@inproceedings{MTMT:34832880,
	title = {Empowering ISPs with Cloud Gaming User Experience Modeling: A NVIDIA GeForce NOW Use-Case},
	url = {https://m2.mtmt.hu/api/publication/34832880},
	author = {Dobreff, Gergely and Frey, Dániel and Báder, Attila and Pašić, Alija},
	booktitle = {2024 27th Conference on Innovation in Clouds, Internet and Networks (ICIN)},
	doi = {10.1109/ICIN60470.2024.10494462},
	unique-id = {34832880},
	abstract = {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.},
	keywords = {PERFORMANCE; Data Collection; Data Collection; Cost effectiveness; Quality of service; Quality of service; Cost effective; User experience; Machine learning models; Quality-of-service; Internet service providers; Quality of service metrics; Network condition; Users' experiences; Cloud gamings; Cloud gaming; user experience modeling; User experience model},
	year = {2024},
	pages = {202-209},
	orcid-numbers = {Pašić, Alija/0000-0001-6346-496X}
}

@article{MTMT:34804256,
	title = {Single and Combined Algorithms for Open Set Classification on Image Datasets},
	url = {https://m2.mtmt.hu/api/publication/34804256},
	author = {AL-SHOUHA, MODAFAR MOHAMMAD MAHMOOD and Szűcs, Gábor},
	doi = {10.14232/actacyb.298356},
	journal-iso = {ACTA CYBERN-SZEGED},
	journal = {ACTA CYBERNETICA},
	volume = {Special Issue of the 13th Conference of PhD Students in Computer Science},
	unique-id = {34804256},
	issn = {0324-721X},
	abstract = {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.},
	year = {2024},
	eissn = {2676-993X},
	pages = {1-26},
	orcid-numbers = {AL-SHOUHA, MODAFAR MOHAMMAD MAHMOOD/0000-0003-2051-4036; Szűcs, Gábor/0000-0002-5781-1088}
}

@inproceedings{MTMT:34785341,
	title = {Model-centric data selection: Refining end-to-end speech recognition},
	url = {https://m2.mtmt.hu/api/publication/34785341},
	author = {Mengke, Dalai and Meng, Yan and Mihajlik, Péter},
	booktitle = {2nd Workshop on Intelligent Infocommunication Networks, Systems and Services},
	doi = {10.3311/WINS2024-001},
	unique-id = {34785341},
	year = {2024},
	pages = {1-5},
	orcid-numbers = {Meng, Yan/0000-0002-2764-0716; Mihajlik, Péter/0000-0001-7532-9773}
}

@inproceedings{MTMT:34782153,
	title = {Efficient Computing of Disaster-Disjoint Paths: Greedy and Beyond},
	url = {https://m2.mtmt.hu/api/publication/34782153},
	author = {Vass, Balázs and Bérczi-Kovács, Erika and Gyimesi, Péter and Tapolcai, János},
	booktitle = {2024 IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2024},
	unique-id = {34782153},
	year = {2024},
	pages = {1-2},
	orcid-numbers = {Bérczi-Kovács, Erika/0000-0003-2259-0868}
}

@inproceedings{MTMT:34782100,
	title = {Efficient Algorithm for Region-Disjoint Survivable Routing in Backbone Networks},
	url = {https://m2.mtmt.hu/api/publication/34782100},
	author = {Bérczi-Kovács, Erika and Gyimesi, Péter and Vass, Balázs and Tapolcai, János},
	booktitle = {IEEE INFOCOM 2024 - IEEE Conference on Computer Communications},
	unique-id = {34782100},
	year = {2024},
	pages = {1-10},
	orcid-numbers = {Bérczi-Kovács, Erika/0000-0003-2259-0868}
}

@article{MTMT:34763417,
	title = {Guest Editorial Special Issue on Selected Papers From the IEEE Sensors 2022 Conference},
	url = {https://m2.mtmt.hu/api/publication/34763417},
	author = {Lee, J.B. and Vida, Rolland},
	doi = {10.1109/JSEN.2024.3361559},
	journal-iso = {IEEE SENS J},
	journal = {IEEE SENSORS JOURNAL},
	volume = {24},
	unique-id = {34763417},
	issn = {1530-437X},
	year = {2024},
	eissn = {1558-1748},
	pages = {7234-7234}
}

