{"id":107,"date":"2021-02-10T08:50:44","date_gmt":"2021-02-10T08:50:44","guid":{"rendered":"http:\/\/www.cs.ubbcluj.ro\/quadeep\/?page_id=107"},"modified":"2023-10-15T21:11:06","modified_gmt":"2023-10-15T21:11:06","slug":"publicatii","status":"publish","type":"page","link":"https:\/\/www.cs.ubbcluj.ro\/quadeep\/ro\/publicatii\/","title":{"rendered":"Publica\u021bii"},"content":{"rendered":"\n

Publica\u021bii \u00een cadrul proiectului<\/h3>\n\n\n\n

2023<\/h4>\n\n\n\n
George Ciubotariu, Gabriela Czibula, Istvan Gergely Czibula, Ioana-Gabriela Chelaru – “Uncovering Behavioural Patterns of One: And Binary-Class SVM-Based Software Defect Predictors<\/a><\/em>“, In Proceedings of the 18th International Conference on Software Technologies – ICSOFT, pp. 249-257 (B-ranked, indexed WoS<\/strong>)<\/td><\/tr>
Anamaria Briciu, Gabriela Czibula, Mihaiela Lupea – “A study on the relevance of semantic features extracted using BERT-based language models for enhancing the performance of software defect classifiers”<\/em>, 27th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES2023), accepted for publication (B-ranked, indexed WoS<\/strong>)<\/td><\/tr>
Gabriela Czibula, Ioana-Gabriela Chelaru, Istvan Gergely Czibula, Arthur Molnar – “An unsupervised learning-based methodology for uncovering behavioural patterns for specific types of software defects<\/em>“, 27th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES2023), accepted for publication (B-ranked, indexed WoS<\/strong>)<\/td><\/tr>
Zsuzsanna Marian-One\u021b, Diana-Lucia Miholca – “Source-code embedding-based software defect prediction<\/em>“, In Proceedings of the 18th International Conference on Software Technologies – ICSOFT, pp. 185-196 (B-ranked, indexed WoS<\/strong>)<\/td><\/tr>
Mariana Maier, Gabriela Czibula, Lavinia Delean – “Using unsupervised learning for mining behavioural patterns from data. A case study for the baccalaureate exam in Romania<\/a>“, <\/em>Studies in Informatics and Control, vol. 32(2), pp. 73-84, 2023 (2022 IF=1.6, Q3<\/strong>)<\/td><\/tr>
Imre-Gergely Mali, Gabriela Czibula – “Policy-Based Reinforcement Learning in the Generalized Rock-Paper-Scissors Game<\/em>, ESANN 2023 proceedings<\/a>“, The 31th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2023), pp. 345-350 (B-ranked, indexed WoS<\/strong>)<\/td><\/tr>
Alexandra-Ioana Albu – “Temporal ensembling-based deep k-nearest neighbours for learning with noisy labels<\/a><\/em>“, The 31th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2023), pp. 483-488\u00a0 (B-ranked, indexed WoS<\/strong>)<\/td><\/tr>
Paul-Dumitru Or\u0103\u0219an, Gabriela Czibula – “Im2Vide0: A Zero-Shot approach using diffusion models for natural language conditioned Image-to-Video<\/em>“, 2023 IEEE 19th International Conference on Intelligent Computer Communication and Processing, 2023, <\/strong>accepted for publication (D-ranked, indexed IEEE<\/strong>)<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

2022<\/h4>\n\n\n\n
Diana-Lucia Miholca, Vlad-Ioan Tomescu, Gabriela Czibula – “An in-depth analysis of the software features\u2019 impact on the performance of deep learning-based software defect predictors<\/a><\/em>“, IEEE Access, 2022, Volume 10, pp. 64801-64818 (B-ranked, indexed WoS, 2021<\/strong> IF=3.476<\/strong>, Q2<\/strong>)<\/td><\/tr>
Mihaiela Lupea, Anamaria Briciu, Istvan-Gergely Czibula, Gabriela Czibula – “SoftId: An autoencoder-based one-class classification model for software authorship identification<\/a><\/em>“, 26th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES 2022), September 7-9, 2022, Procedia Computer Science 207, pp. 716-725 (B-ranked, indexed WoS<\/strong>)<\/td><\/tr>
Gabriela Czibula, Mihaiela Lupea, Anamaria Briciu – “Enhancing the performance of software authorship attribution using an ensemble of deep autoencoders<\/a><\/em>“, Mathematics, Special Issue “Recent Advances in Artificial Intelligence and Machine Learning”, 2022, 10(15):2572 (A-ranked, indexed WoS, 2021 IF=2.592, Q1)<\/strong><\/td><\/tr>
Gabriela Czibula, George Ciubotariu, Mariana Maier, Hannelore-Inge Lisei – “IntelliDaM: A machine learning based framework for enhancing the performance of decision-making processes<\/a>“, A case study for educational data mining<\/em>, IEEE Access, 2022, Volume 10, pp. 80651-80666 (B-ranked, indexed WoS, 2021 IF=3.476, Q2<\/strong>)<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

