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Automatic Fuzzy Architecture Design for Defect Detection via Classifier-Assisted Multiobjective Optimization Approach

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IEEE Transactions on Evolutionary Computation 2025年

作者： Li, Nan Xue, Bing Ma, Lianbo Zhang, Mengjie Northeastern University College of Software Shenyang110819 China Victoria University of Wellington Centre for Data Science and Artificial Intelligence School of Engineering and Computer Science PO Box 600 Wellington6140 New Zealand Foshan Graduate School of Innovation Guangdong 528000 China

Defect recognition is an essential aspect of intelligent manufacturing, but it is a challenging task with noise and unpredictable uncertainties, where convolutional neural networks (CNNs) struggle to achieve good performance. The fuzzy neural network (FNN) emerges as a promising approach to handle uncertainties. However, conventional methods for designing FNNs are tedious and error-prone. A solution is to automatically search for efficient FNN, which can be achieved by neural architecture search (NAS). To achieve NAS for FNN, we propose an efficient classifier-assisted evolutionary multiobjective FNN framework for defect recognition. Considering the characteristics of FNN (e.g., difficult to train and prone to overfitting), we first construct the architecture search as a constrained multiobjective optimization problem, the network accuracy and the architecture size are two conflicting objectives, and the constraint is used to filter out low-quality architectures. Then, we design the search space to incorporate the fuzzy module and develop the corresponding architectural representation and evolutionary operators. Furthermore, the complex regression task of performance evaluation is transformed into a classification task, and a classifier is designed to simplify the performance evaluation process. Massive experiments on four defect recognition datasets (i.e., ELPV, CODEBRIM, MIXEDWM38, and WM-811K) show that the architectures can effectively handle inherent uncertainties from datasets. Our method achieves 94.77% accuracy on ELPV, 81.82% accuracy on CODEBRIM, 98.99% accuracy on MIXEDWM38, and 98.22% accuracy on WM-811K, respectively. © 1997-2012 IEEE.

关键词： Convolutional neural networks

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Defect Screening on Nuclear Power Plant Concrete Structures: A Two-staged Method Based on Contrastive Representation Learning

Defect Screening on Nuclear Power Plant Concrete Structures:...

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International Conference on Advanced Robotics and Mechatronics (ICARM)

作者： Wenlian Huang Guanming Zhu Qiang Huang Zhuangzhuang Chen Jie Chen Jianqiang Li College of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China

Intelligent defect detection methods are important for the surface of the containment of nuclear power plants and face many challenges in the field of computer vision. Due to the irregular shapes and large variation of defects, as well as the similarity of the features between some defects and the background. Most existing deep learning-based defect detection networks suffer from insufficient feature extraction capabilities, making it difficult to detect defects from the background. Inspired by discrete representation learning methods, we propose a two-stage defect detection model called ConVQVAE, which uses a semi-supervised method for representation learning to achieve binary classification for defect detection. In addition, contrastive learning is performed in two stages to enhance the model's diversity representation ability and inter-class disentanglement representation ability. Finally, we experimentally verify that the proposed method can obtain good classification results.

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Enhancing Consistency in Container Migration via TEE: A Secure Architecture

Enhancing Consistency in Container Migration via TEE: A Secu...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Qingyu Gao Liantao Song Yan Lei Feng Wang Lei Wang Shize Zong Yan Ding College of Computer Science National University of Defense Technology Changsha China School of Big Data and Software Engineering Chongqing University Chongqing China College of Computer Science and Electronic Engineering Hunan University Changsha China National Innovation Institution of Defense Technology AMS China

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

Secure migration of multi-tenant containerized cloud servers is critical to maintaining the stability and security of cloud services. However, migrating applications across servers owned by different platforms introduces a range of security challenges. Recent research indicates that Trusted Execution Environments (TEEs) provide effective hardware-based isolation for containers, significantly enhancing data confidentiality and integrity. Despite these advancements, existing technologies still struggle to ensure consistency during the migration of container data. To address these issues, we propose a comprehensive security migration framework that defines the security properties required for container migration and introduces a software-based solution. This solution integrates several technologies, including a secure removal mechanism utilizing monotonic counters to prevent potential fork/rollback attacks and ensure the single-instance nature of migrated containers. Additionally, a two-phase commit-based pre-migration check mechanism is employed to maintain consistency among tightly coupled containers being migrated simultaneously on the same host. Safety analysis confirms the effectiveness of our proposed design, and performance evaluation indicates an additional overhead of approximately 7%.

