DevLan/vispubs huggingface dataset:
'Are They Doing Better In The Clinic Or At Home?': Understanding Clinicians' Needs When Visualizing Wearable Sensor Data Used In Remote Gait Assessments For People With Multiple Sclerosis
Authors: Ayanna Seals, Giuseppina Pilloni, Jin Kim, Raul Sanchez, John-Ross Rizzo, Leigh Charvet, Oded Nov, Graham Dove
Published at CHI, 2022

Walking impairment is a debilitating symptom of Multiple Sclerosis (MS), a disease affecting 2.8 million people worldwide. While clinicians’ in-person observational gait assessments are important, research suggests that data from wearable sensors can indicate early onset of gait impairment, track patients’ responses to treatment, and support remote and longitudinal assessment. We present an inquiry into supporting the transition from research to clinical practice. Co-design by HCI, biomedical, neurology and rehabilitation researchers resulted in a data-rich interface prototype for augmented gait analysis based on visualized sensor data. We used this as a prompt in interviews with ten experienced clinicians from a range of MS rehabilitation roles. We find that clinicians value quantitative sensor data within a whole patient narrative, to help track specific rehabilitation goals, but identify a tension between grasping critical information quickly and more detailed understanding. Based on the findings we make design recommendations for data-rich remote rehabilitation interfaces.
'ShishuShurokkha': A Transformative Justice Approach for Combating Child Sexual Abuse in Bangladesh
Authors: Sharifa Sultana, Sadia Tasnuva Pritha, Rahnuma Tasnim, Anik Das, Rokeya Akter, Shaid Hasan, S. M. Raihanul Alam, Muhammad Ashad Kabir, Syed Ishtiaque Ahmed
Published at CHI, 2022

The challenge of designing against child sexual abuse becomes more complicated in conservative societies where talking about sex is tabooed. Our mix-method study, comprised of an online survey, five FGDs, and 20 semi-structured interviews in Bangladesh, investigates the common nature, location, and time of the abuse, post-incident support, and possible combating strategies. Besides revealing important facts, our findings highlight the need of decentering the design from the victims (children and/or guardians) to the community. Hence, building on the theory of transformative justice, we prototyped and evaluated ‘ShishuShurokkha’ – an online tool that involves the whole community by allowing anonymous bystander reporting, visualizing case-maps, connecting with legal, medical, and social support, and raising awareness. The evaluation of ShishuShurokkha shows the promise for such a communal approach toward combating child sexual abuse, and highlights the needs for sincere involvement of the government, NGOs, the legal, educational, and religious services in this.
A Design Space For Data Visualisation Transformations Between 2D And 3D In Mixed-Reality Environments
Authors: Benjamin Lee, Maxime Cordeil, Arnaud Prouzeau, Bernhard Jenny, Tim Dwyer
Published at CHI, 2022

As mixed-reality (MR) technologies become more mainstream, the delineation between data visualisations displayed on screens or other surfaces and those floating in space becomes increasingly blurred. Rather than the choice of using either a 2D surface or the 3D space for visualising data being a dichotomy, we argue that users should have the freedom to transform visualisations seamlessly between the two as needed. However, the design space for such transformations is large, and practically uncharted. To explore this, we first establish an overview of the different states that a data visualisation can take in MR, followed by how transformations between these states can facilitate common visualisation tasks. We then describe a design space of how these transformations function, in terms of the different stages throughout the transformation, and the user interactions and input parameters that affect it. This design space is then demonstrated with multiple exemplary techniques based in MR.
Accessibility for Color Vision Deficiencies: Challenges and Findings of a Large Scale Study on Paper Figures
Authors: Katrin Angerbauer, Nils Rodrigues, René Cutura, Seyda Öney, Nelusa Pathmanathan, Cristina Morariu, Daniel Weiskopf, Michael Sedlmair
Published at CHI, 2022

We present an exploratory study on the accessibility of images in publications when viewed with color vision deficiencies (CVDs). The study is based on 1,710 images sampled from a visualization dataset (VIS30K) over five years. We simulated four CVDs on each image. First, four researchers (one with a CVD) identified existing issues and helpful aspects in a subset of the images. Based on the resulting labels, 200 crowdworkers provided 30,000 ratings on present CVD issues in the simulated images. We analyzed this data for correlations, clusters, trends, and free text comments to gain a first overview of paper figure accessibility. Overall, about 60 % of the images were rated accessible. Furthermore, our study indicates that accessibility issues are subjective and hard to detect. On a meta-level, we reflect on our study experience to point out challenges and opportunities of large-scale accessibility studies for future research directions.
Annotating Line Charts for Addressing Deception
Authors: Arlen Fan, Yuxin Ma, Michelle Mancenido, Ross Maciejewski
Published at CHI, 2022

