Miracle-dz/UniFAR-FTUs huggingface dataset:
Preserving Agency During Electrical Muscle Stimulation Training Speeds up Reaction Time Directly After Removing EMS

Abstract: Force feedback devices, such as motor-based exoskeletons or wearables based on electrical muscle stimulation (EMS), have the unique potential to accelerate users’ own reaction time (RT). However, this speedup has only been explored while the device is attached to the user. In fact, very little is known regarding whether this faster reaction time still occurs after the user removes the device from their bodies–this is precisely what we investigated by means of a simple reaction time (RT) experiment, in which participants were asked to tap as soon as they saw an LED flashing. Participants experienced this in three EMS conditions: (1) fast-EMS, the electrical impulses were synced with the LED; (2) agency-EMS, the electrical impulse was delivered 40ms faster than the participant’s own RT, which prior work has shown to preserve one’s sense of agency over this movement; and, (3) late-EMS: the impulse was delivered after the participant’s own RT. Our results revealed that the participants’ RT was significantly reduced by approximately 8ms (up to 20ms) only after training with the agency-EMS condition. This finding suggests that the prioritizing agency during EMS training is key to motor-adaptation, i.e., it enables a faster motor response even after the user has removed the EMS device from their body.
What Matters in Professional Drone Pilots’ Practice? An Interview Study to Understand the Complexity of Their Work and Inform Human-Drone Interaction Research

Human-drone interaction is a growing topic of interest within HCI research. Researchers propose many innovative concepts for drone applications, but much of this research does not incorporate knowledge on existing applications already adopted by professionals. This limits the validity of said research. To address this limitation, we present our findings from an in-depth interview study with 10 professional drone pilots. Our participants were armed with significant experience and qualifications – pertinent to both drone operations and a set of applications covering diverse industries. Our findings have resulted in design recommendations that should inform both ends and means of human-drone interaction research. These include, but are not limited to: safety-related protocols, insights from domain-specific use cases, and relevant practices outside of hands-on flight.
Data Agents: Promoting Reflection through Meaningful Representations of Personal Data in Everyday Life

Visual and physical representations of historical personal data have been discussed as artifacts that can lead to self-reflection through meaning-making. However, it is yet unclear how those two concepts relate to each other. We focus on meaningfulness, a part of meaning-making that relates to feelings. In this paper, we present three projects where mundane objects, our data agents, are combined in meaningful ways with personal data with the aim to trigger reflection by placing a person’s individual experience of data in relation to others’. To identify relationships between self-reflection and meaningfulness we use Fleck and Fitzpatrick’s framework to describe the levels of reflection that we found in our projects and Mekler and Hornbæk’s meaning framework to define the depth of reflection. We conclude with a discussion on four themes in which we outline how data agents informed the intersections between our central concepts. This paper, constitutes a first step towards unpacking those relationships and invites for further explorations by the HCI community.
Overlooking Context: How do Defaults and Framing Reduce Deliberation in Smart Home Privacy Decision-Making?

Research has demonstrated that users’ heuristic decision-making processes cause external factors like defaults and framing to influence the outcome of privacy decisions. Proponents of “privacy nudging” have proposed leveraging these effects to guide users’ decisions. Our research shows that defaults and framing not only influence the outcome of privacy decisions, but also the process of evaluating the contextual factors associated with the decision, effectively making the decision-making process more heuristic. In our analysis of an existing dataset of scenario-based smart home privacy decisions, we demonstrate that defaults and framing not only have a direct effect on participants’ decisions; they also moderate the effect of their cognitive appraisals of the presented scenarios on the decision. These results suggest that nudges like defaults and framing exacerbate the well-researched problem that people often employ heuristics rather than making deliberate privacy decisions, and that privacy-setting interfaces should avoid the effects of heuristic decision-making.
Quantitative Data Visualisation on Virtual Globes

Geographic data visualisation on virtual globes is intuitive and widespread, but has not been thoroughly investigated. We explore two main design factors for quantitative data visualisation on virtual globes: i) commonly used primitives (2D bar, 3D bar, circle) and ii) the orientation of these primitives (tangential, normal, billboarded). We evaluate five distinctive visualisation idioms in a user study with 50 participants. The results show that aligning primitives tangentially on the globe’s surface decreases the accuracy of area-proportional circle visualisations, while the orientation does not have a significant effect on the accuracy of length-proportional bar visualisations. We also find that tangential primitives induce higher perceived mental load than other orientations. Guided by these results we design a novel globe visualisation idiom, Geoburst, that combines a virtual globe and a radial bar chart. A preliminary evaluation reports potential benefits and drawbacks of the Geoburst visualisation.
Data Epics: Embarking on Literary Journeys of Home Internet of Things Data

