TY - CHAP AU - Jenei, Attila Zoltán AU - Sztahó, Dávid TI - Contribution of different movement tasks to differential diagnosis of Parkinson’s disease T2 - 2nd Workshop on Intelligent Infocommunication Networks, Systems and Services PB - Budapest University of Technology and Economics CY - Budapest SN - 9789634219446 PY - 2024 SP - 61 EP - 66 PG - 6 SN - 9789634219446 DO - 10.3311/WINS2024-011 UR - https://m2.mtmt.hu/api/publication/34855143 ID - 34855143 LA - English DB - MTMT ER - TY - JOUR AU - Alnahdi, Amad AU - Laszlo, Toka TI - A Survey on Integrating Edge Computing With AI and Blockchain in Maritime Domain, Aerial Systems, IoT, and Industry 4.0. JF - IEEE ACCESS J2 - IEEE ACCESS VL - 12 PY - 2024 SP - 1 EP - 26 PG - 26 SN - 2169-3536 UR - https://m2.mtmt.hu/api/publication/34854968 ID - 34854968 LA - English DB - MTMT ER - TY - CHAP AU - Alwaisi, Shaimaa AU - Al-Radhi, Mohammed Salah AU - Németh, Géza TI - Multi-speaker child speech synthesis in low-resource Hungarian language T2 - 2nd Workshop on Intelligent Infocommunication Networks, Systems and Services PB - Budapest University of Technology and Economics CY - Budapest SN - 9789634219446 PY - 2024 SP - 19 EP - 24 PG - 6 DO - 10.3311/WINS2024-004 UR - https://m2.mtmt.hu/api/publication/34854938 ID - 34854938 LA - English DB - MTMT ER - TY - JOUR AU - Dimitrakopoulos, G. AU - Varga, Pál AU - Gutt, T. AU - Schneider, G. AU - Ehm, H. AU - Hoess, A. AU - Tauber, M. AU - Karathanasopoulou, K. AU - Lackner, A. AU - Delsing, J. TI - Industry 5.0: Research Areas and Challenges With Artificial Intelligence and Human Acceptance JF - IEEE INDUSTRIAL ELECTRONICS MAGAZINE J2 - IEEE IND ELECTRON M PY - 2024 SP - 2 EP - 13 PG - 12 SN - 1932-4529 DO - 10.1109/MIE.2024.3387068 UR - https://m2.mtmt.hu/api/publication/34846759 ID - 34846759 N1 - Department of Informatics and Telematics, Harokopio University of Athens, Athens, Greece Department of Telecommunications and Artificial Intelligence, Budapest University of Technology and Economics, Budapest, Hungary Infineon Technologies, Neubiberg, Germany Infineon Technologies, Dresden, Germany Department of Electrical Engineering, Media, and Computer Science, University of Applied Sciences Amberg-Weiden, Amberg, Germany Research Studios Austria, Vienna, Austria Harokopio University of Athens, Athens, Greece Eesy-Innovation, Unterhaching, Germany Lulea University of Technology, Lulea, Sweden Export Date: 10 May 2024 AB - The industrial landscape is swiftly progressing toward Industry 5.0, marking the fifth revolution characterized by the integration of sustainable practices and digital sovereignty. This article advocates for the adoption, expansion, and implementation of artificial intelligence (AI)-enabled hardware, tools, methods, and semiconductor technologies in the journey toward Industry 5.0. Beyond the initial proposal, the article explores primary research areas and the diverse challenges inherent in this transition. Notably, significant accomplishments in pivotal industrial use cases are appended, providing validation evidence. This comprehensive approach aims to bridge academic advancements with practical industrial application, fostering a symbiotic relationship between humans and machines for increased efficiency, innovation, and adaptability. IEEE LA - English DB - MTMT ER - TY - JOUR AU - Alwaisi, Shaimaa AU - Németh, Géza TI - Advancements in Expressive Speech Synthesis: a Review JF - INFOCOMMUNICATIONS JOURNAL J2 - INFOCOMM J VL - 16 PY - 2024 IS - 1 SP - 35 EP - 46 PG - 12 SN - 2061-2079 DO - 10.36244/ICJ.2024.1.5 UR - https://m2.mtmt.hu/api/publication/34841720 ID - 34841720 AB - In recent years, we have witnessed a fast and wide spread acceptance of speech synthesis technology in, leading to the transition toward a society characterized by a strong desire to incorporate these applications in their daily lives. We provide a comprehensive survey on the recent advancements in the field of expressive Text-To- Speech systems. Among different methods to represent expressivity, this paper focuses the development of ex pressive TTS systems, emphasizing the methodologies employed to enhance the quality and expressiveness of synthetic speech, such as style transfer and improving speaker variability. After that, we point out some of the subjective and objective metrics that are used to evaluate the quality of synthesized speech. Fi nally, we point out the realm of child speech synthesis, a domain that has been neglected for some time. This underscores that the f ield of research in children's speech synthesis is still wide open for exploration and development. Overall, this paper presents a comprehensive overview of historical and contemporary trends and future directions in speech synthesis research. LA - English DB - MTMT ER - TY - CHAP AU - Dobreff, Gergely AU - Frey, Dániel AU - Báder, Attila AU - Pašić, Alija ED - Prosper, Chemouil ED - Barbara, Martini ED - Carmen, Mas Machuca