Deceptive visualizations are visualizations that, whether intentionally or not, lead the reader to an understanding of the data which varies from the actual data. Examples of deceptive visualizations can be found in every digital platform, and, despite their widespread use in the wild, there have been limited efforts to alert laypersons to common deceptive visualization practices. In this paper, we present a tool for annotating line charts in the wild that reads line chart images and outputs text and visual annotations to assess the line charts for distortions and help guide the reader towards an honest understanding of the chart data. We demonstrate the usefulness of our tool through a series of case studies on real-world charts. Finally, we perform a crowdsourced experiment to evaluate the ability of the proposed tool to educate readers about potentially deceptive visualization practices.
AvatAR: An Immersive Analysis Environment for Human Motion Data Combining Interactive 3D Avatars and Trajectories
Authors: Patrick Reipschläger, Frederik Brudy, Raimund Dachselt, Justin Matejka, George W. Fitzmaurice, Fraser Anderson
Published at CHI, 2022

Analysis of human motion data can reveal valuable insights about the utilization of space and interaction of humans with their environment. To support this, we present AvatAR, an immersive analysis environment for the in-situ visualization of human motion data, that combines 3D trajectories with virtual avatars showing people’s detailed movement and posture. Additionally, we describe how visualizations can be embedded directly into the environment, showing what a person looked at or what surfaces they touched, and how the avatar’s body parts can be used to access and manipulate those visualizations. AvatAR combines an AR HMD with a tablet to provide both mid-air and touch interaction for system control, as well as an additional overview device to help users navigate the environment. We implemented a prototype and present several scenarios to show that AvatAR can enhance the analysis of human motion data by making data not only explorable, but experienceable.
BikeAR: Understanding Cyclists' Crossing Decision-Making at Uncontrolled Intersections using Augmented Reality
Authors: Andrii Matviienko, Florian Müller 0003, Dominik Schön, Paul Seesemann, Sebastian Günther 0001, Max Mühlhäuser
Published at CHI, 2022

Cycling has become increasingly popular as a means of transportation. However, cyclists remain a highly vulnerable group of road users. According to accident reports, one of the most dangerous situations for cyclists are uncontrolled intersections, where cars approach from both directions. To address this issue and assist cyclists in crossing decision-making at uncontrolled intersections, we designed two visualizations that: (1) highlight occluded cars through an X-ray vision and (2) depict the remaining time the intersection is safe to cross via a Countdown. To investigate the efficiency of these visualizations, we proposed an Augmented Reality simulation as a novel evaluation method, in which the above visualizations are represented as AR, and conducted a controlled experiment with 24 participants indoors. We found that the X-ray ensures a fast selection of shorter gaps between cars, while the Countdown facilitates a feeling of safety and provides a better intersection overview.
Cicero: A Declarative Grammar for Responsive Visualization
Authors: Hyeok Kim, Ryan A. Rossi, Fan Du, Eunyee Koh, Shunan Guo, Jessica Hullman, Jane Hoffswell
Published at CHI, 2022

Designing responsive visualizations can be cast as applying transformations to a source view to render it suitable for a different screen size. However, designing responsive visualizations is often tedious as authors must manually apply and reason about candidate transformations. We present Cicero, a declarative grammar for concisely specifying responsive visualization transformations which paves the way for more intelligent responsive visualization authoring tools. Cicero’s flexible specifier syntax allows authors to select visualization elements to transform, independent of the source view’s structure. Cicero encodes a concise set of actions to encode a diverse set of transformations in both desktop-first and mobile-first design processes. Authors can ultimately reuse design-agnostic transformations across different visualizations. To demonstrate the utility of Cicero, we develop a compiler to an extended version of Vega-Lite, and provide principles for our compiler. We further discuss the incorporation of Cicero into responsive visualization authoring tools, such as a design recommender.
Classroom Dandelions: Visualising Participant Position, Trajectory and Body Orientation Augments Teachers' Sensemaking
Authors: Gloria Fernández-Nieto, Pengcheng An, Jian Zhao 0010, Simon Buckingham Shum, Roberto Martínez Maldonado
Published at CHI, 2022

Despite the digital revolution, physical space remains the site for teaching and learning embodied knowledge and skills. Both teachers and students must develop spatial competencies to effectively use classroom spaces, enabling fluid verbal and non-verbal interaction. While video permits rich activity capture, it provides no support for quickly seeing activity patterns that can assist learning. In contrast, position tracking systems permit the automated modelling of spatial behaviour, opening new possibilities for feedback. This paper introduces the design rationale for ”Dandelion Diagrams” that integrate participant location, trajectory and body orientation over a variable period. Applied in two authentic teaching contexts (a science laboratory, and a nursing simulation) we show how heatmaps showing only teacher/student location led to misinterpretations that were resolved by overlaying Dandelion Diagrams. Teachers also identified a variety of ways they could aid professional development. We conclude Dandelion Diagrams assisted sensemaking, but discuss the ethical risks of over-interpretation.
ComputableViz: Mathematical Operators as a Formalism for Visualisation Processing and Analysis
Authors: Aoyu Wu, Wai Tong, Haotian Li 0001, Dominik Moritz, Yong Wang 0021, Huamin Qu
Published at CHI, 2022

