Reviewerly/exHarmony huggingface dataset:
Supporting Piggybacked Co-Located Leisure Activities via Augmented Reality

Technology, especially the smartphone, is villainized for taking meaning and time away from in-person interactions and secluding people into “digital bubbles”. We believe this is not an intrinsic property of digital gadgets, but evidence of a lack of imagination in technology design. Leveraging augmented reality (AR) toward this end allows us to create experiences for multiple people, their pets, and their environments. In this work, we explore the design of AR technology that “piggybacks” on everyday leisure to foster co-located interactions among close ties (with other people and pets). We designed, developed, and deployed three such AR applications, and evaluated them through a 41-participant and 19-pet user study. We gained key insights about the ability of AR to spur and enrich interaction in new channels, the importance of customization, and the challenges of designing for the physical aspects of AR devices (e.g., holding smartphones). These insights guide design implications for the novel research space of co-located AR.
Unsettling Care Infrastructures: From the Individual to the Structural in a Digital Maternal and Child Health Intervention

Information services for maternal and child health are increasingly being implemented at scale and integrated into public health infrastructures in Global South countries. These services often disseminate tailored health information and provide channels for families to ask questions to health workers. With increasing uptake, these services are intervening into a highly gendered space and shaping care work and information-seeking in new ways. We present a study of a patient education program and associated WhatsApp-based information service deployed across multiple states in India, drawing on observations, interviews, and analysis of chat records. Building on notions of "unsettling care" [63], we examine what it means to deploy such an intervention in inequitable, fragmented health systems. We find that even as the intervention focuses on individual behavior change, it also runs up against structural issues, such as the overburden of health workers, an illegible health system, and gendered power dynamics that extend beyond the realm of the home. We use our findings to unsettle notions of how the intervention provides care, and to reframe how we might think about the design and implementation of health information services to also engage with structural issues.
How Transgender People and Communities Were Involved in Trans Technology Design Processes

Trans technology – technology created to help address challenges that trans people face – is an important area for innovation that can help improve marginalized people’s lives. We conducted 104 interviews with 115 creators of trans technology to understand how they involved trans people and communities in design processes. We describe projects that used human-centered design processes, as well as design processes that involved trans people in smaller ways, including gathering feedback from users, conducting user testing, or the creators being trans themselves. We show how involving trans people and communities in design is vital for trans technologies to realize their potential for addressing trans needs. Yet we highlight a frequent gap between trans technology design and deployment, and discuss ways to bridge this gap. We argue for the importance of involving community in trans technology design to ensure that trans technology achieves its promise of helping address trans needs and challenges.
Dirty Data in the Newsroom: Comparing Data Preparation in Journalism and Data Science

The work involved in gathering, wrangling, cleaning, and otherwise preparing data for analysis is often the most time consuming and tedious aspect of data work. Although many studies describe data preparation within the context of data science workflows, there has been little research on data preparation in data journalism. We address this gap with a hybrid form of thematic analysis that combines deductive codes derived from existing accounts of data science workflows and inductive codes arising from an interview study with 36 professional data journalists. We extend a previous model of data science work to incorporate detailed activities of data preparation. We synthesize 60 dirty data issues from 16 taxonomies on dirty data and our interview data, and we provide a novel taxonomy to characterize these dirty data issues as discrepancies between mental models. We also identify four challenges faced by journalists: diachronic, regional, fragmented, and disparate data sources.
Toward Inclusive Mindsets: Design Opportunities to Represent Neurodivergent Work Experiences to Neurotypical Co-Workers in Virtual Reality

Inclusive workplaces require mutual efforts between neurotypical (NT) and neurodivergent (ND) employees to understand one another's viewpoints and experiences. Currently, the majority of inclusivity training places the burden of change on NDs to conform to NT social-behavioral standards. Our research examines moving toward a more equal effort distribution by exploring virtual reality (VR) design opportunities to build NTs' understanding of ND workplace experiences. Using participatory design, including generative toolkits and design meetings, we surfaced two main themes that could bridge gaps in understanding: (1) NTs' recognition of NDs' strengths and efforts at work, and (2) NTs' understanding of NDs' differences. We present a strengths-based assessment of ND traits in the workplace, focusing on how workplaces can support NDs' success. Finally, we propose VR simulation designs that communicate these themes to represent ND experiences, emphasizing their strengths and viewpoints so that NT co-workers can better empathize and accommodate them.
Understanding How Low Vision People Read Using Eye Tracking

