Miracle-dz/UniFAR-FTUs huggingface dataset:
CoArgue : Fostering Lurkers’ Contribution to Collective Arguments in Community-based QA Platforms

In Community-Based Question Answering (CQA) platforms, people can participate in discussions about non-factoid topics by marking their stances, providing premises, or arguing for the opinions they support, which forms “collective arguments”. The sustainable development of collective arguments relies on a big contributor base, yet most of the frequent CQA users are lurkers who seldom speak out. With a formative study, we identified detailed obstacles preventing lurkers from contributing to collective arguments. We consequently designed a processing pipeline for extracting and summarizing augmentative elements from question threads. Based on this we built CoArgue, a tool with navigation and chatbot features to support CQA lurkers’ motivation and ability in making contributions. Through a within-subject study (N=24), we found that, compared to a Quora-like baseline, participants perceived CoArgue as significantly more useful in enhancing their motivation and ability to join collective arguments and found the experience to be more engaging and productive.
Are You Killing Time? Predicting Smartphone Users’ Time-killing Moments via Fusion of Smartphone Sensor Data and Screenshots

Time-killing on smartphones has become a pervasive activity, and could be opportune for delivering content to their users. This research is believed to be the first attempt at time-killing detection, which leverages the fusion of phone-sensor and screenshot data. We collected nearly one million user-annotated screenshots from 36 Android users. Using this dataset, we built a deep-learning fusion model, which achieved a precision of 0.83 and an AUROC of 0.72. We further employed a two-stage clustering approach to separate users into four groups according to the patterns of their phone-usage behaviors, and then built a fusion model for each group. The performance of the four models, though diverse, yielded better average precision of 0.87 and AUROC of 0.76, and was superior to that of the general/unified model shared among all users. We investigated and discussed the features of the four time-killing behavior clusters that explain why the models’ performance differ.
3D Building Plans: Supporting Navigation by People who are Blind or have Low Vision in Multi-Storey Buildings

Independent travel and navigation in new environments, in particular multi-storey buildings, is a major challenge for people who are blind or have low vision (BLV). Using tactile maps as part of orientation and mobility (O&M) training, BLV people can build a cognitive map of an environment before visiting. Tactile maps of multi-level environments, however, have received little attention. We investigated the usefulness of 3D printed models of buildings, through a user study with nine BLV adults. Three designs were evaluated: flat, overlapped-sliding and overlapped-rotating. All three designs were reported to be useful, usable, engaging and allowed participants to build a cognitive map of the building. There was a strong user preference for the overlapped presentations, which were reported to be more effective in supporting cross-floor spatial knowledge. This exploration of the design space of 3D building plans demonstrates their value and we hope will encourage their provision in O&M training.
New Monte Carlo global weight method for the approximate solution of the nonlinear Boltzmann equation

We develop new weight modifications of the method of direct statistical simulation for the approximate solution of the nonlinear Boltzmann equation by using the ingenious procedure proposed by the authors, viz. the stratification of collision distribution in a many-particle system by the number of a pair of interacting particles. We check the appropriate weight algorithms for the model problem with the known solution. They allow us to estimate the variations of functionals under small changes of parameters.
Multi-Platform Content Creation: The Configuration of Creator Ecology through Platform Prioritization, Content Synchronization, and Audience Management

Online platforms like YouTube and Instagram have enabled the platformization and monetization of creative work, allowing content creators to derive revenue and thrive in a creator economy. While much work has been done to understand content creation on single platforms, the creative practice often involves content creators’ agency and practice to interact with multiple platforms and make strategic decisions to optimize such interactions. In this paper, we use an interview study with 21 cross-platform creators to understand how they negotiate with platforms in their creative practices through the construction of creator ecology. We found that participants developed priorities among platforms based on varied criteria, paid attention to cross-platform content synchronization, and stressed managing and converting audiences across platforms to grow their fanbase. Our findings highlight the complex interplay between creator agency and labor, as well as yield implications for future design possibilities of creator empowerment and support.
Tasks of a Different Color: How Crowdsourcing Practices Differ per Complex Task Type and Why This Matters

Crowdsourcing in China is a thriving industry. Among its most interesting structures, we find crowdfarms, in which crowdworkers self-organize as small organizations to tackle macrotasks. Little, however, is known as to which practices these crowdfarms use to tackle the macrotasks, and this goes hand in hand with the current practice of the HCI research community to treat all forms of complex crowdsourcing work as practically the same. However, macrotasks differ substantially regarding structure and decomposability. Treating them under one umbrella term - macrotasking - can lead to an imprecise understanding of the workforce involved. We address this gap by examining the work practices of 31 Chinese crowdfarms on the four main macrotask types, namely: modular, interlaced, wicked, and container macrotasks. Our results confirm essential differences in how these nascent crowd organizations address different macrotasks and shed light on what platforms can do to improve the uptake of such work.
Mental Health and the Metaverse: Ample Opportunities or Alarming Threats for Mental Health in Immersive Worlds?

