@CONFERENCE{MTMT:31880767,
	title = {Episode clustering and episodic labeling. The meeting point of artificial intelligence, deep neural networks, and human intelligence},
	url = {https://m2.mtmt.hu/api/publication/31880767},
	author = {Lőrincz, András and Faragó, Kinga Bettina and Fóthi, Áron and Milacski, Zoltán Ádám and Bryar, Rahman and Sárkány, András and Varga, Viktor},
	booktitle = {Learning},
	unique-id = {31880767},
	year = {2017},
	pages = {20-22},
	orcid-numbers = {Lőrincz, András/0000-0002-1280-3447; Milacski, Zoltán Ádám/0000-0002-3135-2936; Varga, Viktor/0000-0003-0410-6171}
}

@book{MTMT:1653130,
	title = {Proceedings of the 10th Symposium on Programming Languages and Software Tools. SPLST 2007 : Dobogókő, Hungary, 14-16 June 2007},
	url = {https://m2.mtmt.hu/api/publication/1653130},
	isbn = {9789634639251},
	editor = {Horváth, Zoltán and Kozma, László and Zsók, Viktória},
	publisher = {ELTE Eötvös Kiadó},
	unique-id = {1653130},
	year = {2007},
	orcid-numbers = {Horváth, Zoltán/0000-0001-9213-2681; Zsók, Viktória/0000-0003-4414-6813}
}

@CONFERENCE{MTMT:1962642,
	title = {Proving the Correctness of Mobile Code in B},
	url = {https://m2.mtmt.hu/api/publication/1962642},
	author = {Istenes, Zoltán and Kozsik, Tamás and Hoch, Csaba and Tóth, László Attila},
	booktitle = {6th Joint Conference on Mathematics and Computer Science, MaCS ’06},
	unique-id = {1962642},
	year = {2006},
	pages = {45},
	orcid-numbers = {Kozsik, Tamás/0000-0003-4484-9172}
}

@article{MTMT:1639730,
	title = {Proving the correctness of mobile Java code},
	url = {https://m2.mtmt.hu/api/publication/1639730},
	author = {Istenes, Zoltán and Kozsik, Tamás and Hoch, Csaba and Tóth, László Attila},
	journal-iso = {PU.M.A PURE MATH APPL},
	journal = {PURE MATHEMATICS AND APPLICATIONS},
	volume = {17},
	unique-id = {1639730},
	issn = {1218-4586},
	year = {2006},
	eissn = {1788-800X},
	pages = {323-342},
	orcid-numbers = {Kozsik, Tamás/0000-0003-4484-9172}
}

@article{MTMT:2597911,
	title = {Intelligent high-performance crawlers used to reveal topic-specific structure of the www},
	url = {https://m2.mtmt.hu/api/publication/2597911},
	author = {Lőrincz, András and Kókai, I and Meretei, A},
	doi = {10.1142/S0129054102001230},
	journal-iso = {INT J FOUND COMPUT S},
	journal = {INTERNATIONAL JOURNAL OF FOUNDATIONS OF COMPUTER SCIENCE},
	volume = {13},
	unique-id = {2597911},
	issn = {0129-0541},
	abstract = {The slogan that «information is power» has undergone a slight change. Today, «information updating» is in the focus of interest. The largest source of information today is the World Wide Web. Fast search methods are needed to utilize this enormous source of information. In this paper our novel crawler using support vector classification and on-line reinforcement learning is described. We launched crawler searches from different sites, including sites that offer, at best, very limited information about the search subject. This case may correspond to typical searches of non-experts. Results indicate that the considerable performance improvement of our crawler over other known crawlers is due to its on-line adaptation property. We used our crawler to characterize basic topic-specific properties of WWW environments. It was found that topic-specific regions have a broad distribution of valuable documents. Expert sites are excellent starting points, whereas mailing lists can form trape for the crawler. These properties of the WWW and the emergence of intelligent «high-performance» crawlers that monitor and search for novel information together predict a significant increase of communication load on the WWW in the near future. © 2002 World Scientific Publishing Company.},
	keywords = {ADAPTATION; reinforcement learning; Internet; crawler},
	year = {2002},
	eissn = {1793-6373},
	pages = {477-495},
	orcid-numbers = {Lőrincz, András/0000-0002-1280-3447}
}

