Miracle-dz/UniFAR-FTUs huggingface dataset:
Affective Typography: The Effect of AI-Driven Font Design on Empathetic Story Reading

When people listen to stories, the words are colored with emotions conveyed through prosodic features beyond the text alone. Visual font design provides an opportunity to enhance the empathic quality of a story compared to plain text. In this paper, we present the design, implementation, and evaluation of Affective Typography (AffType), an AI-driven system that extracts prosodic information and sentiment from speech and maps these properties to typographic styles.1 We conduct a crowdsourced study (N = 140) to assess how different font design elements impact readers’ empathy with personal stories. While our empathy survey results were not statistically significant, we found that participants had a preference for color to express emotion and saw an increase in average empathy for stories with color-based text alterations. In addition, we offer design insights as to what display features best convey emotional qualities of personal stories for future applications that use affective fonts to create more expressive digital text.
Climate Coach: A Dashboard for Open-Source Maintainers to Overview Community Dynamics

Open-source software projects have become an integral part of our daily life, supporting virtually every software we use today. Since open-source software forms the digital infrastructure, maintaining them is of utmost importance. We present Climate Coach, a dashboard that helps open-source project maintainers monitor the health of their community in terms of team climate and inclusion. Through a literature review and an exploratory survey (N=18), we identified important signals that can reflect a project’s health, and display them on a dashboard. We evaluated and refined our dashboard through two rounds of think-aloud studies (N=19). We then conducted a two-week longitudinal diary study (N=10) to test the usefulness of our dashboard. We found that displaying signals that are related to a project’s inclusion help improve maintainers’ management strategies.
A Case Study on the Design and Use of an Annotation and Analytical Tool Tailored To Lead Climbing

Annotating sport performances enables to quantitatively and qualitatively analyze them, and profile athletes to identify their strengths and weaknesses. We present the case study of the design and use of an annotation and analytical tool tailored to lead climbing analysis, developed with and for the French climbing federation. We used an iterative design cycle mostly fueled by virtual meetings with the federation trainer and analyst to identify requirements and implement essential features over time. We complemented these meetings with two workshops involving them, as well as French athletes competing at the international level, to identify the tool advantages and limitations. We contribute a list of insights based on the design process and feedback from stakeholders that inform the design of annotation and analytical tools for lead climbing and potentially other sports.
Human-Computer Interaction and AI: What practitioners need to know to design and build effective AI system from a human perspective

AI and ML are now essential parts of many systems that are currently being built. What should CHI practitioners know about the possibilities and potential drawbacks of building AI systems? Understanding the human side of AI/ML based systems requires understanding both how the system-side AI works, but also how people think about, understand, and use AI tools and systems. This course will cover what AI components and systems currently exist, how to design and build usable systems with AI components, along with how the mental models of AI/ML tools operate. These models lead to user expectations of how AI systems function, and ultimately, to design guidelines that avoid disappointing end-users by accidentally creating unintelligible AI tools. We'll also cover the ethics of AI, including data collection, algorithmic and data fairness considerations, along with other risks of AI.
SwellSense: Creating 2.5D interactions with micro-capsule paper

In this paper, we propose SwellSense, a fabrication technique to screen print stretchable circuits onto a special micro-capsule paper, creating localized swelling patterns with sensing capabilities. This simple technique will allow users to create a wide range of paper-based tactile interactive devices, which are mostly maintaining 2D planar form factor but can also be curved or folded into 3D interactive artifacts. We frst present the design guidelines to support various tactile interaction design including basic tactile graphic geometries, patterns with directional density, or fner interactive textures with embedded sensing such as touch sensor, pressure sensor, and mechanical switch. We then provide a design editor to enable users to design more creatively using the SwellSense technique. We provide a technical evaluation and user evaluation to validate the basic performance of SwellSense. Lastly, we demonstrate several application examples and conclude with a discussion on current limitations and future work.
Investigating the Physiological and Psychological Effect of an Interactive Musical Interface for Stress and Anxiety Reduction

Music is a powerful tool for managing negative affect, due to its portability, accessibility, and unique ability to impact mood. In an effort to explore uses of personalized music as an anxiety management intervention, we designed an interface featuring 14 novel musical fragments with adjusted tempo, instrumentation, and rhythm and allowed users to navigate freely. We conducted a pilot study to test the efficacy of this approach for reducing stress and anxiety. Through survey and biometric data, we found that our approach can effectively reduce stress when enabling participants to personalize their musical stimuli. This suggests significant value for conducting larger-scale studies, prompting us to present our findings to support future work toward building personalized musical interventions that alleviate symptoms of stress and anxiety.
A Smart Contract Top-N Recommendation Method Based on Implicit Feedback