The proportion of the global burden of disease attributable to mental disorders has been continuously increasing since its first recording and is already at a critical level. While responses to the global mental health crisis are urgently required, implementation of countermeasures has so far been inadequate. Due to potential cost-effectiveness and scalability, technological solutions might play an important role in addressing current and projected mental health needs. The various proposed applications of the metaverse might provide unique opportunities in this regard, while also potentially creating additional mental health strain on individuals. In this panel, we will discuss the challenges and opportunities the metaverse might pose for the future of mental health, and the steps we can take now to harness the metaverse for positive change in global mental health – or prevent it from exacerbating already pressing concerns.
Speak in Public: an Innovative Tool for the Treatment of Stuttering through Virtual Reality, Biosensors, and Speech Emotion Recognition

Stuttering is a common speech disorder that affects over 80 million people worldwide. Exposure therapy (ET) is a well-established approach to treating stuttering, but it can be complex and risky to simulate challenging real-life situations. Virtual reality (VR) is a promising tool that can help overcome these limitations. This article presents Speak in Public, a system that combines VR, biosensors, and speech emotion recognition to provide objective measures of patients’ stress and emotional state during ET sessions. In a preliminary study with five participants, we found that the use of VR successfully replicated stressful situations as indicated by biosensors. Additionally, speech emotion recognition showed that patients primarily experienced fear during these activities. Findings are preliminary, and further research with larger samples is needed to validate them. This study highlights the potential of VR and biosensors to enhance ET for stuttering and provides insights for future research in this area.
FabTouch: A Tool to Enable Communication and Design of Tactile and Affective Fabric Experiences

The tactile experience of fabric is not only a sensory experience but also an affective one. Our choice of fabric products, like clothing, is often based on how they feel. Effectively communicating such experiences is crucial for designing tactile fabric experiences. However, there remains a lack of comprehensive understanding of the fabric tactile and affective experiences, preventing the development of tools to facilitate the communication of these experiences. In this paper, we examine the fabric experiences of 27 participants towards nine cotton samples. We combine qualitative and quantitative methods to create FabTouch, a novel tool to facilitate a dialogue in the design of fabric experiences. We found six phases of fabric touch experiences including fabric touch responses, sensory associations, and emotional responses. Initial feedback from designers suggested that FabTouch could enrich design processes both in practice and in education and can create inspiration for physical and digital design explorations.
Supporting Aim Assistance Algorithms through a Rapidly Trainable, Personalized Model of Players’ Spatial and Temporal Aiming Ability

Multiplayer digital games can use aim assistance to help people with different levels of aiming ability to play together. To dynamically provide each player with the right amount of assistance, an aim assistance algorithm needs a model of the player’s ability that can be measured and updated during gameplay. The model must be based on difficulty parameters such as target speed, size, and duration, that can be adjusted in-game to change aiming difficulty, and must account for player’s spatial and temporal aiming abilities. To satisfy these requirements, we present the novel dynamic spatiotemporal model of a player’s aiming ability, based on difficulty parameters that can be manipulated in a game. In a crowdsourced experiment with 72 participants, the model was found to accurately predict how close to a target a player can aim and to converge rapidly with a small set of observations of aiming tasks.
SkinPaper: Exploring Opportunities for Woven Paper as a Wearable Material for On-Skin Interactions

Paper circuitry has been extensively explored by HCI researchers as a means of creating interactive objects. However, these approaches focus on creating desktop or handheld objects, and paper as a wearable material remains under-explored. We present SkinPaper, a fabrication approach using silicone-treated washi paper to weave lightweight and easy-to-fabricate on-skin interactions. We adopt techniques from paper weaving and basketry weaving practices to create paper-woven structures that can conform to the body. Our approach uses off-the-shelf materials to facilitate a highly customizable fabrication process. We showcase eight case studies to illustrate our approach’s two to three-dimensional forms. To understand the expressiveness of the design space, we conducted a workshop study in which weavers created paper-woven on-skin interactions. We draw insights from the studies to understand the opportunities for paper-woven on-skin interactions.
Auditory Seasoning Filters: Altering Food Perception via Augmented Sonic Feedback of Chewing Sounds

