DevLan/vispubs huggingface dataset:
Communicating Consequences: Visual Narratives, Abstraction, and Polysemy in Rural Bangladesh
Authors: Sharifa Sultana, Syed Ishtiaque Ahmed, Jeffrey M. Rzeszotarski
Published at CHI, 2023

Information communication and visualization practices reflect two centuries of developments of conventions and best practices which may not be reflective of global audiences’ methods for conveying information. Contrasting between rural traditional visual culture and contemporary HCI and data-visualization, we argue that an understanding of traditional practices for information visualization is required for building rich data-narratives and making data-driven systems more accessible and culturally situated. Our ten-month ethnographic study investigates how rural Bangladeshi communities construct narratives through visual media. 1 Our observation, interviews, and FGDs (n=54) expose how participants convey risk management, decision-making, and monetary management practices to their peers. We find that villagers used a rich network of polysemic symbols and abstractions to manifest subjectivity, factuality, consequence, situatedness, and uncertainty; varied visual attributes for constructing narratives; and emphasized material relations among components in visuals. These findings inform the design of future systems for decision support in a culturally situated manner.
Computational Notebooks as Co-Design Tools: Engaging Young Adults Living with Diabetes, Family Carers, and Clinicians with Machine Learning Models
Authors: Amid Ayobi, Jacob Hughes, Christopher J. Duckworth, Jakub J. Dylag, Sam James, Paul Marshall, Matthew Guy, Anitha Kumaran, Adriane Chapman, Michael J. Boniface, Aisling Ann O'Kane
Published at CHI, 2023

Engaging end user groups with machine learning (ML) models can help align the design of predictive systems with people's needs and expectations. We present a co-design study investigating the benefits and challenges of using computational notebooks to inform ML models with end user groups. We used a computational notebook to engage young adults, carers, and clinicians with an example ML model that predicted health risk in diabetes care. Through co-design workshops and retrospective interviews, we found that participants particularly valued using the interactive data visualisations of the computational notebook to scaffold multidisciplinary learning, anticipate benefits and harms of the example ML model, and create fictional feature importance plots to highlight care needs. Participants also reported challenges, from running code cells to managing information asymmetries and power imbalances. We discuss the potential of leveraging computational notebooks as interactive co-design tools to meet end user needs early in ML model lifecycles.
ConceptEVA: Concept-Based Interactive Exploration and Customization of Document Summaries
Authors: Xiaoyu Zhang, Jianping Kelvin Li, Po-Wei Chi, Senthil K. Chandrasegaran, Kwan-Liu Ma
Published at CHI, 2023

With the most advanced natural language processing and artificial intelligence approaches, effective summarization of long and multi-topic documents—such as academic papers—for readers from different domains still remains a challenge. To address this, we introduce ConceptEVA, a mixed-initiative approach to generate, evaluate, and customize summaries for long and multi-topic documents. ConceptEVA incorporates a custom multi-task longformer encoder decoder to summarize longer documents. Interactive visualizations of document concepts as a network reflecting both semantic relatedness and co-occurrence help users focus on concepts of interest. The user can select these concepts and automatically update the summary to emphasize them. We present two iterations of ConceptEVA evaluated through an expert review and a within-subjects study. We find that participants’ satisfaction with customized summaries through ConceptEVA is higher than their own manually-generated summary, while incorporating critique into the summaries proved challenging. Based on our findings, we make recommendations for designing summarization systems incorporating mixed-initiative interactions.
CrossCode: Multi-level Visualization of Program Execution
Authors: Devamardeep Hayatpur, Daniel Wigdor, Haijun Xia
Published at CHI, 2023