Data visualizations are created and shared on the web at an unprecedented speed, raising new needs and questions for processing and analyzing visualizations after they have been generated and digitized. However, existing formalisms focus on operating on a single visualization instead of multiple visualizations, making it challenging to perform analysis tasks such as sorting and clustering visualizations. Through a systematic analysis of previous work, we abstract visualization-related tasks into mathematical operators such as union and propose a design space of visualization operations. We realize the design by developing ComputableViz, a library that supports operations on multiple visualization specifications. To demonstrate its usefulness and extensibility, we present multiple usage scenarios concerning processing and analyzing visualization, such as generating visualization embeddings and automatically making visualizations accessible. We conclude by discussing research opportunities and challenges for managing and exploiting the massive visualizations on the web.
CrossData: Leveraging Text-Data Connections for Authoring Data Documents
Authors: Zhutian Chen, Haijun Xia
Published at CHI, 2022

Data documents play a central role in recording, presenting, and disseminating data. Despite the proliferation of applications and systems designed to support the analysis, visualization, and communication of data, writing data documents remains a laborious process, requiring a constant back-and-forth between data processing and writing tools. Interviews with eight professionals revealed that their workflows contained numerous tedious, repetitive, and error-prone operations. The key issue that we identified is the lack of persistent connection between text and data. Thus, we developed CrossData, a prototype that treats text-data connections as persistent, interactive, first-class objects. By automatically identifying, establishing, and leveraging text-data connections, CrossData enables rich interactions to assist in the authoring of data documents. An expert evaluation with eight users demonstrated the usefulness of CrossData, showing that it not only reduced the manual effort in writing data documents but also opened new possibilities to bridge the gap between data exploration and writing.
Data Every Day: Designing and Living with Personal Situated Visualizations
Authors: Nathalie Bressa, Jo Vermeulen, Wesley Willett
Published at CHI, 2022

We explore the design and utility of situated manual self-tracking visualizations on dedicated displays that integrate data tracking into existing practices and physical environments. Situating self-tracking tools in relevant locations is a promising approach to enable reflection on and awareness of data without needing to rely on sensorized tracking or personal devices. In both a long-term autobiographical design process and a co-design study with six participants, we rapidly prototyped and deployed 30 situated self-tracking applications over a ten month period. Grounded in the experience of designing and living with these trackers, we contribute findings on logging and data entry, the use of situated displays, and the visual design and customization of trackers. Our results demonstrate the potential of customizable dedicated self-tracking visualizations that are situated in relevant physical spaces, and suggest future research opportunities and new potential applications for situated visualizations.
Designing for Knowledge Construction to Facilitate the Uptake of Open Science: Laying out the Design Space
Authors: Leonie Disch, Angela Fessl, Viktoria Pammer-Schindler
Published at CHI, 2022

The uptake of open science resources needs knowledge construction on the side of the readers/receivers of scientific content. The design of technologies surrounding open science resources can facilitate such knowledge construction, but this has not been investigated yet. To do so, we first conducted a scoping review of literature, from which we draw design heuristics for knowledge construction in digital environments. Subsequently, we grouped the underlying technological functionalities into three design categories: i) structuring and supporting collaboration, ii) supporting the learning process, and iii) structuring, visualising and navigating (learning) content. Finally, we mapped the design categories and associated design heuristics to core components of popular open science platforms. This mapping constitutes a design space (design implications), which informs researchers and designers in the HCI community about suitable functionalities for supporting knowledge construction in existing or new digital open science platforms.
Designing Word Filter Tools for Creator-led Comment Moderation
Authors: Shagun Jhaver, Quan Ze Chen, Detlef Knauss, Amy X. Zhang
Published at CHI, 2022

Online social platforms centered around content creators often allow comments on content, where creators can then moderate the comments they receive. As creators can face overwhelming numbers of comments, with some of them harassing or hateful, platforms typically provide tools such as word filters for creators to automate aspects of moderation. From needfinding interviews with 19 creators about how they use existing tools, we found that they struggled with writing good filters as well as organizing and revising their filters, due to the difficulty of determining what the filters actually catch. To address these issues, we present FilterBuddy, a system that supports creators in authoring new filters or building from pre-made ones, as well as organizing their filters and visualizing what comments are captured by them over time. We conducted an early-stage evaluation of FilterBuddy with YouTube creators, finding that participants see FilterBuddy not just as a moderation tool, but also a means to organize their comments to better understand their audiences.
Diff in the Loop: Supporting Data Comparison in Exploratory Data Analysis
Authors: April Yi Wang, Will Epperson, Robert A. DeLine, Steven Mark Drucker
Published at CHI, 2022