@article{MTMT:2097495,
	title = {Relating priming and repetition suppression.},
	url = {https://m2.mtmt.hu/api/publication/2097495},
	author = {Lőrincz, András and Szirtes, G and Takács, B and Biederman, I and Vogels, R},
	journal-iso = {INT J NEURAL SYST},
	journal = {INTERNATIONAL JOURNAL OF NEURAL SYSTEMS},
	volume = {12},
	unique-id = {2097495},
	issn = {0129-0657},
	abstract = {We present a prototype of a recently proposed two stage model of the entorhinal-hippocampal loop. Our aim is to form a general computational model of the sensory neocortex. The model--grounded on pure information theoretic principles--accounts for the most characteristic features of long-term memory (LTM), performs bottom-up novelty detection, and supports noise filtering. Noise filtering can also serve to correct the temporal ordering of information processing. Surprisingly, as we examine the temporal characteristics of the model, the emergent dynamics can be interpreted as perceptual priming, a fundamental type of implicit memory. In the model's framework, computational results support the hypothesis of a strong correlation between perceptual priming and repetition suppression and this correlation is a direct consequence of the temporal ordering in forming the LTM. We also argue that our prototype offers a relatively simple and coherent explanation of priming and its relation to a general model of information processing by the brain.},
	keywords = {Animals; Humans; MEMORY; hippocampus; RECOGNITION; ARTICLE; VISION; methodology; human; animal; Neural Pathways; physiology; Neural Inhibition; Models, Neurological; ENTORHINAL CORTEX; biological model; Nerve Net; Photic Stimulation; nerve cell inhibition; neocortex; nerve cell network; photostimulation; Visual Perception; nerve tract; Recognition (Psychology)},
	year = {2002},
	eissn = {1793-6462},
	pages = {187-201},
	orcid-numbers = {Lőrincz, András/0000-0002-1280-3447}
}

@article{MTMT:2097494,
	title = {Ockham's razor at work: Modeling of the "homunculus"},
	url = {https://m2.mtmt.hu/api/publication/2097494},
	author = {Lőrincz, András and Póczos, B and Szirtes, G and Takács, B},
	doi = {10.1023/A:1019996320835},
	journal-iso = {BRAIN MIND},
	journal = {BRAIN AND MIND},
	volume = {3},
	unique-id = {2097494},
	issn = {1389-1987},
	abstract = {There is a broad consensus about the fundamental role of the hippocampal system (hippocampus and its adjacent areas) in the encoding and retrieval of episodic memories. This paper presents a functional model of this system. Although memory is not a single-unit cognitive function, we took the view that the whole system of the smooth, interrelated memory processes may have a common basis. That is why we follow the Ockham's razor principle and minimize the size or complexity of our model assumption set. The fundamental assumption is the requirement of solving the so called "homunculus fallacy", which addresses the issue of interpreting the input. Generative autoassociators seem to offer a resolution of the paradox. Learning to represent and to recall information, in these generative networks, imply maximization of information transfer, sparse representation and novelty recognition. A connectionist architecture, which integrates these aspects as model constraints, is derived. Numerical studies demonstrate the novelty recognition and noise filtering properties of the architecture. Finally, we conclude that the derived connectionist architecture can be related to the neurobiological substrate.},
	keywords = {MODEL; MEMORY; hippocampus; RECOGNITION; ARTICLE; RECALL; human; priority journal; neurobiology; learning; cognition; Information retrieval; MMI; Homunculus fallacy; Generative networks; Functional modeling},
	year = {2002},
	eissn = {1573-3300},
	pages = {187-220},
	orcid-numbers = {Lőrincz, András/0000-0002-1280-3447}
}

