Mohammad J. Abdel‐Rahman

Dania Refai, Saleh Abo-Soud, Mohammad Abdel-Rahman

The performance of learning models heavily relies on the availability and adequacy of training data. To address the dataset adequacy issue, researchers have extensively explored data augmentation (DA) as a promising approach. DA generates new data instances through transformations applied to the available data, thereby increasing dataset size and variability. This approach has enhanced model performance and accuracy, particularly in addressing class imbalance problems in classification tasks. However, few studies have explored DA for the Arabic language, relying on traditional approaches such as paraphrasing or noising-based techniques. In this paper, we propose a new Arabic DA method that employs the recent powerful modeling technique, namely the AraGPT-2, for the augmentation process. The generated sentences are evaluated in terms of context, semantics, diversity, and novelty using the Euclidean, cosine, Jaccard, and BLEU distances. Finally, the AraBERT transformer is used on sentiment classification tasks to evaluate the classification performance of the augmented Arabic dataset. The experiments were conducted on four sentiment Arabic datasets: AraSarcasm, ASTD, ATT, and MOVIE. The selected datasets vary in size, label number, and unbalanced classes. The results show that the proposed methodology enhanced the Arabic sentiment text classification on all datasets with an increase in F1 score by 4% in AraSarcasm, 6% in ASTD, 9% in ATT, and 13% in MOVIE.

Alaa S. Arabiyat, Mohammad J. Abdel-Rahman

Double-active reconfigurable intelligent surface (RIS)-assisted wireless systems can improve coverage and achievable rate in blockage-dominated environments. Still, their joint resource allocation is challenging due to the coupling among RIS placement, amplification power allocation, and reflecting-element assignment. The resulting problem is linearly constrained, non-convex, and involves both continuous and discrete variables, making conventional iterative solvers such as block coordinate descent (BCD) computationally expensive for real-time deployment. This paper proposes a \underline{c}onstraint-\underline{a}ware \underline{l}earning \underline{o}ptimization (CALO) framework for data-driven joint resource allocation in double-active RIS-assisted networks. CALO reformulates the decision variables into grouped fractional representations and maps them to physical resources through constraint-preserving transformations, ensuring that distance, power, and element-budget constraints are satisfied by construction. A straight-through estimator is incorporated to enable differentiable learning over discrete reflecting-element assignments, while a regret-driven hinge objective uses the BCD solution as a reference and encourages performance improvement beyond solver imitation. Simulation results show that CALO achieves $100\%$ feasibility across all tested configurations, improves the achievable rate over BCD in both urban and rural scenarios, and reduces online inference time by orders of magnitude. These results demonstrate the effectiveness of structure-aware learning for feasible and real-time optimization in active multi-RIS wireless systems.