducing a Gaussian weight to penalize layers with large thickness changes. For layer ordering, three design requirements are proposed and implemented through a hierarchical clustering algorithm. Quantitative experiments and user studies demonstrate that SineStream improves the readability and aesthetics of streamgraphs compared to state-of-the-art methods.

Abstract

While graph drawing focuses more on the aesthetic representation of node-link diagrams, graph visualization takes into account other visual metaphors making them useful for graph exploration tasks in information visualization and visual analytics. Although there are aesthetic graph drawing criteria that describe how a graph should be presented to make it faster and more reliably explorable, many controlled and uncontrolled empirical user studies flourished over the past years. The goal of them is to uncover how well the human user performs graph-specific tasks, in many cases compared to previously designed graph visualizations. Due to the fact that many parameters in a graph dataset as well as the visual representation of them might be varied and many user studies have been conducted in this space, a state-of-the-art survey is needed to understand evaluation results and findings to inform the future design, research, and application of graph visualizations. In this article, we classify the present literature on the topmost level into graph interpretation, graph memorability, and graph creation where the users with their tasks stand in focus of the evaluation, not the computational aspects. As another outcome of this work, we identify the white spots in this field and sketch ideas for future research directions.

Abstract

Image-guided drawing can compensate for the lack of skills but often requires a significant number of repetitive strokes to create textures. Existing automatic stroke synthesis methods are usually limited to predefined styles or require indirect manipulation that may break the spontaneous flow of drawing. We present a method to autocomplete repetitive short strokes during users’ normal drawing process. Users can draw over a reference image as usual. At the same time, our system silently analyzes the input strokes and the reference to infer strokes that follow users’ input style when certain repetition is detected. Our key idea is to jointly analyze image regions and operation history for detecting and predicting repetitions. The proposed system can reduce tedious repetitive inputs while being fully under user control.

Abstract

Dimensionality Reduction (DR) is a popular technique that is often used in Machine Learning and Visualization communities to analyze high-dimensional data. The approach is empirically proven to be powerful for uncovering previously unseen structures in the data. While observing the results of the intermediate optimization steps of DR algorithms, we coincidently discovered the artistic beauty of the DR process. With enthusiasm for the beauty, we decided to look at DR from a generative art lens rather than their technical application aspects and use DR techniques to create artwork. Particularly, we use the optimization process to generate images, by drawing each intermediate step of the optimization process with some opacity over the previous intermediate result. As another alternative input, we used a neural-network model for face-landmark detection, to apply DR to portraits, while maintaining some facial properties, resulting in abstracted facial avatars. In this work, we provide such a collection of such artwork.

Abstract

A common enhancement of scatterplots represents points as small multiples, glyphs, or thumbnail images. As this encoding often results in overlaps, a general strategy is to alter the position of the data points, for instance, to a grid-like structure. Previous approaches rely on solving expensive optimization problems or on dividing the space that alter the global structure of the scatterplot. To find a good balance between efficiency and neighborhood and layout preservation, we propose Hagrid, a technique that uses space-filling curves (SFCs) to “gridify” a scatterplot without employing expensive collision detection and handling mechanisms. Using SFCs ensures that the points are plotted close to their original position, retaining approximately the same global structure. The resulting scatterplot is mapped onto a rectangular or hexagonal grid, using Hilbert and Gosper curves. We discuss and evaluate the theoretic runtime of our approach and quantitatively compare our approach to three state-of-the-art gridifying approaches, DGrid, Small multiples with gaps SMWG, and CorrelatedMultiples CMDS, in an evaluation comprising 339 scatterplots. Here, we compute several quality measures for neighborhood preservation together with an analysis of the actual runtimes. The main results show that, compared to the best other technique, Hagrid is faster by a factor of four, while achieving similar or even better quality of the gridified layout. Due to its computational efficiency, our approach also allows novel applications of gridifying approaches in interactive settings, such as removing local overlap upon hovering over a scatterplot.

Abstract

Abstract While there are many visualization techniques for exploring numeric data, only a few work with categorical data. One prominent example is Parallel Sets, showing data frequencies instead of data points - analogous to parallel coordinates for numerical data. As nominal data does not have an intrinsic order, the design of Parallel Sets is sensitive to visual clutter due to overlaps, crossings, and subdivision of ribbons hindering readability and pattern detection. In this paper, we propose a set of quality metrics, called ParSetgnostics (Parallel Sets diagnostics), which aim to improve Parallel Sets by reducing clutter. These quality metrics quantify important properties of Parallel Sets such as overlap, orthogonality, ribbon width variance, and mutual information to optimize the category and dimension ordering. By conducting a systematic correlation analysis between the individual metrics, we ensure their distinctiveness. Further, we evaluate the clutter reduction effect of ParSetgnostics by reconstructing six datasets from previous publications using Parallel Sets measuring and comparing their respective properties. Our results show that ParSetgostics facilitates multi-dimensional analysis of categorical data by automatically providing optimized Parallel Set designs with a clutter reduction of up to 81\% compared to the originally proposed Parallel Sets visualizations.

Abstract

The technological advances of smartphones facilitate the transformation of learning from the classroom to an activity that can happen anywhere and anytime. While micro-learning fosters ubiquitous learning, this flexibility comes at the cost of having an uncontrolled learning environment. To this point, we know little about the usage of mobile learning applications, particularly the occurrence of interruptions and the harm they cause. By diverting users’ attention away from the learning task, interruptions can potentially compromise learning performance. We present a four-week in-the-wild study (N = 12) where we investigate learning behavior and the occurrence of interruptions based on device logging and experience sampling questionnaires. We recorded 276 interruptions in 327 learning sessions and found that interruption type as well as users’ context influence learning sessions and the severity of the interruption (i.e., session termination likeliness). We discuss challenges and opportunities for the design of automated mechanisms to detect and mitigate interruptions in mobile learning.

Abstract

Collaborative exploration of scientific data sets across large high-resolution displays requires both high visual detail as wellas low-latency transfer of image data (oftentimes inducing the need to trade one for the other). In this work, we present a system thatdynamically adapts the encoding quality in such systems in a way that reduces the required bandwidth without impacting the detailsperceived by one or more observers. Humans perceive sharp, colourful details, in the small foveal region around the centre of the fieldof view, while information in the periphery is perceived blurred and colourless. We account for this by tracking the gaze of observers,and respectively adapting the quality parameter of each macroblock used by the H.264 encoder, considering the so-called visual acuityfall-off. This allows to substantially reduce the required bandwidth with barely noticeable changes in visual quality, which is crucial forcollaborative analysis across display walls at different locations. We demonstrate the reduced overall required bandwidth and the highquality inside the foveated regions using particle rendering and parallel coordinate

Abstract

Both visual detail and a low-latency transfer of image data are required for collaborative exploration of scientific data sets across large high-resolution displays. In this work, we present an approach that reduces the resolution before the encoding and uses temporal upscaling to reconstruct the full resolution image, reducing the overall latency and the required bandwidth without significantly impacting the details perceived by observers. Our approach takes advantage of the fact that humans do not perceive the full details of moving objects by providing a perfect reconstruction for static parts of the image, while non-static parts are reconstructed with a lower quality. This strategy enables a substantial reduction of the encoding latency and the required bandwidth with barely noticeable changes in visual quality, which is crucial for collaborative analysis across display walls at different locations. Additionally, our approach can be combined with other techniques aiming to reduce the required bandwidth while keeping the quality as high as possible, such as foveated encoding. We demonstrate the reduced overall latency, the required bandwidth, as well as the high image quality using different visualisations.

Abstract

Predicting and capturing an analyst’s intent behind a selection in a data visualization is valuable in two scenarios: First, a successful prediction of a pattern an analyst intended to select can be used to auto-complete a partial selection which, in turn, can improve the correctness of the selection. Second, knowing the intent behind a selection can be used to improve recall and reproducibility. In this paper, we introduce methods to infer analyst’s intents behind selections in data visualizations, such as scatterplots. We describe intents based on patterns in the data, and identify algorithms that can capture these patterns. Upon an interactive selection, we compare the selected items with the results of a large set of computed patterns, and use various ranking approaches to identify the best pattern for an analyst’s selection. We store annotations and the metadata to reconstruct a selection, such as the type of algorithm and its parameterization, in a provenance graph. We present a prototype system that implements these methods for tabular data and scatterplots. Analysts can select a prediction to auto-complete partial selections and to seamlessly log their intents. We discuss implications of our approach for reproducibility and reuse of analysis workflows. We evaluate our approach in a crowd-sourced study, where we show that auto-completing selection improves accuracy, and that we can accurately capture pattern-based intent.

Abstract

This paper introduces Attentive Implicit Representation Networks (AIR-Nets), a simple, but highly effective architecture for 3D reconstruction from point clouds. Since representing 3D shapes in a local and modular fashion increases generalization and reconstruction quality, AIR-Nets encode an input point cloud into a set of local latent vectors anchored in 3D space, which locally describe the object’s geometry, as well as a global latent description, enforcing global consistency. Our model is the first grid-free, encoder-based approach that locally describes an implicit function. The vector attention mechanism from 62 serves as main point cloud processing module, and allows for permutation invariance and translation equivariance. When queried with a 3D coordinate, our decoder gathers information from the global and nearby local latent vectors in order to predict an occupancy value. Experiments on the ShapeNet dataset 7 show that AIR-Nets significantly outperform previous state-of-the-art encoder-based, implicit shape learning methods and especially dominate in the sparse setting. Furthermore, our model generalizes well to the FAUST dataset 1 in a zero-shot setting. Finally, since AIR-Nets use a sparse latent representation and follow a simple operating scheme, the model offers several exiting avenues for future work. Our code is available at https: //github.com/SimonGiebenhain/AIR-Nets.

Abstract

In visual interactive labeling, users iteratively assign labels to data items until the machine model reaches an acceptable accuracy. A crucial step of this process is to inspect the model's accuracy and decide whether it is necessary to label additional elements. In scenarios with no or very little labeled data, visual inspection of the predictions is required. Similarity-preserving scatterplots created through a dimensionality reduction algorithm are a common visualization that is used in these cases. Previous studies investigated the effects of layout and image complexity on tasks like labeling. However, model evaluation has not been studied systematically. We present the results of an experiment studying the influence of image complexity and visual grouping of images on model accuracy estimation. We found that users outperform traditional automated approaches when estimating a model's accuracy. Furthermore, while the complexity of images impacts the overall performance, the layout of the items in the plot has little to no effect on estimations.