Program visualizations help to form useful mental models of how programs work, and to reason and debug code. But these visualizations exist at a fixed level of abstraction, e.g., line-by-line. In contrast, programmers switch between many levels of abstraction when inspecting program behavior. Based on results from a formative study of hand-designed program visualizations, we designed CrossCode, a web-based program visualization system for JavaScript that leverages structural cues in syntax, control flow, and data flow to aggregate and navigate program execution across multiple levels of abstraction. In an exploratory qualitative study with experts, we found that CrossCode enabled participants to maintain a strong sense of place in program execution, was conducive to explaining program behavior, and helped track changes and updates to the program state.
Data Abstraction Elephants: The Initial Diversity of Data Representations and Mental Models
Authors: Katy Williams, Alex Bigelow, Katherine E. Isaacs
Published at CHI, 2023

Two people looking at the same dataset will create different mental models, prioritize different attributes, and connect with different visualizations. We seek to understand the space of data abstractions associated with mental models and how well people communicate their mental models when sketching. Data abstractions have a profound influence on the visualization design, yet it’s unclear how universal they may be when not initially influenced by a representation. We conducted a study about how people create their mental models from a dataset. Rather than presenting tabular data, we presented each participant with one of three datasets in paragraph form, to avoid biasing the data abstraction and mental model. We observed various mental models, data abstractions, and depictions from the same dataset, and how these concepts are influenced by communication and purpose-seeking. Our results have implications for visualization design, especially during the discovery and data collection phase.
Data, Data, Everywhere: Uncovering Everyday Data Experiences for People with Intellectual and Developmental Disabilities
Authors: Keke Wu, Michelle Ho Tran, Emma Petersen, Varsha Koushik, Danielle Albers Szafir
Published at CHI, 2023

Data is everywhere but may not be accessible to everyone. Conventional data visualization tools and guidelines often do not actively consider the specific needs and abilities of people with Intellectual and Developmental Disabilities (IDD), leaving them excluded from data-driven activities and vulnerable to ethical issues. To understand the needs and challenges people with IDD have with data, we conducted 15 semi-structured interviews with individuals with IDD and their caregivers. Our algorithmic interview approach situated data in the lived experiences of people with IDD to uncover otherwise hidden data encounters in their everyday life. Drawing on findings and observations, we characterize how they conceptualize data, when and where they use data, and what barriers exist when they interact with data. We use our results as a lens to reimagine the role of visualization in data accessibility and establish a critical near-term research agenda for cognitively accessible visualization.
DataDancing: An Exploration of the Design Space For Visualisation View Management for 3D Surfaces and Spaces
Authors: Jiazhou Liu, Barrett Ens, Arnaud Prouzeau, Jim Smiley, Isobel Kara Nixon, Sarah Goodwin, Tim Dwyer
Published at CHI, 2023

Recent studies have explored how users of immersive visualisation systems arrange data representations in the space around them. Generally, these have focused on placement centred at eye-level in absolute room coordinates. However, work in HCI exploring full-body interaction has identified zones relative to the user’s body with different roles. We encapsulate the possibilities for visualisation view management into a design space (called “DataDancing”). From this design space we extrapolate a variety of view management prototypes, each demonstrating a different combination of interaction techniques and space use. The prototypes are enabled by a full-body tracking system including novel devices for torso and foot interaction. We explore four of these prototypes, encompassing standard wall and table-style interaction as well as novel foot interaction, in depth through a qualitative user study. Learning from the results, we improve the interaction techniques and propose two hybrid interfaces that demonstrate interaction possibilities of the design space.
DataHalo: A Customizable Notification Visualization System for Personalized and Longitudinal Interactions
Authors: GuHyun Han, Jaehun Jung, Young-Ho Kim, Jinwook Seo
Published at CHI, 2023

