DevLan/vispubs huggingface dataset:
TimeSplines: Sketch-Based Authoring of Flexible and Idiosyncratic Timelines
Authors: Anna Offenwanger, Matthew Brehmer, Fanny Chevalier, Theophanis Tsandilas
Published at Vis, 2023

Timelines are essential for visually communicating chronological narratives and reflecting on the personal and cultural significance of historical events. Existing visualization tools tend to support conventional linear representations, but fail to capture personal idiosyncratic conceptualizations of time. In response, we built TimeSplines, a visualization authoring tool that allows people to sketch multiple free-form temporal axes and populate them with heterogeneous, time-oriented data via incremental and lazy data binding. Authors can bend, compress, and expand temporal axes to emphasize or de-emphasize intervals based on their personal importance; they can also annotate the axes with text and figurative elements to convey contextual information. The results of two user studies show how people appropriate the concepts in TimeSplines to express their own conceptualization of time, while our curated gallery of images demonstrates the expressive potential of our approach.
TimeTuner: Diagnosing Time Representations for Time-Series Forecasting with Counterfactual Explanations
Authors: Jianing Hao, Qing Shi, Yilin Ye, Wei Zeng 0004
Published at Vis, 2023

Deep learning (DL) approaches are being increasingly used for time-series forecasting, with many efforts devoted to designing complex DL models. Recent studies have shown that the DL success is often attributed to effective data representations, fostering the fields of feature engineering and representation learning. However, automated approaches for feature learning are typically limited with respect to incorporating prior knowledge, identifying interactions among variables, and choosing evaluation metrics to ensure that the models are reliable. To improve on these limitations, this paper contributes a novel visual analytics framework, namely TimeTuner, designed to help analysts understand how model behaviors are associated with localized correlations, stationarity, and granularity of time-series representations. The system mainly consists of the following two-stage technique: We first leverage counterfactual explanations to connect the relationships among time-series representations, multivariate features and model predictions. Next, we design multiple coordinated views including a partition-based correlation matrix and juxtaposed bivariate stripes, and provide a set of interactions that allow users to step into the transformation selection process, navigate through the feature space, and reason the model performance. We instantiate TimeTuner with two transformation methods of smoothing and sampling, and demonstrate its applicability on real-world time-series forecasting of univariate sunspots and multivariate air pollutants. Feedback from domain experts indicates that our system can help characterize time-series representations and guide the feature engineering processes.
Too Many Cooks: Exploring How Graphical Perception Studies Influence Visualization Recommendations in Draco
Authors: Zehua Zeng, Junran Yang, Dominik Moritz, Jeffrey Heer, Leilani Battle
Published at Vis, 2023

Findings from graphical perception can guide visualization recommendation algorithms in identifying effective visualization designs. However, existing algorithms use knowledge from, at best, a few studies, limiting our understanding of how complementary (or contradictory) graphical perception results influence generated recommendations. In this paper, we present a pipeline of applying a large body of graphical perception results to develop new visualization recommendation algorithms and conduct an exploratory study to investigate how results from graphical perception can alter the behavior of downstream algorithms. Specifically, we model graphical perception results from 30 papers in Draco—a framework to model visualization knowledge—to develop new recommendation algorithms. By analyzing Draco-generated algorithms, we showcase the feasibility of our method to (1) identify gaps in existing graphical perception literature informing recommendation algorithms, (2) cluster papers by their preferred design rules and constraints, and (3) investigate why certain studies can dominate Draco's recommendations, whereas others may have little influence. Given our findings, we discuss the potential for mutually reinforcing advancements in graphical perception and visualization recommendation research.
TopoSZ: Preserving Topology in Error-Bounded Lossy Compression
Authors: Lin Yan 0003, Xin Liang 0001, Hanqi Guo 0001, Bei Wang 0001
Published at Vis, 2023

