TY  - JOUR
AU  - Péter, Róbert
AU  - Szántó, Zsolt
AU  - Biacsi, Zoltán
AU  - Berend, Gábor
AU  - Bilicki, Vilmos
TI  - Multilingual Analysis and Visualization of Bibliographic Metadata and Texts with the AVOBMAT Research Tool
JF  - Journal of Open Humanities Data
VL  - 10
PY  - 2024
PG  - 10
SN  - 2059-481X
DO  - 10.5334/johd.175
UR  - https://m2.mtmt.hu/api/publication/34431216
ID  - 34431216
LA  - English
DB  - MTMT
ER  - 

TY  - JOUR
AU  - Nagy, Balázs
AU  - Hegedűs, István
AU  - Sándor, Noémi
AU  - Egedi, Balázs
AU  - Mehmood, Haaris
AU  - Saravanan, Karthikeyan
AU  - Lóki, Gábor
AU  - Kiss, Ákos
TI  - Privacy-preserving Federated Learning and its application to natural language processing
JF  - KNOWLEDGE-BASED SYSTEMS
J2  - KNOWL-BASED SYST
VL  - 268
PY  - 2023
PG  - 12
SN  - 0950-7051
DO  - 10.1016/j.knosys.2023.110475
UR  - https://m2.mtmt.hu/api/publication/33731155
ID  - 33731155
LA  - English
DB  - MTMT
ER  - 

TY  - JOUR
AU  - Balogh, Réka
AU  - Imre, Nóra
AU  - Gosztolya, Gábor
AU  - Hoffmann, Ildikó
AU  - Pákáski, Magdolna
AU  - Kálmán, János
TI  - The Role of Silence in Verbal Fluency Tasks – A New Approach for the Detection of Mild Cognitive Impairment
JF  - JOURNAL OF THE INTERNATIONAL NEUROPSYCHOLOGICAL SOCIETY
J2  - J INT NEUROPSYCH SOC
VL  - 29
PY  - 2023
IS  - 1
SP  - 46
EP  - 58
PG  - 13
SN  - 1355-6177
DO  - 10.1017/S1355617721001454
UR  - https://m2.mtmt.hu/api/publication/32656290
ID  - 32656290
LA  - English
DB  - MTMT
ER  - 

TY  - CHAP
AU  - Devroye, N.
AU  - Mohammadi, N.
AU  - Mulgund, A.
AU  - Naik, H.
AU  - Shekhar, R.
AU  - Turán, György
AU  - Wei, Y.
AU  - Zefran, M.
ED  - IEEE, .
TI  - Interpreting Deep-Learned Error-Correcting Codes
T2  - 2022 IEEE International Symposium on Information Theory (ISIT)
PB  - IEEE
CY  - New York, New York
SN  - 9781665421591
PY  - 2022
SP  - 2457
EP  - 2462
PG  - 6
DO  - 10.1109/ISIT50566.2022.9834599
UR  - https://m2.mtmt.hu/api/publication/33713914
ID  - 33713914
LA  - English
DB  - MTMT
ER  - 

TY  - CHAP
AU  - Mulgund, A.
AU  - Shekhar, R.
AU  - Devroye, N.
AU  - Turán, György
AU  - Zefran, M.
TI  - Evaluating interpretations of deep-learned error-correcting codes
T2  - 2022 58th Annual Allerton Conference on Communication, Control, and Computing (Allerton)
PB  - IEEE
SN  - 9798350399981
PY  - 2022
SP  - 1
EP  - 8
PG  - 8
DO  - 10.1109/Allerton49937.2022.9929417
UR  - https://m2.mtmt.hu/api/publication/33713905
ID  - 33713905
LA  - English
DB  - MTMT
ER  - 

TY  - JOUR
AU  - Vincze, Veronika
AU  - Főző, Eszter
AU  - Kicsi, András
AU  - Vidács, László
TI  - SZIRA: Szövegfeldolgozó Információs Rendszer és Adattár a szerzőazonosítás szolgálatában
JF  - ALKALMAZOTT NYELVTUDOMÁNY
J2  - ALKALMAZOTT NYELVTUDOMÁNY
VL  - 22
PY  - 2022
IS  - Különszám
SP  - 52
EP  - 73
PG  - 22
SN  - 1587-1061
DO  - 10.18460/ANY.K.2022.005
UR  - https://m2.mtmt.hu/api/publication/33675581
ID  - 33675581
LA  - Hungarian
DB  - MTMT
ER  - 