The experience of what we eat depends not only on the taste of the food, but also on other modalities of sensory feedback. Perceptual research has shown the potential of altering visual, olfactory, and textural food cues to affect flavor, texture, and satiety. Recently, the HCI community has leveraged such research to encourage healthy eating, but the resulting tools often require specialised and/or invasive devices. Ubiquitous and unobtrusive, audio feedback-based tools could alleviate those drawbacks, but research in this area has been limited to food texture. We expand on prior psychology research by exploring a wide range of auditory feedback styles to modify not only flavor attributes but also appetite-related measures. We present Auditory Seasoning, a mobile app that offers various curated audio modes to alter chewing sounds. In a Pringles-tasting experiment (N=37), this tool significantly influenced food perception and eating behavior beyond texture alone. Based on these results, we discuss design implications to create custom real-world flavor/satiety-enhancing tools.
Collision Design

Collision, "the violent encounter of a moving body with another", is poorly understood in HCI. When we discuss people colliding with the physical artifacts we create, or colliding with each other while using our systems, this is primarily treated as a hazard, something which we should design to avoid. However many other human activities involve situations where deliberate exposure to risk of collision may in fact have positive aspects. In this paper we discuss how the ’risk matrix’, a widely used risk-management tool, which categorizes risks in terms of likelihood and severity, may limit interaction in unintended ways. We discuss reframings of this matrix in relation to design concepts of ’adventure’, ’disempowerment/agency’ and ’consent’. and show that a range of design spaces for collisions exist which may be fruitful to explore.
Special Interest Group on Creativity and Cultures in Computing

Research on creativity support tools (CSTs) has a long history in Human-Computer Interaction (HCI); however, researchers often focus on developing novel CSTs and verifying them in a controlled lab setting, rather than on capturing the creative process in the wild. In reality, creative activity is exploratory, laborious, and involves multiple CSTs; which together form a creativity support environment or ecology. Creative activity is also social, cultural, and collaborative with people distributing, modifying, and reacting to the creations of others. This process can inspire subsequent iterations. To understand and support open-ended, culturally embedded, collaborative creativity, HCI researchers are seeking new methods to study the sociocultural aspects of creativity support. This Special Interest Group on Creativity and Cultures in Computing (SIGCCC) invites diverse researchers to provide a forum for CST discussions from a wide sociocultural lens. The participants will identify and discuss the state-of-the-art and conceptualize future directions for creativity support research.
Partial Discharge Pattern Recognition of GIS Based on CBAM-ResNet

Different types of partial discharge (PD) cause different damage to gas-insulated switchgear (GIS), so it is very important to correctly identify the type of PD for evaluating the GIS insulation condition. The traditional PD pattern recognition algorithm has the limitations of low recognition accuracy and slow recognition speed in engineering applications. This work proposed a Convolutional Block Attention Module Residual Network (CBAM-ResNet) for the GIS PD pattern recognition. In particular, the Squeeze-and-Excitation mechanism was used to improve the recognition accuracy. The GIS phase resolved partial discharge (PRPD) image is used as the input of the pattern recognition model. The results show that the recognition accuracy of the proposed method is as high as 89%, and has a recognition accuracy of 92.72%, 90.19%, 89.36%, and 93.62% for the four PD types, respectively. Compared with normal CNN and SVM recognition algorithms, the PD pattern recognition accuracy of the proposed method has been greatly improved. And the CBAM-ResNet for PD pattern recognition has a good application prospect in engineering practice.
uKnit: A Position-Aware Reconfigurable Machine-Knitted Wearable for Gestural Interaction and Passive Sensing using Electrical Impedance Tomography

