Miracle-dz/UniFAR-FTUs huggingface dataset:
Sleep Planning with Awari: Uncovering the Materiality of Body Rhythms using Research through Design

With the increasing adoption of body tracking technology, users are able to collect bio-data which designers struggle to make legible or actionable. This work focused on increasing this technology-mediated bodily understanding through exploring the material properties of the body rhythms that govern the sleep behaviours being tracked. Building from a workshop with non-normative sleepers, we reframe sleep tracking to be about understanding and manipulating body rhythms. We explore these rhythms through the RtD process of designing the Awari alertness-forecast and scheduling application in four iterations. This resulted in three non-exclusive categories of rhythmic influence: Slow & Cyclical, Pressure & Release, and Anchored. Through a better understanding of how they interact, their inertia, and their material properties for interaction we encourage the design of technology to shape, and be shaped, by the complex rhythms of life. We discuss ways in which this can democratise medical-models, and make actionable complex bodily processes.
BAGEL: An Approach to Automatically Detect Navigation-Based Web Accessibility Barriers for Keyboard Users

The Web has become an essential part of many people’s daily lives, enabling them to complete everyday and essential tasks online and access important information resources. The ability to navigate the Web via the keyboard interface is critical to people with various types of disabilities. However, modern websites often violate web accessibility guidelines for keyboard navigability. In this paper, we present a novel approach for automatically detecting web accessibility barriers that prevent or hinder keyboard users’ ability to navigate web pages. An extensive evaluation of our technique on real-world subjects showed that our technique was able to detect navigation-based keyboard accessibility barriers in web applications with high precision and recall.
The Modern Informed Citizen: Understanding Trade-offs in Digital News Consumption

In local and global contexts, the link between individual news consumption and beneficial or harmful effects is debated. Some communication theories posit that individuals must stay up-to-date with news to make civic choices like voting, volunteering, and participating in community organizing. Nonetheless, there is increasing evidence that overuse or overreliance on news can have harmful effects on mental health and lead to news avoidance. In a contested and polarized news landscape, understanding how individuals can sustain productive, healthy engagement with civic information is vital. In my work, I draw on literature in the communication and journalism fields to understand the impact of shifting media ecosystems on individuals. My studies employ methods from computational social science and human-computer interaction to analyze news content and exposure in more nuanced ways than previously available. I outline my agenda to run experiments to identify risk-benefit trade-offs at the individual level for engaging with evolving technological news systems.
A Novel Pose Control Scheme with a Non-Iterative Neural Network for Redundant Robots

Pose (i.e., position and orientation) control of robots has been widely applied in industrial factories and medical fields. This paper presents and investigates a quadratic programming (QP) solution to pose control of redundant robots. In the QP formulation, 1) a novel performance index is designed using unit quaternions to be minimized for orientation control; 2) a kinematic equation is established and incorporated into the QP as an equality constraint for position control; and 3) joint-angle and joint-velocity limits are handled in the form of bound constraints in the QP to ensure robot safety. One way to solve the involved QP problem is to employ iterative algorithms such as traditional recurrent neural networks, which may lead to unsatisfactory solution accuracy and/or failure of real-time control. Differing from iterative algorithms, this paper proposes a non-iterative solver for the QP problem. To handle the bound constraints in the QP, a maxQ function is introduced to obtain an equivalent QP with only equality constraints. Then, the QP is further transformed into a nonlinear equation according to the Karush-Kuhn-Tucker conditions. After that, a new zeroing neural network is designed and implemented to solve the resultant nonlinear equation and the original QP problem in a non-iterative manner. Comparative computer simulations and physical experiments are conducted on a Franka Emika Panda robot with seven joints. Simulative and experimental results substantiate the efficacy of the QP solution to pose control of redundant robots.
Investigating the Effects of Individual Spatial Abilities on Virtual Reality Object Manipulation

Object manipulation in 3D space, meaning translating, rotating, and scaling, is ubiquitous in virtual reality (VR), and several interaction techniques have been developed in the past to optimize the task performance and usability. However, preliminary research indicates that individual spatial abilities also have an impact. Yet, it was never investigated if users’ spatial abilities influence VR object manipulation. We assessed this in a user study (N=66) using 21 manipulation tasks defined in a Fitts’ law-related approach. As interaction techniques, we chose gizmos for simultaneously manipulating 1 and 3 degrees of freedom (DOF) and a handle bar metaphor for 7 DOF. Higher spatial abilities resulted in significantly shorter task completion time and more targeted manipulations, while task accuracy was unaffected. However, an optimized interaction technique could compensate individual disadvantages. We propose seven guidelines on spatial abilities in interaction technique design and research to personalize and improve VR applications.
GenAICHI 2023: Generative AI and HCI at CHI 2023

