Large language models (LLMs) have significantly advanced natural language processing (NLP) by breaking through a variety of tasks like summarization, text generation and sentiment analysis. This study addresses aspect-based sentiment analysis (ABSA) for Arabic, a low-resource language. ABSA, which identifies aspect categories and their sentiment polarity, comprises subtasks like aspect category detection (ACD) and aspect category polarity (ACP). Focusing on the aspect of category polarity, we investigate zero-shot, few-shot, and chain-of-thought (CoT) prompting strategies using the Gaza Reviews and HAAD Book Reviews datasets. Our experiments demonstrate that CoT prompting outperforms other methods, achieving F1-scores of 70 % on the Gaza Reviews dataset and 67% on the HAAD Book Reviews dataset.
The rapid expansion of the Internet of Things (IoT) and the growing need for real-time data processing have accelerated the development of decentralized computing paradigms. This paper presents a detailed comparative analysis of edge computing and fog computing architectures, exploring their distinct roles in modern decentralized networks. By evaluating architectural differences and performance metrics, we assess the advantages and limitations of each approach. The proposed methodology combines theoretical analysis with comparative frameworks. Our findings indicate that edge computing excels in ultra-low-latency applications with minimal infrastructure, while fog computing offers greater scalability and efficient resource management for complex distributed systems. We conclude that the choice between edge and fog computing depends on application-specific requirements, with hybrid models emerging as a promising solution for future decentralized networks. This study enhances the understanding of decentralized computing architectures and provides actionable insights for system designers and network architects.
The growing scale and complexity of IoT (Internet of Things) networks demand robust security models that provide both secure identity authentication and reliable trust evaluation. While Self-Sovereign Identity (SSI) enables decentralized and user-centric authentication, ensuring the continuous trustworthiness of participating nodes remains a challenge. This paper presents a unified framework that combines SSI-based decentralized authentication with IOTA-ledger-assisted trust evaluation to provide an integrated security architecture for IoT systems. The proposed architecture guarantees that only authenticated nodes can participate in the network while continuously assessing their behavior and trustworthiness through immutable and tamper-resistant trust scores. Our approach is, to the best of our knowledge, the first to unify SSI-based authentication with IOTA-assisted trust evaluation, offering a holistic IoT security framework.
Cyberbullying is an increasing threat on social media, with serious consequences for mental health, particularly among young people. Despite growing global efforts to address this problem, developing accurate detection systems remains challenging for low-resource languages in both text and audio modalities, such as Arabic and its dialects. This paper presents a binary-labeled dataset for cyberbullying detection in Moroccan Darija. The dataset merges over 4,000 newly collected YouTube comments with the OMCD (Offensive Moroccan Comments Dataset) corpus of 8,024 comments, originally labeled for offensive language. To better reflect the nature of cyberbullying, the entire corpus was reannotated from scratch, clearly distinguishing between bullying and non-bullying content, including subtle forms like sarcasm, shaming, and indirect aggression. Several Arabic transformer models were fine-tuned and evaluated using standard classification metrics. The results show that dialectspecific models, particularly DarijaBERT, achieve the best performance, underlining the importance of context-aware annotation and in-domain pre-training for cyberbullying detection in lowresource settings.
This study introduces a novel centrality metric designed to identify influential actors in social media networks, based on two key parameters: relative degree superiority ($\mathbf{x}$ over y) and the minimization of shared neighbors. Our measure examines direct influence and also takes into account neighborhood non-redundancy. Both of these elements shape network dynamics. Centrality measures used in social media networks are different. They generally only consider direct connections. They often miss part of the network dynamics. We conducted theoretical analyses and empirical validations across various datasets to compare our metric against existing measures, highlighting its unique advantages in understanding network influence. According to our research, this measure improves the accuracy of influencing social media networks contributing to our understanding of the dynamic and structural characteristics of real-world systems.
