AHCI RESEARCH GROUP

Publications

Papers published in international journals,
proceedings of conferences, workshops and books.

OUR RESEARCH

Scientific Publications

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2024

Haramina, E.; Paladin, M.; Petričušić, Z.; Posarić, F.; Drobnjak, A.; Botički, I.

Learning Algorithms Concepts in a Virtual Reality Escape Room Proceedings Article

In: S., Babic; Z., Car; M., Cicin-Sain; D., Cisic; P., Ergovic; T.G., Grbac; V., Gradisnik; S., Gros; A., Jokic; A., Jovic; D., Jurekovic; T., Katulic; M., Koricic; V., Mornar; J., Petrovic; K., Skala; D., Skvorc; V., Sruk; M., Svaco; E., Tijan; N., Vrcek; B., Vrdoljak (Ed.): ICT Electron. Conv., MIPRO - Proc., pp. 2057–2062, Institute of Electrical and Electronics Engineers Inc., 2024, ISBN: 979-835038249-5 (ISBN).

Abstract | Links | BibTeX | Tags: 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

@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}

}

2023

Park, J.; Choi, J.; Kim, S. -L.; Bennis, M.

Enabling the Wireless Metaverse via Semantic Multiverse Communication Proceedings Article

In: Annu. IEEE Commun.Soc. Conf. Sens., Mesh Ad Hoc Commun. Netw. workshops, pp. 85–90, IEEE Computer Society, 2023, ISBN: 21555486 (ISSN); 979-835030052-9 (ISBN).

Abstract | Links | BibTeX | Tags: 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

@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}

}