DevLan/vispubs huggingface dataset:
graphiti: Sketch-based Graph Analytics for Images and Videos
Authors: Nazmus Saquib, Faria Huq, Syed Arefinul Haque
Published at CHI, 2022

Graph analytics is currently performed using a combination of code, symbolic algebra, and network visualizations. The analyst has to work with symbolic and abstract forms of data to construct and analyze graphs. We locate unique design opportunities at the intersection of computer vision and graph analytics, by utilizing visual variables extracted from images/videos and some direct manipulation and pen interaction techniques. We also summarize commonly used graph operations and graphical representations (graphs, simplicial complexes, hypergraphs), and map them to a few brushes and direct manipulation actions. The mapping enables us to visually construct and analyze a wide range of graphs on top of images, videos, and sketches. The design framework is implemented as a sketch-based notebook interface to demonstrate the design possibilities. User studies with scientists from various fields reveal innovative use cases for such an embodied interaction paradigm for graph analytics.
HAExplorer: Understanding Interdependent Biomechanical Motions with Interactive Helical Axes
Authors: Pepe Eulzer, Robert Rockenfeller, Kai Lawonn
Published at CHI, 2022

The helical axis is a common tool used in biomechanical modeling to parameterize the motion of rigid objects. It encodes an object’s rotation around and translation along a unique axis. Visualizations of helical axes have helped to make kinematic data tangible. However, the analysis process often remains tedious, especially if complex motions are examined. We identify multiple key challenges: the absence of interactive tools for the computation and handling of helical axes, visual clutter in axis representations, and a lack of contextualization. We solve these issues by providing the first generalized framework for kinematic analysis with helical axes. Axis sets can be computed on-demand, interactively filtered, and explored in multiple coordinated views. We iteratively developed and evaluated the HAExplorer with active biomechanics researchers. Our results show that the techniques we introduce open up the possibility to analyze non-planar, compound, and interdependent motion data.
i-LaTeX : Manipulating Transitional Representations between LaTeX Code and Generated Documents
Authors: Camille Gobert, Michel Beaudouin-Lafon
Published at CHI, 2022

Document description languages such as LaTeX are used extensively to author scientific and technical documents, but editing them is cumbersome: code-based editors only provide generic features, while WYSIWYG interfaces only support a subset of the language. Our interviews with 11 LaTeX users highlighted their difficulties dealing with textually-encoded abstractions and with the mappings between source code and document output. To address some of these issues, we introduce Transitional Representations for document description languages, which enable the visualisation and manipulation of fragments of code in relation to their generated output. We present i-LaTeX, a LaTeX editor equipped with Transitional Representations of formulae, tables, images, and grid layouts. A 16-participant experiment shows that Transitional Representations let them complete common editing tasks significantly faster, with fewer compilations, and with a lower workload. We discuss how Transitional Representations affect editing strategies and conclude with directions for future work.
Infosonics: Accessible Infographics for People who are Blind using Sonification and Voice
Authors: Leona M. Holloway, Cagatay Goncu, Alon Ilsar, Matthew Butler 0002, Kim Marriott
Published at CHI, 2022

Data visualisations are increasingly used online to engage readers and enable independent analysis of the data underlying news stories. However, access to such infographics is problematic for readers who are blind or have low vision (BLV). Equitable access to information is a basic human right and essential for independence and inclusion. We introduce infosonics, the audio equivalent of infographics, as a new style of interactive sonification that uses a spoken introduction and annotation, non-speech audio and sound design elements to present data in an understandable and engaging way. A controlled user evaluation with 18 BLV adults found a COVID-19 infosonic enabled a clearer mental image than a traditional sonification. Further, infosonics prove complementary to text descriptions and facilitate independent understanding of the data. Based on our findings, we provide preliminary suggestions for infosonics design, which we hope will enable BLV people to gain equitable access to online news and information.
Interpolating Happiness: Understanding the Intensity Gradations of Face Emojis Across Cultures
Authors: Andrey Krekhov, Katharina Emmerich, Johannes Fuchs 0001, Jens Harald Krüger
Published at CHI, 2022

