DevLan/vispubs huggingface dataset:
Sensitive Pictures: Emotional Interpretation in the Museum
Authors: Steve Benford, Anders Sundnes Løvlie, Karin Ryding, Paulina Rajkowska, Edgar Bodiaj, Dimitrios Paris Darzentas, Harriet R. Cameron, Jocelyn Spence, Joy Egede, Bogdan Spanjevic
Published at CHI, 2022

Museums are interested in designing emotional visitor experiences to complement traditional interpretations. HCI is interested in the relationship between Affective Computing and Affective Interaction. We describe Sensitive Pictures, an emotional visitor experience co-created with the Munch art museum. Visitors choose emotions, locate associated paintings in the museum, experience an emotional story while viewing them, and self-report their response. A subsequent interview with a portrayal of the artist employs computer vision to estimate emotional responses from facial expressions. Visitors are given a souvenir postcard visualizing their emotional data. A study of 132 members of the public (39 interviewed) illuminates key themes: designing emotional provocations; capturing emotional responses; engaging visitors with their data; a tendency for them to align their views with the system's interpretation; and integrating these elements into emotional trajectories. We consider how Affective Computing can hold up a mirror to our emotions during Affective Interaction
Slide-Tone and Tilt-Tone: 1-DOF Haptic Techniques for Conveying Shape Characteristics of Graphs to Blind Users
Authors: Danyang Fan, Alexa Fay Siu, Wing-Sum Adrienne Law, Raymond Ruihong Zhen, Sile O'Modhrain, Sean Follmer
Published at CHI, 2022

We increasingly rely on up-to-date, data-driven graphs to understand our environments and make informed decisions. However, many of the methods blind and visually impaired users (BVI) rely on to access data-driven information do not convey important shape-characteristics of graphs, are not refreshable, or are prohibitively expensive. To address these limitations, we introduce two refreshable, 1-DOF audio-haptic interfaces based on haptic cues fundamental to object shape perception. Slide-tone uses finger position with sonification, and Tilt-tone uses fingerpad contact inclination with sonification to provide shape feedback to users. Through formative design workshops (n = 3) and controlled evaluations (n = 8), we found that BVI participants appreciated the additional shape information, versatility, and reinforced understanding these interfaces provide; and that task accuracy was comparable to using interactive tactile graphics or sonification alone. Our research offers insight into the benefits, limitations, and considerations for adopting these haptic cues into a data visualization context.
Smooth as Steel Wool: Effects of Visual Stimuli on the Haptic Perception of Roughness in Virtual Reality
Authors: Sebastian Günther 0001, Julian Rasch, Dominik Schön, Florian Müller 0003, Martin Schmitz, Jan Riemann, Andrii Matviienko, Max Mühlhäuser
Published at CHI, 2022

Haptic Feedback is essential for lifelike Virtual Reality (VR) experiences. To provide a wide range of matching sensations of being touched or stroked, current approaches typically need large numbers of different physical textures. However, even advanced devices can only accommodate a limited number of textures to remain wearable. Therefore, a better understanding is necessary of how expectations elicited by different visualizations affect haptic perception, to achieve a balance between physical constraints and great variety of matching physical textures. In this work, we conducted an experiment (N=31) assessing how the perception of roughness is affected within VR. We designed a prototype for arm stroking and compared the effects of different visualizations on the perception of physical textures with distinct roughnesses. Additionally, we used the visualizations’ real-world materials, no-haptics and vibrotactile feedback as baselines. As one result, we found that two levels of roughness can be sufficient to convey a realistic illusion.
STRAIDE: A Research Platform for Shape-Changing Spatial Displays based on Actuated Strings
Authors: Severin Engert, Konstantin Klamka, Andreas Peetz, Raimund Dachselt
Published at CHI, 2022

