Abstract Object detection is vital for automated surface defect inspection, yet most models suffer from bloated architectures and poor performance on multi‑class, multi‑scale tasks involving large‑size images, limiting their use on edge devices. We propose YOLO‑MSD, a lightweight surface defect detection model that integrates two key designs: (1) a novel four-scale backbone that effectively extracts small and multi-scale targets from large-size images by enhancing feature representation across different scale resolutions, and (2) a streamlined feature‑pyramid neck that boosts cross‑scale fusion while reducing parameters and computational cost. Extensive experiments on five public datasets verify the model’s effectiveness. On the PCB, HRIPCB and GC10‑DET datasets featuring high-resolution images, YOLO‑MSD achieves 96.67% mAP , 96.62% mAP and 69.09% mAP , respectively, while maintaining a low parameter count and computational complexity. It also outperforms most advanced models on two additional public datasets and achieves 20.82 FPS with a power consumption of 6.95 W on the PCB dataset when deployed on a Jetson Xavier NX edge device. These results demonstrate the accuracy, efficiency, and deployability of YOLO‑MSD for industrial surface‑defect detection.

The shuffle model of local differential privacy is an advanced method of privacy amplification designed to enhance privacy protection with high utility. It achieves this by randomly shuffling sensitive data, making linking individual data points to specific individuals more challenging. However, most existing studies have focused on the shuffle model based on (ε0,0)-Locally Differentially Private (LDP) randomizers, with limited consideration for complex scenarios such as (ε0,δ0)-LDP or personalized LDP (PLDP). This hinders a comprehensive understanding of the shuffle model's potential and limits its application in various settings. To bridge this research gap, we propose a generalized shuffle framework that can be applied to PLDP setting. This generalization allows for a broader exploration of the privacy-utility trade-off and facilitates the design of privacy-preserving analyses in diverse contexts. We prove that the shuffled PLDP process approximately preserves μ-Gaussian Differential Privacy with μ = O(1/√n). This approach allows us to avoid the limitations and potential inaccuracies associated with inequality estimations. To strengthen the privacy guarantee, we improve the lower bound by utilizing hypothesis testing instead of relying on rough estimations like the Chernoff bound or Hoeffding's inequality. Furthermore, extensive comparative evaluations clearly show that our approach outperforms existing methods in achieving strong central privacy guarantees while preserving the utility of the global model. We have also carefully designed corresponding algorithms for average function, frequency estimation, and stochastic gradient descent.

The shuffle model of Differential Privacy (DP) has gained significant attention in privacy-preserving data analysis due to its remarkable tradeoff between privacy and utility. It is characterized by adding a shuffling procedure after each user’s locally differentially private perturbation, which leads to a privacy amplification effect, meaning that the privacy guarantee of a small level of noise, say ϵ0, can be enhanced to $O\left( {{\varepsilon _0}/\sqrt n } \right)$ (the smaller, the more private) after shuffling all n users’ perturbed data. Most studies in the shuffle DP focus on proving a tighter privacy guarantee of privacy amplification. However, the current results assume that the local privacy budget ϵ0is within a limited range. In addition, there remains a gap between the tightest lower bound and the known upper bound of the privacy amplification. In this work, we push forward the state-of-the-art by making the following contributions. Firstly, we present the first asymptotically optimal analysis of Ŕenyi Differential Privacy (RDP) in the shuffle model without constraints on ϵ0. Secondly, we introduce hypothesis testing for privacy amplification through shuffling, offering a distinct analysis technique and a tighter upper bound. Furthermore, we propose a DP-SGD algorithm based on RDP. Experiments demonstrate that our approach outperforms existing methods significantly at the same privacy level.

As a robust real-time mapping technology for virtual reality, Digital Twin (DT) offers significant potential in the Internet of Things (IoT) context. When combined with IoT, DT enables excellent online monitoring and management capabilities for physical entities. Additionally, this collaboration has led to the development of a smart monitoring system that leverages DT and deep learning technologies to provide a user-friendly monitoring platform and enable real-time anomaly detection. In detail, we first establish a DT-based monitoring platform to ensure the mapping of physical entities to virtual environments. Furthermore, we employ a deep learning-based anomaly detection method for real-time monitoring. The system is implemented in a smart laboratory environment to evaluate our approach. Based on the collected data, our method shows excellent performance in anomaly detection. Experimental results demonstrate the effectiveness of our strategy in achieving the stated goals. In conclusion, leveraging deep learning and DT techniques, this solution offers a robust and reliable solution for IoT active monitoring systems, facilitating highly relevant and valuable real-world applications.

