Editorial - Christian Gütl (Journal of Universal Computer Science, 2024)
Dear Readers, It gives me great pleasure to announce the second regular issue of 2024. I would like to thank all the authors for their sound research and the editorial board for the extremely valuable reviews and suggestions for improvement. These contributions together with the generous support of the consortium members enable us to run our journal and maintain its quality. I would still like to expand our editorial board: If you are a tenured associate professor or above with a good publication record, please apply to join our editorial board. We are also interested in high-quality proposals for special issues on new topics and emerging trends. And finally, we are still looking for some financial support for 2024 to cover all our expenses. We would be very grateful if your library or institution can support us. We would then be happy to add it to our consortium list. In this regular issue, I am very pleased to present 6 accepted papers by 19 authors from 9 countries: Canada, China, Croatia, India, Kazakhstan, México, Sri Lanka, Ukraine, and Vietnam. Petra Grd, Igor Tomičić, and Ena Barčić from Croatia address in their article a multi-step methodology for face shape classification that is based on the potential of transfer learning and a pretrained EfficientNetV2S neural network. Lizbeth Alejandra Hernández-González, Ulises Juárez-Martínez, Jezreel Mejía, and Alberto Aguilar-Laserre from México focus their research on applying the naturalistic programming paradigm within a software development process using a naturalistic software development method. In a joint research, Shanshan Jia from China, Gaukhar A. Kamalova from the Republic of Kazakhstan, and Dmytro Mykhalevskiy from Ukraine report on a mobile handover technique aligning with the neighbour discovery paradigm in 6LoWPAN. Ajay Kumar from India is investigating a mechanism to assess machine learning approaches for software effort estimation (SEE) modeling in the context of accuracy measures, specifically exploring machine learning techniques for SEE modeling as a multi-criteria decision making (MCDM) problem. Anne Perera and Amitha Caldera from Sri Lanka conduct a comprehensive review on sentiment analysis in the context of mix of languages, phonetic typing and lexical borrowing in web communication. And last but not least, in a collaboration between researchers from Vietnam and Canada, Tien Quang Dam, Nghia Thinh Nguyen, Trung Viet Le, Tran Duc Le, Sylvestre Uwizeyemungu, and Thang Le-Dinh look into malware detection methods, specifically leveraging machine learning to encode critical information from portable executable (PE) headers into visual representations of ransomware samples. Enjoy Reading! Cordially, Christian Gütl, Managing Editor-in-Chief Graz University of Technology, Graz, Austria