Automatic personality recognition is a task that attempts to automatically infer personality traits from a variety of data sources, including Text. Our words, whether spoken or written, reveal a lot about who we are. As people speak different languages, each with its own set of characteristics and level of complexity, identifying their personalities automatically might be language-dependent. This task requires an annotated text corpus with personality traits. However, the lack of corpora for languages other than English makes the task extremely challenging. We concentrated our efforts in this paper on the Arabic language in particular because it is understudied and lacks a corpus, despite being one of the most widely spoken languages in the world. Our primary goal was constructing our “MSAPersonality” dataset, which consists of 267 texts in modern standard Arabic that have been annotated with the Big Five personality traits. To evaluate the dataset and its potential for classification and regression, we used text preprocessing techniques, feature extraction, and machine learning algorithms. We obtained promising experimental results. Therefore, further research into predicting personality from Arabic text can be conducted.

The Medina of Fez, a UNESCO World Heritage Site, is a cultural and historical treasure requiring innovative approaches for its preservation and promotion in the digital era. This paper presents MedinaOnto, an ontology dedicated to modeling the built cultural heritage of the Medina of Fez, developed in alignment with established standards like CIDOC-CRM, FOAF, and GEOSPARQL. The ontology aims to structure and interconnect knowledge about the Medina’s buildings while integrating social, geospatial, and historical dimensions. Based on the Core Data Standard and enriched with structured data from open sources such as Wikidata and DBpedia, the ontology aims to represent a wide range of heritage entities, including monuments, historic buildings, and culturally significant spaces. The modeling approach followed a structured ontology engineering methodology, combining domain analysis, concept extraction, the formulation of Competency Questions, and iterative refinement. The evaluation of the ontology was conducted through logical consistency tests, scenario-based validation, and SPARQL queries derived from Competency Questions. The results confirmed that MedinaOnto enables robust semantic reasoning and effective data querying. Designed as a reusable and scalable semantic framework, Medina Onto targets both public and heritage professionals, with a focus on valorization, mediation, and research.

Context and relevance. This study investigates the Big Five personality traits within the Moroccan context, exploring their relationship with age, gender, and education level. Objective. This study aimed to assess the psychometric properties of the Arabic BFI within the Moroccan population and to explore the relationship between personality traits and various demographic factors. Methods and materials. Using the Arabic version of the Big Five Inventory (BFI), we surveyed 1537 individuals across Morocco. Results. The Arabic BFI displayed acceptable internal consistency (Cronbach's alpha = 0,72), and good convergent and discriminant validity. There were gender differences, with women scoring higher on neuroticism and men scoring higher on openness and conscientiousness. Age showed significant associations, with neuroticism decreasing and extraversion, agreeableness, and conscientiousness increasing as individuals grew older. Conclusions. These findings confirm that the instrument can be used in Arabic-speaking populations and provide valuable insights into the nature of personality traits within the Moroccan population in the North African context.

Optical character recognition (OCR) is one of the widely used pattern recognition systems. However, the research on ancient Arabic writing recognition has suffered from a lack of interest for decades, despite the availability of thousands of historical documents. One of the reasons for this lack of interest is the absence of a standard dataset, which is fundamental for building and evaluating an OCR system. In 2022, we published a database of ancient Arabic words as the only public dataset of characters written in Al-Mojawhar Moroccan calligraphy. Therefore, such a database needs to be studied and evaluated. In this paper, we explored the proposed database and investigated the recognition of Al-Mojawhar Arabic characters. We studied feature extraction by using the most popular descriptors used in Arabic OCR. The studied descriptors were associated with different machine learning classifiers to build recognition models and verify their performance. In order to compare the learned and handcrafted features on the proposed dataset, we proposed a deep convolutional neural network for character recognition. Regarding the complexity of the character shapes, the results obtained were very promising, especially by using the convolutional neural network model, which gave the highest accuracy score.