JUCS - Journal of Universal Computer Science 29(2): 98-99, doi: 10.3897/jucs.102031
Editorial
expand article infoChristian Gütl
‡ Graz University of Technology, Graz, Austria
Open Access
Abstract

Dear Readers, 

It gives me great pleasure to announce the second regular issue of 2023. 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. 

Still, I would 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. 

In this regular issue, I am very pleased to introduce 4 accepted papers involving 16 authors from 6 different countries. 

In a collaboration between researchers from South Africa and the USA, Trienko Grobler, Manfred Habeck, Lynette van Zijl and Jaco Geldenhuys address improved algorithms for combinatorial generation of bordered box repetition-free words based on tree-based search space and graph-based search space. In another joint research between Algeria and France, Mehdi Rouissat, Mohammed Belkheir, Hicham Sid Ahmed Belkhira, Sofiane Boukli Hacen, Pascal Lorenz and Merahi Bouziani describe a new technique to mitigate the version number attack for IoT networks, reducing control overhead by 83% and energy consumption by 74%. Amira Samir, Huda Amin Maghawry and Nagwa Badr from Egypt aim in their research to increase the effectiveness of the graphical user interface testing process of mobile applications by proposing an enhanced combinatorial-based metaheuristic approach that has been compared with monkey, frequency, random and greedy approaches. Qusai Y. Shambour, Mosleh M. Abualhaj and Ahmad Adel Abu-Shareha from Jordan propose an effective multi-criteria recommender algorithm for personalized restaurant recommendations by exploiting users’ and items’ implicit similarities to eliminate the sparseness of rating information.  

Enjoy Reading! 

Cordially, 

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