: Emotion recognition plays a crucial role in enabling intelligent human–computer interaction, yet research in low-resource languages such as Urdu remains limited, particularly in multimodal settings. This study proposes a multimodal deep learning framework for Urdu emotion recognition by integrating speech and text modalities. The approach leverages transformer-based models, namely wav2vec 2.0 for audio representation and MuRIL for text representation, combined using a late fusion strategy for classification. Experiments were conducted on the UMED dataset, consisting of 8269 multimodal instances across five emotion classes. The proposed multimodal model achieved an accuracy of 0.701 and an F1-score of 0.6915, outperforming unimodal baselines, where the audio-only and text-only models achieved accuracies of 0.6681 and 0.5085, respectively. Furthermore, the proposed approach surpasses the existing UMEDNet benchmark, demonstrating the effectiveness of transformer-based feature extraction and multimodal late fusion for Urdu emotion recognition. The results highlight the complementary nature of speech and text modalities and demonstrate that independently learned modality-specific classifiers combined through decision-level fusion can improve emotion recognition performance in low-resource languages. However, the performance improvement over alternative fusion strategies was relatively modest, indicating that more advanced multimodal interaction mechanisms may further enhance recognition performance.
Muhammad Sheraz, Adil Majeed, Shehzad Khalid et al.· Computer Modeling in Enginee...· 0 citations
: Membership Inference Attacks (MIAs) pose a significant privacy risk in machine learning by enabling adversaries to infer whether specific data samples were used during training, particularly in sensitive domains such as social media and mental health analytics. To address this challenge, this paper proposes HEbdMIA, a lightweight homomorphic encryption-based defense that operates at the post-inference stage by encrypting model output logits without requiring retraining or architectural modifications. The proposed approach preserves the relative ordering of predictions while obscuring confidence patterns exploited by MIAs. Experimental evaluation on DepInferAttack and BotInferAttack demonstrates that HEbdMIA achieves a reduction in MIA success rates of 31.0% and 27.3%, respectively, with an associated accuracy decrease of 29.3% and 26.4%, reflecting a controlled privacy and utility trade off. Additional analysis using precision, recall, F1-score, and ROC-AUC confirms a substantial decline in adversarial inference capability. These findings indicate that HEbdMIA provides an effective, scalable, and deployment-friendly solution for enhancing privacy in real-world machine learning systems.
Akash Shah, M. A. Wani, R. Chaturvedi et al.· Computers, Materials & C...· 0 citations
It is shown that the enhanced Zynerator framework reduces development effort, strengthens security posture, and accelerates DevSecOps adoption, indicating that DevSecOps-aware model-driven engineering offers a viable pathway toward secure, auto-mated software delivery.
Younes Zouani, Mohamed Lachgar, Youssef Harrati et al.· International Journal of Adv...· 0 citations