Reviewerly/exHarmony huggingface dataset:
VR Almost There: Simulating Co-located Multiplayer Experiences in Social Virtual Reality

Consumer social virtual reality (VR) applications have recently started to enable social interactions at a distance. Yet it is still relatively unknown if and to what extent such applications provide meaningful social experiences in cases where in-person leisure activities are not feasible. To explore this, we developed a custom social VR application and conducted an exploratory lab study with 25 dyads in which we compared an in-person and a virtual version of a co-located multiplayer scenario. Our mixed-methods analysis revealed that both scenarios created a socially rich atmosphere and strengthened the social closeness between players. However, the lack of facial animations, limited body language, and a low field of view led to VR's main social experiential limitations: a reduced mutual awareness and emotional understanding compared to the in-person scenario. We derive implications for social VR design and research as well as game user research.
Characterizing the Technology Needs of Vulnerable Populations for Participation in Research and Design by Adopting Maslow’s Hierarchy of Needs

While various frameworks and heuristics exist within the HCI community to guide research and design for vulnerable populations, most are centered on the researcher's involvement. In this work, we developed a conceptual framework for supporting the participation of vulnerable populations in the research and design of technologies. Building upon Maslow's hierarchy of needs, we synthesized 84 research articles that focus on vulnerable populations and technology to develop our framework. This framework conceptualizes both the barriers, such as lack of technology access and digital literacy, and assets, like social relationships, that impact effective participation in research and design. Using our framework can guide researchers in identifying and fulfilling the technology-related needs of vulnerable populations, leading to more empowering research participation for these groups. The framework's guiding questions offer researchers the opportunity to reflect on their approach prior to and during their collaboration with vulnerable populations in technology research and design.
Why, when, and from whom: considerations for collecting and reporting race and ethnicity data in HCI

Engaging diverse participants in HCI research is critical for creating safe, inclusive, and equitable technology. However, there is a lack of guidelines on when, why, and how HCI researchers collect study participants' race and ethnicity. Our paper aims to take the first step toward such guidelines by providing a systematic review and discussion of the status quo of race and ethnicity data collection in HCI. Through an analysis of 2016–2021 CHI proceedings and a survey with 15 authors who published in these proceedings, we found that reporting race and ethnicity of participants is very rare (<3%) and that researchers are far from consensus. Drawing from multidisciplinary literature and our findings, we devise considerations for HCI researchers to decide why, when, and from whom to collect race and ethnicity data. For truly inclusive, equitable technologies, we encourage deliberate decisions rather than default omissions.
Troubling Collaboration: Matters of Care for Visualization Design Study

A common research process in visualization is for visualization researchers to collaborate with domain experts to solve particular applied data problems. While there is existing guidance and expertise around how to structure collaborations to strengthen research contributions, there is comparatively little guidance on how to navigate the implications of, and power produced through the socio-technical entanglements of collaborations. In this paper, we qualitatively analyze reflective interviews of past participants of collaborations from multiple perspectives: visualization graduate students, visualization professors, and domain collaborators. We juxtapose the perspectives of these individuals, revealing tensions about the tools that are built and the relationships that are formed — a complex web of competing motivations. Through the lens of matters of care, we interpret this web, concluding with considerations that both trouble and necessitate reformation of current patterns around collaborative work in visualization design studies to promote more equitable, useful, and care-ful outcomes.
ThrowIO: Actuated TUIs that Facilitate “Throwing and Catching” Spatial Interaction with Overhanging Mobile Wheeled Robots

We introduce ThrowIO, a novel style of actuated tangible user interface that facilitates throwing and catching spatial interaction powered by mobile wheeled robots on overhanging surfaces. In our approach, users throw and stick objects that are embedded with magnets to an overhanging ferromagnetic surface where wheeled robots can move and drop them at desired locations, allowing users to catch them. The thrown objects are tracked with an RGBD camera system to perform closed-loop robotic manipulations. By computationally facilitating throwing and catching interaction, our approach can be applied in many applications including kinesthetic learning, gaming, immersive haptic experience, ceiling storage, and communication. We demonstrate the applications with a proof-of-concept system enabled by wheeled robots, ceiling hardware design, and software control. Overall, ThrowIO opens up novel spatial, dynamic, and tangible interaction for users via overhanging robots, which has great potential to be integrated into our everyday space.
PopBlends: Strategies for Conceptual Blending with Large Language Models