关键词： Cloud computing Privacy Runtime Scalability Containers Stability analysis Safety Security Servers Virtualization

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Multi-view aggregation and multi-relation alignment for few-shot fine-grained recognition

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Expert Systems with Applications 2025年 283卷

作者： Chen, Jiale Xu, Feng Lyu, Xin Zeng, Tao Li, Xin Chen, Shangjing College of Computer Science and Software Engineering Hohai University Nanjing210024 China School of Computer Engineering Jiangsu Ocean University Lianyungang222005 China Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing211106 China

Few-shot fine-grained recognition (FS-FGR) aims to recognize nuanced categories with a limited number of labeled samples that were not encountered during training. Previous work has made significant progress by enhancing the learning of foreground refined regions and the alignment of consistent semantics. However, the detrimental impact of insufficient background diversity on constructing representative category prototypes has been overlooked. Meanwhile, the alignment of semantically consistent features has been hampered by the reliance on singular metrics, resulting in suboptimal feature extraction. To address the limitations above, a novel framework with multi-view aggregation and multi-relation alignment (MVRA) is proposed. In this framework, we strive to refine category prototypes by generating and consolidating multiple views from limited learnable samples. Specifically, we generate foreground-refined views, pinpointing discriminative regions, and background-obfuscated views, broadening the landscape of background diversity. Further, without relying on the entire prior, a global label assignment module is designed to automatically assign reliable labels to the query set samples. Finally, armed with these credible labels, the multi-relation alignment module harnesses the enriched views and their semantic congruencies, facilitating robust feature extraction. The effectiveness and outstanding performance of MVRA are evaluated through extensive experiments conducted on three fine-grained benchmark datasets. © 2025

关键词： Feature extraction

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Generative Semantic Communication for Joint Image Transmission and Segmentation

arXiv

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arXiv 2024年

作者： Yuan, Weiwen Ren, Jinke Wang, Chongjie Zhang, Ruichen Wei, Jun Kim, Dong In Cui, Shuguang FNii-Shenzhen China SSE United Kingdom Shenzhen China College of Computing and Data Science Nanyang Technological University Singapore College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea Republic of

Semantic communication has emerged as a promising technology for enhancing communication efficiency. However, most existing research emphasizes single-task reconstruction, neglecting model adaptability and generalization across multi-task systems. In this paper, we propose a novel generative semantic communication system that supports both image reconstruction and segmentation tasks. Our approach builds upon semantic knowledge bases (KBs) at both the transmitter and receiver, with each semantic KB comprising a source KB and a task KB. The source KB at the transmitter leverages a hierarchical Swin-Transformer, a generative AI scheme, to extract multi-level features from the input image. Concurrently, the counterpart source KB at the receiver utilizes hierarchical residual blocks to generate task-specific knowledge. Furthermore, the two task KBs adopt a semantic similarity model to map different task requirements into pre-defined task instructions, thereby facilitating the feature selection of the source KBs. Additionally, we develop a unified residual block-based joint source and channel (JSCC) encoder and two task-specific JSCC decoders to achieve the two image tasks. In particular, a generative diffusion model is adopted to construct the JSCC decoder for the image reconstruction task. Experimental results demonstrate that our multi-task generative semantic communication system outperforms previous single-task communication systems in terms of peak signal-to-noise ratio and segmentation accuracy. Copyright © 2024, The Authors. All rights reserved.

