Editorial

Abstract

Dear Readers, It is my great pleasure to announce the second regular issue of 2022. In this issue, various topical aspects of computer science are covered by 17 authors from 6 countries in 5 articles. I would like to thank all the authors for their sound research and the editorial board for the highly valuable review effort and comments for improvement. These contributions, together with the generous support of the consortium members, sustain the quality of our journal. 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. Please consider yourself and encourage your colleagues to submit high-quality articles or special issues for our journal. In the second regular issue, I am very pleased to introduce the following 5 accepted articles: In a collaborative research between Italy and Ireland, Luca Calderoni, Dario Maio, and Paolo Palmieri compare two probabilistic data structures for association queries derived from the well-known Bloom filter, and extend and optimize the functionality of the shifting Bloom filter, which is applicable to any non-trivial number of subsets. Zeinab Ghasemi-Naraghi, Ahmad Nickabadi and Reza Safabakhsh from Iran focus their research on multi-task learning, more specifically, they introduce a novel multi-task loss function to capture homoscedastic uncertainty in multi regression tasks models without increasing the complexity of the network. Daniel Gómez, Luis Llana and Cristóbal Pareja from Spain present and experimentally review a parallel version of the Brandes algorithm implemented in Spark to compute the betweenness centrality measures. Vít Novotný, Michal Štefánik, Eniafe Festus Ayetiran, Petr Sojka and Radim Řehůřek from the Czech Republic propose and evaluate a constrained positional model that adapts the sparse attention mechanism from neural machine translation to improve the speed of the positional model. Mahmut Ünver, Atilla Ergüzen and Erdal Erdal from Turkey focus their research on an approach based on distributed file systems for managing large amounts of data generated by distance education.