AHCI RESEARCH GROUP
Publications
Papers published in international journals,
proceedings of conferences, workshops and books.
OUR RESEARCH
Scientific Publications
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You can use the tag cloud to select only the papers dealing with specific research topics.
You can expand the Abstract, Links and BibTex record for each paper.
2025
Chang, K. -Y.; Lee, C. -F.
Enhancing Virtual Restorative Environment with Generative AI: Personalized Immersive Stress-Relief Experiences Proceedings Article
In: V.G., Duffy (Ed.): Lect. Notes Comput. Sci., pp. 132–144, Springer Science and Business Media Deutschland GmbH, 2025, ISBN: 03029743 (ISSN); 978-303193501-5 (ISBN).
Abstract | Links | BibTeX | Tags: Artificial intelligence generated content, Artificial Intelligence Generated Content (AIGC), Electroencephalography, Electroencephalography (EEG), Generative AI, Immersive, Immersive environment, Mental health, Physical limitations, Restorative environment, Stress relief, Virtual reality exposure therapies, Virtual reality exposure therapy, Virtual Reality Exposure Therapy (VRET), Virtualization
@inproceedings{chang_enhancing_2025,
title = {Enhancing Virtual Restorative Environment with Generative AI: Personalized Immersive Stress-Relief Experiences},
author = {K. -Y. Chang and C. -F. Lee},
editor = {Duffy V.G.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105007759157&doi=10.1007%2f978-3-031-93502-2_9&partnerID=40&md5=ee620a5da9b65e90ccb1eaa75ec8b724},
doi = {10.1007/978-3-031-93502-2_9},
isbn = {03029743 (ISSN); 978-303193501-5 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {Lect. Notes Comput. Sci.},
volume = {15791 LNCS},
pages = {132–144},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {In today’s fast-paced world, stress and mental health challenges are becoming more common. Restorative environments help people relax and recover emotionally, and Virtual Reality Exposure Therapy (VRET) offers a way to experience these benefits beyond physical limitations. However, most VRET applications rely on pre-designed content, limiting their adaptability to individual needs. This study explores how Generative AI can enhance VRET by creating personalized, immersive environments that better match users’ preferences and improve relaxation. To evaluate the impact of AI-generated restorative environments, we combined EEG measurements with user interviews. Thirty university students participated in the study, experiencing two different modes: static mode and walking mode. The EEG results showed an increase in Theta (θ) and High Beta (β) brain waves, suggesting a state of deep immersion accompanied by heightened cognitive engagement and mental effort. While participants found the experience enjoyable and engaging, the AI-generated environments tended to create excitement and focus rather than conventional relaxation. These findings suggest that for AI-generated environments in VRET to be more effective for stress relief, future designs should reduce cognitive load while maintaining immersion. This study provides insights into how AI can enhance relaxation experiences and introduces a new perspective on personalized digital stress-relief solutions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.},
keywords = {Artificial intelligence generated content, Artificial Intelligence Generated Content (AIGC), Electroencephalography, Electroencephalography (EEG), Generative AI, Immersive, Immersive environment, Mental health, Physical limitations, Restorative environment, Stress relief, Virtual reality exposure therapies, Virtual reality exposure therapy, Virtual Reality Exposure Therapy (VRET), Virtualization},
pubstate = {published},
tppubtype = {inproceedings}
}
In today’s fast-paced world, stress and mental health challenges are becoming more common. Restorative environments help people relax and recover emotionally, and Virtual Reality Exposure Therapy (VRET) offers a way to experience these benefits beyond physical limitations. However, most VRET applications rely on pre-designed content, limiting their adaptability to individual needs. This study explores how Generative AI can enhance VRET by creating personalized, immersive environments that better match users’ preferences and improve relaxation. To evaluate the impact of AI-generated restorative environments, we combined EEG measurements with user interviews. Thirty university students participated in the study, experiencing two different modes: static mode and walking mode. The EEG results showed an increase in Theta (θ) and High Beta (β) brain waves, suggesting a state of deep immersion accompanied by heightened cognitive engagement and mental effort. While participants found the experience enjoyable and engaging, the AI-generated environments tended to create excitement and focus rather than conventional relaxation. These findings suggest that for AI-generated environments in VRET to be more effective for stress relief, future designs should reduce cognitive load while maintaining immersion. This study provides insights into how AI can enhance relaxation experiences and introduces a new perspective on personalized digital stress-relief solutions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
2024
Liew, Z. Q.; Xu, M.; Lim, W. Y. Bryan; Niyato, D.; Kim, D. I.
