With the rapid development of Large Language Models (LLMs), a large number of machine learning models have been developed to assist programming tasks including the generation of program code from natural language input. However, how to evaluate such LLMs for this task is still an open problem despite of the great amount of research efforts that have been made and reported to evaluate and compare them. This paper provides a critical review of the existing work on the testing and evaluation of these tools with a focus on two key aspects: the benchmarks and the metrics used in the evaluations. Based on the review, further research directions are discussed.

They lack a systematic review ARGE language models (LLMs) [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] have posed a groundbreaking impact on artificial and discussion on preference learning, especially preference intelligence (AI), transforming the opinions of people on the modeling methods, which are critical to capturing human potential of AI systems for understanding and applying human intentions for LM alignment [13]. To further explore effective languages. These neural network language models with largescale preference learning approaches for better LLM alignment, parameters (mainly over 10 billion) are initially pretrained we present a comprehensive review of human preference on large corpora collected from a wide range of learning methods applicable to language models, examining sources, a remarkable part of which is on the Internet [11]. After LLM alignment methods from the perspective of preference pre-training by imitating how humans use natural languages learning. By analyzing a wide range of alignment approaches in the text data, the foundation LLMs acquire strong and within the preference learning framework, we outline the general language skills [1, 12]. On the other hand, foundation holistic picture of introducing human preference into LLMs, LLMs are observed to have difficulty in understanding or enabling insights to be drawn from every aspect of human responding to diverse human instruction appropriately [13], preference learning for various domains.

Generating realistic images to accurately predict changes in the structure of brain MRI is a crucial tool for clinicians. Such applications help assess patients' outcomes and analyze how diseases progress at the individual level. However, existing methods for this task present some limitations. Some approaches attempt to model the distribution of MRI scans directly by conditioning the model on patients' ages, but they fail to explicitly capture the relationship between structural changes in the brain and time intervals, especially on age-unbalanced datasets. Other approaches simply rely on interpolation between scans, which limits their clinical application as they do not predict future MRIs. To address these challenges, we propose a Temporally-Aware Diffusion Model (TADM), which introduces a novel approach to accurately infer progression in brain MRIs. TADM learns the distribution of structural changes in terms of intensity differences between scans and combines the prediction of these changes with the initial baseline scans to generate future MRIs. Furthermore, during training, we propose to leverage a pre-trained Brain-Age Estimator (BAE) to refine the model's training process, enhancing its ability to produce accurate MRIs that match the expected age gap between baseline and generated scans. Our assessment, conducted on the OASIS-3 dataset, uses similarity metrics and region sizes computed by comparing predicted and real follow-up scans on 3 relevant brain regions. TADM achieves large improvements over existing approaches, with an average decrease of 24% in region size error and an improvement of 4% in similarity metrics. These evaluations demonstrate the improvement of our model in mimicking temporal brain neurodegenerative progression compared to existing methods. Our approach will benefit applications, such as predicting patient outcomes or improving treatments for patients.

Generative artificial intelligence (AI) has brought revolutionary innovations in various fields, including medicine. However, it also exhibits limitations. In response, retrieval-augmented generation (RAG) provides a potential solution, enabling models to generate more accurate contents by leveraging the retrieval of external knowledge. With the rapid advancement of generative AI, RAG can pave the way for connecting this transformative technology with medical applications and is expected to bring innovations in equity, reliability, and personalization to health care.

In recent years, predictive maintenance (PMx) has gained prominence for its potential to enhance efficiency, automation, accuracy, and cost-effectiveness while reducing human involvement. Importantly, PMx has evolved in tandem with digital advancements, such as Big Data and the Internet of Things (IOT). These technological strides have enabled Artificial Intelligence (AI) to revolutionize PMx processes, with increasing capacities for real-time automation of monitoring, analysis, and prediction tasks. However, PMx still faces challenges such as poor explainability and sample inefficiency in data-driven methods and high complexity in physics-based models, hindering broader adoption. This paper posits that Digital Twins (DTs) can be integrated into PMx to overcome these challenges, paving the way for more automated PMx applications across various stakeholders. Despite their potential, current DTs have not fully matured to bridge existing gaps. Our paper provides a comprehensive roadmap for DT evolution, addressing current limitations to foster large-scale automated PMx progression. We structure our approach in three stages: First, we reference prior work where we identified and defined the Information Requirements (IRs) and Functional Requirements (FRs) for PMx, forming the blueprint for a unified framework. Second, we conduct a literature review to assess current DT applications integrating these IRs and FRs, revealing standardized DT models and tools that support automated PMx. Lastly, we highlight gaps in current DT implementations, particularly those IRs and FRs not fully supported, and outline the necessary components for a comprehensive, automated PMx system. Our paper concludes with research directions aimed at seamlessly integrating DTs into the PMx paradigm to achieve this ambitious vision.

