DevLan/vispubs huggingface dataset:
ASTF: Visual Abstractions of Time-Varying Patterns in Radio Signals
Authors: Ying Zhao 0001, Luhao Ge, Huixuan Xie, Genghuai Bai, Zhao Zhang, Qiang Wei, Yun Lin 0005, Yuchao Liu, Fangfang Zhou
Published at Vis, 2022

A time-frequency diagram is a commonly used visualization for observing the time-frequency distribution of radio signals and analyzing their time-varying patterns of communication states in radio monitoring and management. While it excels when performing short-term signal analyses, it becomes inadaptable for long-term signal analyses because it cannot adequately depict signal time-varying patterns in a large time span on a space-limited screen. This research thus presents an abstract signal time-frequency (ASTF) diagram to address this problem. In the diagram design, a visual abstraction method is proposed to visually encode signal communication state changes in time slices. A time segmentation algorithm is proposed to divide a large time span into time slices. Three new quantified metrics and a loss function are defined to ensure the preservation of important time-varying information in the time segmentation. An algorithm performance experiment and a user study are conducted to evaluate the effectiveness of the diagram for long-term signal analyses.
BeauVis: A Validated Scale for Measuring the Aesthetic Pleasure of Visual Representations
Authors: Tingying He, Petra Isenberg, Raimund Dachselt, Tobias Isenberg 0001
Published at Vis, 2022

We developed and validated a rating scale to assess the aesthetic pleasure (or beauty) of a visual data representation: the BeauVis scale. With our work we offer researchers and practitioners a simple instrument to compare the visual appearance of different visualizations, unrelated to data or context of use. Our rating scale can, for example, be used to accompany results from controlled experiments or be used as informative data points during in-depth qualitative studies. Given the lack of an aesthetic pleasure scale dedicated to visualizations, researchers have mostly chosen their own terms to study or compare the aesthetic pleasure of visualizations. Yet, many terms are possible and currently no clear guidance on their effectiveness regarding the judgment of aesthetic pleasure exists. To solve this problem, we engaged in a multi-step research process to develop the first validated rating scale specifically for judging the aesthetic pleasure of a visualization (osf.io/fxs76). Our final BeauVis scale consists of five items, “enjoyable,” “likable,” “pleasing,” “nice,” and “appealing.” Beyond this scale itself, we contribute (a) a systematic review of the terms used in past research to capture aesthetics, (b) an investigation with visualization experts who suggested terms to use for judging the aesthetic pleasure of a visualization, and (c) a confirmatory survey in which we used our terms to study the aesthetic pleasure of a set of 3 visualizations.
Breaking the Fourth Wall of Data Stories through Interaction
Authors: Yang Shi 0007, Tian Gao, Xiaohan Jiao, Nan Cao
Published at Vis, 2022

Interaction is increasingly integrating into data stories to support data exploration and explanation. Interaction can also be combined with the narrative device, breaking the fourth wall (BTFW), to build a deeper connection between readers and data stories. BTFW interaction directly addresses readers by requiring their input. Such user input is then integrated into the narrative or visuals of data stories to encourage readers to inspect the stories more closely. In this work, we explore the design patterns of BTFW interaction commonly used in data stories. Six design patterns were identified through the analysis of 58 high-quality data stories collected from a range of online sources. Specifically, the data stories were categorized using a coding framework, including the input of BTFW interaction provided by readers and the output of BTFW interaction generated by data stories to respond to the input. To explore the benefits as well as concerns of using BTFW interaction, we conducted a three-session user study including the reading, interview, and recall sessions. The results of our user study suggested that BTFW interaction has a positive impact on self-story connection, user engagement, and information recall. We also discussed design implications to address the possible negative effects on the interactivity-comprehensibility balance, information privacy, and the learning curve of interaction brought by BTFW interaction.
Calibrate: Interactive Analysis of Probabilistic Model Output
Authors: Peter Xenopoulos, João Rulff, Luis Gustavo Nonato, Brian Barr, Cláudio T. Silva
Published at Vis, 2022

