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 expand the Abstract, Links and BibTex record for each paper.
2024
Jones, D.; Gračanin, D.; Azab, M.
Augmented Reality Research: Benefit or Detriment for Neurodiverse People Proceedings Article
In: U., Eck; M., Sra; J., Stefanucci; M., Sugimoto; M., Tatzgern; I., Williams (Ed.): Proc. - IEEE Int. Symp. Mixed Augment. Real. Adjunct, ISMAR-Adjunct, pp. 26–28, Institute of Electrical and Electronics Engineers Inc., 2024, ISBN: 979-833150691-9 (ISBN).
Abstract | Links | BibTeX | Tags: Anonymity, Attention Deficit, Augmented Reality, Benefit/risk, Cyber Attack, Cyber attacks, Cyber Defense, Cyber-attacks, Cyber-defense, Language Model, Model training, Potential risks, Privacy invasions, Quality of life, Training data
@inproceedings{jones_augmented_2024,
title = {Augmented Reality Research: Benefit or Detriment for Neurodiverse People},
author = {D. Jones and D. Gračanin and M. Azab},
editor = {Eck U. and Sra M. and Stefanucci J. and Sugimoto M. and Tatzgern M. and Williams I.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85214361441&doi=10.1109%2fISMAR-Adjunct64951.2024.00015&partnerID=40&md5=c2e684986face0f49335d711fecf58c2},
doi = {10.1109/ISMAR-Adjunct64951.2024.00015},
isbn = {979-833150691-9 (ISBN)},
year = {2024},
date = {2024-01-01},
booktitle = {Proc. - IEEE Int. Symp. Mixed Augment. Real. Adjunct, ISMAR-Adjunct},
pages = {26–28},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {The intersection of technology and innovation has always been a double-edged sword for humanity, offering both profound benefits and potential risks. This paper examines the positive and negative impacts of augmented reality (AR) and generative artificial intelligence (GAI) on neurodiverse users (NDU). While AR, coupled with large language models (LLM), has the potential to revolutionize the diagnosis and training environments for NDUs, inherent biases in LLM training data, which predominantly reflects neurotypical user (NTU) content, pose significant risks. These biases can result in environments and interactions that are less accessible and potentially harmful to NDUs. The paper explores the implications of these biases, including the possibility of privacy invasion and the misuse of technology for diagnosing undiagnosed NDUs, leading to severe personal and professional consequences. The study advocates for industry-wide collaboration to mitigate these biases, develop NDU-specific datasets, and create secure AR frameworks that safeguard the neurodiverse population while enhancing their quality of life. © 2024 IEEE.},
keywords = {Anonymity, Attention Deficit, Augmented Reality, Benefit/risk, Cyber Attack, Cyber attacks, Cyber Defense, Cyber-attacks, Cyber-defense, Language Model, Model training, Potential risks, Privacy invasions, Quality of life, Training data},
pubstate = {published},
tppubtype = {inproceedings}
}
The intersection of technology and innovation has always been a double-edged sword for humanity, offering both profound benefits and potential risks. This paper examines the positive and negative impacts of augmented reality (AR) and generative artificial intelligence (GAI) on neurodiverse users (NDU). While AR, coupled with large language models (LLM), has the potential to revolutionize the diagnosis and training environments for NDUs, inherent biases in LLM training data, which predominantly reflects neurotypical user (NTU) content, pose significant risks. These biases can result in environments and interactions that are less accessible and potentially harmful to NDUs. The paper explores the implications of these biases, including the possibility of privacy invasion and the misuse of technology for diagnosing undiagnosed NDUs, leading to severe personal and professional consequences. The study advocates for industry-wide collaboration to mitigate these biases, develop NDU-specific datasets, and create secure AR frameworks that safeguard the neurodiverse population while enhancing their quality of life. © 2024 IEEE.
Truong, V. T.; Le, H. D.; Le, L. B.
Trust-Free Blockchain Framework for AI-Generated Content Trading and Management in Metaverse Journal Article
In: IEEE Access, vol. 12, pp. 41815–41828, 2024, ISSN: 21693536 (ISSN).