Abstract Currently, 2D anomaly detection has demonstrated outstanding performance. However, 2D images limit the improvement of anomaly detection accuracy without utilizing depth information. Therefore, this paper proposes a Dual Reconstruction viAInpainting Network for 3D industrial anomaly detection (DRAIN). Firstly, we design a 3D reconstruction network using an encoder-decoder-based U-shaped network for processing RGB images and depth images. Subsequently, accurate anomaly segmentation is implemented through a 3D segmentation network. We introduce a lightweight MLP module to enhance segmentation performance to capture long-range dependencies in the reconstructed images. Furthermore, we propose a dual attention-based information entropy fusion module to expedite feature fusion in the inference process, aiming for enhanced deployment in the industry. Extensive experiments demonstrate that DRAIN achieves a 94.3% AUROC on the 3D anomaly detection dataset MVTec 3D-AD, surpassing other research methods. Graphical abstract Overall architecture for 3D industrial anomaly detection via dual reconstruction network

Abstract With the wide application of deep learning in anomaly detection (AD), industrial vision AD has achieved remarkable success. However, current AD usually focuses on anomaly localization and rarely investigates anomaly classification. Furthermore, anomaly classification is currently requested for quality management and anomaly reason analysis. Therefore, it is essential to classify anomalies while improving the accuracy of AD. This paper designs a novel multi-classification AD (MCAD) framework to achieve high-accuracy AD with an anomaly classification function. In detail, the proposal model based on relational knowledge distillation consists of two components. The first one employs a teacher–student AD model, utilizing a relational knowledge distillation approach to transfer the interrelationships of images. The teacher–student critical layer feature activation values are used in the knowledge transfer process to achieve anomaly detection. The second component realizes anomaly multi-classification using the lightweight convolutional neural network. Our proposal has achieved 98.95, 96.04, and 92.94% AUROC AD results on MNIST, FashionMNIST, and CIFAR10 datasets. Meanwhile, we earn 97.58 and 98.10% AUROC for AD and localization in the MVTecAD dataset. The average classification accuracy of anomaly classification has reached 76.37% in fifteen categories of the MVTec-AD dataset. In particular, the classification accuracy of the leather category has gained 95.24%. The results on the MVTec-AD dataset show that MCAD achieves excellent detection, localization, and classification results.

(Source) Code summarization aims to automatically generate summaries/comments for given code snippets in the form of natural language. Such summaries play a key role in helping developers understand and maintain source code. Existing code summarization techniques can be categorized into extractive methods and abstractive methods . The extractive methods extract a subset of important statements and keywords from the code snippet using retrieval techniques and generate a summary that preserves factual details in important statements and keywords. However, such a subset may miss identifier or entity naming, and consequently, the naturalness of the generated summary is usually poor. The abstractive methods can generate human-written-like summaries leveraging encoder-decoder models. However, the generated summaries often miss important factual details. To generate human-written-like summaries with preserved factual details, we propose a novel extractive-and-abstractive framework. The extractive module in the framework performs the task of extractive code summarization, which takes in the code snippet and predicts important statements containing key factual details. The abstractive module in the framework performs the task of abstractive code summarization, which takes in the code snippet and important statements in parallel and generates a succinct and human-written-like natural language summary. We evaluate the effectiveness of our technique, called EACS, by conducting extensive experiments on three datasets involving six programming languages. Experimental results show that EACS significantly outperforms state-of-the-art techniques for all three widely used metrics, including BLEU, METEOR, and ROUGH-L. In addition, the human evaluation demonstrates that the summaries generated by EACS have higher naturalness and informativeness and are more relevant to given code snippets.

(Source) code search is widely concerned by software engineering researchers because it can improve the productivity and quality of software development. Given a functionality requirement usually described in a natural language sentence, a code search system can retrieve code snippets that satisfy the requirement from a large-scale code corpus, e.g., GitHub. To realize effective and efficient code search, many techniques have been proposed successively. These techniques improve code search performance mainly by optimizing three core components, including query understanding component, code understanding component, and query-code matching component. In this article, we provide a 3-dimensional perspective survey for code search. Specifically, we categorize existing code search studies into query-end optimization techniques, code-end optimization techniques, and match-end optimization techniques according to the specific components they optimize. These optimization techniques are proposed to enhance the performance of specific components, and thus the overall performance of code search. Considering that each end can be optimized independently and contributes to the code search performance, we treat each end as a dimension. Therefore, this survey is 3-dimensional in nature, and it provides a comprehensive summary of each dimension in detail. To understand the research trends of the three dimensions in existing code search studies, we systematically review 68 relevant literatures. Different from existing code search surveys that only focus on the query end or code end or introduce various aspects shallowly (including codebase, evaluation metrics, modeling technique, etc.), our survey provides a more nuanced analysis and review of the evolution and development of the underlying techniques used in the three ends. Based on a systematic review and summary of existing work, we outline several open challenges and opportunities at the three ends that remain to be addressed in future work.