Publications

@inproceedings{logothetis_interactive_2025,

title = {Interactive Learning Through Conversational Avatars and Immersive VR: Enhancing Diabetes Education and Self-Management},

author = {I. Logothetis and K. Diakogiannis and N. Vidakis},

editor = {Fang X.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105008266480&doi=10.1007%2f978-3-031-92578-8_27&partnerID=40&md5=451274dfa3ef0b3f1b39c7d5a665ee3b},

doi = {10.1007/978-3-031-92578-8_27},

isbn = {03029743 (ISSN); 978-303192577-1 (ISBN)},

year  = {2025},

date = {2025-01-01},

booktitle = {Lect. Notes Comput. Sci.},

volume = {15816 LNCS},

pages = {415–429},

publisher = {Springer Science and Business Media Deutschland GmbH},

abstract = {Diabetes is a chronic disease affecting a large population of the world. Education and self-management of diabetes are crucial. Technologies such as Virtual Reality (VR) have presented promising results in healthcare education, while studies suggest that Artificial Intelligence (AI) can help in learning by further engaging the learner. This study aims to educate users on the entire routine of managing diabetes. The serious game utilizes VR for realistic interaction with diabetes tools and generative AI through a conversational avatar that acts as an assistant instructor. In this way, it allows users to practice diagnostic and therapeutic interventions in a controlled virtual environment, helping to build their understanding and confidence in diabetes management. To measure the effects of the proposed serious game, presence, and perceived agency were measured. Preliminary results indicate that this setup aids in the engagement and immersion of learners, while the avatar can provide helpful information during gameplay. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.},

keywords = {Artificial intelligence, Chronic disease, Computer aided instruction, Diabetes Education, Diagnosis, E-Learning, Education management, Engineering education, Gamification, Immersive virtual reality, Interactive computer graphics, Interactive learning, Large population, Learning systems, NUI, Self management, Serious game, Serious games, simulation, Virtual Reality},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{tsai_development_2025,

title = {The Development of an Interactive IoT Cross-Media Survey System and Real-Time Re-presentation of Mass Learning},

author = {Y. -J. Tsai and S. -T. Liu and S. -C. Hsu},

editor = {Wei J. and Margetis G.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105008756188&doi=10.1007%2f978-3-031-93061-4_10&partnerID=40&md5=c487828eeacfdf18cf4e726e6ce28146},

doi = {10.1007/978-3-031-93061-4_10},

isbn = {03029743 (ISSN); 978-303193060-7 (ISBN)},

year  = {2025},

date = {2025-01-01},

booktitle = {Lect. Notes Comput. Sci.},

volume = {15823 LNCS},

pages = {145–157},

publisher = {Springer Science and Business Media Deutschland GmbH},

abstract = {In this study, we propose the Interactive IoT Cross-Media Survey System, integrating tangible interaction in a game-like manner with real-time data re-presentation. This system was implemented in the “STEM with A” Interactive Exploration Hall at National Tsing Hua University in 2020. It enabled participants to use their smartphones as tangible user interfaces to “scoop-up questions” from interactive sensing points within the exhibition areas. After completing the questions, participants could “pour-in” their responses and observe digital data re-presentation artworks generated from survey results, showcasing mass learning outcomes. Furthermore, the data re-presentation content was tailored to participants’ group characteristics, showing how their responses impact the group’s overall learning outcomes with each “pour-in response.” The study achieved several key outcomes: (1) transforming traditional surveys into a gamified survey system, enhancing participants’ engagement, (2) providing real-time, group-based data re-presentations, enabling participants to contribute to the group’s learning outcomes, and (3) implementing a grouping mechanism to foster collaboration within groups and healthy competition between them. This system provides flexible and customizable data re-presentation, making it suitable for diverse environments requiring real-time data-driven engagement. Future applications can integrate emerging technologies, such as generative AI to dynamically generate questions or virtual reality to offer immersive experiences. Additionally, data re-presentations can be designed as dynamic mass artistic creations, allowing participants to become co-creators of an evolving collective masterpiece. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.},