Pop culture is an important aspect of communication. On social media people often post pop culture reference images that connect an event, product or other entity to a pop culture domain. Creating these images is a creative challenge that requires finding a conceptual connection between the users' topic and a pop culture domain. In cognitive theory, this task is called conceptual blending. We present a system called PopBlends that automatically suggests conceptual blends. The system explores three approaches that involve both traditional knowledge extraction methods and large language models. Our annotation study shows that all three methods provide connections with similar accuracy, but with very different characteristics. Our user study shows that people found twice as many blend suggestions as they did without the system, and with half the mental demand. We discuss the advantages of combining large language models with knowledge bases for supporting divergent and convergent thinking.
PromptInfuser: Bringing User Interface Mock-ups to Life with Large Language Models

Large Language Models have enabled novices without machine learning (ML) experience to quickly prototype ML functionalities with prompt programming. This paper investigates incorporating prompt-based prototyping into designing functional user interface (UI) mock-ups. To understand how infusing LLM prompts into UI mock-ups might affect the prototyping process, we conduct a exploratory study with five designers, and find that this capability might significantly speed up creating functional prototypes, inform designers earlier on how their designs will integrate ML, and enable user studies with functional prototypes earlier. From these findings, we built PromptInfuser, a Figma plugin for authoring LLM-infused mock-ups. PromptInfuser introduces two novel LLM-interactions: input-output, which makes content interactive and dynamic, and frame-change, which directs users to different frames depending on their natural language input. From initial observations, we find that PromptInfuser has the potential to transform the design process by tightly integrating UI and AI prototyping in a single interface.
ParaGlassMenu: Towards Social-Friendly Subtle Interactions in Conversations

Interactions with digital devices during social settings can reduce social engagement and interrupt conversations. To overcome these drawbacks, we designed ParaGlassMenu, a semi-transparent circular menu that can be displayed around a conversation partner’s face on Optical See-Through Head-Mounted Display (OHMD) and interacted subtly using a ring mouse. We evaluated ParaGlassMenu with several alternative approaches (Smartphone, Voice assistant, and Linear OHMD menus) by manipulating Internet-of-Things (IoT) devices in a simulated conversation setting with a digital partner. Results indicated that the ParaGlassMenu offered the best overall performance in balancing social engagement and digital interaction needs in conversations. To validate these findings, we conducted a second study in a realistic conversation scenario involving commodity IoT devices. Results confirmed the utility and social acceptance of the ParaGlassMenu. Based on the results, we discuss implications for designing attention-maintaining subtle interaction techniques on OHMDs.
A Hybrid Framework of Blockchain and IoT Technology in the Pharmaceutical Industry: A Comprehensive Study

The pharmaceutical company is key to having a strong healthcare system, and excellent healthcare is essential for every society and economy. However, there are significant concerns with medication safety and security as a result of fake and inferior medical items, which constitute a significant hazard and harm to consumers’ health. Globally, drug counterfeiting is a severe problem that endangers the public’s health as well as the health of consumers. The global business of manufacturing fake medications generates enormous annual revenue. To have a quality healthcare system, the pharmaceutical industry is of great importance and plays a vital role in medicine and pharmaceutical supplies. With emerging computer technologies like blockchain and IoT cutting across several industries and sectors and revolutionizing the world, a systematic literature review of various articles chosen from different databases was carried out in this study to analyze and evaluate application areas of this technology in the pharmaceutical industry and existing frameworks that have been proposed to solve problems faced in the pharmaceutical industry. The outcome of this review showed that the application of the blockchain and IoT hybrid framework can assist in reducing the drug counterfeit problem and provide solutions to most of the problems faced in the pharmaceutical industry. This study also proposed a framework that addressed the drawbacks of existing frameworks in the pharmaceutical industry.
Research Products at Scale

In this paper, I argue that scale is an important quality of research products [32] and reflect upon the lessons learnt from designing and producing research products at scales ranging from one-offs to those reproduced in thousands. I describe details from a body of research completed by our studio over twenty years by examining four research projects that were designed, developed, and manufactured at four distinct levels of scale. I draw out details from these projects that have not previously been reported and discuss the methodological implications of growth not just for design and production, but also for strategies for engaging with participants. In addition, I discuss particular features of research products produced at the four levels of scale and describe the benefits and trade-offs of producing research products at different scales.
Studying Exploration &amp; Long-Term Use of Voice Assistants by Older Adults

While past research has examined older adults’ voice assistant (VA) use, it is unclear whether VAs provide enough value to sustain use when compared to technologies such as smartphones. Research also suggests that barriers around structured command input may limit use. In order to investigate these gaps in adoption, we conducted interviews with ten older adults in a long-term care community who have adopted Alexa devices for at least one year. Participants learned to use Alexa through a training program that encouraged exploration. They used Alexa to complement their daily routines, improve their mood, engage in cognitively stimulating activities, and support socialization with others. We discuss our findings in the context of prior work, describe strategies to promote VA learning and adoption, and present design recommendations to support aging.
What is in the Cards: Exploring Uses, Patterns, and Trends in Design Cards