Existing error-bounded lossy compression techniques control the pointwise error during compression to guarantee the integrity of the decompressed data. However, they typically do not explicitly preserve the topological features in data. When performing post hoc analysis with decompressed data using topological methods, preserving topology in the compression process to obtain topologically consistent and correct scientific insights is desirable. In this paper, we introduce TopoSZ, an error-bounded lossy compression method that preserves the topological features in 2D and 3D scalar fields. Specifically, we aim to preserve the types and locations of local extrema as well as the level set relations among critical points captured by contour trees in the decompressed data. The main idea is to derive topological constraints from contour-tree-induced segmentation from the data domain, and incorporate such constraints with a customized error-controlled quantization strategy from the SZ compressor (version 1.4). Our method allows users to control the pointwise error and the loss of topological features during the compression process with a global error bound and a persistence threshold.
TransforLearn: Interactive Visual Tutorial for the Transformer Model
Authors: Lin Gao, Zekai Shao, Ziqin Luo, Haibo Hu 0002, Cagatay Turkay, Siming Chen 0001
Published at Vis, 2023

The widespread adoption of Transformers in deep learning, serving as the core framework for numerous large-scale language models, has sparked significant interest in understanding their underlying mechanisms. However, beginners face difficulties in comprehending and learning Transformers due to its complex structure and abstract data representation. We present TransforLearn, the first interactive visual tutorial designed for deep learning beginners and non-experts to comprehensively learn about Transformers. TransforLearn supports interactions for architecture-driven exploration and task-driven exploration, providing insight into different levels of model details and their working processes. It accommodates interactive views of each layer's operation and mathematical formula, helping users to understand the data flow of long text sequences. By altering the current decoder-based recursive prediction results and combining the downstream task abstractions, users can deeply explore model processes. Our user study revealed that the interactions of TransforLearn are positively received. We observe that TransforLearn facilitates users' accomplishment of study tasks and a grasp of key concepts in Transformer effectively.
Transitioning to a Commercial Dashboarding System: Socio-Technical Observations and Opportunities
Authors: Conny Walchshofer, Vaishali Dhanoa, Marc Streit, Miriah Meyer
Published at Vis, 2023

Many long-established, traditional manufacturing businesses are becoming more digital and data-driven to improve their production. These companies are embracing visual analytics in these transitions through their adoption of commercial dashboarding systems. Although a number of studies have looked at the technical challenges of adopting these systems, very few have focused on the socio-technical issues that arise. In this paper, we report on the results of an interview study with 17 participants working in a range of roles at a long-established, traditional manufacturing company as they adopted Microsoft Power BI. The results highlight a number of socio-technical challenges the employees faced, including difficulties in training, using and creating dashboards, and transitioning to a modern digital company. Based on these results, we propose a number of opportunities for both companies and visualization researchers to improve these difficult transitions, as well as opportunities for rethinking how we design dashboarding systems for real-world use.
TROPHY: A Topologically Robust Physics-Informed Tracking Framework for Tropical Cyclones
Authors: Lin Yan 0003, Hanqi Guo 0001, Thomas Peterka, Bei Wang 0001, Jiali Wang
Published at Vis, 2023

