Reviewerly/exHarmony huggingface dataset:
‘There’s more to it than allure...’ – Navigating Socio-cultural Roles of Digital Jewellery

Over the recent years, digital jewellery has been creatively explored in research and commercial products. However, the symbolic layers of these artefacts which are inherent to traditional jewellery are not yet understood. Exploring the anthropological dimensions of wearable ornaments is necessary for digital jewellery to become enduring assets of personal importance. Drawing on participatory design methods, we conducted an explorative workshop with experts in design, art, and technology (N=16) discussing the socio-technical dimensions of designing interactive jewellery. In this work, we build upon a multidisciplinary framework describing traditional jewellery from an anthropological perspective. We examine whether and how novel digital ornaments fulfil five major socio-cultural roles of jewellery. Our work contributes insights for designing digital jewellery with social relevance and hints new directions for future implementations.
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.
Using Geometric Features of Drag-and-Drop Trajectories to Understand Students’ Learning

Herein, we present two studies on how students’ Psychological State of Decision difficulty (PSD) relates to two aspects of learning, i.e., guessing behavior and learning achievement. To measure PSD, we extracted geometric features from trajectories of drag-and-drop touch interactions collected while students aged 7–10 played a math game on a tablet device. In the first study, we explored whether eight geometric features extracted from 97,303 trial trajectories could be grouped to understand students’ PSD. In the second study, we examined whether the two aspects of learning could be predicted using the data collected from 187 students with geometric features indicating their PSD. This work provides empirical evidence that geometric features can be grouped into two types of PSD in the context of learning, including conflict and uncertainty. Moreover, our results demonstrate that data on students’ PSD collected from drag-and-drop trajectories can be used to predict learning.
Toward a Multilingual Conversational Agent: Challenges and Expectations of Code-mixing Multilingual Users

Multilingual speakers tend to interleave two or more languages when communicating. This communication strategy is called code-mixing, and it has surged with today's ever-increasing linguistic and cultural diversity. Because of their communication style, multilinguals who use conversational agents have specific needs and expectations which are currently not being met by conversational systems. While research has been undertaken on code-mixing conversational systems, previous works have rarely focused on the code-mixing users themselves to discover their genuine needs. This work furthers our understanding of the challenges faced by code-mixing users in conversational agent interaction, unveils the key factors that users consider in code-mixing scenarios, and explores expectations that users have for future conversational agents capable of code-mixing. This study discusses the design implications of our findings and provides a guide on how to alleviate the challenges faced by multilingual users and how to improve the conversational agent user experience for multilingual users.
An Enhanced Energy-Efficient Web Service Composition Algorithm Based on the Firefly Algorithm

Numerous web services with the same function but different service qualities are constantly emerging on the network. Optimizing web service composition based on multiple candidate services sets an urgent problem in the service composition neighborhood. This paper modifies the traditional Firefly algorithm and adds exchange and mutation mechanisms to optimize the Web service composition efficiently in multiple candidate service sets. Meanwhile, it discretizes the continuous space of its solution set and better adapts to the service composition optimization problem. Experimental results show that compared with the GA, IA, SA, ACO, FACO, and EFACO algorithms, this algorithm has better optimization performance, faster speed, and higher energy efficiency for solving service composition optimization problems in the case of large-scale data. The higher the combined complexity of the solution, the stronger the performance compared to other algorithms. It can better deal with the increasingly complex situation of Web service composition problems.
ResType: Invisible and Adaptive Tablet Keyboard Leveraging Resting Fingers

Text entry on tablet touchscreens is a basic need nowadays. Tablet keyboards require visual attention for users to locate keys, thus not supporting efficient touch typing. They also take up a large proportion of screen space, which affects the access to information. To solve these problems, we propose ResType, an adaptive and invisible keyboard on three-state touch surfaces (e.g. tablets with unintentional touch prevention). ResType allows users to rest their hands on it and automatically adapts the keyboard to the resting fingers. Thus, users do not need visual attention to locate keys, which supports touch typing. We quantitatively explored users' resting finger patterns on ResType, based on which we proposed an augmented Bayesian decoding algorithm for ResType, with 96.3% top-1 and 99.0% top-3 accuracies. After a 5-day evaluation, ResType achieved 41.26 WPM, outperforming normal tablet keyboards by 13.5% and reaching 86.7% of physical keyboards. It solves the occlusion problem while maintaining comparable typing speed with current methods on visible tablet keyboards.
Let warnings interrupt the interaction and explain: designing and evaluating phishing email warnings

