DevLan/vispubs huggingface dataset:
Visualization of Speech Prosody and Emotion in Captions: Accessibility for Deaf and Hard-of-Hearing Users
Authors: Caluã de Lacerda Pataca, Matthew Watkins, Roshan L. Peiris, Sooyeon Lee, Matt Huenerfauth
Published at CHI, 2023

Speech is expressive in ways that caption text does not capture, with emotion or emphasis information not conveyed. We interviewed eight Deaf and Hard-of-Hearing (dhh) individuals to understand if and how captions’ inexpressiveness impacts them in online meetings with hearing peers. Automatically captioned speech, we found, lacks affective depth, lending it a hard-to-parse ambiguity and general dullness. Interviewees regularly feel excluded, which some understand is an inherent quality of these types of meetings rather than a consequence of current caption text design. Next, we developed three novel captioning models that depicted, beyond words, features from prosody, emotions, and a mix of both. In an empirical study, 16 dhh participants compared these models with conventional captions. The emotion-based model outperformed traditional captions in depicting emotions and emphasis, with only a moderate loss in legibility, suggesting its potential as a more inclusive design for captions.
VizProg: Identifying Misunderstandings By Visualizing Students' Coding Progress
Authors: Ashley Ge Zhang, Yan Chen 0033, Steve Oney
Published at CHI, 2023

Programming instructors often conduct in-class exercises to help them identify students that are falling behind and surface students’ misconceptions. However, as we found in interviews with programming instructors, monitoring students’ progress during exercises is difficult, particularly for large classes. We present VizProg, a system that allows instructors to monitor and inspect students’ coding progress in real-time during in-class exercises. VizProg represents students’ statuses as a 2D Euclidean spatial map that encodes the students’ problem-solving approaches and progress in real-time. VizProg allows instructors to navigate the temporal and structural evolution of students’ code, understand relationships between code, and determine when to provide feedback. A comparison experiment showed that VizProg helped to identify more students’ problems than a baseline system. VizProg also provides richer and more comprehensive information for identifying important student behavior. By managing students’ activities at scale, this work presents a new paradigm for improving the quality of live learning.
VRGit: A Version Control System for Collaborative Content Creation in Virtual Reality
Authors: Lei Zhang, Ashutosh Agrawal, Steve Oney, Anhong Guo
Published at CHI, 2023

Immersive authoring tools allow users to intuitively create and manipulate 3D scenes while immersed in Virtual Reality (VR). Collaboratively designing these scenes is a creative process that involves numerous edits, explorations of design alternatives, and frequent communication with collaborators. Version Control Systems (VCSs) help users achieve this by keeping track of the version history and creating a shared hub for communication. However, most VCSs are unsuitable for managing the version history of VR content because their underlying line differencing mechanism is designed for text and lacks the semantic information of 3D content; and the widely adopted commit model is designed for asynchronous collaboration rather than real-time awareness and communication in VR. We introduce VRGit, a new collaborative VCS that visualizes version history as a directed graph composed of 3D miniatures, and enables users to easily navigate versions, create branches, as well as preview and reuse versions directly in VR. Beyond individual uses, VRGit also facilitates synchronous collaboration in VR by providing awareness of users’ activities and version history through portals and shared history visualizations. In a lab study with 14 participants (seven groups), we demonstrate that VRGit enables users to easily manage version history both individually and collaboratively in VR.
We are the Data: Challenges and Opportunities for Creating Demographically Diverse Anthropographics
Authors: Priya Dhawka, Helen Ai He, Wesley Willett
Published at CHI, 2023

