huashen218/convxai-cia-dataset huggingface dataset:
Title: Let’s Frets! Assisting Guitar Students During Practice via Capacitive Sensing
Authors: family: Marky
given: Karola; family: Weiß
given: Andreas; family: Matviienko
given: Andrii; family: Brandherm
given: Florian; family: Wolf
given: Sebastian; family: Schmitz
given: Martin; family: Krell
given: Florian; family: Müller
given: Florian; family: Mühlhäuser
given: Max; family: Kosch
given: Thomas
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: capacitive sensing, musical instruments, support setup

Abstract: Learning a musical instrument requires regular exercise. However, students are often on their own during their practice sessions due to the limited time with their teachers, which increases the likelihood of mislearning playing techniques. To address this issue, we present Let’s Frets - a modular guitar learning system that provides visual indicators and capturing of finger positions on a 3D-printed capacitive guitar fretboard. We based the design of Let’s Frets on requirements collected through in-depth interviews with professional guitarists and teachers. In a user study (N=24), we evaluated the feedback modules of Let’s Frets against fretboard charts. Our results show that visual indicators require the least time to realize new finger positions while a combination of visual indicators and position capturing yielded the highest playing accuracy. We conclude how Let’s Frets enables independent practice sessions that can be translated to other musical instruments.
Title: “I Don’t Want People to Look At Me Differently”: Designing User-Defined Above-the-Neck Gestures for People with Upper Body Motor Impairments
Authors: family: Zhao
given: Xuan; family: Fan
given: Mingming; family: Han
given: Teng
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: user-defined gestures, gesture elicitation, above-the-neck gestures, people with motor impairments

Abstract: Recent research proposed eyelid gestures for people with upper-body motor impairments (UMI) to interact with smartphones without finger touch. However, such eyelid gestures were designed by researchers. It remains unknown what eyelid gestures people with UMI would want and be able to perform. Moreover, other above-the-neck body parts (e.g., mouth, head) could be used to form more gestures. We conducted a user study in which 17 people with UMI designed above-the-neck gestures for 26 common commands on smartphones. We collected a total of 442 user-defined gestures involving the eyes, the mouth, and the head. Participants were more likely to make gestures with their eyes and preferred gestures that were simple, easy-to-remember, and less likely to draw attention from others. We further conducted a survey (N=24) to validate the usability and acceptance of these user-defined gestures. Results show that user-defined gestures were acceptable to both people with and without motor impairments.
Title: Understanding Gesture Input Articulation with Upper-Body Wearables for Users with Upper-Body Motor Impairments
Authors: family: Vatavu
given: Radu-Daniel; family: Ungurean
given: Ovidiu-Ciprian
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: smartglasses, Motor impairments, accessible input, gesture input, wearables., smart rings, smartwatches

Abstract: We examine touchscreen stroke-gestures and mid-air motion-gestures articulated by users with upper-body motor impairments with devices worn on the wrist, finger, and head. We analyze users’ gesture input performance in terms of production time, articulation consistency, and kinematic measures, and contrast the performance of users with upper-body motor impairments with that of a control group of users without impairments. Our results, from two datasets of 7,290 stroke-gestures and 3,809 motion-gestures collected from 28 participants, reveal that users with upper-body motor impairments take twice as much time to produce stroke-gestures on wearable touchscreens compared to users without impairments, but articulate motion-gestures equally fast and with similar acceleration. We interpret our findings in the context of ability-based design and propose ten implications for accessible gesture input with upper-body wearables for users with upper-body motor impairments.
Title: Identifying factors that inhibit self-care behavior among individuals with severe spinal cord injury
Authors: family: Motahar
given: Tamanna; family: Ghosh
given: Isha; family: Wiese
given: Jason
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: Self-Care, Pressure Relief, Assistive Technologies, Spinal Cord Injury

Abstract: Individuals with spinal cord injury (SCI) need to perform numerous self-care behaviors, some very frequently. Pressure reliefs (PRs), which prevent life-threatening pressure ulcers (PUs), are one such behavior. We conducted a qualitative study with seven individuals with severe SCI—who depend on power wheelchairs—to explore their current PR behavior and the potential for technology to facilitate PR adherence. While our participants were highly motivated to perform PRs because of prior PUs, we found that their understanding of how and when to perform a PR differed by individual, and that while they sometimes forgot to perform PR, in other cases contextual factors made it difficult to perform a PR. Our findings provide insight into the complexity of this design space, identify design considerations for designing technology to facilitate these behaviors, and demonstrate the opportunity for personal informatics to be more inclusive by supporting the needs of this population.
Title: Understanding How People with Limited Mobility Use Multi-Modal Input
Authors: family: Wentzel
given: Johann; family: Junuzovic
given: Sasa; family: Devine
given: James; family: Porter
given: John; family: Mott
given: Martez
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems

