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
Liu, G.; Du, H.; Wang, J.; Niyato, D.; Kim, D. I.
Contract-Inspired Contest Theory for Controllable Image Generation in Mobile Edge Metaverse Journal Article
In: IEEE Transactions on Mobile Computing, vol. 24, no. 8, pp. 7389–7405, 2025, ISSN: 15361233 (ISSN), (Publisher: Institute of Electrical and Electronics Engineers Inc.).
Abstract | Links | BibTeX | Tags: Contest Theory, Deep learning, Deep reinforcement learning, Diffusion Model, Generative adversarial networks, Generative AI, High quality, Image generation, Image generations, Immersive technologies, Metaverses, Mobile edge computing, Reinforcement Learning, Reinforcement learnings, Resource allocation, Resources allocation, Semantic data, Virtual addresses, Virtual environments, Virtual Reality
@article{liu_contract-inspired_2025,
title = {Contract-Inspired Contest Theory for Controllable Image Generation in Mobile Edge Metaverse},
author = {G. Liu and H. Du and J. Wang and D. Niyato and D. I. Kim},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105000066834&doi=10.1109%2FTMC.2025.3550815&partnerID=40&md5=f95abb0df00e3112fa2c15ee77eb41bc},
doi = {10.1109/TMC.2025.3550815},
issn = {15361233 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {IEEE Transactions on Mobile Computing},
volume = {24},
number = {8},
pages = {7389–7405},
abstract = {The rapid advancement of immersive technologies has propelled the development of the Metaverse, where the convergence of virtual and physical realities necessitates the generation of high-quality, photorealistic images to enhance user experience. However, generating these images, especially through Generative Diffusion Models (GDMs), in mobile edge computing environments presents significant challenges due to the limited computing resources of edge devices and the dynamic nature of wireless networks. This paper proposes a novel framework that integrates contract-inspired contest theory, Deep Reinforcement Learning (DRL), and GDMs to optimize image generation in these resource-constrained environments. The framework addresses the critical challenges of resource allocation and semantic data transmission quality by incentivizing edge devices to efficiently transmit high-quality semantic data, which is essential for creating realistic and immersive images. The use of contest and contract theory ensures that edge devices are motivated to allocate resources effectively, while DRL dynamically adjusts to network conditions, optimizing the overall image generation process. Experimental results demonstrate that the proposed approach not only improves the quality of generated images but also achieves superior convergence speed and stability compared to traditional methods. This makes the framework particularly effective for optimizing complex resource allocation tasks in mobile edge Metaverse applications, offering enhanced performance and efficiency in creating immersive virtual environments. © 2025 Elsevier B.V., All rights reserved.},
note = {Publisher: Institute of Electrical and Electronics Engineers Inc.},
keywords = {Contest Theory, Deep learning, Deep reinforcement learning, Diffusion Model, Generative adversarial networks, Generative AI, High quality, Image generation, Image generations, Immersive technologies, Metaverses, Mobile edge computing, Reinforcement Learning, Reinforcement learnings, Resource allocation, Resources allocation, Semantic data, Virtual addresses, Virtual environments, Virtual Reality},
pubstate = {published},
tppubtype = {article}
}
The rapid advancement of immersive technologies has propelled the development of the Metaverse, where the convergence of virtual and physical realities necessitates the generation of high-quality, photorealistic images to enhance user experience. However, generating these images, especially through Generative Diffusion Models (GDMs), in mobile edge computing environments presents significant challenges due to the limited computing resources of edge devices and the dynamic nature of wireless networks. This paper proposes a novel framework that integrates contract-inspired contest theory, Deep Reinforcement Learning (DRL), and GDMs to optimize image generation in these resource-constrained environments. The framework addresses the critical challenges of resource allocation and semantic data transmission quality by incentivizing edge devices to efficiently transmit high-quality semantic data, which is essential for creating realistic and immersive images. The use of contest and contract theory ensures that edge devices are motivated to allocate resources effectively, while DRL dynamically adjusts to network conditions, optimizing the overall image generation process. Experimental results demonstrate that the proposed approach not only improves the quality of generated images but also achieves superior convergence speed and stability compared to traditional methods. This makes the framework particularly effective for optimizing complex resource allocation tasks in mobile edge Metaverse applications, offering enhanced performance and efficiency in creating immersive virtual environments. © 2025 Elsevier B.V., All rights reserved.