Anthropographics are human-shaped visualizations that aim to emphasize the human importance of datasets and the people behind them. However, current anthropographics tend to employ homogeneous human shapes to encode data about diverse demographic groups. Such anthropographics can obscure important differences between groups and contemporary designs exemplify the lack of inclusive approaches for representing human diversity in visualizations. In response, we explore the creation of demographically diverse anthropographics that communicate the visible diversity of demographically distinct populations. Building on previous anthropographics research, we explore strategies for visualizing datasets about people in ways that explicitly encode diversity—illustrating these approaches with examples in a variety of visual styles. We also critically reflect on strategies for creating diverse anthropographics, identifying social and technical challenges that can result in harmful representations. Finally, we highlight a set of forward-looking research opportunities for advancing the design and understanding of diverse anthropographics.
When do data visualizations persuade? The impact of prior attitudes on learning about correlations from scatterplot visualizations
Authors: Douglas Markant, Milad Rogha, Alireza Karduni, Ryan Wesslen, Wenwen Dou
Published at CHI, 2023

Data visualizations are vital to scientific communication on critical issues such as public health, climate change, and socioeconomic policy. They are often designed not just to inform, but to persuade people to make consequential decisions (e.g., to get vaccinated). Are such visualizations persuasive, especially when audiences have beliefs and attitudes that the data contradict? In this paper we examine the impact of existing attitudes (e.g., positive or negative attitudes toward COVID-19 vaccination) on changes in beliefs about statistical correlations when viewing scatterplot visualizations with different representations of statistical uncertainty. We find that strong prior attitudes are associated with smaller belief changes when presented with data that contradicts existing views, and that visual uncertainty representations may amplify this effect. Finally, even when participants’ beliefs about correlations shifted their attitudes remained unchanged, highlighting the need for further research on whether data visualizations can drive longer-term changes in views and behavior.
Who Do We Mean When We Talk About Visualization Novices?
Authors: Alyxander Burns, Christiana Lee, Ria Chawla, Evan Peck, Narges Mahyar
Published at CHI, 2023

As more people rely on visualization to inform their personal and collective decisions, researchers have focused on a broader range of audiences, including “novices.” But successfully applying, interrogating, or advancing visualization research for novices demands a clear understanding of what “novice” means in theory and practice. Misinterpreting who a “novice” is could lead to misapplying guidelines and overgeneralizing results. In this paper, we investigated how visualization researchers define novices and how they evaluate visualizations intended for novices. We analyzed 79 visualization papers that used “novice,” “non-expert,” “laypeople,” or “general public” in their titles or abstracts. We found ambiguity within papers and disagreement between papers regarding what defines a novice. Furthermore, we found a mismatch between the broad language describing novices and the narrow population representing them in evaluations (i.e., young people, students, and US residents). We suggest directions for inclusively supporting novices in both theory and practice.
Why Combining Text and Visualization Could Improve Bayesian Reasoning: A Cognitive Load Perspective
Authors: Melanie Bancilhon, Amanda Wright, Sunwoo Ha, R. Jordan Crouser, Alvitta Ottley
Published at CHI, 2023

Investigations into using visualization to improve Bayesian reasoning and advance risk communication have produced mixed results, suggesting that cognitive ability might affect how users perform with different presentation formats. Our work examines the cognitive load elicited when solving Bayesian problems using icon arrays, text, and a juxtaposition of text and icon arrays. We used a three-pronged approach to capture a nuanced picture of cognitive demand and measure differences in working memory capacity, performance under divided attention using a dual-task paradigm, and subjective ratings of self-reported effort. We found that individuals with low working memory capacity made fewer errors and experienced less subjective workload when the problem contained an icon array compared to text alone, showing that visualization improves accuracy while exerting less cognitive demand. We believe these findings can considerably impact accessible risk communication, especially for individuals with low working memory capacity.
Working with Forensic Practitioners to Understand the Opportunities and Challenges for Mixed-Reality Digital Autopsy
Authors: Vahid Pooryousef, Maxime Cordeil, Lonni Besançon, Christophe Hurter, Tim Dwyer, Richard Bassed
Published at CHI, 2023

