Miracle-dz/UniFAR-FTUs huggingface dataset:
Lived Experiences of Mental Workload in Everyday Life

We can now buy consumer brain-computer interface devices to help us meditate and focus, but what are we aiming to achieve? Mental workload (MWL) is an established concept, and as a form of personal data could be useful for making positive life changes. However, MWL is typically only studied for isolated tasks to avoid overload and underload. We investigated lived experiences of MWL, aiming to understand how tracking such data could implicate our everyday lives. 19 participants, that had previously experienced tracking their mental workload, took part in interviews and an Interpretive Phenomenological Analysis identified four superordinate themes. Results point towards mixed and changing perceptions of MWL and the importance of fluctuating between MWL levels in daily life in terms of performances, perceptions, and wellbeing. These findings are captured in an apparent Cycle, which outside factors can disrupt, and we discuss these cycles in terms of personal informatics and work performance.
Supercharging Trial-and-Error for Learning Complex Software Applications

Despite an abundance of carefully-crafted tutorials, trial-and-error remains many people’s preferred way to learn complex software. Yet, approaches to facilitate trial-and-error (such as tooltips) have evolved very little since the 1980s. While existing mechanisms work well for simple software, they scale poorly to large feature-rich applications. In this paper, we explore new techniques to support trial-and-error in complex applications. We identify key benefits and challenges of trial-and-error, and introduce a framework with a conceptual model and design space. Using this framework, we developed three techniques: ToolTrack to keep track of trial-and-error progress; ToolTrip to go beyond trial-and-error of single commands by highlighting related commands that are frequently used together; and ToolTaste to quickly and safely try commands. We demonstrate how these techniques facilitate trial-and-error, as illustrated through a proof-of-concept implementation in the CAD software Fusion 360. We conclude by discussing possible scenarios and outline directions for future research on trial-and-error.
Cocomix: Utilizing Comments to Improve Non-Visual Webtoon Accessibility

Webtoon is a type of digital comics read online where readers can leave comments to share their thoughts on the story. While it has experienced a surge in popularity internationally, people with visual impairments cannot enjoy webtoon with the lack of an accessible format. While traditional image description practices can be adopted, resulting descriptions cannot preserve webtoons’ unique values such as control over the reading pace and social engagement through comments. To improve the webtoon reading experience for BLV users, we propose Cocomix, an interactive webtoon reader that leverages comments into the design of novel webtoon interactions. Since comments can identify story highlights and provide additional context, we designed a system that provides 1) comments-based adaptive descriptions with selective access to details and 2) panel-anchored comments for easy access to relevant descriptive comments. Our evaluation (N=12) showed that Cocomix users could adapt the description for various needs and better utilize comments.
Supporting Anaesthetists during ‘Red Blanket’ Trauma Surgery: An Analysis of Work Practices and Requirements for a Head-Worn Display Support System

‘Red blanket’ (RB) processes accelerate the surgical treatment of patients who have suffered severe trauma. Although the rapid start of surgery has positive effects on patient outcomes, the short time available to prepare for a case and the limited information available at the outset of care can be challenging for physicians. To support the lead of an RB team in making the right clinical decisions quickly, this work proposes a head-worn display (HWD) based information system. HWD could be valuable in RB settings, because they provide information in a continuous and hands-free manner, potentially allowing physicians to access case relevant data while treating the patient. Through an interview study with seven anaesthetists, we studied work practices of RB team leads and identified user requirements for the HWD based support system. We discuss opportunities and challenges for HWDs in RB settings and provide a detailed description of the developed HWD application.
Negative Emotions, Positive Outcomes? Exploring the Communication of Negativity in Serious Data Stories

Recent work has highlighted that emotion is key to the user experience with data stories. However, limited attention has been paid to negative emotions specifically. This work investigates the outcomes of negative emotions in the context of serious data stories and examines how they can be augmented by design methods from the perspectives of both storytellers and viewers. First, we conducted a workshop with 9 data story experts to understand the possible benefits of eliciting negative emotions in serious data stories and 19 potential design methods that contribute to negative emotions. Based on the findings from the workshop, we then conducted a lab study with 35 participants to explore the outcomes of eliciting negative emotions as well as the effectiveness of the design methods. The results indicated that negative emotions mainly facilitated contemplative experiences and long-term memory. Besides, the design methods showed varied effectiveness in augmenting negative emotions and being recalled.
Glancee: An Adaptable System for Instructors to Grasp Student Learning Status in Synchronous Online Classes