While being able to read with screen magnifiers, low vision people have slow and unpleasant reading experiences. Eye tracking has the potential to improve their experience by recognizing fine-grained gaze behaviors and providing more targeted enhancements. To inspire gaze-based low vision technology, we investigate the suitable method to collect low vision users' gaze data via commercial eye trackers and thoroughly explore their challenges in reading based on their gaze behaviors. With an improved calibration interface, we collected the gaze data of 20 low vision participants and 20 sighted controls who performed reading tasks on a computer screen; low vision participants were also asked to read with different screen magnifiers. We found that, with an accessible calibration interface and data collection method, commercial eye trackers can collect gaze data of comparable quality from low vision and sighted people. Our study identified low vision people's unique gaze patterns during reading, building upon which, we propose design implications for gaze-based low vision technology.
Private and Mutual Authentication Protocols for Internet of Things

The Internet of Things (IoT) consists of a range of devices that communicate with other devices that are surrounding them. Secure communications between IoT devices have been an essential requirement. However, most existing authentication protocols fail to provide privacy protection for devices such that sensitive information including device owner’s identity and device type can be leaked. In this paper, based on attribute-based encryption (ABE), we propose two private and mutual authentication protocols to protect the privacy of IoT devices. The first protocol is a three-message key exchange protocol, following the public-key encryption mode of the Internet Key Exchange (IKE). The second protocol is a one-round key exchange protocol, thereby providing simplicity and efficiency of messages communicated between the participants. Both of our protocols can be proved secure in the defined model. Finally, we implement the two protocols in an open source framework.
Showing Flow: Comparing Usability of Chord and Sankey Diagrams

Chord and Sankey diagrams are two common techniques for visualizing flows. Chord diagrams use a radial layout with a single circular axis, and Sankey diagrams use a left-to-right layout with two vertical axes. Previous work suggests both strengths and weaknesses of the radial approach, but little is known about the usability and interpretability of these two layout styles for showing flow. We carried out a study where participants answered questions using equivalent Chord and Sankey diagrams. We measured completion time, errors, perceived effort, and preference. Our results show that participants took substantially longer to answer questions with Chord diagrams and made more errors; participants also rated Chord as requiring more effort, and strongly preferred Sankey diagrams. Our study identifies and explains limitations of the popular Chord layout, provides new understanding about radial vs. linear layouts that can help guide visualization designers, and identifies possible design improvements for both visualization types.
GazeReader: Detecting Unknown Word Using Webcam for English as a Second Language (ESL) Learners

Automatic unknown word detection techniques can enable new applications for assisting English as a Second Language (ESL) learners, thus improving their reading experiences. However, most modern unknown word detection methods require dedicated eye-tracking devices with high precision that are not easily accessible to end-users. In this work, we propose GazeReader, an unknown word detection method only using a webcam. GazeReader tracks the learner's gaze and then applies a transformer-based machine learning model that encodes the text information to locate the unknown word. We applied knowledge enhancement including term frequency, part of speech, and named entity recognition to improve the performance. The user study indicates that the accuracy and F1-score of our method were 98.09% and 75.73%, respectively. Lastly, we explored the design scope for ESL reading and discussed the findings.
Marking Material Interactions with Computer Vision

The electronics-centered approach to physical computing presents challenges when designers build tangible interactive systems due to its inherent emphasis on circuitry and electronic components. To explore an alternative physical computing approach we have developed a computer vision (CV) based system that uses a webcam, computer, and printed fiducial markers to create functional tangible interfaces. Through a series of design studios, we probed how designers build tangible interfaces with this CV-driven approach. In this paper, we apply the annotated portfolio method to reflect on the fifteen outcomes from these studios. We observed that CV markers offer versatile materiality for tangible interactions, afford the use of democratic materials for interface construction, and engage designers in embodied debugging with their own vision as a proxy for CV. By sharing our insights, we inform other designers and educators who seek alternative ways to facilitate physical computing and tangible interaction design.
MechCircuit: Augmenting Laser-Cut Objects with Integrated Electronics, Mechanical Structures and Magnets

Laser cutting revolutionizes the creation of personal-fabricated prototypes. These objects can have transformable properties by adopting different materials and be interactive by integrating electronic circuits. However, circuits in laser-cut objects always have limited movements, which refrains laser cutting from achieving interactive prototypes with more complex movable functions like mechanisms. We propose MechCircuit, a design and fabrication pipeline for making mechanical-electronical objects with laser cutting. We leverage the neodymium magnet's natures of magnetism and conductivity to integrate electronics and mechanical structure joints into prototypes. We conduct the evaluation to explore technological parameters and assess the practical feasibility of the fabrication pipeline. And we organized a user-observing workshop for non-expert users. Through the outcoming prototypes, the result demonstrates the feasibility of MechCircuit as a useful and inspiring prototyping method.
Defining Patterns for a Conversational Web