Phishing represents one of the most spread and effective cyber-attacks of our times. Warning messages are commonly employed in email clients to notify users about the possible danger and let them decide on their own. However, the high success rate of phishing attacks shows that the existing warnings are not yet adequate. This study contributes by proposing two novel warning dialogs for email clients that prevent users from immediately accessing the content of phishing emails. Specifically, the first one alerts the users against the potential scam, and the second one also reports explanations about the possible causes of the scam. A comparative between-subjects experiment with 300 participants has been performed. Results show that the proposed warnings better defend users from phishing emails than the warnings at the state-of-the-art. In addition, explanations resulted useful in preventing users from discarding genuine emails where warnings are displayed incorrectly due to misclassification of the email.
Fencing Hallucination: An Interactive Installation for Fencing with AI and Synthesizing Chronophotographs

Fencing Hallucination is a multi-screen interactive installation that enables real-time human-AI interaction in the form of a Fencing game and generates a chronophotograph based on the audience's movement. It mitigates the conflicts between interactivity, modality variety, and computational limitation in creative AI tools. Fencing Hallucination captures the audience's pose data as an input to the Multilayer Perceptron(MLP), which generates the virtual AI Fencer's pose data. It also uses the audience's pose to synthesize the chronophotograph. The system first represents pose data as stick figures. Then it uses a diffusion model to perform image-to-image translations, converting the stick figures into a series of realistic fencing images. Finally, it combines all images with an additive effect into one image as the result. This multi-step process overcomes the challenge of preserving both the overall motion patterns and fine details when synthesizing a chronophotograph.
“I Won’t Go Speechless”: Design Exploration on a Real-Time Text-To-Speech Speaking Tool for Videoconferencing

The COVID-19 pandemic has shifted many business activities to non-face-to-face activities, and videoconferencing has become a new paradigm. However, conference spaces isolated from surrounding interferences are not always readily available. People frequently participate in public places with unexpected crowds or acquaintances, such as cafés, living rooms, and shared offices. These environments have surrounding limitations that potentially cause challenges in speaking up during videoconferencing. To alleviate these issues and support the users in speaking-restrained spatial contexts, we propose a text-to-speech (TTS) speaking tool as a new speaking method to support active videoconferencing participation. We derived the possibility of a TTS speaking tool and investigated the empirical challenges and user expectations of a TTS speaking tool using a technology probe and participatory design methodology. Based on our findings, we discuss the need for a TTS speaking tool and suggest design considerations for its application in videoconferencing.
Modeling the Trade-off of Privacy Preservation and Activity Recognition on Low-Resolution Images

A computer vision system using low-resolution image sensors can provide intelligent services (e.g., activity recognition) but preserve unnecessary visual privacy information from the hardware level. However, preserving visual privacy and enabling accurate machine recognition have adversarial needs on image resolution. Modeling the trade-off of privacy preservation and machine recognition performance can guide future privacy-preserving computer vision systems using low-resolution image sensors. In this paper, using the at-home activity of daily livings (ADLs) as the scenario, we first obtained the most important visual privacy features through a user survey. Then we quantified and analyzed the effects of image resolution on human and machine recognition performance in activity recognition and privacy awareness tasks. We also investigated how modern image super-resolution techniques influence these effects. Based on the results, we proposed a method for modeling the trade-off of privacy preservation and activity recognition on low-resolution images.
Designing Accessible Visualizations for People with Intellectual and Developmental Disabilities

