Reviewerly/exHarmony huggingface dataset:
Probing a Community-Based Conversational Storytelling Agent to Document Digital Stories of Housing Insecurity

Despite the central role that stories play in social movement-building, they are difficult to sustainably document for many reasons. To explore this challenge, this paper describes the design of a community-based conversational storytelling agent (CSA) to document digital stories of housing insecurity. Building on insights from an ongoing grassroots project, the Anti-Eviction Mapping Project, we share how a study initially focused on CSA-support opened an investigation of the role that artificial intelligence may play in housing justice movements. Drawing from 17 interviews with narrators of housing insecurity experiences and collectors of such stories, we find that collectors perceive opportunities to expand means of documentation with multimedia and multi-language support. Meanwhile, some narrators perceive potential for a CSA to offer therapeutic storytelling experiences and document otherwise unrecorded stories. Yet, CSA encounters also surface perils of machine bias, as well as reduced possibilities of human connections and relations.
The Effects of Avatar and Environment on Thermal Perception and Skin Temperature in Virtual Reality

Humans' thermal regulation and subjective perception of temperature is highly plastic and depends on the visual appearance of the surrounding environment. Previous work shows that an environment's color temperature affects the experienced temperature. As virtual reality (VR) enables visual immersion, recent work suggests that a VR scene's color temperature also affects experienced temperature. It is, however, unclear if an avatar's appearance also affects users' thermal perception and if a change in thermal perception even influences the body temperature. Therefore, we conducted a study with 32 participants performing a task in an ice or fire world while having ice or fire hands. We show that being in a fire world or having fire hands increases the perceived temperature. We even show that having fire hands decreases the hand temperature compared to having ice hands. We discuss the implications for the design of VR systems and future research directions.
Slide4N: Creating Presentation Slides from Computational Notebooks with Human-AI Collaboration

Data scientists often have to use other presentation tools (e.g., Microsoft PowerPoint) to create slides to communicate their analysis obtained using computational notebooks. Much tedious and repetitive work is needed to transfer the routines of notebooks (e.g., code, plots) to the presentable contents on slides (e.g., bullet points, figures). We propose a human-AI collaborative approach and operationalize it within Slide4N, an interactive AI assistant for data scientists to create slides from computational notebooks. Slide4N leverages advanced natural language processing techniques to distill key information from user-selected notebook cells and then renders them in appropriate slide layouts. The tool also provides intuitive interactions that allow further refinement and customization of the generated slides. We evaluated Slide4N with a two-part user study, where participants appreciated this human-AI collaborative approach compared to fully-manual or fully-automatic methods. The results also indicate the usefulness and effectiveness of Slide4N in slide creation tasks from notebooks.
Through Their Eyes and In Their Shoes: Providing Group Awareness During Collaboration Across Virtual Reality and Desktop Platforms

Many collaborative data analysis situations benefit from collaborators utilizing different platforms. However, maintaining group awareness between team members using diverging devices is difficult, not least because common ground diminishes. A person using head-mounted VR cannot physically see a user on a desktop computer even while co-located, and the desktop user cannot easily relate to the VR user’s 3D workspace. To address this, we propose the “eyes-and-shoes” principles for group awareness and abstract them into four levels of techniques. Furthermore, we evaluate these principles with a qualitative user study of 6 participant pairs synchronously collaborating across distributed desktop and VR head-mounted devices. In this study, we vary the group awareness techniques between participants and explore two visualization contexts within participants. The results of this study indicate that the more visual metaphors and views of participants diverge, the greater the level of group awareness is needed. A copy of this paper, the study preregistration, and all supplemental materials required to reproduce the study are available on OSF (link).
Let’s Face It: Influence of Facial Expressions on Social Presence in Collaborative Virtual Reality

As the world becomes more interconnected, physical separation between people increases. Existing collaborative Virtual Reality (VR) applications, designed to bridge this distance, are not yet sufficient in providing a sense of social connection comparable to face-to-face interactions. Possible reasons are the limited multimodality of VR systems and the lack of non-verbal cues in VR avatars. We systematically investigated how facial expressions influence Social Presence in two collaborative VR tasks. We explored four types of facial expressions: eyes and mouth movements, their combination, and no expressions, for two types of explanations: verbal and graphical. To examine how these expressions influence Social Presence, we conducted a controlled VR experiment (N = 48), in which participants had to explain a specific term to their counterpart. Our results demonstrate that eye and mouth movements positively influence Social Presence in VR. Particularly, combining verbal explanations and eye movements induces the highest feeling of co-presence.
FlexiChain 3.0: Distributed Ledger Technology-Based Intelligent Transportation for Vehicular Digital Asset Exchange in Smart Cities