@article{MTMT:2097493,
	title = {Fast adapting value estimation-based hybrid architecture for searching the world-wide web},
	url = {https://m2.mtmt.hu/api/publication/2097493},
	author = {Kókai, I and Lőrincz, András},
	doi = {10.1016/S1568-4946(02)00025-X},
	journal-iso = {APPL SOFT COMPUT},
	journal = {APPLIED SOFT COMPUTING},
	volume = {2},
	unique-id = {2097493},
	issn = {1568-4946},
	abstract = {The slogan that information is power has undergone a slight change. Today, information updating is in the focus of interest. The largest source of information is the world-wide web. Fast search methods are in need for this enormous source. In this paper a hybrid architecture that combines soft support vector classification and reinforcement learning for value estimation is introduced for the evaluation of a link (a document) and its neighboring links (or documents), called the context of a document. The method is motivated by (i) large differences between such contexts on the web, (ii) the facilitation of goal oriented search using context classifiers, and (iii) attractive fast adaptation properties, that could counteract diversity of web environments. We demonstrate that value estimation-based fast adaptation offers considerable improvement over other known search methods. © 2002 Elsevier Science B.V. All rights reserved.},
	keywords = {Artificial intelligence; Problem solving; SEARCH; reinforcement learning; Internet; Small world; World Wide Web; VECTORS; Computer architecture; Support vector machines; Classification (of information); Online searching; Fast adaptations; SVM; Fast adaptation},
	year = {2002},
	eissn = {1872-9681},
	pages = {11-23},
	orcid-numbers = {Lőrincz, András/0000-0002-1280-3447}
}

@inproceedings{MTMT:2097491,
	title = {Anonymity and accountability in self-organizing electronic communities},
	url = {https://m2.mtmt.hu/api/publication/2097491},
	author = {Farkas, C and Meretei, A and Ziegler, Gábor and Lőrincz, András},
	booktitle = {ACM Workshop on Privacy in the Electronic Society},
	doi = {10.1145/644527.644536},
	unique-id = {2097491},
	abstract = {In this paper we study the problem of anonymity versus accountability in electronic communities. We argue that full anonymity may present a security risk that is unacceptable in certain applications; therefore, anonymity and accountability are both needed. To resolve the inherent contradiction between anonymity and accountability in a flexible manner, we introduce the concepts of internal and external accountabilities. Intuitively, internal accountability applies to virtual users only, and is governed by the policy of a group (a community). In contrast, external accountability is needed to address issues related to misuse if the activity is to be penalized in real life according to internal rules or external laws. We provide a set of protocols to ensure that users' virtual and real identities cannot be disclosed unnecessarily, and allow users to monitor the data collected about them as well as to terminate their membership (both real and virtual) under certain conditions. We develop a general conceptual model of electronic Editorial Board (e-EB). In our thinking, there are deep connections between anonymity and self-organization. In turn, the concept of self-organizing e-EB (SO-eEB) is introduced here, and a robotic example is provided. Finally, SO-eEB is specialized to Anonymous and Accountable Self-Organizing Communities (A2SOCs), that fully supports internal and external accountability while providing anonymity.},
	keywords = {Internet; information science; accountability; AUTHENTICATION; Electronic communities; Self-organizing community; Data privacy; Privacy; Anonymity},
	year = {2002},
	pages = {81-90},
	orcid-numbers = {Lőrincz, András/0000-0002-1280-3447}
}

@article{MTMT:2072373,
	title = {The mystery of structure and function of sensory processing areas of the neocortex: A resolution},
	url = {https://m2.mtmt.hu/api/publication/2072373},
	author = {Lőrincz, András and Szatmary, B and Szirtes, G},
	doi = {10.1023/A:1020262214821},
	journal-iso = {J COMPUT NEUROSCI},
	journal = {JOURNAL OF COMPUTATIONAL NEUROSCIENCE},
	volume = {13},
	unique-id = {2072373},
	issn = {0929-5313},
	year = {2002},
	eissn = {1573-6873},
	pages = {187-205},
	orcid-numbers = {Lőrincz, András/0000-0002-1280-3447}
}