People struggle with the overflow of smartphone notifications but often face two challenges: (1) prioritizing the informative notifications as they wish and (2) retaining the delivered information as long as they want to utilize it. In this paper, we present DataHalo, a customizable notification visualization system that represents notifications as prolonged ambient visualizations on the home screen. DataHalo supports keyword-based filtering and categorization, and draws graphical marks based on time-varying importance model to enable longitudinal interaction with the notifications. We evaluated DataHalo through a usability study (N = 17), from which we improved the interface. We then conducted a three-week deployment study (N = 12) to assess how people use DataHalo in their domestic contexts. Our study revealed that people generated various visualization settings for different kinds of apps. Drawing on both quantitative and qualitative findings, we discussed implications for supporting effective notification management through customizable ambient visualizations.
DataLev: Mid-air Data Physicalisation Using Acoustic Levitation
Authors: Lei Gao, Pourang Irani, Sriram Subramanian, Gowdham Prabhakar, Diego Martínez Plasencia, Ryuji Hirayama
Published at CHI, 2023

Data physicalisation is a technique that encodes data through the geometric and material properties of an artefact, allowing users to engage with data in a more immersive and multi-sensory way. However, current methods of data physicalisation are limited in terms of their reconfigurability and the types of materials that can be used. Acoustophoresis—a method of suspending and manipulating materials using sound waves—offers a promising solution to these challenges. In this paper, we present DataLev, a design space and platform for creating reconfigurable, multimodal data physicalisations with enriched materiality using acoustophoresis. We demonstrate the capabilities of DataLev through eight examples and evaluate its performance in terms of reconfigurability and materiality. Our work offers a new approach to data physicalisation, enabling designers to create more dynamic, engaging, and expressive artefacts.
DataParticles: Block-based and Language-oriented Authoring of Animated Unit Visualizations
Authors: Yining Cao, Jane L. E, Zhutian Chen, Haijun Xia
Published at CHI, 2023

Unit visualizations have been widely used in data storytelling within interactive articles and videos. However, authoring data stories that contain animated unit visualizations is challenging due to the tedious, time-consuming process of switching back and forth between writing a narrative and configuring the accompanying visualizations and animations. To streamline this process, we present DataParticles, a block-based story editor that leverages the latent connections between text, data, and visualizations to help creators flexibly prototype, explore, and iterate on a story narrative and its corresponding visualizations. To inform the design of DataParticles, we interviewed 6 domain experts and studied a dataset of 44 existing animated unit visualizations to identify the narrative patterns and congruence principles they employed. A user study with 9 experts showed that DataParticles can significantly simplify the process of authoring data stories with animated unit visualizations by encouraging exploration and supporting fast prototyping.
DataPilot: Utilizing Quality and Usage Information for Subset Selection during Visual Data Preparation
Authors: Arpit Narechania, Fan Du, Atanu R. Sinha, Ryan A. Rossi, Jane Hoffswell, Shunan Guo, Eunyee Koh, Shamkant B. Navathe, Alex Endert
Published at CHI, 2023

Selecting relevant data subsets from large, unfamiliar datasets can be difficult. We address this challenge by modeling and visualizing two kinds of auxiliary information: (1) quality – the validity and appropriateness of data required to perform certain analytical tasks; and (2) usage – the historical utilization characteristics of data across multiple users. Through a design study with 14 data workers, we integrate this information into a visual data preparation and analysis tool, DataPilot. DataPilot presents visual cues about “the good, the bad, and the ugly” aspects of data and provides graphical user interface controls as interaction affordances, guiding users to perform subset selection. Through a study with 36 participants, we investigate how DataPilot helps users navigate a large, unfamiliar tabular dataset, prepare a relevant subset, and build a visualization dashboard. We find that users selected smaller, effective subsets with higher quality and usage, and with greater success and confidence.
DeepLens: Interactive Out-of-distribution Data Detection in NLP Models
Authors: Da Song, Zhijie Wang, Yuheng Huang, Lei Ma 0003, Tianyi Zhang 0001
Published at CHI, 2023