Smart contracts on Ethereum evolve rapidly and lack explicit feedback, so it becomes necessary to use implicit feedback for recommendations. This paper proposes a collaborative filtering recommendation algorithm based on the user preferences list (UPLS-CF) to solve the above problems. We propose a pseudo rating generator to convert the implicit feedback data into explicit ratings and use collaborative filtering-based recommendation algorithm to complete top-N recommendations for smart contracts. In addition, we introduce user preference information to improve the accuracy of recommendations. Extensive experiments on datasets show that the algorithm proposed in this paper improves Precision by 15.975%, Recall by 12.116%, and HR by 20.815%. The results show that it can improve the algorithm's accuracy and can effectively recommend smart contracts to Ethereum users to combine the proposed algorithm with the user preference information.
Attached to “The Algorithm”: Making Sense of Algorithmic Precarity on Instagram

This work explores how users navigate the opaque and ever-changing algorithmic processes that dictate visibility on Instagram through the lens of Attachment Theory. We conducted thematic analysis on 1,100 posts and comments on r/Instagram to understand how users engage in collective sensemaking with regards to Instagram’s algorithms, user-perceived punishments, and strategies to counteract algorithmic precarity. We found that the unpredictability in how Instagram rewards or punishes a user can lead to distress, hypervigilance, and a need to appease “the algorithm’’. We therefore frame these findings through Attachment Theory, drawing upon the metaphor of Instagram as an unreliable paternalistic figure that inconsistently rewards users [74]. User experiences are then contextualized through the lens of anxious, avoidant, disorganized, and secure attachment. We conclude by making suggestions for fostering secure attachment towards the Instagram algorithm, by suggesting potential strategies to help users successfully cope with uncertainty.
A Multi-layer Graph Readout Motor Imagery Classification Model with Graph Neural Network Based on Channel Correlation Features

At present, most classification methods for motor imagery EEG signals require complex feature extraction steps, which are time-consuming and lack generality, and basically do not use the spatial location information between EEG channels. In this study, a graph neural network is introduced for end-to-end learning to complete the classification of four types of MI tasks, aiming to improve the accuracy. This paper proposes a method to generate the adjacency matrix required for graph convolution based on brain functional connections. This method can dynamically adjust the graph structure for different samples and fully consider the correlation information between channels. In addition, a cascaded network composed of graph convolutional neural networks and gated recurrent units is designed to simultaneously extract spatial information and time-frequency information in EEG signals, which can capture relevant hidden representations of different human MI patterns. Experiments are performed on the BCI Competition IV 2a dataset, with an accuracy of 99.61% and a standard deviation of 0.29, and the proposed method outperforms existing methods. The experimental results show that the proposed method improves the accuracy of four classification by automatically extracting the spatial and time information from EEG signals, overcomes the influence of individual differences on classification, and promotes the development of brain-computer interface systems.
SAWSense: Using Surface Acoustic Waves for Surface-bound Event Recognition

Enabling computing systems to understand user interactions with everyday surfaces and objects can drive a wide range of applications. However, existing vibration-based sensors (e.g., accelerometers) lack the sensitivity to detect light touch gestures or the bandwidth to recognize activity containing high-frequency components. Conversely, microphones are highly susceptible to environmental noise, degrading performance. Each time an object impacts a surface, Surface Acoustic Waves (SAWs) are generated that propagate along the air-to-surface boundary. This work repurposes a Voice PickUp Unit (VPU) to capture SAWs on surfaces (including smooth surfaces, odd geometries, and fabrics) over long distances and in noisy environments. Our custom-designed signal acquisition, processing, and machine learning pipeline demonstrates utility in both interactive and activity recognition applications, such as classifying trackpad-style gestures on a desk and recognizing 16 cooking-related activities, all with >97% accuracy. Ultimately, SAWs offer a unique signal that can enable robust recognition of user touch and on-surface events.
A Consensus-Based Joint Optimization Algorithm for Multi-agent Collaborative Dynamic Task Allocation

In team-based task allocation, due to the full utilization of individual computing resources and getting rid of single node dependence, the advantages of distributed methods are always stronger than centralized ones. Therefore, in this paper, the multi-UAV system adopts a distributed architecture. We propose a consensus-based joint optimization algorithm. In the phase of task sequence construction, an improved differential evolution algorithm is used in the early stage, while the local adjustment is carried out in the later stage after converging to a stable solution. Then, for task sequence coordination among agents, the algorithm utilizes a conflict resolution strategy based on consensus principle. After the above-mentioned multiple rounds of task allocation, a conflict-free optimized solution is obtained. And tests confirm superior performance when compared with existing collaborative task allocation algorithms.
Investigating Wrist Deflection Scrolling Techniques for Extended Reality

