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MOCO ’24, May 30–June 02, 2024, Utrecht, Netherlands, This research introduces an approach for translating traditional dance knowledge into interactive computational models extending beyond static dance performance recordings. Specifically, this paper presents the concept of "Human-AI co-dancing," which involves integrating human dancers with virtual dance partners powered by models derived from dance principles. To demonstrate this concept, the research focuses on the choreographic principles deconstructed from the knowledge of traditional Thai dance. The principles are analyzed and translated into computational procedures that dynamically manipulate the movements of a virtual character by altering animation keyframes and the motions of individual joints in real-time. We developed an interactive system that enables dancers to improvise alongside the virtual agent. The system incorporates voice control functionality, allowing the dancer, choreographer, and even the audience to participate in altering the choreography of the virtual agents by adjusting parameters that represent traditional Thai dance elements. Human-AI rehearsals yielded intriguing artistic results, with hybrid movement aesthetics emerging from the synergy and friction between humans and machines. The resulting dance production, "Cyber Subin," demonstrates the potential of combining intangible cultural heritage, intelligent technology, and posthuman choreography to expand artistic expression and preserve traditional wisdom in a contemporary context.

CHI '24: Proceedings of the CHI Conference on Human Factors in Computing Systems May 11–16, 2024, Honolulu, HI, USA, Fostering students’ interests in learning is considered to have many positive downstream effects. Large language models have opened up new horizons for generating content tuned to one’s interests, yet it is unclear in what ways and to what extent this customization could have positive effects on learning. To explore this novel dimension, we conducted a between-subjects online study (n=272) featuring different variations of a generative AI vocabulary learning app that enables users to personalize their learning examples. Participants were randomly assigned to control (sentence sourced from pre-existing text) or experimental conditions (generated sentence or short story based on users’ text input). While we did not observe a difference in learning performance between the conditions, the analysis revealed that generative AI-driven context personalization positively affected learning motivation. We discuss how these results relate to previous findings and underscore their significance for the emerging field of using generative AI for personalized learning.

29th International Conference on Intelligent User Interfaces March 18–21, 2024, Greenville, SC, USA, Despite significant historical progress, discrimination and social stigma continue to impact the lives of LGBTQIA+ individuals. The use of AI-generated virtual characters offers a unique opportunity to facilitate advocacy by engaging individuals in simulated conversations that can foster understanding, education, and empathy. This paper explores the potential of AI simulations to help individuals practice LGBTQIA+ advocacy, while also acknowledging the need for ethical considerations and addressing concerns about oversimplification or perpetuation of stereotypes. By combining technological innovation with a commitment to inclusivity, we aim to contribute to the ongoing struggle for equality in both the legal framework and the hearts and minds of the community. We present a study evaluating virtual characters driven by generative conversational AI simulating the social interactions surrounding “coming out of the closet”, a rite of passage associated with LGBTQIA+ communities. In our study, virtual characters embodied as queer individuals engage with users in a text-based conversation simulation paired with visual representations. We investigate how the interactions between the virtual characters and a user influence the user’s comfort, confidence, empathy and sympathy. The AI simulation includes distinct visual personas deployed in a series of conditions. We present findings from our deployments involving 307 users. Finally, we discuss the design implications of our work on the potential future of embodied, self-actuated and openly LGBTQIA+ intelligent agents.

AI-based virtual assistants are increasingly used to support daily ideation tasks. The values or bias present in these agents can influence output in hidden ways. They may also affect how people perceive the ideas produced with these AI agents and lead to implications for the design of AI-based tools. We explored the effects of AI agents with different values on the ideation process and user perception of idea quality, ownership, agent competence, and values present in the output. Our study tasked 180 participants with brainstorming practical solutions to a set of problems with AI agents of different values. Results show no significant difference in self-evaluation of idea quality and perception of the agent based on value alignment; however, ideas generated reflected the AI's values and feeling of ownership is affected. This highlights an intricate interplay between AI values and human ideation, suggesting careful design considerations for future AI-supported brainstorming tools.

We introduce "Future You," an interactive, brief, single-session, digital chat intervention designed to improve future self-continuity--the degree of connection an individual feels with a temporally distant future self--a characteristic that is positively related to mental health and wellbeing. Our system allows users to chat with a relatable yet AI-powered virtual version of their future selves that is tuned to their future goals and personal qualities. To make the conversation realistic, the system generates a "synthetic memory"--a unique backstory for each user--that creates a throughline between the user's present age (between 18-30) and their life at age 60. The "Future You" character also adopts the persona of an age-progressed image of the user's present self. After a brief interaction with the "Future You" character, users reported decreased anxiety, and increased future self-continuity. This is the first study successfully demonstrating the use of personalized AI-generated characters to improve users' future self-continuity and wellbeing.