keywords = {Cross-media, Data Re-presentation, Internet of Things, IoT Cross-Media System, IoT cross-medium system, Learning outcome, Learning systems, Mass Learning, Media systems, Smart phones, Smartphone, Smartphones, STEM with A, Survey System, Survey systems, Surveying, Tangible User Interface, Tangible user interfaces, User interfaces, Virtual Reality},

pubstate = {published},

tppubtype = {inproceedings}

}

@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}

}

@inproceedings{domenichini_ai-driven_2024,

title = {An AI-Driven Approach for Enhancing Engagement and Conceptual Understanding in Physics Education},

author = {D. Domenichini and A. Bucchiarone and F. Chiarello and G. Schiavo and G. Fantoni},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85199035695&doi=10.1109%2fEDUCON60312.2024.10578670&partnerID=40&md5=4cf9f89e97664ae6d618a90f2dbc23e0},

doi = {10.1109/EDUCON60312.2024.10578670},

isbn = {21659559 (ISSN); 979-835039402-3 (ISBN)},

year  = {2024},

date = {2024-01-01},

booktitle = {IEEE Global Eng. Edu. Conf., EDUCON},

publisher = {IEEE Computer Society},

abstract = {This Work in Progress paper introduces the design of an innovative educational system that leverages Artificial Intelligence (AI) to address challenges in physics education. The primary objective is to create a system that dynamically adapts to the individual needs and preferences of students while maintaining user-friendliness for teachers, allowing them to tailor their teaching methods. The emphasis is on fostering motivation and engagement, achieved through the implementation of a gamified virtual environment and a strong focus on personalization. Our aim is to develop a system capable of autonomously generating learning activities and constructing effective learning paths, all under the supervision and interaction of teachers. The generation of learning activities is guided by educational taxonomies that delineate and categorize the cognitive processes involved in these activities. The proposed educational system seeks to address challenges identified by Physics Education Research (PER), which offers valuable insights into how individuals learn physics and provides strategies to enhance the overall quality of physics education. Our specific focus revolves around two crucial aspects: concentrating on the conceptual understanding of physics concepts and processes, and fostering knowledge integration and coherence across various physics topics. These aspects are deemed essential for cultivating enduring knowledge and facilitating practical applications in the field of physics. © 2024 IEEE.},

keywords = {Adaptive Learning, Artificial intelligence, Artificial intelligence in education, Artificial Intelligence in Education (AIED), Conceptual Understanding, Educational System, Educational systems, Gamification, Generative AI, generative artificial intelligence, Learning Activity, Learning systems, Physics Education, Teachers', Teaching, Virtual Reality},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{williams_deep_2024,

title = {Deep HoriXons - 3D Virtual Generative AI Assisted Campus for Deep Learning AI and Cybersecurity},

author = {R. Williams},

editor = {Blowers M. and Wysocki B.T.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85196555361&doi=10.1117%2f12.3011374&partnerID=40&md5=ff7392a37a51044c79d4d2824c9cf46b},

doi = {10.1117/12.3011374},

isbn = {0277786X (ISSN); 978-151067434-9 (ISBN)},

year  = {2024},

date = {2024-01-01},

booktitle = {Proc SPIE Int Soc Opt Eng},

volume = {13058},

publisher = {SPIE},

abstract = {This abstract outlines two significant innovations in AI and cybersecurity education within the "Deep HoriXons" 3D virtual campus, addressing the urgent need for skilled professionals in these domains. First, the paper introduces "Deep HoriXons," an immersive 3D virtual learning environment designed to democratize and enhance the educational experience for AI and cybersecurity. This innovation is notable for its global accessibility and ability to simulate real-world scenarios, providing an interactive platform for experiential learning, which is a marked departure from traditional educational models. The second innovation discussed is the strategic integration of ChatGPT as a digital educator and tutor within this virtual environment. ChatGPT's role is pivotal in offering tailored, real-time educational support, making complex AI and cybersecurity concepts more accessible and engaging for learners. This application of ChatGPT is an innovation worth noting for its ability to adapt to individual learning styles, provide interactive scenario-based learning, and support a deeper understanding of technical subjects through dynamic, responsive interaction. Together, these innovations represent a significant advancement in the field of AI and cybersecurity education, addressing the critical talent shortage by making high-quality, interactive learning experiences accessible on a global scale. The paper highlights the importance of these innovations in creating a skilled workforce capable of tackling the evolving challenges in AI and cybersecurity, underscoring the need for ongoing research and development in this area. © 2024 SPIE.},

