This paper provides a comprehensive review of the literature concerning the utilization of Natural Language Processing (NLP) techniques, with a particular focus on transformer-based large language models (LLMs) trained using Big Code, within the domain of AI-assisted programming tasks. LLMs, augmented with software naturalness, have played a crucial role in facilitating AI-assisted programming applications, including code generation, code completion, code translation, code refinement, code summarization, defect detection, and clone detection. Notable examples of such applications include the GitHub Copilot powered by OpenAI’s Codex and DeepMind AlphaCode. This paper presents an overview of the major LLMs and their applications in downstream tasks related to AI-assisted programming. Furthermore, it explores the challenges and opportunities associated with incorporating NLP techniques with software naturalness in these applications, with a discussion on extending AI-assisted programming capabilities to Apple’s Xcode for mobile software development. This paper also presents the challenges of and opportunities for incorporating NLP techniques with software naturalness, empowering developers with advanced coding assistance and streamlining the software development process.

In the dynamic landscape of contemporary education, the evolution of teaching strategies such as blended learning and flipped classrooms has highlighted the need for efficient and effective generation of multiple-choice questions (MCQs). To address this, we introduce MCQGen , a novel generative artificial intelligence framework designed for the automated creation of MCQs. MCQGen uniquely integrates a large language model (LLM) with retrieval-augmented generation and advanced prompt engineering techniques, drawing from an extensive external knowledge base. This integration significantly enhances the ability of the LLM to produce educationally relevant questions that align with both the goals of educators and the diverse learning needs of students. The framework employs innovative prompt engineering, combining chain-of-thought and self-refine prompting techniques, to enhance performance of the LLM. This process leads to the generation of questions that are not only contextually relevant and challenging but also reflective of common student misconceptions, contributing effectively to personalized learning experiences and enhancing student engagement and understanding. Our extensive evaluations showcase the effectiveness of MCQGen in producing high-quality MCQs for various educational needs and learning styles. The framework demonstrates its potential to significantly reduce the time and expertise required for MCQ creation, marking its practical utility in modern education. In essence, MCQGen offers an innovative and robust solution for the automated generation of MCQs, enhancing personalized learning in the digital era.

The evolution of network technologies has significantly transformed global communication, information sharing, and connectivity. Traditional networks, relying on static configurations and manual interventions, face substantial challenges such as complex management, inefficiency, and susceptibility to human error. The rise of artificial intelligence (AI) has begun to address these issues by automating tasks like network configuration, traffic optimization, and security enhancements. Despite their potential, integrating AI models in network engineering encounters practical obstacles including complex configurations, heterogeneous infrastructure, unstructured data, and dynamic environments. Generative AI, particularly large language models (LLMs), represents a promising advancement in AI, with capabilities extending to natural language processing tasks like translation, summarization, and sentiment analysis. This paper aims to provide a comprehensive review exploring the transformative role of LLMs in modern network engineering. In particular, it addresses gaps in the existing literature by focusing on LLM applications in network design and planning, implementation, analytics, and management. It also discusses current research efforts, challenges, and future opportunities, aiming to provide a comprehensive guide for networking professionals and researchers. The main goal is to facilitate the adoption and advancement of AI and LLMs in networking, promoting more efficient, resilient, and intelligent network systems.

In the age of social media, the rapid spread of misinformation and rumors has led to the emergence of infodemics, where false information poses a significant threat to society. To combat this issue, we introduce TrumorGPT, a novel generative artificial intelligence solution designed for fact-checking in the health domain. TrumorGPT aims to distinguish “trumors”, which are health-related rumors that turn out to be true, providing a crucial tool in differentiating between mere speculation and verified facts. This framework leverages a large language model (LLM) with few-shot learning for semantic health knowledge graph construction and semantic reasoning. TrumorGPT incorporates graph-based retrieval-augmented generation (GraphRAG) to address the hallucination issue common in LLMs and the limitations of static training data. GraphRAG involves accessing and utilizing information from regularly updated semantic health knowledge graphs that consist of the latest medical news and health information, ensuring that fact-checking by TrumorGPT is based on the most recent data. Evaluating with extensive healthcare datasets, TrumorGPT demonstrates superior performance in fact-checking for public health claims. Its ability to effectively conduct fact-checking across various platforms marks a critical step forward in the fight against health-related misinformation, enhancing trust and accuracy in the digital information age.

The COVID-19 pandemic brought not only global devastation but also an unprecedented infodemic of false or misleading information that spread rapidly through online social networks. Network analysis plays a crucial role in the science of fact-checking by modeling and learning the risk of infodemics through statistical processes and computation on mega-sized graphs. This paper proposes MEGA, M achine Learning- E nhanced G raph A nalytics, a framework that combines feature engineering and graph neural networks to enhance the efficiency of learning performance involving massive graphs. Infodemic risk analysis is a unique application of the MEGA framework, which involves detecting spambots by counting triangle motifs and identifying influential spreaders by computing the distance centrality. The MEGA framework is evaluated using the COVID-19 pandemic Twitter dataset, demonstrating superior computational efficiency and classification accuracy

The rapid global spread of the coronavirus disease (COVID-19) has severely impacted daily life worldwide. As potential solutions, various digital contact tracing (DCT) strategies have emerged to mitigate the virus’s spread while maintaining economic and social activities. The computational epidemiology problems of DCT often involve parameter optimization through learning processes, making it crucial to understand how to apply machine learning techniques for effective DCT optimization. While numerous research studies on DCT have emerged recently, most existing reviews primarily focus on DCT application design and implementation. This paper offers a comprehensive overview of privacy-preserving machine learning-based DCT in preparation for future pandemics. We propose a new taxonomy to classify existing DCT strategies into forward, backward, and proactive contact tracing. We then categorize several DCT apps developed during the COVID-19 pandemic based on their tracing strategies. Furthermore, we derive three research questions related to computational epidemiology for DCT and provide a detailed description of machine learning techniques to address these problems. We discuss the challenges of learning-based DCT and suggest potential solutions. Additionally, we include a case study demonstrating the review’s insights into the pandemic response. Finally, we summarize the study’s limitations and highlight promising future research directions in DCT.