Forensic practitioners analyse intrinsic 3D data daily on 2D screens. We explore novel immersive visualisation techniques that enable digital autopsy through analysis of 3D imagery. We employ a user-centred design process involving four rounds of user feedback: (1) formative interviews eliciting opportunities and requirements for mixed-reality digital autopsies; (2) a larger workshop identifying our prototype’s limitations and further use-cases and interaction ideas; (3+4) two rounds of qualitative user validation of successive prototypes of novel interaction techniques for pathologist sensemaking. Overall, we find MR holds great potential to enable digital autopsy, initially to supplement physical autopsy, but ultimately to replace it. We found that experts were able to use our tool to perform basic virtual autopsy tasks, MR setup promotes exploration and sense making of cause of death, and subject to limitations of current MR technology, the proposed system is a valid option for digital autopsies, according to experts’ feedback. – Warning: This paper contains sensitive images which are 3D visualisation of deceased people.
Zeno: An Interactive Framework for Behavioral Evaluation of Machine Learning
Authors: Ángel Alexander Cabrera, Erica Fu, Donald Bertucci, Kenneth Holstein, Ameet Talwalkar, Jason I. Hong, Adam Perer
Published at CHI, 2023

Machine learning models with high accuracy on test data can still produce systematic failures, such as harmful biases and safety issues, when deployed in the real world. To detect and mitigate such failures, practitioners run behavioral evaluation of their models, checking model outputs for specific types of inputs. Behavioral evaluation is important but challenging, requiring that practitioners discover real-world patterns and validate systematic failures. We conducted 18 semi-structured interviews with ML practitioners to better understand the challenges of behavioral evaluation and found that it is a collaborative, use-case-first process that is not adequately supported by existing task- and domain-specific tools. Using these findings, we designed zeno, a general-purpose framework for visualizing and testing AI systems across diverse use cases. In four case studies with participants using zeno on real-world models, we found that practitioners were able to reproduce previous manual analyses and discover new systematic failures.
A Comparison of Spatiotemporal Visualizations for 3D Urban Analytics
Authors: Roberta C. Ramos Mota, Nivan Ferreira, Julio Daniel Silva, Marius Horga, Marcos Lage, Luis Ceferino, Usman R. Alim, Ehud Sharlin, Fabio Miranda 0001
Published at Vis, 2022

Recent technological innovations have led to an increase in the availability of 3D urban data, such as shadow, noise, solar potential, and earthquake simulations. These spatiotemporal datasets create opportunities for new visualizations to engage experts from different domains to study the dynamic behavior of urban spaces in this under explored dimension. However, designing 3D spatiotemporal urban visualizations is challenging, as it requires visual strategies to support analysis of time-varying data referent to the city geometry. Although different visual strategies have been used in 3D urban visual analytics, the question of how effective these visual designs are at supporting spatiotemporal analysis on building surfaces remains open. To investigate this, in this paper we first contribute a series of analytical tasks elicited after interviews with practitioners from three urban domains. We also contribute a quantitative user study comparing the effectiveness of four representative visual designs used to visualize 3D spatiotemporal urban data: spatial juxtaposition, temporal juxtaposition, linked view, and embedded view. Participants performed a series of tasks that required them to identify extreme values on building surfaces over time. Tasks varied in granularity for both space and time dimensions. Our results demonstrate that participants were more accurate using plot-based visualizations (linked view, embedded view) but faster using color-coded visualizations (spatial juxtaposition, temporal juxtaposition). Our results also show that, with increasing task complexity, plot-based visualizations perform better in preserving efficiency (time, accuracy) compared to color-coded visualizations. Based on our findings, we present a set of takeaways with design recommendations for 3D spatiotemporal urban visualizations for researchers and practitioners. Lastly, we report on a series of interviews with four practitioners, and their feedback and suggestions for further work on the visualizations to support 3D spatiotemporal urban data analysis.
A Design Space for Surfacing Content Recommendations in Visual Analytic Platforms
Authors: Zhilan Zhou, Wenyuan Wang, Mengtian Guo, Yue Wang 0035, David Gotz
Published at Vis, 2022