Abstract: People with limited mobility often use multiple devices when interacting with computing systems, but little is known about the impact these multi-modal configurations have on daily computing use. A deeper understanding of the practices, preferences, obstacles, and workarounds associated with accessible multi-modal input can uncover opportunities to create more accessible computer applications and hardware. We explored how people with limited mobility use multi-modality through a three-part investigation grounded in the context of video games. First, we surveyed 43 people to learn about their preferred devices and configurations. Next, we conducted semi-structured interviews with 14 participants to understand their experiences and challenges with using, configuring, and discovering input setups. Lastly, we performed a systematic review of 74 YouTube videos to illustrate and categorize input setups and adaptations in-situ. We conclude with a discussion on how our findings can inform future accessibility research for current and emerging computing technologies.
Title: Stylette: Styling the Web with Natural Language
Authors: family: Kim
given: Tae Soo; family: Choi
given: DaEun; family: Choi
given: Yoonseo; family: Kim
given: Juho
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: End-User Programming, Natural Language Interface, Web Design, Machine Learning

Abstract: End-users can potentially style and customize websites by editing them through in-browser developer tools. Unfortunately, end-users lack the knowledge needed to translate high-level styling goals into low-level code edits. We present Stylette, a browser extension that enables users to change the style of websites by expressing goals in natural language. By interpreting the user’s goal with a large language model and extracting suggestions from our dataset of 1.7 million web components, Stylette generates a palette of CSS properties and values that the user can apply to reach their goal. A comparative study (N=40) showed that Stylette lowered the learning curve, helping participants perform styling changes 35% faster than those using developer tools. By presenting various alternatives for a single goal, the tool helped participants familiarize themselves with CSS through experimentation. Beyond CSS, our work can be expanded to help novices quickly grasp complex software or programming languages.
Title: VocabEncounter: NMT-powered Vocabulary Learning by Presenting Computer-Generated Usages of Foreign Words into Users’ Daily Lives
Authors: family: Arakawa
given: Riku; family: Yakura
given: Hiromu; family: Kobayashi
given: Sosuke
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: natural language processing, vocabulary learning, neural mechanical translation

Abstract: We demonstrate that recent natural language processing (NLP) techniques introduce a new paradigm of vocabulary learning that benefits from both micro and usage-based learning by generating and presenting the usages of foreign words based on the learner’s context. Then, without allocating dedicated time for studying, the user can become familiarized with how the words are used by seeing the example usages during daily activities, such as Web browsing. To achieve this, we introduce VocabEncounter, a vocabulary-learning system that suitably encapsulates the given words into materials the user is reading in near real time by leveraging recent NLP techniques. After confirming the system’s human-comparable quality of generating translated phrases by involving crowdworkers, we conducted a series of user studies, which demonstrated its effectiveness on learning vocabulary and its favorable experiences. Our work shows how NLP-based generation techniques can transform our daily activities into a field for vocabulary learning.
Title: Affinder: Expressing Concepts of Situations that Afford Activities using Context-Detectors
Authors: family: Louie
given: Ryan; family: Gergle
given: Darren; family: Zhang
given: Haoqi
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: block-based programming, context-aware programming, context-features, bridging challenges, design fixation, expressing concepts of situations

Abstract: Context-aware applications have the potential to act opportunistically to facilitate human experiences and activities, from reminding us of places to perform personal activities, to identifying coincidental moments to engage in digitally-mediated shared experiences. However, despite the availability of context-detectors and programming frameworks for defining how such applications should trigger, designers lack support for expressing their human concepts of a situation and the experiences and activities they afford (e.g., situations to toss a frisbee) when context-features are made available at the level of locations (e.g., parks). This paper introduces Affinder, a block-based programming environment that supports constructing concept expressions that effectively translate their conceptions of a situation into a machine representation using available context features. During pilot testing, we discovered three bridging challenges that arise when expressing situations that cannot be encoded directly by a single context-feature. To overcome these bridging challenges, Affinder provides designers (1) an unlimited vocabulary search for discovering features they may have forgotten; (2) prompts for reflecting and expanding their concepts of a situation and ideas for foraging for context-features; and (3) simulation and repair tools for identifying and resolving issues with the precision of concept expressions on real use-cases. In a comparison study, we found that Affinder’s core functions helped designers stretch their concepts of how to express a situation, find relevant context-features matching their concepts, and recognize when the concept expression operated differently than intended on real-world cases. These results show that Affinder and tools that support bridging can improve a designer’s ability to express their concepts of a human situation into detectable machine representations—thus pushing the boundaries of how computing systems support our activities in the world.
Title: Two Heads Are Better Than One: A Dimension Space for Unifying Human and Artificial Intelligence in Shared Control
Authors: family: Cimolino
given: Gabriele; family: Graham
given: T.C. Nicholas
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: Design Space Analysis, Shared Control, Human-Machine Cooperation

