publications | Kentaro Takahira

Conference Articles

TangibleNet: Synchronous Network Data Storytelling through Tangible Interactions in Augmented Reality

Kentaro Takahira, Wong Kam-Kwai, Leni Yang, and 3 more authors

In Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems (CHI ’25, acceptance rate: 24%), Yokohama, Japan, 2025

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Synchronous data-driven storytelling with network visualizations presents significant challenges due to the complexity of real-time manipulation of network components. While existing research addresses asynchronous scenarios, there is a lack of effective tools for live presentations. To address this gap, we developed TangibleNet, a projector-based AR prototype that allows presenters to interact with node-link diagrams using double-sided magnets during live presentations. The design process was informed by interviews with professionals experienced in synchronous data storytelling and workshops with 14 HCI/VIS researchers. Insights from the interviews helped identify key design considerations for integrating physical objects as interactive tools in presentation contexts. The workshops contributed to the development of a design space mapping user actions to interaction commands for node-link diagrams. Evaluation with 12 participants confirmed that TangibleNet supports intuitive interactions and enhances presenter autonomy, demonstrating its effectiveness for synchronous network-based data storytelling.
🏅 VR-Mediated Cognitive Defusion: A Comparative Study for Managing Negative Thoughts

Kento Shigyo, Yi-Fan Cao, Kentaro Takahira, and 2 more authors

In Proceedings of the 32nd ACM International Conference on Multimedia (MM ’24, acceptance rate: 25%), Melbourne VIC, Australia, 2024

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This work received an Honerable Mention Award from Multimedia Conference 2024 [top 1%]

The growing prevalence of psychological disorders underscores the critical importance of mental health research in today’s society. In psychotherapy, particularly Acceptance and Commitment Therapy (ACT), cognitive exercises employing mental imagery are used to manage negative thoughts. However, the challenge of maintaining vivid imagery diminishes their therapeutic effectiveness. Virtual reality (VR) offers untapped potential for increasing engagement and therapeutic efficacy. However, there is still a gap in exploration regarding how to effectively leverage the potential of VR to enhance traditional cognitive exercises with mental imagery. This study investigates the effective HCI design and the comparative efficacy of a VR-mediated exercise for promoting cognitive defusion to address negative thoughts grounded in ACT. Using a co-design approach with clinicians and potential users of postgraduate students, we developed a VR system that materializes negative thoughts into tangible objects. This allows users to visually modify and transpose these objects onto a surface, facilitating mental detachment from negative thoughts. In an evaluation study with 20 non-clinical participants, divided into VR and mental imagery groups, we assessed the impact of the cognitive defusion exercise on their perception of negative thoughts and psychological measures using standardized questionnaires. Results show improvement in both groups, with significant enhancements in negative thought perception and mental detachment from negative thoughts exclusively in the VR group, whereas the mental imagery group did not demonstrate significant changes. Interviews emphasize the VR’s capability to present vivid visualizations of negative thoughts effortlessly, highlighting its effectiveness and engagement in psychotherapy to facilitate cognitive exercises.
Echoes in the Gallery: A Collaborative Immersive Analytics System for Analyzing Audience Reactions in Virtual Reality Exhibitions

Linping Yuan, Wai Tong, Kentaro Takahira, and 2 more authors

In MERCADO workshop IEEE Visualization Conference, Melbourne VIC, Australia, 2023

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Virtual reality (VR) has enriched the exhibition experience, yet curators and artists often face challenges in curation due to disagreements in the spatial arrangement of exhibits. Although VR applications permit audiences to express their reactions, these multimodal and semantically rich data remain underutilized in spatial arrangement deliberations. Based on insights from semi-structured interviews with curators and artists, we outline design requirements to enhance collaboration in analyzing visitors’ reactions, focusing on traffic flow analysis, angle-induced reactions, what-if analysis, and cross-view annotations. To meet these requirements, we propose a novel collaborative immersive analytic system that visualizes audience flow and individual reactions within VR spaces, facilitating more effective communication and decision-making between artists and curators. Future research will explore advanced visual design techniques to address scalability issues and in-place spatial comparison. We will also finish the development and conduct usability studies.
Contract-Based Inter-User Usage Coordination in Free-Floating Car Sharing

Kentaro Takahira and Shigeo Matsubara

In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI ’21, acceptance rate: 21%), Vancouver, Canada, 2021

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We propose a novel distributed user-car matching method based on a contract between users to mitigate the imbalance problem between vehicle distribution and demand in free-floating car sharing. Previous regulation methods involved an incentive system based on the predictions of origin-destination (OD) demand obtained from past usage history. However, the difficulty these methods have in obtaining accurate data limits their applicability. To overcome this drawback, we introduce contract-based coordination among drop-off and pick-up users in which an auction is conducted for drop-off users’ intended drop-off locations. We theoretically analyze the proposed method regarding the upper bound of its efficiency. We also compare it with a baseline method and non-regulation scenario on a free-floating car-sharing simulator. The experimental results show that the proposed method achieves a higher social surplus than the existing method.

Journal Articles

Forecasting Internally Displaced People’s Movements with Artificial Intelligence

Ayaka Oishi, Takeshi Teshima, Kunikazu Akao, and 11 more authors

Digital Innovations, Business and Society in Africa: New Frontiers and a Shared Strategic Vision, 2022

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The rise of big data and artificial intelligence (AI) has paved the way for data-driven interventions in the field of international development. In this paper, a group of researchers (i) summarizes policies and implications of the use of advanced technology in the field and (ii) presents the result of the study they conducted which applies machine learning to forecast internally displaced people’s (IDPs) movements in the Democratic Republic of the Congo. Despite methodological limitations, the results confer an exposition on how machine learning models and open-source data could enhance the predictive insights of forced displacement. Our approach could be used to predict not only IDP flows but also refugee flows, expanding the use of machine learning for social good. To counter future crises triggered by climate change and the COVID-19 pandemic, we believe our approach has a great possibility to support the effective distribution of limited funds and supplies. This study underscores the benefit of AI and highlights issues in implementations. Future research will need to widen target regions and periods as well as to include the pragmatic aspects of the implementations.