Computer Science

By optimizing aesthetics, graph diagrams can be generated that are easier to read and understand. However, the challenge lies in identifying suitable aesthetics. We present a novel approach based on repertory grids to explore the design space of aesthetics systematically. We applied our approach with three independent groups of participants to systematically identify graph aesthetics. In all three cases, we were able to reproduce the aesthetics with positively evaluated influence on readability without any prior knowledge. We also applied our approach to two- and three-dimensional domain-specific software visualizations to demonstrate its versatility. In this case, we were also able to acquire several aesthetics that are relevant for perceiving the visualization.

The ongoing COVID-19 pandemic has raised concerns for many regarding personal and public health implications, financial security and economic stability. Alongside many other unprecedented challenges, there are increasing concerns over social isolation and mental health. We introduce \textit{Expressive Interviewing}--an interview-style conversational system that draws on ideas from motivational interviewing and expressive writing. Expressive Interviewing seeks to encourage users to express their thoughts and feelings through writing by asking them questions about how COVID-19 has impacted their lives. We present relevant aspects of the system's design and implementation as well as quantitative and qualitative analyses of user interactions with the system. In addition, we conduct a comparative evaluation with a general purpose dialogue system for mental health that shows our system potential in helping users to cope with COVID-19 issues.

Extended Reality (XR) technology - such as virtual and augmented reality - is now widely used in Human Computer Interaction (HCI), social science and psychology experimentation. However, these experiments are predominantly deployed in-lab with a co-present researcher. Remote experiments, without co-present researchers, have not flourished, despite the success of remote approaches for non-XR investigations. This paper summarises findings from a 30-item survey of 46 XR researchers to understand perceived limitations and benefits of remote XR experimentation. Our thematic analysis identifies concerns common with non-XR remote research, such as participant recruitment, as well as XR-specific issues, including safety and hardware variability. We identify potential positive affordances of XR technology, including leveraging data collection functionalities builtin to HMDs (e.g. hand, gaze tracking) and the portability and reproducibility of an experimental setting. We suggest that XR technology could be conceptualised as an interactive technology and a capable data-collection device suited for remote experimentation.

Cyber-Physical-Social Systems (CPSS) performance for industry 4.0 is highly context-dependent, where three design areas arise: the artifact itself, the human-agent collaboration, and the organizational settings. Current HF&E tools are limited to conceptualize and anticipate future human-agent work situations with a fine-grained perspective. This paper explores how rich in-sights from work analysis can be translated into formative design patterns that provide finer guidance in conceptualizing the human-agent collaboration and the organizational settings. The current manual work content elicited is disaggregated into functional requirements. Each function is then scrutinized by a multidisciplinary design team that decides its feasibility and nature (autonomy function, human function, or hybrid function). By doing so, we uncover the technical capabilities of the CPSS in comparison with subject-matter experts` work activity. We called this concept technological coverage. The framework thereof allows close collaboration with design stakeholders to define detailed HAT configurations. We then imagined joint activity scenarios based on end-users work activity, the technological capabilities, and the interaction requirements to perform the work. We use a study on technological innovation in the aircraft maintenance domain to illustrate the framework`s early phases

Transcription, annotation, digitization and/or visualization are common transformations that historical documents such as national records, birth/death registers, university records, letters or books undergo. Reasons for those transformations span from the (physical) protection of the original materials to disclosure of 'hidden' information or patterns within the documents. Even though such transformations bring new insights and perspectives on the documents, they also modify the documents' content, structure, and/or artifactual form and thus, occlude prior knowledge and interpretation. When it comes to visualization as a means to transform historical documents from written to abstract visual form, there is typically little acknowledgment or even understanding of the previous transformation steps these documents have gone through. The 'tremendous rhetorical force' of visualization, we argue, should not be at the expense of the multiple pasts, contexts, and curators that are inherent in historical record collections. Rather, the urgent question for the fields of visualization and the (digital) humanities is how to better support awareness of these multiple layers of interpretation and the people behind them when representing historical documents. We begin to address this question based on a collection of historical university records by (a) investigating common transformation processes of historical documents, and (b) discussing opportunities and challenges for making such transformations transparent through what we call 'provenance-driven visualization'; the idea for a visualization that makes visible the layers of transformation (including interpretation, re-structuring, and curation) inherent in historical documents.