This workshop applies human centered themes to a new and powerful technology, generative artificial intelligence (AI). Unlike AI systems that produce decisions or descriptions, generative AI systems can produce new and creative content that can include images, texts, music, video, code, and other forms of design. The results are often similar to results produced by humans. However, it is not yet clear how humans make sense of generative AI algorithms or their outcomes. It is also not yet clear how humans can control and more generally, interact with, these powerful capabilities in ethical ways. Finally, it is not clear what kinds of collaboration patterns will emerge when creative humans and creative technologies work together. Following a successful workshop in 2022, we convene the interdisciplinary research domain of generative AI and HCI. Participation in this invitational workshop is open to seasoned scholars and early career researchers. We solicit descriptions of completed projects, works-in-progress, and provocations. Together we will develop theories and practices in this intriguing new domain.
Understanding Moderators’ Conflict and Conflict Management Strategies with Streamers in Live Streaming Communities

As each micro community centered around the streamer attempts to set its own guidelines in live streaming communities, it is common for volunteer moderators (mods) and the streamer to disagree on how to handle various situations. In this study, we conducted an online survey (N=240) with live streaming mods to explore their commitment to the streamer to grow the micro community and the different styles in which they handle conflicts with the streamer. We found that 1) mods apply more active and cooperative styles than passive and assertive styles to manage conflicts, but they might be forced to do so, and 2) mods with strong commitments to the streamer would like to apply styles showing either high concerns for the streamer or low concerns for themselves. We reflect on how these results can affect micro community development and recommend designs to mitigate conflict and strengthen commitment.
To Do or Not To Do? Managing Intentions with Technology

An overarching goal of human-computer interaction is to build technologies that empower users to do what they want to do i.e. to realise their intentions. To achieve high-level goals and values, people have to do certain things and not do other things. Written for the CHI 2023 Doctoral Consortium, this article briefly describes the initial results from two studies which explore how reminders can help people to exhibit and inhibit behaviours, respectively. The first study demonstrates that metacognitive reflection guides reminder setting for everyday intentions. The second study explores how reminders to reflect can empower individuals to use social media more intentionally. These findings are used to inform future directions for research and design.
Multi-point STM: Effects of Drawing Speed and Number of Focal Points on Users’ Responses using Ultrasonic Mid-Air Haptics

Spatiotemporal modulation (STM) is used to render tactile patterns with ultrasound arrays. Previous research only explored the effects of single-point STM parameters, such as drawing speed (Vd). Here we explore the effects of multi-point STM on both perceptual (intensity) and emotional (valence/arousal) responses. This introduces a new control parameter for STM - the number of focal points (Nfp) – on top of conventional STM parameter (Vd). Our results from a study with 30 participants showed a negative effect of Nfp on perceived intensity and arousal, but no significant effects on valence. We also found the effects of Vd still aligned with prior results for single-point, even when different Nfp were used, suggesting that effects observed from single-point also apply to multi-point STM. We finally derive recommendations, such as using single-point STM to produce stimuli with higher intensity and/or arousal, or using multi-point STM for milder and more relaxing (less arousing) experiences.
Investigating Semantically-enhanced Exploration of GAN Latent Space via a Digital Mood Board

During past decades, Artificial Intelligence (AI) has been consistently used in Creativity Support Tools (CSTs). Recently, with the development of generative AI models, particularly Generative Adversarial Nets (GAN) in Computer Vision, it became possible that AI directly generates visual ideas. However, there were rarely any work in creativity research that harnessed the design ideas generated by such models directly for design space exploration. To that end we designed a digital mood board as a technology probe to support semantically-enhanced exploration of the StyleGAN latent space. We compared the mood board with traditional gallery display. Preliminary results showed that such design offered a more enjoyable and explicit way of exploring AI generated visual ideas, but gallery display was found to be more straightforward and less demanding.
ARound the Smartphone: Investigating the Effects of Virtually-Extended Display Size on Spatial Memory