Analyzing classification model performance is a crucial task for machine learning practitioners. While practitioners often use count-based metrics derived from confusion matrices, like accuracy, many applications, such as weather prediction, sports betting, or patient risk prediction, rely on a classifier's predicted probabilities rather than predicted labels. In these instances, practitioners are concerned with producing a calibrated model, that is, one which outputs probabilities that reflect those of the true distribution. Model calibration is often analyzed visually, through static reliability diagrams, however, the traditional calibration visualization may suffer from a variety of drawbacks due to the strong aggregations it necessitates. Furthermore, count-based approaches are unable to sufficiently analyze model calibration. We present Calibrate, an interactive reliability diagram that addresses the aforementioned issues. Calibrate constructs a reliability diagram that is resistant to drawbacks in traditional approaches, and allows for interactive subgroup analysis and instance-level inspection. We demonstrate the utility of Calibrate through use cases on both real-world and synthetic data. We further validate Calibrate by presenting the results of a think-aloud experiment with data scientists who routinely analyze model calibration.
ChartWalk: Navigating large collections of text notes in electronic health records for clinical chart review
Authors: Nicole Sultanum, Farooq Naeem, Michael Brudno, Fanny Chevalier
Published at Vis, 2022

Before seeing a patient for the first time, healthcare workers will typically conduct a comprehensive clinical chart review of the patient's electronic health record (EHR). Within the diverse documentation pieces included there, text notes are among the most important and thoroughly perused segments for this task; and yet they are among the least supported medium in terms of content navigation and overview. In this work, we delve deeper into the task of clinical chart review from a data visualization perspective and propose a hybrid graphics+text approach via ChartWalk, an interactive tool to support the review of text notes in EHRs. We report on our iterative design process grounded in input provided by a diverse range of healthcare professionals, with steps including: (a) initial requirements distilled from interviews and the literature, (b) an interim evaluation to validate design decisions, and (c) a task-based qualitative evaluation of our final design. We contribute lessons learned to better support the design of tools not only for clinical chart reviews but also other healthcare-related tasks around medical text analysis.
CohortVA: A Visual Analytic System for Interactive Exploration of Cohorts based on Historical Data
Authors: Wei Zhang, Jason K. Wong, Xumeng Wang, Youcheng Gong, Rongchen Zhu, Kai Liu, Zihan Yan, Siwei Tan, Huamin Qu, Siming Chen 0001, Wei Chen 0001
Published at Vis, 2022

In history research, cohort analysis seeks to identify social structures and figure mobilities by studying the group-based behavior of historical figures. Prior works mainly employ automatic data mining approaches, lacking effective visual explanation. In this paper, we present CohortVA, an interactive visual analytic approach that enables historians to incorporate expertise and insight into the iterative exploration process. The kernel of CohortVA is a novel identification model that generates candidate cohorts and constructs cohort features by means of pre-built knowledge graphs constructed from large-scale history databases. We propose a set of coordinated views to illustrate identified cohorts and features coupled with historical events and figure profiles. Two case studies and interviews with historians demonstrate that CohortVA can greatly enhance the capabilities of cohort identifications, figure authentications, and hypothesis generation.
Communicating Uncertainty in Digital Humanities Visualization Research
Authors: Georgia Panagiotidou, Houda Lamqaddam, Jeroen Poblome, Koenraad Brosens, Katrien Verbert, Andrew Vande Moere
Published at Vis, 2022

Due to their historical nature, humanistic data encompass multiple sources of uncertainty. While humanists are accustomed to handling such uncertainty with their established methods, they are cautious of visualizations that appear overly objective and fail to communicate this uncertainty. To design more trustworthy visualizations for humanistic research, therefore, a deeper understanding of its relation to uncertainty is needed. We systematically reviewed 126 publications from digital humanities literature that use visualization as part of their research process, and examined how uncertainty was handled and represented in their visualizations. Crossing these dimensions with the visualization type and use, we identified that uncertainty originated from multiple steps in the research process from the source artifacts to their datafication. We also noted how besides known uncertainty coping strategies, such as excluding data and evaluating its effects, humanists also embraced uncertainty as a separate dimension important to retain. By mapping how the visualizations encoded uncertainty, we identified four approaches that varied in terms of explicitness and customization. This work contributes with two empirical taxonomies of uncertainty and it's corresponding coping strategies, as well as with the foundation of a research agenda for uncertainty visualization in the digital humanities. Our findings further the synergy among humanists and visualization researchers, and ultimately contribute to the development of more trustworthy, uncertainty-aware visualizations.
Comparative Evaluation of Bipartite, Node-Link, and Matrix-Based Network Representations
Authors: Moataz Abdelaal, Nathan Daniel Schiele, Katrin Angerbauer, Kuno Kurzhals, Michael Sedlmair, Daniel Weiskopf
Published at Vis, 2022