We frequently utilize face emojis to express emotions in digital communication. But how wholly and precisely do such pictographs sample the emotional spectrum, and are there gaps to be closed? Our research establishes emoji intensity scales for seven basic emotions: happiness, anger, disgust, sadness, shock, annoyance, and love. In our survey (N = 1195), participants worldwide assigned emotions and intensities to 68 face emojis. According to our results, certain feelings, such as happiness or shock, are visualized by manifold emojis covering a broad spectrum of intensities. Other feelings, such as anger, have limited and only very intense representative visualizations. We further emphasize that the cultural background influences emojis’ perception: for instance, linear-active cultures (e.g., UK, Germany) rate the intensity of such visualizations higher than multi-active (e.g., Brazil, Russia) or reactive cultures (e.g., Indonesia, Singapore). To summarize, our manuscript promotes future research on more expressive, culture-aware emoji design.
Investigating Potentials of Shape-Changing Displays for Sound Zones
Authors: Stine S. Johansen, Timothy Merritt 0001, Rune Møberg Jacobsen, Peter Axel Nielsen, Jesper Kjeldskov
Published at CHI, 2022

In this paper, we investigate the use of shape-change for interaction with sound zones. A core challenge to designing interaction with sound zone systems is to support users’ understanding of the unique spatial properties of sound zones. Shape-changing interfaces present new opportunities for addressing this. We present a structured investigation into this. We leveraged the knowledge of 12 sound experts to define a set of basic shapes and movements. Then, we constructed a prototype and conducted an elicitation study with 17 novice users, investigating the experience of these shapes and movements. Our findings show that physical visualizations of sound zones can be useful in supporting users’ experience of sound zones. We present a framework of 4 basic pattern categories that prompt different sound zone experiences and outline further research directions for shape-change in supporting sound zone interaction.
Juvenile Graphical Perception: A Comparison between Children and Adults
Authors: Liudas Panavas, Amy E. Worth, Tarik Crnovrsanin, Tejas Sathyamurthi, Sara Cordes, Michelle A. Borkin, Cody Dunne
Published at CHI, 2022

Data visualization is pervasive in the lives of children as they encounter graphs and charts in early education and online media. In spite of this prevalence, our guidelines and understanding of how children perceive graphs stem primarily from studies conducted with adults. Previous psychology and education research indicates that children’s cognitive abilities are different from adults. Therefore, we conducted a classic graphical perception study on a population of children aged 8–12 enrolled in the Ivy After School Program in Boston, MA and adult computer science students enrolled in Northeastern University to determine how accurately participants judge differences in particular graphical encodings. We record the accuracy of participants’ answers for five encodings most commonly used with quantitative data. The results of our controlled experiment show that children have remarkably similar graphical perception to adults, but are consistently less accurate at interpreting the visual encodings. We found similar effectiveness rankings, relative differences in error between the different encodings, and patterns of bias across encoding types. Based on our findings, we provide design guidelines and recommendations for creating visualizations for children. This paper and all supplemental materials are available at https://osf.io/ygrdv.
Making Data Tangible: A Cross-disciplinary Design Space for Data Physicalization
Authors: S. Sandra Bae, Clement Zheng, Mary Etta West, Ellen Yi-Luen Do, Samuel Huron, Danielle Albers Szafir
Published at CHI, 2022

