This work participates in SemEval 2024 Task 1 on Semantic Textural Relatedness (STR) in Track A (supervised regression) in two languages, English and Moroccan Arabic. The task consists of providing a score of how two sentences relate to each other. The system developed in this work leveraged a cross-encoder with a merged fine-tuned Low-Rank Adapter (LoRA). The system was ranked eighth in English with a Spearman coefficient of 0.842, while Moroccan Arabic was ranked seventh with a score of 0.816. Moreover, various experiments were conducted to see the impact of different models and adapters on the performance and accuracy of the system.

Source code plagiarism is a critical ethical issue in computer science education where students use someone else’s work as their own. It can be treated as a binary classification problem where the output can be either: yes (plagiarism found) or no (plagiarism not found). In this research, we have taken the open-source dataset ‘SOCO’, which contains two programming languages (PLs), namely Java and C/C++ (although our method could be applied to any PL). Source codes should be converted to vector representations that capture both the syntax and semantics of the text, known as contextual embeddings. These embeddings would be generated using source code pre-trained models (CodePTMs). The cosine similarity scores of three different CodePTMs were selected as features. The classifier selection and parameter tuning were conducted with the assistance of Automated Machine Learning (AutoML). The selected classifiers were tested, initially on Java, and the proposed approach produced average to high results compared to other published research, and surpassed the baseline (the JPlag plagiarism detection tool). For C/C++, the approach outperformed other research work and produced the highest ranking score.