Abstract

Video quality assessment (VQA) methods focus on particular degradation types, usually artificially induced on a small set of reference videos. Hence, most traditional VQA methods under-perform in-the-wild. Deep learning approaches have had limited success due to the small size and diversity of existing VQA datasets, either artificial or authentically distorted. We introduce a new in-the-wild VQA dataset that is substantially larger and diverse: KonVid-150k. It consists of a coarsely annotated set of 153,841 videos having five quality ratings each, and 1,596 videos with a minimum of 89 ratings each. Additionally, we propose new efficient VQA approaches (MLSP-VQA) relying on multi-level spatially pooled deep-features (MLSP). They are exceptionally well suited for training at scale, compared to deep transfer learning approaches. Our best method, MLSP-VQA-FF, improves the Spearman rank-order correlation coefficient (SRCC) performance metric on the commonly used KoNViD-1k in-the-wild benchmark dataset to 0.82. It surpasses the best existing deep-learning model (0.80 SRCC) and hand-crafted feature-based method (0.78 SRCC). We further investigate how alternative approaches perform under different levels of label noise, and dataset size, showing that MLSP-VQA-FF is the overall best method for videos in-the-wild. Finally, we show that the MLSP-VQA models trained on KonVid-150k sets the new state-of-the-art for cross-test performance on KoNViD-1k and LIVE-Qualcomm with a 0.83 and 0.64 SRCC, respectively. For KoNViD-1k this inter-dataset testing outperforms intra-dataset experiments, showing excellent generalization.

Abstract

Hybrid user interfaces combine cross-reality devices (e. g., head-mounted display) with other heterogeneous device technologies (e. g., smartphone) to compensate for the disadvantages of one device with the advantages of the other, such as addressing the lack of haptic feedback in mid-air interaction with touchscreen input. Such hybrid user interfaces typically involve the synchronous use of multiple input and output technologies. In this work, we instead consider the asynchronous use of heterogeneous devices (e. g., using a desktop and virtual reality device in sequence). While the sequential use of different technologies does not necessarily offset the individual device-specific disadvantages, it allows users to choose the more appropriate device for a particular sub-task. In this context, transitional interfaces play an essential role in enabling the switch between devices, while allowing the user to maintain a mental connection between realities and seamlessly continue with their task where they left off.

Abstract

Recent research in the area of immersive analytics demonstrated the utility of head-mounted augmented reality devices for visual data analysis. However, it can be challenging to use the by default supported mid-air gestures to interact with visualizations in augmented reality (e.g. due to limited precision). Touch-based interaction (e.g. via mobile devices) can compensate for these drawbacks, but is limited to two-dimensional input. In this work we present STREAM: Spatially-aware Tablets combined with Augmented Reality Head-Mounted Displays for the multimodal interaction with 3D visualizations. We developed a novel eyes-free interaction concept for the seamless transition between the tablet and the augmented reality environment. A user study reveals that participants appreciated the novel interaction concept, indicating the potential for spatially-aware tablets in augmented reality. Based on our findings, we provide design insights to foster the application of spatially-aware touch devices in augmented reality and research implications indicating areas that need further investigation.

Abstract

The human microbiome is largely shaped by the chemical interactions of its microbial members, which includes cross-talk via shared signals or quenching of the signalling of other species. Quorum sensing is a process that allows microbes to coordinate their behaviour in dependence of their population density and to adjust gene expression accordingly. We present the Quorum Sensing Database (QSDB), a comprehensive database of all published sensing and quenching relations between organisms and signalling molecules of the human microbiome, as well as an interactive web interface that allows browsing the database, provides graphical depictions of sensing mechanisms as Systems Biology Graphical Notation diagrams and links to other databases.Database URL: QSDB (Quorum Sensing DataBase) is freely available via an interactive web interface and as a downloadable csv file at http://qsdb.org.

Abstract

The recent availability of affordable and lightweight tracking sensors allows researchers to collect large and complex movement data sets. To explore and analyse these data, applications are required that are capable of handling the data while providing an environment that enables the analyst(s) to focus on the task of investigating the movement in the context of the geographic environment it occurred in. We present an extensible, open-source framework for collaborative analysis of geospatial–temporal movement data with a use case in collective behaviour analysis. The framework TEAMwISE supports the concurrent usage of several program instances, allowing to have different perspectives on the same data in collocated or remote set-ups. The implementation can be deployed in a variety of immersive environments, for example, on a tiled display wall and mobile VR devices.

Abstract

Current tracking technology such as GPS data loggers allows biologists to remotely collect large amounts of movement data for a large variety of species. Extending, and often replacing interpretation based on observation, the analysis of the collected data supports research on animal behaviour, on impact factors such as climate change and human intervention on the globe, as well as on conservation programs. However, this analysis is difficult, due to the nature of the research questions and the complexity of the data sets. It requires both automated analysis, for example, for the detection of behavioural patterns, and human inspection, for example, for interpretation, inclusion of previous knowledge, and for conclusions on future actions and decision making. For this analysis and inspection, the movement data needs to be put into the context of environmental data, which helps to interpret the behaviour. Thus, a major challenge is to design and develop methods and intuitive interfaces that integrate the data for analysis by biologists. We present a concept and implementation for the visual analysis of cheetah movement data in a web-based fashion that allows usage both in the field and in office environments.

Abstract

Dynamic Mode Decomposition (DMD) is a data-driven and model-free decomposition technique. It is suitable for revealing spatio-temporal features of both numerically and experimentally acquired data. Conceptually, DMD performs a low-dimensional spectral decomposition of the data into the following components: the modes, called DMD modes, encode the spatial contribution of the decomposition, whereas the DMD amplitudes specify their impact. Each associated eigenvalue, referred to as DMD eigenvalue, characterizes the frequency and growth rate of the DMD mode. In this paper, we demonstrate how the components of DMD can be utilized to obtain temporal and spatial information from time-dependent flow fields. We begin with the theoretical background of DMD and its application to unsteady flow. Next, we examine the conventional process with DMD mathematically and put it in relationship to the discrete Fourier transform. Our analysis shows that the current use of DMD components has several drawbacks. To resolve these problems we adjust the components and provide new and meaningful insights into the decomposition: we show that our improved components capture the spatio-temporal patterns of the flow better. Moreover, we remove redundancies in the decomposition and clarify the interplay between components, allowing users to understand the impact of components. These new representations, which respect the spatio-temporal character of DMD, enable two clustering methods that segment the flow into physically relevant sections and can therefore be used for the selection of DMD components. With a number of typical examples, we demonstrate that the combination of these techniques allows new insights with DMD for unsteady flow.

Abstract

Abstract After a long period of scepticism, more and more publications describe basic research but also practical approaches to how abstract data can be presented in immersive environments for effective and efficient data understanding. Central aspects of this important research question in immersive analytics research are concerned with the use of 3D for visualization, the embedding in the immersive space, the combination with spatial data, suitable interaction paradigms and the evaluation of use cases. We provide a characterization that facilitates the comparison and categorization of published works and present a survey of publications that gives an overview of the state of the art, current trends, and gaps and challenges in current research.

Abstract

In recent years, research on immersive environments has experienced a new wave of interest, and immersive analytics has been established as a new research field. Every year, a vast amount of different techniques, applications, and user studies are published that focus on employing immersive environments for visualizing and analyzing data. Nevertheless, immersive analytics is still a relatively unexplored field that needs more basic research in many aspects and is still viewed with skepticism. Rightly so, because in our opinion, many researchers do not fully exploit the possibilities offered by immersive environments and, on the contrary, sometimes even overestimate the power of immersive visualizations. Although a growing body of papers has demonstrated individual advantages of immersive analytics for specific tasks and problems, the general benefit of using immersive environments for effective analytic tasks remains controversial. In this article, we reflect on when and how immersion may be appropriate for the analysis and present four guiding scenarios. We report on our experiences, discuss the landscape of assessment strategies, and point out the directions where we believe immersive visualizations have the greatest potential.

Abstract

The world is still under the influence of the COVID-19 pandemic. Even though vaccines are deployed as rapidly as possible, it is still necessary to use other measures to reduce the spread of the virus. Measures such as social distancing or wearing a mask receive a lot of criticism. Therefore, we want to demonstrate a serious game to help the players understand these measures better and show them why they are still necessary. The player of the game has to avoid other agents to keep their risk of a COVID-19 infection low. The game uses Virtual Reality through a Head-Mounted-Display to deliver an immersive and enjoyable experience. Gamification elements are used to engage the user with the game while they explore various environments. We also implemented visualizations that help the user with social distancing.

Abstract

Abstract In recent years guider-follower approaches show a promising solution to the challenging problem of last-mile or indoor pedestrian navigation without micro-maps or indoor floor plans for path planning. However, the success of such guider-follower approaches is highly dependent on a set of manually and carefully chosen image or video checkpoints. This selection process is tedious and error-prone. To address this issue, we first conduct a pilot study to understand how users as guiders select critical checkpoints from a video recorded while walking along a route, leading to a set of criteria for automatic checkpoint selection. By using these criteria, including visibility, stairs and clearness, we then implement this automation process. The key behind our technique is a lightweight, effective algorithm using left-hand-side and right-hand-side objects for path occlusion detection, which benefits both automatic checkpoint selection and occlusion-aware path annotation on selected image checkpoints. Our experimental results show that our automatic checkpoint selection method works well in different navigation scenarios. The quality of automatically selected checkpoints is comparable to that of manually selected ones and higher than that of checkpoints by alternative automatic methods.

Abstract

Recent advances in the automated detection of motorcycle riders’ helmet use have enabled road safety actors to process large scale video data efficiently and with high accuracy. To distinguish drivers from passengers in helmet use, the most straightforward way is to train a multi-class classifier, where each class corresponds to a specific combination of rider position and individual riders’ helmet use. However, such strategy results in long-tailed data distribution, with critically low class samples for a number of uncommon classes. In this paper, we propose a novel approach to address this limitation. Let n be the maximum number of riders a motorcycle can hold, we encode the helmet use on a motorcycle as a vector with 2n bits, where the first n bits denote if the encoded positions have riders, and the latter n bits denote if the rider in the corresponding position wears a helmet. With the novel helmet use positional encoding, we propose a deep learning model that stands on existing image classification architecture. The model simultaneously trains 2n binary classifiers, which allows more balanced samples for training. This method is simple to implement and requires no hyperparameter tuning. Experimental results demonstrate our approach outperforms the state-of-the-art approaches by 1.9% accuracy.

Abstract

We present an integrated approach for creating and assigning color palettes to different visualizations such as multi-class scatterplots, line, and bar charts. While other methods separate the creation of colors from their assignment, our approach takes data characteristics into account to produce color palettes, which are then assigned in a way that fosters better visual discrimination of classes. To do so, we use a customized optimization based on simulated annealing to maximize the combination of three carefully designed color scoring functions: point distinctness, name difference, and color discrimination. We compare our approach to state-of-the-art palettes with a controlled user study for scatterplots and line charts, furthermore we performed a case study. Our results show that Palettailor, as a fully-automated approach, generates color palettes with a higher discrimination quality than existing approaches. The efficiency of our optimization allows us also to incorporate user modifications into the color selection process.