keywords = {3D virtual campus, AI and cybersecurity education, AI talent pipeline, ChatGPT digital tutor, CompTIA Security+, Computer aided instruction, Cyber security, Cyber-security educations, Cybersecurity, Deep learning, E-Learning, Immersive, Learning systems, Virtual campus, Virtual learning environments, Virtual Reality},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{liu_classmeta_2024,

title = {ClassMeta: Designing Interactive Virtual Classmate to Promote VR Classroom Participation},

author = {Z. Liu and Z. Zhu and L. Zhu and E. Jiang and X. Hu and K. Peppler and K. Ramani},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85194868458&doi=10.1145%2f3613904.3642947&partnerID=40&md5=0592b2f977a2ad2e6366c6fa05808a6a},

doi = {10.1145/3613904.3642947},

isbn = {979-840070330-0 (ISBN)},

year  = {2024},

date = {2024-01-01},

booktitle = {Conf Hum Fact Comput Syst Proc},

publisher = {Association for Computing Machinery},

abstract = {Peer influence plays a crucial role in promoting classroom participation, where behaviors from active students can contribute to a collective classroom learning experience. However, the presence of these active students depends on several conditions and is not consistently available across all circumstances. Recently, Large Language Models (LLMs) such as GPT have demonstrated the ability to simulate diverse human behaviors convincingly due to their capacity to generate contextually coherent responses based on their role settings. Inspired by this advancement in technology, we designed ClassMeta, a GPT-4 powered agent to help promote classroom participation by playing the role of an active student. These agents, which are embodied as 3D avatars in virtual reality, interact with actual instructors and students with both spoken language and body gestures. We conducted a comparative study to investigate the potential of ClassMeta for improving the overall learning experience of the class. © 2024 Copyright held by the owner/author(s)},

keywords = {3D Avatars, Behavioral Research, Classroom learning, Collaborative learning, Computational Linguistics, Condition, E-Learning, Human behaviors, Language Model, Large language model, Learning experiences, Learning systems, pedagogical agent, Pedagogical agents, Students, Three dimensional computer graphics, Virtual Reality, VR classroom},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{pester_conversational_2024,

title = {Conversational Agents, Virtual Worlds, and Beyond: A Review of Large Language Models Enabling Immersive Learning},

author = {A. Pester and A. Tammaa and C. Gütl and A. Steinmaurer and S. A. El-Seoud},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85199068668&doi=10.1109%2fEDUCON60312.2024.10578895&partnerID=40&md5=1b904fd8a5e06d7ced42a328c028bbb7},

doi = {10.1109/EDUCON60312.2024.10578895},

isbn = {21659559 (ISSN); 979-835039402-3 (ISBN)},

year  = {2024},

date = {2024-01-01},

booktitle = {IEEE Global Eng. Edu. Conf., EDUCON},

publisher = {IEEE Computer Society},

abstract = {Large Language Models represent a significant breakthrough in Natural Language Processing research and opened a wide range of application domains. This paper demonstrates the successful integration of Large Language Models into immersive learning environments. The review highlights how this emerging technology aligns with pedagogical principles, enhancing the effectiveness of current educational systems. It also reflects recent advancements in integrating Large Language Models, including fine-tuning, hallucination reduction, fact-checking, and human evaluation of generated results. © 2024 IEEE.},

keywords = {Computational Linguistics, Computer aided instruction, Conversational Agents, Education, Immersive learning, Language Model, Large language model, Learning systems, Literature reviews, LLM, Metaverse, Metaverses, Natural language processing systems, Pedagogy, Survey literature review, Virtual Reality, Virtual worlds},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{clocchiatti_character_2024,