We present STRAIDE, a string-actuated interactive display environment that allows to explore the promising potential of shape-changing interfaces for casual visualizations. At the core, we envision a platform that spatially levitates elements to create dynamic visual shapes in space. We conceptualize this type of tangible mid-air display and discuss its multifaceted design dimensions. Through a design exploration, we realize a physical research platform with adjustable parameters and modular components. For conveniently designing and implementing novel applications, we provide developer tools ranging from graphical emulators to in-situ augmented reality representations. To demonstrate STRAIDE’s reconfigurability, we further introduce three representative physical setups as a basis for situated applications including ambient notifications, personal smart home controls, and entertainment. They serve as a technical validation, lay the foundations for a discussion with developers that provided valuable insights, and encourage ideas for future usage of this type of appealing interactive installation.
Structure-aware Visualization Retrieval
Authors: Haotian Li 0001, Yong Wang 0021, Aoyu Wu, Huan Wei, Huamin Qu
Published at CHI, 2022

With the wide usage of data visualizations, a huge number of Scalable Vector Graphic (SVG)-based visualizations have been created and shared online. Accordingly, there has been an increasing interest in exploring how to retrieve perceptually similar visualizations from a large corpus, since it can benefit various downstream applications such as visualization recommendation. Existing methods mainly focus on the visual appearance of visualizations by regarding them as bitmap images. However, the structural information intrinsically existing in SVG-based visualizations is ignored. Such structural information can delineate the spatial and hierarchical relationship among visual elements, and characterize visualizations thoroughly from a new perspective. This paper presents a structure-aware method to advance the performance of visualization retrieval by collectively considering both the visual and structural information. We extensively evaluated our approach through quantitative comparisons, a user study and case studies. The results demonstrate the effectiveness of our approach and its advantages over existing methods.
Supporting Accessible Data Visualization Through Audio Data Narratives
Authors: Alexa F. Siu, Gene S.-H. Kim, Sile O'Modhrain, Sean Follmer
Published at CHI, 2022

Online data visualizations play an important role in informing public opinion but are often inaccessible to screen reader users. To address the need for accessible data representations on the web that provide direct, multimodal, and up-to-date access to the data, we investigate audio data narratives –which combine textual descriptions and sonification (the mapping of data to non-speech sounds). We conduct two co-design workshops with screen reader users to define design principles that guide the structure, content, and duration of a data narrative. Based on these principles and relevant auditory processing characteristics, we propose a dynamic programming approach to automatically generate an audio data narrative from a given dataset. We evaluate our approach with 16 screen reader users. Findings show with audio narratives, users gain significantly more insights from the data. Users describe data narratives help them better extract and comprehend the information in both the sonification and description.
Supporting Data-Driven Basketball Journalism through Interactive Visualization
Authors: Yu Fu, John T. Stasko
Published at CHI, 2022

Basketball writers and journalists report on the sport that millions of fans follow and love. However, the recent emergence of pervasive data about the sport and the growth of new forms of sports analytics is changing writers’ jobs. While these writers seek to leverage the data and analytics to create engaging, data-driven stories, they typically lack the technical background to perform analytics or efficiently explore data. We investigated and analyzed the work and context of basketball writers, interviewed nine stakeholders to understand the challenges from a holistic view. Based on what we learned, we designed and constructed two interactive visualization systems that support rapid and in-depth sports data exploration and sense-making to enhance their articles and reporting. We deployed the systems during the recent NBA playoffs to gather initial feedback. This article describes the visualization design study we conducted, the resulting visualization systems, and what we learned to potentially help basketball writers in the future.
Supporting the Contact Tracing Process with WiFi Location Data: Opportunities and Challenges
Authors: Kaely Hall, Dong Whi Yoo, Wenrui Zhang, Mehrab Bin Morshed, Vedant Das Swain, Gregory D. Abowd, Munmun De Choudhury, Alex Endert, John T. Stasko, Jennifer G. Kim
Published at CHI, 2022