关键词： Decoding

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IBATree: A Novel Method for Interpretable Cancer Cell Diagnosis Using Information Bottleneck Attribution

IBATree: A Novel Method for Interpretable Cancer Cell Diagno...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Muhammad Umair Raza Jie Chen Adil Nawaz Faisal Saeed Victor C.M. Leung Jianqiang Li Zhaoxia Wang College of Computer Science and Software Engineering Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Department of Gastroenterology Shenzhen Children’s Hospital China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Deep Neural Networks (DNNs) have demonstrated remarkable performance in classification and regression tasks on RGB-based pathological inputs. The network’s prediction mechanism must be interpretable to establish trust in the clinical routine. One principal approach to interpretation is feature attribution. Feature attribution methods identify the importance of input features for the output prediction. Building on the Information Bottleneck Attribution (IBA) method, we recognize the RGB’s input regions with high mutual information with the network’s output for each prediction. IBA identifies input regions that have sufficient predictive information. In this paper, we introduce "IBATree", a novel approach that combines IBA with decision trees to enhance both the interpretability and accuracy of DNNs in cancer cell classification. Our method leverages the information bottleneck framework to inject noise into feature maps and then isolates the most informative features for model predictions while maintaining high classification performance. We evaluated our proposed approach on three datasets—CNMC, ISBI2016, and BreaKHis—demonstrating competitive accuracy and producing clear interpretations. This makes IBATree particularly suitable for clinical applications, where understanding the rationale behind predictions is crucial. Our results show that IBATree provides reliable predictions and valuable insights into feature importance, paving the way for its application in various biomedical domains.

关键词： Pathology Accuracy Biological system modeling Noise Predictive models Reliability Decision trees Biological cells Cancer Biomedical imaging

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Identifying Key Tag Distribution in Large-Scale RFID Systems

Identifying Key Tag Distribution in Large-Scale RFID Systems

引用

International Workshop on Quality of Service

作者： Yanyan Wang Jia Liu Shen-Huan Lyu Zhihao Qu Bin Tang Baoliu Ye Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University China College of Computer Science and Software Engineering Hohai University China State Key Laboratory for Novel Software Technology Nanjing University China

ISBN: (数字)9798350350128

ISBN: (纸本)9798350350135

With the proliferation of RFID-enabled applications, multiple readers are required for the complete coverage of numerous tags in a large-scale RFID system. In this scenario, we sometimes pay more attention to a subset of tags instead of all, which are referred to as key tags. In this paper, we study an under-investigated problem key tag distribution identification, which aims to identify which key tags are beneath which readers. This is crucial for efficiently managing specific items of interest, which can quickly pinpoint key tags and help RFID readers covering these tags collaborate to improve the tag inventory efficiency. Since key tags typically make up a small part of all tags, it is time consuming to deal with all tags in the traditional way. We propose a protocol called Kadept that identifies the key tag distribution by using a sophisticatedly designed filter that teases out key tags as well as assigns each of them a singleton slot for response. With this design, a great number of trivial (non-key) tags will keep silent and free up bandwidth resources for key tags, and each key tag is sorted in a collision-free way and can be identified with only 1-bit response, which significantly improves the time efficiency. We theoretically analyze how can we optimize protocol parameters of Kadept and conduct extensive simulations under different tag distribution scenarios. Compared with the state-of-the-art, Kadept can improve the time efficiency by a factor of 3.7×, when the ratio of key tags to all tags is 0.1.

关键词： Analytical models Protocols Quality of service Bandwidth Radiofrequency identification

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Explicit Analysis of Age of Synchronization in IoT Networks

Explicit Analysis of Age of Synchronization in IoT Networks

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IEEE International Conference on Information Communication and Signal Processing (ICICSP)

作者： Zhiwei Jin Siyuan Zhou Yunquan Dong Bin Tang Guoping Tan College of Information Science and Engineering Hohai University Nanjing China Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing China College of Computer Science and Software Engineering Hohai University Nanjing China School of Electronic and Information Engineering Nanjing University of Information Science and Technology Nanjing China