This work investigates and compares the performance of node-link diagrams, adjacency matrices, and bipartite layouts for visualizing networks. In a crowd-sourced user study ($\mathrm{n}=150$), we measure the task accuracy and completion time of the three representations for different network classes and properties. In contrast to the literature, which covers mostly topology-based tasks (e.g., path finding) in small datasets, we mainly focus on overview tasks for large and directed networks. We consider three overview tasks on networks with 500 nodes: (T1) network class identification, (T2) cluster detection, and (T3) network density estimation, and two detailed tasks: (T4) node in-degree vs. out-degree and (T5) representation mapping, on networks with 50 and 20 nodes, respectively. Our results show that bipartite layouts are beneficial for revealing the overall network structure, while adjacency matrices are most reliable across the different tasks.
Comparison Conundrum and the Chamber of Visualizations: An Exploration of How Language Influences Visual Design
Authors: Aimen Gaba, Vidya Setlur, Arjun Srinivasan, Jane Hoffswell, Cindy Xiong
Published at Vis, 2022

The language for expressing comparisons is often complex and nuanced, making supporting natural language-based visual comparison a non-trivial task. To better understand how people reason about comparisons in natural language, we explore a design space of utterances for comparing data entities. We identified different parameters of comparison utterances that indicate what is being compared (i.e., data variables and attributes) as well as how these parameters are specified (i.e., explicitly or implicitly). We conducted a user study with sixteen data visualization experts and non-experts to investigate how they designed visualizations for comparisons in our design space. Based on the rich set of visualization techniques observed, we extracted key design features from the visualizations and synthesized them into a subset of sixteen representative visualization designs. We then conducted a follow-up study to validate user preferences for the sixteen representative visualizations corresponding to utterances in our design space. Findings from these studies suggest guidelines and future directions for designing natural language interfaces and recommendation tools to better support natural language comparisons in visual analytics.
Computing a Stable Distance on Merge Trees
Authors: Brian Bollen, Pasindu Tennakoon, Joshua A. Levine
Published at Vis, 2022

Distances on merge trees facilitate visual comparison of collections of scalar fields. Two desirable properties for these distances to exhibit are 1) the ability to discern between scalar fields which other, less complex topological summaries cannot and 2) to still be robust to perturbations in the dataset. The combination of these two properties, known respectively as stability and discriminativity, has led to theoretical distances which are either thought to be or shown to be computationally complex and thus their implementations have been scarce. In order to design similarity measures on merge trees which are computationally feasible for more complex merge trees, many researchers have elected to loosen the restrictions on at least one of these two properties. The question still remains, however, if there are practical situations where trading these desirable properties is necessary. Here we construct a distance between merge trees which is designed to retain both discriminativity and stability. While our approach can be expensive for large merge trees, we illustrate its use in a setting where the number of nodes is small. This setting can be made more practical since we also provide a proof that persistence simplification increases the outputted distance by at most half of the simplified value. We demonstrate our distance measure on applications in shape comparison and on detection of periodicity in the von Kármán vortex street.
ConceptExplainer: Interactive Explanation for Deep Neural Networks from a Concept Perspective
Authors: Jinbin Huang, Aditi Mishra, Bum Chul Kwon, Chris Bryan
Published at Vis, 2022

Traditional deep learning interpretability methods which are suitable for model users cannot explain network behaviors at the global level and are inflexible at providing fine-grained explanations. As a solution, concept-based explanations are gaining attention due to their human intuitiveness and their flexibility to describe both global and local model behaviors. Concepts are groups of similarly meaningful pixels that express a notion, embedded within the network's latent space and have commonly been hand-generated, but have recently been discovered by automated approaches. Unfortunately, the magnitude and diversity of discovered concepts makes it difficult to navigate and make sense of the concept space. Visual analytics can serve a valuable role in bridging these gaps by enabling structured navigation and exploration of the concept space to provide concept-based insights of model behavior to users. To this end, we design, develop, and validate ConceptExplainer, a visual analytics system that enables people to interactively probe and explore the concept space to explain model behavior at the instance/class/global level. The system was developed via iterative prototyping to address a number of design challenges that model users face in interpreting the behavior of deep learning models. Via a rigorous user study, we validate how ConceptExplainer supports these challenges. Likewise, we conduct a series of usage scenarios to demonstrate how the system supports the interactive analysis of model behavior across a variety of tasks and explanation granularities, such as identifying concepts that are important to classification, identifying bias in training data, and understanding how concepts can be shared across diverse and seemingly dissimilar classes.
Constrained Dynamic Mode Decomposition
Authors: Tim Krake, Daniel Klötzl, Bernd Eberhardt, Daniel Weiskopf
Published at Vis, 2022