Abstract | Links | BibTeX | Tags: AI-generated content, AI-generated content (AIGC), Artificial intelligence, Asset management, Assets management, Block-chain, Blockchain, Commerce, Content distribution networks, Cyber-attacks, Decentralised, Decentralized application, Digital asset management, Digital system, Generative AI, Metaverse, Metaverses, Plagiarism, Security, Trustless service, Virtual Reality
@article{truong_trust-free_2024,
title = {Trust-Free Blockchain Framework for AI-Generated Content Trading and Management in Metaverse},
author = {V. T. Truong and H. D. Le and L. B. Le},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85188472793&doi=10.1109%2fACCESS.2024.3376509&partnerID=40&md5=301939c1faef0c5a7b56d9feadce27ee},
doi = {10.1109/ACCESS.2024.3376509},
issn = {21693536 (ISSN)},
year = {2024},
date = {2024-01-01},
journal = {IEEE Access},
volume = {12},
pages = {41815–41828},
abstract = {The rapid development of the metaverse and generative Artificial Intelligence (GAI) has led to the emergence of AI-Generated Content (AIGC). Unlike real-world products, AIGCs are represented as digital files, thus vulnerable to plagiarism and leakage on the Internet. In addition, the trading of AIGCs in the virtual world is prone to various trust issues between the involved participants. For example, some customers may try to avoid the payment after receiving the desired AIGC products, or the content sellers refuse to grant the products after obtaining the license fee. Existing digital asset management (DAM) systems often rely on a trusted third-party authority to mitigate these issues. However, this might lead to centralization problems such as the single-point-of-failure (SPoF) when the third parties are under attacks or being malicious. In this paper, we propose MetaTrade, a blockchain-empowered DAM framework that is designed to tackle these urgent trust issues, offering secured AIGC trading and management in the trustless metaverse environment. MetaTrade eliminates the role of the trusted third party, without requiring trust assumptions among participants. Numerical results show that MetaTrade offers higher performance and lower trading cost compared to existing platforms, while security analysis reveals that the framework is resilient against plagiarism, SPoF, and trust-related attacks. To showcase the feasibility of the design, a decentralized application (DApp) has been built on top of MetaTrade as a marketplace for metaverse AIGCs. © 2013 IEEE.},
keywords = {AI-generated content, AI-generated content (AIGC), Artificial intelligence, Asset management, Assets management, Block-chain, Blockchain, Commerce, Content distribution networks, Cyber-attacks, Decentralised, Decentralized application, Digital asset management, Digital system, Generative AI, Metaverse, Metaverses, Plagiarism, Security, Trustless service, Virtual Reality},
pubstate = {published},
tppubtype = {article}
}
The rapid development of the metaverse and generative Artificial Intelligence (GAI) has led to the emergence of AI-Generated Content (AIGC). Unlike real-world products, AIGCs are represented as digital files, thus vulnerable to plagiarism and leakage on the Internet. In addition, the trading of AIGCs in the virtual world is prone to various trust issues between the involved participants. For example, some customers may try to avoid the payment after receiving the desired AIGC products, or the content sellers refuse to grant the products after obtaining the license fee. Existing digital asset management (DAM) systems often rely on a trusted third-party authority to mitigate these issues. However, this might lead to centralization problems such as the single-point-of-failure (SPoF) when the third parties are under attacks or being malicious. In this paper, we propose MetaTrade, a blockchain-empowered DAM framework that is designed to tackle these urgent trust issues, offering secured AIGC trading and management in the trustless metaverse environment. MetaTrade eliminates the role of the trusted third party, without requiring trust assumptions among participants. Numerical results show that MetaTrade offers higher performance and lower trading cost compared to existing platforms, while security analysis reveals that the framework is resilient against plagiarism, SPoF, and trust-related attacks. To showcase the feasibility of the design, a decentralized application (DApp) has been built on top of MetaTrade as a marketplace for metaverse AIGCs. © 2013 IEEE.