Machine Learning (ML) has been widely used in Natural Language Processing (NLP) applications. A fundamental assumption in ML is that training data and real-world data should follow a similar distribution. However, a deployed ML model may suffer from out-of-distribution (OOD) issues due to distribution shifts in the real-world data. Though many algorithms have been proposed to detect OOD data from text corpora, there is still a lack of interactive tool support for ML developers. In this work, we propose DeepLens, an interactive system that helps users detect and explore OOD issues in massive text corpora. Users can efficiently explore different OOD types in DeepLens with the help of a text clustering method. Users can also dig into a specific text by inspecting salient words highlighted through neuron activation analysis. In a within-subjects user study with 24 participants, participants using DeepLens were able to find nearly twice more types of OOD issues accurately with 22% more confidence compared with a variant of DeepLens that has no interaction or visualization support.
Deimos: A Grammar of Dynamic Embodied Immersive Visualisation Morphs and Transitions
Authors: Benjamin Lee, Arvind Satyanarayan, Maxime Cordeil, Arnaud Prouzeau, Bernhard Jenny, Tim Dwyer
Published at CHI, 2023

We present Deimos, a grammar for specifying dynamic embodied immersive visualisation morphs and transitions. A morph is a collection of animated transitions that are dynamically applied to immersive visualisations at runtime and is conceptually modelled as a state machine. It is comprised of state, transition, and signal specifications. States in a morph are used to generate animation keyframes, with transitions connecting two states together. A transition is controlled by signals, which are composable data streams that can be used to enable embodied interaction techniques. Morphs allow immersive representations of data to transform and change shape through user interaction, facilitating the embodied cognition process. We demonstrate the expressivity of Deimos in an example gallery and evaluate its usability in an expert user study of six immersive analytics researchers. Participants found the grammar to be powerful and expressive, and showed interest in drawing upon Deimos’ concepts and ideas in their own research.
Designing Resource Allocation Tools to Promote Fair Allocation: Do Visualization and Information Framing Matter?
Authors: Arnav Verma, Luiz Augusto de Macêdo Morais, Pierre Dragicevic, Fanny Chevalier
Published at CHI, 2023

Studies on human decision-making focused on humanitarian aid have found that cognitive biases can hinder the fair allocation of resources. However, few HCI and Information Visualization studies have explored ways to overcome those cognitive biases. This work investigates whether the design of interactive resource allocation tools can help to promote allocation fairness. We specifically study the effect of presentation format (using text or visualization) and a specific framing strategy (showing resources allocated to groups or individuals). In our three crowdsourced experiments, we provided different tool designs to split money between two fictional programs that benefit two distinct communities. Our main finding indicates that individual-framed visualizations and text may be able to curb unfair allocations caused by group-framed designs. This work opens new perspectives that can motivate research on how interactive tools and visualizations can be engineered to combat cognitive biases that lead to inequitable decisions.
DRAVA: Aligning Human Concepts with Machine Learning Latent Dimensions for the Visual Exploration of Small Multiples
Authors: Qianwen Wang, Sehi L'Yi, Nils Gehlenborg
Published at CHI, 2023

Latent vectors extracted by machine learning (ML) are widely used in data exploration (e.g., t-SNE) but suffer from a lack of interpretability. While previous studies employed disentangled representation learning (DRL) to enable more interpretable exploration, they often overlooked the potential mismatches between the concepts of humans and the semantic dimensions learned by DRL. To address this issue, we propose Drava, a visual analytics system that supports users in 1) relating the concepts of humans with the semantic dimensions of DRL and identifying mismatches, 2) providing feedback to minimize the mismatches, and 3) obtaining data insights from concept-driven exploration. Drava provides a set of visualizations and interactions based on visual piles to help users understand and refine concepts and conduct concept-driven exploration. Meanwhile, Drava employs a concept adaptor model to fine-tune the semantic dimensions of DRL based on user refinement. The usefulness of Drava is demonstrated through application scenarios and experimental validation.
ESCAPE: Countering Systematic Errors from Machine's Blind Spots via Interactive Visual Analysis
Authors: Yongsu Ahn, Yu-Ru Lin, Panpan Xu, Zeng Dai
Published at CHI, 2023

