@article{MTMT:34623343,
	title = {Formal verification for quantized neural networks},
	url = {https://m2.mtmt.hu/api/publication/34623343},
	author = {Kovásznai, Gergely and Kiss, D.H. and Mlinkó, P.},
	doi = {10.33039/ami.2023.04.003},
	journal-iso = {ANN MATH INFORM},
	journal = {ANNALES MATHEMATICAE ET INFORMATICAE},
	volume = {57},
	unique-id = {34623343},
	issn = {1787-5021},
	abstract = {Despite of deep neural networks are being successfully used in many fields of computing, it is still challenging to verify their trustiness. Previously it has been shown that binarized neural networks can be verified by being encoded into Boolean constraints. In this paper, we generalize this encoding to quantized neural networks (QNNs). We demonstrate how to implement QNNs in Python, using the Tensorflow and Keras libraries. Also, we demonstrate how to implement a Boolean encoding of QNNs, as part of our tool that is able to run a variety of solvers to verify QNNs. © 2023, Eszterhazy Karoly College. All rights reserved.},
	keywords = {Artificial intelligence; Neural network; formal verification; Python; SAT; SMT; Deep learning; constraint programming; Keras},
	year = {2023},
	eissn = {1787-6117},
	pages = {36-48}
}

@article{MTMT:34534646,
	title = {Integer Programming Based Optimization of Power Consumption for Data Center Networks},
	url = {https://m2.mtmt.hu/api/publication/34534646},
	author = {Kovásznai, Gergely and Al-Shamarti, Mohammed},
	doi = {10.14232/actacyb.299115},
	journal-iso = {ACTA CYBERN-SZEGED},
	journal = {ACTA CYBERNETICA},
	unique-id = {34534646},
	issn = {0324-721X},
	abstract = {With the quickly developing data centers in smart cities, reducing energy consumption and improving network performance, as well as economic benefits, are essential research topics. In particular, Data Center Networks do not always run at full capacity, which leads to significant energy consumption. This paper experiments with a range of optimization tools to find the optimal solutions for the Integer Linear Programming (ILP) model of network power consumption. The study reports on experiments under three communication patterns (near, long, and random), measuring runtime and memory consumption in order to evaluate the performance of different ILP solvers.While the results show that, for near traffic pattern, most of the tools rapidly converge to the optimal solution, CP-SAT provides the most stable performance and outperforms the other solvers for the long traffic pattern. On the other hand, for random traffic pattern, Gurobi can be considered to be the best choice, since it is able to solve all the benchmark instances under the time limit and finds solutions faster by 1 or 2 orders of magnitude than the other solvers do.},
	year = {2023},
	eissn = {2676-993X}
}

@misc{MTMT:34439161,
	title = {Proceedings of the 16th International Conference on Automata and Formal Languages},
	url = {https://m2.mtmt.hu/api/publication/34439161},
	doi = {10.4204/EPTCS.386},
	editor = {Gazdag, Zsolt and Iván, Szabolcs and Kovásznai, Gergely},
	publisher = {Electronic Proceedings in Theoretical Computer Science (EPTCS)},
	unique-id = {34439161},
	year = {2023},
	orcid-numbers = {Iván, Szabolcs/0000-0003-0406-7117}
}

@article{MTMT:34131002,
	title = {Special Issue on Applied Informatics},
	url = {https://m2.mtmt.hu/api/publication/34131002},
	author = {Kovásznai, Gergely and Varga, Imre},
	journal-iso = {INFOCOMM J},
	journal = {INFOCOMMUNICATIONS JOURNAL},
	volume = {15},
	unique-id = {34131002},
	issn = {2061-2079},
	year = {2023},
	eissn = {2061-2125},
	pages = {1-1}
}

@article{MTMT:34130917,
	title = {Survey of Routing Techniques-Based Optimization of Energy Consumption in SD-DCN},
	url = {https://m2.mtmt.hu/api/publication/34130917},
	author = {Al-Shamarti, Mohammed and Kovásznai, Gergely and Malik, Ali and de Fréin, Ruairí},
	doi = {10.36244/ICJ.2023.5.6},
	journal-iso = {INFOCOMM J},
	journal = {INFOCOMMUNICATIONS JOURNAL},
	volume = {15},
	unique-id = {34130917},
	issn = {2061-2079},
	abstract = {The increasing power consumption of Data Center Networks (DCN) is becoming a major concern for network operators. The object of this paper is to provide a survey of state-of-the-art methods for reducing energy consumption via (1) enhanced scheduling and (2) enhanced aggregation of traffic flows using Software-Defined Networks (SDN), focusing on the advantages and disadvantages of these approaches. We tackle a gap in the literature for a review of SDN-based energy saving techniques and discuss the limitations of multi-controller solutions in terms of constraints on their performance. The main finding of this survey paper is that the two classes of SDNbased methods, scheduling and flow aggregation, significantly reduce energy consumption in DCNs. We also suggest that Machine Learning has the potential to further improve these classes of solutions and argue that hybrid ML-based solutions are the next frontier for the field. The perspective gained as a consequence of this analysis is that advanced ML-based solutions and multi-controller-based solutions may address the limitations of the state-of-the-art, and should be further explored for energy optimization in DCNs.},
	year = {2023},
	eissn = {2061-2125},
	pages = {35-42}
}

