The transition from Industry 4.0 to Industry 5.0 marks a paradigm shift from technology-driven automation toward secure, resilient, and human-centric manufacturing. While Industry 4.0 enhanced efficiency through cyber-physical systems (CPS), the Internet of Things (IoT), and artificial intelligence (AI), it often overlooks human involvement and introduces heightened cybersecurity risks. Industry 5.0 seeks to overcome these limitations by emphasizing sustainability, resilience, and human-centricity through collaboration between humans and intelligent systems. As a step toward maturing the Industry 5.0 paradigm, we propose the Self-X-based secure human-cyber-physical system (SSHCPS) as a new conceptual framework for autonomous manufacturing. This study introduces an original architecture that integrates Self-X capabilities, human-in-the-loop (HITL) interaction, and cybersecurity (CS). The architecture is structured into four interlinked modules: HITL, Digital Twin, Physical Twin, and CS. As the foundation of this architecture, we categorized the Self-X terms from the literature, merged them into 23 Self-X principles, redefined them, and mapped them into the core values of Industry 5.0 and the SSHCPS modules. Furthermore, we demonstrate the applicability of SSHCPS through potential applications such as lightless and de-urbanized factories, humanoid-enabled SMEs, and the aerospace industry, while also identifying key technical challenges and future direction. This study establishes SSHCPS as a forward-looking foundation for secure, efficient, and human-centered autonomous manufacturing systems in the Industry 5.0 era.

Quantum computers are evolving at a rapid pace and are considered next-generation computers with high computational capabilities. However, due to the unique characteristics of qubits, state-of-the-art quantum computers are vulnerable to noise caused by qubit instability. To overcome this, highperformance computing (HPC) systems are utilized for quantum circuit simulations to evaluate complex quantum algorithms with great accuracy. However, quantum circuit simulations have high computational demands, and the data volume increases exponentially as the number of qubits increases. In this paper, we propose SWIFTN, a quantum circuit simulation optimization framework for HPC systems with scalability. To achieve this, it enhances parallelism by dividing the tensor networks and distributing them across multiple GPUs and nodes. Additionally, it reduces computational costs by bypassing tasks through intermittent tensor contraction. Finally, to mitigate the degradation in accuracy due to intermittent tensor contraction,SWIFTNperforms amplitude adjustments. We implement and evaluateSWIFTNusing a Perlmutter supercomputer. Our evaluation results using popular quantum algorithm benchmark (i.e., QAOA) shows thatSWIFTNcan improve the performance by$7.85 \times$with 99.997 % accuracy.

Various scientific applications with different I/O characteristics are executed in HPC systems. However, underlying parallel file systems are unaware of these characteristics of applications, and using a single fixed file layout for all applications can degrade the performance of HPC systems. In this paper, we propose A2FL, an autonomous and adaptive file layout adjustment scheme that optimizes parallel file system configurations by analyzing the access pattern of the applications. The key steps of A2FL are as follows: (1) A2FL initially intercepts the I/O operations of the application, recording their access patterns in real-time. (2) The access patterns are then transformed into a graphical representation used for predicting I/O performance and providing adjustment recommendations. (3) A2FL autonomously adjusts the file layout based on the prediction results, delivering an optimal file layout within the parallel file system. Moreover, we propose A2FL-Compound which analyzes an access pattern by dividing it into smaller components to optimize the file layout in a fine-grained manner. Our evaluations demonstrate that A2FL significantly enhances I/O performance, with improvements of up to 65.9× compared to the default file layout.

Cybersecurity has become an inevitable concern in the healthcare industry due to the rapid growth of the Internet of Health Things (IoHT). The IoHT is revolutionizing healthcare by enabling remote access to hospital equipment, real-time patient monitoring, and urgent alerts to patients and hospitals. However, the convenience of these systems also makes them vulnerable to cyberattacks, with hackers seeking to disrupt health services or extort money through ransomware attacks. Efficiently detecting multiple threats is a challenging task because IoHT generates large temporal data and system log information. In this paper, we propose time series classification models for the identification of potential cyberattacks in IoHT networks. First, we introduce Neighborhood Component Analysis (NCA) with modifications of the regularization parameter to select the vital input features. With the selected features, we propose two LSTM-based models: Directed Acyclic Graph-based Long Short-Term Memory (DAG-LSTM) and Projected Layer-based Long Short-Term Memory (PL-LSTM) for detecting cyberattacks. We evaluate the existing time series classification models (i.e., GRU, LSTM, and Bi-LSTM) and proposed models (i.e., DAG-LSTM and PL-LSTM) using real-world IoHT data. We also validate the models by applying a non-parametric statistical test, Friedman test. Our evaluation results show that the proposed DAG-LSTM achieves the highest accuracy with 99.89% training and 92.04% an average testing accuracy.

As data is becoming more and more important, Internet of Things (IoT) devices are widely used to collect information and process data from various industries, such as finance, autonomous driving, and smart factories. To address the limited computational power of IoT devices in processing real-time data, both edge computing, which utilizes nearby computers with greater computation capabilities, and cloud computing with even more processing power, are widely adopted solutions. As these systems have heterogeneous software and hardware configurations, it can be challenging to understand the behavior of the application from the perspective of different resources. In this article, we propose an efficient logging and monitoring system in large-scale, heterogeneous environments for IoT and edge applications. To do this, our scheme first collects system resource usage data from each compute node using the operating system’s native system analysis tool. Then, it consolidates the system resource usage information from multiple nodes into an integrated database which creates a comprehensive view of the system. Finally, our scheme provides global system resource information in terms of specific jobs and nodes, providing a comprehensive understanding of complex heterogeneous hardware/software stacks. Our evaluation, using IoT and edge workloads in heterogeneous systems, demonstrates the efficiency of logging and monitoring schemes. The average network usage for Windows and Linux is 0.12 and 1.29 kB/s, respectively, resulting in minimal network overhead. In addition, the proposed scheme shows negligible overhead in terms of both runtime (up to 0.73%) and storage (0.0474%).