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Now showing 1 - 10 of 36
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    Sketching Vocabulary for Crowd Motion
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Mathew, C. D. Tharindu; Benes, Bedrich; Aliaga, Daniel; Dominik L. Michels; Soeren Pirk
    This paper proposes and evaluates a sketching language to author crowd motion. It focuses on the path, speed, thickness, and density parameters of crowd motion. A sketch-based vocabulary is proposed for each parameter and evaluated in a user study against complex crowd scenes. A sketch recognition pipeline converts the sketches into a crowd simulation. The user study results show that 1) participants at various skill levels and can draw accurate crowd motion through sketching, 2) certain sketch styles lead to a more accurate representation of crowd parameters, and 3) sketching allows to produce complex crowd motions in a few seconds. The results show that some styles although accurate actually are less preferred over less accurate ones.
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    LineageD: An Interactive Visual System for Plant Cell Lineage Assignments based on Correctable Machine Learning
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Hong, Jiayi; Trubuil, Alain; Isenberg, Tobias; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
    We describe LineageD-a hybrid web-based system to predict, visualize, and interactively adjust plant embryo cell lineages. Currently, plant biologists explore the development of an embryo and its hierarchical cell lineage manually, based on a 3D dataset that represents the embryo status at one point in time. This human decision-making process, however, is time-consuming, tedious, and error-prone due to the lack of integrated graphical support for specifying the cell lineage. To fill this gap, we developed a new system to support the biologists in their tasks using an interactive combination of 3D visualization, abstract data visualization, and correctable machine learning to modify the proposed cell lineage. We use existing manually established cell lineages to obtain a neural network model. We then allow biologists to use this model to repeatedly predict assignments of a single cell division stage. After each hierarchy level prediction, we allow them to interactively adjust the machine learning based assignment, which we then integrate into the pool of verified assignments for further predictions. In addition to building the hierarchy this way in a bottom-up fashion, we also offer users to divide the whole embryo and create the hierarchy tree in a top-down fashion for a few steps, improving the ML-based assignments by reducing the potential for wrong predictions. We visualize the continuously updated embryo and its hierarchical development using both 3D spatial and abstract tree representations, together with information about the model's confidence and spatial properties. We conducted case study validations with five expert biologists to explore the utility of our approach and to assess the potential for LineageD to be used in their daily workflow. We found that the visualizations of both 3D representations and abstract representations help with decision making and the hierarchy tree top-down building approach can reduce assignments errors in real practice.
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    Neural Flow Map Reconstruction
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Sahoo, Saroj; Lu, Yuzhe; Berger, Matthew; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
    In this paper we present a reconstruction technique for the reduction of unsteady flow data based on neural representations of time-varying vector fields. Our approach is motivated by the large amount of data typically generated in numerical simulations, and in turn the types of data that domain scientists can generate in situ that are compact, yet useful, for post hoc analysis. One type of data commonly acquired during simulation are samples of the flow map, where a single sample is the result of integrating the underlying vector field for a specified time duration. In our work, we treat a collection of flow map samples for a single dataset as a meaningful, compact, and yet incomplete, representation of unsteady flow, and our central objective is to find a representation that enables us to best recover arbitrary flow map samples. To this end, we introduce a technique for learning implicit neural representations of time-varying vector fields that are specifically optimized to reproduce flow map samples sparsely covering the spatiotemporal domain of the data. We show that, despite aggressive data reduction, our optimization problem - learning a function-space neural network to reproduce flow map samples under a fixed integration scheme - leads to representations that demonstrate strong generalization, both in the field itself, and using the field to approximate the flow map. Through quantitative and qualitative analysis across different datasets we show that our approach is an improvement across a variety of data reduction methods, and across a variety of measures ranging from improved vector fields, flow maps, and features derived from the flow map.