\ufeff<\/p>\n\n\n\n

2021<\/h4>\n\n\n\n
Anamaria Briciu, Gabriela Czibula, Mihaiela Lupea – “AutoAt: A deep autoencoder-based classification model for supervised authorship attribution<\/a><\/em>“, 25th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES 2021), September 8-10, 2021, Procedia Computer Science 192, pp. 397-406 (B-ranked, indexed WoS<\/strong>)<\/td><\/tr>
Vlad-Ioan Tomescu, Gabriela Czibula, \u0218tefan Ni\u021bic\u0103 – “A study on using deep autoencoders for imbalanced binary classification<\/a><\/em>“, 25th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES 2021), September 8-10, 2021, Procedia Computer Science 192, pp. 119-128 (B-ranked, indexed WoS<\/strong>)<\/td><\/tr>
George Ciubotariu, Vlad-Ioan Tomescu, Gabriela Czibula – “Enhancing the performance of image classification through features automatically learned from depth-maps<\/a><\/em>“, 13th International Conference on Computer Vision Systems, September 22-24, 2021, LNCS 12899, pp. 68-81 (C-ranked<\/strong>)<\/td><\/tr>
Zsuzsanna One\u021b-Marian, Gabriela Czibula, Mariana Maier – “Using self-organizing maps for comparing students’ academic performance in online and traditional learning environments<\/a><\/em>“, Studies in Informatics and Control (SIC) journal, 30(4), 2021, pp. 17-28 (C-ranked, indexed WoS<\/strong>, IF 2020=1.649<\/strong>)<\/td><\/tr>
Maria-M\u0103d\u0103lina Mircea, Rare\u0219 Boian and Gabriela Czibula – “A machine learning approach for data protection in virtual reality therapy applications<\/a><\/em>“, 2021 IEEE 17th International Conference on Intelligent Computer Communication and Processing, 2021, pp. 367-374, DOI: 10.1109\/ICCP53602.2021.9733574 (D-ranked, indexed WoS<\/strong>)<\/td><\/tr>
Diana-Lucia Miholca – “New Conceptual Cohesion Metrics: Assessment for Software Defect Prediction<\/a><\/em>“, 23rd International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, SYNASC 2021, pp. 163-170, DOI: 10.1109\/SYNASC54541.2021.00036 (D-ranked, indexed WoS<\/strong>)<\/td><\/tr>
Mariana-Ioana Maier, Gabriela Czibula, Zsuzsanna One\u021b-Marian – “Towards Using Deep Autoencoders for Comparing Traditional and Synchronous Online Learning in Assessing Students’ Academic Performance<\/a><\/em>“, Mathematics, Engineering Mathematics – special issue on Didactics and Technology in Mathematical Education, 2021, 9(22), 2870 (A-ranked,<\/strong> 2020 IF=2.258, Q1<\/strong>)<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

<\/p>\n\n\n\n

Publica\u021bii conexe<\/h3>\n\n\n\n

Acestea sunt publica\u021bii premerg\u0103toare proiectului QuaDeeP. Au contribuit la modelarea ideii de proiect \u0219i servesc la ilustrarea eforturilor anterioare ale grupului nostru de cercetare.<\/p>\n\n\n\n