Smartphones conveniently place large information spaces in the palms of our hands. While research has shown that larger screens positively affect spatial memory, workload, and user experience, smartphones remain fairly compact for the sake of device ergonomics and portability. Thus, we investigate the use of hybrid user interfaces to virtually increase the available display size by complementing the smartphone with an augmented reality head-worn display. We thereby combine the benefits of familiar touch interaction with the near-infinite visual display space afforded by augmented reality. To better understand the potential of virtually-extended displays and the possible issues of splitting the user’s visual attention between two screens (real and virtual), we conducted a within-subjects experiment with 24 participants completing navigation tasks using different virtually-augmented display sizes. Our findings reveal that a desktop monitor size represents a “sweet spot” for extending smartphones with augmented reality, informing the design of hybrid user interfaces.
Multivariate Interdependent Discretization InDiscovering The Best Correlated Attribute

The decision tree is one of the most widely used and practical methods in data mining. However, many discretization algorithms developed in this field focus on the univariate only, which discretize continuous-valued attributes independently, without considering the interdependent relationship between other attributes, at most taking the class attribute into account. Such univariate discretization is inadequate to handle the critical problems especially owned in the medical domain. In this paper, we propose a new multivariate discretization method called Multivariate Interdependent Discretization for Continuous Attributes – MIDCA. This method incorporates the normalized relief and information measures to look for the best correlated attribute with respect to each continuous-valued attribute being discretized, and using the discovered best correlated attribute as the interdependent attribute to carry out the multivariate discretization. We believe that a good multivariate discretization scheme for continuous-valued attributes should rely highly on their perfect correlated attributes respectively. Among an attribute space, each attribute should have at least one most relevant attribute that may be different from others. Our novel multivariate discretization algorithm can minimize the uncertainty between the interdependent attribute and the continuous-valued attribute being discretized and at the same time maximize their correlation. Such a method can be used as a pre-processing step for the learning algorithms. The empirical results demonstrate a comparison of performance between MIDCA and various discretization methods for two decision tree algorithms ID3 and C4.5 on twelve real-life datasets from UCI repository.
Different Researchers, Different Results? Analyzing the Influence of Researcher Experience and Data Type During Qualitative Analysis of an Interview and Survey Study on Security Advice

When conducting qualitative research it is necessary to decide how many researchers should be involved in coding the data: Is one enough or are more coders beneficial? To offer empirical evidence for this question, we designed a series of studies investigating qualitative coding. We replicated and extended a usable security and privacy study by Ion et al. to gather both simple survey data and complex interview data. We had a total of 65 students and seven researchers analyze different parts of this data. We analyzed the codebook creation process, similarity of outcomes, inter-rater reliability, and compared the student to the researcher outcomes. We also surveyed five years of SOUPS-PC members about their views on coding. The reviewers view on coding practices for complex and simple data are almost identical. However, our results suggest that the coding process can be different for the two types of data, with complex data benefiting more from interaction between coders.
IntroBot: Exploring the Use of Chatbot-assisted Familiarization in Online Collaborative Groups

Many people gather online and form teams with strangers to collaborate on tasks. However, while intrateam trust and cohesion are critical for team performance, such characteristics take time to establish and are harder to build up through computer-mediated communication. Building on prior research that has shown that enhancing familiarity between members can help, we hypothesized that the use of a chatbot to support the familiarization of ad hoc teammates can help their collaboration. As such, we designed IntroBot, a chatbot that builds on an online discussion facilitator framework and leverages the social media data of users to assist their familiarization process. Through a between-subjects study (N=60), we found that participants who used IntroBot reported higher levels of trust, cohesion, and interaction quality, as well as generated more ideas in a collaborative brainstorming task. We discuss insights gained from our study, and present opportunities for the future of chatbot-assisted collaboration.
3D Printable Play-Dough: New Biodegradable Materials and Creative Possibilities for Digital Fabrication

Play-dough is a brightly-colored, easy-to-make, and familiar material. We have developed and tested custom play-dough materials that can be employed in 3D printers designed for clay. This paper introduces a set of recipes for 3D printable play-dough along with an exploration of these materials’ print characteristics. We explore the design potential of play-dough as a sustainable fabrication material, highlighting its recyclability, compostability, and repairability. We demonstrate how custom-color prints can be designed and constructed and describe how play-dough can be used as a support material for clay 3D prints. We also present a set of example artifacts made from play-dough and discuss opportunities for future research.
Inhabiting Interconnected Spaces: How Users Shape and Appropriate Their Smart Home Ecosystems