Sousa, R. T.; Oliveira, E. A. M.; Cintra, L. M. F.; Filho, A. R. G. Galvão
Transformative Technologies for Rehabilitation: Leveraging Immersive and AI-Driven Solutions to Reduce Recidivism and Promote Decent Work Proceedings Article
In: Proc. - IEEE Conf. Virtual Real. 3D User Interfaces Abstr. Workshops, VRW, pp. 168–171, Institute of Electrical and Electronics Engineers Inc., 2025, ISBN: 9798331514846 (ISBN).
Abstract | Links | BibTeX | Tags: AI- Driven Rehabilitation, Artificial intelligence- driven rehabilitation, Emotional intelligence, Engineering education, Generative AI, generative artificial intelligence, Immersive, Immersive technologies, Immersive Technology, Language Model, Large language model, large language models, Skills development, Social Reintegration, Social skills, Sociology, Vocational training
@inproceedings{sousa_transformative_2025,
title = {Transformative Technologies for Rehabilitation: Leveraging Immersive and AI-Driven Solutions to Reduce Recidivism and Promote Decent Work},
author = {R. T. Sousa and E. A. M. Oliveira and L. M. F. Cintra and A. R. G. Galvão Filho},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105005140551&doi=10.1109%2FVRW66409.2025.00042&partnerID=40&md5=a8dbe15493fd8361602d049f2b09efe3},
doi = {10.1109/VRW66409.2025.00042},
isbn = {9798331514846 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {Proc. - IEEE Conf. Virtual Real. 3D User Interfaces Abstr. Workshops, VRW},
pages = {168–171},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {The reintegration of incarcerated individuals into society presents significant challenges, particularly in addressing barriers related to vocational training, social skill development, and emotional rehabilitation. Immersive technologies, such as Virtual Reality and Augmented Reality, combined with generative Artificial Intelligence (AI) and Large Language Models, offer innovative opportunities to enhance these areas. These technologies create practical, controlled environments for skill acquisition and behavioral training, while generative AI enables dynamic, personalized, and adaptive experiences. This paper explores the broader potential of these integrated technologies in supporting rehabilitation, reducing recidivism, and fostering sustainable employment opportunities and these initiatives align with the overarching equity objective of ensuring Decent Work for All, reinforcing the commitment to inclusive and equitable progress across diverse communities, through the transformative potential of immersive and AI-driven systems in correctional systems. © 2025 Elsevier B.V., All rights reserved.},
keywords = {AI- Driven Rehabilitation, Artificial intelligence- driven rehabilitation, Emotional intelligence, Engineering education, Generative AI, generative artificial intelligence, Immersive, Immersive technologies, Immersive Technology, Language Model, Large language model, large language models, Skills development, Social Reintegration, Social skills, Sociology, Vocational training},
pubstate = {published},
tppubtype = {inproceedings}
}
The reintegration of incarcerated individuals into society presents significant challenges, particularly in addressing barriers related to vocational training, social skill development, and emotional rehabilitation. Immersive technologies, such as Virtual Reality and Augmented Reality, combined with generative Artificial Intelligence (AI) and Large Language Models, offer innovative opportunities to enhance these areas. These technologies create practical, controlled environments for skill acquisition and behavioral training, while generative AI enables dynamic, personalized, and adaptive experiences. This paper explores the broader potential of these integrated technologies in supporting rehabilitation, reducing recidivism, and fostering sustainable employment opportunities and these initiatives align with the overarching equity objective of ensuring Decent Work for All, reinforcing the commitment to inclusive and equitable progress across diverse communities, through the transformative potential of immersive and AI-driven systems in correctional systems. © 2025 Elsevier B.V., All rights reserved.