Synchronous online learning has become a trend in recent years. However, instructors often face the challenge of inferring audiences’ reactions and learning status without seeing their faces in video feeds, which prevents instructors from establishing connections with students. To solve this problem, based on a need-finding survey with 67 college instructors, we propose Glancee, a real-time interactive system with adaptable configurations, sidebar-based visual displays, and comprehensive learning status detection algorithms. Then, we conduct a within-subject user study in which 18 college instructors deliver lectures online with Glancee and two baselines, EngageClass and ZoomOnly. Results show that Glancee can effectively support online teaching and is perceived to be significantly more helpful than the baselines. We further investigate how instructors’ emotions, behaviors, attention, cognitive load, and trust are affected during the class. Finally, we offer design recommendations for future online teaching assistant systems.
Self-Determination Theory in HCI: Shaping a Research Agenda

Self-determination theory (SDT) has become one of the most frequently used and well-validated theories used in HCI research, modelling the relation of basic psychological needs, intrinsic motivation, positive experience and wellbeing. This makes it a prime candidate for a ‘motor theme’ driving more integrated, systematic, theory-guided research. However, its use in HCI has remained superficial and disjointed across various application domains like games, health and wellbeing, or learning. This workshop therefore convenes researchers across HCI to co-create a research agenda on how SDT-informed HCI research can maximise its progress in the coming years.
ABS: A formally correct software tool for space-efficient symbolic synthesis

We present ABS, a software for Abstraction-Based Synthesis of controllers for continuous-state control systems. The tool distinguishes itself from previously known such software by being formally correct, i.e., any controller synthesized by ABS is mathematically guaranteed to solve the control problem provided as input. ABS achieves this quality by providing an input language with mathematically defined semantics and a respective compiler, and by carefully taking into account all numerical and rounding errors that might be incurred at either compile- or run-time. To mitigate computational overhead caused by the aforementioned approach, ABS implements, e.g. on-the-fly synthesis algorithms with greatly reduced memory requirement. The tool is currently applicable to invariance and reachability problems and requires state measurement. We discuss structure, algorithmic details and basic usage of ABS, and we demonstrate on two examples that its performance compares favorably with that of competing, not formally correct synthesis software. The source code of ABS is publicly available. See http://www.reiszig. de/gunther/pubs/ABS.html
The Effect of the Vergence-Accommodation Conflict on Virtual Hand Pointing in Immersive Displays

Previous work hypothesized that for Virtual Reality (VR) and Augmented Reality (AR) displays a mismatch between disparities and optical focus cues, known as the vergence and accommodation conflict (VAC), affects depth perception and thus limits user performance in 3D selection tasks within arm’s reach (peri-personal space). To investigate this question, we built a multifocal stereo display, which can eliminate the influence of the VAC for pointing within the investigated distances. In a user study, participants performed a virtual hand 3D selection task with targets arranged laterally or along the line of sight, with and without a change in visual depth, in display conditions with and without the VAC. Our results show that the VAC influences 3D selection performance in common VR and AR stereo displays and that multifocal displays have a positive effect on 3D selection performance with a virtual hand.
Compete, Collaborate, Investigate: Exploring the Social Structures of Open Source Intelligence Investigations

Online investigations are increasingly conducted by individuals with diverse skill levels and experiences, with mixed results. Novice investigations often result in vigilantism or doxxing, while expert investigations have greater success rates and fewer mishaps. Many of these experts are involved in a community of practice known as Open Source Intelligence (OSINT), with an ethos and set of techniques for conducting investigations using only publicly available data. Through semi-structured interviews with 14 expert OSINT investigators from nine different organizations, we examine the social dynamics of this community, including the collaboration and competition patterns that underlie their investigations. We also describe investigators’ use of and challenges with existing OSINT tools, and implications for the design of social computing systems to better support crowdsourced investigations.
Cognitive Load Theory in Computing Education Research: A Review

One of the most commonly cited theories in computing education research is cognitive load theory (CLT), which explains how learning is affected by the bottleneck of human working memory and how teaching may work around that limitation. The theory has evolved over a number of decades, addressing shortcomings in earlier versions; other issues remain and are being debated by the CLT community. We conduct a systematic mapping review of how CLT has been used across a number of leading computing education research (CER) forums since 2010. We find that the most common reason to cite CLT is to mention it briefly as a design influence; authors predominantly cite old versions of the theory; hypotheses phrased in terms of cognitive load components are rare; and only a small selection of cognitive load measures have been applied, sparsely. Overall, the theory’s evolution and recent themes in CLT appear to have had limited impact on CER so far. We recommend that studies in CER explain which version of the theory they use and why; clearly distinguish between load components (e.g., intrinsic and extraneous load); phrase hypotheses in terms of load components a priori; look further into validating different measures of cognitive load; accompany cognitive load measures with complementary constructs, such as motivation; and explore themes such as collaborative CLT and individual differences in working-memory capacity.
“Alexa, Do You Want to Build a Snowman?” Characterizing Playful Requests to Conversational Agents