title = {Character Animation Pipeline based on Latent Diffusion and Large Language Models},

author = {A. Clocchiatti and N. Fumero and A. M. Soccini},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187217072&doi=10.1109%2fAIxVR59861.2024.00067&partnerID=40&md5=d88b9ba7c80d49b60fd0d7acd5e7c4f0},

doi = {10.1109/AIxVR59861.2024.00067},

isbn = {979-835037202-1 (ISBN)},

year  = {2024},

date = {2024-01-01},

booktitle = {Proc. - IEEE Int. Conf. Artif. Intell. Ext. Virtual Real., AIxVR},

pages = {398–405},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {Artificial intelligence and deep learning techniques are revolutionizing the film production pipeline. The majority of the current screenplay-to-animation pipelines focus on understanding the screenplay through natural language processing techniques, and on the generation of the animation through custom engines, missing the possibility to customize the characters. To address these issues, we propose a high-level pipeline for generating 2D characters and animations starting from screenplays, through a combination of Latent Diffusion Models and Large Language Models. Our approach uses ChatGPT to generate character descriptions starting from the screenplay. Then, using that data, it generates images of custom characters with Stable Diffusion and animates them according to their actions in different scenes. The proposed approach avoids well-known problems in generative AI tools such as temporal inconsistency and lack of control on the outcome. The results suggest that the pipeline is consistent and reliable, benefiting industries ranging from film production to virtual, augmented and extended reality content creation. © 2024 IEEE.},

keywords = {Animation, Animation pipeline, Artificial intelligence, Augmented Reality, Character animation, Computational Linguistics, Computer animation, Deep learning, Diffusion, E-Learning, Extended reality, Film production, Generative art, Language Model, Learning systems, Learning techniques, Natural language processing systems, Pipelines, Production pipelines, Virtual Reality},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{haramina_learning_2024,

title = {Learning Algorithms Concepts in a Virtual Reality Escape Room},

author = {E. Haramina and M. Paladin and Z. Petričušić and F. Posarić and A. Drobnjak and I. Botički},

editor = {Babic S. and Car Z. and Cicin-Sain M. and Cisic D. and Ergovic P. and Grbac T.G. and Gradisnik V. and Gros S. and Jokic A. and Jovic A. and Jurekovic D. and Katulic T. and Koricic M. and Mornar V. and Petrovic J. and Skala K. and Skvorc D. and Sruk V. and Svaco M. and Tijan E. and Vrcek N. and Vrdoljak B.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85198221737&doi=10.1109%2fMIPRO60963.2024.10569447&partnerID=40&md5=8a94d92d989d1f0feb84eba890945de8},

doi = {10.1109/MIPRO60963.2024.10569447},

isbn = {979-835038249-5 (ISBN)},

year  = {2024},

date = {2024-01-01},

booktitle = {ICT Electron. Conv., MIPRO - Proc.},

pages = {2057–2062},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {Although the standard way to learn algorithms is by coding, learning through games is another way to obtain knowledge while having fun. Virtual reality is a computer-generated three-dimensional environment in which the player is fully immersed by having external stimuli mostly blocked out. In the game presented in this paper, players are enhancing their algorithms skills by playing an escape room game. The goal is to complete the room within the designated time by solving puzzles. The puzzles change for every playthrough with the use of generative artificial intelligence to provide every player with a unique experience. There are multiple types of puzzles such as. time complexity, sorting algorithms, searching algorithms, and code execution. The paper presents the results of a study indicating students' preference for learning through gaming as a method of acquiring algorithms knowledge. © 2024 IEEE.},

keywords = {Artificial intelligence, Computational complexity, Computer generated three dimensional environment, E-Learning, Education, Escape room, Extended reality, generative artificial intelligence, Learn+, Learning, Learning algorithms, Learning systems, Puzzle, puzzles, user experience, User study, User testing, Users' experiences, Virtual Reality},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{krauss_opportunities_2024,