@inproceedings{MTMT:33293087,
	title = {ML-Based Online Traffic Classification for SDNs},
	url = {https://m2.mtmt.hu/api/publication/33293087},
	author = {Al-Shamarti, Mohammed and Kovásznai, Gergely and Abboosh, M. and Malik, A. and Frein, R.D.},
	booktitle = {2022 IEEE 2nd Conference on Information Technology and Data Science (CITDS)},
	doi = {10.1109/CITDS54976.2022.9914138},
	unique-id = {33293087},
	abstract = {Traffic classification is a crucial aspect for Software-Defined Networking functionalities. This paper is a part of an on-going project aiming at optimizing power consumption in the environment of software-defined datacenter networks. We have developed a novel routing strategy that can blindly balance between the power consumption and the quality of service for the incoming traffic flows. In this paper, we demonstrate how to classify the network traffic flows so that the quality of service of each flow-class can be guaranteed efficiently. This is achieved by creating a dataset that encompasses different types of network traffic such as video, VoIP, game and ICMP. The performance of a number of Machine Learning techniques is compared and the results are reported. As part of future work, we will incorporate classification into the power consumption model towards achieving further advances in this research area. © 2022 IEEE.},
	keywords = {CLASSIFICATION; Learning systems; machine learning; machine learning; E-learning; Dataset; Dataset; Quality of service; Machine-learning; Classification (of information); Electric power utilization; SDN; SDN; Routing strategies; Software-defined networkings; data center networks; Incoming traffic; Quality-of-service; traffic classification; On-line traffic},
	year = {2022},
	pages = {217-222}
}

@article{MTMT:32545784,
	title = {An Adaptive Routing Framework for Efficient Power Consumption in Software-Defined Datacenter Networks},
	url = {https://m2.mtmt.hu/api/publication/32545784},
	author = {Al-Shamarti, Mohammed and Kovásznai, Gergely and Rácz, Anett and Malik, Ali and de Fréin, Ruairí},
	doi = {10.3390/electronics10233027},
	journal = {ELECTRONICS (SWITZ)},
	volume = {10},
	unique-id = {32545784},
	year = {2021},
	eissn = {2079-9292},
	pages = {3027},
	orcid-numbers = {Kovásznai, Gergely/0000-0001-8455-0218; Rácz, Anett/0000-0002-6373-3194; Malik, Ali/0000-0002-2866-0743; de Fréin, Ruairí/0000-0002-3912-1470}
}

@article{MTMT:32169794,
	title = {Portfolio solver for verifying Binarized Neural Networks},
	url = {https://m2.mtmt.hu/api/publication/32169794},
	author = {Kovásznai, Gergely and Gajdár, Krisztián and Narodytska, Nina},
	doi = {10.33039/ami.2021.03.007},
	journal-iso = {ANN MATH INFORM},
	journal = {ANNALES MATHEMATICAE ET INFORMATICAE},
	volume = {53},
	unique-id = {32169794},
	issn = {1787-5021},
	year = {2021},
	eissn = {1787-6117},
	pages = {183-200}
}

@article{MTMT:32842987,
	title = {Preface},
	url = {https://m2.mtmt.hu/api/publication/32842987},
	author = {Kovásznai, Gergely and Fazekas, I. and Tómács, Tibor},
	journal-iso = {CEUR WORKSHOP PROC},
	journal = {CEUR WORKSHOP PROCEEDINGS},
	volume = {2650},
	unique-id = {32842987},
	year = {2020},
	eissn = {1613-0073}
}

@misc{MTMT:32169856,
	title = {Szenzorhálózatok optimalizálása formális módszerekkel},
	url = {https://m2.mtmt.hu/api/publication/32169856},
	author = {Kovásznai, Gergely},
	unique-id = {32169856},
	year = {2020}
}