@inproceedings{MTMT:34795664,
	title = {Towards a Simulation as a Service Platform for the Cloud-to-Things Continuum},
	url = {https://m2.mtmt.hu/api/publication/34795664},
	author = {Wilson, Valdez and Baniata, Hamza and Márkus, András and Kertész, Attila},
	booktitle = {Euro-Par 2023: Parallel Processing Workshops},
	doi = {10.1007/978-3-031-48803-0_6},
	unique-id = {34795664},
	year = {2024},
	pages = {65-75},
	orcid-numbers = {Baniata, Hamza/0000-0003-1978-3175; Kertész, Attila/0000-0002-9457-2928}
}

@article{MTMT:34167552,
	title = {Distributed scalability tuning for evolutionary sharding optimization with Random-equivalent security in permissionless Blockchain},
	url = {https://m2.mtmt.hu/api/publication/34167552},
	author = {Baniata, Hamza and Anaqreh, Ahmad and Kertész, Attila},
	doi = {10.1016/j.iot.2023.100955},
	journal-iso = {INTERNET THINGS-NETH},
	journal = {INTERNET OF THINGS},
	volume = {24},
	unique-id = {34167552},
	issn = {2543-1536},
	year = {2023},
	eissn = {2542-6605},
	orcid-numbers = {Baniata, Hamza/0000-0003-1978-3175; Anaqreh, Ahmad/0000-0002-3971-2684; Kertész, Attila/0000-0002-9457-2928}
}

@mastersthesis{MTMT:34134015,
	title = {DISSECT-CF-Fog: A Simulation Environment for Analysing the Cloud-to-Thing Continuum},
	url = {https://m2.mtmt.hu/api/publication/34134015},
	author = {Márkus, András},
	doi = {10.14232/phd.11551},
	publisher = {Universití of Szeged},
	unique-id = {34134015},
	year = {2023}
}

@mastersthesis{MTMT:34133816,
	title = {The Applicability of Fuzzy Theory in Machine Learning},
	url = {https://m2.mtmt.hu/api/publication/34133816},
	author = {Hussain, Abrar},
	doi = {10.14232/phd.11349},
	publisher = {Universití of Szeged},
	unique-id = {34133816},
	year = {2023}
}

@mastersthesis{MTMT:34132517,
	title = {Adaptation of Speaker and Speech Recognition Methods for the Automatic Screening of Speech Disorders using Machine Learning [Beszélő- és beszédfelismerési módszerek adaptálása betegséges automatikus előszűrésére gépi tanulás segítségével]},
	url = {https://m2.mtmt.hu/api/publication/34132517},
	author = {José Vicente, Egas López},
	doi = {10.14232/phd.11491},
	publisher = {Universití of Szeged},
	unique-id = {34132517},
	abstract = {Jelen doktori értekezés olyan módszereket mutat be, amelyek bizonyos betegségekben vagy egészségi állapotban szenvedő egyének nemverbális kommunikációjának kiaknázását célozzák azok automatikus szűrésére. Konkrétabban, a nemverbális kommunikáció egyik pillérét, a paralingvisztikát alkalmaztuk olyan technikák feltárására, amelyek felhasználhatók az alanyok beszédének modellezésére. A paralingvisztika a kommunikáció egy nem lexikális összetevője, amely az intonáción, a hangmagasságon, a beszéd sebességén stb. alapszik, és amely automatikusan feldolgozható és elemezhető. Ezt Computational Paralinguistics-nak hívják, amely úgy definiálható, mint a beszélő beszédében lévő nemverbális látens minták számítási algoritmusok segítségével történő modellezése. A gépi tanulás segítségével modelleket mutatunk be mind a paralingvisztikai, mind az orvosi célú beszédelemzés különböző forgatókönyveiből, amelyek alkalmasak egy adott betegséggel (például az Alzheimer-kór, Parkinson-kór, depresszió) élő alanyok egészségi állapotának automatikus becslésére.},
	year = {2023}
}