The proliferation of Large Language Models (LLMs) poses challenges in detecting and mitigating digital deception, as these models can emulate human conversational patterns and facilitate chat-based social engineering (CSE) attacks. This study investigates the dual capabilities of LLMs as both facilitators and defenders against CSE threats. We develop a novel dataset, SEConvo, simulating CSE scenarios in academic and recruitment contexts, and designed to examine how LLMs can be exploited in these situations. Our findings reveal that, while off-the-shelf LLMs generate high-quality CSE content, their detection capabilities are suboptimal, leading to increased operational costs for defense. In response, we propose ConvoSentinel, a modular defense pipeline that improves detection at both the message and the conversation levels, offering enhanced adaptability and cost-effectiveness. The retrieval-augmented module in ConvoSentinel identifies malicious intent by comparing messages to a database of similar conversations, enhancing CSE detection at all stages. Our study highlights the need for advanced strategies to leverage LLMs in cybersecurity.

Medicine and deep learning-based artificial intelligence (AI) engineering represent two distinct fields each with decades of published history. With such history comes a set of terminology that has a specific way in which it is applied. However, when two distinct fields with overlapping terminology start to collaborate, miscommunication and misunderstandings can occur. This narrative review aims to give historical context for these terms, accentuate the importance of clarity when these terms are used in medical AI contexts, and offer solutions to mitigate misunderstandings by readers from either field. Through an examination of historical documents, including articles, writing guidelines, and textbooks, this review traces the divergent evolution of terms for data sets and their impact. Initially, the discordant interpretations of the word 'validation' in medical and AI contexts are explored. Then the data sets used for AI evaluation are classified, namely random splitting, cross-validation, temporal, geographic, internal, and external sets. The accurate and standardized description of these data sets is crucial for demonstrating the robustness and generalizability of AI applications in medicine. This review clarifies existing literature to provide a comprehensive understanding of these classifications and their implications in AI evaluation. This review then identifies often misunderstood terms and proposes pragmatic solutions to mitigate terminological confusion. Among these solutions are the use of standardized terminology such as 'training set,' 'validation (or tuning) set,' and 'test set,' and explicit definition of data set splitting terminologies in each medical AI research publication. This review aspires to enhance the precision of communication in medical AI, thereby fostering more effective and transparent research methodologies in this interdisciplinary field.

Graph neural networks (GNNs) have emerged as a powerful tool for solving combinatorial optimization problems (COPs), exhibiting state-of-the-art performance in both graph-structured and non-graph-structured domains. However, existing approaches lack a unified framework capable of addressing a wide range of COPs. After presenting a summary of representative COPs and a brief review of recent advancements in GNNs for solving COPs, this paper proposes a unified framework for solving COPs based on GNNs, including graph representation of COPs, equivalent conversion of non-graph structured COPs to graph-structured COPs, graph decomposition, and graph simplification. The proposed framework leverages the ability of GNNs to effectively capture the relational information and extract features from the graph representation of COPs, offering a generic solution to COPs that can address the limitations of state-of-the-art in solving non-graph-structured and highly complex graph-structured COPs.

This paper presents a comprehensive review of cardiorespiratory auscultation sensing devices which is useful for understanding the theoretical aspects of sensing devices, as well as practical notes to design novel sensing devices. One of the methods to design a stethoscope is using electret condenser microphones (ECM). In this paper, we first introduce the acoustic properties of the heart and lungs, as well as a brief history of stethoscope evolution. Then, we discuss the basic concept of ECM sensors and a recent stethoscope based on this technology. In response to the limitations of ECM-based systems, we explore the potential of microelectromechanical systems (MEMS), particularly focusing on piezoelectric transducer (PZT) sensors. This paper comprehensively reviews sensing technologies, emphasizing innovative MEMS-based designs for wearable cardiopulmonary auscultation in the past decade. To our knowledge, this is the first paper to summarize ECM and MEMS applications for heart and lung sound analysis. Keywords: Micro-electro-mechanical Systems (MEMS); Electret Condenser Microphone (ECM); Wearable Sensing Devices; Cardiorespiratory Auscultation; Phonocardiography (PCG); Heart Sound; Lung Sound

The recent development of Foundation Models (FMs), represented by large language models, vision transformers, and multimodal models, has been making a significant impact on both academia and industry. Compared with small-scale models, FMs have a much stronger demand for high-volume data during the pre-training phase. Although general FMs can be pre-trained on data collected from open sources such as the Internet, domain-specific FMs need proprietary data, posing a practical challenge regarding the amount of data available due to privacy concerns. Federated Learning (FL) is a collaborative learning paradigm that breaks the barrier of data availability from different participants. Therefore, it provides a promising solution to customize and adapt FMs to a wide range of domain-specific tasks using distributed datasets whilst preserving privacy. This survey paper discusses the potentials and challenges of synergizing FL and FMs and summarizes core techniques, future directions, and applications. A periodically updated paper collection on FM-FL is available at https://github.com/lishenghui/awesome-fm-fl.