AI-Generated Bidding for Immersive AIGC Services in Mobile Edge-Empowered Metaverse Proceedings Article
In: Int. Conf. Inf. Networking, pp. 305–309, IEEE Computer Society, 2024, ISBN: 19767684 (ISSN); 979-835033094-6 (ISBN).
Abstract | Links | BibTeX | Tags: Artificial intelligence generated bid, Artificial intelligence generated content, Bidding mechanism, Bidding models, Budget constraint, Budget control, Budget-constraint bidding, Constrained optimization, Content services, Immersive, Learning systems, Metaverses, Mobile edge computing, Reinforcement Learning, Semantics, Virtual tour
@inproceedings{liew_ai-generated_2024,
title = {AI-Generated Bidding for Immersive AIGC Services in Mobile Edge-Empowered Metaverse},
author = {Z. Q. Liew and M. Xu and W. Y. Bryan Lim and D. Niyato and D. I. Kim},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85198324990&doi=10.1109%2fICOIN59985.2024.10572159&partnerID=40&md5=271f5c45e8e95f01b42acaee89599bd5},
doi = {10.1109/ICOIN59985.2024.10572159},
isbn = {19767684 (ISSN); 979-835033094-6 (ISBN)},
year = {2024},
date = {2024-01-01},
booktitle = {Int. Conf. Inf. Networking},
pages = {305–309},
publisher = {IEEE Computer Society},
abstract = {Recent advancements in Artificial Intelligence Generated Content (AIGC) provide personalized and immersive content generation services for applications such as interactive advertisements, virtual tours, and metaverse. With the use of mobile edge computing (MEC), buyers can bid for the AIGC service to enhance their user experience in real-time. However, designing strategies to optimize the quality of the services won can be challenging for budget-constrained buyers. The performance of classical bidding mechanisms is limited by the fixed rules in the strategies. To this end, we propose AI-generated bidding (AIGB) to optimize the bidding strategies for AIGC. AIGB model uses reinforcement learning model to generate bids for the services by learning from the historical data and environment states such as remaining budget, budget consumption rate, and quality of the won services. To obtain quality AIGC service, we propose a semantic aware reward function for the AIGB model. The proposed model is tested with a real-world dataset and experiments show that our model outperforms the classical bidding mechanism in terms of the number of services won and the similarity score. © 2024 IEEE.},
keywords = {Artificial intelligence generated bid, Artificial intelligence generated content, Bidding mechanism, Bidding models, Budget constraint, Budget control, Budget-constraint bidding, Constrained optimization, Content services, Immersive, Learning systems, Metaverses, Mobile edge computing, Reinforcement Learning, Semantics, Virtual tour},
pubstate = {published},
tppubtype = {inproceedings}
}
Recent advancements in Artificial Intelligence Generated Content (AIGC) provide personalized and immersive content generation services for applications such as interactive advertisements, virtual tours, and metaverse. With the use of mobile edge computing (MEC), buyers can bid for the AIGC service to enhance their user experience in real-time. However, designing strategies to optimize the quality of the services won can be challenging for budget-constrained buyers. The performance of classical bidding mechanisms is limited by the fixed rules in the strategies. To this end, we propose AI-generated bidding (AIGB) to optimize the bidding strategies for AIGC. AIGB model uses reinforcement learning model to generate bids for the services by learning from the historical data and environment states such as remaining budget, budget consumption rate, and quality of the won services. To obtain quality AIGC service, we propose a semantic aware reward function for the AIGB model. The proposed model is tested with a real-world dataset and experiments show that our model outperforms the classical bidding mechanism in terms of the number of services won and the similarity score. © 2024 IEEE.