Editorial

Abstract

Dear Readers,  It gives me great pleasure to announce the third regular issue of 2023. In this issue, 4 papers by 11 authors from 5 countries cover various topical aspects of computer science. In an ongoing effort to further strengthen our journal, I would like to expand the editorial board: If you are a tenured associate professor or above with a strong publication record, you are welcome to apply to join our editorial board. We are also interested in high-quality proposals for special issues on new topics and trends.  As always, I would like to thank all the authors for their sound research and the editorial board for their extremely valuable review effort and suggestions for improvement. These contributions, together with the generous support of the consortium members, sustain the quality of our journal.  In the third regular issue, I am very pleased to introduce the following 4 accepted articles: Gerardo Matturro from Uruguay reports on his research findings on undergraduate software engineering over a seven-year period, specifically on students' motivation to participate in research projects, skills acquired, and their perceptions of benefits. Uma Priya D and P. Santhi Thilagam from India propose an approach to cluster heterogeneous JSON documents using the similarity fusion method based on structural, semantic and contextual measures of JSON schemas. Zeinab Rahimi and Mehrnoush Shamsfard from Iran present a hybrid contradiction detection approach that can detect seven categories of contradictions in Persian texts: Antonymy, negation, numerical, factive, structural, lexical and world knowledge, which is based on a novel data mining method and a transformer-based deep neural method for contradiction detection. In their joint research between Estonia, Spain and India, Shashi Kant Shankar, Adolfo Ruiz-Calleja, Luis P. Prieto, María Jesús Rodríguez-Triana, Pankaj Chejara, and Sandesh Tripathi discuss a modular and modifiable infrastructure for data preparation, organization, and fusion to partially support the development of context-aware multimodal learning analytics solutions.  Enjoy Reading! Cordially,