Shehzad Ashraf Chaudhry

Ephrem Afele Retta, Richard Sutcliffe, Jabar Mahmood et al.

In a conventional Speech emotion recognition (SER) task, a classifier for a given language is trained on a pre-existing dataset for that same language. However, where training data for a language does not exist, data from other languages can be used instead. We experiment with cross-lingual and multilingual SER, working with Amharic, English, German and URDU. For Amharic, we use our own publicly-available Amharic Speech Emotion Dataset (ASED). For English, German and Urdu we use the existing RAVDESS, EMO-DB and URDU datasets. We followed previous research in mapping labels for all datasets to just two classes, positive and negative. Thus we can compare performance on different languages directly, and combine languages for training and testing. In Experiment 1, monolingual SER trials were carried out using three classifiers, AlexNet, VGGE (a proposed variant of VGG), and ResNet50. Results averaged for the three models were very similar for ASED and RAVDESS, suggesting that Amharic and English SER are equally difficult. Similarly, German SER is more difficult, and Urdu SER is easier. In Experiment 2, we trained on one language and tested on another, in both directions for each pair: Amharic<->German, Amharic<->English, and Amharic<->Urdu. Results with Amharic as target suggested that using English or German as source will give the best result. In Experiment 3, we trained on several non-Amharic languages and then tested on Amharic. The best accuracy obtained was several percent greater than the best accuracy in Experiment 2, suggesting that a better result can be obtained when using two or three non-Amharic languages for training than when using just one non-Amharic language. Overall, the results suggest that cross-lingual and multilingual training can be an effective strategy for training a SER classifier when resources for a language are scarce.

Somnath Kumar, Kunal Dey, Vikas Srivastava et al.

The rapid growth of the Internet of Things (IoT) introduces challenges in secure authentication and delegation due to the limited computational capabilities of devices. Proxy signature schemes offer an effective solution by enabling controlled delegation of signing rights to more capable entities, such as gateway nodes. However, most existing schemes rely on classical assumptions that are likely to be broken by quantum adversaries. In this work, we address these challenges by proposing an isogeny-based post-quantum proxy signature scheme, \textit{CSI-PS}. The scheme leverages the hardness of the Group Action Inverse Problem (GAIP) to ensure quantum-resistant security while maintaining efficiency suitable for resource-constrained environments. We further demonstrate its applicability in IoT architectures through a gateway-based delegation model. Our analysis shows that the proposed scheme strikes an effective balance between security and efficiency in terms of computation and communication overhead, along with provable security under the EUF-CMA notion.

Hafeez Ur Rehman, Anwar Ghani, Shehzad Ashraf Chaudhry et al.

Very recently, Barman et al. proposed a multi-server authentication protocol using fuzzy commitment. The authors claimed that their protocol provides anonymity while resisting all known attacks. In this paper, we analyze that Barman et al.'s protocol is still vulnerable to anonymity violation attack and impersonation based on the stolen smart attack; moreover, it has scalability issues. We then propose an improved and enhanced protocol to overcome the security weaknesses of Barman et al.'s scheme. The security of the proposed protocol is verified using BAN logic and widely accepted automated AVISPA tool. The BAN logic and automated AVISPA along with the informal analysis ensures the robustness of the scheme against all known attacks