Designing a data physicalization requires a myriad of different considerations. Despite the cross-disciplinary nature of these considerations, research currently lacks a synthesis across the different communities data physicalization sits upon, including their approaches, theories, and even terminologies. To bridge these communities synergistically, we present a design space that describes and analyzes physicalizations according to three facets: context (end-user considerations), structure (the physical structure of the artifact), and interactions (interactions with both the artifact and data). We construct this design space through a systematic review of 47 physicalizations and analyze the interrelationships of key factors when designing a physicalization. This design space cross-pollinates knowledge from relevant HCI communities, providing a cohesive overview of what designers should consider when creating a data physicalization while suggesting new design possibilities. We analyze the design decisions present in current physicalizations, discuss emerging trends, and identify underlying open challenges.
Neo: Generalizing Confusion Matrix Visualization to Hierarchical and Multi-Output Labels
Authors: Jochen Görtler, Fred Hohman, Dominik Moritz, Kanit Wongsuphasawat, Donghao Ren, Rahul Nair, Marc Kirchner, Kayur Patel
Published at CHI, 2022

The confusion matrix, a ubiquitous visualization for helping people evaluate machine learning models, is a tabular layout that compares predicted class labels against actual class labels over all data instances. We conduct formative research with machine learning practitioners at Apple and find that conventional confusion matrices do not support more complex data-structures found in modern-day applications, such as hierarchical and multi-output labels. To express such variations of confusion matrices, we design an algebra that models confusion matrices as probability distributions. Based on this algebra, we develop Neo, a visual analytics system that enables practitioners to flexibly author and interact with hierarchical and multi-output confusion matrices, visualize derived metrics, renormalize confusions, and share matrix specifications. Finally, we demonstrate Neo’s utility with three model evaluation scenarios that help people better understand model performance and reveal hidden confusions.
One Week in the Future: Previs Design Futuring for HCI Research
Authors: Alexander Ivanov 0004, Tim Au Yeung, Kathryn Blair, Kurtis Thorvald Danyluk, Georgina Freeman, Marcus Friedel, Carmen Hull, Michael Yuk-Shing Hung, Sydney Pratte, Wesley Willett
Published at CHI, 2022

We explore the use of cinematic “pre-visualization” (previs) techniques as a rapid ideation and design futuring method for human computer interaction (HCI) research. Previs approaches, which are widely used in animation and film production, use digital design tools to create medium-fidelity videos that capture richer interaction, motion, and context than sketches or static illustrations. When used as a design futuring method, previs can facilitate rapid, iterative discussions that reveal tensions, challenges, and opportunities for new research. We performed eight one-week design futuring sprints, in which individual HCI researchers collaborated with a lead designer to produce concept sketches, storyboards, and videos that examined future applications of their research. From these experiences, we identify recurring themes and challenges and present a One Week Futuring Workbook that other researchers can use to guide their own futuring sprints. We also highlight how variations of our approach could support other speculative design practices.
Pandemic Displays: Considering Hygiene on Public Touchscreens in the Post-Pandemic Era
Authors: Ville Mäkelä, Jonas Winter, Jasmin Schwab, Michael Koch 0001, Florian Alt
Published at CHI, 2022

The COVID-19 pandemic created unprecedented questions for touch-based public displays regarding hygiene, risks, and general awareness. We study how people perceive and consider hygiene on shared touchscreens, and how touchscreens could be improved through hygiene-related functions. First, we report the results from an online survey (n = 286). Second, we present a hygiene concept for touchscreens that visualizes prior touches and provides information about the cleaning of the display and number of prior users. Third, we report the feedback for our hygiene concept from 77 participants. We find that there is demand for improved awareness of public displays’ hygiene status, especially among those with stronger concerns about COVID-19. A particularly desired detail is when the display has been cleaned. For visualizing prior touches, fingerprints worked best. We present further considerations for designing for hygiene on public displays.
Paracentral and near-peripheral visualizations: Towards attention-maintaining secondary information presentation on OHMDs during in-person social interactions
Authors: Nuwan Janaka, Chloe Haigh, Hyeong Cheol Kim, Shan Zhang, Shengdong Zhao
Published at CHI, 2022