title = {Opportunities and Challenges in Developing Educational AI-Assistants for the Metaverse},

author = {C. Krauss and L. Bassbouss and M. Upravitelev and T. -S. An and D. Altun and L. Reray and E. Balitzki and T. El Tamimi and M. Karagülle},

editor = {Sottilare R.A. and Schwarz J.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85196214138&doi=10.1007%2f978-3-031-60609-0_16&partnerID=40&md5=9a66876cb30e9e5d287a86e6cfa66e05},

doi = {10.1007/978-3-031-60609-0_16},

isbn = {03029743 (ISSN); 978-303160608-3 (ISBN)},

year  = {2024},

date = {2024-01-01},

booktitle = {Lect. Notes Comput. Sci.},

volume = {14727 LNCS},

pages = {219–238},

publisher = {Springer Science and Business Media Deutschland GmbH},

abstract = {The paper explores the opportunities and challenges for metaverse learning environments with AI-Assistants based on Large Language Models. A proof of concept based on popular but proprietary technologies is presented that enables a natural language exchange between the user and an AI-based medical expert in a highly immersive environment based on the Unreal Engine. The answers generated by ChatGPT are not only played back lip-synchronously, but also visualized in the VR environment using a 3D model of a skeleton. Usability and user experience play a particularly important role in the development of the highly immersive AI-Assistant. The proof of concept serves to illustrate the opportunities and challenges that lie in the merging of large language models, metaverse applications and educational ecosystems, which are self-contained research areas. Development strategies, tools and interoperability standards will be presented to facilitate future developments in this triangle of tension. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.},

keywords = {3D modeling, AI-assistant, AI-Assistants, Computational Linguistics, Computer aided instruction, Concept-based, E-Learning, Education, Interoperability, Language Model, Large language model, large language models, Learning Environments, Learning systems, Learning Technologies, Learning technology, LLM, Metaverse, Metaverses, Natural language processing systems, Proof of concept, User interfaces, Virtual assistants, Virtual Reality},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{sarshartehrani_enhancing_2024,

title = {Enhancing E-Learning Experience Through Embodied AI Tutors in Immersive Virtual Environments: A Multifaceted Approach for Personalized Educational Adaptation},

author = {F. Sarshartehrani and E. Mohammadrezaei and M. Behravan and D. Gracanin},

editor = {Sottilare R.A. and Schwarz J.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85196174389&doi=10.1007%2f978-3-031-60609-0_20&partnerID=40&md5=3801d0959781b1a191a3eb14f47bd8d8},

doi = {10.1007/978-3-031-60609-0_20},

isbn = {03029743 (ISSN); 978-303160608-3 (ISBN)},

year  = {2024},

date = {2024-01-01},

booktitle = {Lect. Notes Comput. Sci.},

volume = {14727 LNCS},

pages = {272–287},

publisher = {Springer Science and Business Media Deutschland GmbH},

abstract = {As digital education transcends traditional boundaries, e-learning experiences are increasingly shaped by cutting-edge technologies like artificial intelligence (AI), virtual reality (VR), and adaptive learning systems. This study examines the integration of AI-driven personalized instruction within immersive VR environments, targeting enhanced learner engagement-a core metric in online education effectiveness. Employing a user-centric design, the research utilizes embodied AI tutors, calibrated to individual learners’ emotional intelligence and cognitive states, within a Python programming curriculum-a key area in computer science education. The methodology relies on intelligent tutoring systems and personalized learning pathways, catering to a diverse participant pool from Virginia Tech. Our data-driven approach, underpinned by the principles of educational psychology and computational pedagogy, indicates that AI-enhanced virtual learning environments significantly elevate user engagement and proficiency in programming education. Although the scope is limited to a single academic institution, the promising results advocate for the scalability of such AI-powered educational tools, with potential implications for distance learning, MOOCs, and lifelong learning platforms. This research contributes to the evolving narrative of smart education and the role of large language models (LLMs) in crafting bespoke educational experiences, suggesting a paradigm shift towards more interactive, personalized e-learning solutions that align with global educational technology trends. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.},