Tropical cyclones (TCs) are among the most destructive weather systems. Realistically and efficiently detecting and tracking TCs are critical for assessing their impacts and risks. In particular, the eye is a signature feature of a mature TC. Therefore, knowing the eyes' locations and movements is crucial for both operational weather forecasts and climate risk assessments. Recently, a multilevel robustness framework has been introduced to study the critical points of time-varying vector fields. The framework quantifies the robustness (i.e., structural stability) of critical points across varying neighborhoods. By relating the multilevel robustness with critical point tracking, the framework has demonstrated its potential in cyclone tracking. An advantage is that it identifies cyclonic features using only 2D wind vector fields, which is encouraging as most tracking algorithms require multiple dynamic and thermodynamic variables at different altitudes. A disadvantage is that the framework does not scale well computationally for datasets containing a large number of cyclones. This paper introduces a topologically robust physics-informed tracking framework (TROPHY) for TC tracking. The main idea is to integrate physical knowledge of TC to drastically improve the computational efficiency of multilevel robustness framework for large-scale climate datasets. First, during preprocessing, we propose a physics-informed feature selection strategy to filter 90% of critical points that are short-lived and have low stability, thus preserving good candidates for TC tracking. Second, during in-processing, we impose constraints during the multilevel robustness computation to focus only on physics-informed neighborhoods of TCs. We apply TROPHY to 30 years of 2D wind fields from reanalysis data in ERA5 and generate a number of TC tracks. In comparison with the observed tracks, we demonstrate that TROPHY can capture TC characteristics (e.g., frequency, intensity, duration, latitudes with maximum intensity, and genesis) that are comparable to and sometimes even better than a well-validated TC tracking algorithm that requires multiple dynamic and thermodynamic scalar fields.
Unraveling the Design Space of Immersive Analytics: A Systematic Review
Authors: David Saffo, Sara Di Bartolomeo, Tarik Crnovrsanin, Laura South, Justin Raynor, Caglar Yildirim, Cody Dunne
Published at Vis, 2023

Immersive analytics has emerged as a promising research area, leveraging advances in immersive display technologies and techniques, such as virtual and augmented reality, to facilitate data exploration and decision-making. This paper presents a systematic literature review of 73 studies published between 2013-2022 on immersive analytics systems and visualizations, aiming to identify and categorize the primary dimensions influencing their design. We identified five key dimensions:  Academic Theory and Contribution,  Immersive Technology,  Data,  Spatial Presentation, and  Visual Presentation. Academic Theory and Contribution assess the motivations behind the works and their theoretical frameworks. Immersive Technology examines the display and input modalities, while Data dimension focuses on dataset types and generation. Spatial Presentation discusses the environment, space, embodiment, and collaboration aspects in IA, and Visual Presentation explores the visual elements, facet and position, and manipulation of views. By examining each dimension individually and cross-referencing them, this review uncovers trends and relationships that help inform the design of immersive systems visualizations. This analysis provides valuable insights for researchers and practitioners, offering guidance in designing future immersive analytics systems and shaping the trajectory of this rapidly evolving field. A free copy of this paper and all supplemental materials are available at osf.io/5ewaj.
VideoPro: A Visual Analytics Approach for Interactive Video Programming
Authors: Jianben He, Xingbo Wang 0001, Kamkwai Wong, Xijie Huang, Changjian Chen, Zixin Chen, Fengjie Wang, Min Zhu, Huamin Qu
Published at Vis, 2023

Constructing supervised machine learning models for real-world video analysis require substantial labeled data, which is costly to acquire due to scarce domain expertise and laborious manual inspection. While data programming shows promise in generating labeled data at scale with user-defined labeling functions, the high dimensional and complex temporal information in videos poses additional challenges for effectively composing and evaluating labeling functions. In this paper, we propose VideoPro, a visual analytics approach to support flexible and scalable video data programming for model steering with reduced human effort. We first extract human-understandable events from videos using computer vision techniques and treat them as atomic components of labeling functions. We further propose a two-stage template mining algorithm that characterizes the sequential patterns of these events to serve as labeling function templates for efficient data labeling. The visual interface of VideoPro facilitates multifaceted exploration, examination, and application of the labeling templates, allowing for effective programming of video data at scale. Moreover, users can monitor the impact of programming on model performance and make informed adjustments during the iterative programming process. We demonstrate the efficiency and effectiveness of our approach with two case studies and expert interviews.
Vimo: Visual Analysis of Neuronal Connectivity Motifs
Authors: Jakob Troidl, Simon Warchol, Jinhan Choi, Jordan Matelsky, Nagaraju Dhanyasi, Xueying Wang, Brock A. Wester, Donglai Wei 0001, Jeff W. Lichtman, Hanspeter Pfister, Johanna Beyer
Published at Vis, 2023