Chen, Y.; Yan, Y.; Yang, G.
Bringing Microbiology to Life in Museum: Using Mobile VR and LLM-Powered Virtual Character for Children's Science Learning Proceedings Article
In: Chui, K. T.; Jaikaeo, C.; Niramitranon, J.; Kaewmanee, W.; Ng, K. -K.; Ongkunaruk, P. (Ed.): pp. 83–87, Institute of Electrical and Electronics Engineers Inc., 2025, ISBN: 9798331595500 (ISBN).
Abstract | Links | BibTeX | Tags: Computer aided instruction, E-Learning, Engineering education, Experimental groups, Immersive technologies, Informal learning, Language Model, Large language model, large language models, Learning systems, Microbiology, Mobile virtual reality, Museum, Museums, Science education, Science learning, Virtual addresses, Virtual character, Virtual Reality, Virtual reality system
@inproceedings{chen_bringing_2025,
title = {Bringing Microbiology to Life in Museum: Using Mobile VR and LLM-Powered Virtual Character for Children's Science Learning},
author = {Y. Chen and Y. Yan and G. Yang},
editor = {K. T. Chui and C. Jaikaeo and J. Niramitranon and W. Kaewmanee and K. -K. Ng and P. Ongkunaruk},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105015708152&doi=10.1109%2FISET65607.2025.00025&partnerID=40&md5=77ae9a4829656155010abc280a817a72},
doi = {10.1109/ISET65607.2025.00025},
isbn = {9798331595500 (ISBN)},
year = {2025},
date = {2025-01-01},
pages = {83–87},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Although the increasing advantages of immersive technology-enhanced museum informal learning in children's science education, the application of mobile virtual reality (MVR) technology combined with large language models (LLM) in this environment has not yet been fully explored. Furthermore, virtual character, as an intelligent learning assistant, is capable of providing personalized guidance and instant feedback to children through natural language interactions, but its potential in museum learning has yet to be fully tapped. To address these gaps, this study investigates the effectiveness of integrating MVR with LLM-powered virtual character in promoting children's microbiology learning during museum activities. In this paper, the technology-enhanced POE (Prediction-observation-explanation) learning model was studied, and the corresponding MVR system was designed and developed to carry out microbial learning activities. A quasiexperimental design was used with 60 children aged 10-12. The experimental group learned via an MVR system combining LLM-powered virtual character, while the control group used traditional methods. Results showed the experimental group significantly outperformed the control group in both academic achievement and learning motivation, including attention, confidence, and satisfaction. This provides evidence for using immersive technologies in informal learning and offers insights into applying LLM-powered virtual character in science education. © 2025 Elsevier B.V., All rights reserved.},
keywords = {Computer aided instruction, E-Learning, Engineering education, Experimental groups, Immersive technologies, Informal learning, Language Model, Large language model, large language models, Learning systems, Microbiology, Mobile virtual reality, Museum, Museums, Science education, Science learning, Virtual addresses, Virtual character, Virtual Reality, Virtual reality system},
pubstate = {published},
tppubtype = {inproceedings}
}
Although the increasing advantages of immersive technology-enhanced museum informal learning in children's science education, the application of mobile virtual reality (MVR) technology combined with large language models (LLM) in this environment has not yet been fully explored. Furthermore, virtual character, as an intelligent learning assistant, is capable of providing personalized guidance and instant feedback to children through natural language interactions, but its potential in museum learning has yet to be fully tapped. To address these gaps, this study investigates the effectiveness of integrating MVR with LLM-powered virtual character in promoting children's microbiology learning during museum activities. In this paper, the technology-enhanced POE (Prediction-observation-explanation) learning model was studied, and the corresponding MVR system was designed and developed to carry out microbial learning activities. A quasiexperimental design was used with 60 children aged 10-12. The experimental group learned via an MVR system combining LLM-powered virtual character, while the control group used traditional methods. Results showed the experimental group significantly outperformed the control group in both academic achievement and learning motivation, including attention, confidence, and satisfaction. This provides evidence for using immersive technologies in informal learning and offers insights into applying LLM-powered virtual character in science education. © 2025 Elsevier B.V., All rights reserved.