In this paper, we use fiction as a method to complicate the commonplace narratives of data as intangible and objective, in the particular context of Internet of Things (IoT) in the home. We, a team of two design researchers, partnered with a fiction writer and a single IoT enthusiast, Susan, to create The Data Epics: four short stories based on Susan's monthly home IoT data logs. The Data Epics revealed new imaginaries for data, showing new world-views and lively data, but also surfaced data's entanglement in meshes and hierarchies, and concerns about control and power. Our work also examines the labor of tending to and interpreting data and a particular interest in anomalies. We conclude with discussions of how data imaginaries from fiction might be imperfect, but are uniquely generative, offering a path to get closer to IoT data by trying things on and zooming in and slowing down.
Design and Evaluation of CFC-targeted Security Nudges

Aimed at breaking up the myth of the “average user”, the notion of personalization promises to solve the contradiction between the population’s heterogeneity and one-size-fits-all security nudges. To further explore the promising avenue, we propose to design personalized security nudges targeted at different mindsets and make the Consideration for Future Consequences (CFC) the testbed. Namely, we designed two CFC-targeted security nudges, Promotion and Prevention, for the individuals who care about future and immediate consequences, respectively. An online survey (N = 145) was conducted to test their effectiveness. Results show that both the nudges can improve users’ security attitudes, while the moderation effects imply that the Promotion nudge is merely effective for the users having deep concerns about future consequences. The findings indicate the feasibility of designing security nudges targeted at future orientations and illustrate the importance of tailoring nudges according to individuals’ differences.
“Oops...”: Mobile Message Deletion in Conversation Error and Regret Remediation

Message deletion in mobile messaging apps allows people to “unsay” things they have said. This paper explores how and why people use (or do not use) this feature within remediation strategies after a communication error is identified. We present findings from a multi-stage survey designed to explore people’s general experiences of the message deletion feature (N = 401), peoples’ experiences of using this feature during the remediation of an error (N = 70), and receivers’ perceptions around recent message deletions (N = 68). While people are typically aware of the deletion feature, it is infrequently used. When used, it is primarily done so to improve conversations by reducing confusion between conversation partners. We found people being aware of message deletions creating information-gaps which can provoke curiosity in recipients, causing them to develop narratives to help address the uncertainty. We found concerns amongst senders that these narratives would be of a negative nature, having an undesirable impact on how others perceive them. We use our findings to suggest ways in which mobile messaging apps could improve conversational experiences around erroneous and regrettable messages.
Privacy Norms for Smart Home Personal Assistants

Smart Home Personal Assistants (SPA) have a complex ecosystem that enables them to carry out various tasks on behalf of the user with just voice commands. SPA capabilities are continually growing, with over a hundred thousand third-party skills in Amazon Alexa, covering several categories, from tasks within the home (e.g. managing smart devices) to tasks beyond the boundaries of the home (e.g. purchasing online, booking a ride). In the SPA ecosystem, information flows through several entities including SPA providers, third-party skills providers, providers of Smart Devices, other users and external parties. Prior studies have not explored privacy norms in the SPA ecosystem, i.e., the acceptability of these information flows. In this paper, we study privacy norms in SPAs based on Contextual Integrity through a large-scale study with 1,738 participants. We also study the influence that the Contextual Integrity parameters and personal factors have on the privacy norms. Further, we identify the similarities in terms of the Contextual Integrity parameters of the privacy norms studied to distill more general privacy norms, which could be useful, for instance, to establish suitable privacy defaults in SPA. We finally provide recommendations for SPA and third-party skill providers based on the privacy norms studied.
The Effects of Warmth and Competence Perceptions on Users' Choice of an AI System

People increasingly rely on Artificial Intelligence (AI) based systems to aid decision-making in various domains and often face a choice between alternative systems. We explored the effects of users' perception of AI systems' warmth (perceived intent) and competence (perceived ability) on their choices. In a series of studies, we manipulated AI systems' warmth and competence levels. We show that, similar to the judgments of other people, there is often primacy for warmth over competence. Specifically, when faced with a choice between a high-competence system and a high-warmth system, more participants preferred the high-warmth system. Moreover, the precedence of warmth persisted even when the high-warmth system was overtly deficient in its competence compared to an alternative high competence-low warmth system. The current research proposes that it may be vital for AI systems designers to consider and communicate the system's warmth characteristics to its potential users.
A Simulation Model of Intermittently Controlled Point-and-Click Behaviour