Due to the enormous amounts of data being generated between users, Intelligent Transportation Systems (ITS) are complex Cyber-Physical Systems that necessitate a reliable and safe infrastructure. Internet of Vehicles (IoV) is the term that describes the interconnection for every single node, device, sensor, and actuator that are Internet enabled, whether attached or unattached to vehicles. A single smart vehicle will generate a huge amount of data. Concurrently, it needs an instant response to avoid accidents since vehicles are fast-moving objects. In this work, we explore Distributed Ledger Technology (DLT) and collect data about consensus algorithms and their applicability to be used in the IoV as the backbone of ITS. Multiple distributed ledger networks are currently in operation. Some are used in finance or supply chains, and others are used for general decentralized applications. Despite the secure and decentralized nature of the blockchain, each of these networks has trade-offs and compromises. Based on the analysis of consensus algorithms, a conclusion has been made to design one that fits the requirements of ITS-IOV. FlexiChain 3.0 is proposed in this work to serve as a Layer0 network for different stakeholders in the IoV. A time analysis has been conducted and shows a capacity of 2.3 transactions per second, which is an acceptable speed to be used in IoV. Moreover, a security analysis was conducted as well and shows high security and high independence of the node number in terms of security level per the number of participants.
PumpVR: Rendering the Weight of Objects and Avatars through Liquid Mass Transfer in Virtual Reality

Perceiving objects’ and avatars’ weight in Virtual Reality (VR) is important to understand their properties and naturally interact with them. However, commercial VR controllers cannot render weight. Controllers presented by previous work are single-handed, slow, or only render a small mass. In this paper, we present PumpVR that renders weight by varying the controllers’ mass according to the properties of virtual objects or bodies. Using a bi-directional pump and solenoid valves, the system changes the controllers’ absolute weight by transferring water in or out with an average error of less than 5%. We implemented VR use cases with objects and avatars of different weights to compare the system with standard controllers. A study with 24 participants revealed significantly higher realism and enjoyment when using PumpVR to interact with virtual objects. Using the system to render body weight had significant effects on virtual embodiment, perceived exertion, and self-perceived fitness.
Social Digital Cyborgs: The Collaborative Design Process of JIZAI ARMS

Half a century since the concept of a cyborg was introduced, digital cyborgs, enabled by the spread of wearable robotics, are the focus of much research in recent times. We introduce JIZAI ARMS, a supernumerary robotic limb system consisting of a wearable base unit with six terminals and detachable robot arms controllable by the wearer. The system was designed to enable social interaction between multiple wearers, such as an exchange of arm(s), and explore possible interactions between digital cyborgs in a cyborg society. This paper describes the JIZAI ARMS’ design process, an interdisciplinary collaboration between human augmentation researchers, product designers, a system architect, and manufacturers, to realize a technically complex system while considering the aesthetics of a digital cyborg. We also provide an autobiographical report of our first impressions of using the JIZAI ARMS and use our findings to speculate on a model of potential social interactions between digital cyborgs.
AI in the Shell: Towards an Understanding of Integrated Embodiment

With technologies becoming increasingly intelligent, the interaction paradigm of Human-Computer Integration where computers and human form a partnership emerged. Most of these works considered computers as separate from users’ embodiment. However, in recent years, technologies are becoming increasingly closer and even interwoven with the human body. Our work asks whether computers can incorporate into one’s embodiment and form a partnership. We call this integrated embodiment. Such a paradigm might facilitate a more direct and intimate partnership between humans and computers. To exemplify the paradigm, we present AI-in-the-Shell, an exoskeleton-based system that enables users to experience having an AI residing in their body. The AI-powered system can make independent decisions and actuate the user’s body to better support their daily tasks and experiences, e.g., to enhance their game performance. We hope this work can extend the current understanding of Human-Computer Integration, and step towards a more complete understanding of integrated embodiment.
Surfacing Livingness in Microbial Displays

In recent years, there has been a notable proliferation and diversification of works in HCI, that integrate living microorganisms; an imperative lifeform dominating ecosystems of our planet. Yet despite the growing interest, there is a lack of structured lenses with which designers can strategize their processes of surfacing livingness; a material quality inherent in living artefacts with a potential to enrich user experiences and to initiate mutualistic care between humans and microorganisms. Through a systematic artefacts review and a case study on Flavobacteria, we have developed and instantiated a Taxonomy of Surfacing Livingness in Microbial Displays, consisting of six microbe-sensitive, tuneable mechanisms for human noticing of microorganisms: 1) Canvassing, 2) Marking, 3) Magnifying, 4) Translating, 5) Nudging, and 6) Molecular Programming. The taxonomy invites diverse and adaptable ways of generating and crafting microbial displays; towards overcoming microbe-specific surfacing constraints, integrating diverse stakeholders’ values, and enabling nuanced address of microbial welfare.
FP-Conv-CM: Fuzzy Probabilistic Convolution C-Means