Abstract

Approaches based on order-adaptive regularisation belong to the most accurate variational methods for computing the optical flow. By locally deciding between first- and second-order regularisation, they are applicable to scenes with both fronto-parallel and ego-motion. So far, however, existing order-adaptive methods have a decisive drawback. While the involved first- and second-order smoothness terms already make use of anisotropic concepts, the underlying selection process itself is still isotropic in that sense that it locally chooses the same regularisation order for all directions. In our paper, we address this shortcoming. We propose a generalised order-adaptive approach that allows to select the local regularisation order for each direction individually. To this end, we split the order-adaptive regularisation across and along the locally dominant direction and perform an energy competition for each direction separately. This in turn offers another advantage. Since the parameters can be chosen differently for both directions, the approach allows for a better adaption to the underlying scene. Experiments for MPI Sintel and KITTI 2015 demonstrate the usefulness of our approach. They not only show improvements compared to an isotropic selection scheme. They also make explicit that our approach is able to improve the results from state-of-the-art learning-based approaches, if applied as a final refinement step – thereby achieving top results in both benchmarks.

Abstract

In subjective full-reference image quality assessment, a reference image is distorted at increasing distortion levels. The differences between perceptual image qualities of the reference image and its distorted versions are evaluated, often using degradation category ratings (DCR). However, the DCR has been criticized since differences between rating categories on this ordinal scale might not be perceptually equidistant, and observers may have different understandings of the categories. Pair comparisons (PC) of distorted images, followed by Thurstonian reconstruction of scale values, overcomes these problems. In addition, PC is more sensitive than DCR, and it can provide scale values in fractional, just noticeable difference (JND) units that express a precise perceptional interpretation. Still, the comparison of images of nearly the same quality can be difficult. We introduce boosting techniques embedded in more general triplet comparisons (TC) that increase the sensitivity even more. Boosting amplifies the artefacts of distorted images, enlarges their visual representation by zooming, increases the visibility of the distortions by a flickering effect, or combines some of the above. Experimental results show the effectiveness of boosted TC for seven types of distortion (color diffusion, jitter, high sharpen, JPEG 2000 compression, lens blur, motion blur, multiplicative noise). For our study, we crowdsourced over 1.7 million responses to triplet questions. We give a detailed analysis of the data in terms of scale reconstructions, accuracy, detection rates, and sensitivity gain. Generally, boosting increases the discriminatory power and allows to reduce the number of subjective ratings without sacrificing the accuracy of the resulting relative image quality values. Our technique paves the way to fine-grained image quality datasets, allowing for more distortion levels, yet with high-quality subjective annotations. We also provide the details for Thurstonian scale reconstruction from TC and our annotated dataset, KonFiG-IQA , containing 10 source images, processed using 7 distortion types at 12 or even 30 levels, uniformly spaced over a span of 3 JND units.

Abstract

A plethora of dimensionality reduction techniques have emerged over the past decades, leaving researchers and analysts with a wide variety of choices for reducing their data, all the more so given some techniques come with additional parametrization (e.g. t-SNE, UMAP, etc.). Recent studies are showing that people often use dimensionality reduction as a black-box regardless of the specific properties the method itself preserves. Hence, evaluating and comparing 2D projections is usually qualitatively decided, by setting projections side-by-side and letting human judgment decide which projection is the best. In this work, we propose a quantitative way of evaluating projections, that nonetheless places human perception at the center. We run a comparative study, where we ask people to select 'good' and 'misleading' views between scatterplots of low-level projections of image datasets, simulating the way people usually select projections. We use the study data as labels for a set of quality metrics whose purpose is to discover and quantify what exactly people are looking for when deciding between projections. With this proxy for human judgments, we use it to rank projections on new datasets, explain why they are relevant, and quantify the degree of subjectivity in projections selected.

Abstract

Interactive visualization is a valuable tool for introductory or advanced courses in general relativity as well as for public outreach to provide a deeper understanding of the visual implications due to curved spacetime. In particular, the extreme case of a black hole where the curvature becomes so strong that even light cannot escape, benefits from an interactive visualization where students can investigate the distortion effects by moving objects around. However, the most commonly used technique of four-dimensional general-relativistic ray tracing is still too slow for interactive frame rates. Therefore, we propose an efficient and adaptive polygon rendering method that takes light deflection and light travel time into account. An additional advantage of this method is that it provides a natural demonstration of how multiple images occur and how light travel time affects them. Finally, we present our method using three example scenes: a triangle passing behind a black hole, a sphere orbiting a black hole and an accretion disk with different inclination angles.

Abstract

The logos of metal bands can be by turns gaudy, uncouth, or nearly illegible. Yet, these logos work: they communicate sophisticated notions of genre and emotional affect. In this paper we use the design considerations of metal logos to explore the space of "illegible semantics": the ways that text can communicate information at the cost of readability, which is not always the most important objective. In this work, drawing on formative visualization theory, professional design expertise, and empirical assessments of a corpus of metal band logos, we describe a design space of metal logos and present a tool through which logo characteristics can be explored through visualization. We investigate ways in which logo designers imbue their text with meaning and consider opportunities and implications for visualization more widely.

Abstract

We propose to use a quality estimator and evolutionary methods to search the latent space of generative adversarial networks trained on small, difficult datasets, or both. The new method leads to the generation of significantly higher quality images while preserving the original generator’s diversity. Human raters preferred an image from the new version with frequency 83.7% for Cats, 74% for FashionGen, 70.4% for Horses, and 69.2% for Artworks - minor improvements for the already excellent GANs for faces. This approach applies to any quality scorer and GAN generator.

Abstract

Super-resolution aims at increasing the resolution and level of detail within an image. The current state of the art in general single-image super-resolution is held by NESRGAN+, which injects a Gaussian noise after each residual layer at training time. In this paper, we harness evolutionary methods to improve NESRGAN+ by optimizing the noise injection at inference time. More precisely, we use Diagonal CMA to optimize the injected noise according to a novel criterion combining quality assessment and realism. Our results are validated by the PIRM perceptual score and a human study. Our method outperforms NESRGAN+ on several standard super-resolution datasets. More generally, our approach can be used to optimize any method based on noise injection.

Abstract

Simulations of cosmic evolution are a means to explain the formation of the universe as we see it today. The resulting data of such simulations comprise numerous physical quantities, which turns their analysis into a complex task. Here, we analyze such high-dimensional and time-varying particle data using various visualization techniques from the fields of particle visualization, flow visualization, volume visualization, and information visualization. Our approach employs specialized filters to extract and highlight the development of so-called active galactic nuclei and filament structures formed by the particles. Additionally, we calculate X-ray emission of the evolving structures in a preprocessing step to complement visual analysis. Our approach is integrated into a single visual analytics framework to allow for analysis of star formation at interactive frame rates. Finally, we lay out the methodological aspects of our work that led to success at the 2019 IEEE SciVis Contest.

Abstract

We introduce inverted stippling, a method to mimic an inversion technique used by artists when performing stippling. To this end, we extend Linde-Buzo-Gray (LBG) stippling to multi-class LBG (MLBG) stippling with multiple layers. MLBG stippling couples the layers stochastically to optimize for per-layer and overall blue-noise properties. We propose a stipple-based filling method to generate solid color backgrounds for inverting areas. Our experiments demonstrate the effectiveness of MLBG in terms of reducing overlapping and intensity accuracy. In addition, we showcase MLBG with color stippling and dynamic multi-class blue-noise sampling, which is possible due to its support for temporal coherence.

Abstract

Despite the success of contextualized language models on various NLP tasks, it is still unclear what these models really learn. In this paper, we contribute to the current efforts of explaining such models by exploring the continuum between function and content words with respect to contextualization in BERT, based on linguistically-informed insights. In particular, we utilize scoring and visual analytics techniques: we use an existing similarity-based score to measure contextualization and integrate it into a novel visual analytics technique, presenting the model's layers simultaneously and highlighting intra-layer properties and inter-layer differences. We show that contextualization is neither driven by polysemy nor by pure context variation. We also provide insights on why BERT fails to model words in the middle of the functionality continuum.

Abstract

Although image quality assessment (IQA) in-the-wild has been researched in computer vision, it is still challenging to precisely estimate perceptual image quality in the presence of real-world complex and composite distortions. In order to improve machine learning solutions for IQA, we consider side information denoting the presence of distortions besides the basic quality ratings in IQA datasets. Specifically, we extend one of the largest in-the-wild IQA databases, KonIQ-10k, to KonIQ++, by collecting distortion annotations for each image, aiming to improve quality prediction together with distortion identification. We further explore the interactions between image quality and distortion by proposing a novel IQA model, which jointly predicts image quality and distortion by recurrently refining task-specific features in a multi-stage fusion framework. Our dataset KonIQ++, along with the model, boosts IQA performance and generalization ability, demonstrating its potential for solving the challenging authentic IQA task. The proposed model can also accurately predict distinct image defects, suggesting its application in image processing tasks such as image colorization and deblurring.

Abstract

Mobile intervention studies employ mobile devices to observe participants’ behavior change over several weeks. Researchers regularly monitor high-dimensional data streams to ensure data quality and prevent data loss (e.g., missing engagement or malfunctions). The multitude of problem sources hampers possible automated detection of such irregularities – providing a use case for interactive dashboards. With the advent of untethered head-mounted AR devices, these dashboards can be placed anywhere in the user's physical environment, leveraging the available space and allowing for flexible information arrangement and natural navigation. In this work, we present the user-centered design and the evaluation of IDIAR: Interactive Dashboards in AR, combining a head-mounted display with the familiar interaction of a smartphone. A user study with 15 domain experts for mobile intervention studies shows that participants appreciated the multimodal interaction approach. Based on our findings, we provide implications for research and design of interactive dashboards in AR.

Abstract

We propose a data-driven space-filling curve method for 2D and 3D visualization. Our flexible curve traverses the data elements in the spatial domain in a way that the resulting linearization better preserves features in space compared to existing methods. We achieve such data coherency by calculating a Hamiltonian path that approximately minimizes an objective function that describes the similarity of data values and location coherency in a neighborhood. Our extended variant even supports multiscale data via quadtrees and octrees. Our method is useful in many areas of visualization including multivariate or comparative visualization ensemble visualization of 2D and 3D data on regular grids or multiscale visual analysis of particle simulations. The effectiveness of our method is evaluated with numerical comparisons to existing techniques and through examples of ensemble and multivariate datasets.

Abstract

In a planar L-drawing of a directed graph (digraph) each edge e is represented as a polyline composed of a vertical segment starting at the tail of e and a horizontal segment ending at the head of e. Distinct edges may overlap, but not cross. Our main focus is on bimodal graphs, i.e., digraphs admitting a planar embedding in which the incoming and outgoing edges around each vertex are contiguous. We show that every plane bimodal graph without 2-cycles admits a planar L-drawing. This includes the class of upward-plane graphs. Finally, outerplanar digraphs admit a planar L-drawing -- although they do not always have a bimodal embedding -- but not necessarily with an outerplanar embedding.