We present a novel simulation model of point-and-click behaviour that is applicable both when a target is stationary or moving. To enable more realistic simulation than existing models, the model proposed in this study takes into account key features of the user and the external environment, such as intermittent motor control, click decision-making, visual perception, upper limb kinematics and the effect of input device. The simulated user’s point-and-click behaviour is formulated as a Markov decision process (MDP), and the user’s policy of action is optimised through deep reinforcement learning. As a result, our model successfully and accurately reproduced the trial completion time, distribution of click endpoints, and cursor trajectories of real users. Through an ablation study, we showed how the simulation results change when the model’s sub-modules are individually removed. The implemented model and dataset are publicly available.
UISketch: A Large-Scale Dataset of UI Element Sketches

This paper contributes the first large-scale dataset of 17,979 hand-drawn sketches of 21 UI element categories collected from 967 participants, including UI/UX designers, front-end developers, HCI, and CS grad students, from 10 different countries. We performed a perceptual study with this dataset and found out that UI/UX designers can recognize the UI element sketches with ~96% accuracy. To compare human performance against computational recognition methods, we trained the state-of-the-art DNN-based image classification models to recognize the UI elements sketches. This study revealed that the ResNet-152 model outperforms other classification networks and detects unknown UI element sketches with 91.77% accuracy (chance is 4.76%). We have open-sourced the entire dataset of UI element sketches to the community intending to pave the way for further research in utilizing AI to assist the conversion of lo-fi UI sketches to higher fidelities.
What Makes Videos Accessible to Blind and Visually Impaired People?

User-generated videos are an increasingly important source of information online, yet most online videos are inaccessible to blind and visually impaired (BVI) people. To find videos that are accessible, or understandable without additional description of the visual content, BVI people in our formative studies reported that they used a time-consuming trial-and-error approach: clicking on a video, watching a portion, leaving the video, and repeating the process. BVI people also reported video accessibility heuristics that characterize accessible and inaccessible videos. We instantiate 7 of the identified heuristics (2 audio-related, 2 video-related, and 3 audio-visual) as automated metrics to assess video accessibility. We collected a dataset of accessibility ratings of videos by BVI people and found that our automatic video accessibility metrics correlated with the accessibility ratings (Adjusted R2 = 0.642). We augmented a video search interface with our video accessibility metrics and predictions. BVI people using our augmented video search interface selected an accessible video more efficiently than when using the original search interface. By integrating video accessibility metrics, video hosting platforms could help people surface accessible videos and encourage content creators to author more accessible products, improving video accessibility for all.
Towards “Avatar-Friendly” 3D Manipulation Techniques: Bridging the Gap Between Sense of Embodiment and Interaction in Virtual Reality

Avatars, the users’ virtual representations, are becoming ubiquitous in virtual reality applications. In this context, the avatar becomes the medium which enables users to manipulate objects in the virtual environment. It also becomes the users’ main spatial reference, which can not only alter their interaction with the virtual environment, but also the perception of themselves. In this paper, we review and analyse the current state-of-the-art for 3D object manipulation and the sense of embodiment. Our analysis is twofold. First, we discuss the impact that the avatar can have on object manipulation. Second, we discuss how the different components of a manipulation technique (i.e. input, control and feedback) can influence the user’s sense of embodiment. Throughout the analysis, we crystallise our discussion with practical guidelines for VR application designers and we propose several research topics towards “avatar-friendly’’ manipulation techniques.
Immersion, Flow and Usability in video games

Context: Nowadays, video games look and feel increasingly realistic, and provide engaging and immersive experiences to players. Understanding what makes games so appealing and immersive is important to grasp in order to provide better experiences. Objectives: To provide a better understanding of the concepts of flow, immersion and usability in video games and understand how usability interact with these different concepts. Method: 20 participants took part in user tests and answered a survey about usability and immersion. Results: Usability had a positive effect on immersion which was fully mediated by appeal. Learnability was correlated with absorption by activity, and usability was correlated with fluency of performance. Conclusions: Usability and appeal are both contributing to the feeling of immersion which in turn increases the general appreciation of the game. Immersion and flow are both relevant to understand players’ appreciation of the game.
A Chinese-Language Validation of the Video Game Demand Scale (VGDS-C): Measuring the Cognitive, Emotional, Physical, and Social Demands of Video Games

Video games are engaging multimedia experiences that require players’ cognitive, emotional, physical (in terms of controllers and exertion), and social faculties. Recent theorizing has suggested that these dimensions of demand can explain processes by which players engage with and respond to gameplay. A relatively new measure—the five-factor, 26-item Video Game Demand Scale (VGDS)—has been tested for dimensionality and measurement validity with English- and German-speaking players, but not for other play populations. Given the popularity of video games among Chinese-speaking players, this brief report demonstrates a successful translation of VGDS into Traditional Chinese (VGDS-C). A sample of N = 863 Chinese speakers in Taiwan were asked to recall and describe a recent gaming experience before completing the VGDS-C along with other gaming-related measures (tests of construct validity). VGDS-C was shown to be a reliable and valid way of assessing players’ perceptions of the myriad demands of video gaming.
Haptic Empathy: Conveying Emotional Meaning through Vibrotactile Feedback

