Determining the author of a text is a difficult task. Here, we compare multiple Artificial Intelligence techniques for classifying literary texts written by multiple authors by taking into account a limited number of speech parts (prepositions, adverbs, and conjunctions). We also introduce a new dataset composed of texts written in the Romanian language on which we have run the algorithms. The compared methods are artificial neural networks, multi-expression programming, k-nearest neighbour, support vector machines, and decision trees with C5.0. Numerical experiments show, first of all, that the problem is difficult, but some algorithms are able to generate acceptable error rates on the test set.

Being around for decades, the problem of Authorship Attribution is still very much in focus currently. Some of the more recent instruments used are the pre-trained language models, the most prevalent being BERT.Here we used such a model to detect the authorship of texts written in the Romanian language. The dataset used is highly unbalanced, i.e., significant differences in the number of texts per author, the sources from which the texts were collected, the time period in which the authors lived and wrote these texts, the medium intended to be read (i.e., paper or online), and the type of writing (i.e., stories, short stories, fairy tales, novels, literary articles, and sketches). The results are better than expected, sometimes exceeding 87% macro-accuracy.

In this paper we conducted an investigation on the performance of the students during the second semester of the academic year 2020-2021. We looked at the performance results obtained by students on the laboratory work, practical and final exams while we were forced by the Covid pandemic to move entirely into an online education system. Our focus was to determine the impact of a consistent behaviour (or lack of it) on the final student performance. We determined that, even in an online setting, a good involvement (in terms of attendance and good performance) guarantees good final results. The investigations were performed using the Formal Concept Analysis, which is a very powerful instrument already used by us in previous research in order to detect student behaviour in using an e-learning portal. Another set of results showed that the change of the final mark computation formula to be based in a higher proportion on the lab work was closer to the actual overall performance of students