Haider Hadi Abbas

Background: Recent advancements in Natural Language Processing (NLP) have been significantly influenced by transformer models. However, challenges related to scalability, discrepancies between pretraining and finetuning, and suboptimal performance on tasks with diverse and limited data remain. The integration of Reinforcement Learning (RL) with transformers has emerged as a promising approach to address these limitations. Objective: This article aims to evaluate the performance of a transformer-based NLP model integrated with RL across multiple tasks, including translation, sentiment analysis, and text summarization. Additionally, the study seeks to assess the model's efficiency in real-time operations and its fairness. Methods: The hybrid model's effectiveness was evaluated using task-oriented metrics such as BLEU, F1, and ROUGE scores across various task difficulties, dataset sizes, and demographic samples. Fairness was measured based on demographic parity and equalized odds. Scalability and real-time performance were assessed using accuracy and latency metrics. Results: The hybrid model consistently outperformed the baseline transformer across all evaluated tasks, demonstrating higher accuracy, lower error rates, and improved fairness. It also exhibited robust scalability and significant reductions in latency, enhancing its suitability for real-time applications. Conclusion: This article illustrates that the proposed hybrid model effectively addresses issues related to scale, diversity, and fairness in NLP. Its flexibility and efficacy make it a valuable tool for a wide range of linguistic and practical applications. Future research should focus on improving time complexity and exploring the use of deep unsupervised learning for low-resource languages.

  Background: Quantum computing has posed a profound threat to the classical cryptographic systems as it is advancing at an exponential rate with the help of quantum algorithms like Shor’s and Grover’s which can easily decipher the Rivest–Shamir–Adleman (RSA) and Elliptic Curve Cryptography (ECC) algorithms. Huge requirements for cryptographic frameworks that can withstand quantum hacking have inspired Quantum Key Distribution (QKD), Post-Quantum Cryptography (PQC), and systems that use both. Objective: The aim of this article is to review the performance, scalability and integration of quantum-secure cryptographic services, with a practical lens on how they can be used in real-time environments like self-driving cars, industrial IoT, and intelligent health systems. It also aims at establishing the drawback of the current model and directions for further enhancement. Methods: The study employs simulative experimentation to understand lest exposures to quantum algorithms and rates cryptographic systems on standards such as latency, Quantum Bit Error Rate (QBER), computational overhead, scalability, and cost. Comparative assessment furniture integrated analysis of QKD, PQC, and hybrid system by identifying the advantages and disadvantage of each system. Results: As a result, adopting hybrid systems provided the best or comparable median results with lowest latency in real-time applications of ~45 ms or lower compared to alternative Multi-Access Edge Computing (MEC) architectures and types of security elements at high scalability. Thus, QKD, while being exceptional in security, has the problem of scalability, while PQC had average results on the given parameters. Conclusion : Quantum threats are adequately dealt with by hybrid cryptographic systems as this study has also pointed out. It is seen that initiation to future work may someday distribute resources effectively, expedite PQC standardization, and embrace artificially intelligent network frameworks for flexibility and expansiveness across different networks.