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.
Cultivating Visualization Literacy for Children Through Curiosity and Play
Authors: Sandra Bae, Rishi Vanukuru, Ruhan Yang, Peter Gyory, Ran Zhou 0003, Ellen Yi-Luen Do, Danielle Albers Szafir
Published at Vis, 2022

Fostering data visualization literacy (DVL) as part of childhood education could lead to a more data literate society. However, most work in DVL for children relies on a more formal educational context (i.e., a teacher-led approach) that limits children's engagement with data to classroom-based environments and, consequently, children's ability to ask questions about and explore data on topics they find personally meaningful. We explore how a curiosity-driven, child-led approach can provide more agency to children when they are authoring data visualizations. This paper explores how informal learning with crafting physicalizations through play and curiosity may foster increased literacy and engagement with data. Employing a constructionist approach, we designed a do-it-yourself toolkit made out of everyday materials (e.g., paper, cardboard, mirrors) that enables children to create, customize, and personalize three different interactive visualizations (bar, line, pie). We used the toolkit as a design probe in a series of in-person workshops with 5 children (6 to 11-year-olds) and interviews with 5 educators. Our observations reveal that the toolkit helped children creatively engage and interact with visualizations. Children with prior knowledge of data visualization reported the toolkit serving as more of an authoring tool that they envision using in their daily lives, while children with little to no experience found the toolkit as an engaging introduction to data visualization. Our study demonstrates the potential of using the constructionist approach to cultivate children's DVL through curiosity and play.
D-BIAS: A Causality-Based Human-in-the-Loop System for Tackling Algorithmic Bias
Authors: Bhavya Ghai, Klaus Mueller 0001
Published at Vis, 2022

With the rise of AI, algorithms have become better at learning underlying patterns from the training data including ingrained social biases based on gender, race, etc. Deployment of such algorithms to domains such as hiring, healthcare, law enforcement, etc. has raised serious concerns about fairness, accountability, trust and interpretability in machine learning algorithms. To alleviate this problem, we propose D-BIAS, a visual interactive tool that embodies human-in-the-loop AI approach for auditing and mitigating social biases from tabular datasets. It uses a graphical causal model to represent causal relationships among different features in the dataset and as a medium to inject domain knowledge. A user can detect the presence of bias against a group, say females, or a subgroup, say black females, by identifying unfair causal relationships in the causal network and using an array of fairness metrics. Thereafter, the user can mitigate bias by refining the causal model and acting on the unfair causal edges. For each interaction, say weakening/deleting a biased causal edge, the system uses a novel method to simulate a new (debiased) dataset based on the current causal model while ensuring a minimal change from the original dataset. Users can visually assess the impact of their interactions on different fairness metrics, utility metrics, data distortion, and the underlying data distribution. Once satisfied, they can download the debiased dataset and use it for any downstream application for fairer predictions. We evaluate D-BIAS by conducting experiments on 3 datasets and also a formal user study. We found that D-BIAS helps reduce bias significantly compared to the baseline debiasing approach across different fairness metrics while incurring little data distortion and a small loss in utility. Moreover, our human-in-the-loop based approach significantly outperforms an automated approach on trust, interpretability and accountability.
Dashboard Design Patterns
Authors: Benjamin Bach, Euan Freeman, Alfie Abdul-Rahman, Cagatay Turkay, Saiful Khan, Yulei Fan, Min Chen 0001
Published at Vis, 2022

This paper introduces design patterns for dashboards to inform dashboard design processes. Despite a growing number of public examples, case studies, and general guidelines there is surprisingly little design guidance for dashboards. Such guidance is necessary to inspire designs and discuss tradeoffs in, e.g., screenspace, interaction, or information shown. Based on a systematic review of 144 dashboards, we report on eight groups of design patterns that provide common solutions in dashboard design. We discuss combinations of these patterns in “dashboard genres” such as narrative, analytical, or embedded dashboard. We ran a 2-week dashboard design workshop with 23 participants of varying expertise working on their own data and dashboards. We discuss the application of patterns for the dashboard design processes, as well as general design tradeoffs and common challenges. Our work complements previous surveys and aims to support dashboard designers and researchers in co-creation, structured design decisions, as well as future user evaluations about dashboard design guidelines. Detailed pattern descriptions and workshop material can be found online: https://dashboarddesignpatterns.github.io