Optical see-through Head-Mounted Displays (OST HMDs, OHMDs) are known to facilitate situational awareness while accessing secondary information. However, information displayed on OHMDs can cause attention shifts, which distract users from natural social interactions. We hypothesize that information displayed in paracentral and near-peripheral vision can be better perceived while the user is maintaining eye contact during face-to-face conversations. Leveraging this idea, we designed a circular progress bar to provide progress updates in paracentral and near-peripheral vision. We compared it with textual and linear progress bars under two conversation settings: a simulated one with a digital conversation partner and a realistic one with a real partner. Results show that a circular progress bar can effectively reduce notification distractions without losing eye contact and is more preferred by users. Our findings highlight the potential of utilizing the paracentral and near-peripheral vision for secondary information presentation on OHMDs.
Preferences and Effectiveness of Sleep Data Visualizations for Smartwatches and Fitness Bands
Authors: Alaul Islam, Ranjini Aravind, Tanja Blascheck, Anastasia Bezerianos, Petra Isenberg
Published at CHI, 2022

We present the findings of four studies related to the visualization of sleep data on wearables with two form factors: smartwatches and fitness bands. Our goal was to understand the interests, preferences, and effectiveness of different sleep visualizations by form factor. In a survey, we showed that wearers were mostly interested in weekly sleep duration, and nightly sleep phase data. Visualizations of this data were generally preferred over purely text-based representations, and the preferred chart type for fitness bands, and smartwatches was often the same. In one in-person pilot study, and two crowdsourced studies, we then tested the effectiveness of the most preferred representations for different tasks, and found that participants performed simple tasks effectively on both form factors but more complex tasks benefited from the larger smartwatch size. Lastly, we reflect on our crowdsourced study methodology for testing the effectiveness of visualizations for wearables. Supplementary material is available at https://osf.io/yz8ar/.
Pretty Princess vs. Successful Leader: Gender Roles in Greeting Card Messages
Authors: Jiao Sun, Tongshuang Wu, Yue Jiang, Ronil Awalegaonkar, Xi Victoria Lin, Diyi Yang
Published at CHI, 2022

People write personalized greeting cards on various occasions. While prior work has studied gender roles in greeting card messages, systematic analysis at scale and tools for raising the awareness of gender stereotyping remain under-investigated. To this end, we collect a large greeting card message corpus covering three different occasions (birthday, Valentine’s Day and wedding) from three sources (exemplars from greeting message websites, real-life greetings from social media and language model generated ones). We uncover a wide range of gender stereotypes in this corpus via topic modeling, odds ratio and Word Embedding Association Test (WEAT). We further conduct a survey to understand people’s perception of gender roles in messages from this corpus and if gender stereotyping is a concern. The results show that people want to be aware of gender roles in the messages, but remain unconcerned unless the perceived gender roles conflict with the recipient’s true personality. In response, we developed GreetA, an interactive visualization and writing assistant tool to visualize fine-grained topics in greeting card messages drafted by the users and the associated gender perception scores, but without suggesting text changes as an intervention.
Put a Label On It! Approaches for Constructing and Contextualizing Bar Chart Physicalizations
Authors: Kim Sauvé, Argenis Ramirez Gomez, Steven Houben
Published at CHI, 2022

Physicalizations represent data through their tangible and material properties. In contrast to screen-based visualizations, there is currently very limited understanding of how to label or annotate physicalizations to support people in interpreting the data encoded by the physicalization. Because of its spatiality, contextualization through labeling or annotation is crucial to communicate data across different orientations. In this paper, we study labeling approaches as part of the overall construction process of bar chart physicalizations. We designed a toolkit of physical tokens and paper data labels and asked 16 participants to construct and contextualize their own data physicalizations. We found that (i) the construction and contextualization of physicalizations is a highly intertwined process, (ii) data labels are integrated with physical constructs in the final design, and (iii) these are both influenced by orientation changes. We contribute with an understanding of the role of data labeling in the creation and contextualization of physicalizations.
Recommendations for Visualization Recommendations: Exploring Preferences and Priorities in Public Health
Authors: Calvin S. Bao, Siyao Li, Sarah G. Flores, Michael Correll, Leilani Battle
Published at CHI, 2022