Abstract

Among the many changes brought about by the COVID-19 pandemic, one of the most pressing for scientific research concerns user testing. For the researchers who conduct studies with human participants, the requirements for social distancing have created a need for reflecting on methodologies that previously seemed relatively straightforward. It has become clear from the emerging literature on the topic and from first-hand experiences of researchers that the restrictions due to the pandemic affect every aspect of the research pipeline. The current paper offers an initial reflection on user-based research, drawing on the authors' own experiences and on the results of a survey that was conducted among researchers in different disciplines, primarily psychology, human-computer interaction (HCI), and visualization communities. While this sampling of researchers is by no means comprehensive, the multi-disciplinary approach and the consideration of different aspects of the research pipeline allow us to examine current and future challenges for user-based research. Through an exploration of these issues, this paper also invites others in the VIS-as well as in the wider-research community, to reflect on and discuss the ways in which the current crisis might also present new and previously unexplored opportunities.

Abstract

Schematic transit maps (often called "metro maps" in the literature) are important to produce comprehensible visualizations of complex public transit networks. In this work, we investigate the problem of automatically drawing such maps on an octilinear grid with an arbitrary (but optimal) number of edge bends. Our approach can naturally deal with obstacles that should be respected in the final drawing (points of interest, rivers, coastlines) and can prefer grid edges near the real-world course of a line. This allows our drawings to be combined with existing maps, for example as overlays in map services. We formulate an integer linear program which can be used to solve the problem exactly. We also provide a fast approximation algorithm which greedily calculates shortest paths between node candidates on the underlying octilinear grid graph. Previous work used local search techniques to update node positions until a local optimum was found, but without guaranteeing octilinearity. We can thus calculate nearly optimal metro maps in a fraction of a second even for complex networks, enabling the interactive use of our method in map editors.

Abstract

The development of linguistic corpora is fraught with various problems of annotation and representation. These constitute a very real challenge for the development and use of annotated corpora, but as yet not much literature exists on how to address the underlying problems. In this paper, we identify and discuss five sources of representation problems, which are independent though interrelated: ambiguity, variation, uncertainty, error and bias. We outline and characterize these sources, discussing how their improper treatment can have stark consequences for research outcomes. Finally, we discuss how an adequate treatment can inform corpus-related linguistic research, both computational and theoretical, improving the reliability of research results and NLP models, as well as informing the more general reproducibility issue.

Abstract

This paper describes DiaSense, a system developed for Task 1 `Unsupervised Lexical Semantic Change Detection' of SemEval 2020. In DiaSense, contextualized word embeddings are used to model word sense changes. This allows for the calculation of metrics which mimic human intuitions about the semantic relatedness between individual use pairs of a target word for the assessment of lexical semantic change. DiaSense is able to detect lexical semantic change in English, German, Latin and Swedish (accuracy = 0.728). Moreover, DiaSense differentiates between weak and strong change.

Abstract

We present a pipeline that, given a weighted graph as an input, produces a planar grid embedding where all edges are represented as axis-aligned straight lines with their Euclidean length matching their edge weight (if such an embedding exists). Being able to compute such embeddings is important for visualization purposes but is additionally helpful to solve certain optimization problems faster, as e.g. the Steiner tree problem. Our embedding pipeline consists of three main steps: In the first step, we identify rigid substructures which we call puzzle pieces. In the second step, we merge puzzle pieces if possible. In the third and last step, we compute the final embedding (or decide that such an embedding does not exist) via backtracking. We describe suitable data structures and engineering techniques for accelerating all steps of the pipeline along the way. Experiments on a large variety of input graphs demonstrate the applicability and scalability of our approach.

Abstract

Class separation is an important concept in machine learning and visual analytics. However, the comparison of class separation for datasets with varying dimensionality is non-trivial, given a) the various possible structural characteristics of datasets and b) the plethora of separation measures that exist. Building upon recent findings in visualization research about the qualitative and quantitative evaluation of class separation for 2D dimensionally reduced data using scatterplots, this research addresses the visual analysis of class separation measures for high-dimensional data. We present SepEx, an interactive visualization approach for the assessment and comparison of class separation measures for multiple datasets. SepEx supports analysts with the comparison of multiple separation measures over many high-dimensional datasets, the effect of dimensionality reduction on measure outputs by supporting nD to 2D comparison, and the comparison of the effect of different dimensionality reduction methods on measure outputs. We demonstrate SepEx in a scenario on 100 two-class 5D datasets with a linearly increasing amount of separation between the classes, illustrating both similarities and nonlinearities across 11 measures.

Abstract

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.

Abstract

Comparing data distributions is a core focus in descriptive statistics, and part of most data analysis processes across disciplines. In particular, comparing distributions entails numerous tasks, ranging from identifying global distribution properties, comparing aggregated statistics (e.g., mean values), to the local inspection of single cases. While various specialized visualizations have been proposed (e.g., box plots, histograms, or violin plots), they are not usually designed to support more than a few tasks, unless they are combined. In this paper, we present the v-plot designer; a technique for authoring custom hybrid charts, combining mirrored bar charts, difference encodings, and violin-style plots. v-plots are customizable and enable the simultaneous comparison of data distributions on global, local, and aggregation levels. Our system design is grounded in an expert survey that compares and evaluates 20 common visualization techniques to derive guidelines for the task-driven selection of appropriate visualizations. This knowledge externalization step allowed us to develop a guiding wizard that can tailor v-plots to individual tasks and particular distribution properties. Finally, we confirm the usefulness of our system design and the user-guiding process by measuring the fitness for purpose and applicability in a second study with four domain and statistic experts.

Abstract

The ability to perceive patterns in parallel coordinates plots (PCPs) is heavily influenced by the ordering of the dimensions. While the community has proposed over 30 automatic ordering strategies, we still lack empirical guidance for choosing an appropriate strategy for a given task. In this paper, we first propose a classification of tasks and patterns and analyze which PCP reordering strategies help in detecting them. Based on our classification, we then conduct an empirical user study with 31 participants to evaluate reordering strategies for cluster identification tasks. We particularly measure time, identification quality, and the users' confidence for two different strategies using both synthetic and real-world datasets. Our results show that, somewhat unexpectedly, participants tend to focus on dissimilar rather than similar dimension pairs when detecting clusters, and are more confident in their answers. This is especially true when increasing the amount of clutter in the data. As a result of these findings, we propose a new reordering strategy based on the dissimilarity of neighboring dimension pairs.

Abstract

Data-informed decision-making processes play a fundamental role across disciplines.To support these processes, knowledge needs to be extracted from high-dimensional(HD) and complex datasets. Visualizations play hereby a key role in identifying andunderstanding patterns within the data. However, the choice of visual mappingheavily influences the effectiveness of the visualization. While one design choice isuseful for a particular task, the very same design can make another analysis taskmore difficult, or even impossible. This doctoral thesis advances the quality andpattern-driven optimization of visualizations in two core areas by addressing theresearch question:“How can we effectively design visualizations to highlight patterns –using automatic and user-driven approaches?”The first part of the thesis deals with the question“how can we automaticallymeasure the quality of a particular design to optimize the layout?”We summarizethe state-of-the-art in quality-metrics research, describe the underlying concepts,optimization goals, constraints, and discuss the requirements of the algorithms.While numerous quality metrics exist for all major HD visualizations, researchlacks empirical studies to choose a particular technique for a given analysis task.In particular for parallel coordinates (PCP) and star glyphs, two frequently usedtechniques for high-dimensional data, no study exists which evaluates the impact ofdifferent axes orderings. Therefore, this thesis contributes an empirical study anda novel quality metric for both techniques. Based on our findings in the PCP study,we also contribute a formalization of how standard parallel coordinates distort theperception of patterns, in particular clusters. To minimize the effect, we propose anautomatic rendering technique.The second part of the thesis is user-centered and addresses the question“howcan analysts support the design of visualization to highlight particular patterns?”We contribute two techniques: Thev-plot designeris a chart authoring tool todesign custom hybrid charts for the comparative analysis of data distributions. Itautomatically recommends basic charts (e.g., box plots, violin-typed visualizations,and bar charts) and optimizes a custom hybrid chart called v-plot based on a setof analysis tasks.SMARTexploreuses a table metaphor and combines easy-to-applyinteraction with pattern-driven layouts of rows and columns and an automaticallycomputed reliability analysis based on statistical measures.In summary, this thesis contributes quality-metrics and user-driven approachesto advance the quality- and pattern-driven optimization of high-dimensional datavisualizations. The quality metrics and the grounding of the user-centered techniquesare derived from empirical user studies while the effectiveness of the implementedtools is shown by domain expert evaluations.

Abstract

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.

Abstract

We present an approach for visual analysis of high-dimensional measurement data with varying sampling rates in the context of an experimental post-surgery study performed on a porcine surrogate model. The study aimed at identifying parameters suitable for diagnosing and prognosticating the volume state-a crucial and difficult task in intensive care medicine. In intensive care, most assessments not only depend on a single measurement but a plethora of mixed measurements over time. Even for trained experts, efficient and accurate analysis of such multivariate time-dependent data remains a challenging task. We present a linked-view post hoc visual analysis application that reduces data complexity by combining projection-based time curves for overview with small multiples for details on demand. Our approach supports not only the analysis of individual patients but also the analysis of ensembles by adapting existing techniques using non-parametric statistics. We evaluated the effectiveness and acceptance of our application through expert feedback with domain scientists from the surgical department using real-world data: the results show that our approach allows for detailed analysis of changes in patient state while also summarizing the temporal development of the overall condition. Furthermore, the medical experts believe that our method can be transferred from medical research to the clinical context, for example, to identify the early onset of a sepsis.

Abstract

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.

Abstract

We report on the state-of-the-art of software visualization.To ensure reproducibility, we adopted the Systematic Literature Review methodology. That is, we analyzed 1440 entries from IEEE Xplore and ACM Digital Library databases. We selected 105 relevant full papers published in 2013–2019, which we classified based on the aspect of the software system that is supported (i.e., structure, behavior, and evolution). For each paper, we extracted main dimensions that characterize software visualizations, such as software engineering tasks, roles of users, information visualization techniques, and media used to display visualizations. We provide researchers in the field an overview of the state-of-the-art in software visualization and highlight research opportunities. We also help developers to identify suitable visualizations for their particular context by matching software visualizations to development concerns and concrete details to obtain available visualization tools.

Abstract

Motivated by the fact that in a space where shortest paths are unique, no two shortest paths meet twice, we study a question posed by Greg Bodwin: Given a geodetic graph G, i.e., an unweighted graph in which the shortest path between any pair of vertices is unique, is there a philogeodetic drawing of G, i.e., a drawing of G in which the curves of any two shortest paths meet at most once? We answer this question in the negative by showing the existence of geodetic graphs that require some pair of shortest paths to cross at least four times. The bound on the number of crossings is tight for the class of graphs we construct. Furthermore, we exhibit geodetic graphs of diameter two that do not admit a philogeodetic drawing.