keywords = {Adaptive Learning, Artificial intelligence, Artificial intelligence in education, Computer aided instruction, Computer programming, E - learning, E-Learning, Education computing, Embodied artificial intelligence, Engineering education, Immersive Virtual Environments, Learner Engagement, Learning experiences, Learning systems, Multi-faceted approach, Personalized Instruction, Traditional boundaries, Virtual Reality},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{liang_iknowisee_2024,

title = {iKnowiSee: AR Glasses with Language Learning Translation System and Identity Recognition System Built Based on Large Pre-trained Models of Language and Vision and Internet of Things Technology},

author = {Q. Liang and Y. Chen and W. Li and M. Lai and W. Ni and H. Qiu},

editor = {Zhang L. and Yu W. and Wang Q. and Laili Y. and Liu Y.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85200663840&doi=10.1007%2f978-981-97-3948-6_2&partnerID=40&md5=a0324ba6108674b1d39a338574269d60},

doi = {10.1007/978-981-97-3948-6_2},

isbn = {18650929 (ISSN); 978-981973947-9 (ISBN)},

year  = {2024},

date = {2024-01-01},

booktitle = {Commun. Comput. Info. Sci.},

volume = {2139 CCIS},

pages = {12–24},

publisher = {Springer Science and Business Media Deutschland GmbH},

abstract = {AR glasses used in daily life have made good progress and have some practical value.However, the current design concept of AR glasses is basically to simply port the content of a cell phone and act as a secondary screen for the phone. In contrast, the AR glasses we designed are based on actual situations, focus on real-world interactions, and utilize IoT technology with the aim of enabling users to fully extract and utilize the digital information in their lives. We have created two innovative features, one is a language learning translation system for users to learn foreign languages, which integrates a large language model with an open vocabulary recognition model to fully extract the visual semantic information of the scene; and the other is a social conferencing system, which utilizes the IoT cloud, pipe, edge, and end development to reduce the cost of communication and improve the efficiency of exchanges in social situations. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.},

keywords = {Augmented Reality, Glass, Identity recognition, Internet of Things, Internet of things technologies, IoT, Language learning, Learning systems, LLM, Object Detection, Objects detection, Open Vocabulary Object Detection, Recognition systems, Semantics, Telephone sets, Translation (languages), Translation systems, Visual languages, Wearable computers, Wearable device, Wearable devices},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{marin-morales_gaze_2023,

title = {Gaze and Head Movement Patterns of Depressive Symptoms During Conversations with Emotional Virtual Humans},

author = {J. Marín-Morales and J. Llanes-Jurado and M. E. Minissi and L. Gómez-Zaragozá and A. Altozano and M. Alcaniz},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184656388&doi=10.1109%2fACII59096.2023.10388134&partnerID=40&md5=143cdd8530e17a7b64bdf88f3a0496ab},

doi = {10.1109/ACII59096.2023.10388134},

isbn = {979-835032743-4 (ISBN)},

year  = {2023},

date = {2023-01-01},

booktitle = {Int. Conf. Affect. Comput. Intell. Interact., ACII},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {Depressive symptoms involve dysfunctional social attitudes and heightened negative emotional states. Identifying biomarkers requires data collection in realistic environments that activate depression-specific phenomena. However, no previous research analysed biomarkers in combination with AI-powered conversational virtual humans (VH) for mental health assessment. This study aims to explore gaze and head movements patterns related to depressive symptoms during conversations with emotional VH. A total of 105 participants were evenly divided into a control group and a group of subjects with depressive symptoms (SDS). They completed six semi-guided conversations designed to evoke basic emotions. The VHs were developed using a cognitive-inspired framework, enabling real-time voice-based conversational interactions powered by a Large Language Model, and including emotional facial expressions and lip synchronization. They have embedded life-history, context, attitudes, emotions and motivations. Signal processing techniques were applied to obtain gaze and head movements features, and heatmaps were generated. Then, parametric and non-parametric statistical tests were applied to evaluate differences between groups. Additionally, a two-dimensional t-SNE embedding was created and combined with k-means clustering. Results indicate that SDS exhibited shorter blinks and longer saccades. The control group showed affiliative lateral head gyros and accelerations, while the SDS demonstrated stress-related back-and-forth movements. SDS also displayed the avoidance of eye contact. The exploratory multivariate statistical unsupervised learning achieved 72.3% accuracy. The present study analyse biomarkers in affective processes with multiple social contextual factors and information modalities in ecological environments, and enhances our understanding of gaze and head movements patterns in individuals with depressive symptoms, ultimately contributing to the development of more effective assessments and intervention strategies. © 2023 IEEE.},