ED - Panagiotis, Papadimitriou ED - Davide, Borsatti ED - Stéphane, Rovedakis TI - Empowering ISPs with Cloud Gaming User Experience Modeling: A NVIDIA GeForce NOW Use-Case T2 - 2024 27th Conference on Innovation in Clouds, Internet and Networks (ICIN) PB - IEEE CY - Piscataway (NJ) SN - 9798350393767 T3 - International Conference on Intelligence in Next Generation Networks, ISSN 2162-3414 PY - 2024 SP - 202 EP - 209 PG - 8 DO - 10.1109/ICIN60470.2024.10494462 UR - https://m2.mtmt.hu/api/publication/34832880 ID - 34832880 N1 - Orange Budapest University of Technology and Economics, Faculty of Electrical Engineering and Informatics, Hungary Ericsson Hungary, Hungary Conference code: 198793 Export Date: 3 May 2024 AB - Cloud gaming has emerged as a cost-effective and accessible gaming solution, with platforms like NVIDIA GeForce NOW leading the way. The rapid growth of this industry, projected to reach 6.8 billion USD by 2028, has sparked the need for enhanced user experience models to optimize cloud and network infrastructure. In our study, we conducted a comprehensive analysis of the in-game performance of the popular NVIDIA GeForce NOW cloud gaming platform under varying network conditions. Our research focused on quality of service (QoS) metrics, particularly the WebRTC logs, and their relationship with user experience, defined as in-game performance. Standardized and repeatable measurements from the GeForce NOW platform were used, where the player was asked to complete training exercises of fast-paced games under different network conditions. This paper analyses and proposes machine learning (ML) models that estimate the user experience of cloud gaming. The models are trained on the low-level network- and application-related QoS metrics extracted from WebRTC logs. Our contribution demonstrates that ML models can accurately estimate in-game performance from QoS parameters, highlighting network latency's greater impact on the player's gaming experience than packet loss, bandwidth, and jitter. With our novel model, internet service providers (ISPs) can effectively estimate user experience using only network-related metrics, enabling network optimization and enhancing gaming services. This research deepens our understanding of cloud gaming user experience and offers insights for refining cloud gaming services. © 2024 IEEE. LA - English DB - MTMT ER - TY - JOUR AU - AL-SHOUHA, MODAFAR MOHAMMAD MAHMOOD AU - Szűcs, Gábor TI - Single and Combined Algorithms for Open Set Classification on Image Datasets JF - ACTA CYBERNETICA J2 - ACTA CYBERN-SZEGED VL - Special Issue of the 13th Conference of PhD Students in Computer Science PY - 2024 SP - 1 EP - 26 PG - 26 SN - 0324-721X DO - 10.14232/actacyb.298356 UR - https://m2.mtmt.hu/api/publication/34804256 ID - 34804256 AB - Generally, classification models have closed nature, and they are constrained by the number of classes in the training data. Hence, classifying "unknown" - OOD (out-of-distribution) - samples is challenging, especially in the so called "open set" problem. We propose and investigate different solutions - single and combined algorithms - to tackle this task, where we use and expand a K-classifier to be able to identify K+1 classes. They do not require any retraining or modification on the K-classifier architecture. We show their strengths when avoiding type I or type II errors is fundamental. We also present a mathematical representation for the task to estimate the K+1 classification accuracy, and an inequality that defines its boundaries. Additionally, we introduce a formula to calculate the exact K+1 classification accuracy. LA - English DB - MTMT ER - TY - CHAP AU - Mengke, Dalai AU - Meng, Yan AU - Mihajlik, Péter TI - Model-centric data selection: Refining end-to-end speech recognition T2 - 2nd Workshop on Intelligent Infocommunication Networks, Systems and Services PB - Budapest University of Technology and Economics CY - Budapest SN - 9789634219446 PY - 2024 SP - 1 EP - 5 PG - 5 DO - 10.3311/WINS2024-001 UR - https://m2.mtmt.hu/api/publication/34785341 ID - 34785341 LA - English DB - MTMT ER - TY - CHAP AU - Vass, Balázs AU - Bérczi-Kovács, Erika AU - Gyimesi, Péter AU - Tapolcai, János TI - Efficient Computing of Disaster-Disjoint Paths: Greedy and Beyond T2 - 2024 IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2024 PB - Institute of Electrical and Electronics Engineers (IEEE) CY - Vancouver PY - 2024 SP - 1 EP - 2 PG - 2 UR - https://m2.mtmt.hu/api/publication/34782153 ID - 34782153 LA - English DB - MTMT ER - TY - CHAP AU - Bérczi-Kovács, Erika AU - Gyimesi, Péter AU - Vass, Balázs AU - Tapolcai, János TI - Efficient Algorithm for Region-Disjoint Survivable Routing in Backbone Networks T2 - IEEE INFOCOM 2024 - IEEE Conference on Computer Communications PB - IEEE CY - Piscataway (NJ) PY - 2024 SP - 1 EP - 10 PG - 10 UR - https://m2.mtmt.hu/api/publication/34782100 ID - 34782100 LA - English DB - MTMT ER -