The classification algorithms were developed to label the objects with the same characteristics into similar groups. In Arabic poetry, poems belong to different eras based on the time they were written in. Changes in time resulted in changes in the style of writing Arabic poetry. Hence, this study is testing the use of different classification algorithms to discover their ability to classify poems in their correct era. This process can be automated by studying the linguistic changes that happened to the Arabic poetry in terms of the words used in the hemistichs structure (part of poem line) without considering poems rhyme. In this paper, we studied applying different classification algorithms to discover poems written in the Abbasid and Andalusian eras. Data have been collected and classifiers have been trained on an overall dataset that contains 30,058 words from 10,895 poetic hemistich collected from both eras. The experiments obtained 70.50% accuracy rate using the support vector machine classifier when tested with a sample of random poem lines. The study showed that it is possible to distinguish, with decent accuracy, poems from different eras based on identifying discriminant features that can be used in the classification.

In recent years and with the advancement of semantic technologies, shared and published online data have become necessary to improve research and development in all fields. While many datasets are publicly available in social and economic domains, most lack standardization. Unlike the medical field, where terms and concepts are well defined using controlled vocabulary and ontologies, social datasets are not. Experts such as the National Consortium for the Study of Terrorism and Responses to Terrorism (START) collect data on global incidents and publish them in the Global Terrorism Database (GTD). Thus, the data are deficient in the technical modeling of its metadata. In this paper, we proposed GTD ontology (GTDOnto) to organize and model knowledge about global incidents, targets, perpetrators, weapons, and other related information. Based on the NeOn methodology, the goal is to build on the effort of START and present controlled vocabularies in a machine-readable format that is interoperable and can be reused to describe potential incidents in the future. The GTDOnto was implemented with the Web Ontology Language (OWL) using the Protégé editor and evaluated by answering competency questions, domain experts’ opinions, and running examples of GTDOnto for representing actual incidents. The GTDOnto can further be used to leverage the publishing of GTD as a knowledge graph that visualizes related incidents and build further applications to enrich its content.

This study investigates the challenges faced by highly qualified women in the 19th Jordanian parliamentary elections and proposes solutions based on the experiences of female candidates. Employing a mixed-methods approach, the research integrates quantitative data from surveys and qualitative insights from in-depth interviews with female candidates. Key findings reveal significant obstacles such as inadequate networking among female parliamentarians, the pervasive influence of a tribal culture that favors male candidates and the limited public perception of women’s legislative roles. Despite the existence of a women’s quota system, female candidates often struggle for recognition and support as their electoral success is frequently tied to tribal affiliations rather than qualifications. Additionally, the criteria for voting predominantly focus on candidates’ abilities as service providers, overshadowing their potential contributions as legislators. The study emphasizes the necessity for improved public perceptions of women’s political participation and suggests several recommendations, including increasing the number of female quota seats, conducting comprehensive research on women’s political achievements and developing strategic media initiatives to promote their representation. Furthermore, it advocates facilitating candidacy pathways for highly qualified women by removing restrictive resignation requirements and ensuring access to leadership opportunities across sectors. By implementing these recommendations, the study aims at enhancing women’s political representation and contributing to a more equitable legislative process in Jordan.

Flocking is a behavior where a group of objects travel, move or collaborate together. By learning more about flocking behavior, we might be able to apply this knowledge in different contexts such as computer graphics, games, and education. A key steppingstone for understanding flocking behavior is to be able to simulate it. However, simulating behaviors of large numbers of objects is highly compute-intensive task because of the n-squared complexity of nearest neighbor for separating n objects. The work in this paper presents an efficient nearest neighbor method based on the k-dimensional trees (KD trees). To evaluate the proposed approach, we apply it using Unity-3D game engine, together with other conventional nearest neighbor methods. The Unity-3D game simulation engine allows users to utilize interaction design tools for programming and animating flocking behaviors. Results showed that the proposed approach outperform other conventional nearest neighbor approaches. The proposed approach can be used to enhance digital games quality and simulations.