Conversational Agents (CAs) such as Apple’s Siri and Amazon’s Alexa are well-suited for task-oriented interactions (“Call Jason”), but other interaction types are often beyond their capabilities. One notable example is playful requests: for example, people ask their CAs personal questions (“What’s your favorite color? ”) or joke with them, sometimes at their expense (“Find Nemo”). Failing to recognize playfulness causes user dissatisfaction and abandonment, destroying the precious rapport with the CA. Today, playful CA behavior is achieved through manually curated replies to hard-coded questions. We take a step towards understanding and scaling playfulness by characterizing playful opportunities. To map the problem’s landscape, we draw inspiration from humor theories and analyze real user data. We present a taxonomy of playful requests and explore its prevalence in real Alexa traffic. We hope to inspire new avenues towards more human-like CAs.
Photography Community Codesign (a.k.a. Codesign in a Global Pandemic)

Codesign brings together users and creators of a product to collaboratively determine how that product will manifest. In this case study of codesign, the Adobe Lightroom team collaborated with 12 photographers in a 4-day workshop to envision an in-app community space for photographic expression and learning. We discuss findings from the codesign workshop, including core values for the envisioned photography community: authenticity, connection and growth, and how we translated these themes to the design of the product. This case study demonstrates a successful codesign effort within the context of user-centered design at a large company.
A Network-Based, Multidisciplinary Approach to Intention Inference

The ability to successfully infer private behavioral intentions using publicly-available digital records has a far-reaching impact. Unlike other private attributes such as demographic attributes, an intention oftentimes leads to a near-future behavior. Prior knowledge about such future behaviors can be seen as “actionable intelligence” and constitutes a significantly bigger risk to users’ privacy than knowledge of non-behavioral attributes. In this paper, we present a novel, multidisciplinary methodology for behavioral-intention inference. Using Bayesian-Networks, we model a behavioral intention using a set of causes that influence the intention’s formation, a set of effects that are caused by the intention, and various dependency relations within and between those sets. Unlike the methodologies used in prior attribute-inference works, which are oftentimes tailored to a single target attribute, our methods can be applied to different types of intentions from a diverse set of domains as we demonstrate by applying our model to multiple real-world intention-inference tasks.
‘Are They Doing Better In The Clinic Or At Home?’: Understanding Clinicians’ Needs When Visualizing Wearable Sensor Data Used In Remote Gait Assessments For People With Multiple Sclerosis

Walking impairment is a debilitating symptom of Multiple Sclerosis (MS), a disease affecting 2.8 million people worldwide. While clinicians’ in-person observational gait assessments are important, research suggests that data from wearable sensors can indicate early onset of gait impairment, track patients’ responses to treatment, and support remote and longitudinal assessment. We present an inquiry into supporting the transition from research to clinical practice. Co-design by HCI, biomedical, neurology and rehabilitation researchers resulted in a data-rich interface prototype for augmented gait analysis based on visualized sensor data. We used this as a prompt in interviews with ten experienced clinicians from a range of MS rehabilitation roles. We find that clinicians value quantitative sensor data within a whole patient narrative, to help track specific rehabilitation goals, but identify a tension between grasping critical information quickly and more detailed understanding. Based on the findings we make design recommendations for data-rich remote rehabilitation interfaces.
Dynamic Robotic Fibers: Liquid Crystal Elastomers for Programmable and Reversible Shape-Changing Behaviors

This work aims to provide early-stage insights into an electro-thermally actuated liquid crystal elastomer (LCE) fiber for novel shape-changing behaviors that are both programmable and reversible. We build a control system and experimentally investigate the electro-thermal characteristics and actuation, identifying four categories of fiber behavior: Oscillating Tip, Oscillating Fiber, Tilting and Bending. These key parameters illustrate the broad application potential of the proposed approach within a functional, communication and expressive context. Our contributions are threefold a. ) the control of an electro-thermal responsive LCE fiber, b. ) directions for soft robotic device integration c. ) early-stage insights into fiber shape-changing behaviors towards application.
Stability of discrete-time switched linear systems with ω-regular switching sequences

In this paper, we develop tools to analyze stability properties of discrete-time switched linear systems driven by switching signals belonging to a given ω- regular language. More precisely, we assume switching signals to be generated by a Büchi automaton where the alphabet corresponds to the modes of the switched system. We define notions of attractivity and uniform stability for this type of systems and also of uniform exponential stability when the considered Büchi automaton is deterministic. We then provide sufficient conditions to check these properties using Lyapunov and automata theoretic techniques. For a subclass of such systems with invertible matrices, we show that these conditions are also necessary. We finally show an example of application in the context of synchronization of oscillators over a communication network.
Let's Talk @Clubhouse: Exploring Voice-Centered Social Media Platform and its Opportunities, Challenges, and Design Guidelines