Soft computing models based on fuzzy or probabilistic approaches provide decision system makers with the necessary capabilities to deal with imprecise and incomplete information. Hybrid systems based on different soft computing approaches with complementary qualities and principles have also become popular. On the one hand, fuzzy logic makes its decisions on the basis of the degree of membership but gives no information on the frequency of an event; on the other hand, the probability informs us of the frequency of the event but gives no information on the degree of membership to a set. In this work, we propose a new measure that implements both fuzzy and probabilistic notions (i.e., the degree of membership and the frequency) while exploiting the ability of the convolution operator to combine functions on continuous intervals. This measure evaluates both the degree of membership and the frequency of objects/events in the design of decision support systems. We show, using concrete examples, the drawbacks of fuzzy logic and probability-based approaches taken separately, and we then show how a fuzzy probabilistic convolution measure allows the correction of these drawbacks. Based on this measure, we introduce a new clustering method named Fuzzy-Probabilistic-Convolution-C-Means (FP-Conv-CM). Fuzzy C-Means (FCM), Probabilistic K-Means (PKM), and FP-Conv-CM were tested on multiple datasets and compared on the basis of two performance measures based on the Silhouette metric and the Dunn’s Index. FP-Conv-CM was shown to improve on both metrics. In addition, FCM, PKM, and FP-Conv-CM were used for multiple image compression tasks and were compared based on three performance measures: Mean Square Error (MSE), Peak Signal-to-Noise Ratio (PSNR), and Structural SImilarity Index (SSIM). The proposed FP-Conv-CM method shows improvements in all these three measures as well.
OK Google, Let's Learn: Using Voice User Interfaces for Informal Self-Regulated Learning of Health Topics among Younger and Older Adults

In this paper, we present Health Buddy, a voice agent integrated into commercially available Voice User Interfaces (VUIs) to support informal self-regulated learning (SRL) of health-related topics through multiple learning strategies and examine the efficacy of Health Buddy on learning outcomes for younger and older adults. We conducted a mixed-factorial-design experiment with 26 younger and 25 older adults, assigned to three SRL strategies (within-subjects): monologue, dialogue-based scaffolding building, and conceptual diagramming. We found that while younger adults benefit more from scaffolding building and conceptual diagramming, both younger and older adults showed equivalent learning outcomes. Furthermore, interaction fluency (operationalized by the number of conversational breakdowns) was associated with learning outcomes regardless of age. While older adults did not experience less fluent conversations, interaction fluency affected their technology acceptance toward VUIs more than younger ones. Our study discusses age-related learning differences and has implications for designing VUI-based learning programs for older adults.
Don’t Just Tell Me, Ask Me: AI Systems that Intelligently Frame Explanations as Questions Improve Human Logical Discernment Accuracy over Causal AI explanations

Critical thinking is an essential human skill. Despite the importance of critical thinking, research reveals that our reasoning ability suffers from personal biases and cognitive resource limitations, leading to potentially dangerous outcomes. This paper presents the novel idea of AI-framed Questioning that turns information relevant to the AI classification into questions to actively engage users' thinking and scaffold their reasoning process. We conducted a study with 204 participants comparing the effects of AI-framed Questioning on a critical thinking task; discernment of logical validity of socially divisive statements. Our results show that compared to no feedback and even causal AI explanations of an always correct system, AI-framed Questioning significantly increase human discernment of logically flawed statements. Our experiment exemplifies a future style of Human-AI co-reasoning system, where the AI becomes a critical thinking stimulator rather than an information teller.
Towards a Consensus Gesture Set: A Survey of Mid-Air Gestures in HCI for Maximized Agreement Across Domains

Mid-air gesture-based systems are becoming ubiquitous. Many mid-air gestures control different kinds of interactive devices, applications, and systems. They are, however, still targeted at specific devices in specific domains and are not necessarily consistent across domain boundaries. A comprehensive evaluation of the transferability of gesture vocabulary between domains is also lacking. Consequently, interaction designers cannot decide which gestures to use for which domain. In this systematic literature review, we contribute to the future research agenda in this area, based on an analysis of 172 papers. As part of our analysis, we clustered gestures according to the dimensions of an existing taxonomy to identify their common characteristics in different domains, and we investigated the extent to which existing mid-air gesture sets are consistent across different domains. We derived a consensus gesture set containing 22 gestures based on agreement rates calculation and considered their transferability across different domains.
Unmaking as Emancipation: Lessons and Reflections from Luddism