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    SurfNet: Learning Surface Representations via Graph Convolutional Network
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Han, Jun; Wang, Chaoli; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
    For scientific visualization applications, understanding the structure of a single surface (e.g., stream surface, isosurface) and selecting representative surfaces play a crucial role. In response, we propose SurfNet, a graph-based deep learning approach for representing a surface locally at the node level and globally at the surface level. By treating surfaces as graphs, we leverage a graph convolutional network to learn node embedding on a surface. To make the learned embedding effective, we consider various pieces of information (e.g., position, normal, velocity) for network input and investigate multiple losses. Furthermore, we apply dimensionality reduction to transform the learned embeddings into 2D space for understanding and exploration. To demonstrate the effectiveness of SurfNet, we evaluate the embeddings in node clustering (node-level) and surface selection (surface-level) tasks. We compare SurfNet against state-of-the-art node embedding approaches and surface selection methods. We also demonstrate the superiority of SurfNet by comparing it against a spectral-based mesh segmentation approach. The results show that SurfNet can learn better representations at the node and surface levels with less training time and fewer training samples while generating comparable or better clustering and selection results.
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    VIBE: A Design Space for VIsual Belief Elicitation in Data Journalism
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Mahajan, Shambhavi; Chen, Bonnie; Karduni, Alireza; Kim, Yea-Seul; Wall, Emily; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
    The process of forming, expressing, and updating beliefs from data plays a critical role in data-driven decision making. Effectively eliciting those beliefs has potential for high impact across a broad set of applications, including increased engagement with data and visualizations, personalizing visualizations, and understanding users' visual reasoning processes, which can inform improved data analysis and decision making strategies (e.g., via bias mitigation). Recently, belief-driven visualizations have been used to elicit and visualize readers' beliefs in a visualization alongside data in narrative media and data journalism platforms such as the New York Times and FiveThirtyEight. However, there is little research on different aspects that constitute designing an effective belief-driven visualization. In this paper, we synthesize a design space for belief-driven visualizations based on formative and summative interviews with designers and visualization experts. The design space includes 7 main design considerations, beginning with an assumed data set, then structured according to: from who, why, when, what, and how the belief is elicited, and the possible feedback about the belief that may be provided to the visualization viewer. The design space covers considerations such as the type of data parameter with optional uncertainty being elicited, interaction techniques, and visual feedback, among others. Finally, we describe how more than 24 existing belief-driven visualizations from popular news media outlets span the design space and discuss trends and opportunities within this space.
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    Seeing Through Sounds: Mapping Auditory Dimensions to Data and Charts for People with Visual Impairments
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Wang, Ruobin; Jung, Crescentia; Kim, Yea-Seul; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
    Sonification can be an effective medium for people with visual impairments to understand data in visualizations. However, there are no universal design principles that apply to various charts that encode different data types. Towards generalizable principles, we conducted an exploratory experiment to assess how different auditory channels (e.g., pitch, volume) impact the data and visualization perception among people with visual impairments. In our experiment, participants evaluated the intuitiveness and accuracy of the mapping of auditory channels on different data and chart types. We found that participants rated pitch to be the most intuitive, while the number of tappings and the length of sounds yielded the most accurate perception in decoding data. We study how audio channels can intuitively represent different charts and demonstrate that data-level perception might not directly transfer to chart-level perception as participants reflect on visual aspects of the charts while listening to audio. We conclude by how future experiments can be designed to establish a robust ranking for creating audio charts.
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    HyperNP: Interactive Visual Exploration of Multidimensional Projection Hyperparameters
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Appleby, Gabriel; Espadoto, Mateus; Chen, Rui; Goree, Samuel; Telea, Alexandru C.; Anderson, Erik W.; Chang, Remco; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
    Projection algorithms such as t-SNE or UMAP are useful for the visualization of high dimensional data, but depend on hyperparameters which must be tuned carefully. Unfortunately, iteratively recomputing projections to find the optimal hyperparameter values is computationally intensive and unintuitive due to the stochastic nature of such methods. In this paper we propose HyperNP, a scalable method that allows for real-time interactive hyperparameter exploration of projection methods by training neural network approximations. A HyperNP model can be trained on a fraction of the total data instances and hyperparameter configurations that one would like to investigate and can compute projections for new data and hyperparameters at interactive speeds. HyperNP models are compact in size and fast to compute, thus allowing them to be embedded in lightweight visualization systems. We evaluate the performance of HyperNP across three datasets in terms of performance and speed. The results suggest that HyperNP models are accurate, scalable, interactive, and appropriate for use in real-world settings.