Conversational agents are emerging as channels for a natural and accessible interaction with digital services. Their benefits span across a wide range of usage scenarios and address visual impairments and any situational impairments that may take advantage of voice-based interactions. A few works highlighted the potential and the feasibility of adopting conversational agents for making the Web truly accessible for everyone. Yet, there is still a lack of concrete guidance in designing conversational experiences for browsing the Web. This paper illustrates a human-centered process that involved 26 blind and visually impaired people to investigate their difficulties when using assistive technology for accessing the Web, and their attitudes and preferences on adopting conversational agents. In response to the identified challenges, the paper introduces patterns for conversational Web browsing. It also discusses design implications that can promote Conversational AI as a technology to enhance Web accessibility.
Cyclists’ Use of Technology While on Their Bike

Cycling continues to grow in popularity, both as a means to commute and for exercise. While there is a plethora of research studying technology use in vehicular travel, cycling remains a relatively understudied area—especially within HCI. We conducted an ethnography, adopting an ethnomethodological lens, to study cyclists as they use their bicycles for routine purposes. Through the use of a handlebar-mounted 360-degree action video camera, we conducted our study longitudinally with participants over a number of weeks. Our analysis explicates our participants accountable use of different electronic technologies while on the go and in this paper we present four fragments of their use of different technologies as exemplars from our corpus. Our paper offers insights into the use of technology on bicycles, including how cyclists select moments of opportunity to use technology for different purposes. We conclude by offering design implications for the design of interactive technologies for cyclists.
SpeakFaster Observer: Long-Term Instrumentation of Eye-Gaze Typing for Measuring AAC Communication

Accelerating communication for users with severe motor and speech impairments, in particular for eye-gaze-based augmentative and alternative communication (AAC) device users, is a longstanding area of research. However, observation of such users' communication over extended durations has been limited. This case study presents the real-world experience of developing and field-testing a tool for observing and curating the gaze typing-based communication of an eye-gaze AAC user with amyotrophic lateral sclerosis (ALS). With the intent to observe and develop technology to accelerate eye-gaze typed communication, we designed a tool and a protocol called the SpeakFaster Observer to measure everyday conversational text entry by the gaze-typing user, as well as several consenting conversation partners of the AAC user. We detail the design of the Observer software and data curation protocol, along with considerations for privacy protection. The deployment of the data protocol from November 2021 to April 2022 yielded a rich dataset of gaze-based AAC text entry from everyday life, consisting of 130+ hours of gaze keystrokes and 5,000+ curated speech utterances from the AAC user and the conversation partners. We present the key statistics of the data, including the speed (8.1 ± 3.9 words per minute) and keystroke saving rate (-0.14 ± 0.83) of gaze typing, patterns of utterance repetition and reuse, and the temporal dynamics of conversation turn-taking in gaze-based communication. We share our findings and also open source our data collection tools to further research in this domain.
Playing with Feedback: Unpredictability, Immediacy, and Entangled Agency in the No-input Mixing Desk

Feedback is a process that is common in both acoustic and electronic musical instruments, but rare in digital musical tools or creative digital tools more generally. This paper examines the musical use of the 'no-input mixing desk'—or 'feedback mixer', a sound mixing desk fed back on itself—to explore how and why feedback is appealing for musicians. Twenty interviews were conducted with musicians who have used no-input mixing desk in their practice. Thematic analysis is used to explore the interview data. Results highlight the enjoyment and creative fulfilment of working with systems that can't be fully predicted or understood, a sense of gestural immediacy, sensitivity and tactility often perceived as lacking in digital instruments, and an affinity with acoustic instruments in terms of the scope for surprise and exploration.
LYDSPOR: AN URBAN SOUND EXPERIENCE WEAVING TOGETHER PAST AND PRESENT THROUGH VIBRATING BODIES

In this paper we present LYDSPOR; a site-specific sound experience, created in Elsinore, Denmark, consisting of two physical installations and an app-based soundwalk, which together allow people to feel and sense in their bodies certain narrative fragments of the history of the city. LYDSPOR is the result of a 1,5 year-long research project which had the aim of adopting soma design methods by drawing on affective interaction design and on an understanding of bodies as always multiple, relational, and never only human. Through an analysis of the design process and its outcome, the paper contributes an in-depth understanding of how to combine somatic and affective design approaches when creating site-specific sonic augmentations for historical dissemination in public space.
Not All Spacings are Created Equal: The Effect of Text Spacings in On-the-go Reading Using Optical See-Through Head-Mounted Displays

