Omar Hammad

Khaled Alanezi, Nuha Albadi, Omar Hammad et al.

Online reviews have become essential for users to make informed decisions in everyday tasks ranging from planning summer vacations to purchasing groceries and making financial investments. A key problem in using online reviews is the overabundance of online that overwhelms the users. As a result, recommendation systems for providing helpfulness of reviews are being developed. This paper argues that cultural background is an important feature that impacts the nature of a review written by the user, and must be considered as a feature in assessing the helpfulness of online reviews. The paper provides an in-depth study of differences in online reviews written by users from different cultural backgrounds and how incorporating culture as a feature can lead to better review helpfulness recommendations. In particular, we analyze online reviews originating from two distinct cultural spheres, namely Arabic and Western cultures, for two different products, hotels and books. Our analysis demonstrates that the nature of reviews written by users differs based on their cultural backgrounds and that this difference varies based on the specific product being reviewed. Finally, we have developed six different review helpfulness recommendation models that demonstrate that taking culture into account leads to better recommendations.

Habiba Kausar, Saeed Anwar, Omar Jamal Hammad et al.

Face super-resolution aims to recover high-quality facial images from severely degraded low-resolution inputs, but remains challenging due to the loss of fine structural details and identity-specific features. This work introduces SwinIFS, a landmark-guided super-resolution framework that integrates structural priors with hierarchical attention mechanisms to achieve identity-preserving reconstruction at both moderate and extreme upscaling factors. The method incorporates dense Gaussian heatmaps of key facial landmarks into the input representation, enabling the network to focus on semantically important facial regions from the earliest stages of processing. A compact Swin Transformer backbone is employed to capture long-range contextual information while preserving local geometry, allowing the model to restore subtle facial textures and maintain global structural consistency. Extensive experiments on the CelebA benchmark demonstrate that SwinIFS achieves superior perceptual quality, sharper reconstructions, and improved identity retention; it consistently produces more photorealistic results and exhibits strong performance even under 8x magnification, where most methods fail to recover meaningful structure. SwinIFS also provides an advantageous balance between reconstruction accuracy and computational efficiency, making it suitable for real-world applications in facial enhancement, surveillance, and digital restoration. Our code, model weights, and results are available at https://github.com/Habiba123-stack/SwinIFS.