Touch plays an essential role in communicating emotions and intensifying interpersonal communication[4]. A lot of research focuses on how to create or improve haptic interfaces looking into challenges and possibilities that the haptic technology can offer[30]. The objective of this research is to investigate whether people can share subjective feelings through simple vibrotactile feedback. In an initial experiment, we used the TECHTILE toolkit[19] to record 28 vibration sample sets for 4 different emotions (joy, anger, sadness, relaxation). We then replayed the vibrations to test how well they could be recognized. The results support the hypothesis that people can use vibration feedback as a medium for expressing specific subjective feelings. It also indicates some universalities in affective vibrotactile stimuli that even strangers with little to no knowledge about the senders could recognize the emotional meanings.
Safety, Connection and Reflection: Designing with Therapists for Children with Serious Emotional Behaviour Issues

Designing technologies to support mental health in children is a growing area of research. However less is known about how to design with therapists for children with serious emotional behaviour issues. We conducted a contextual enquiry at an organisation that provides therapy treatment for children of trauma backgrounds. A co-design with therapists of a reflective storytelling activity provided insight into how to integrate design research activities with therapy approaches. Our analysis produced a framework summarising the important elements of a facilitated reflective experience. The framework is intended to guide the design of technologies to support safety, connection, and reflection in scaffolding social emotional learning towards improved emotional behaviour for children. Future directions for applying the framework include: augmenting existing therapy activities with technology, technology to support children learning how to self-regulate their emotions outside the clinic, and technology to help parents in emotion coaching their child.
Human-AI Interaction in Human Resource Management: Understanding Why Employees Resist Algorithmic Evaluation at Workplaces and How to Mitigate Burdens

Recently, Artificial Intelligence (AI) has been used to enable efficient decision-making in managerial and organizational contexts, ranging from employment to dismissal. However, to avoid employees’ antipathy toward AI, it is important to understand what aspects of AI employees like and/or dislike. In this paper, we aim to identify how employees perceive current human resource (HR) teams and future algorithmic management. Specifically, we explored what factors negatively influence employees’ perceptions of AI making work performance evaluations. Through in-depth interviews with 21 workers, we found that 1) employees feel six types of burdens (i.e., emotional, mental, bias, manipulation, privacy, and social) toward AI's introduction to human resource management (HRM), and that 2) these burdens could be mitigated by incorporating transparency, interpretability, and human intervention to algorithmic decision-making. Based on our findings, we present design efforts to alleviate employees’ burdens. To leverage AI for HRM in fair and trustworthy ways, we call for the HCI community to design human-AI collaboration systems with various HR stakeholders.
User Perceptions and Adoption of Plug Load Management Systems in the Workplace

Smart energy management systems incorporate advanced sensing and control technologies that enable users to monitor and reduce their energy consumption through interactive visualisations and automated control features. Plug load management systems (PLMS), in particular, are applications of such systems targeting electrical devices found in homes and workplaces. While past studies have mostly focused on PLMS adoption in homes, past literature indicates several key differences in user motivation as office users typically do not bear the cost of their consumption. This reduces their motivation to embrace such systems, resulting in low adoption rates. In our research, we examined user perception of adopting PLMS in the workplace through a series of focus group discussions and an online survey guided by findings from the focus group discussions. By analysing the quantitative and qualitative responses from 101 participants, we identified six design implications to guide the development of future PLMS in the workplace.
vMirror: Enhancing the Interaction with Occluded or Distant Objects in VR with Virtual Mirrors

Interacting with out of reach or occluded VR objects can be cumbersome. Although users can change their position and orientation, such as via teleporting, to help observe and select, doing so frequently may cause loss of spatial orientation or motion sickness. We present vMirror, an interactive widget leveraging reflection of mirrors to observe and select distant or occluded objects. We first designed interaction techniques for placing mirrors and interacting with objects through mirrors. We then conducted a formative study to explore a semi-automated mirror placement method with manual adjustments. Next, we conducted a target-selection experiment to measure the effect of the mirror’s orientation on users’ performance. Results showed that vMirror can be as efficient as direct target selection for most mirror orientations. We further compared vMirror with teleport technique in a virtual treasure hunt game and measured participants’ task performance and subjective experiences. Finally, we discuss vMirorr user experience and present future directions.