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1. Cross-Cultural Affective Evaluation of Kawaii Robots in Virtual Spaces

   Laohakangvalvit, Tipporn; Sripian, Peeraya; Sugaya, Midori; Ohkura, Michiko; Berque, Dave; Chiba, Hiroko (July 2023, Springer)

   Rauterberg, Matthias (Ed.)

   In modern society, robots have been increasingly involved in human lives in various scenarios. As a future society with human-robot interaction is approaching, it is important to consider how to develop robots that give positive impressions for a variety of users. Based on an Affective Engineering approach, affective values can strengthen the impact of the first impressions of products. Kawaiiness is one affective value that can be a key factor in developing robots with positive impressions. In this research, we carried out a collaborative project to design and develop kawaii robot prototypes in virtual spaces by American and Japanese university students. We then performed an experiment on affective evaluation of those robots using 10 adjectives: kawaii/cute, approachable, scary, trustworthy, cool, beautiful, polite, comfortable, and soft.We previously presented our statistical analysis results for the adjective “kawaii/cute.” However, the results of other adjectives, which potentially have effects on the robot impression, have not been presented yet. Therefore, in this paper, we present our further analysis of several adjectives to clarify their relationship with kawaii/cute and robot features across genders and cultures. In addition, we statistically analyzed the effects of robot pairs, adjectives, genders, and cultures. The results suggest that robots with features such as more animal-like, rounder, and shorter tend to increase positive impressions such as kawaii, approachability, beauty, comfortable, and softness. Also, we found no difference across gender and culture for the impressions on kawaii robots, which shows the possibility of expanding the concept of kawaii robots worldwide. 

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2. “We Feel LikeWe’re Winging It:” A Study on Navigating Open-Source Dependency Abandonment

   Miller, Courtney; Christian Kaestner; Vasilescu, Bogdan (December 2023, Proceedings of the ACM SIGSOFT International Symposium on the Foundations of Software Engineering)

   While lots of research has explored howto prevent maintainers from abandoning the open-source projects that serve as our digital infrastructure, there are very few insights on addressing abandonment when it occurs. We argue open-source sustainability research must expand its focus beyond trying to keep particular projects alive, to also cover the sustainable use of open source by supporting users when they face potential or actual abandonment.We interviewed 33 developers who have experienced open-source dependency abandonment. Often, they used multiple strategies to cope with abandonment, for example, first reaching out to the community to find potential alternatives, then switching to a community-accepted alternative if one exists. We found many developers felt they had little to no support or guidance when facing abandonment, leaving them to figure out what to do through a trial-and-error process on their own. Abandonment introduces cost for otherwise seemingly free dependencies, but users can decide whether and how to prepare for abandonment through a number of different strategies, such as dependency monitoring, building abstraction layers, and community involvement. In many cases, community members can invest in resources that help others facing the same abandoned dependency, but often do not because of the many other competing demands on their time – a form of the volunteer’s dilemma. We discuss cost reduction strategies and ideas to overcome this volunteer’s dilemma. Our findings can be used directly by open-source users seeking resources on dealing with dependency abandonment, or by researchers to motivate future work supporting the sustainable use of open source. 

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3. Artificial Intelligence and Impression Management: Consequences of Autonomous Conversational Agents Communicating on One’s Behalf

   https://doi.org/10.1093/hcr/hqac009  

   Endacott, Camille G.; Leonardi, Paul M. (April 2022, Human Communication Research)

   Abstract Artificially intelligent communication technologies (AICTs) that operate autonomously with high degrees of conversational fluency can make communication decisions on behalf of their principal users and communicate with those principals’ audiences on their behalf. In this study, we explore how the involvement of AICTs in communication activities shapes how principals engage in impression management and how their communication partners form impressions of them. Through an inductive, comparative field study of users of two AI scheduling technologies, we uncover three communicative practices through which principals engaged in impression management when AICTs communicate on their behalf: interpretation, diplomacy, and staging politeness. We also uncover three processes through which communication partners formed impressions of principals when communicating with them via AICTs: confirmation, transference, and compartmentalization. We show that communication partners can transfer impressions of AICTs to principals themselves and outline the conditions under which such transference is and is not likely. We discuss the implications of these findings for the study of technological mediation of impression management and formation in the age of artificial intelligence and present propositions to guide future empirical research. 

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4. Innovations and advances in instrumentation at the W. M. Keck Observatory, vol. II

   https://doi.org/10.1117/12.2628630  

   Kassis, Marc .; Allen, Steve; Alvarez, Carlos; Banyal, Ravinder; Bertz, Robert; Beichman, Charles; Brown, Aaron; Brown, Matthew; Cabak, Gerald; Bundy, Kevin; et al (August 2022, Innovations and advances in instrumentation at the W. M. Keck Observatory, vol. II)

   Evans, Christopher J.; Bryant, Julia J.; Motohara, Kentaro (Ed.)

   Since the start of science operations in 1993, the twin 10-meter W. M. Keck Observatory (WMKO) telescopes have continued to maximize their scientific impact and to produce transformative discoveries that keep the observing community on the frontiers of astronomical research. Upgraded capabilities and new instrumentation are provided though collaborative partnerships with Caltech, the University of California, and the University of Hawaii instrument development teams, as well as industry and other organizations. This paper summarizes the performance of recently commissioned infrastructure projects, technology upgrades, and new additions to the suite of observatory instrumentation. We also provide a status of projects currently in design or development phases and, since we keep our eye on the future, summarize projects in exploratory phases that originate from our 2022 strategic plan developed in collaboration with our science community to adapt and respond to evolving science needs. 

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5. The Effectiveness of Multi-Label Classification and Multi-Output Regression in Social Trait Recognition

   https://doi.org/10.3390/s21124127  

   Farlessyost, Will; Grant, Kelsey-Ryan; Davis, Sara R.; Feil-Seifer, David; Hand, Emily M. (June 2021, Sensors)

   First impressions make up an integral part of our interactions with other humans by providing an instantaneous judgment of the trustworthiness, dominance and attractiveness of an individual prior to engaging in any other form of interaction. Unfortunately, this can lead to unintentional bias in situations that have serious consequences, whether it be in judicial proceedings, career advancement, or politics. The ability to automatically recognize social traits presents a number of highly useful applications: from minimizing bias in social interactions to providing insight into how our own facial attributes are interpreted by others. However, while first impressions are well-studied in the field of psychology, automated methods for predicting social traits are largely non-existent. In this work, we demonstrate the feasibility of two automated approaches—multi-label classification (MLC) and multi-output regression (MOR)—for first impression recognition from faces. We demonstrate that both approaches are able to predict social traits with better than chance accuracy, but there is still significant room for improvement. We evaluate ethical concerns and detail application areas for future work in this direction. 

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