Contact tracers assist in containing the spread of highly infectious diseases such as COVID-19 by engaging community members who receive a positive test result in order to identify close contacts. Many contact tracers rely on community member’s recall for those identifications, and face limitations such as unreliable memory. To investigate how technology can alleviate this challenge, we developed a visualization tool using de-identified location data sensed from campus WiFi and provided it to contact tracers during mock contact tracing calls. While the visualization allowed contact tracers to find and address inconsistencies due to gaps in community member’s memory, it also introduced inconsistencies such as false-positive and false-negative reports due to imperfect data, and information sharing hesitancy. We suggest design implications for technologies that can better highlight and inform contact tracers of potential areas of inconsistencies, and further present discussion on using imperfect data in decision making.
Symphony: Composing Interactive Interfaces for Machine Learning
Authors: Alex Bäuerle, Ángel Alexander Cabrera, Fred Hohman, Megan Maher, David Koski, Xavier Suau, Titus Barik, Dominik Moritz
Published at CHI, 2022

Interfaces for machine learning (ML), information and visualizations about models or data, can help practitioners build robust and responsible ML systems. Despite their benefits, recent studies of ML teams and our interviews with practitioners (n=9) showed that ML interfaces have limited adoption in practice. While existing ML interfaces are effective for specific tasks, they are not designed to be reused, explored, and shared by multiple stakeholders in cross-functional teams. To enable analysis and communication between different ML practitioners, we designed and implemented Symphony, a framework for composing interactive ML interfaces with task-specific, data-driven components that can be used across platforms such as computational notebooks and web dashboards. We developed Symphony through participatory design sessions with 10 teams (n=31), and discuss our findings from deploying Symphony to 3 production ML projects at Apple. Symphony helped ML practitioners discover previously unknown issues like data duplicates and blind spots in models while enabling them to share insights with other stakeholders.
Tangible Globes for Data Visualisation in Augmented Reality
Authors: Kadek Ananta Satriadi, Jim Smiley, Barrett Ens, Maxime Cordeil, Tobias Czauderna, Benjamin Lee, Ying Yang, Tim Dwyer, Bernhard Jenny
Published at CHI, 2022

Head-mounted augmented reality (AR) displays allow for the seamless integration of virtual visualisation with contextual tangible references, such as physical (tangible) globes. We explore the design of immersive geospatial data visualisation with AR and tangible globes. We investigate the “tangible-virtual interplay” of tangible globes with virtual data visualisation, and propose a conceptual approach for designing immersive geospatial globes. We demonstrate a set of use cases, such as augmenting a tangible globe with virtual overlays, using a physical globe as a tangible input device for interacting with virtual globes and maps, and linking an augmented globe to an abstract data visualisation. We gathered qualitative feedback from experts about our use case visualisations, and compiled a summary of key takeaways as well as ideas for envisioned future improvements. The proposed design space, example visualisations and lessons learned aim to guide the design of tangible globes for data visualisation in AR.
Telling Stories from Computational Notebooks: AI-Assisted Presentation Slides Creation for Presenting Data Science Work
Authors: Chengbo Zheng, Dakuo Wang, April Yi Wang, Xiaojuan Ma
Published at CHI, 2022

Creating presentation slides is a critical but time-consuming task for data scientists. While researchers have proposed many AI techniques to lift data scientists’ burden on data preparation and model selection, few have targeted the presentation creation task. Based on the needs identified from a formative study, this paper presents NB2Slides, an AI system that facilitates users to compose presentations of their data science work. NB2Slides uses deep learning methods as well as example-based prompts to generate slides from computational notebooks, and take users’ input (e.g., audience background) to structure the slides. NB2Slides also provides an interactive visualization that links the slides with the notebook to help users further edit the slides. A follow-up user evaluation with 12 data scientists shows that participants believed NB2Slides can improve efficiency and reduces the complexity of creating slides. Yet, participants questioned the future of full automation and suggested a human-AI collaboration paradigm.
The Pattern is in the Details: An Evaluation of Interaction Techniques for Locating, Searching, and Contextualizing Details in Multivariate Matrix Visualizations
Authors: Yalong Yang 0001, Wenyu Xia, Fritz Lekschas, Carolina Nobre, Robert Krüger, Hanspeter Pfister
Published at CHI, 2022