Data science is characterized by evolution: since data science is exploratory, results evolve from moment to moment; since it can be collaborative, results evolve as the work changes hands. While existing tools help data scientists track changes in code, they provide less support for understanding the iterative changes that the code produces in the data. We explore the idea of visualizing differences in datasets as a core feature of exploratory data analysis, a concept we call Diff in the Loop (DITL). We evaluated DITL in a user study with 16 professional data scientists and found it helped them understand the implications of their actions when manipulating data. We summarize these findings and discuss how the approach can be generalized to different data science workflows.
Do You See What I Hear? - Peripheral Absolute and Relational Visualisation Techniques for Sound Zones
Authors: Rune Møberg Jacobsen, Niels van Berkel, Mikael B. Skov, Stine S. Johansen, Jesper Kjeldskov
Published at CHI, 2022

Sound zone technology allows multiple simultaneous sound experiences for multiple people in the same room without interference. However, given the inherent invisible and intangible nature of sound zones, it is unclear how to communicate the position and size of sound zones to users. This paper compares two visualisation techniques; absolute visualisation, relational visualisation, as well as a baseline condition without visualisations. In a within-subject experiment (N = 33), we evaluated these techniques for effectiveness and efficiency across four representative tasks. Our findings show that the absolute and relational visualisation techniques increase effectiveness in multi-user tasks but not in single-user tasks. The efficiency for all tasks was improved using visualisations. We discuss the potential of visualisations for sound zones and highlight future research opportunities for sound zone interaction.
Do You See What You Mean? Using Predictive Visualizations to Reduce Optimism in Duration Estimates
Authors: Morgane Koval, Yvonne Jansen
Published at CHI, 2022

Making time estimates, such as how long a given task might take, frequently leads to inaccurate predictions because of an optimistic bias. Previous attempts to alleviate this bias, including decomposing the task into smaller components and listing potential surprises, have not shown any major improvement. This article builds on the premise that these procedures may have failed because they involve compound probabilities and mixture distributions which are difficult to compute in one’s head. We hypothesize that predictive visualizations of such distributions would facilitate the estimation of task durations. We conducted a crowdsourced study in which 145 participants provided different estimates of overall and sub-task durations and we used these to generate predictive visualizations of the resulting mixture distributions. We compared participants’ initial estimates with their updated ones and found compelling evidence that predictive visualizations encourage less optimistic estimates.
FluidMeet: Enabling Frictionless Transitions Between In-Group, Between-Group, and Private Conversations During Virtual Breakout Meetings
Authors: Erzhen Hu, Md. Aashikur Rahman Azim, Seongkook Heo
Published at CHI, 2022

People often form small conversation groups during physical gatherings to have ad-hoc and informal conversations. As these groups are loosely defined, others can often overhear and join the conversation. However, current video-conferencing tools only allow for strict boundaries between small conversation groups, inhibiting fluid group formations and between-group conversations. This isolates small-group conversations from others and leads to inefficient transitions between conversations. We present FluidMeet, a virtual breakout meeting system that employs flexible conversation boundaries and cross-group conversation visualizations to enable fluid conversation group formations and ad-hoc, informal conversations. FluidMeet enables out-group members to overhear group conversations while allowing conversation groups to control their shared level of context. Users within conversation groups can also quickly switch between in-group and private conversations. A study of FluidMeet showed that it encouraged users to break group boundaries, made them feel less isolated in group conversations, and facilitated communication across different groups.
GANSlider: How Users Control Generative Models for Images using Multiple Sliders with and without Feedforward Information
Authors: Hai Dang, Lukas Mecke, Daniel Buschek
Published at CHI, 2022

We investigate how multiple sliders with and without feedforward visualizations influence users’ control of generative models. In an online study (N=138), we collected a dataset of people interacting with a generative adversarial network (StyleGAN2) in an image reconstruction task. We found that more control dimensions (sliders) significantly increase task difficulty and user actions. Visual feedforward partly mitigates this by enabling more goal-directed interaction. However, we found no evidence of faster or more accurate task performance. This indicates a tradeoff between feedforward detail and implied cognitive costs, such as attention. Moreover, we found that visualizations alone are not always sufficient for users to understand individual control dimensions. Our study quantifies fundamental UI design factors and resulting interaction behavior in this context, revealing opportunities for improvement in the UI design for interactive applications of generative models. We close by discussing design directions and further aspects.