Abstract

To characterize the building blocks of a legacy software system (e.g., structure, dependencies), programmers usually spend a long time navigating its source code. Yet, modern integrated development environments (IDEs) do not provide appropriate means to efficiently achieve complex software comprehension tasks. To deal with this unfulfilled need, we present Hunter, a tool for the visualization of JavaScript applications. Hunter visualizes source code through a set of coordinated views that include a node-link diagram that depicts the dependencies among the components of a system, and a treemap that helps programmers to orientate when navigating its structure. In this paper, we report on a controlled experiment that evaluates Hunter. We asked 16 participants to solve a set of software comprehension tasks, and assessed their effectiveness in terms of (i) user performance (i.e., completion time, accuracy, and attention), and (ii) user experience (i.e., emotions, usability). We found that when using Hunter programmers required significantly less time to complete various software comprehension tasks and achieved a significantly higher accuracy. We also found that the node-link diagram panel of Hunter gets most of the attention of programmers, whereas the source code panel does so in Visual Studio Code. Moreover, programmers considered that Hunter exhibits a good user experience.

Abstract

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.

Abstract

While visualisation often strives for abstraction, the interactive exploration of large scientific data sets like densely sampled 3Dfields or massive particle data sets still benefits from rendering their graphical representation in large detail on high-resolutiondisplays such as Powerwalls or tiled display walls driven by multiple GPUs or even GPU clusters. Such visualisation systemsare typically rather unique in their setup of hardware and software which makes transferring a visualisation application fromone high-resolution system to another one a complicated task. As more and more such visualisation systems get installed,collaboration becomes desirable in the sense of sharing such a visualisation running on one site in real time with another high-resolution display on a remote site while at the same time communicating via video and audio. Since typical video conferencesolutions or web-based collaboration tools often cannot deal with resolutions exceeding 4K, with stereo displays or with multi-GPU setups, we designed and implemented a new system based on state-of-the-art hardware and software technologies totransmit high-resolution visualisations including video and audio streams via the internet to remote large displays and back.Our system architecture is built on efficient capturing, encoding and transmission of pixel streams and thus supports a multitudeof configurations combining audio and video streams in a generic approac

Abstract

In this paper, we introduce a visualization technique aimed to help machine learning experts to analyze the hidden states of layers in recurrent neural networks (RNNs). Our technique allows the user to visually inspect how hidden states store and process information throughout the feeding of an input sequence into the network. It can answer questions such as which parts of the input data had a higher impact on the prediction and how the model correlates each hidden state configuration with a certain output. Our visualization comprises several components: our input visualization shows the input sequence and how it relates to the output (using color coding); hidden states are visualized by nonlinear projection to 2-D visualization space via t-SNE in order to understand the shape of the space of hidden states; time curves are employed to show the details of the evolution of hidden state configurations; and a time-multi-class heatmap matrix visualizes the evolution of expected predictions for multi-class classifiers. To demonstrate the capability of our approach, we discuss two typical use cases for long short-term memory (LSTM) models applied to two widely used natural language processing (NLP) datasets.

Abstract

The Joint Workshop on Aesthetic and Technical Quality Assessment of Multimedia and Media Analytics for Societal Trends (ATQAM/ MAST) aims to bring together researchers and professionals working in fields ranging from computer vision, multimedia computing, multimodal signal processing to psychology and social sciences. It is divided into two tracks: ATQAM and MAST. ATQAM track: Visual quality assessment techniques can be divided into image and video technical quality assessment (IQA and VQA, or broadly TQA) and aesthetics quality assessment (AQA). While TQA is a long-standing field, having its roots in media compression, AQA is relatively young. Both have received increased attention with developments in deep learning. The topics have mostly been studied separately, even though they deal with similar aspects of the underlying subjective experience of media. The aim is to bring together individuals in the two fields of TQA and AQA for the sharing of ideas and discussions on current trends, developments, issues, and future directions. MAST track: The research area of media content analytics has been traditionally used to refer to applications involving inference of higher-level semantics from multimedia content. However, multimedia is typically created for human consumption, and we believe it is necessary to adopt a human-centered approach to this analysis, which would not only enable a better understanding of how viewers engage with content but also how they impact each other in the process.

Abstract

We propose ClaVis, a visual analytics system for comparative analysis of classification models. ClaVis allows users to visually compare the performance and behavior of tens to hundreds of classifiers trained with different hyperparameter configurations. Our approach is plugin-based and classifier-agnostic and allows users to add their own datasets and classifier implementations. It provides multiple visualizations, including a multivariate ranking, a similarity map, a scatterplot that reveals correlations between parameters and scores, and a training history chart. We demonstrate the effectivity of our approach in multiple case studies for training classification models in the domain of natural language processing.

Abstract

Every day 1.8+ billion images are being uploaded to Facebook, Instagram, Flickr, Snapchat, and WhatsApp 6. The exponential growth of visual media has made quality assessment become increasingly important for various applications, from image acquisition, synthesis, restoration, and enhancement, to image search and retrieval, storage, and recognition.There have been two related but different classes of visual quality assessment techniques: image quality assessment (IQA) and image aesthetics assessment (IAA). As perceptual assessment tasks, subjective IQA and IAA share some common underlying factors that affect user judgments. Moreover, they are similar in methodology (especially NR-IQA in-the-wild and IAA). However, the emphasis for each is different: IQA focuses on low-level defects e.g. processing artefacts, noise, and blur, while IAA puts more emphasis on abstract and higher-level concepts that capture the subjective aesthetics experience, e.g. established photographic rules encompassing lighting, composition, and colors, and personalized factors such as personality, cultural background, age, and emotion.IQA has been studied extensively over the last decades 3, 14, 22. There are three main types of IQA methods: full-reference (FR), reduced-reference (RR), and no-reference (NR). Among these, NRIQA is the most challenging as it does not depend on reference images or impose strict assumptions on the distortion types and level. NR-IQA techniques can be further divided into those that predict the global image score 1, 2, 10, 17, 26 and patch-based IQA 23, 25, naming a few of the more recent approaches.

Abstract

Deep learning methods for image quality assessment (IQA) are limited due to the small size of existing datasets. Extensive datasets require substantial resources both for generating publishable content and annotating it accurately. We present a systematic and scalable approach to creating KonIQ-10k, the largest IQA dataset to date, consisting of 10,073 quality scored images. It is the first in-the-wild database aiming for ecological validity, concerning the authenticity of distortions, the diversity of content, and quality-related indicators. Through the use of crowdsourcing, we obtained 1.2 million reliable quality ratings from 1,459 crowd workers, paving the way for more general IQA models. We propose a novel, deep learning model (KonCept512), to show an excellent generalization beyond the test set (0.921 SROCC), to the current state-of-the-art database LIVE-in-the-Wild (0.825 SROCC). The model derives its core performance from the InceptionResNet architecture, being trained at a higher resolution than previous models (512 × 384 ). Correlation analysis shows that KonCept512 performs similar to having 9 subjective scores for each test image.

Abstract

Image quality is a key issue affecting the performance of biometric systems. Ensuring the quality of iris images acquired in unconstrained imaging conditions in visible light poses many challenges to iris recognition systems. Poor-quality iris images increase the false rejection rate and decrease the performance of the systems by quality filtering. Methods that can accurately predict iris image quality can improve the efficiency of quality-control protocols in iris recognition systems. We propose a fast blind/no-reference metric for predicting iris image quality. The proposed metric is based on statistical features of the sign and the magnitude of local image intensities. The experiments, conducted with a reference iris recognition system and three datasets of iris images acquired in visible light, showed that the quality of iris images strongly affects the recognition performance and is highly correlated with the iris matching scores. Rejecting poor-quality iris images improved the performance of the iris recognition system. In addition, we analyzed the effect of iris image quality on the accuracy of the iris segmentation module in the iris recognition system.

Abstract

Our work for the first time evaluates the effectiveness of visual and acoustic warning systems in an accident situation using a realistic, immersive driving simulation. In a first experiment, 70 participants were trained to complete a course at high speed. The course contained several forks where a wrong turn would lead to the car falling off a cliff and crashing - these forks were indicated either with a visual warning sign for a first, no-sound group or with a visual and auditory warning cue for a second, sound group. In a testing phase, right after the warning signals were given, trees suddenly fell on the road, leaving the (fatal) turn open. Importantly, in the no-sound group, 18 out of 35 people still chose this turn, whereas in the sound group only 5 out of 35 people did so - the added sound therefore had a large and significant increase in situational awareness. We found no other differences between the groups concerning age, physiological responses, or driving experience. In a second replication experiment, the setup was repeated with another 70 participants without emphasis on driving speed. Results fully confirmed the previous findings with 17 out of 35 people in the no-sound group versus only 6 out of 35 in the sound group choosing the turn to the cliff. With these two experiments using a one-shot design to avoid pre-meditation and testing naïve, rapid decision-making, we provide clear evidence for the advantage of visual-auditory in-vehicle warning systems for promoting situational awareness.

Abstract

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.

Abstract

Movement-compensating interactions like teleportation are commonly deployed techniques in virtual reality environments. Although practical, they tend to cause disorientation while navigating. Previous studies show the effectiveness of orientation-supporting tools, such as trails, in reducing such disorientation and reveal different strengths and weaknesses of individual tools. However, to date, there is a lack of a systematic comparison of those tools when teleportation is used as a movement-compensating technique, in particular under consideration of different tasks. In this paper, we compare the effects of three orientation-supporting tools, namely minimap, trail, and heatmap. We conducted a quantitative user study with 48 participants to investigate the accuracy and efficiency when executing four exploration and search tasks. As dependent variables, task performance, completion time, space coverage, amount of revisiting, retracing time, and memorability were measured. Overall, our results indicate that orientation-supporting tools improve task completion times and revisiting behavior. The trail and heatmap tools were particularly useful for speed-focused tasks, minimal revisiting, and space coverage. The minimap increased memorability and especially supported retracing tasks. These results suggest that virtual reality systems should provide orientation aid tailored to the specific tasks of the users.

Abstract

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.

Abstract

Eye movement data analysis plays an important role in examining human cognitive processes and perceptions. Such analysis at times needs data recording from additional sources too during experiments. In this paper, we study a pair programming based collaboration using two eye trackers, stimulus recording, and an external camera recording. To analyze the collected data, we introduce the EyeSAC system that synchronizes the data from different sources and that removes the noisy and missing gazes from eye tracking data with the help of visual feedback from the external recording. The synchronized and cleaned data is further annotated using our system and then exported for further analysis.

Abstract

Many applications in eye tracking have been increasingly employing neural networks to solve machine learning tasks. In general, neural networks have achieved impressive results in many problems over the past few years, but they still suffer from the lack of interpretability due to their black-box behavior. While previous research on explainable AI has been able to provide high levels of interpretability for models in image classification and natural language processing tasks, little effort has been put into interpreting and understanding networks trained with eye movement datasets. This paper discusses the importance of developing interpretability methods specifically for these models. We characterize the main problems for interpreting neural networks with this type of data, how they differ from the problems faced in other domains, and why existing techniques are not sufficient to address all of these issues. We present preliminary experiments showing the limitations that current techniques have and how we can improve upon them. Finally, based on the evaluation of our experiments, we suggest future research directions that might lead to more interpretable and explainable neural networks for eye tracking.