Visualization amplifies cognition and helps a viewer see the trends, patterns, and outliers in data. However, conventional visualization tools and guidelines do not actively consider the unique needs and abilities of people with Intellectual and Developmental Disabilities (IDD), leaving them excluded from data-driven activities and vulnerable to ethical issues in everyday life. My dissertation work explores the challenges and opportunities of cognitively accessible visualization. Through mixed-method approaches and close collaboration with people with IDD, my team and I ran experiments and developed guidelines to improve current visualizations, we interviewed people with IDD and gained initial understandings of their daily data experiences, and we are currently in the process of revising a participatory design workshop to create accessible visualizations for and with this population. For the remaining dissertation work, I hope to further expand our knowledge of cognitively accessible visualization, translating what I have learned from these experiences into a graphical user interface that supports people with IDD with their self-advocacy and self-expression using personally relevant data. My ultimate career goal is to theorize cognitively accessible visualization and empower people with IDD to make informed decisions and generate meaningful discoveries through accessible visual analytics.
Introduction to Authentication using Behavioral Biometrics

The trend of ubiquitous computing goes in parallel with ubiquitous authentication, as users must confirm their identity several times a day on their devices. Passwords are increasingly superseded by biometrics for their inherent drawbacks, and Behavioral Biometrics are particularly promising for increased usability and user experience. This course provides participants with an introduction to the overall topic, covering all phases of creating novel authentication schemes. We introduce important aspects of evaluating Behavioral Biometrics and provide an overview of technical machine-learning techniques in a hands-on session, inviting practitioners and researchers to extend their knowledge of Behavioral Biometrics.
Unraveling The Complexity: A User-Centered Design Process For Narrative Visualization

In this case study, we introduce a user-centered design process for developing Metroverse, a narrative visualization platform that communicates urban economic composition and growth opportunities for cities. The primary challenge in making Metroverse stems from the complexity of the underlying research and data, both of which need to be effectively communicated to a wide range of end-users with different backgrounds. To unravel the complexity of the research, and to design the platform, we followed a user-centered design process. Our design process brought together researchers, designers, and various end-users, who collectively guided the design of the narrative visualization. Engaging end-users in the early phases of the project allowed us to identify the valuable insights in the data and subsequently design effective visualizations that convey those insights. We believe findings from our process can provide a template for similar projects that require translating complex research data and methodologies into user-friendly story structures.
Wow! I Have Tiny Hands: Design And Evaluation Of Adaptive Virtual Hands For Small Object Selection Within Arms Length In Dense Virtual Environment

Object selection is essential to VR applications, but selecting small objects within arms' reach in dense virtual environments can be challenging. We introduce Tiny Hands, which allows reducing the size of the dominant virtual hand in immersive VR to navigate efficiently and accurately select the desired object. This study compares the tiny hands technique with ray casting and pinch-to-select techniques. Our results indicate that the tiny hands technique is significantly faster and more accurate than the other two techniques. It is also significantly easier to use and learn, perceived as natural, playful, and the most preferred technique. Participants appreciated its ability to resize virtual hands for selecting small objects and ease in navigating a dense VE. They also liked its similarity to mouse and cursor-based interactions and flexibility in adjusting hand size according to various object sizes. Participants perceived tiny hands as potentially fatigued for prolonged use and reported a sense of incorrect depth perception when hand sizes are too small.
Tuning Endpoint-variability Parameters by Observed Error Rates to Obtain Better Prediction Accuracy of Pointing Misses

Error rates (ERs) in target-pointing tasks are typically modelled in two steps: predicting the click-point variability (σ) based on target sizes and then computing the probability that a click falls outside a target. This is an indirect approach if the researcher's purpose is to achieve the accurate prediction of ERs because the model coefficients are optimized to predict σ accurately in the first step. We compared the prediction accuracies of this method with a more direct technique in which the coefficients used for σ are determined in such a way as to optimize the closeness between observed and predicted ERs. Our re-analysis of eight datasets from mouse- and touch-based pointing studies showed that the latter approach consistently outperforms the conventional one if the starting values for the parameter search are appropriate (which can be achieved by hyperparameter optimization), thus enabling the interface configuration on the basis of accurately predicted ERs.
You Know What I’m Saying: Designing Conversational Strategies of AI Agent for Tip of the Tongue Phenomenon

