Princess Sumaya University for Technology

This study investigates the attitudes of foreign students at Amman Arab University (AAU) in Jordan toward learning Arabic, shedding light on the teaching approaches of Arabic dialect, particularly Jordanian Arabic (JA), while exploring their perspective on Arabic diglossia. The study examines faculty member’s academic views specifically those of the English department and basic sciences, incorporating linguistics, translation scholars, and Arabic lecturers. The findings show that while students stigmatize the Jordanian dialect, recognizing its grammar inferiority to modern standard Arabic (MSA), they contradictorily express a keen interest in learning the dialect due to its cultural and practical relevance to Arabic-speaking societies. Contrarily and strikingly, faculty members, despite their tolerance of the vernacular in informal contexts, hold an opposing opinion that prevents the inclusion of an Arabic curriculum for non-native speakers. Moreover, the study affirms the divergence in perspectives which showcases the tension between the traditional view of H variety being the “proper” variety of the language and the practical demand of the dialect for effective communications in the Arab world. While emphasizing the significance of presenting these contradictions in Arabic language pedagogy, the study introduces the integration of dialect teaching alongside MSA to simply align with students’ linguistic needs.

Biometric authentication is widely regarded as more secure and reliable than conventional approaches like passwords and PINs. Nonetheless, many current systems rely on active user participation, such as fingerprint scanning or facial recognition, which can disrupt tasks, increase the likelihood of errors, and raise privacy concerns. To address these challenges, this study introduces a continuous, seamless authentication framework that utilizes vital signs for passive identity verification across various activities, including resting, walking, and running. The framework analyzes physiological indicators such as Heart Rate (HR), Heart Rate Variability (HRV), Skin Temperature, Peripheral Oxygen Saturation (SpO2), and Breathing Rate to provide zero-effort authentication without requiring user intervention. Multiple machine learning algorithms, including Decision Tree, Random Forest, XGBoost, Gradient Boosting, and K-Nearest Neighbors, were implemented and compared to identify the most effective predictive model. The methodology involved data collection, preprocessing, model construction, evaluation, and comparison. Experimental results revealed that the XGBoost Classifier achieved the highest accuracy at 96%. Overall, the proposed framework demonstrates strong reliability, scalability, adaptability, and flexibility, making it suitable for practical deployment. By continuously verifying identity without interrupting user activity, it improves both security and usability, offering a modern and convenient alternative to traditional authentication methods.

Software defect prediction aims to identify faults in code before they cause failures. It helps developers locate problematic modules early in the development cycle. Early detection reduces the cost of fixing defects and prevents them from affecting users. Just-in-Time defect prediction is the concept of detecting buggy commits early on in the development stage rather than in the release. While recent research focuses on improving the accuracy of pre-diction faulty commits, fewer studies explore how to do that task with minimal effort. In this study, we propose an effort-aware JIT defect prediction pipeline that utilizes code pre-trained models finetuned with Low Rank Adaptation (LoRA) and a feedback loop mechanism. The aim is to improve fault detection while reducing effort. Our experiments evaluate four different transformer-based models: CodeBERT, JavaBERT, UniXcoder, and RoBERTa on ApacheJIT. Our results illustrate CodeBERT and UniXcoder are benefiting the most from the feedback loop with the highest gain in F1 score, where CodeBERT highest gain is 0.14 and UniXcoder is 0.02. RoBERTa performed the best across experiments with an F1 score of 0.73. However, Popt metric remains static with limited gain that reached maximum 0.02, suggesting that further experiments are needed to improve performance.

The metaverse—a persistent, multiuser fusion of digitally augmented reality and computer-generated virtuality— is emerging as a programmable substrate for identity, assets, and interaction. Its heterogeneous stack (XR clients, engines/SDKs, Web3 rails, wallets, marketplaces) enlarges the attack surface. This paper contributes: (i) a structured threat taxonomy specialized for Web3/XR platforms; (ii) explicit system and adversary models; (iii) a risk quantification scheme combining behavioral and on-chain signals; and (iv) a data-driven defense architecture aligning decentralized identity, wallet/custody guardrails, analytics, AI-aided detection, and policy instrumentation. We further instantiate these controls in the Medical MeTAI context, where confidentiality, integrity, and provenance requirements are stringent.

Reconfigurable intelligent surfaces (RIS) have emerged as a promising approach to enhance the secrecy performance of wireless networks from a physical layer security perspective. However, its effectiveness critically depends on the availability of accurate instantaneous channel state information (CSI) at the RIS controller. This work evaluates the impact of outdated CSI on the secrecy performance of a multiple RIS-aided wireless network. To this end, a closed-form expression for the secrecy outage probability (SOP) is derived assuming the availability of an outdated CSI at the RIS controller. The obtained results demonstrate the network’s secrecy degradation level due to the CSI outdatedness, while the increase in the number of RISs can compensate for such a secrecy loss. For instance, incorporating three RISs improves the SOP by two orders of magnitude with respect to a single RIS for 25% of CSI outdatedness. Furthermore, the results reveal that, under specific scenarios of several channel outdatedness, a baseline single RIS with a perfect CSI can outperform multiple RISs with outdated CSI. These findings highlight a critical limitation in incorporating multiple RISs in a wireless network with severe channel aging, requiring the increase of the RISs size or the number of RISs to combat its effect.