Matrix visualizations are widely used to display large-scale network, tabular, set, or sequential data. They typically only encode a single value per cell, e.g., through color. However, this can greatly limit the visualizations’ utility when exploring multivariate data, where each cell represents a data point with multiple values (referred to as details). Three well-established interaction approaches can be applicable in multivariate matrix visualizations (or MMV): focus+context, pan&zoom, and overview+detail. However, there is little empirical knowledge of how these approaches compare in exploring MMV. We report on two studies comparing them for locating, searching, and contextualizing details in MMV. We first compared four focus+context techniques and found that the fisheye lens overall outperformed the others. We then compared the fisheye lens, to pan&zoom and overview+detail. We found that pan&zoom was faster in locating and searching details, and as good as overview+detail in contextualizing details.
Understanding and Designing Avatar Biosignal Visualizations for Social Virtual Reality Entertainment
Authors: Sueyoon Lee, Abdallah El Ali, Maarten W. A. Wijntjes, Pablo César
Published at CHI, 2022

Visualizing biosignals can be important for social Virtual Reality (VR), where avatar non-verbal cues are missing. While several biosignal representations exist, designing effective visualizations and understanding user perceptions within social VR entertainment remains unclear. We adopt a mixed-methods approach to design biosignals for social VR entertainment. Using survey (N=54), context-mapping (N=6), and co-design (N=6) methods, we derive four visualizations. We then ran a within-subjects study (N=32) in a virtual jazz-bar to investigate how heart rate (HR) and breathing rate (BR) visualizations, and signal rate, influence perceived avatar arousal, user distraction, and preferences. Findings show that skeuomorphic visualizations for both biosignals allow differentiable arousal inference; skeuomorphic and particles were least distracting for HR, whereas all were similarly distracting for BR; biosignal perceptions often depend on avatar relations, entertainment type, and emotion inference of avatars versus spaces. We contribute HR and BR visualizations, and considerations for designing social VR entertainment biosignal visualizations.
Understanding Visual Investigation Patterns Through Digital "Field" Observations
Authors: Irene Rae, Feng Zhou, Martin Bilsing, Philipp Bunge
Published at CHI, 2022

An extensive body of work in visual analytics has examined how users conduct analyses in scientific and academic settings, identifying and categorizing user goals and the actions they undertake to achieve them. However, most of this work has studied the analysis process in simulated or isolated environments, leading to a gap in connecting these findings to large-scale business (enterprise) contexts, where visual analysis is most needed to make sense of the large amounts of data being generated. In this work, we conducted digital ”field” observations to understand how users conduct visual analyses in an enterprise setting, where they operate within a large ecosystem of systems and people. From these observations, we identified four common objectives, six recurring visual investigation patterns, and five emergent themes. We also performed a quantitative analysis of logs over 2530 user sessions from a second visual analysis product to validate that our patterns were not product-specific.
VisGuide: User-Oriented Recommendations for Data Event Extraction
Authors: Yu-Rong Cao, Xiao-Han Li, Jia-Yu Pan, Wen-Chieh Lin
Published at CHI, 2022

Data exploration systems have become popular tools with which data analysts and others can explore raw data and organize their observations. However, users of such systems who are unfamiliar with their datasets face several challenges when trying to extract data events of interest to them. Those challenges include progressively discovering informative charts, organizing them into a logical order to depict a meaningful fact, and arranging one or more facts to illustrate a data event. To alleviate them, we propose VisGuide—a data exploration system that generates personalized recommendations to aid users’ discovery of data events in breadth and depth by incrementally learning their data exploration preferences and recommending meaningful charts tailored to them. As well as user preferences, VisGuide’s recommendations simultaneously consider sequence organization and chart presentation. We conducted two user studies to evaluate 1) the usability of VisGuide and 2) user satisfaction with its recommendation system. The results of those studies indicate that VisGuide can effectively help users create coherent and user-oriented visualization trees that represent meaningful data events.
Visualization Accessibility in the Wild: Challenges Faced by Visualization Designers
Authors: Shakila Cherise S. Joyner, Amalia Riegelhuth, Kathleen Garrity, Yea-Seul Kim, Nam Wook Kim
Published at CHI, 2022