The phenomenon of being unable to recall a specific word or term from memory is called "tip-of-the-tongue" (TOT). Despite its significance, potential applications for solving TOT from the user experience (UX) and human–computer interface (HCI) perspectives are yet to be fully explored. With the advent of advanced artificial intelligence (AI) interfaces and technologies, new opportunities have emerged to address TOT. This study aims to investigate the applicability of AI in addressing TOT and the roles of conversation design strategies in mitigating this phenomenon. To this end, we developed a prototype called "Tip-pong," which is a turn-taking user interface that provides semantic and episodic clues, as well as targeted and optional answers to assist users in resolving TOT. Through a quantitative and qualitative study involving 23 participants, we evaluated the prototype and found that while the type of clue had no significant impact on usability, users preferred optional answers over targeted answers. Additionally, we found that allowing users to experience the process of approaching the answer on their own was more effective than providing the correct answer immediately. Based on these findings, we discuss the design implications for the development of AI agents to solve TOT in everyday life.
Memory Tracer &amp; Memory Compass: Investigating Personal Location Histories as a Design Material for Everyday Reminiscence

With the massive adoption of smartphones, location trackers, and GPS-based applications, data is being generated that captures people's geographic locations in more precise detail than ever before. Personal location history archives offer a potentially valuable and overlooked resource for supporting reminiscence and recollection of the past. Yet, little design research has explored how location histories can be used as a material in designing such experiences. To investigate this space, we engaged in a practice-based design research process that resulted in two design artifacts. Memory Tracer is a tangible device that occasionally, yet perpetually surfaces locations from the past bound to today's date. Memory Compass is a smartwatch application that uses a 'casting' interaction enabling a user to retrieve and explore locations from their past, across space and time. We unpack and reflect on key decisions in our design process and conclude with opportunities for future HCI research and practice.
DAPIE: Interactive Step-by-Step Explanatory Dialogues to Answer Children’s Why and How Questions

Children acquire an understanding of the world by asking "why" and "how" questions. Conversational agents (CAs) like smart speakers or voice assistants can be promising respondents to children's questions as they are more readily available than parents or teachers. However, CAs' answers to "why" and "how" questions are not designed for children, as they can be difficult to understand and provide little interactivity to engage the child. In this work, we propose design guidelines for creating interactive dialogues that promote children's engagement and help them understand explanations. Applying these guidelines, we propose DAPIE, a system that answers children's questions through interactive dialogue by employing an AI-based pipeline that automatically transforms existing long-form answers from online sources into such dialogues. A user study (N=16) showed that, with DAPIE, children performed better in an immediate understanding assessment while also reporting higher enjoyment than when explanations were presented sentence-by-sentence.
Exploring Long-Term Mediated Relations with a Shape-Changing Thing: A Field Study of coMorphing Stool

This paper presents a long-term field study of the coMorphing stool: a computational thing that can change shape in response to the surrounding light. We deployed 5 coMorphing stools to 5 participants' homes over 9 months. As co-speculators, the participants reflected on their mediated relations with the coMorphing stool. Findings suggest that they perceived the subtle transformations of the coMorphing stool in the early days of the deployment. After becoming familiar with these features, they interpreted their daily entanglements with the coMorphing stool in diverse personalized ways. Over time, the co-speculators accepted the coMorphing stool as part of their homes. These findings contribute new empirical insights to the shape-changing research field in HCI and enrich discussions on higher-level concepts in postphenomenology. Reflecting on these experiences promotes further HCI explorations on computational things.
VisLab: Enabling Visualization Designers to Gather Empirically Informed Design Feedback

When creating a visualization, designers face various conflicting design choices. They typically rely on their hunches to deal with intricate trade-offs or resort to feedback from their colleagues. On the other hand, researchers have long used empirical methods to derive useful quantitative insights into visualization designs. Taking inspiration from this research tradition, we developed VisLab, an open-source online system to complement the existing qualitative feedback practice and help visualization practitioners run experiments to gather empirically informed design feedback. We surveyed practitioners' perceptions of quantitative feedback and analyzed the research literature to inform VisLab's motivation and design. VisLab operationalizes the experiment process using templates and dashboards to make empirical methods amenable for practitioners while supporting sharing and remixing experiments to aid knowledge exchange and validation. We demonstrated the validity of experiments in VisLab and evaluated the usability and potential usefulness of VisLab in visualization design practice.