@article{MTMT:34431216,
	title = {Multilingual Analysis and Visualization of Bibliographic Metadata and Texts with the AVOBMAT Research Tool},
	url = {https://m2.mtmt.hu/api/publication/34431216},
	author = {Péter, Róbert and Szántó, Zsolt and Biacsi, Zoltán and Berend, Gábor and Bilicki, Vilmos},
	doi = {10.5334/johd.175},
	journal = {Journal of Open Humanities Data},
	volume = {10},
	unique-id = {34431216},
	year = {2024},
	eissn = {2059-481X},
	orcid-numbers = {Berend, Gábor/0000-0002-3845-4978}
}

@article{MTMT:33731155,
	title = {Privacy-preserving Federated Learning and its application to natural language processing},
	url = {https://m2.mtmt.hu/api/publication/33731155},
	author = {Nagy, Balázs and Hegedűs, István and Sándor, Noémi and Egedi, Balázs and Mehmood, Haaris and Saravanan, Karthikeyan and Lóki, Gábor and Kiss, Ákos},
	doi = {10.1016/j.knosys.2023.110475},
	journal-iso = {KNOWL-BASED SYST},
	journal = {KNOWLEDGE-BASED SYSTEMS},
	volume = {268},
	unique-id = {33731155},
	issn = {0950-7051},
	year = {2023},
	eissn = {1872-7409},
	orcid-numbers = {Nagy, Balázs/0000-0002-0599-242X; Hegedűs, István/0000-0002-5356-2192; Egedi, Balázs/0000-0003-2989-9374; Lóki, Gábor/0000-0002-2843-827X; Kiss, Ákos/0000-0003-3077-7075}
}

@article{MTMT:32656290,
	title = {The Role of Silence in Verbal Fluency Tasks – A New Approach for the Detection of Mild Cognitive Impairment},
	url = {https://m2.mtmt.hu/api/publication/32656290},
	author = {Balogh, Réka and Imre, Nóra and Gosztolya, Gábor and Hoffmann, Ildikó and Pákáski, Magdolna and Kálmán, János},
	doi = {10.1017/S1355617721001454},
	journal-iso = {J INT NEUROPSYCH SOC},
	journal = {JOURNAL OF THE INTERNATIONAL NEUROPSYCHOLOGICAL SOCIETY},
	volume = {29},
	unique-id = {32656290},
	issn = {1355-6177},
	year = {2023},
	eissn = {1469-7661},
	pages = {46-58},
	orcid-numbers = {Gosztolya, Gábor/0000-0002-2864-6466; Pákáski, Magdolna/0000-0001-8067-5435; Kálmán, János/0000-0001-5319-5639}
}

@inproceedings{MTMT:33713914,
	title = {Interpreting Deep-Learned Error-Correcting Codes},
	url = {https://m2.mtmt.hu/api/publication/33713914},
	author = {Devroye, N. and Mohammadi, N. and Mulgund, A. and Naik, H. and Shekhar, R. and Turán, György and Wei, Y. and Zefran, M.},
	booktitle = {2022 IEEE International Symposium on Information Theory (ISIT)},
	doi = {10.1109/ISIT50566.2022.9834599},
	unique-id = {33713914},
	year = {2022},
	pages = {2457-2462}
}

@inproceedings{MTMT:33713905,
	title = {Evaluating interpretations of deep-learned error-correcting codes},
	url = {https://m2.mtmt.hu/api/publication/33713905},
	author = {Mulgund, A. and Shekhar, R. and Devroye, N. and Turán, György and Zefran, M.},
	booktitle = {2022 58th Annual Allerton Conference on Communication, Control, and Computing (Allerton)},
	doi = {10.1109/Allerton49937.2022.9929417},
	unique-id = {33713905},
	year = {2022},
	pages = {1-8}
}

@article{MTMT:33675581,
	title = {SZIRA: Szövegfeldolgozó Információs Rendszer és Adattár a szerzőazonosítás szolgálatában},
	url = {https://m2.mtmt.hu/api/publication/33675581},
	author = {Vincze, Veronika and Főző, Eszter and Kicsi, András and Vidács, László},
	doi = {10.18460/ANY.K.2022.005},
	journal-iso = {ALKALMAZOTT NYELVTUDOMÁNY},
	journal = {ALKALMAZOTT NYELVTUDOMÁNY},
	volume = {22},
	unique-id = {33675581},
	issn = {1587-1061},
	year = {2022},
	eissn = {2498-4442},
	pages = {52-73},
	orcid-numbers = {Vincze, Veronika/0000-0002-9844-2194; Vidács, László/0000-0002-0319-3915}
}

@{MTMT:33576741,
	title = {Temporal variables of speech in Parkinson’s Disease in three spontaneous speaking tasks},
	url = {https://m2.mtmt.hu/api/publication/33576741},
	author = {Bóna, Judit and Gosztolya, Gábor and Hoffmann, Ildikó and Klivényi, Péter and Tóth, Alinka and Svindt, Veronika and Tóth, László and Lőrincz, András},
	booktitle = {Book of Abstracts : The 11th scientific conference with international participation Speech Research, Faculty of Humanities and Social Sciences, Zagreb, Croatia, December 8 - 10 2022},
	unique-id = {33576741},
	year = {2022},
	pages = {28-29},
	orcid-numbers = {Bóna, Judit/0000-0003-2369-1636; Gosztolya, Gábor/0000-0002-2864-6466; Klivényi, Péter/0000-0002-5389-3266; Svindt, Veronika/0000-0002-6027-9029; Tóth, László/0000-0003-0161-1375; Lőrincz, András/0000-0002-1280-3447}
}