Card-based design tools–design cards–increasingly present opportunities to support practitioners. However, the breadth and depth of the design card landscape remain underexplored. In this work, we surveyed 103 design practitioners to assess current usages and associated barriers. Additionally, we analyzed and classified 161 decks of design cards from 1952-2020. We held a workshop with four experienced practitioners to generate initial categories, and then coded the remaining decks. We found that the cards contain seven different types of design knowledge: Creative Inspiration; Human Insights; Material & Domain; Methods & Tooling; Problem Definition; Team Building; and Values in Practice. The content of these cards can support designers across design stages; however, most are intended to support the early stages of design (e.g., research and ideation) rather than later design stages (e.g., prototyping and implementation). We share additional patterns uncovered and provide recommendations to support the future development and adoption of these tools.
ChartDetective: Easy and Accurate Interactive Data Extraction from Complex Vector Charts

Extracting underlying data from rasterized charts is tedious and inaccurate; values might be partially occluded or hard to distinguish, and the quality of the image limits the precision of the data being recovered. To address these issues, we introduce a semi-automatic system leveraging vector charts to extract the underlying data easily and accurately. The system is designed to make the most of vector information by relying on a drag-and-drop interface combined with selection, filtering, and previsualization features. A user study showed that participants spent less than 4 minutes to accurately recover data from charts published at CHI with diverse styles, thousands of data points, a combination of different encodings, and elements partially or completely occluded. Compared to other approaches relying on raster images, our tool successfully recovered all data, even when hidden, with a 78% lower relative error.
When XR and AI Meet - A Scoping Review on Extended Reality and Artificial Intelligence

Research on Extended Reality (XR) and Artificial Intelligence (AI) is booming, which has led to an emerging body of literature in their intersection. However, the main topics in this intersection are unclear, as are the benefits of combining XR and AI. This paper presents a scoping review that highlights how XR is applied in AI research and vice versa. We screened 2619 publications from 203 international venues published between 2017 and 2021, followed by an in-depth review of 311 papers. Based on our review, we identify five main topics at the intersection of XR and AI, showing how research at the intersection can benefit each other. Furthermore, we present a list of commonly used datasets, software, libraries, and models to help researchers interested in this intersection. Finally, we present 13 research opportunities and recommendations for future work in XR and AI research.
How Data Analysts Use a Visualization Grammar in Practice

Visualization grammars, often based on the Grammar of Graphics (GoG), have much potential for augmenting data analysis in a programming environment. However, we do not know how analysts conceptualize grammar abstractions, or how a visualization grammar works with data analysis in practice. Therefore, we qualitatively analyzed how experienced analysts (N = 6) from TidyTuesday, a social data project, wrangled and visualized data using GoG-based ggplot2 without given tasks in R Markdown. Though participants' analysis and customization needs could mismatch with GoG component design, their analysis processes aligned with the goal of GoG to expedite visualization iteration. We also found a feedback loop and tight coupling between visualization and data transformation code, explaining both participants' productivity and their errors. From these results, we discuss how future visualization grammars can become more practical for analysts and how visualization grammar and analysis tools can better integrate within a programming (i.e., computational notebook) environment.
“What if everyone is able to program?” – Exploring the Role of Software Development in Science Fiction

For decades, research around emerging technologies has been inspired by science fiction and vice versa. While so far almost only the technologies themselves have been considered, we explore the underlying software development and programming approaches. We therefore conduct a detailed media content analysis of twenty-seven movies that examines the role of software development in science fiction by identifying and investigating new approaches to programming and how software development is conceptualized portrayed within science fiction scenes. With the additional analysis of eighteen design fiction stories exploring the scenario "What if everyone is able to program?", we envision potential impacts of the democratization of software development on business and society. Our study opens new discussions and perspectives, by investigating the current vision of the future of programming and uncovers new approaches to software development which can serve as a starting point for further research in the HCI community.
ProxSituated Visualization

Existing situated visualization models assume the user is able to directly interact with the objects and spaces to which the data refers (known as physical referents). We review a growing body of work exploring scenarios where the user interacts with a proxy representation of the physical referent rather than immediately with the object itself. This introduces a complex mixture of immediate situatedness and proxies of situatedness that goes beyond the expressiveness of current models. We propose an extended model of situated visualization that encompasses Immediate Situated Visualization and ProxSituated (Proxy of Situated) Visualization. Our model describes a set of key entities involved in proxSituated scenarios and important relationships between them. From this model, we derive design dimensions and apply them to existing situated visualization work. The resulting design space allows us to describe and evaluate existing scenarios, as well as to creatively generate new conceptual scenarios.
Binary Coati Optimization Algorithm- Multi- Kernel Least Square Support Vector Machine-Extreme Learning Machine Model (BCOA-MKLSSVM-ELM): A New Hybrid Machine Learning Model for Predicting Reservoir Water Level