@article{MTMT:34763097,
	title = {An Edge Cloud Based Coordination Platform for Multi-user AR Applications},
	url = {https://m2.mtmt.hu/api/publication/34763097},
	author = {Sonkoly, Balázs and Nagy, Bálint György and Dóka, János and Kecskés-Solymosi, Zsófia and Czentye, János Emánuel and Formanek, Bence and Jocha, Dávid and Gerő, Balázs Péter},
	doi = {10.1007/s10922-024-09809-9},
	journal-iso = {J NETW SYST MANAG},
	journal = {JOURNAL OF NETWORK AND SYSTEMS MANAGEMENT},
	volume = {32},
	unique-id = {34763097},
	issn = {1064-7570},
	abstract = {Augmented Reality (AR) applications can reshape our society enabling novel ways of interactions and immersive experiences in many fields. However, multi-user and collaborative AR applications pose several challenges. The expected user experience requires accurate position and orientation information for each device and precise synchronization of the respective coordinate systems in real-time. Unlike mobile phones or AR glasses running on battery with constrained resource capacity, cloud and edge platforms can provide the computing power for the core functions under the hood. In this paper, we propose a novel edge cloud based platform for multi-user AR applications realizing an essential coordination service among the users. The latency critical, computation intensive Simultaneous Localization And Mapping (SLAM) function is offloaded from the device to the edge cloud infrastructure. Our solution is built on open-source SLAM libraries and the Robot Operating System (ROS). Our contribution is threefold. First, we propose an extensible, edge cloud based AR architecture. Second, we develop a proof-of-concept prototype supporting multiple devices and building on an AI-based SLAM selection component. Third, a dedicated measurement methodology is described, including energy consumption aspects as well, and the overall performance of the system is evaluated via real experiments.},
	year = {2024},
	eissn = {1573-7705},
	orcid-numbers = {Sonkoly, Balázs/0000-0002-4640-388X; Nagy, Bálint György/0000-0001-9917-952X; Dóka, János/0000-0002-6565-7981; Kecskés-Solymosi, Zsófia/0000-0002-4376-6693; Czentye, János Emánuel/0000-0001-7075-309X}
}

@article{MTMT:34760727,
	title = {Jövőbe látó lélektan – Gépi tanulás a pszichológiai kutatásmódszertanban},
	url = {https://m2.mtmt.hu/api/publication/34760727},
	author = {Damsa, Andrei and Püski, Marcell and Tulics, Miklós Gábriel},
	doi = {10.1556/0016.2023.00084},
	journal-iso = {M PSZICH SZLE},
	journal = {MAGYAR PSZICHOLÓGIAI SZEMLE},
	volume = {79},
	unique-id = {34760727},
	issn = {0025-0279},
	abstract = {Háttér és célkitűzés A pszichológiai kutatásmódszertan eljárásait (főképp a p értékre építkező bizonyításokat) számos kritika érte az utóbbi évtizedek során. A kutatói elfogultság és a módszertanok (például az adatgyűjtés, az adatszelekció vagy a statisztikai próbák) könnyű manipulálhatósága teret adott a félrevezető és nehezen reprodukálható kutatásoknak. A gépi tanulás elterjedése megfigyelhető a pszichológia területén is, új eszköztárat biztosítva a kutatók számára. Az eljárás áthelyezi a hangsúlyt a statisztikai bizonyításról az előrejelzésre, valamint az ehhez kapcsolódó validációs folyamatokra, ezáltal lecsökkentve a kutatói szubjektivitás hatását. Jelen tanulmány célja gyakorlati példákon keresztül betekintést nyújtani a gépi tanulás módszertanába, fókuszálva a pszichológiai alkalmazhatóságára. Módszer A vizsgálati szakasz első részében két, a gépi tanulás használatára irányuló tanulmány kerül bemutatásra a humán döntéshozatali mechanizmusok, valamint a pandémiás helyzet okozta mentális hatások területére vonatkozóan. A vizsgálati szakasz második részében egy klasszifikációs feladat (filmpreferencia és nemi identitás kapcsolata) keretén belül kerül összehasonlításra egy nem parametrikus statisztikai módszer és két, gépi tanuláson alapuló eljárás. Eredmények A kapott eredmények bemutatják a gépi tanulás által nyújtott előnyöket (validációs eljárások és többletinformáció kinyerése), párhuzamot vonva a nem parametrikus eljárással. Következtetések A tanulmány népszerűsíteni és alátámasztani hivatott a gépi tanulás alkalmazhatóságát a kutatói szektorban tevékenykedő pszichológusok számára. A bemutatott kutatás reprodukálhatóságának érdekében az adatok és programozási kódsorok szabadon felhasználhatók a tanulmányban megadott elérhetőségeken keresztül.},
	year = {2024},
	eissn = {1588-2799},
	pages = {1-18}
}