Abstract: Shared control is an emerging interaction paradigm in which a human and an AI partner collaboratively control a system. Shared control unifies human and artificial intelligence, making the human’s interactions with computers more accessible, safe, precise, effective, creative, and playful. This form of interaction has independently emerged in contexts as varied as mobility assistance, driving, surgery, and digital games. These domains each have their own problems, terminology, and design philosophies. Without a common language for describing interactions in shared control, it is difficult for designers working in one domain to share their knowledge with designers working in another. To address this problem, we present a dimension space for shared control, based on a survey of 55 shared control systems from six different problem domains. This design space analysis tool enables designers to classify existing systems, make comparisons between them, identify higher-level design patterns, and imagine solutions to novel problems.
Title: Supporting Serendipitous Discovery and Balanced Analysis of Online Product Reviews with Interaction-Driven Metrics and Bias-Mitigating Suggestions
Authors: family: Jasim
given: Mahmood; family: Collins
given: Christopher; family: Sarvghad
given: Ali; family: Mahyar
given: Narges
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: product review exploration, bias mitigation model, serendipity

Abstract: In this study, we investigate how supporting serendipitous discovery and analysis of online product reviews can encourage readers to explore reviews more comprehensively prior to making purchase decisions. We propose two interventions — Exploration Metrics that can help readers understand and track their exploration patterns through visual indicators and a Bias Mitigation Model that intends to maximize knowledge discovery by suggesting sentiment and semantically diverse reviews. We designed, developed, and evaluated a text analytics system called Serendyze, where we integrated these interventions. We asked 100 crowd workers to use Serendyze to make purchase decisions based on product reviews. Our evaluation suggests that exploration metrics enabled readers to efficiently cover more reviews in a balanced way, and suggestions from the bias mitigation model influenced readers to make confident data-driven decisions. We discuss the role of user agency and trust in text-level analysis systems and their applicability in domains beyond review exploration.
Title: Shared Interest: Measuring Human-AI Alignment to Identify Recurring Patterns in Model Behavior
Authors: family: Boggust
given: Angie; family: Hoover
given: Benjamin; family: Satyanarayan
given: Arvind; family: Strobelt
given: Hendrik
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: saliency methods, interpretability, machine learning, human-computer interaction

Abstract: Saliency methods — techniques to identify the importance of input features on a model’s output — are a common step in understanding neural network behavior. However, interpreting saliency requires tedious manual inspection to identify and aggregate patterns in model behavior, resulting in ad hoc or cherry-picked analysis. To address these concerns, we present Shared Interest: metrics for comparing model reasoning (via saliency) to human reasoning (via ground truth annotations). By providing quantitative descriptors, Shared Interest enables ranking, sorting, and aggregating inputs, thereby facilitating large-scale systematic analysis of model behavior. We use Shared Interest to identify eight recurring patterns in model behavior, such as cases where contextual features or a subset of ground truth features are most important to the model. Working with representative real-world users, we show how Shared Interest can be used to decide if a model is trustworthy, uncover issues missed in manual analyses, and enable interactive probing.
Title: Q-Chef: The impact of surprise-eliciting systems on food-related decision-making
Authors: family: Grace
given: Kazjon; family: Finch
given: Elanor; family: Gulbransen-Diaz
given: Natalia; family: Henderson
given: Hamish
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: mixed methods, curiosity, recommender systems, food, surprise

Abstract: Choosing what to eat requires navigating a large volume of information and various competing factors. While recommendation systems are an effective approach to assist users with this culinary decision-making, they typically prioritise similarity to a query or user profile to give relevant results. This can expose users to an increasingly narrow band of phenomena, which could compromise dietary diversity, a factor in dietary quality. We designed Q-Chef, which combines a recipe recommendation system with a personalised model of surprise, and conducted a study to identify if surprise-eliciting recipes affect food decisions. Our study utilises a rigorous thematic analysis with over 40 participants to explore how computational models of surprise influence recipe choice. We also explored how these factors differed when people were presented with “surprising-yet-tasty” recipes, as opposed to just “tasty” recipes, and identified that being presented with surprising choices is more likely to elicit situational interest and prompt reflection on choices. We conclude with a set of suggestions for the design of future surprise-eliciting recipe systems.
Title: Scaling Creative Inspiration with Fine-Grained Functional Aspects of Ideas
Authors: family: Hope
given: Tom; family: Tamari
given: Ronen; family: Hershcovich
given: Daniel; family: Kang
given: Hyeonsu B; family: Chan
given: Joel; family: Kittur
given: Aniket; family: Shahaf
given: Dafna
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems

Abstract: Large repositories of products, patents and scientific papers offer an opportunity for building systems that scour millions of ideas and help users discover inspirations. However, idea descriptions are typically in the form of unstructured text, lacking key structure that is required for supporting creative innovation interactions. Prior work has explored idea representations that were either limited in expressivity, required significant manual effort from users, or dependent on curated knowledge bases with poor coverage. We explore a novel representation that automatically breaks up products into fine-grained functional aspects capturing the purposes and mechanisms of ideas, and use it to support important creative innovation interactions: functional search for ideas, and exploration of the design space around a focal problem by viewing related problem perspectives pooled from across many products. In user studies, our approach boosts the quality of creative search and inspirations, substantially outperforming strong baselines by 50-60%.
Title: EmoGlass: an End-to-End AI-Enabled Wearable Platform for Enhancing Self-Awareness of Emotional Health
Authors: family: Yan
given: Zihan; family: Wu
given: Yufei; family: Zhang
given: Yang; family: Chen
given: Xiang 'Anthony'
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: Mobile health, Emotion sensing, Facial expression detection, Mental health, Wearable

Abstract: Often, emotional disorders are overlooked due to their lack of awareness, resulting in potential mental issues. Recent advances in sensing and inference technology provide a viable path to wearable facial-expression-based emotion recognition. However, most prior work has explored only laboratory settings and few platforms are geared towards end-users in everyday lives or provide personalized emotional suggestions to promote self-regulation. We present EmoGlass, an end-to-end wearable platform that consists of emotion detection glasses and an accompanying mobile application. Our single-camera-mounted glasses can detect seven facial expressions based on partial face images. We conducted a three-day out-of-lab study (N=15) to evaluate the performance of EmoGlass. We iterated on the design of the EmoGlass application for effective self-monitoring and awareness of users’ daily emotional states. We report quantitative and qualitative findings, based on which we discuss design recommendations for future work on sensing and enhancing awareness of emotional health.
Title: Algorithmic Management Reimagined For Workers and By Workers: Centering Worker Well-Being in Gig Work
Authors: family: Zhang
given: Angie; family: Boltz
given: Alexander; family: Wang
given: Chun Wei; family: Lee
given: Min Kyung
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: worker-centered work design, Algorithmic management, participatory design, worker well-being, gig work

Abstract: Prior research has studied the detrimental impact of algorithmic management on gig workers and strategies that workers devise in response. However, little work has investigated alternative platform designs to promote worker well-being, particularly from workers’ own perspectives. We use a participatory design approach wherein workers explore their algorithmic imaginaries to co-design interventions that center their lived experiences, preferences, and well-being in algorithmic management. Our interview and participatory design sessions highlight how various design dimensions of algorithmic management, including information asymmetries and unfair, manipulative incentives, hurt worker well-being. Workers generate designs to address these issues while considering competing interests of the platforms, customers, and themselves, such as information translucency, incentives co-configured by workers and platforms, worker-centered data-driven insights for well-being, and collective driver data sharing. Our work offers a case study that responds to a call for designing worker-centered digital work and contributes to emerging literature on algorithmic work.
Title: Tech Help Desk: Support for Local Entrepreneurs Addressing the Long Tail of Computing Challenges
Authors: family: Kotturi
given: Yasmine; family: Johnson
given: Herman T; family: Skirpan
given: Michael; family: Fox
given: Sarah E; family: Bigham
given: Jeffrey P; family: Pavel
given: Amy
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: entrepreneurship, technical assistance, small business, community engagement, action research

Abstract: Even entrepreneurs whose businesses are not technological (e.g., handmade goods) need to be able to use a wide range of computing technologies in order to achieve their business goals. In this paper, we follow a participatory action research approach and collaborate with various stakeholders at an entrepreneurial co-working space to design “Tech Help Desk”, an on-going technical service for entrepreneurs. Our model for technical assistance is strategic, in how it is designed to fit the context of local entrepreneurs, and responsive, in how it prioritizes emergent needs. From our engagements with 19 entrepreneurs and support personnel, we reflect on the challenges with existing technology support for non-technological entrepreneurs. Our work highlights the importance of ensuring technological support services can adapt based on entrepreneurs’ ever-evolving priorities, preferences and constraints. Furthermore, we find technological support services should maintain broad technical support for entrepreneurs’ long tail of computing challenges.