Information

Industry 5.0 represents a paradigm shift toward human–AI collaboration in manufacturing, incorporating unprecedented volumes of robots, Internet of Things (IoT) devices, Augmented/Virtual Reality (AR/VR) systems, and smart devices. This extensive interconnectivity introduces significant cybersecurity vulnerabilities. While AI has proven effective for cybersecurity applications, including intrusion detection, malware identification, and phishing prevention, cybersecurity professionals have shown reluctance toward adopting black-box machine learning solutions due to their opacity. This hesitation has accelerated the development of explainable artificial intelligence (XAI) techniques that provide transparency into AI decision-making processes. This systematic review examines XAI-based intrusion detection systems (IDSs) for Industry 5.0 environments. We analyze how explainability impacts cybersecurity through the critical lens of adversarial XAI (Adv-XIDS) approaches. Our comprehensive analysis of 135 studies investigates XAI’s influence on both advanced deep learning and traditional shallow architectures for intrusion detection. We identify key challenges, opportunities, and research directions for implementing trustworthy XAI-based cybersecurity solutions in high-stakes Industry 5.0 applications. This rigorous analysis establishes a foundational framework to guide future research in this rapidly evolving domain.

Transmission lines in complex outdoor environments often suffer external damage in construction areas, severely affecting the stability of power systems. Traditional manual detection methods have problems of low efficiency and poor real-time performance. In deep learning-based detection methods, standard convolution has a large parameter count and computational complexity, making it difficult to deploy on edge devices; while lightweight depthwise separable convolution offers low computational cost, it suffers from insufficient feature extraction capability. This limitation stems from its independent processing of each channel’s information, making it unable to simultaneously meet the practical requirements for both lightweight design and high detection accuracy in transmission line monitoring applications. To address the above problems, this study proposes LFRE-YOLO, a lightweight external damage detection algorithm for transmission lines based on YOLOv10n. This study proposes LFRE-YOLO, a lightweight external damage detection algorithm based on YOLOv10n. First, we design a lightweight feature reuse and enhancement convolution (LFREConv) that overcomes the limitations of traditional depthwise separable convolution through cascaded dual depthwise convolution structure and residual connection mechanisms, significantly expanding the effective receptive field with minimal parameter increment and compensating for information loss caused by independent channel processing in depthwise convolution through feature reuse strategies. Second, based on LFREConv, we propose an efficient lightweight feature extraction module (LFREBlock) that achieves cross-channel information interaction enhancement and channel importance modeling. Additionally, we propose a lightweight feature reuse and enhancement detection head (LFRE-Head) that applies LFREConv to the regression branch, achieving comprehensive lightweight design of the detection head while maintaining spatial localization accuracy. Finally, we employ layer-adaptive magnitude-based pruning (LAMP) to prune the trained model, further optimizing the network structure through layer-wise adaptive pruning. Experimental results demonstrate significant improvements over YOLOv10n baseline: mAP50 increased from 92.0% to 94.1%, mAP50-95 improved from 66.2% to 70.2%, while reducing parameters from 2.27 M to 0.99 M, computational complexity from 6.5 G to 3.1 G, and achieving 86.9 FPS inference speed, making it suitable for resource-constrained edge computing environments.

Exchangeability is a foundational concept in Bayesian statistics, crucial for ensuring the validity and generalizability of inferences from experimental data. This paper presents a theoretical and computational framework for understanding the role of exchangeability in the reliability of scientific conclusions, with specific reference to psychology, neuroimaging, and clinical trials. We build on de Finetti’s representation theorem to show how exchangeability enables using hierarchical Bayesian models, and we analyze how its violation can lead to interpretative errors and paradoxes, such as Simpson’s Paradox. In addition to theoretical discussion, we present practical strategies to evaluate and enforce exchangeability, including randomization, matching, stratification, and hierarchical modeling. We also introduce computational tools—such as the Shuffle Test and Stratified Bootstrap—to empirically test for exchangeability and detect latent structures in the data. The novelty of this work lies in unifying theoretical reasoning and empirical testing within a single framework that bridges de Finetti’s representation theorem and resampling-based diagnostics. By providing concrete tools to evaluate exchangeability prior to model fitting, the proposed approach introduces a pre-analysis verification step that strengthens the reliability and transparency of Bayesian inference. Our results emphasize that exchangeability is not merely a technical assumption, but a structural property that governs the coherence and informational integrity of the data. This framework provides both conceptual clarity and operational tools for researchers aiming to perform robust Bayesian inference in complex and heterogeneous datasets.