@inproceedings{MTMT:33117870,
	title = {State Space Modelling of a Final Exam Scheduling Problem},
	url = {https://m2.mtmt.hu/api/publication/33117870},
	author = {Trautsch, László Kálmán and Erdős, Szilvia},
	booktitle = {Proceedings of the Automation and Applied Computer Science Workshop 2022 (AACS'22)},
	unique-id = {33117870},
	year = {2022},
	pages = {115-126},
	orcid-numbers = {Trautsch, László Kálmán/0000-0002-1589-3265}
}

@article{MTMT:31640942,
	title = {ECN-based Mitigation of Congestion in Urban Traffic Networks},
	url = {https://m2.mtmt.hu/api/publication/31640942},
	author = {Alekszejenko, Levente and Dobrowiecki, Tadeusz},
	doi = {10.52825/scp.v1i.94},
	journal-iso = {SUMO Conf Proc},
	journal = {SUMO Conference Proceedings},
	volume = {1},
	unique-id = {31640942},
	year = {2022},
	eissn = {2750-4425},
	pages = {123-136}
}

@CONFERENCE{MTMT:32515161,
	title = {Beosztástervezési problémák igazságosságának vizsgálata},
	url = {https://m2.mtmt.hu/api/publication/32515161},
	author = {Erdős, Szilvia and Kővári, Bence András},
	booktitle = {XXXIV. Magyar Operációkutatási Konferencia: Absztraktok könyve},
	unique-id = {32515161},
	year = {2021},
	pages = {101}
}

@CONFERENCE{MTMT:32236537,
	title = {Új betegségek képi osztályozását segítő few-shot tanulás mély neurális hálózattal},
	url = {https://m2.mtmt.hu/api/publication/32236537},
	author = {Szűcs, Gábor and Németh, Marcell István},
	booktitle = {Képfeldolgozók és Alakfelismerők társaságának 13. konferenciája},
	unique-id = {32236537},
	year = {2021},
	orcid-numbers = {Szűcs, Gábor/0000-0002-5781-1088}
}

@inproceedings{MTMT:32107799,
	title = {Qualitative reasoning assisted empirical system identification},
	url = {https://m2.mtmt.hu/api/publication/32107799},
	author = {Földvári, András and Pataricza, András},
	booktitle = {Proceedings of the 28th PhD Mini-Symposium},
	unique-id = {32107799},
	year = {2021},
	pages = {12-15},
	orcid-numbers = {Földvári, András/0000-0002-4559-6990; Pataricza, András/0000-0002-6516-129X}
}

@inproceedings{MTMT:32087460,
	title = {The difficulties of interpreting fairness in real-world scheduling problems},
	url = {https://m2.mtmt.hu/api/publication/32087460},
	author = {Erdős, Szilvia},
	booktitle = {Proceedings of the Automation and Applied Computer Science Workshop 2021},
	unique-id = {32087460},
	abstract = {Fairness is a very general concept, interpreted in many different ways in various fields, notably in social choice theory, game theory, economics, and law. The question of fairness is yet to be defined in the context of scheduling. 
		In previous scheduling researches, the word "fair" is used; however, in various different meanings, e.g. the term is used for those unique problems that are being dealt with. Thus, a more general definition of fairness is needed, which derives from mathematical formulas.
		In decision support systems, a term for fairness is defined via the concept of Lipschitz property, which may be suitable for solving the question of fairness in real-world scheduling problems. A basic conception is proposed, based on this concept and the Earthmover distance.},
	keywords = {Scheduling; Operations research; FAIRNESS; Lipschitz property; Earthmover distance},
	year = {2021},
	pages = {194-203}
}

@book{MTMT:32038979,
	title = {Risk Mitigation of Facial Recognition},
	url = {https://m2.mtmt.hu/api/publication/32038979},
	author = {Fábián, István and Gulyás, Gábor György},
	publisher = {BME Department of Automation and Applied Informatics},
	unique-id = {32038979},
	year = {2021}
}

@article{MTMT:31995398,
	title = {Double-View Matching Network for Few-Shot Learning to Classify Covid-19 in X-ray images},
	url = {https://m2.mtmt.hu/api/publication/31995398},
	author = {Szűcs, Gábor and Németh, Marcell István},
	doi = {10.36244/ICJ.2021.1.4},
	journal-iso = {INFOCOMM J},
	journal = {INFOCOMMUNICATIONS JOURNAL},
	volume = {13},
	unique-id = {31995398},
	issn = {2061-2079},
	abstract = {The research topic presented in this paper belongs to small training data problem in machine learning (especially in deep learning), it intends to help the work of those working in medicine by analyzing pathological X-ray recordings, using only very few images. This scenario is a particularly hot issue nowadays: how could a new disease for which only limited data are available be diagnosed using features of previous diseases? In this problem, so-called few-shot learning, the difficulty of the classification task is to learn the unique feature characteristics associated with the classes. Although there are solutions, but if the images come from different views, they will not handle these views well. We proposed an improved method, so-called Double-View Matching Network (DVMN based on the deep neural network), which solves the few-shot learning problem as well as the different views of the pathological recordings in the images. The main contribution of this is the convolutional neural network for feature extraction and handling the multi-view in image representation. Our method was tested in the classification of images showing unknown COVID-19 symptoms in an environment designed for learning a few samples, with prior meta-learning on images of other diseases only. The results show that DVMN reaches better accuracy on multi-view dataset than simple Matching Network without multi-view handling.},
	year = {2021},
	eissn = {2061-2125},
	pages = {26-34},
	orcid-numbers = {Szűcs, Gábor/0000-0002-5781-1088}
}

@inproceedings{MTMT:31973190,
	title = {Compensation of the major drawback of oscillometric blood pressure measurement},
	url = {https://m2.mtmt.hu/api/publication/31973190},
	author = {Nagy, Péter and Jobbágy, Ákos},
	booktitle = {Proceedings of the 28th PhD Mini-Symposium},
	unique-id = {31973190},
	year = {2021},
	pages = {16-19},
	orcid-numbers = {Jobbágy, Ákos/0000-0001-6569-6000}
}

@article{MTMT:31810913,
	title = {FFT-Based Identification of Gilbert–Elliott Data Loss Models},
	url = {https://m2.mtmt.hu/api/publication/31810913},
	author = {Palkó, András and Sujbert, László},
	doi = {10.1109/TIM.2020.3039623},
	journal-iso = {IEEE T INSTRUM MEAS},
	journal = {IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT},
	volume = {70},
	unique-id = {31810913},
	issn = {0018-9456},
	year = {2021},
	eissn = {1557-9662},
	orcid-numbers = {Palkó, András/0000-0003-1586-4082; Sujbert, László/0000-0002-9968-9383}
}