Due to the pandemic, social media has become an essential route to satisfy socializing needs. Expanding from dominant services like Facebook and Instagram, a new wave caused a stir—Clubhouse as a voice-centered social media platform. Despite its worldwide popularity after its launch in 2020, general properties of Clubhouse have not been actively discussed. Accordingly, this study explores Clubhouse's opportunities and challenges as a voice-centered social media through its user experiences. We conducted interviews with regular Clubhouse users (N=26) to gain insight into their motivation, social networking, and conversations. Findings highlight that voice is effective for establishing social relationships via interactivity and intimacy, mutual respect, and convenience from ephemerality. Conversely, users reported patterns of the privacy paradox and the oligopoly of communication. Design guidelines for future voice-centric social media are proposed. Our initial study of Clubhouse will encourage more dialogues on voice-centered social media and its potential as a major platform.
ASTEROIDS: Exploring Swarms of Mini-Telepresence Robots for Physical Skill Demonstration

Online synchronous tutoring allows for immediate engagement between instructors and audiences over distance. However, tutoring physical skills remains challenging because current telepresence approaches may not allow for adequate spatial awareness, viewpoint control of the demonstration activities scattered across an entire work area, and the instructor’s sufficient awareness of the audience. We present Asteroids, a novel approach for tangible robotic telepresence, to enable workbench-scale physical embodiments of remote people and tangible interactions by the instructor. With Asteroids, the audience can actively control a swarm of mini-telepresence robots, change camera positions, and switch to other robots’ viewpoints. Demonstrators can perceive the audiences’ physical presence while using tangible manipulations to control the audience’s viewpoints and presentation flow. We conducted an exploratory evaluation for Asteroids with 12 remote participants in a model-making tutorial scenario with an architectural expert demonstrator. Results suggest our unique features benefitted participants’ engagement, sense of presence, and understanding.
Am I a Resource-Poor Language? Data Sets, Embeddings, Models and Analysis for four different NLP Tasks in Telugu Language

Due to the lack of a large annotated corpus, many resource-poor Indian languages struggle to reap the benefits of recent deep feature representations in Natural Language Processing (NLP). Moreover, adopting existing language models trained on large English corpora for Indian languages is often limited by data availability, rich morphological variation, syntax, and semantic differences. In this paper, we explore the traditional to recent efficient representations to overcome the challenges of a low resource language, Telugu. In particular, our main objective is to mitigate the low-resource problem for Telugu. Overall, we present several contributions to a resource-poor language viz. Telugu. (i) a large annotated data (35,142 sentences in each task) for multiple NLP tasks such as sentiment analysis, emotion identification, hate-speech detection, and sarcasm detection, (ii) we create different lexicons for sentiment, emotion, and hate-speech for improving the efficiency of the models, (iii) pretrained word and sentence embeddings, and (iv) different pretrained language models for Telugu such as ELMo-Te, BERT-Te, RoBERTa-Te, ALBERT-Te, and DistilBERT-Te on a large Telugu corpus consisting of 8,015,588 sentences (1,637,408 sentences from Telugu Wikipedia and 6,378,180 sentences crawled from different Telugu websites). Further, we show that these representations significantly improve the performance of four NLP tasks and present the benchmark results for Telugu. We argue that our pretrained embeddings are competitive or better than the existing multilingual pretrained models: mBERT, XLM-R, and IndicBERT. Lastly, the fine-tuning of pretrained models show higher performance than linear probing results on four NLP tasks with the following F1-scores: Sentiment (68.72), Emotion (58.04), Hate-Speech (64.27), and Sarcasm (77.93). We also experiment on publicly available Telugu datasets (Named Entity Recognition, Article Genre Classification, and Sentiment Analysis) and find that our Telugu pretrained language models (BERT-Te and RoBERTa-Te) outperform the state-of-the-art system except for the sentiment task. We open-source our corpus, four different datasets, lexicons, embeddings, and code https://github.com/Cha14ran/DREAM-T. The pretrained Transformer models for Telugu are available at https://huggingface.co/ltrctelugu.
Authoring Virtual Peer Interactions for Lecture Videos

Lecture videos have become a prevalent learning resource, due to the rising popularity of educational video platforms and the necessity for remote learning during the COVID-19 pandemic. However, lecture videos limit feasible pedagogical approaches, since there are no interactions that students would engage with and learn from in synchronous classes. In this study, we proposed virtual peers - virtual TAs and students that interact with each other as though they are watching the video for the first time together - to help real students learn from observing their interactions. We conducted two rounds of interviews and participatory designs with instructors and TAs to design an authoring tool that would help them create engaging virtual characters and author five types of pedagogically valuable interactions. The design received favorable preliminary feedback from instructors. They expect the tool to add valuable components that are missing from lecture videos with minimal additional effort on their parts.