Basyoni, L.; Qayyum, A.; Shaban, K. Bashir; Elmahjub, E.; Al-Ali, A.; Halabi, O.; Qadir, J.
Generative AI-Driven Metaverse: The Promises and Challenges of AI-Generated Content Journal Article
In: IEEE Open Journal of the Computer Society, 2025, ISSN: 26441268 (ISSN), (Publisher: Institute of Electrical and Electronics Engineers Inc.).
Abstract | Links | BibTeX | Tags: Artificial intelligence technologies, Dynamic virtual environment, Essential elements, Immersive technologies, Interactive virtual environments, Metaverses, Physical world, Rich dynamics, Virtual environments, Virtual Reality, Virtual worlds, Wireless communications
@article{basyoni_generative_2025,
title = {Generative AI-Driven Metaverse: The Promises and Challenges of AI-Generated Content},
author = {L. Basyoni and A. Qayyum and K. Bashir Shaban and E. Elmahjub and A. Al-Ali and O. Halabi and J. Qadir},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105018067689&doi=10.1109%2FOJCS.2025.3616501&partnerID=40&md5=1c24838c13edfdb187842c663121483d},
doi = {10.1109/OJCS.2025.3616501},
issn = {26441268 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {IEEE Open Journal of the Computer Society},
abstract = {Recent advancements in Artificial Intelligence (AI) and immersive technologies, such as Extended Reality (XR), coupled with complementary innovations like 5G/6G wireless communications, are paving the way for fully realized AI-XR metaverses. AI will play a pivotal role in this transformation, enabling the seamless convergence of virtual and physical worlds. Among the various AI applications, Generative AI (GenAI) stands out for creating rich, dynamic, and interactive virtual environments—essential elements for sustained metaverse growth and user engagement. To facilitate a deeper understanding of how GenAI will be integrated and utilized within the metaverse, we provide a comprehensive overview of GenAI and Artificial Intelligence-Generated Content (AIGC). Specifically, we examine the potential influence of GenAI's on future metaverses, exploring both the opportunities it offers and the major challenges associated with its deployment. Additionally, we investigate the robustness of AIGC detection techniques against adversarial attacks, highlighting less-explored risks posed by adversarial examples. Finally, we highlight various open research issues related to GenAI, AIGC, the metaverse, and responsible innovation that merit further exploration. © 2025 Elsevier B.V., All rights reserved.},
note = {Publisher: Institute of Electrical and Electronics Engineers Inc.},
keywords = {Artificial intelligence technologies, Dynamic virtual environment, Essential elements, Immersive technologies, Interactive virtual environments, Metaverses, Physical world, Rich dynamics, Virtual environments, Virtual Reality, Virtual worlds, Wireless communications},
pubstate = {published},
tppubtype = {article}
}
Recent advancements in Artificial Intelligence (AI) and immersive technologies, such as Extended Reality (XR), coupled with complementary innovations like 5G/6G wireless communications, are paving the way for fully realized AI-XR metaverses. AI will play a pivotal role in this transformation, enabling the seamless convergence of virtual and physical worlds. Among the various AI applications, Generative AI (GenAI) stands out for creating rich, dynamic, and interactive virtual environments—essential elements for sustained metaverse growth and user engagement. To facilitate a deeper understanding of how GenAI will be integrated and utilized within the metaverse, we provide a comprehensive overview of GenAI and Artificial Intelligence-Generated Content (AIGC). Specifically, we examine the potential influence of GenAI's on future metaverses, exploring both the opportunities it offers and the major challenges associated with its deployment. Additionally, we investigate the robustness of AIGC detection techniques against adversarial attacks, highlighting less-explored risks posed by adversarial examples. Finally, we highlight various open research issues related to GenAI, AIGC, the metaverse, and responsible innovation that merit further exploration. © 2025 Elsevier B.V., All rights reserved.