Data visualizations are now widely used across many disciplines. However, many of them are not easily accessible for visually impaired people. In this work, we use three-staged mixed methods to understand the current practice of accessible visualization design for visually impaired people. We analyzed 95 visualizations from various venues to inspect how they are made inaccessible. To understand the rationale and context behind the design choices, we also conducted surveys with 144 practitioners in the U.S. and follow-up interviews with ten selected survey participants. Our findings include the difficulties of handling modern complex and interactive visualizations and the lack of accessibility support from visualization tools in addition to personal and organizational factors making it challenging to perform accessible design practices.
Visualizing Instructions for Physical Training: Exploring Visual Cues to Support Movement Learning from Instructional Videos
Authors: Alessandra Semeraro, Laia Turmo Vidal
Published at CHI, 2022

Instructional videos for physical training have gained popularity in recent years among sport and fitness practitioners, due to the proliferation of affordable and ubiquitous forms of online training. Yet, learning movement this way poses challenges: lack of feedback and personalised instructions, and having to rely on personal imitation capacity to learn movements. We address some of these challenges by exploring visual cues’ potential to help people imitate movements from instructional videos. With a Research through Design approach, focused on strength training, we augmented an instructional video with different sets of visual cues: directional cues, body highlights, and metaphorical visualizations. We tested each set with ten practitioners over three recorded sessions, with follow-up interviews. Through thematic analysis, we derived insights on the effect of each set of cues for supporting movement learning. Finally, we generated design takeaways to inform future HCI work on visual cues for instructional training videos.
Visualizing Urban Accessibility: Investigating Multi-Stakeholder Perspectives through a Map-based Design Probe Study
Authors: Manaswi Saha, Siddhant Patil, Emily Cho, Evie Yu-Yen Cheng, Chris Horng, Devanshi Chauhan, Rachel Kangas, Richard McGovern, Anthony Li, Jeffrey Heer, Jon E. Froehlich
Published at CHI, 2022

Urban accessibility assessments are challenging: they involve varied stakeholders across decision-making contexts while serving a diverse population of people with disabilities. To better support urban accessibility assessment using data visualizations, we conducted a three-part interview study with 25 participants across five stakeholder groups using map visualization probes. We present a multi-stakeholder analysis of visualization needs and sensemaking processes to explore how interactive visualizations can support stakeholder decision making. In particular, we elaborate how stakeholders’ varying levels of familiarity with accessibility, geospatial analysis, and specific geographic locations influences their sensemaking needs. We then contribute 10 design considerations for geovisual analytic tools for urban accessibility communication, planning, policymaking, and advocacy.
VoxLens: Making Online Data Visualizations Accessible with an Interactive JavaScript Plug-In
Authors: Ather Sharif, Olivia H. Wang, Alida T. Muongchan, Katharina Reinecke, Jacob O. Wobbrock
Published at CHI, 2022

JavaScript visualization libraries are widely used to create online data visualizations but provide limited access to their information for screen-reader users. Building on prior findings about the experiences of screen-reader users with online data visualizations, we present VoxLens, an open-source JavaScript plug-in that—with a single line of code—improves the accessibility of online data visualizations for screen-reader users using a multi-modal approach. Specifically, VoxLens enables screen-reader users to obtain a holistic summary of presented information, play sonified versions of the data, and interact with visualizations in a “drill-down” manner using voice-activated commands. Through task-based experiments with 21 screen-reader users, we show that VoxLens improves the accuracy of information extraction and interaction time by 122% and 36%, respectively, over existing conventional interaction with online data visualizations. Our interviews with screen-reader users suggest that VoxLens is a “game-changer” in making online data visualizations accessible to screen-reader users, saving them time and effort.