Editorial

Dear Readers, It gives me great pleasure to announce the sixth regular issue of 2023. In this issue, 5 papers cover various topical aspects of computer science by 16 authors from 4 countries. 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 authors for their sound research and the editorial board and our guest reviewers 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 this regular issue, I am very pleased to introduce the following 5 accepted articles: In a collaborative research effort between Morocco and Spain, Soukaina Benabdelouahab, José A. García-Berná, Chaimae Moumouh, Juan M. Carrillo-de-Gea, Jaber El Bouhdidi, Yacine El Younoussi, and José L. Fernández-Alemán report on their bibliometric study on e-learning software engineering education by conducting an analysis of 10,603 publications, dating from 1954 to 2020. Achraf Boumhidi, Abdessamad Benlahbib and El Habib Nfaoui from Morocco propose in their research a financially-oriented reputation system that generates a single numerical value from user-generated content on Twitter toward cryptocurrencies by applying a sentiment polarity extractor based on the fine-tuned auto-regressive language model named XLNet. Samuel Caetano da Silva and Ivandré Paraboni from Brazil focus on politically-oriented information inference from text by a series of experiments to compare a number of strategies for political bias and ideology inference from text data using sequence-based BERT models, syntax- and semantics-driven features. Burak Gülmez from Türkiye discusses a model developed for disease detection from images of cotton leaves applying a deep convolutional neural network model and the grey wolf optimization algorithm. This model outperforms the ResNet50, VGG19, and InceptionV3 models frequently used in the literature. Last but not least, José L. López Ruiz, Ángeles Verdejo Espinosa and Macarena Espinilla Estévez from Spain discuss a new methodology for the optimization of Bluetooth anchors for location-relevant systems in an enclosed space using a machine learning-based inference model based on different configurations of the BLE anchors located in the enclosed environment. Enjoy Reading! Cordially, Christian Gütl, Managing Edito