@inproceedings{MTMT:33096492,
	title = {Identification of Subjects Wearing a Surgical Mask from Their Speech by Means of X-vectors and Fisher Vectors},
	url = {https://m2.mtmt.hu/api/publication/33096492},
	author = {José Vicente, Egas López and Gosztolya, Gábor},
	booktitle = {Modeling Decisions for Artificial Intelligence},
	doi = {10.1007/978-3-031-13448-7_9},
	unique-id = {33096492},
	year = {2022},
	pages = {108-118},
	orcid-numbers = {Gosztolya, Gábor/0000-0002-2864-6466}
}

@article{MTMT:33055336,
	title = {Using the Bag-of-Audio-Words approach for emotion recognition},
	url = {https://m2.mtmt.hu/api/publication/33055336},
	author = {Kiss-Vetráb, Mercedes and Gosztolya, Gábor},
	doi = {10.2478/ausi-2022-0001},
	journal-iso = {ACTA UNIV SAP INFORM},
	journal = {ACTA UNIVERSITATIS SAPIENTIAE INFORMATICA},
	volume = {14},
	unique-id = {33055336},
	issn = {1844-6086},
	abstract = {The problem of varying length recordings is a well-known issue in paralinguistics. We investigated how to resolve this problem using the bag-of-audio-words feature extraction approach. The steps of this technique involve preprocessing, clustering, quantization and normalization. The bag-of-audio-words technique is competitive in the area of speech emotion recognition, but the method has several parameters that need to be precisely tuned for good efficiency. The main aim of our study was to analyse the effectiveness of bag-of-audio-words method and try to find the best parameter values for emotion recognition. We optimized the parameters one-by-one, but built on the results of each other. We performed the feature extraction, using openSMILE. Next we transformed our features into same-sized vectors with openXBOW, and finally trained and evaluated SVM models with 10-fold-crossvalidation and UAR. In our experiments, we worked with a Hungarian emotion database. According to our results, the emotion classification performance improves with the bag-of-audio-words feature representation. Not all the BoAW parameters have the optimal settings but later we can make clear recommendations on how to set bag-of-audio-words parameters for emotion detection tasks.},
	year = {2022},
	eissn = {2066-7760},
	pages = {1-21},
	orcid-numbers = {Kiss-Vetráb, Mercedes/0000-0002-3914-2036; Gosztolya, Gábor/0000-0002-2864-6466}
}

@article{MTMT:33041903,
	title = {Using contextual knowledge in interactive fault localization},
	url = {https://m2.mtmt.hu/api/publication/33041903},
	author = {Horváth, Ferenc and Beszédes, Árpád and Vancsics, Béla and Balogh, Gergő and Vidács, László and Gyimóthy, Tibor},
	doi = {10.1007/s10664-022-10190-x},
	journal-iso = {EMPIR SOFTW ENG},
	journal = {EMPIRICAL SOFTWARE ENGINEERING},
	volume = {27},
	unique-id = {33041903},
	issn = {1382-3256},
	abstract = {Tool support for automated fault localization in program debugging is limited because state-of-the-art algorithms often fail to provide efficient help to the user. They usually offer a ranked list of suspicious code elements, but the fault is not guaranteed to be found among the highest ranks. In Spectrum-Based Fault Localization (SBFL) – which uses code coverage information of test cases and their execution outcomes to calculate the ranks –, the developer has to investigate several locations before finding the faulty code element. Yet, all the knowledge she a priori has or acquires during this process is not reused by the SBFL tool. There are existing approaches in which the developer interacts with the SBFL algorithm by giving feedback on the elements of the prioritized list. We propose a new approach called iFL which extends interactive approaches by exploiting contextual knowledge of the user about the next item in the ranked list (e. g., a statement), with which larger code entities (e. g., a whole function) can be repositioned in their suspiciousness. We implemented a closely related algorithm proposed by Gong et al. , called Talk . First, we evaluated iFL using simulated users, and compared the results to SBFL and Talk . Next, we introduced two types of imperfections in the simulation: user’s knowledge and confidence levels. On SIR and Defects4J, results showed notable improvements in fault localization efficiency, even with strong user imperfections. We then empirically evaluated the effectiveness of the approach with real users in two sets of experiments: a quantitative evaluation of the successfulness of using iFL , and a qualitative evaluation of practical uses of the approach with experienced developers in think-aloud sessions.},
	year = {2022},
	eissn = {1573-7616},
	orcid-numbers = {Horváth, Ferenc/0000-0002-8442-7970; Beszédes, Árpád/0000-0002-5421-9302; Vancsics, Béla/0000-0003-4584-3733; Balogh, Gergő/0000-0002-6781-5453; Vidács, László/0000-0002-0319-3915; Gyimóthy, Tibor/0000-0002-2123-7387}
}