Over the last decade, smart home technology (SHT) has become an integral part of modern households. As a result, smart home ecosystems blend with daily social life, appropriated and integrated into personalised domestic environments. The lived experience of inhabiting smart home ecosystems, however, is not yet understood, resulting in a mismatch between ecosystem design and inhabitants’ needs. Drawing on contextual inquiry methods, we conducted an explorative interview study (N=20) with SHT users in their homes. Our thematic analysis reveals how users shape their smart home ecosystems (SHEs), considering social relationships at home, perceived ownership of SHTs, and expected key benefits. Notably, our analysis shows that household members consciously choose ‘their’ level of SHT interconnectedness, reflecting social, spatial and functional affinities between systems. Following our findings, we formulate five implications for designing future SHTs. Our work contributes insights on the dynamics and appropriation of smart home ecosystems by their inhabitants.
Modeling Touch-based Menu Selection Performance of Blind Users via Reinforcement Learning

Although menu selection has been extensively studied in HCI, most existing studies have focused on sighted users, leaving blind users’ menu selection under-studied. In this paper, we propose a computational model that can simulate blind users’ menu selection performance and strategies, including the way they use techniques like swiping, gliding, and direct touch. We assume that selection behavior emerges as an adaptation to the user’s memory of item positions based on experience and feedback from the screen reader. A key aspect of our model is a model of long-term memory, predicting how a user recalls and forgets item position based on previous menu selections. We compare simulation results predicted by our model against data obtained in an empirical study with ten blind users. The model correctly simulated the effect of the menu length and menu arrangement on selection time, the action composition, and the menu selection strategy of the users.
Four Challenges for IML Designers: Lessons of an Interactive Customer Segmentation Prototype in a Global Manufacturing Company

Interactive Machine Learning (IML) apps permeate all aspects of businesses, including sales. Customer segmentation is integral for sales, to identify customer groups to serve them appropriately. However, the novelty of such apps in a sales context raises the question: what challenges designers of such apps will face, in a sales context? To explore this question, we report our reflections on an IML study for customer clustering. We used data from a global manufacturing company to cluster customers using the Recency, Frequency, and Monetary (RFM) method. We applied a machine learning clustering model (K-Means) and discussed with seven seasoned sales managers the interaction with clusters. We report four challenges and foresee that designers of such systems will face, in the context of sales operations.
Log-it: Supporting Programming with Interactive, Contextual, Structured, and Visual Logs

Logging is a widely used technique for inspecting and understanding programs. However, the presentation of logs still often takes its ancient form of a linear stream of text that resides in a terminal, console, or log file. Despite its simplicity, interpreting log output is often challenging due to the large number of textual logs that lack structure and context. We conducted content analysis and expert interviews to understand the practices and challenges inherent in logging. These activities demonstrated that the current representation of logs does not provide the rich structures programmers need to interpret them or the program’s behavior. We present Log-it, a logging interface that enables programmers to interactively structure and visualize logs in situ. A user study with novices and experts showed that Log-it’s syntax and interface have a minimal learning curve, and the interactive representations and organizations of logs help programmers easily locate, synthesize, and understand logs.
Creating Inclusive Voices for the 21st Century: A Non-Binary Text-to-Speech for Conversational Assistants

As voice assistant usage continues to grow, their homogeneity becomes even more problematic with the UNESCO report, “I’d Blush if I could” showing that designing only feminine voice assistants encourages negative behavior, both with virtual assistants and with real people [3]. While masculine text-to-speech (TTS) voices exist, ones that cover the full range of gender presentations, such as non-binary or gender-ambiguous voices are largely missing. In this paper, we present a method of creating a non-binary TTS voice and an example voice, Sam, created with input from the non-binary and transgender communities. We have open-sourced the resulting voice, along with the process and data used to create it. Finally, we present results from a large-scale survey showing that non-binary individuals are more likely to prefer a non-binary voice assistant compared to cisgendered individuals and discuss differences across age and gender.
Research Products at Scale: Learnings from Designing Devices in Multiples of Ones, Tens, Hundreds and Thousands

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.