As knowledge around bio-digital interaction continues to unfold, there are new opportunities for HCI researchers to integrate biology as a design and computational material. Our motivation for the workshop is to bring together interdisciplinary researchers with interest in exploring the next generation of biological HCI and exploring novel bio-digital interfaces implicating diverse contexts, scales, and stakeholders. The workshop aims to provide a space for interactive discussions, presentations, and brainstorming regarding opportunities and approaches for HCI around bio-digital interfaces. We invite researchers from both academia and industry to submit a short position paper in the following areas: Synthetic Biology, Biological Circuits, Do-It-Yourself Biology (DIYBio), Biomimetic Interfaces, Living Interfaces, Living Artefacts, and Bio-ethics. We will evaluate submissions on fit, ability to stimulate discussion, and contribution to HCI. On our website we have included examples of past work in this area to help inspire and inform position papers. Our website will host a recording of the entire workshop session with accepted papers to support asynchronous viewing for participants who are unable to attend in-person or synchronously., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Materials and Manufacturing

The ability to discern between true and false information is essential to making sound decisions. However, with the recent increase in AI-based disinformation campaigns, it has become critical to understand the influence of deceptive systems on human information processing. In experiment (N=128), we investigated how susceptible people are to deceptive AI systems by examining how their ability to discern true news from fake news varies when AI systems are perceived as either human fact-checkers or AI fact-checking systems, and when explanations provided by those fact-checkers are either deceptive or honest. We find that deceitful explanations significantly reduce accuracy, indicating that people are just as likely to believe deceptive AI explanations as honest AI explanations. Although before getting assistance from an AI-system, people have significantly higher weighted discernment accuracy on false headlines than true headlines, we found that with assistance from an AI system, discernment accuracy increased significantly when given honest explanations on both true headlines and false headlines, and decreased significantly when given deceitful explanations on true headlines and false headlines. Further, we did not observe any significant differences in discernment between explanations perceived as coming from a human fact checker compared to an AI-fact checker. Similarly, we found no significant differences in trust. These findings exemplify the dangers of deceptive AI systems and the need for finding novel ways to limit their influence human information processing.

© 2020 Owner/Author. Having good mentors and role models is important for personal growth. However, they are not always available at the time of need. Some of our personal heroes have passed away leaving only their wisdom through writings and other artifacts. We present Wearable Wisdom, an intelligent, audio-based system for mediating wisdom and advice from mentors and personal heroes to a user. It does so by performing automated semantic analysis on the collected wisdom database and generating a simulated voice of a mentor sharing relevant wisdom and advice with the user. The results show that our platform is statistically superior in delivering relevant, yet abstract wisdom as well as providing more inspiration compared to control. We describe the implementation of the Wearable Wisdom system, report on a user study, and discuss potential applications of wisdom computation for supporting personal growth and motivation.

© 2020 Owner/Author. Having good mentors and role models is important for personal growth. However, they are not always available at the time of need. Some of our personal heroes have passed away leaving only their wisdom through writings and other artifacts. We present Wearable Wisdom, an intelligent, audio-based system for mediating wisdom and advice from mentors and personal heroes to a user. It does so by performing automated semantic analysis on the collected wisdom database and generating a simulated voice of a mentor sharing relevant wisdom and advice with the user. The results show that our platform is statistically superior in delivering relevant, yet abstract wisdom as well as providing more inspiration compared to control. We describe the implementation of the Wearable Wisdom system, report on a user study, and discuss potential applications of wisdom computation for supporting personal growth and motivation.

Video represents the majority of internet traffic today, driving a continual race between the generation of higher quality content, transmission of larger file sizes, and the development of network infrastructure. In addition, the recent COVID-19 pandemic fueled a surge in the use of video conferencing tools. Since videos take up considerable bandwidth (~100 Kbps to a few Mbps), improved video compression can have a substantial impact on network performance for live and pre-recorded content, providing broader access to multimedia content worldwide. We present a novel video compression pipeline, called Txt2Vid, which dramatically reduces data transmission rates by compressing webcam videos ("talking-head videos") to a text transcript. The text is transmitted and decoded into a realistic reconstruction of the original video using recent advances in deep learning based voice cloning and lip syncing models. Our generative pipeline achieves two to three orders of magnitude reduction in the bitrate as compared to the standard audio-video codecs (encoders-decoders), while maintaining equivalent Quality-of-Experience based on a subjective evaluation by users (n = 242) in an online study. The Txt2Vid framework opens up the potential for creating novel applications such as enabling audio-video communication during poor internet connectivity, or in remote terrains with limited bandwidth. The code for this work is available at https://github.com/tpulkit/txt2vid.git., Comment: 11 pages, 8 figures, 2 table. Addition of statistical analysis of results. Reorganization and rewriting of text to make it clearer