The latest research in expertise assessment of soccer players has affirmed the importance of perceptual skills (especially for decision making) by focusing either on high experimental control or on a realistic presentation. To assess the perceptual skills of athletes in an optimized manner, we captured omnidirectional in-field scenes and showed these to 12 expert, 10 intermediate and 13 novice soccer goalkeepers on virtual reality glasses. All scenes were shown from the same natural goalkeeper perspective and ended after the return pass to the goalkeeper. Based on their gaze behavior we classified their expertise with common machine learning techniques. This pilot study shows promising results for objective classification of goalkeepers expertise based on their gaze behaviour and provided valuable insight to inform the design of training systems to enhance perceptual skills of athletes.

Usage of smartphones and tablets have been increasing rapidly with multi-touch interaction and powerful configurations. Performing tasks on mobile phones become more complex as people age, thereby increasing their cognitive workload. In this context, we conducted an eye tracking study with 50 participants between the age of 20 to 60 years and above, living in Bangalore, India. This paper focuses on visual nature of interaction with mobile user interfaces. The study aims to investigate how aging affects user experience on mobile phones while performing complex tasks, and estimate cognitive workload using eye tracking metrics. The study consisted of five tasks that were performed on an android mobile phone under naturalistic scenarios using eye tracking glasses. We recorded ocular parameters like fixation rate, saccadic rate, average fixation duration, maximum fixation duration and standard deviation of pupil dilation for left and right eyes respectively for each participant. Results from our study show that aging has a bigger effect on performance of using mobile phones irrespective of any complex task given to them. We noted that, participants aged between 50 to 60+ years had difficulties in completing tasks and showed increased cognitive workload. They took longer fixation duration to complete tasks which involved copy-paste operations. Further, we identifed design implications and provided design recommendations for designers and manufacturers.

According to the CDC, one key step of preventing oneself from contracting coronavirus (COVID-19) is to avoid touching eyes, nose, and mouth with unwashed hands. However, touching one's face is a frequent and spontaneous behavior---one study observed subjects touching their faces on average 23 times per hour. Creative solutions have emerged amongst some recent commercial and hobbyists' projects, yet most either are closed-source or lack validation in performance. We develop FaceOff---a sensing technique using a commodity wrist-worn accelerometer to detect face-touching behavior based on the specific motion pattern of raising one's hand towards the face. We report a survey (N=20) that elicits different ways people touch their faces, an algorithm that temporally ensembles data-driven models to recognize when a face touching behavior occurs and results from a preliminary user testing (N=3 for a total of about 90 minutes).

Distal facial Electromyography (EMG) can be used to detect smiles and frowns with reasonable accuracy. It capitalizes on volume conduction to detect relevant muscle activity, even when the electrodes are not placed directly on the source muscle. The main advantage of this method is to prevent occlusion and obstruction of the facial expression production, whilst allowing EMG measurements. However, measuring EMG distally entails that the exact source of the facial movement is unknown. We propose a novel method to estimate specific Facial Action Units (AUs) from distal facial EMG and Computer Vision (CV). This method is based on Independent Component Analysis (ICA), Non-Negative Matrix Factorization (NNMF), and sorting of the resulting components to determine which is the most likely to correspond to each CV-labeled action unit (AU). Performance on the detection of AU06 (Orbicularis Oculi) and AU12 (Zygomaticus Major) was estimated by calculating the agreement with Human Coders. The results of our proposed algorithm showed an accuracy of 81% and a Cohen's Kappa of 0.49 for AU6; and accuracy of 82% and a Cohen's Kappa of 0.53 for AU12. This demonstrates the potential of distal EMG to detect individual facial movements. Using this multimodal method, several AU synergies were identified. We quantified the co-occurrence and timing of AU6 and AU12 in posed and spontaneous smiles using the human-coded labels, and for comparison, using the continuous CV-labels. The co-occurrence analysis was also performed on the EMG-based labels to uncover the relationship between muscle synergies and the kinematics of visible facial movement.

As open source software (OSS) becomes increasingly mature and popular, there are significant challenges with properly accounting for usability concerns for the diverse end users. Participatory design, where multiple stakeholders collaborate on iterating the design, can be an efficient way to address the usability concerns for OSS projects. However, barriers such as a code-centric mindset and insufficient tool support often prevent OSS teams from effectively including end users in participatory design methods. This paper proposes preliminary contributions to this problem through the user-centered exploration of (1) a set of design guidelines that capture the needs of OSS participatory design tools, (2) two personas that represent the characteristics of OSS designers and end users, and (3) a low-fidelity prototype tool for end user involvement in OSS projects. This work paves the road for future studies about tool design that would eventually help improve OSS usability.