TY  - CHAP
AU  - Bóna, Judit
AU  - Gosztolya, Gábor
AU  - Hoffmann, Ildikó
AU  - Klivényi, Péter
AU  - Tóth, Alinka
AU  - Svindt, Veronika
AU  - Tóth, László
AU  - Lőrincz, András
ED  - Dobrić, Arnalda
ED  - Liker, Marko
TI  - Temporal variables of speech in Parkinson’s Disease in three spontaneous speaking tasks
T2  - Book of Abstracts : The 11th scientific conference with international participation Speech Research, Faculty of Humanities and Social Sciences, Zagreb, Croatia, December 8 - 10 2022
PB  - Hrvatsko filološko društvo
CY  - Zágráb
SN  - 9789532961935
PY  - 2022
SP  - 28
EP  - 29
PG  - 2
UR  - https://m2.mtmt.hu/api/publication/33576741
ID  - 33576741
LA  - English
DB  - MTMT
ER  - 

TY  - CHAP
AU  - José Vicente, Egas López
AU  - Gosztolya, Gábor
ED  - Narukawa, Yasuo
ED  - Torra, Vicenç
TI  - Identification of Subjects Wearing a Surgical Mask from Their Speech by Means of X-vectors and Fisher Vectors
T2  - Modeling Decisions for Artificial Intelligence
PB  - Springer Netherlands
CY  - Cham
SN  - 9783031134487
T3  - Lecture Notes in Computer Science, ISSN 0302-9743 ; 13408.
PY  - 2022
SP  - 108
EP  - 118
PG  - 11
DO  - 10.1007/978-3-031-13448-7_9
UR  - https://m2.mtmt.hu/api/publication/33096492
ID  - 33096492
N1  - Chapter 9
LA  - English
DB  - MTMT
ER  - 

TY  - JOUR
AU  - Kiss-Vetráb, Mercedes
AU  - Gosztolya, Gábor
TI  - Using the Bag-of-Audio-Words approach for emotion recognition
JF  - ACTA UNIVERSITATIS SAPIENTIAE INFORMATICA
J2  - ACTA UNIV SAP INFORM
VL  - 14
PY  - 2022
IS  - 1
SP  - 1
EP  - 21
PG  - 21
SN  - 1844-6086
DO  - 10.2478/ausi-2022-0001
UR  - https://m2.mtmt.hu/api/publication/33055336
ID  - 33055336
AB  - 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.
LA  - English
DB  - MTMT
ER  - 

TY  - JOUR
AU  - Horváth, Ferenc
AU  - Beszédes, Árpád
AU  - Vancsics, Béla
AU  - Balogh, Gergő
AU  - Vidács, László
AU  - Gyimóthy, Tibor
TI  - Using contextual knowledge in interactive fault localization
JF  - EMPIRICAL SOFTWARE ENGINEERING
J2  - EMPIR SOFTW ENG
VL  - 27
PY  - 2022
IS  - 6
PG  - 69
SN  - 1382-3256
DO  - 10.1007/s10664-022-10190-x
UR  - https://m2.mtmt.hu/api/publication/33041903
ID  - 33041903
N1  - Department of Software Engineering, University of Szeged, Szeged, Hungary            
            MTA-SZTE Research Group on Artificial Intelligence, University of Szeged, Szeged, Hungary            
            Export Date: 16 February 2023            
            CODEN: ESENF            
            Correspondence Address: Horváth, F.; Department of Software Engineering, Hungary; email: hferenc@inf.u-szeged.hu            
            Funding details: RRF-2.3.1-21-2022-00004            
            Funding details: Magyar Tudományos Akadémia, MTA, TKP2021-NVA-09            
            Funding details: Nemzeti Kutatási, Fejlesztési és Innovaciós Alap, NKFIA            
            Funding text 1: László Vidács was also funded by the János Bolyai Scholarship of the Hungarian Academy of Sciences. Project no. TKP2021-NVA-09 has been implemented with the support provided by the Ministry of Innovation and Technology of Hungary from the National Research, Development and Innovation Fund, financed under the TKP2021-NVA funding scheme. The research was supported by the Ministry of Innovation and Technology NRDI Office within the framework of the Artificial Intelligence National Laboratory Program (RRF-2.3.1-21-2022-00004).
AB  - 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.
LA  - English
DB  - MTMT
ER  -