The promise of visualization recommendation systems is that analysts will be automatically provided with relevant and high-quality visualizations that will reduce the work of manual exploration or chart creation. However, little research to date has focused on what analysts value in the design of visualization recommendations. We interviewed 18 analysts in the public health sector and explored how they made sense of a popular in-domain dataset1 in service of generating visualizations to recommend to others. We also explored how they interacted with a corpus of both automatically- and manually-generated visualization recommendations, with the goal of uncovering how the design values of these analysts are reflected in current visualization recommendation systems. We find that analysts champion simple charts with clear takeaways that are nonetheless connected with existing semantic information or domain hypotheses. We conclude by recommending that visualization recommendation designers explore ways of integrating context and expectation into their systems.
Reflective Spring Cleaning: Using Personal Informatics to Support Infrequent Notification Personalization
Authors: Izabelle F. Janzen, Joanna McGrenere
Published at CHI, 2022

Distracting mobile notifications are a high-profile problem but previous research suggests notification management tools are underused because of the barriers users face in relation to the perceived benefits. We posit that users might be more motivated to personalize if they could view contextual data for how personalizations would have impacted their recent notifications. We propose the ‘Reflective Spring Cleaning’ approach to support notification management through infrequent personalization with visualization of collected notification data. To simplify and contextualize key trends in a user’s notifications, we framed these visualizations within a novel who-what-when data abstraction. We evaluated it through a four-week longitudinal study: 21 participants logged their notifications before and after a personalization session that included suggestions for notification management contextualized against visualizations of their recent notifications. A debriefing interview described their new experience after two more weeks of logging. Our approach encouraged users to critically reflect on their notifications, which frequently inspired them to personalize and improved the experience of the majority.
ReLive: Bridging In-Situ and Ex-Situ Visual Analytics for Analyzing Mixed Reality User Studies
Authors: Sebastian Hubenschmid, Jonathan Wieland, Daniel Immanuel Fink 0001, Andrea Batch, Johannes Zagermann, Niklas Elmqvist, Harald Reiterer
Published at CHI, 2022

The nascent field of mixed reality is seeing an ever-increasing need for user studies and field evaluation, which are particularly challenging given device heterogeneity, diversity of use, and mobile deployment. Immersive analytics tools have recently emerged to support such analysis in situ, yet the complexity of the data also warrants an ex-situ analysis using more traditional non-immersive visual analytics setups. To bridge the gap between both approaches, we introduce ReLive: a mixed-immersion visual analytics framework for exploring and analyzing mixed reality user studies. ReLive combines an in-situ virtual reality view with a complementary ex-situ desktop view. While the virtual reality view allows users to relive interactive spatial recordings replicating the original study, the synchronized desktop view provides a familiar interface for analyzing aggregated data. We validated our concepts in a two-step evaluation consisting of a design walkthrough and an empirical expert user study.
RoleSeer: Understanding Informal Social Role Changes in MMORPGs via Visual Analytics
Authors: Laixin Xie, Ziming Wu, Peng Xu, Wei Li 0094, Xiaojuan Ma, Quan Li
Published at CHI, 2022

Massively multiplayer online role-playing games create virtual communities that support heterogeneous “social roles” determined by gameplay interaction behaviors under a specific social context. For all social roles, formal roles are pre-defined, obvious, and explicitly ascribed to the people holding the roles, whereas informal roles are not well-defined and unspoken. Identifying the informal roles and understanding their subtle changes are critical to designing sociability mechanisms. However, it is nontrivial to understand the existence and evolution of such roles due to their loosely defined, interconvertible, and dynamic characteristics. We propose a visual analytics system, RoleSeer, to investigate informal roles from the perspectives of behavioral interactions and depict their dynamic interconversions and transitions. Two cases, experts’ feedback, and a user study suggest that RoleSeer helps interpret the identified informal roles and explore the patterns behind role changes. We see our approach’s potential in investigating informal roles in a broader range of social games.