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, 2025, ISSN: 15361233 (ISSN).
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=3cb5a2143b9ce4ca7f931a60f1bf239c},
doi = {10.1109/TMC.2025.3550815},
issn = {15361233 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {IEEE Transactions on Mobile Computing},
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. © 2002-2012 IEEE.},
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}
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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. © 2002-2012 IEEE.
Sousa, R. T.; Oliveira, E. A. M.; Cintra, L. M. F.; Filho, A. R. G.
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: 979-833151484-6 (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. Filho},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105005140551&doi=10.1109%2fVRW66409.2025.00042&partnerID=40&md5=89da6954863a272d48c0d8da3760bfb6},
doi = {10.1109/VRW66409.2025.00042},
isbn = {979-833151484-6 (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 IEEE.},
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 IEEE.
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.