Abstract

Technical progress in hardware and software enables us to record gaze data in everyday situations and over long time spans. Among a multitude of research opportunities, this technology enables visualization researchers to catch a glimpse behind performance measures and into the perceptual and cognitive processes of people using visualization techniques. The majority of eye tracking studies performed for visualization research is limited to the analysis of gaze distributions and aggregated statistics, thus only covering a small portion of insights that can be derived from gaze data. We argue that incorporating theories and methodology from psychology and cognitive science will benefit the design and evaluation of eye tracking experiments for visualization. This position paper outlines our experiences with eye tracking in visualization and states the benefits that an interdisciplinary research field on visualization psychology might bring for better understanding how people interpret visualizations.

Abstract

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.

Abstract

We propose a new technique for visual analytics and annotation of long-term pervasive eye tracking data for which a combined analysis of gaze and egocentric video is necessary. Our approach enables two important tasks for such data for hour-long videos from individual participants: (1) efficient annotation and (2) direct interpretation of the results. Exemplary time spans can be selected by the user and are then used as a query that initiates a fuzzy search of similar time spans based on gaze and video features. In an iterative refinement loop, the query interface then provides suggestions for the importance of individual features to improve the search results. A multi-layered timeline visualization shows an overview of annotated time spans. We demonstrate the efficiency of our approach for analyzing activities in about seven hours of video in a case study and discuss feedback on our approach from novices and experts performing the annotation task.

Abstract

Assessing visual similarity in-the-wild, a core ability of the human visual system, is a challenging problem for computer vision methods because of its subjective nature and limited annotated datasets. We make a stride forward, showing that visual similarity can be better studied by isolating its components. We identify color composition similarity as an important aspect and study its interaction with category-level similarity. Color composition similarity considers the distribution of colors and their layout in images. We create predictive models accounting for the global similarity that is beyond pixel-based and patch-based, or histogram level information. Using an active learning approach, we build a large-scale color composition similarity dataset with subjective ratings via crowd-sourcing, the first of its kind. We train a Siamese network using the dataset to create a color similarity metric and descriptors which outperform existing color descriptors. We also provide a benchmark for global color descriptors for perceptual color similarity. Finally, we combine color similarity and category level features for fine-grained visual similarity. Our proposed model surpasses the state-of-the-art performance while using three orders of magnitude less training data. The results suggest that our proposal to study visual similarity by isolating its components, modeling and combining them is a promising paradigm for further development.

Abstract

The picture-wise just noticeable difference (PJND) for a given image and a compression scheme is a statistical quantity giving the smallest distortion that a subject can perceive when the image is compressed with the compression scheme. The PJND is determined with subjective assessment tests for a sample of subjects. We introduce and apply two methods of adjustment where the subject interactively selects the distortion level at the PJND using either a slider or keystrokes. We compare the results and times required to those of the adaptive binary search type approach, in which image pairs with distortions that bracket the PJND are displayed and the difference in distortion levels is reduced until the PJND is identified. For the three methods, two images are compared using the flicker test in which the displayed images alternate at a frequency of 8 Hz. Unlike previous work, our goal is a global one, determining the PJND not only for the original pristine image but also for a sequence of compressed versions. Results for the MCL-JCI dataset show that the PJND measurements based on adjustment are comparable with those of the traditional approach using binary search, yet significantly faster. Moreover, we conducted a crowdsourcing study with side-byside comparisons and forced choice, which suggests that the flicker test is more sensitive than a side-by-side comparison.

Abstract

Automated detection of motorcycle helmet use through video surveillance can facilitate efficient education and enforcement campaigns that increase road safety. However, existing detection approaches have a number of shortcomings, such as the inabilities to track individual motorcycles through multiple frames, or to distinguish drivers from passengers in helmet use. Furthermore, datasets used to develop approaches are limited in terms of traffic environments and traffic density variations. In this paper, we propose a CNN-based multi-task learning (MTL) method for identifying and tracking individual motorcycles, and register rider specific helmet use. We further release the HELMET dataset, which includes 91,000 annotated frames of 10,006 individual motorcycles from 12 observation sites in Myanmar. Along with the dataset, we introduce an evaluation metric for helmet use and rider detection accuracy, which can be used as a benchmark for evaluating future detection approaches. We show that the use of MTL for concurrent visual similarity learning and helmet use classification improves the efficiency of our approach compared to earlier studies, allowing a processing speed of more than 8 FPS on consumer hardware, and a weighted average F-measure of 67.3% for detecting the number of riders and helmet use of tracked motorcycles. Our work demonstrates the capability of deep learning as a highly accurate and resource efficient approach to collect critical road safety related data.

Abstract

The satisfied user ratio (SUR) curve for a lossy image compression scheme, e.g., JPEG, characterizes the complementary cumulative distribution function of the just noticeable difference (JND), the smallest distortion level that can be perceived by a subject when a reference image is compared to a distorted one. A sequence of JNDs can be defined with a suitable successive choice of reference images. We propose the first deep learning approach to predict SUR curves. We show how to apply maximum likelihood estimation and the Anderson-Darling test to select a suitable parametric model for the distribution function. We then use deep feature learning to predict samples of the SUR curve and apply the method of least squares to fit the parametric model to the predicted samples. Our deep learning approach relies on a siamese convolutional neural network, transfer learning, and deep feature learning, using pairs consisting of a reference image and a compressed image for training. Experiments on the MCL-JCI dataset showed state-of-the-art performance. For example, the mean Bhattacharyya distances between the predicted and ground truth first, second, and third JND distributions were 0.0810, 0.0702, and 0.0522, respectively, and the corresponding average absolute differences of the peak signal-to-noise ratio at a median of the first JND distribution were 0.58, 0.69, and 0.58 dB. Further experiments on the JND-Pano dataset showed that the method transfers well to high resolution panoramic images viewed on head-mounted displays.

Abstract

Professional video editing tools can generate slow-motion video by interpolating frames from video recorded at astandard frame rate. Thereby the perceptual quality of such in-terpolated slow-motion videos strongly depends on the underlyinginterpolation techniques. We built a novel benchmark databasethat is specifically tailored for interpolated slow-motion videos(KoSMo-1k). It consists of 1,350 interpolated video sequences,from 30 different content sources, along with their subjectivequality ratings from up to ten subjective comparisons per videopair. Moreover, we evaluated the performance of twelve exist-ing full-reference (FR) image/video quality assessment (I/VQA)methods on the benchmark. In this way, we are able to show thatspecifically tailored quality assessment methods for interpolatedslow-motion videos are needed, since the evaluated methods –despite their good performance on real-time video databases – donot give satisfying results when it comes to frame interpolation.

Abstract

Current benchmarks for optical flow algorithms evaluate the estimation either directly by comparing the predicted flow fields with the ground truth or indirectly by using the predicted flow fields for frame interpolation and then comparing the interpolated frames with the actual frames. In the latter case, objective quality measures such as the mean squared error are typically employed. However, it is well known that for image quality assessment, the actual quality experienced by the user cannot be fully deduced from such simple measures. Hence, we conducted a subjective quality assessment crowdscouring study for the interpolated frames provided by one of the optical flow benchmarks, the Middlebury benchmark. It contains interpolated frames from 155 methods applied to each of 8 contents. For this purpose, we collected forced-choice paired comparisons between interpolated images and corresponding ground truth. To increase the sensitivity of observers when judging minute difference in paired comparisons we introduced a new method to the field of full-reference quality assessment, called artefact amplification. From the crowdsourcing data (3720 comparisons of 20 votes each) we reconstructed absolute quality scale values according to Thurstone’s model. As a result, we obtained a re-ranking of the 155 participating algorithms w.r.t. the visual quality of the interpolated frames. This re-ranking not only shows the necessity of visual quality assessment as another evaluation metric for optical flow and frame interpolation benchmarks, the results also provide the ground truth for designing novel image quality assessment (IQA) methods dedicated to perceptual quality of interpolated images. As a first step, we proposed such a new full-reference method, called WAE-IQA, which weights the local differences between an interpolated image and its ground truth.

Abstract

In immersive augmented reality (IAR), users can wear a head-mounted display to see computer-generated images superimposed to their view of the world. IAR was shown to be beneficial across several domains, e.g., automotive, medicine, gaming and engineering, with positive impacts on, e.g., collaboration and communication. We think that IAR bears a great potential for software engineering but, as of yet, this research area has been neglected. In this vision paper, we elicit potentials and obstacles for the use of IAR in software engineering. We identify possible areas that can be supported with IAR technology by relating commonly discussed IAR improvements to typical software engineering tasks. We further demonstrate how innovative use of IAR technology may fundamentally improve typical activities of a software engineer through a comprehensive series of usage scenarios outlining practical application. Finally, we reflect on current limitations of IAR technology based on our scenarios and sketch research activities necessary to make our vision a reality. We consider this paper to be relevant to academia and industry alike in guiding the steps to innovative research and applications for IAR in software engineering.

Abstract

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.

Abstract

We present a systematic review of 45S papers that report on evaluations in mixed and augmented reality (MR/AR) published in ISMAR, CHI, IEEE VR, and UIST over a span of 11 years (2009-2019). Our goal is to provide guidance for future evaluations of MR/AR approaches. To this end, we characterize publications by paper type (e.g., technique, design study), research topic (e.g., tracking, rendering), evaluation scenario (e.g., algorithm performance, user performance), cognitive aspects (e.g., perception, emotion), and the context in which evaluations were conducted (e.g., lab vs. in-thewild). We found a strong coupling of types, topics, and scenarios. We observe two groups: (a) technology-centric performance evaluations of algorithms that focus on improving tracking, displays, reconstruction, rendering, and calibration, and (b) human-centric studies that analyze implications of applications and design, human factors on perception, usability, decision making, emotion, and attention. Amongst the 458 papers, we identified 248 user studies that involved 5,761 participants in total, of whom only 1,619 were identified as female. We identified 43 data collection methods used to analyze 10 cognitive aspects. We found nine objective methods, and eight methods that support qualitative analysis. A majority (216/248) of user studies are conducted in a laboratory setting. Often (138/248), such studies involve participants in a static way. However, we also found a fair number (30/248) of in-the-wild studies that involve participants in a mobile fashion. We consider this paper to be relevant to academia and industry alike in presenting the state-of-the-art and guiding the steps to designing, conducting, and analyzing results of evaluations in MR/AR.

Abstract

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.

Abstract

Visualization in virtual 3D environments can provide a natural way for users to explore data. Often, arm and short head movements are required for interaction in augmented reality, which can be tiring and strenuous though. In an effort toward more user-friendly interaction, we developed a prototype that allows users to manipulate virtual objects using a combination of eye gaze and an external clicker device. Using this prototype, we performed a user study comparing four different input methods of which head gaze plus clicker was preferred by most participants.