Emancipation is fundamentally a work of unmaking, as it entails undermining, dissolving, and undoing oppressive structures. This paper offers an account of a frequently misunderstood unmaking movement, Luddism. The Luddites were a loosely organized collective of nineteenth century English textile makers who destroyed machines that were replacing their skilled labor and leading to deteriorating working conditions. In this account, we show that the goals and tactics of Luddism have significant alignments with current HCI work in the areas of unmaking and social justice. Through articulation of six characteristics of unmaking in Luddism - practical and symbolic, community-engaged, emancipatory, selective, antagonistic, and enduring - we identify potential limits and opportunities in HCI research and design practice, as currently construed. In doing so, we build upon and extend prior HCI research to suggest unmaking as emancipation, a new category of unmaking around issues of social justice.
HandAvatar: Embodying Non-Humanoid Virtual Avatars through Hands

We propose HandAvatar to enable users to embody non-humanoid avatars using their hands. HandAvatar leverages the high dexterity and coordination of users' hands to control virtual avatars, enabled through our novel approach for automatically-generated joint-to-joint mappings. We contribute an observation study to understand users' preferences on hand-to-avatar mappings on eight avatars. Leveraging insights from the study, we present an automated approach that generates mappings between users' hands and arbitrary virtual avatars by jointly optimizing control precision, structural similarity, and comfort. We evaluated HandAvatar on static posing, dynamic animation, and creative exploration tasks. Results indicate that HandAvatar enables more precise control, requires less physical effort, and brings comparable embodiment compared to a state-of-the-art body-to-avatar control method. We demonstrate HandAvatar's potential with applications including non-humanoid avatar based social interaction in VR, 3D animation composition, and VR scene design with physical proxies. We believe that HandAvatar unlocks new interaction opportunities, especially for usage in Virtual Reality, by letting users become the avatar in applications including virtual social interaction, animation, gaming, or education.
I Need a Third Arm! Eliciting Body-based Interactions with a Wearable Robotic Arm

Wearable robotic arms (WRA) open up a unique interaction space that closely integrates the user's body with an embodied robotic collaborator. This space affords diverse interaction styles, including body movement, hand gestures, or gaze. Yet, it is so-far unexplored which commands are desirable from a user perspective. Contributing findings from an elicitation study (N=14), we provide a comprehensive set of interactions for basic robot control, navigation, object manipulation, and emergency situations, performed when hands are free or occupied. Our study provides insights into preferred body parts, input modalities, and the users' underlying sources of inspiration. Comparing interaction styles between WRAs and off-body robots, we highlight how WRAs enable a range of interactions specific for on-body robots and how users use WRAs both as tools and as collaborators. We conclude by providing guidance on the design of ad-hoc interaction with WRAs informed by user behavior.
RxInfer: A Julia package for reactive real-time Bayesian inference

Bayesian inference realizes optimal information processing through a full commitment to reasoning by probability theory.The Bayesian framework is positioned at the core of modern AI technology for applications such as speech and image recognition and generation, medical analysis, robot navigation, and more.The framework describes how a rational agent should update its beliefs when new information is revealed by the agent's environment.Unfortunately, perfect Bayesian reasoning is generally intractable, since calculations of (often) very high-dimensional integrals are required for many models of interest.As a result, a number of numerical algorithms for approximating Bayesian inference have been developed and implemented in probabilistic programming packages.Successful methods include the Laplace approximation (Gelman et al., 2015), variants of Monte Carlo (MC) sampling (Salimans et al., n.d.)
Prior-knowledge-based self-attention network for 3D human pose estimation

Estimating three-dimensional (3D) human poses from two-dimensional (2D) joints has achieved promising results. However, there is relatively little work focused on exploiting domain-specific knowledge as prior. In this work, we present a learning framework based on prior knowledge for the task of estimating a 3D human pose from a 2D pose. In contrast to other state-of-the-art 3D pose estimation approaches, the proposed method is a systematic analysis pipeline that takes full advantage of prior knowledge based on three observations. The proposed approach can model the spatial and temporal relations between joints to achieve better performance. Our approach formulates the learning network as an encoder–decoder architecture that explicitly encodes prior knowledge about the task. The encoder is a multi-head self-attention network which can capture human joint spatial relations. The decoder is formulated as three separated sub-networks, each sub-network represents a kinematic chain which is derived from our prior knowledge about human motion. The experimental results on the Human3.6M, HumanEva and MPI-INF-3DHP datasets demonstrate the effectiveness of our approach. The code and data are available at https://github.com/XTU-PR-LAB/PK-SAN.