@mastersthesis{MTMT:34132495,
	title = {Integrating Blockchain and Fog Computing Technologies for Efficient Privacy-preserving Systems},
	url = {https://m2.mtmt.hu/api/publication/34132495},
	author = {Baniata, Hamza},
	doi = {10.14232/phd.11555},
	publisher = {Universití of Szeged},
	unique-id = {34132495},
	abstract = {This PhD dissertation concludes a three-year long research journey on the integration of Fog Computing and Blockchain technologies. The main aim of such integration is to address the challenges of each of these technologies, by integrating it with the other. Blockchain technology (BC) is a distributed ledger technology in the form of a distributed transactional database, secured by cryptography, and governed by a consensus mechanism. It was initially proposed for decentralized cryptocurrency applications with practically proven high robustness. Fog Computing (FC) is a geographically distributed computing architecture, in which various heterogeneous devices at the edge of network are ubiquitously connected to collaboratively provide elastic computation services. FC provides enhanced services closer to end-users in terms of time, energy, and network load. The integration of FC with BC can result in more efficient services, in terms of latency and privacy, mostly required by Internet of Things systems.},
	year = {2023},
	orcid-numbers = {Baniata, Hamza/0000-0003-1978-3175}
}

@mastersthesis{MTMT:34087912,
	title = {Code Coverage Measurement and Fault Localization Approaches},
	url = {https://m2.mtmt.hu/api/publication/34087912},
	author = {Horváth, Ferenc},
	unique-id = {34087912},
	year = {2023},
	orcid-numbers = {Horváth, Ferenc/0000-0002-8442-7970}
}

@mastersthesis{MTMT:34079154,
	title = {Software Maintenance Experiments with the A+ Programming Language and the Primitive Obsession Bad Smell},
	url = {https://m2.mtmt.hu/api/publication/34079154},
	author = {Gál, Péter},
	doi = {10.14232/phd.11431},
	publisher = {Universití of Szeged},
	unique-id = {34079154},
	year = {2023}
}

@article{MTMT:33643183,
	title = {Adaptive Savitzky–Golay Filters for Analysis of Copy Number Variation Peaks from Whole-Exome Sequencing Data},
	url = {https://m2.mtmt.hu/api/publication/33643183},
	author = {Ochieng, Peter Juma and Maróti, Zoltán and Dombi, József and Krész, Miklós and Békési, József and Kalmár, Tibor},
	doi = {10.3390/info14020128},
	journal-iso = {INFORMATION-BASEL},
	journal = {INFORMATION (BASEL)},
	volume = {14},
	unique-id = {33643183},
	abstract = {Copy number variation (CNV) is a form of structural variation in the human genome that provides medical insight into complex human diseases; while whole-genome sequencing is becoming more affordable, whole-exome sequencing (WES) remains an important tool in clinical diagnostics. Because of its discontinuous nature and unique characteristics of sparse target-enrichment-based WES data, the analysis and detection of CNV peaks remain difficult tasks. The Savitzky–Golay (SG) smoothing is well known as a fast and efficient smoothing method. However, no study has documented the use of this technique for CNV peak detection. It is well known that the effectiveness of the classical SG filter depends on the proper selection of the window length and polynomial degree, which should correspond with the scale of the peak because, in the case of peaks with a high rate of change, the effectiveness of the filter could be restricted. Based on the Savitzky–Golay algorithm, this paper introduces a novel adaptive method to smooth irregular peak distributions. The proposed method ensures high-precision noise reduction by dynamically modifying the results of the prior smoothing to automatically adjust parameters. Our method offers an additional feature extraction technique based on density and Euclidean distance. In comparison to classical Savitzky–Golay filtering and other peer filtering methods, the performance evaluation demonstrates that adaptive Savitzky–Golay filtering performs better. According to experimental results, our method effectively detects CNV peaks across all genomic segments for both short and long tags, with minimal peak height fidelity values (i.e., low estimation bias). As a result, we clearly demonstrate how well the adaptive Savitzky–Golay filtering method works and how its use in the detection of CNV peaks can complement the existing techniques used in CNV peak analysis.},
	year = {2023},
	eissn = {2078-2489},
	orcid-numbers = {Ochieng, Peter Juma/0000-0001-6497-6481; Maróti, Zoltán/0000-0002-0515-117X; Dombi, József/0000-0001-9459-912X; Krész, Miklós/0000-0002-7547-1128; Békési, József/0000-0003-3820-9777; Kalmár, Tibor/0000-0002-0419-2009}
}

@inproceedings{MTMT:33615794,
	title = {HuBERTUSz: Alacsony paraméterszámú transzformer modellek létrehozása és kiértékelése magyar nyelvre},
	url = {https://m2.mtmt.hu/api/publication/33615794},
	author = {Ficsor, Tamás and Berend, Gábor},
	booktitle = {XIX. Magyar Számítógépes Nyelvészeti Konferencia, MSZNY-2023},
	unique-id = {33615794},
	year = {2023},
	pages = {417-432},
	orcid-numbers = {Ficsor, Tamás/0000-0002-8442-6652; Berend, Gábor/0000-0002-3845-4978}
}