keywords = {Biomarkers, Clustering, Clusterings, Computational Linguistics, Depressive disorder, Depressive symptom, E-Learning, Emotion elicitation, Eye movements, Gaze movements, K-means clustering, Language Model, Large language model, large language models, Learning systems, Mental health, Multivariant analysis, Signal processing, Statistical learning, virtual human, Virtual humans, Virtual Reality},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{park_enabling_2023,

title = {Enabling the Wireless Metaverse via Semantic Multiverse Communication},

author = {J. Park and J. Choi and S. -L. Kim and M. Bennis},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85177465286&doi=10.1109%2fSECON58729.2023.10287438&partnerID=40&md5=b052572fb2f78ce0694c7ae5726c8daf},

doi = {10.1109/SECON58729.2023.10287438},

isbn = {21555486 (ISSN); 979-835030052-9 (ISBN)},

year  = {2023},

date = {2023-01-01},

booktitle = {Annu. IEEE Commun.Soc. Conf. Sens., Mesh Ad Hoc Commun. Netw. workshops},

volume = {2023-September},

pages = {85–90},

publisher = {IEEE Computer Society},

abstract = {Metaverse over wireless networks is an emerging use case of the sixth generation (6G) wireless systems, posing unprecedented challenges in terms of its multi-modal data transmissions with stringent latency and reliability requirements. Towards enabling this wireless metaverse, in this article we propose a novel semantic communication (SC) framework by decomposing the metaverse into human/machine agent-specific semantic multiverses (SMs). An SM stored at each agent comprises a semantic encoder and a generator, leveraging recent advances in generative artificial intelligence (AI). To improve communication efficiency, the encoder learns the semantic representations (SRs) of multi-modal data, while the generator learns how to manipulate them for locally rendering scenes and interactions in the metaverse. Since these learned SMs are biased towards local environments, their success hinges on synchronizing heterogeneous SMs in the background while communicating SRs in the foreground, turning the wireless metaverse problem into the problem of semantic multiverse communication (SMC). Based on this SMC architecture, we propose several promising algorithmic and analytic tools for modeling and designing SMC, ranging from distributed learning and multi-agent reinforcement learning (MARL) to signaling games and symbolic AI. © 2023 IEEE.},

keywords = {Deep learning, Extended reality (XR), Federated learning, Fertilizers, Learn+, Learning systems, Metaverse, Metaverses, Modal analysis, Multi agent systems, Multi-agent reinforcement learning, Multi-modal data, Reinforcement Learning, Semantic communication, Semantics, Signal encoding, Signaling game, Split learning, Symbolic artificial intelligence},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{suryavanshi_advancing_2023,

title = {Advancing Digital Transformation in Indian Higher Education Institutions},

author = {D. P. Suryavanshi and P. R. Kaveri and P. S. Kadlag},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189153416&doi=10.1109%2fICCEBS58601.2023.10448947&partnerID=40&md5=8aff6f6dc84d011ed59e0f8cec9d9318},