Diana-Lucia Miholca, Gabriela Czibula, Vlad Tomescu – “COMET: A conceptual coupling based metrics suite for software defect prediction<\/em><\/a>“, Procedia Computer Science 176, 2020, pp. 31-40.<\/td><\/tr>
Diana-Lucia Miholca, Zsuzsanna One\u021b-Marian \u2013 \u201cAn analysis of aggregated coupling\u2019s suitability for software defect prediction<\/a><\/em>\u201d, 22nd  International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC), 2020, pp. 141\u2013148.<\/td><\/tr>
Mihai Teletin, Gabriela Czibula – “CVSimP: An approach for predicting proteins’ structural similarity using one-shot learning<\/a><\/em>“, 2020 IEEE 14th International Symposium on Applied Computational Intelligence and Informatics (SACI), 2020, pp. 111-116.<\/td><\/tr>
Arthur-Jozsef Molnar, Simona Motogna – “Long-Term Evaluation of Technical Debt in Open-Source Software<\/a><\/em>“, Proceedings of the 14th ACM\/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM), 2020, pp. 1-9.<\/td><\/tr>
Arthur-Jozsef Molnar, Simona Motogna – “Longitudinal Evaluation of Open-source Software Maintainability<\/a><\/em>“, Proceedings of the 15th International Conference on Evaluation of Novel Approaches to Software Engineering (ENASE 2020), 2020, pp. 120-131.<\/td><\/tr>
Istvan Gergely Czibula, Gabriela Czibula, Diana-Lucia Miholca, Zsuzsanna Onet-Marian. – “An aggregated coupling measure for the analysis of object-oriented software systems<\/a><\/em>“, Journal of Systems and Software 148, 2019, pp. 1-20.<\/td><\/tr>
Diana-Lucia Miholca, Gabriela Czibula – “Software Defect Prediction Using a Hybrid Model Based on Semantic Features Learned from the Source Code<\/a><\/em>“, International Conference on Knowledge Science, Engineering and Management, August 28-30, 2019, LNCS 11775, pp. 262\u2013274.<\/td><\/tr>
Arthur-Jozsef Molnar, Alexandra Neam\u0163u, Simona Motogna – “Longitudinal Evaluation of Software Quality Metrics in Open-Source Applications<\/a>“<\/em>, Proceedings of the 14th International Conference on Evaluation of Novel Approaches to Software Engineering (ENASE 2019), 2019, pp. 80\u201391.<\/td><\/tr>
Mihai Teletin, Gabriela Czibula, Carmina Codre – “AutoSimP: An approach for predicting proteins’ structural similarities using an ensemble of deep autoencoders<\/a><\/em>“, International Conference on Knowledge Science, Engineering and Management, August 28-30, 2019, LNCS 11776, pp. 49\u201354.<\/td><\/tr>
Diana-Lucia Miholca, Gabriela Czibula, Istvan Gergely Czibula – “A  novel  approach  for  software  defect  prediction  through  hybridizing  gradual  relational association rules with artificial neural network<\/em>s<\/a>“, Information Sciences 441, 2018, pp. 152 \u2013 170.<\/td><\/tr>
Istvan Gergely Czibula, Gabriela Czibula, Diana-Lucia Miholca – “Enhancing relational association rules with gradualness<\/a><\/em>“, International Journal of Innovative Computing, Information and Control 13(1), 2017, pp. 289-305.<\/td><\/tr>
Gabriela Czibula, Zsuzsanna Marian, Istvan Gergely Czibula – “Detecting software design defects using relational association rule mining<\/em><\/a>“, Knowledge and Information Systems 42(3), <\/strong>2015, pp. 545\u2013577.<\/td><\/tr>
Gabriela Czibula, Zsuzsanna Marian, Istvan Gergely Czibula – “Software defect prediction using relational association rule mining<\/a><\/em>“, Information Sciences 264, 2014, pp. 260-278.<\/td><\/tr>
Gabriela \u015eerban, Alina C\u00e2mpan, Istvan Gergely Czibula – “A Programming Interface For Finding Relational Association Rules<\/a><\/em>“, International Journal of Computers, Communications & Control 1(S), 2006, pp. 439-444.<\/td><\/tr><\/tbody><\/table><\/figure>\n","protected":false},"excerpt":{"rendered":"

Publica\u021bii \u00een cadrul proiectului 2023 George Ciubotariu, Gabriela Czibula, Istvan Gergely Czibula, Ioana-Gabriela Chelaru – “Uncovering Behavioural Patterns of One:… <\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":[],"_links":{"self":[{"href":"https:\/\/www.cs.ubbcluj.ro\/quadeep\/wp-json\/wp\/v2\/pages\/107"}],"collection":[{"href":"https:\/\/www.cs.ubbcluj.ro\/quadeep\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.cs.ubbcluj.ro\/quadeep\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.cs.ubbcluj.ro\/quadeep\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.cs.ubbcluj.ro\/quadeep\/wp-json\/wp\/v2\/comments?post=107"}],"version-history":[{"count":25,"href":"https:\/\/www.cs.ubbcluj.ro\/quadeep\/wp-json\/wp\/v2\/pages\/107\/revisions"}],"predecessor-version":[{"id":334,"href":"https:\/\/www.cs.ubbcluj.ro\/quadeep\/wp-json\/wp\/v2\/pages\/107\/revisions\/334"}],"wp:attachment":[{"href":"https:\/\/www.cs.ubbcluj.ro\/quadeep\/wp-json\/wp\/v2\/media?parent=107"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}