Scrolling in extended reality (XR) is currently performed using handheld controllers or vision-based arm-in-front gestures, which have the limitations of encumbering the user’s hands or requiring a specific arm posture, respectively. To address these limitations, we investigate freehand, posture-independent scrolling driven by wrist deflection. We propose two novel techniques: Wrist Joystick, which uses rate control, and Wrist Drag, which uses position control. In an empirical study of a rapid item acquisition task and a casual browsing task, both Wrist Drag and Wrist Joystick performed on par with a comparable state-of-the-art technique on one of the two tasks. Further, using a relaxed arm-at-side posture, participants retained their arm-in-front performance for both wrist techniques. Finally, we analyze behavioral and ergonomic data to provide design insights for wrist deflection scrolling. Our results demonstrate that wrist deflection provides a promising method for performant scrolling controls while offering additional benefits over existing XR interaction techniques.
Sharing Earthquake Narratives: Making Space for Others in our Autobiographical Design Process

As interaction designers are venturing to design for others based on autobiographical experiences, it becomes particularly relevant to critically distinguish the designer’s voice from others’ experiences. However, few reports go into detail about how self and others mutually shape the design process and how to incorporate external evaluation into these designs. We describe a one-year process involving the design and evaluation of a prototype combining haptics and storytelling, aiming to materialise and share somatic memories of earthquakes experienced by a designer and her partner. We contribute with three strategies for bringing others into our autobiographical processes, avoiding the dilution of first-person voices while critically addressing design flaws that might hinder the representation of our stories.
Non-commutative Iwasawa theory for elliptic curves with multiplicative reduction

Abstract Let $E_{/{\mathbb{Q}}}$ be a semistable elliptic curve, and p ≠ 2 a prime of bad multiplicative reduction. For each Lie extension $\mathbb{Q}$FT/$\mathbb{Q}$ with Galois group G∞ ≅ $\mathbb{Z}$p ⋊ $\mathbb{Z}$p×, we construct p-adic L-functions interpolating Artin twists of the Hasse–Weil L-series of the curve E. Through the use of congruences, we next prove a formula for the analytic λ-invariant over the false Tate tower, analogous to Chern–Yang Lee's results on its algebraic counterpart. If one assumes the Pontryagin dual of the Selmer group belongs to the $\mathfrak{M}_{\mathcal{H}}$(G∞)-category, the leading terms of its associated Akashi series can then be computed, allowing us to formulate a non-commutative Iwasawa Main Conjecture in the multiplicative setting.
Affine formation maneuver control for multiple underactuated quadrotor UAVs with fixed convergence time

This paper proposes an affine formation maneuver control scheme for multiple underactuated quadrotor UAVs, which comprises the distributed fixed-time estimators and tracking controllers, , translational and rotational controllers. The desired states can be obtained within fixed time by the distributed fixed-time estimators. Then, under the proposed fixed-time tracking controllers, all error signals in the control system converge. Meanwhile, an attitude extraction algorithm is adopted to obtain the desired rotational states. A first-order filter is proposed to deal with calculating derivatives for variables. Finally, through numerical simulations, the feasibility of the proposed control scheme is proved.
Evaluation of Submission Limits and Regression Penalties to Improve Student Behavior with Automatic Assessment Systems

Objectives. Automatic assessment systems are widely used to provide rapid feedback for students and reduce grading time. Despite the benefits of increased efficiency and improved pedagogical outcomes, an ongoing challenge is mitigating poor student behaviors when interacting with automatic assessment systems including numerous submissions, trial-and-error, and relying on marking feedback for problem solving. These behaviors negatively affect student learning as well as have significant impact on system resources. This research quantitatively examines how utilizing submission policies such as limiting the number of submissions and applying regression penalties can reduce negative student behaviors. The hypothesis is that both submission policies will have a significant impact on student behavior and reduce both the number of submissions and regressions in student performance. The research questions evaluate the impact on student behavior, determine which submission policy is the most effective, and what submission policy is preferred by students. Participants. The study involved two course sections in two different semesters consisting of a total of 224 students at the University of British Columbia, a research-intensive university. The students were evaluated using an automated assessment system in a large third year database course. Study Methods. The two course sections used an automated assessment system for constructing database design diagrams for assignments and exams. The first section had no limits on the number of submissions for both assignments and exams. The second section had limits for the exams but no limits on assignments. On the midterm, participants were randomly assigned to have either a restriction on the total number of submissions or unlimited submissions but with regression penalties if a graded answer was lower than a previous submission. On the final exam, students were given the option of selecting their submission policy. Student academic performance and submission profiles were compared between the course sections and the different submission policies. Findings. Unrestricted use of automatic grading systems results in high occurrence of undesirable student behavior including trial-and-error guessing and reduced time between submissions without sufficient independent thought. Both submission policies of limiting maximum submissions and utilizing regression penalties significantly reduce these behaviors by up to 85%. Overall, students prefer maximum submission limits, and demonstrate improved behavior and educational outcomes. Conclusions. Automated assessment systems when used for larger problems related to design and programming have benefits when deployed with submission restrictions (maximum attempts or regression penalty) for both improved student learning behaviors and to reduce the computational costs for the system. This is especially important for summative assessment but reasonable limits for formative assessments are also valuable.
“I'm” Lost in Translation: Pronoun Missteps in Crowdsourced Data Sets