The emergent Optical Head-Mounted Display (OHMD) platform has made mobile reading possible by superimposing digital text onto users’ view of the environment. However, mobile reading through OHMD needs to be effectively balanced with the user’s environmental awareness. Hence, a series of studies were conducted to explore how text spacing strategies facilitate such balance. Through these studies, it was found that increasing spacing within the text can significantly enhance mobile reading on OHMDs in both simple and complex navigation scenarios and that such benefits mainly come from increasing the inter-line spacing, but not inter-word spacing. Compared with existing positioning strategies, increasing inter-line spacing improves mobile OHMD information reading in terms of reading speed (11.9% faster), walking speed (3.7% faster), and switching between reading and navigation (106.8% more accurate and 33% faster).
Rethinking Creative Labor: A Sociotechnical Examination of Creativity &amp; Creative Work on TikTok

Social media platform success relies on users to consume, create, and share creative content. While some creatives aspire to become influencers, this is not the goal of all creatives, particularly those with smaller audiences. Through an interview study of 15 creatives on TikTok, we explore the often overlapping intentions for creating and sharing videos, as well as the challenges to maintaining these creative intentions and routines as they are shaped by platform logic. We find platforms introduce impediments which disrupt people's creative routines and alienate people from their overlapping creative intentions; introducing challenges which alienate people from their sense of self, and their audiences. We construct a broader definition of creative labor - the work of professionalizing and monetizing a creative product shared on social media - reflecting on how the routine enactment of creative labor is impacted by infrastructural elements of technology.
Target Detection for Motion Images Using the Improved YOLO Algorithm

The images of motion states are time-varying, and when actually detecting their internal motion targets, the formed detection frames overlap, resulting in small confidence values for the detection frames and low accuracy of the detection results. To address this problem, the authors propose a target detection for motion image using the improved YOLO algorithm. First, the YOLO algorithm is improved using deformable convolution; the edge weights of the front and back views within the image are collated, and the motion image is segmented using the improved YOLO algorithm. Second, the structure formed by the initial convolution is used as the initial detection frame structure, the parallel cross-ratio value is set, the overlap generated by the detection frame is controlled, the parameters of the detection frame compression processing are output, the threshold trigger value relationship is constructed, and finally, the detection of the motion image target is realized. The results show that the target false detection rate of the proposed method is only about 15%. The detection a priori frame height value is 80 pixels, and the average detection time consumed is 6.8ms, which proves that the proposed algorithm can be widely used in motion image target detection to improve the detection level.
Low-Light Image Enhancement Method Using a Modified Gamma Transform and Gamma Filtering-Based Histogram Specification for Convex Combination Coefficients

Recently, a lot of low-light image enhancement methods have been proposed. However, these methods have some problems such as causing fine details lost in bright regions and/or unnatural color tones. In this paper, we propose a new low-light image enhancement method to cope with these problems. In the proposed method, a pixel is represented by a convex combination of white, black, and pure color. Then, an equi-hue plane in RGB color space is represented as a triangle whose vertices correspond to white, black, and pure color. The visibility of low-light image is improved by applying a modified gamma transform to the combination coefficients on an equi-hue plane in RGB color space. The contrast of the image is enhanced by the histogram specification method using the histogram smoothed by a filter with a kernel determined based on a gamma distribution. In the experiments, the effectiveness of the proposed method is verified by the comparison with the state-of-the-art low-light image enhancement methods.
Studying the Co-Evolution of Source Code and Acceptance Tests

Testing is a vital part of achieving good-quality software. Deploying untested code can cause system crashes and unexpected behavior. To reduce these problems, testing should evolve with coding. In addition, test suites should not remain static throughout the software versions. Since whenever software gets updated, new functionalities are added, or existing functionalities are changed, test suites should be updated along with the software. Software repositories contain valuable information about the software systems. Access to older versions and differentiating adjacent versions’ source code and acceptance test changes can provide information about the evolution process of the software. This research proposes a method and implementation to analyze 21 open-source real-world projects hosted on GitHub regarding the co-evolution of both software and its acceptance test suites. Related projects are retrieved from repositories, their versions are analyzed, graphs are created, and analysis related to the co-evolution process is performed. Observations show that the source code is getting updated more frequently than the acceptance tests. They indicate a pattern that source code and acceptance tests do not evolve together. Moreover, the analysis showed that a few acceptance tests test most of the functionalities that take a significant line of code.