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Shared-gaze visualizations (SGV) allow collocated collaborators to understand each other's attention and intentions while working jointly in an augmented reality setting. However, prior work has overlooked user control and privacy over how gaze information can be shared between collaborators. In this work, we examine two methods for visualizing shared-gaze between collaborators: gaze-hover and gaze-trigger. We compare the methods with existing solutions through a paired-user evaluation study in which participants participate in a virtual assembly task. Finally, we contribute an understanding of user perceptions, preferences, and design implications of shared-gaze visualizations in augmented reality. 

Motivational agents are virtual agents that seek to motivate users by providing feedback and guidance. Prior work has shown how certain factors of an agent, such as the type of feedback given or the agent’s appearance, can influence user motivation when completing tasks. However, it is not known how nonverbal mirroring affects an agent’s ability to motivate users. Specifically, would an agent that mirrors be more motivating than an agent that does not? Would an agent trained on real human behaviors be better? We conducted a within-subjects study asking 30 participants to play a “find-the-hidden-object” game while interacting with a motivational agent that would provide hints and feedback on the user’s performance. We created three agents: a Control agent that did not respond to the user’s movements, a simple Mimic agent that mirrored the user’s movements on a delay, and a Complex agent that used a machine-learned behavior model. We asked participants to complete a questionnaire asking them to rate their levels of motivation and perceptions of the agent and its feedback. Our results showed that the Mimic agent was more motivating than the Control agent and more helpful than the Complex agent. We also found that when participants became aware of the mimicking behavior, it can feel weird or creepy; therefore, it is important to consider the detection of mimicry when designing virtual agents. 

Augmented reality (AR) headsets are being utilized in different task-based domains (e.g., healthcare, education) for both adults and children. However, prior work has mainly examined the applicability of AR headsets instead of how to design the visual information being displayed. It is essential to study how visual information should be presented in AR headsets to maximize task performance for both adults and children. Therefore, we conducted two studies (adults vs. children) analyzing distinct design combinations of critical and secondary textual information during a procedural assembly task. We found that while the design of information did not affect adults' task performance, the location of information had a direct effect on children's task performance. Our work contributes new understanding on how to design textual information in AR headsets to aid in adults’ and children's task performance. In addition, we identify specific differences on how to design textual information between adults and children. 

Nanopore technology enables portable, real-time sequencing of microbial populations from clinical and ecological samples. An emerging healthcare application for Nanopore includes point-of-care, timely identification of antibiotic resistance genes (ARGs) to help developing targeted treatments of bacterial infections, and monitoring resistant outbreaks in the environment. While several computational tools exist for classifying ARGs from sequencing data, to date (2022) none have been developed for mobile devices. We present here KARGAMobile, a mobile app for portable, real-time, easily interpretable analysis of ARGs from Nanopore sequencing. KARGAMobile is the porting of an existing ARG identification tool named KARGA; it retains the same algorithmic structure, but it is optimized for mobile devices. Specifically, KARGAMobile employs a compressed ARG reference database and different internal data structures to save RAM usage. The KARGAMobile app features a friendly graphical user interface that guides through file browsing, loading, parameter setup, and process execution. More importantly, the output files are post-processed to create visual, printable and shareable reports, aiding users to interpret the ARG findings. The difference in classification performance between KARGAMobile and KARGA is minimal (96.2% vs . 96.9% f-measure on semi-synthetic datasets of 1 million reads with known resistance ground truth). Using real Nanopore experiments, KARGAMobile processes on average 1 GB data every 23–48 min (targeted sequencing - metagenomics), with peak RAM usage below 500MB, independently from input file sizes, and an average temperature of 49°C after 1 h of continuous data processing. KARGAMobile is written in Java and is available at https://github.com/Ruiz-HCI-Lab/KargaMobile under the MIT license. 

Abstract Biological and biomedical research is increasingly conducted in large, interdisciplinary collaborations to address problems with significant societal impact, such as reducing antibiotic resistance, identifying disease sub-types, and identifying genes that control for drought tolerance in plants. Many of these projects are data driven and involve the collection and analysis of biological data at a large-scale. As a result, life-science projects, which are frequently diverse, large and geographically dispersed, have created unique challenges for collaboration and training. We examine the communication and collaboration challenges in multidisciplinary research through an interview study with 20 life-science researchers. Our results show that both the inclusion of multiple disciplines and differences in work culture influence collaboration in life science. Using these results, we discuss opportunities and implications for designing solutions to better support collaborative tasks and workflows of life scientists. In particular, we show that life science research is increasingly conducted in large, multi-institutional collaborations, and these large groups rely on “mutual respect” and collaboration. However, we found that the interdisciplinary nature of these projects cause technical language barriers and differences in methodology affect trust. We use these findings to guide our recommendations for technology to support life science. We also present recommendations for life science research training programs and note the necessity for incorporating training in project management, multiple language, and discipline culture.