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    A Flip-book of Knot Diagrams for Visualizing Surfaces in 4-Space
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Liu, Huan; Zhang, Hui; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
    Just as 2D shadows of 3D curves lose structure where lines cross, 3D graphics projections of smooth 4D topological surfaces are interrupted where one surface intersects itself. They twist, turn, and fold back on themselves, leaving important but hidden features behind the surface sheets. In this paper, we propose a smart slicing tool that can read the 4D surface in its entropy map and suggest the optimal way to generate cross-sectional images - or ''slices'' - of the surface to visualize its underlying 4D structure. Our visualization thinks of a 4D-embedded surface as a collection of 3D curves stacked in time, very much like a flip-book animation, where successive terms in the sequence differ at most by a critical change. This novel method can generate topologically meaningful visualization to depict complex and unfamiliar 4D surfaces, with the minimum number of cross-sectional diagrams. Our approach has been successfully used to create flip-books of diagrams to visualize a range of known 4D surfaces. In this preliminary study, our results show that the new visualization and slicing tool can help the viewers to understand and describe the complex spatial relationships and overall structures of 4D surfaces.
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    Mobile and Multimodal? A Comparative Evaluation of Interactive Workplaces for Visual Data Exploration
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) León, Gabriela Molina; Lischka, Michael; Luo, Wei; Breiter, Andreas; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
    Mobile devices are increasingly being used in the workplace. The combination of touch, pen, and speech interaction with mobile devices is considered particularly promising for a more natural experience. However, we do not yet know how everyday work with multimodal data visualizations on a mobile device differs from working in the standard WIMP workplace setup. To address this gap, we created a visualization system for social scientists, with a WIMP interface for desktop PCs, and a multimodal interface for tablets. The system provides visualizations to explore spatio-temporal data with consistent WIMP and multimodal interaction techniques. To investigate how the different combinations of devices and interaction modalities affect the performance and experience of domain experts in a work setting, we conducted an experiment with 16 social scientists where they carried out a series of tasks with both interfaces. Participants were significantly faster and slightly more accurate on the WIMP interface. They solved the tasks with different strategies according to the interaction modalities available. The pen was the most used and appreciated input modality. Most participants preferred the multimodal setup and could imagine using it at work. We present our findings, together with their implications for the interaction design of data visualizations.
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    Exploring How Visualization Design and Situatedness Evoke Compassion in the Wild
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Morais, Luiz; Andrade, Nazareno; Sousa, Dandara; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
    This work explores how the design and situatedness of data representations affect people's compassion with a case study concerning harassment episodes in a public place. Results contribute to advancing the understanding of how visualizations can evoke emotions and their impact on prosocial behaviors, such as helping people in need. Recent literature examined the effect of different on-screen data representations on emotion or prosociality, but little has been done concerning visualizations shown in a public place - especially a space contextually relevant to the data - or presented through unconventional media formats such as physical marks. We conducted two in-the-wild studies to investigate how different factors affect people's selfreported compassion and intention to donate. We compared three ways of presenting data about the harassment cases: (1) communicating data only verbally; (2) using a printed poster with aggregated information; and (3) using a physicalization with detailed information about each story. We found that the physicalization influenced people to donate more than only hearing about the data, but it is unclear if the same applied to the poster visualization. Also, passers-by reported a likely small increase in compassion when they saw the physicalization instead of the poster. We also examined the role of situatedness by showing the physicalization in a site that is not contextually relevant to the data. Our results suggest that people had a similar intention to donate and levels of compassion in both places. Those findings may indicate that using specific visualization designs to support campaigns about sensitive causes (e.g., sexual harassment) can increase the emotional response of passers-by and may motivate them to help, independently of where the data representation is shown. Finally, this work also informs on the strengths and weaknesses of using research in the wild to evaluate data visualizations in public spaces.