Classification models learn to generalize the associations between data samples and their target classes. However, researchers have increasingly observed that machine learning practice easily leads to systematic errors in AI applications, a phenomenon referred to as “AI blindspots.” Such blindspots arise when a model is trained with training samples (e.g., cat/dog classification) where important patterns (e.g., black cats) are missing or periphery/undesirable patterns (e.g., dogs with grass background) are misleading towards a certain class. Even more sophisticated techniques cannot guarantee to capture, reason about, and prevent the spurious associations. In this work, we propose ESCAPE, a visual analytic system that promotes a human-in-the-loop workflow for countering systematic errors. By allowing human users to easily inspect spurious associations, the system facilitates users to spontaneously recognize concepts associated misclassifications and evaluate mitigation strategies that can reduce biased associations. We also propose two statistical approaches, relative concept association to better quantify the associations between a concept and instances, and debias method to mitigate spurious associations. We demonstrate the utility of our proposed ESCAPE system and statistical measures through extensive evaluation including quantitative experiments, usage scenarios, expert interviews, and controlled user experiments.
Exploring Chart Question Answering for Blind and Low Vision Users
Authors: Jiho Kim, Arjun Srinivasan, Nam Wook Kim, Yea-Seul Kim
Published at CHI, 2023

Data visualizations can be complex or involve numerous data points, making them impractical to navigate using screen readers alone. Question answering (QA) systems have the potential to support visualization interpretation and exploration without overwhelming blind and low vision (BLV) users. To investigate if and how QA systems can help BLV users in working with visualizations, we conducted a Wizard of Oz study with 24 BLV people where participants freely posed queries about four visualizations. We collected 979 queries and mapped them to popular analytic task taxonomies. We found that retrieving value and finding extremum were the most common tasks, participants often made complex queries and used visual references, and the data topic notably influenced the queries. We compile a list of design considerations for accessible chart QA systems and make our question corpus publicly available to guide future research and development.
Exploring Co-located Interactions with a Shape-Changing Bar Chart
Authors: Miriam Sturdee, Hayat Kara, Jason Alexander
Published at CHI, 2023

Data-physicalizations encode data and meaning through geometry or material properties, providing a non-planar view of data, offering novel opportunities for interrogation, discovery and presentation. This field has explored how single users interact with complex 3D data, but the challenges in the application of this technology to collaborative situations have not been addressed. We describe a study exploring interactions and preferences among co-located individuals using a dynamic data-physicalization in the form of a shape-changing bar chart, and compare this to previous work with single participants. Results suggest that co-located interactions with physical data prompt non-interactive hand gestures, a mirroring of physicalizations, and novel hand gestures in comparison to single participant studies. We also note that behavioural similarities in participants between interactive tabletop studies and data-physicalizations may be capitalised upon for further development of these dynamic representations. Finally, we consider the implications and challenges for the adoption of these types of platforms.
FlowAR: How Different Augmented Reality Visualizations of Online Fitness Videos Support Flow for At-Home Yoga Exercises
Authors: Hye-Young Jo, Laurenz Seidel, Michel Pahud, Mike Sinclair, Andrea Bianchi
Published at CHI, 2023

Online fitness video tutorials are an increasingly popular way to stay fit at home without a personal trainer. However, to keep the screen playing the video in view, users typically disrupt their balance and break the motion flow — two main pillars for the correct execution of yoga poses. While past research partially addressed this problem, these approaches supported only a limited view of the instructor and simple movements. To enable the fluid execution of complex full-body yoga exercises, we propose FlowAR, an augmented reality system for home workouts that shows training video tutorials as always-present virtual static and dynamic overlays around the user. We tested different overlay layouts in a study with 16 participants, using motion capture equipment for baseline performance. Then, we iterated the prototype and tested it in a furnished lab simulating home settings with 12 users. Our results highlight the advantages of different visualizations and the system’s general applicability.