2024
Hutson, J.
Combining Large Language Models and Immersive Technologies to Represent Cultural Heritage in the Metaverse Context Book Section
In: Springer Series on Cultural Computing, vol. Part F2842, pp. 265–281, Springer Science and Business Media Deutschland GmbH, 2024, ISBN: 21959056 (ISSN).
Abstract | Links | BibTeX | Tags: Cultural heritage, Ethical implications, Generative AI, Immersive technologies, Metaverse
@incollection{hutson_combining_2024,
title = {Combining Large Language Models and Immersive Technologies to Represent Cultural Heritage in the Metaverse Context},
author = {J. Hutson},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85194557004&doi=10.1007%2f978-3-031-57746-8_14&partnerID=40&md5=476f9da2f1dedb4ada0f0c6ff6e7d6ca},
doi = {10.1007/978-3-031-57746-8_14},
isbn = {21959056 (ISSN)},
year = {2024},
date = {2024-01-01},
booktitle = {Springer Series on Cultural Computing},
volume = {Part F2842},
pages = {265–281},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {This chapter delves into the intersection of large language models, immersive technologies, and cultural heritage representation in the metaverse. Advancements in natural language processing (NLP) and deep learning enable immersive learning experiences using extended reality (XR) to teach global cultural heritage. Specifically, we propose a model that integrates generative AI, NLP, and XR, incorporating multi-sensory feedback with haptics and olfactory virtual reality (OVR) to engage users in a dialogical relationship with diverse cultures and challenge postcolonial narratives. We explore the potential of cultural heritage to resurrect famous historical personalities and overlooked indigenous peoples using generative AI and metahumans. Use cases in art history are presented, highlighting scaffolded experiences in virtual learning environments (VLEs) for deeper engagement with historical figures and events. Additionally, we address recent safety concerns and limitations of large language models that may inadvertently compromise historical veracity. Ethical implications of misrepresenting historical viewpoints are discussed, emphasizing the need for expert collaboration to ensure historical accuracy and appropriateness. The chapter also elucidates issues of ownership, representation, and cultural appropriation in the context of cultural heritage. It underscores the potential of combining large language models and immersive technologies to offer captivating and educational cultural heritage experiences. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.},
keywords = {Cultural heritage, Ethical implications, Generative AI, Immersive technologies, Metaverse},
pubstate = {published},
tppubtype = {incollection}
}
This chapter delves into the intersection of large language models, immersive technologies, and cultural heritage representation in the metaverse. Advancements in natural language processing (NLP) and deep learning enable immersive learning experiences using extended reality (XR) to teach global cultural heritage. Specifically, we propose a model that integrates generative AI, NLP, and XR, incorporating multi-sensory feedback with haptics and olfactory virtual reality (OVR) to engage users in a dialogical relationship with diverse cultures and challenge postcolonial narratives. We explore the potential of cultural heritage to resurrect famous historical personalities and overlooked indigenous peoples using generative AI and metahumans. Use cases in art history are presented, highlighting scaffolded experiences in virtual learning environments (VLEs) for deeper engagement with historical figures and events. Additionally, we address recent safety concerns and limitations of large language models that may inadvertently compromise historical veracity. Ethical implications of misrepresenting historical viewpoints are discussed, emphasizing the need for expert collaboration to ensure historical accuracy and appropriateness. The chapter also elucidates issues of ownership, representation, and cultural appropriation in the context of cultural heritage. It underscores the potential of combining large language models and immersive technologies to offer captivating and educational cultural heritage experiences. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.