Thesis: S.M., Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, September, 2020, Cataloged from the official PDF version of thesis., Includes bibliographical references., We explore the vision of closed-loop bio-digital interfaces for human augmentation, where the bio-digital system allows for both sensing and writing biological information to the body. Current-generation wearable devices sense an individual's physiological data such as heart rate, respiration, electrodermal activity, and EEG, but lack in sensing their biological counterparts, which drive the majority of individual's physiological signals. On the other hand, biosensors for detecting biochemical markers are currently limited to one-time use, are non-continuous and don't provide flexibility in choosing which biomarker they sense. We believe that the future for wearable biosensors lies in going beyond specific sensing capabilities and becoming a wearable "lab" on body, where a small device can offer a fully integrated and re-configurable system that mimics several processes usually performed in the laboratory for clinical diagnostics and analysis of human health. To illustrate our vision of having a lab on body, we prototyped "Wearable Lab" a bio-digital platform for sensing biochemical and digital data from saliva. Our platform contains digital sensors such as an IMU for activity recognition, as well as an automated system for continuous sampling of biomarkers from saliva by leveraging existing paper-based biochemical sensors. The platform could aid with longitudinal studies of biomarkers and early diagnosis of diseases. We present example data collected from the device, show a preliminary evaluation, and discuss the limitation of our platform., by Pat Pataranutaporn., S.M., S.M. Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences

Pat Pataranutaporn, Ali Shtarbanov, Glenn Fernandes, Jingwen Li, Parinya Punpongsanon, Joe Paradiso, and Pattie Maes. 2020. Wearable Sanitizer: Design and Implementation of an Open-source, On-body Sanitizer. In SIGGRAPH Asia 2020 Emerging Technologies (SA '20). Association for Computing Machinery, New York, NY, USA, Article 1, 1–2. DOI:https://doi.org/10.1145/3415255.3422897., SA '20: SIGGRAPH Asia 2020 [December 4 - 13, 2020 ], During the pandemic, wearables such as face masks and face shields have become broadly adopted, these solutions do reduce infection but do not eliminate infectious agents from surfaces and objects the person may touch. Therefore, regular disinfection of hands and frequently touched surfaces is a critical factor in preventing the spread of infectious diseases ranging from the common cold and flu to SARS and COVID-19. This activity of frequent disinfection requires a high degree of discipline and leads to increased cognitive and physical effort involved in frequent washing of hands or use of a pocket sanitizer. We present an open-source, wearable sanitizer that provides just-in-time, automatic dispensing of alcohol to the wearer's hand or nearby objects using sensors and programmable cues. We systematically explore the design space aiming to create a device that not only seamlessly integrates with the user's body and behavior but also frees their physical and mental faculties for other tasks.

Pat Pataranutaporn, Ali Shtarbanov, Glenn Fernandes, Jingwen Li, Parinya Punpongsanon, Joe Paradiso, and Pattie Maes. 2020. Wearable Sanitizer: Design and Implementation of an Open-source, On-body Sanitizer. In SIGGRAPH Asia 2020 Emerging Technologies (SA '20). Association for Computing Machinery, New York, NY, USA, Article 1, 1–2. DOI:https://doi.org/10.1145/3415255.3422897., SA '20: SIGGRAPH Asia 2020 [December 4 - 13, 2020 ], During the pandemic, wearables such as face masks and face shields have become broadly adopted, these solutions do reduce infection but do not eliminate infectious agents from surfaces and objects the person may touch. Therefore, regular disinfection of hands and frequently touched surfaces is a critical factor in preventing the spread of infectious diseases ranging from the common cold and flu to SARS and COVID-19. This activity of frequent disinfection requires a high degree of discipline and leads to increased cognitive and physical effort involved in frequent washing of hands or use of a pocket sanitizer. We present an open-source, wearable sanitizer that provides just-in-time, automatic dispensing of alcohol to the wearer's hand or nearby objects using sensors and programmable cues. We systematically explore the design space aiming to create a device that not only seamlessly integrates with the user's body and behavior but also frees their physical and mental faculties for other tasks.

Pat Pataranutaporn, Ali Shtarbanov, Glenn Fernandes, Jingwen Li, Parinya Punpongsanon, Joe Paradiso, and Pattie Maes. 2020. Wearable Sanitizer: Design and Implementation of an Open-source, On-body Sanitizer. In SIGGRAPH Asia 2020 Emerging Technologies (SA '20). Association for Computing Machinery, New York, NY, USA, Article 1, 1–2. DOI:https://doi.org/10.1145/3415255.3422897., SA '20: SIGGRAPH Asia 2020 [December 4 - 13, 2020 ], During the pandemic, wearables such as face masks and face shields have become broadly adopted, these solutions do reduce infection but do not eliminate infectious agents from surfaces and objects the person may touch. Therefore, regular disinfection of hands and frequently touched surfaces is a critical factor in preventing the spread of infectious diseases ranging from the common cold and flu to SARS and COVID-19. This activity of frequent disinfection requires a high degree of discipline and leads to increased cognitive and physical effort involved in frequent washing of hands or use of a pocket sanitizer. We present an open-source, wearable sanitizer that provides just-in-time, automatic dispensing of alcohol to the wearer's hand or nearby objects using sensors and programmable cues. We systematically explore the design space aiming to create a device that not only seamlessly integrates with the user's body and behavior but also frees their physical and mental faculties for other tasks.