Abstract

Often, requirements engineering (RE) activities demand project stakeholders to communicate and collaborate with each other towards building a common software product vision. We conjecture that augmented reality (AR) can be a good fit to support such communication and collaboration. In this vision paper, we report on state-of-the-art research at the intersection of AR and RE. We found that requirements elicitation and analysis have been supported by the ability of AR to provision on-the-fly information such as augmented prototypes. We discuss and map the existing challenges in RE to the aspects of AR that can boost the productivity and user experience of existing RE techniques. Finally, we elaborate on various envisioned usage scenarios in which we highlight concrete benefits and challenges of adopting immersive AR to assist project stakeholders in RE activities.

Abstract

We present a novel variant of parallel coordinates plots (PCPs) in which we show clusters in 2D subspaces of multivariate data and emphasize flow between them. We achieve this by duplicating and stacking individual axes vertically. On a high level, our clusterflow layout shows how data points move from one cluster to another in different subspaces. We achieve cluster-based bundling and limit plot growth through the reduction of available vertical space for each duplicated axis. Although we introduce space between clusters, we preserve the readability of intra-cluster correlations by starting and ending with the original slopes from regular PCPs and drawing Hermite spline segments in between. Moreover, our rendering technique enables the visualization of small and large data sets alike. Cluster-flow PCPs can even propagate the uncertainty inherent to fuzzy clustering through the layout and rendering stages of our pipeline. Our layout algorithm is based on A*. It achieves an optimal result with regard to a novel set of cost functions that allow us to arrange axes horizontally (dimension ordering) and vertically (cluster ordering).

Abstract

Super-resolution increases the resolution of an image. Using evolutionary optimization, we optimize the noise injection of a super-resolution method for improving the results. More generally, our approach can be used to optimize any method based on noise injection.

Abstract

The progress of technology has increased research on neuropsychological emotion and attention with virtual reality (VR). However, direct comparisons between conventional two-dimensional (2D) and VR stimulations are lacking. Thus, the present study compared electroencephalography (EEG) correlates of explicit task and implicit emotional attention between 2D and VR stimulation. Participants (n = 16) viewed angry and neutral faces with equal size and distance in both 2D and VR, while they were asked to count one of the two facial expressions. For the main effects of emotion (angry vs. neutral) and task (target vs. nontarget), established event related potentials (ERP), namely the late positive potential (LPP) and the target P300, were replicated. VR stimulation compared to 2D led to overall bigger ERPs but did not interact with emotion or task effects. In the frequency domain, alpha/beta-activity was larger in VR compared to 2D stimulation already in the baseline period. Of note, while alpha/beta event related desynchronization (ERD) for emotion and task conditions were seen in both VR and 2D stimulation, these effects were significantly stronger in VR than in 2D. These results suggest that enhanced immersion with the stimulus materials enabled by VR technology can potentiate induced brain oscillation effects to implicit emotion and explicit task effects.

Abstract

In this paper we present a case study in which Visual Analytic methods for interactive data exploration are applied to the study of historical linguistics. We discuss why diachronic linguistic data poses special challenges for Visual Analytics and show how these are handled in a collaboratively developed web-based tool: HistoBankVis. HistoBankVis allows an immediate and efficient interaction with underlying diachronic data and we go through an investigation of the interplay between case marking and word order in Icelandic and Old Saxon to illustrate its features. We then discuss challenges posed by the lack of annotation standardization across different corpora as well as the problems we encountered with respect to errors, uncertainty and issues of data provenance. Overall we conclude that the integration of Visual Analytics methodology into the study of language change has an immense potential but that the full realization of its potential will depend on whether issues of data interoperability and annotation standards can be resolved.

Abstract

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.

Abstract

We propose and study a generalization to the well-known problem of polyline simplification. Instead of a single polyline, we are given a set of l polylines possibly sharing some line segments and bend points. Our goal is to minimize the number of bend points in the simplified bundle with respect to some error tolerance δ (measuring Fréchet distance) but under the additional constraint that shared parts have to be simplified consistently. We show that polyline bundle simplification is NP-hard to approximate within a factor n^(1/3 - ε) for any ε > 0 where n is the number of bend points in the polyline bundle. This inapproximability even applies to instances with only l=2 polylines. However, we identify the sensitivity of the solution to the choice of δ as a reason for this strong inapproximability. In particular, we prove that if we allow δ to be exceeded by a factor of 2 in our solution, we can find a simplified polyline bundle with no more thanO(log (l + n)) ⋅ OPT bend points in polytime, providing us with an efficient bi-criteria approximation. As a further result, we show fixed-parameter tractability in the number of shared bend points.

Abstract

Given a polyline consisting of n segments, we study the problem of selecting k of its segments such that the maximum induced gap length without a selected segment is minimized. This optimization problem has applications in the domains of trajectory visualization and facility location. We design several heuristics and exact algorithms for simple polylines, along with algorithm engineering techniques to achieve good practical running times even for large values of n and k. The fastest exact algorithm is based on dynamic programming and exhibits a running time of O(nk) while using space linear in n. Furthermore, we consider incremental problem variants. For the case where a given set of k segments shall be augmented by a single additional segment, we devise an optimal algorithm which runs in O(k + log n) on a suitable polyline representation. If not only a single segment but k' segments shall be added, we can compute the optimal segment set in time O(nk') by modifying the dynamic programming approach for the original problem. Experiments on large sets of real-world trajectories as well as artificial polylines show the trade-offs between quality and running time of the different approaches.

Abstract

With the growing interest in Immersive Analytics, there is also a need for novel and suitable input modalities for such applications. We explore eye tracking, head tracking, hand motion tracking, and data gloves as input methods for a 2D tracing task and compare them to touch input as a baseline in an exploratory user study (N=20). We compare these methods in terms of user experience, workload, accuracy, and time required for input. The results show that the input method has a significant influence on these measured variables. While touch input surpasses all other input methods in terms of user experience, workload, and accuracy, eye tracking shows promise in respect of the input time. The results form a starting point for future research investigating input methods.

Abstract

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.

Abstract

Appropriate evaluation is a key component in visualization research. It is typically based on empirical studies that assess visualization components or complete systems. While such studies often include the user of the visualization, empirical research is not necessarily restricted to user studies but may also address the technical performance of a visualization system such as its computational speed or memory consumption. Any such empirical experiment faces the issue that the underlying visualization is becoming increasingly sophisticated, leading to an increasingly difficult evaluation in complex environments. Therefore, many of the established methods of empirical studies can no longer capture the full complexity of the evaluation. One promising solution is the use of data-rich observations that we can acquire during studies to obtain more reliable interpretations of empirical research. For example, we have been witnessing an increasing availability and use of physiological sensor information from eye tracking, electrodermal activity sensors, electroencephalography, etc. Other examples are various kinds of logs of user activities such as mouse, keyboard, or touch interaction. Such data-rich empirical studies promise to be especially useful for studies in the wild and similar scenarios outside of the controlled laboratory environment. However, with the growing availability of large, complex, time-dependent, heterogeneous, and unstructured observational data, we are facing the new challenge of how we can analyze such data. This challenge can be addressed by establishing the subfield of visualization for visualization (Vis4Vis): visualization as a means of analyzing and communicating data from empirical studies to advance visualization research.

Abstract

Video streaming under real-time constraints is an increasingly widespread application. Many recent video encoders are unsuitable for this scenario due to theoretical limitations or run time requirements. In this paper, we present a framework for the perceptual evaluation of foveated video coding schemes. Foveation describes the process of adapting a visual stimulus according to the acuity of the human eye. In contrast to traditional region-of-interest coding, where certain areas are statically encoded at a higher quality, we utilize feedback from an eye-tracker to spatially steer the bit allocation scheme in real-time. We evaluate the performance of an H.264 based foveated coding scheme in a lab environment by comparing the bitrates at the point of just noticeable distortion (JND). Furthermore, we identify perceptually optimal codec parameterizations. In our trials, we achieve an average bitrate savings of 63.24% at the JND in comparison to the unfoveated baseline.

Abstract

This paper presents current methodologies and challenges in the context of subjective quality assessment with a focus on adaptively encoded video streams.

Abstract

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.

Abstract

Saliency has been widely studied in relation to image quality assessment (IQA). The optimal use of saliency in IQA metrics, however, is nontrivial and largely depends on whether saliency can be accurately predicted for images containing various distortions. Although tremendous progress has been made in saliency modelling, very little is known about whether and to what extent state-of-the-art methods are beneficial for saliency prediction of distorted images. In this paper, we analyse the ability of deep learning versus traditional algorithms in predicting saliency, based on an IQA-aware saliency benchmark, the SIQ288 database. Building off the variations in model performance, we make recommendations for model selections for IQA applications.

Abstract

We propose a photographic method to show scalar values of high dynamic range (HDR) by color mapping for 2D visualization. We combine (1) tone-mapping operators that transform the data to the display range of the monitor while preserving perceptually important features, based on a systematic evaluation, and (2) simulated glares that highlight high-value regions. Simulated glares are effective for highlighting small areas (of a few pixels) that may not be visible with conventional visualizations; through a controlled perception study, we confirm that glare is preattentive. The usefulness of our overall photographic HDR visualization is validated through the feedback of expert users.

Abstract

Gaze tracking in 3D has the potential to improve interaction with objects and visualizations in augmented reality. However, previous research showed that subjective perception of distance varies between real and virtual surroundings. We wanted to determine whether objectively measured 3D gaze depth through eye tracking also exhibits differences between entirely real and augmented environments. To this end, we conducted an experiment (N = 25) in which we used Microsoft HoloLens with a binocular eye tracking add-on from Pupil Labs. Participants performed a task that required them to look at stationary real and virtual objects while wearing a HoloLens device. We were not able to find significant differences in the gaze depth measured by eye tracking. Finally, we discuss our findings and their implications for gaze interaction in immersive analytics, and the quality of the collected gaze data.

Abstract

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.

Abstract

User-adaptive systems are a current trend in technological development. Such systems are designed to sense the user’s status based on ongoing interaction and automatically change certain features (e.g. content, interface, or interaction capabilities) in order to provide a targeted, personalized experience. In this scenario, users are likely to adapt to the evolving characteristics of the interaction (Burge et al., 2008), changing their own behavior to correctly interact with such systems and thereby leading to dynamics of mutual adaptation between human and machine. We investigated such mutual adaptation dynamics within a visuo-motor adaptation paradigm. Participants were instructed to perform fast pointing movements on a graphic tablet as accurately as possible, while also seeking to minimize the error between target and feedback location on a screen in front of them. The feedback location reflected the pointing performance of the user according to the underlying tablet-to-screen mapping, which changed systematically over time due to the introduction of a step offset. Concurrently, an adaptive algorithm corrected the feedback location according to an estimation of the participant’s error, thus contributing to the reduction of the displayed error over trials. In different experimental conditions, the extent of such contributions varied systematically, and we measured the adaptive performance of the human-machine system as a whole, as well as the underlying motor performance of participants. The greater the correction introduced by the adaptive algorithm, the more effective was the joint system in reducing visual error after the introduction of the step offset. On the other hand, when considering human’s motor behavior alone, the pointing error did not decrease, but tended to increase over time with higher contributions from the algorithm. Our findings indicate that, in order to obtain desired outcomes from interactions with user-adaptive technology, the sensorimotor mechanisms underlying such interactions must be considered.