Recent advances in high-resolution connectomics provide researchers with access to accurate petascale reconstructions of neuronal circuits and brain networks for the first time. Neuroscientists are analyzing these networks to better understand information processing in the brain. In particular, scientists are interested in identifying specific small network motifs, i.e., repeating subgraphs of the larger brain network that are believed to be neuronal building blocks. Although such motifs are typically small (e.g., 2–6 neurons), the vast data sizes and intricate data complexity present significant challenges to the search and analysis process. To analyze these motifs, it is crucial to review instances of a motif in the brain network and then map the graph structure to detailed 3D reconstructions of the involved neurons and synapses. We present Vimo, an interactive visual approach to analyze neuronal motifs and motif chains in large brain networks. Experts can sketch network motifs intuitively in a visual interface and specify structural properties of the involved neurons and synapses to query large connectomics datasets. Motif instances (MIs) can be explored in high-resolution 3D renderings. To simplify the analysis of MIs, we designed a continuous focus&context metaphor inspired by visual abstractions. This allows users to transition from a highly-detailed rendering of the anatomical structure to views that emphasize the underlying motif structure and synaptic connectivity. Furthermore, Vimo supports the identification of motif chains where a motif is used repeatedly (e.g., 2–4 times) to form a larger network structure. We evaluate Vimo in a user study and an in-depth case study with seven domain experts on motifs in a large connectome of the fruit fly, including more than 21,000 neurons and 20 million synapses. We find that Vimo enables hypothesis generation and confirmation through fast analysis iterations and connectivity highlighting.
VIRD: Immersive Match Video Analysis for High-Performance Badminton Coaching
Authors: Tica Lin, Alexandre Aouididi, Zhutian Chen, Johanna Beyer, Hanspeter Pfister, Jui-Hsien Wang
Published at Vis, 2023

Badminton is a fast-paced sport that requires a strategic combination of spatial, temporal, and technical tactics. To gain a competitive edge at high-level competitions, badminton professionals frequently analyze match videos to gain insights and develop game strategies. However, the current process for analyzing matches is time-consuming and relies heavily on manual note-taking, due to the lack of automatic data collection and appropriate visualization tools. As a result, there is a gap in effectively analyzing matches and communicating insights among badminton coaches and players. This work proposes an end-to-end immersive match analysis pipeline designed in close collaboration with badminton professionals, including Olympic and national coaches and players. We present VIRD, a VR Bird (i.e., shuttle) immersive analysis tool, that supports interactive badminton game analysis in an immersive environment based on 3D reconstructed game views of the match video. We propose a top-down analytic workflow that allows users to seamlessly move from a high-level match overview to a detailed game view of individual rallies and shots, using situated 3D visualizations and video. We collect 3D spatial and dynamic shot data and player poses with computer vision models and visualize them in VR. Through immersive visualizations, coaches can interactively analyze situated spatial data (player positions, poses, and shot trajectories) with flexible viewpoints while navigating between shots and rallies effectively with embodied interaction. We evaluated the usefulness of VIRD with Olympic and national-level coaches and players in real matches. Results show that immersive analytics supports effective badminton match analysis with reduced context-switching costs and enhances spatial understanding with a high sense of presence.
VisGrader: Automatic Grading of D3 Visualizations
Authors: Matthew Hull, Vivian Pednekar, Hannah Murray, Nimisha Roy, Emmanuel Tung, Susanta Routray, Connor Guerin, Justin Chen, Zijie J. Wang, Seongmin Lee 0007, M. Mahdi Roozbahani, Duen Horng Chau
Published at Vis, 2023