A scarf is inherently reconfigurable: wearers often use it as a neck wrap, a shawl, a headband, a wristband, and more. We developed uKnit, a scarf-like soft sensor with scarf-like reconfigurability, built with machine knitting and electrical impedance tomography sensing. Soft wearable devices are comfortable and thus attractive for many human-computer interaction scenarios. While prior work has demonstrated various soft wearable capabilities, each capability is device- and location-specific, being incapable of meeting users’ various needs with a single device. In contrast, uKnit explores the possibility of one-soft-wearable-for-all. We describe the fabrication and sensing principles behind uKnit, demonstrate several example applications, and evaluate it with 10-participant user studies and a washability test. uKnit achieves 88.0%/78.2% accuracy for 5-class worn-location detection and 80.4%/75.4% accuracy for 7-class gesture recognition with a per-user/universal model. Moreover, it identifies respiratory rate with an error rate of 1.25 bpm and detects binary sitting postures with an average accuracy of 86.2%.
Exploring the Use of Personalized AI for Identifying Misinformation on Social Media

This work aims to explore how human assessments and AI predictions can be combined to identify misinformation on social media. To do so, we design a personalized AI which iteratively takes as training data a single user’s assessment of content and predicts how the same user would assess other content. We conduct a user study in which participants interact with a personalized AI that learns their assessments of a feed of tweets, shows its predictions of whether a user would find other tweets (in)accurate, and evolves according to the user feedback. We study how users perceive such an AI, and whether the AI predictions influence users’ judgment. We find that this influence does exist and it grows larger over time, but it is reduced when users provide reasoning for their assessment. We draw from our empirical observations to identify design implications and directions for future work.
Research on Sampling Estimation Method for Complex Networks-Oriented

As an important innovation element in the new round of industrial revolution, big data plays an important role in the development of digital economy. As an important carrier of network digital platform economy, researchers have found that most of the real networks are neither traditional regular networks nor completely random networks, but complex networks with certain statistical rules. Complex network has the characteristics of small world and scale-free. Its network structure is complex, its scale is huge, and its individuals are independent and connected. At the same time, there are a large number of users in the network, carrying tens of thousands of information. The traditional network analysis method is not comprehensive, so it is difficult to grasp the whole picture of the network environment. Therefore, this paper introduces a method to solve the network data dilemma by improving the sampling estimation. The data information closely related to the research variables found in the network is introduced into the model-aided estimation method as auxiliary information, and the whole information is studied through the local information of the network. Facing the huge scale of network data, it is an important technology with high efficiency and low cost, which provides a way to quickly obtain data and analysis results.
Models of Applied Privacy (MAP): A Persona Based Approach to Threat Modeling

The paradigm of Privacy by Design aims to integrate privacy early in the product development life cycle. One element of this is to conduct threat modeling with developers to identify privacy threats that engender from the architecture design of the product. In this paper, we propose a systematic lightweight privacy threat modeling framework (MAP) based on attacker personas that is both easy to operationalize and scale. MAP leverages existing privacy threat frameworks to provide an operational roadmap based on relevant threat actors, associated threats, and resulting harm to individuals as well as organizations. We implement MAP as a persona picker tool that threat modelers can use as a menu select to identify, investigate, and remediate relevant threats based on product developer’s scope of privacy risk. We conclude by testing the framework using a repository of 207 privacy breaches extracted from the VERIS Community Database.
In-vehicle Performance and Distraction for Midair and Touch Directional Gestures

We compare the performance and level of distraction of expressive directional gesture input in the context of in-vehicle system commands. Center console touchscreen swipes and midair swipe-like movements are tested in 8-directions, with 8-button touchscreen tapping as a baseline. Participants use these input methods for intermittent target selections while performing the Lane Change Task in a virtual driving simulator. Input performance is measured with time and accuracy, cognitive load with deviation of lane position and speed, and distraction from frequency of off-screen glances. Results show midair gestures were less distracting and faster, but with lower accuracy. Touchscreen swipes and touchscreen tapping are comparable across measures. Our work provides empirical evidence for vehicle interface designers and manufacturers considering midair or touch directional gestures for centre console input.
Linguistic Dead-Ends and Alphabet Soup: Finding Dark Patterns in Japanese Apps

Dark patterns are deceptive and malicious properties of user interfaces that lead the end-user to do something different from intended or expected. While now a key topic in critical computing, most work has been conducted in Western contexts. Japan, with its booming app market, is a relatively uncharted context that offers culturally- and linguistically-sensitive differences in design standards, contexts of use, values, and language, all of which could influence the presence and expression of dark patterns. In this work, we analyzed 200 popular mobile apps in the Japanese market. We found that most apps had dark patterns, with an average of 3.9 per app. We also identified a new class of dark pattern: “Linguistic Dead-Ends” in the forms of “Untranslation” and “Alphabet Soup. ” We outline the implications for design and research practice, especially for future cross-cultural research on dark patterns.
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.