Recommendation algorithms have been leveraged in various ways within visualization systems to assist users as they perform of a range of information tasks. One common focus for these techniques has been the recommendation of content, rather than visual form, as a means to assist users in the identification of information that is relevant to their task context. A wide variety of techniques have been proposed to address this general problem, with a range of design choices in how these solutions surface relevant information to users. This paper reviews the state-of-the-art in how visualization systems surface recommended content to users during users' visual analysis; introduces a four-dimensional design space for visual content recommendation based on a characterization of prior work; and discusses key observations regarding common patterns and future research opportunities.
A Framework for Multiclass Contour Visualization
Authors: Sihang Li, Jiacheng Yu, Mingxuan Li, Le Liu, Xiaolong Zhang 0001, Xiaoru Yuan
Published at Vis, 2022

Multiclass contour visualization is often used to interpret complex data attributes in such fields as weather forecasting, computational fluid dynamics, and artificial intelligence. However, effective and accurate representations of underlying data patterns and correlations can be challenging in multiclass contour visualization, primarily due to the inevitable visual cluttering and occlusions when the number of classes is significant. To address this issue, visualization design must carefully choose design parameters to make visualization more comprehensible. With this goal in mind, we proposed a framework for multiclass contour visualization. The framework has two components: a set of four visualization design parameters, which are developed based on an extensive review of literature on contour visualization, and a declarative domain-specific language (DSL) for creating multiclass contour rendering, which enables a fast exploration of those design parameters. A task-oriented user study was conducted to assess how those design parameters affect users' interpretations of real-world data. The study results offered some suggestions on the value choices of design parameters in multiclass contour visualization.
A Scanner Deeply: Predicting Gaze Heatmaps on Visualizations Using Crowdsourced Eye Movement Data
Authors: Sungbok Shin, Sunghyo Chung, Sanghyun Hong 0001, Niklas Elmqvist
Published at Vis, 2022

Visual perception is a key component of data visualization. Much prior empirical work uses eye movement as a proxy to understand human visual perception. Diverse apparatus and techniques have been proposed to collect eye movements, but there is still no optimal approach. In this paper, we review 30 prior works for collecting eye movements based on three axes: (1) the tracker technology used to measure eye movements; (2) the image stimulus shown to participants; and (3) the collection methodology used to gather the data. Based on this taxonomy, we employ a webcam-based eyetracking approach using task-specific visualizations as the stimulus. The low technology requirement means that virtually anyone can participate, thus enabling us to collect data at large scale using crowdsourcing: approximately 12,000 samples in total. Choosing visualization images as stimulus means that the eye movements will be specific to perceptual tasks associated with visualization. We use these data to propose a Scanner Deeply, a virtual eyetracker model that, given an image of a visualization, generates a gaze heatmap for that image. We employ a computationally efficient, yet powerful convolutional neural network for our model. We compare the results of our work with results from the DVS model and a neural network trained on the Salicon dataset. The analysis of our gaze patterns enables us to understand how users grasp the structure of visualized data. We also make our stimulus dataset of visualization images available as part of this paper's contribution.
A Unified Comparison of User Modeling Techniques for Predicting Data Interaction and Detecting Exploration Bias
Authors: Sunwoo Ha, Shayan Monadjemi, Roman Garnett, Alvitta Ottley
Published at Vis, 2022