Abstract

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.

Abstract

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).

Abstract

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

Abstract

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.

Abstract

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.

Abstract

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.

Abstract

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.

Abstract

The Satisfied User Ratio (SUR) curve for a lossy image compression scheme, e.g., JPEG, characterizes the probability distribution of the Just Noticeable Difference (JND) level, the smallest distortion level that can be perceived by a subject. We propose the first deep learning approach to predict such SUR curves. Instead of the direct approach of regressing the SUR curve itself for a given reference image, our model is trained on pairs of images, original and compressed. Relying on a Siamese Convolutional Neural Network (CNN), feature pooling, a fully connected regression-head, and transfer learning, we achieved a good prediction performance. Experiments on the MCL-JCI dataset showed a mean Bhattacharyya distance between the predicted and the original JND distributions of only 0.072.

Abstract

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

Abstract

We propose an effective deep learning approach to aesthetics quality assessment that relies on a new type of pre-trained features, and apply it to the AVA data set, the currently largest aesthetics database. While previous approaches miss some of the information in the original images, due to taking small crops, down-scaling or warping the originals during training, we propose the first method that efficiently supports full resolution images as an input, and can be trained on variable input sizes. This allows us to significantly improve upon the state of the art, increasing the Spearman rank-order correlation coefficient (SRCC) of ground-truth mean opinion scores (MOS) from the existing best reported of 0.612 to 0.756. To achieve this performance, we extract multi-level spatially pooled (MLSP) features from all convolutional blocks of a pre-trained InceptionResNet-v2 network, and train a custom shallow Convolutional Neural Network (CNN) architecture on these new features.

Abstract

Analysing and understanding brain function and disorder is the main focus of neuroscience. Due to the high complexity of the brain, directionality of the signal and changing activity over time, visual exploration and data analysis are difficult. For this reason, a vast amount of research challenges are still unsolved. We explored different challenges of the visual analysis of brain data and the design of corresponding immersive environments in collaboration with experts from the biomedical domain. We built a prototype of an immersive virtual reality environment to explore the design space and to investigate how brain data analysis can be supported by a variety of design choices. Our environment can be used to study the effect of different visualisations and combinations of brain data representation, as for example network layouts, anatomical mapping or time series. As a long-term goal, we aim to aid neuro-scientists in a better understanding of brain function and disorder.

Abstract

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.

Abstract

Understanding the movement of animals is important for a wide range of scientific interests including migration, disease spread, collective movement behaviour and analysing motion in relation to dynamic changes of the environment such as wind and thermal lifts. Particularly, the three-dimensional (3D) spatial–temporal nature of bird movement data, which is widely available with high temporal and spatial resolution at large volumes, presents a natural option to explore the potential of immersive analytics (IA). We investigate the requirements and benefits of a wide range of immersive environments for explorative visualization and analytics of 3D movement data, in particular regarding design considerations for such 3D immersive environments, and present prototypes for IA solutions. Tailored to biologists studying bird movement data, the immersive solutions enable geo-locational time-series data to be investigated interactively, thus enabling experts to visually explore interesting angles of a flock and its behaviour in the context of the environment. The 3D virtual world presents the audience with engaging and interactive content, allowing users to ‘fly with the flock’, with the potential to ascertain an intuitive overview of often complex datasets, and to provide the opportunity thereby to formulate and at least qualitatively assess hypotheses. This work also contributes to ongoing research efforts to promote better understanding of bird migration and the associated environmental factors at the global scale, thereby providing a visual vehicle for driving public awareness of environmental issues and bird migration patterns.

Abstract

Recent advances in tracking technology allow biologists to collect large amounts of movement data for a variety of species. Analysis of the collected data supports research on animal behaviour, influence of impact factors such as climate change and human intervention, as well as conservation programs. Analysis of the movement data is difficult, due to the nature of the research questions and the complexity of the data sets. It requires both automated analysis, e.g. for the detection of behavioural patterns, and human inspection, e.g. for interpretation, inclusion of previous knowledge, and for conclusions on future actions and decision making. We present a concept and implementation for the visual analysis of cheetah movement data in a web-based fashion that allows usage both in the field and in office environments.

Abstract

Current artificially distorted image quality assessment (IQA) databases are small in size and limited in content. Larger IQA databases that are diverse in content could benefit the development of deep learning for IQA. We create two datasets, the Konstanz Artificially Distorted Image quality Database (KADID-10k) and the Konstanz Artificially Distorted Image quality Set (KADIS-700k). The former contains 81 pristine images, each degraded by 25 distortions in 5 levels. The latter has 140,000 pristine images, with 5 degraded versions each, where the distortions are chosen randomly. We conduct a subjective IQA crowdsourcing study on KADID-10k to yield 30 degradation category ratings (DCRs) per image. We believe that the annotated set KADID-10k, together with the unlabelled set KADIS-700k, can enable the full potential of deep learning based IQA methods by means of weakly-supervised learning.

Abstract

Current benchmarks for optical flow algorithms evaluate the estimation quality by comparing their predicted flow field with the ground truth, and additionally may compare interpolated frames, based on these predictions, with the correct frames from the actual image sequences. For the latter comparisons, objective measures such as mean square errors are applied. However, for applications like image interpolation, the expected user's quality of experience cannot be fully deduced from such simple quality measures. Therefore, we conducted a subjective quality assessment study by crowdsourcing for the interpolated images provided in one of the optical flow benchmarks, the Middlebury benchmark. We used paired comparisons with forced choice and reconstructed absolute quality scale values according to Thurstone's model using the classical least squares method. The results give rise to a re-ranking of 141 participating algorithms w.r.t. visual quality of interpolated frames mostly based on optical flow estimation. Our re-ranking result shows the necessity of visual quality assessment as another evaluation metric for optical flow and frame interpolation benchmarks.

Abstract

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.

Abstract

This work presents a visual analytics approach to explore microsaccade distributions in high-frequency eye tracking data. Research studies often apply filter algorithms and parameter values for microsaccade detection. Even when the same algorithms are employed, different parameter values might be adopted across different studies. In this paper, we present a visual analytics system (VisME) to promote reproducibility in the data analysis of microsaccades. It allows users to interactively vary the parametric values for microsaccade filters and evaluate the resulting influence on microsaccade behavior across individuals and on a group level. In particular, we exploit brushing-and-linking techniques that allow the microsaccadic properties of space, time, and movement direction to be extracted, visualized, and compared across multiple views. We demonstrate in a case study the use of our visual analytics system on data sets collected from natural scene viewing and show in a qualitative usability study the usefulness of this approach for eye tracking researchers. We believe that interactive tools such as VisME will promote greater transparency in eye movement research by providing researchers with the ability to easily understand complex eye tracking data sets; such tools can also serve as teaching systems. VisME is provided as open source software.

Abstract

The advent of modern and affordable augmented reality head sets like Microsoft HoloLens has sparked new interest in using virtual and augmented reality technology in the analysis of molecular data. For all visualisation in immersive, mixed-reality scenarios, a sufficiently high rendering speed is an important factor, which leads to the issue of limited processing power available on fully untethered devices facing the situation of handling computationally expensive visualisations. Recent research shows that the space-filling model of even small data sets from the Protein Data Bank (PDB) cannot be rendered at desirable frame rates on the HoloLens. In this work, we report on how to improve the rendering speed of atom-based visualisation of proteins and how the rendering of more abstract representations of the molecules compares against it. We complement our findings with in-depth GPU and CPU performance numbers.

Abstract

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.

Abstract

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.

Abstract

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.

Abstract

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.

Abstract

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.

Abstract

Visual analytics (VA) research provides helpful solutions for interactive visual data analysis when exploring large and complex datasets. Due to recent advances in eye tracking technology, promising opportunities arise to extend these traditional VA approaches. Therefore, we discuss foundations for eye tracking support in VA systems. We first review and discuss the structure and range of typical VA systems. Based on a widely used VA model, we present five comprehensive examples that cover a wide range of usage scenarios. Then, we demonstrate that the VA model can be used to systematically explore how concrete VA systems could be extended with eye tracking, to create supportive and adaptive analytics systems. This allows us to identify general research and application opportunities, and classify them into research themes. In a call for action, we map the road for future research to broaden the use of eye tracking and advance visual analytics.

Abstract

The Tiled Stereoscopic 3D Display Wall (TS3DW) is a monitor system consisting of six consumer 3D TVs. Two monitors reside on a mobile display mount. One standard configuration is to use them in a 135-degree angle to each other, having one mobile mount in the center, and one at each side. In this way, the system can be transported to multiple locations across a campus as well as used in different application scenarios. This system was already used for a number of research projects and presentations.

Abstract

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.

Abstract

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.

Abstract

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.

Abstract

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.

Abstract

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.

Abstract

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.

Abstract

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

Abstract

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

Abstract

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.

Abstract

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.

Abstract

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.

Abstract

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.

Abstract

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.

Abstract

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.

Abstract

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.

Abstract

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.

Abstract

Chlamydomonas reinhardtii cells have been in the focus of research for more than a decade, in particular due to its use as alternative source for energy production. However, the molecular processes in these cells are still not completely known, and 3D visualisations may help to understand these complex interactions and processes. In previous work, we presented the stereoscopic 3D (S3D) visualisation of a complete Chlamydomonas reinhardtii cell created with the 3D modelling framework Blender. This animation contained already a scene showing an illustrative membrane model of the thylakoid membrane. During discussion with domain experts, shortcomings of the visualisation for several detailed analysis questions have been identified and it was decided to redefine it.A new modelling and visualisation pipeline based on a Membrane Packing Algorithm was developed, which can be configured via a user interface, enabling the composition of membranes employing published material. An expert user study was conducted to evaluate this new approach, with half the participants having a biology and the other half having an informatics background. The new and old Chlamydomonas thylakoid membrane models were presented on a S3D back projection system. The evaluation results reveal that the majority of participants preferred the new, more realistic membrane visualisation. However, the opinion varied with the expertise, leading to valuable conclusions for future visualisations. Interestingly, the S3D presentation of molecular structures lead to a positive change in opinion regarding S3D technology.

Abstract

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.

Abstract

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.

Abstract

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.

Abstract

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

Abstract

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.

Abstract

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

Abstract

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.

Abstract

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.

Abstract

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.

Abstract

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.

Abstract

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.

Abstract

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.

Abstract

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.

Abstract

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.

Abstract

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.

Abstract

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.

Abstract

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.

Abstract

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.

Abstract

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.

Abstract

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.

Abstract

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.

Abstract

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.

Abstract

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.

Abstract

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.

Abstract

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.

Abstract

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.

Abstract

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.

Abstract

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.

Abstract

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.

Abstract

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.

Abstract

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.

Abstract

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.

Abstract

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.

Abstract

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.

Abstract

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.

Abstract

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.

Abstract

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.