Chandrashekar, N. Donekal; Lee, A.; Azab, M.; Gracanin, D.
Understanding User Behavior for Enhancing Cybersecurity Training with Immersive Gamified Platforms Journal Article
In: Information (Switzerland), vol. 15, no. 12, 2024, ISSN: 20782489 (ISSN).
Abstract | Links | BibTeX | Tags: Artificial intelligence, Critical infrastructures, Cyber attacks, Cyber security, Cyber systems, Cyber-attacks, Cybersecurity, Decisions makings, Digital infrastructures, digital twin, Extended reality, Gamification, Immersive, Network Security, simulation, Technical vulnerabilities, Training, user behavior, User behaviors
@article{donekal_chandrashekar_understanding_2024,
title = {Understanding User Behavior for Enhancing Cybersecurity Training with Immersive Gamified Platforms},
author = {N. Donekal Chandrashekar and A. Lee and M. Azab and D. Gracanin},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85213435167&doi=10.3390%2finfo15120814&partnerID=40&md5=134c43c7238bae4923468bc6e46c860d},
doi = {10.3390/info15120814},
issn = {20782489 (ISSN)},
year = {2024},
date = {2024-01-01},
journal = {Information (Switzerland)},
volume = {15},
number = {12},
abstract = {In modern digital infrastructure, cyber systems are foundational, making resilience against sophisticated attacks essential. Traditional cybersecurity defenses primarily address technical vulnerabilities; however, the human element, particularly decision-making during cyber attacks, adds complexities that current behavioral studies fail to capture adequately. Existing approaches, including theoretical models, game theory, and simulators, rely on retrospective data and static scenarios. These methods often miss the real-time, context-specific nature of user responses during cyber threats. To address these limitations, this work introduces a framework that combines Extended Reality (XR) and Generative Artificial Intelligence (Gen-AI) within a gamified platform. This framework enables continuous, high-fidelity data collection on user behavior in dynamic attack scenarios. It includes three core modules: the Player Behavior Module (PBM), Gamification Module (GM), and Simulation Module (SM). Together, these modules create an immersive, responsive environment for studying user interactions. A case study in a simulated critical infrastructure environment demonstrates the framework’s effectiveness in capturing realistic user behaviors under cyber attack, with potential applications for improving response strategies and resilience across critical sectors. This work lays the foundation for adaptive cybersecurity training and user-centered development across critical infrastructure. © 2024 by the authors.},
keywords = {Artificial intelligence, Critical infrastructures, Cyber attacks, Cyber security, Cyber systems, Cyber-attacks, Cybersecurity, Decisions makings, Digital infrastructures, digital twin, Extended reality, Gamification, Immersive, Network Security, simulation, Technical vulnerabilities, Training, user behavior, User behaviors},
pubstate = {published},
tppubtype = {article}
}
In modern digital infrastructure, cyber systems are foundational, making resilience against sophisticated attacks essential. Traditional cybersecurity defenses primarily address technical vulnerabilities; however, the human element, particularly decision-making during cyber attacks, adds complexities that current behavioral studies fail to capture adequately. Existing approaches, including theoretical models, game theory, and simulators, rely on retrospective data and static scenarios. These methods often miss the real-time, context-specific nature of user responses during cyber threats. To address these limitations, this work introduces a framework that combines Extended Reality (XR) and Generative Artificial Intelligence (Gen-AI) within a gamified platform. This framework enables continuous, high-fidelity data collection on user behavior in dynamic attack scenarios. It includes three core modules: the Player Behavior Module (PBM), Gamification Module (GM), and Simulation Module (SM). Together, these modules create an immersive, responsive environment for studying user interactions. A case study in a simulated critical infrastructure environment demonstrates the framework’s effectiveness in capturing realistic user behaviors under cyber attack, with potential applications for improving response strategies and resilience across critical sectors. This work lays the foundation for adaptive cybersecurity training and user-centered development across critical infrastructure. © 2024 by the authors.