The visual analytics community has proposed several user modeling algorithms to capture and analyze users' interaction behavior in order to assist users in data exploration and insight generation. For example, some can detect exploration biases while others can predict data points that the user will interact with before that interaction occurs. Researchers believe this collection of algorithms can help create more intelligent visual analytics tools. However, the community lacks a rigorous evaluation and comparison of these existing techniques. As a result, there is limited guidance on which method to use and when. Our paper seeks to fill in this missing gap by comparing and ranking eight user modeling algorithms based on their performance on a diverse set of four user study datasets. We analyze exploration bias detection, data interaction prediction, and algorithmic complexity, among other measures. Based on our findings, we highlight open challenges and new directions for analyzing user interactions and visualization provenance.
A Visual Analytics System for Improving Attention-based Traffic Forecasting Models
Authors: Seungmin Jin, Hyunwook Lee, Cheonbok Park, Hyeshin Chu, Yunwon Tae, Jaegul Choo, Sungahn Ko
Published at Vis, 2022

With deep learning (DL) outperforming conventional methods for different tasks, much effort has been devoted to utilizing DL in various domains. Researchers and developers in the traffic domain have also designed and improved DL models for forecasting tasks such as estimation of traffic speed and time of arrival. However, there exist many challenges in analyzing DL models due to the black-box property of DL models and complexity of traffic data (i.e., spatio-temporal dependencies). Collaborating with domain experts, we design a visual analytics system, AttnAnalyzer, that enables users to explore how DL models make predictions by allowing effective spatio-temporal dependency analysis. The system incorporates dynamic time warping (DTW) and Granger causality tests for computational spatio-temporal dependency analysis while providing map, table, line chart, and pixel views to assist user to perform dependency and model behavior analysis. For the evaluation, we present three case studies showing how AttnAnalyzer can effectively explore model behaviors and improve model performance in two different road networks. We also provide domain expert feedback.
Affective Learning Objectives for Communicative Visualizations
Authors: Elsie Lee-Robbins, Eytan Adar
Published at Vis, 2022

When designing communicative visualizations, we often focus on goals that seek to convey patterns, relations, or comparisons (cognitive learning objectives). We pay less attention to affective intents–those that seek to influence or leverage the audience's opinions, attitudes, or values in some way. Affective objectives may range in outcomes from making the viewer care about the subject, strengthening a stance on an opinion, or leading them to take further action. Because such goals are often considered a violation of perceived ‘neutrality’ or are ‘political,’ designers may resist or be unable to describe these intents, let alone formalize them as learning objectives. While there are notable exceptions–such as advocacy visualizations or persuasive cartography–we find that visualization designers rarely acknowledge or formalize affective objectives. Through interviews with visualization designers, we expand on prior work on using learning objectives as a framework for describing and assessing communicative intent. Specifically, we extend and revise the framework to include a set of affective learning objectives. This structured taxonomy can help designers identify and declare their goals and compare and assess designs in a more principled way. Additionally, the taxonomy can enable external critique and analysis of visualizations. We illustrate the use of the taxonomy with a critical analysis of an affective visualization.
Animated Vega-Lite: Unifying Animation with a Grammar of Interactive Graphics
Authors: Jonathan Zong, Josh Pollock, Dylan Wootton, Arvind Satyanarayan
Published at Vis, 2022

We present Animated Vega-Lite, a set of extensions to Vega-Lite that model animated visualizations as time-varying data queries. In contrast to alternate approaches for specifying animated visualizations, which prize a highly expressive design space, Animated Vega-Lite prioritizes unifying animation with the language's existing abstractions for static and interactive visualizations to enable authors to smoothly move between or combine these modalities. Thus, to compose animation with static visualizations, we represent time as an encoding channel. Time encodings map a data field to animation keyframes, providing a lightweight specification for animations without interaction. To compose animation and interaction, we also represent time as an event stream; Vega-Lite selections, which provide dynamic data queries, are now driven not only by input events but by timer ticks as well. We evaluate the expressiveness of our approach through a gallery of diverse examples that demonstrate coverage over taxonomies of both interaction and animation. We also critically reflect on the conceptual affordances and limitations of our contribution by interviewing five expert developers of existing animation grammars. These reflections highlight the key motivating role of in-the-wild examples, and identify three central tradeoffs: the language design process, the types of animated transitions supported, and how the systems model keyframes.