@inproceedings{MTMT:34751452,
	title = {Momentum Matters: Investigating High-Pressure Situations in the NBA Through Scoring Probability},
	url = {https://m2.mtmt.hu/api/publication/34751452},
	author = {Mihalyi, Balazs and Biczók, Gergely and Toka, László},
	booktitle = {Machine Learning and Data Mining for Sports Analytics},
	doi = {10.1007/978-3-031-53833-9_7},
	volume = {2035 CCIS},
	unique-id = {34751452},
	abstract = {One of the defining characteristics of real basketball stars, and even great role players, is how well they perform under immense mental pressure. In this paper, we present a method to identify high-pressure situations during a basketball game through shooting success. In order to calculate the amount of pressure a team is facing going into a game, we use a prediction model to determine the importance of the given game for that team to reach their end-of-season goal. The model relies on features referring to game context, recent form, and pre-season aspirations. We then investigate the impact of our pre-game pressure metric, along with other factors, on the shooting performance of NBA players on six seasons’ worth of data. We find that shotmaking in the NBA is mainly impacted by the so-called momentum, i.e., when a team outscores their opponent significantly over a short period of time. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.},
	keywords = {PERFORMANCE; PERFORMANCE; high pressure; Basketball; Sports; scoring; scoring; MOMENTUM; MOMENTUM; Prediction modelling; Short periods; shooting performance; Basketball games; mental pressure; mental pressure; Pressure situation},
	year = {2024},
	pages = {77-90},
	orcid-numbers = {Toka, László/0000-0003-1045-9205}
}

@inproceedings{MTMT:34751450,
	title = {Pass Receiver and Outcome Prediction in Soccer Using Temporal Graph Networks},
	url = {https://m2.mtmt.hu/api/publication/34751450},
	author = {Rahimian, Pegah and Kim, H. and Schmid, M. and Toka, László},
	booktitle = {Machine Learning and Data Mining for Sports Analytics},
	doi = {10.1007/978-3-031-53833-9_5},
	volume = {2035 CCIS},
	unique-id = {34751450},
	abstract = {This paper explores the application of the Temporal Graph Network (TGN) model to predict the receiver and outcome of a pass in soccer. We construct two TGN models that estimate receiver selection probabilities (RSP) and receiver prediction probabilities (RPP) to predict the intended and actual receivers of a given pass attempt, respectively. Then, based on these RSP and RPP, we compute the success probability (CPSP) of each passing option that the pass is successfully sent to the intended receiver as well as the overall pass success probability (OPSP) of a given situation. The proposed framework provides deeper insights into the context around passes in soccer by quantifying the tendency of passers’ choice of passing options, difficulties of the options, and the overall difficulty of a given passing situation at once. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.},
	keywords = {Forecasting; Sports; Multi agent systems; outcome prediction; Network models; Temporal graphs; Multi agent; Graph neural networks; Graph Networks; Soccer analytics; Multi-Agent Analysis; Pass Outcome Prediction; Pass Receiver Prediction; Temporal Graph Network; Multi-agent analyse; Pass outcome prediction; Pass receiver prediction; Soccer analytic; Temporal graph network},
	year = {2024},
	pages = {52-63},
	orcid-numbers = {Toka, László/0000-0003-1045-9205}
}