Predicting reservoir water levels helps manage droughts and floods. Predicting reservoir water level is complex because it depends on factors such as climate parameters and human intervention. Therefore, predicting water level needs robust models. Our study introduces a new model for predicting reservoir water levels. An extreme learning machine, the multi-kernel least square support vector machine model (MKLSSVM), is developed to predict the water level of a reservoir in Malaysia. The study also introduces a novel optimization algorithm for selecting inputs. While the LSSVM model may not capture nonlinear components of the time series data, the extreme learning machine (ELM) model—MKLSSVM model can capture nonlinear and linear components of the time series data. A coati optimization algorithm is introduced to select input scenarios. The MKLSSVM model takes advantage of multiple kernel functions. The extreme learning machine model—multi-kernel least square support vector machine model also takes the benefit of both the ELM model and MKLSSVM model models to predict water levels. This paper’s novelty includes introducing a new method for selecting inputs and developing a new model for predicting water levels. For water level prediction, lagged rainfall and water level are used. In this study, we used extreme learning machine (ELM)-multi-kernel least square support vector machine (ELM-MKLSSVM), extreme learning machine (ELM)-LSSVM-polynomial kernel function (PKF) (ELM-LSSVM-PKF), ELM-LSSVM-radial basis kernel function (RBF) (ELM-LSSVM-RBF), ELM-LSSVM-Linear Kernel function (LKF), ELM, and MKLSSVM models to predict water level. The testing means absolute of the same models was 0.710, 0.742, 0.832, 0.871, 0.912, and 0.919, respectively. The Nash–Sutcliff efficiency (NSE) testing of the same models was 0.97, 0.94, 0.90, 0.87, 0.83, and 0.18, respectively. The ELM-MKLSSVM model is a robust tool for predicting reservoir water levels.
NFTDisk: Visual Detection of Wash Trading in NFT Markets

With the growing popularity of Non-Fungible Tokens (NFT), a new type of digital assets, various fraudulent activities have appeared in NFT markets. Among them, wash trading has become one of the most common frauds in NFT markets, which attempts to mislead investors by creating fake trading volumes. Due to the sophisticated patterns of wash trading, only a subset of them can be detected by automatic algorithms, and manual inspection is usually required. We propose NFTDisk, a novel visualization for investors to identify wash trading activities in NFT markets, where two linked visualization modules are presented: a radial visualization module with a disk metaphor to overview NFT transactions and a flow-based visualization module to reveal detailed NFT flows at multiple levels. We conduct two case studies and an in-depth user interview with 14 NFT investors to evaluate NFTDisk. The results demonstrate its effectiveness in exploring wash trading activities in NFT markets.
CALVI: Critical Thinking Assessment for Literacy in Visualizations

Visualization misinformation is a prevalent problem, and combating it requires understanding people’s ability to read, interpret, and reason about erroneous or potentially misleading visualizations, which lacks a reliable measurement: existing visualization literacy tests focus on well-formed visualizations. We systematically develop an assessment for this ability by: (1) developing a precise definition of misleaders (decisions made in the construction of visualizations that can lead to conclusions not supported by the data), (2) constructing initial test items using a design space of misleaders and chart types, (3) trying out the provisional test on 497 participants, and (4) analyzing the test tryout results and refining the items using Item Response Theory, qualitative analysis, a wrong-due-to-misleader score, and the content validity index. Our final bank of 45 items shows high reliability, and we provide item bank usage recommendations for future tests and different use cases. Related materials are available at: https://osf.io/pv67z/.
Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence

Biases in Artificial Intelligence (AI) systems or their results are one important issue that demands AI explainability. Despite the prevalence of AI applications, the general public are not necessarily equipped with the ability to understand how the black-box algorithms work and how to deal with biases. To inform designs for explainable AI (XAI), we conducted in-depth interviews with major stakeholders, both end-users (n = 24) and engineers (n = 15), to investigate how they made sense of AI applications and the associated biases according to situations of high and low stakes. We discussed users' perceptions and attributions about AI biases and their desired levels and types of explainability. We found that personal relevance and boundaries as well as the level of stake are two major dimensions for developing user trust especially during biased situations and informing XAI designs.