doi = {10.1109/ICCEBS58601.2023.10448947},

isbn = {979-835039458-0 (ISBN)},

year  = {2023},

date = {2023-01-01},

booktitle = {Intell. Comput. Control Eng. Bus. Syst., ICCEBS},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {The paper focuses on advancing the use of Digital Transformation in Indian Higher Education Institutions, although India being a developing country it is important for the educational institution to practice transformation in various forms. The paper covers the detail literature study and conclude with various opinions that have been generated through primary data collection. The objective of the study is to identify the need of digital transformation for education environment by two major methods literature study and stakeholder data analysis. Technological expectation was also studied using questionnaires. The study also analyzed related studies that had been done in the past using the Vosviewer programme for the years 1980 to 2004 for Scopus dataset in order to understand the year-by-year publications, research articles, and book chapters in the subject of Digital Transformation in Higher Education. The majority of stakeholders concur that using digital transformation technologies like IoT, AI & ChatGpt, Generative AI, Augmented reality in higher education is essential for implementing NEP 2020 and successfully integrating digital technologies. The paper covers a detail discussion including literature review on various aspects of digital transformation in education institutes. It also covers opinion from various stakeholders to understand actual outcomes expected from the study which was conducted. The current study uses a mixed research methodology because the questionnaire includes both quantitative and qualitative questions. A sample of 40 respondents was collected, representing the four main stakeholders in education: students, faculty, businesspeople, and educationalists. The responses were analysed using the SPSS Percentage and mean. The newly adopted educational policy NEP 2020 encourages the use of technology and skill-based learning. The importance of technology in teaching and learning processes has been emphasized in numerous research papers in order to improve the teaching-learning process and its outcomes. The thorough assessment of the literature was carried out utilizing the VOS viewer to evaluate the pertinent studies and pinpoint any gaps. © 2023 IEEE.},

keywords = {Augmented Reality, Data Analysis, Data collection, Data handling, Developing countries, Digital revolution, Digital transformation, E-Learning, Educational Institution, Educational institutions, Engineering education, High educations, Higher education institutions, Information analysis, Learning systems, Literature studies, Metadata, Primary data, Stakeholder, Stakeholders, Technology Adoption},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{stacchio_evaluating_2023,

title = {Evaluating Human Aesthetic and Emotional Aspects of 3D generated content through eXtended Reality},

author = {L. Stacchio and C. Scorolli and G. Marfia},

editor = {De Filippo A. and Milano M. and Presutti V. and Saffiotti A.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85176617276&partnerID=40&md5=14d9d23320d6ed236cbb4b0c562bec06},

isbn = {16130073 (ISSN)},

year  = {2023},

date = {2023-01-01},

booktitle = {CEUR Workshop Proc.},

volume = {3519},

pages = {38–49},

publisher = {CEUR-WS},

abstract = {The Metaverse era is rapidly shaping novel and effective tools particularly useful in the entertainment and creative industry. A fundamental role is played by modern generative deep learning models, that can be used to provide varied and high-quality multimedia content, considerably lowering costs while increasing production efficiency. The goodness of such models is usually evaluated quantitatively with established metrics on data and humans using simple constructs such as the Mean Opinion Score. However, these scales and scores don't take into account the aesthetical and emotional components, which could play a role in positively controlling the automatic generation of multimedia content while at the same time introducing novel forms of human-in-the-loop in generative deep learning. Furthermore, considering data such as 3D models/scenes, and 360° panorama images and videos, conventional display hardware may not be the most effective means for human evaluation. A first solution to such a problem could consist of employing eXtendend Reality paradigms and devices. Considering all such aspects, we here discuss a recent contribution that adopted a well-known scale to evaluate the aesthetic and emotional experience of watching a 360° video of a musical concert in Virtual Reality (VR) compared to a classical 2D webstream, showing that adopting fully immersive VR experience could be a possible path to follow. © 2023 CEUR-WS. All rights reserved.},

keywords = {aesthetic evaluation, Creative industries, Deep learning, Effective tool, Emotional aspect, Entertainment industry, Esthetic evaluation, Extended reality, generative artificial intelligence, Human-in-the-loop, Learning systems, Metaverses, Multimedia contents, Production efficiency, Three dimensional computer graphics, Virtual Reality},

pubstate = {published},

tppubtype = {inproceedings}

}