As virtual assistants continue to be taken up globally, there is an ever-greater need for these speech-based systems to communicate naturally in a variety of languages. Crowdsourcing initiatives have focused on multilingual translation of big, open data sets for use in natural language processing (NLP). Yet, language translation is often not one-to-one, and biases can trickle in. In this late-breaking work, we focus on the case of pronouns translated between English and Japanese in the crowdsourced Tatoeba database. We found that masculine pronoun biases were present overall, even though plurality in language was accounted for in other ways. Importantly, we detected biases in the translation process that reflect nuanced reactions to the presence of feminine, neutral, and/or non-binary pronouns. We raise the issue of translation bias for pronouns and offer a practical solution to embed plurality in NLP data sets.
LURM: A LiDAR-based Urban Road-Map Representation

In this paper, we propose the LiDAR-based Urban Road-Map (LURM), an efficient 3D map representation, for autonomous robot navigation in general urban environment. We created LURM by an online mapping framework which incrementally merges local 2D occupancy grid maps (2D-OGM). Specifically, the LURM representation can be summarized as three contributions. First, we solve the challenging problem of creating local 2D-OGM in non-structured urban scenes by a real-time delimitation of traversable and curb regions in LiDAR point cloud. Second, we can achieve accurate 3D road mapping in general urban road scenarios by a probabilistic fusion scheme. Third, the LURM representation can be applied for localization purposes in general environment thanks to the generated 3D-OGM, the sparse local point-cloud encoding and effective global LiDAR-Iris descriptor. We compare it with the popular Octomap method, and our map representation is more favorable in terms of efficiency, scalability and compactness.
Studying Children’s Object Interaction in Virtual Reality: A Manipulative Gesture Taxonomy for VR Hand Tracking

In this paper, we propose a taxonomy for the classification of children’s gestural input elicited from spatial puzzle play in VR hand tracking. The taxonomy builds on the existing manipulative gesture taxonomy in human-computer interaction, and offers two main analytical categories; Goal-directed actions and Hand kinematics as complementary dimensions for analysing gestural input. Based on our study with eight children (aged between 7-14), we report the qualitative results for describing the categories for analysis and quantitative results for their frequency in occurring in children’s interaction with the objects during the spatial task. This taxonomy is an initial step towards capturing the complexity of manipulative gestures in relation to mental rotation actions, and helps designers and developers to understand and study children’s gestures as an input for object interaction as well as an indicator for spatial thinking strategies in VR hand tracking systems.
Eye Movement Analysis of Human Visual Recognition Processes with Camera Eye Tracker: Higher Mean and Variance of Fixation Duration for Familiar Images

Eye movements could provide information on a person’s cognitive context. Previous works showed that human visual recognition processes could be detected via electrooculography (EOG) data and revealed top-ranked eye movement features for detection. However, they lack analysis on how eye movements differ when viewing recognised (familiar) and unrecognised (unfamiliar) images. EOG methods are less prevalent and have limited scalability for detecting other cognitive processes. Our study with 34 participants showed that saccade count and fixation count decreased while the mean and variance of fixation duration increased with image familiarity. We discuss the differences in eye movements when seeing images of different categories and lessons learnt for our next steps to enable implicit interactions using wearable camera-based eye trackers.
Towards a Reflection in Creative Experience Questionnaire

Reflection is underexplored in Creativity Support Tool (CST) research, partly due to its ambiguous nature. We suggest that researchers could benefit from a measure of a CST’s capacity to support reflection. To this end, we detail the first stages of development of the Reflection in Creative Experience Questionnaire (RiCE) – a lightweight questionnaire for differentiating between creative user experiences which exhibit more or less moments of reflection. We develop RiCE through i) an expert review of questionnaire items (n=10) and ii) an exploratory factor analysis (n=300) of the reviewed items. We also present a user study testing RiCE (n=58) across two time points (one week apart) with novel interfaces designed for creative writing and music making. Although we do not confirm validity, we identify four factors for RiCE which we suggest are interpretable in a conceptually meaningful way. Our formative studies contribute towards supporting future explorations on reflection with CSTs.