Manually grading D3 data visualizations is a challenging endeavor, and is especially difficult for large classes with hundreds of students. Grading an interactive visualization requires a combination of interactive, quantitative, and qualitative evaluation that are conventionally done manually and are difficult to scale up as the visualization complexity, data size, and number of students increase. We present VisGrader, a first-of-its kind automatic grading method for D3 visualizations that scalably and precisely evaluates the data bindings, visual encodings, interactions, and design specifications used in a visualization. Our method enhances students' learning experience, enabling them to submit their code frequently and receive rapid feedback to better inform iteration and improvement to their code and visualization design. We have successfully deployed our method and auto-graded D3 submissions from more than 4000 students in a visualization course at Georgia Tech, and received positive feedback for expanding its adoption.
VISPUR: Visual Aids for Identifying and Interpreting Spurious Associations in Data-Driven Decisions
Authors: Xian Teng, Yongsu Ahn, Yu-Ru Lin
Published at Vis, 2023

Big data and machine learning tools have jointly empowered humans in making data-driven decisions. However, many of them capture empirical associations that might be spurious due to confounding factors and subgroup heterogeneity. The famous Simpson's paradox is such a phenomenon where aggregated and subgroup-level associations contradict with each other, causing cognitive confusions and difficulty in making adequate interpretations and decisions. Existing tools provide little insights for humans to locate, reason about, and prevent pitfalls of spurious association in practice. We propose Vispur, a visual analytic system that provides a causal analysis framework and a human-centric workflow for tackling spurious associations. These include a Confounder Dashboard, which can automatically identify possible confounding factors, and a Subgroup Viewer, which allows for the visualization and comparison of diverse subgroup patterns that likely or potentially result in a misinterpretation of causality. Additionally, we propose a Reasoning Storyboard, which uses a flow-based approach to illustrate paradoxical phenomena, as well as an interactive Decision Diagnosis panel that helps ensure accountable decision-making. Through an expert interview and a controlled user experiment, our qualitative and quantitative results demonstrate that the proposed “de-paradox” workflow and the designed visual analytic system are effective in helping human users to identify and understand spurious associations, as well as to make accountable causal decisions.
Vistrust: a Multidimensional Framework and Empirical Study of Trust in Data Visualizations
Authors: Hamza Elhamdadi, Adam Stefkovics, Johanna Beyer, Eric Mörth, Hanspeter Pfister, Cindy Xiong Bearfield, Carolina Nobre
Published at Vis, 2023

Trust is an essential aspect of data visualization, as it plays a crucial role in the interpretation and decision-making processes of users. While research in social sciences outlines the multi-dimensional factors that can play a role in trust formation, most data visualization trust researchers employ a single-item scale to measure trust. We address this gap by proposing a comprehensive, multidimensional conceptualization and operationalization of trust in visualization. We do this by applying general theories of trust from social sciences, as well as synthesizing and extending earlier work and factors identified by studies in the visualization field. We apply a two-dimensional approach to trust in visualization, to distinguish between cognitive and affective elements, as well as between visualization and data-specific trust antecedents. We use our framework to design and run a large crowd-sourced study to quantify the role of visual complexity in establishing trust in science visualizations. Our study provides empirical evidence for several aspects of our proposed theoretical framework, most notably the impact of cognition, affective responses, and individual differences when establishing trust in visualizations.
Visual Analysis of Displacement Processes in Porous Media using Spatio-Temporal Flow Graphs
Authors: Alexander Straub, Nikolaos Karadimitriou, Guido Reina, Steffen Frey, Holger Steeb, Thomas Ertl
Published at Vis, 2023

We developed a new approach comprised of different visualizations for the comparative spatio-temporal analysis of displacement processes in porous media. We aim to analyze and compare ensemble datasets from experiments to gain insight into the influence of different parameters on fluid flow. To capture the displacement of a defending fluid by an invading fluid, we first condense an input image series to a single time map. From this map, we generate a spatio-temporal flow graph covering the whole process. This graph is further simplified to only reflect topological changes in the movement of the invading fluid. Our interactive tools allow the visual analysis of these processes by visualizing the graph structure and the context of the experimental setup, as well as by providing charts for multiple metrics. We apply our approach to analyze and compare ensemble datasets jointly with domain experts, where we vary either fluid properties or the solid structure of the porous medium. We finally report the generated insights from the domain experts and discuss our contribution's advantages, generality, and limitations.