ISBN: (数字)9798350355895

ISBN: (纸本)9798350355901

The Internet of Things (IoT) has the capability to support the synchronous transmission of dynamically sensed environmental status information to base stations. To improve transmission timeliness, previous studies have commonly utilized Age of Information (AoI) as a metric for network performance. However, AoI falls short in capturing status synchronicity, as synchronization requires knowledge of updates from both the transmitter and the receiver, which AoI cannot reflect due to its inability to track changes at the transmitter. In this work, we adopt the Age of Synchronization (AoS) as a metric to quantify the time elapsed since the freshest information at the receiver becomes desynchronized. Specifically, we develop a model for an IoT communication network to derive closed-form expressions for the average peak AoS (PAoS) and average AoS, considering discrete geometric and uniformly distributed packet arrivals. Ad-ditionally, the analysis has been carried out for both first-come-first-served (FCFS) and last-come-first-served with preemptive resume (LCFS-PR) service disciplines. Within these results, we prove that network's synchronization performance worsens with longer packet arrival intervals but improves with higher arrival rates. LCFS-PR strategy makes synchronization independent of packet arrival, solely influenced by service rate. Additionally, AoS consistently remains lower than AoI in the same system, with both tending to converge as packet arrival rate rises.

关键词： Measurement Closed-form solutions Transmitters Processor scheduling Wireless networks Receivers Signal processing Synchronization Internet of Things Queueing analysis

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ZombieCoin3.0: On the Looming of a Novel Botnet Fortified by Distributed Ledger Technology and Internet of Things 23

ZombieCoin3.0: On the Looming of a Novel Botnet Fortified by...

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23rd IEEE International Conference on High Performance Computing and Communications, 7th IEEE International Conference on data science and Systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and Big data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Gao, Haoyu Li, Leixiao Lin, Hao Chang, Xiangyang Wan, Jianxiong Li, Jie Zhu, Fangyuan College of Data Science and Application Inner Mongolia University of Technology China Inner Mongolia Autonomous Reg. Eng. and Technol. Research Center of Big Data Based Software Service China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781665494571

P2P Botnet is famous for the resilience against termination. However, its dependence on Neighbor List (NL) makes it susceptible to infiltration and poison, also leading to a dearth of adequate protection of Botmaster's identity. So as to overcome these weaknesses, the novel botnet infrastructure combined with blockchain emerges. But due to some flaws such like high latency and expensive transactions fees on blockchain, these new schemes can't satisfy the need of constructing hyper large-scale botnet in practice. We propose ZombieCoin3.0, a Botnet underlying overlay leveraging IoT devices to be maintainers and integrated with Distributed Ledger Technology (DLT), which is able to deploy bots fully on blockchain. And it is versatile for file transfer, data feedback and other command and control scenarios that many contemporaries are impotent to cope with. Our proposal eliminates the P2P botnets' dependence on NL and the bottlenecks that other blockchain combined botnets have. Experiments' results authenticated that the effectiveness of our proposal as well as low latency of communication and nearly zero overhead of cost other comparatives unable to attain are achieved. © 2021 IEEE.

关键词： Costs Distributed ledger Botnet Blockchains Proposals Internet of Things Low latency communication

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Efficient Routing Protection Algorithm in Large-Scale Networks

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Computers, Materials & Continua 2021年第2期66卷 1733-1744页

作者： Haijun Geng Han Zhang Yangyang Zhang School of Software Engineering Shanxi UniversityTaiyuan030006China Institute of Big Data Science and Industry Shanxi UniversityTaiyuan030006China School of Cyber Space and Technology Beihang UniversityBeijing100191China College of Engineering Northeastern University Boston02115USA

With an increasing urgent demand for fast recovery routing mechanisms in large-scale networks,minimizing network disruption caused by network failure has become ***,a large number of relevant studies have shown that network failures occur on the Internet inevitably and *** current routing protocols deployed on the Internet adopt the reconvergence mechanism to cope with network *** the reconvergence process,the packets may be lost because of inconsistent routing information,which reduces the network’s availability greatly and affects the Internet service provider’s(ISP’s)service quality and reputation ***,improving network availability has become an urgent *** such,the Internet engineering Task Force suggests the use of downstream path criterion(DC)to address all single-link failure ***,existing methods for implementing DC schemes are time consuming,require a large amount of router CPU resources,and may deteriorate router ***,the computation overhead introduced by existing DC schemes is significant,especially in large-scale ***,this study proposes an efficient intra-domain routing protection algorithm(ERPA)in large-scale *** analysis indicates that the time complexity of ERPA is less than that of constructing a shortest path *** results show that ERPA can reduce the computation overhead significantly compared with the existing algorithms while offering the same network availability as DC.

关键词： Large-scale network shortest path tree time complexity network failure real-time and mission-critical applications

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