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An End-to-End Classification Network Model Based on Hybrid Supervision for Industrial Surface Defect Detection

International Journal of Pattern Recognition and Artificial Intelligence 2025年第5期39卷

作者： Qin, Runbing Chen, Ningjiang Gan, Shukun Guangxi University School of Computer Electronics and Information Nanning530004 China Education Department of Guangxi Zhuang Autonomous Region Key Laboratory of Parallel Distributed and Intelligent Computing Guangxi University Nanning530004 China Guangxi Intelligent Digital Services Research Center of Engineering Technology Nanning530004 China

Industrial part surface defect detection aims to precisely locate defects in images, which is crucial for quality control in manufacturing. The traditional method needs to be designed in advance, but it has shortcomings in terms of generalization ability. Deep neural network-based defect detection faces issues such as low resolution, data scarcity, labeling costs, and high computation. Therefore, an improved solution is necessary to enhance industrial product quality control efficiency and accuracy. A new method is proposed that utilizes end-to-end training of a two-stage neural network based on segmentation with an extended training process. The gradient flow from the classification to the segmentation network is adjusted to prevent unstable features from interfering with learning. To address the problem of image oversampling and undersampling during training, a frequency sampling scheme for negative samples is introduced. Additionally, positive pixels in the region-based segmentation mask are weighted using the distance transform algorithm so that regions with a high probability of defects can be detected without detailed annotation. Experiments are conducted across three distinct defect datasets by applying hybrid supervision encompassing diverse conditions. In the optimal case, the AP rate of the proposed model on the DAGM and KolektorSDD datasets reaches 100%, and the AP rate on the Severstal Steel dataset reaches 98.99%. The experiments’ results show that the detection accuracy has improved greatly, showing how well the proposed method works in the industry. © 2025 World Scientific Publishing Company.

关键词： Deep neural networks

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Galaxyfly: A novel family of flexible-radix low-diameter topologies for large-scales interconnection networks 16

Galaxyfly: A novel family of flexible-radix low-diameter top...

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30th International Conference on Supercomputing, ICS 2016

作者： Lei, Fei Dong, Dezun Liao, Xiangke Su, Xing Li, Cunlu National Laboratory for Parallel and Distributed Processing Collaborative Innovation Center of High Performance Computing College of Computer National University of Defense Technology Changsha410073 China

ISBN: (纸本)9781450343619

Interconnection network plays an essential role in the architecture of large-scale high performance computing (HPC) systems. In the paper, we construct a novel family of lowdiameter topologies, Galaxyfly, using techniques of algebraic graphs over finite fields. Galaxyfly is guaranteed to retain a small constant diameter while achieving a flexible tradeoff between network scale and bisection bandwidth. Galaxyfly lowers the demands for high radix of network routers and is able to utilize routers with merely moderate radix to build exascale interconnection networks. We present effective congestion-aware routing algorithms for Galaxyfly by exploring its algebraic property. We conduct extensive simulations and analysis to evaluate the performance, cost and power consumption of Galaxyfly against state-of-the-art topologies. The results show that our design achieves better performance than most existing topologies under various routing algorithms and traffic patterns, and is cost-effective to deploy for exascale HPC systems. © 2016 ACM.

关键词： Topology

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Adaptive and augmented active anomaly detection on dynamic network traffic streams

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Frontiers of Information Technology & Electronic Engineering 2024年第3期25卷 446-460页

作者： Bin LI Yijie WANG Li CHENG National Key Laboratory of Parallel and Distributed Computing College of ComputerNational University of Defense TechnologyChangsha 410073China College of System Engineering National University of Defense TechnologyChangsha 410073China

Active anomaly detection queries labels of sampled instances and uses them to incrementally update the detection model,and has been widely adopted in detecting network ***,existing methods cannot achieve desirable performance on dynamic network traffic streams because(1)their query strategies cannot sample informative instances to make the detection model adapt to the evolving stream and(2)their model updating relies on limited query instances only and fails to leverage the enormous unlabeled instances on *** address these issues,we propose an active tree based model,adaptive and augmented active prior-knowledge forest(A3PF),for anomaly detection on network trafic streams.A prior-knowledge forest is constructed using prior knowledge of network attacks to find feature subspaces that better distinguish network anomalies from normal *** one hand,to make the model adapt to the evolving stream,a novel adaptive query strategy is designed to sample informative instances from two aspects:the changes in dynamic data distribution and the uncertainty of *** the other hand,based on the similarity of instances in the neighborhood,we devise an augmented update method to generate pseudo labels for the unlabeled neighbors of query instances,which enables usage of the enormous unlabeled instances during model *** experiments on two benchmarks,CIC-IDS2017 and UNSW-NB15,demonstrate that A3PF achieves significant improvements over previous active methods in terms of the area under the receiver operating characteristic curve(AUC-ROC)(20.9%and 21.5%)and the area under the precision-recall curve(AUC-PR)(44.6%and 64.1%).

关键词： Active anomaly detection Network traffic streams Pseudo labels Prior knowledge of network attacks

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A New Method for Evaluating Core Developers In Open Source Software 13

A New Method for Evaluating Core Developers In Open Source S...

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13th IEEE International Conference on Software Engineering and Service Science, ICSESS 2022

作者： Song, Yulin Wang, Tao Shen, Yang Chang, Junsheng National University of Defense Technology National Laboratory for Parallel and Distributed Processing Hunan Province Changsha China National University of Defense Technology Institute for Quantum Information State Key Laboratory of High Performance Computing Hunan Province Changsha China

ISBN: (数字)9781665410328

ISBN: (纸本)9781665410311

The high-quality development of Open Source Software(OSS) benefits from the contributions of developers. An important research question is how to truly assess the extent of contribution of all developers in open source software and effectively identify the core developers to avoid their loss. In this paper, we use the undirected weighted network model to build a Developer Collaboration Network(DCN) based on developers and their contributions to the GitHub community's open source software Rails, analyse the characteristics of developer partnerships through the Developer Collaboration Network, and propose a Developer Personal Impact Factor(DPIF) to evaluate the importance of developer, select the top 10 core developers in Rails, focus on their participation in the open source software process, and predicting the changes of their contributions. The experimental results show that core developers with extensive partnerships are more likely to associate with developers with fewer partnerships. The average degree of developer contribution does not increase with more developers. Developer partnerships are characterized by close localization and loose overall relationships. The Developer Personal Impact Factor identifies the core developers in open source software well. The Developer Personal Impact Factor gives a ranking in which the core team members rank in the top 1.17% on average. © 2022 IEEE.

关键词： Open source software

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PDRL-CM: An efficient cooperative caching management method for vehicular networks based on deep reinforcement learning

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Ad Hoc Networks 2025年 176卷

作者： Pingjie Ou Ningjiang Chen Long Yang School of Computer and Electronic Information Guangxi University Nanning 530004 Guangxi China Key Laboratory of Parallel Distributed and Intelligent Computing Education Department of Guangxi Zhuang Autonomous Region Nanning 530004 Guangxi China The Guangxi Intelligent Digital Services Research Center of Engineering Technology Nanning 530004 Guangxi China

In vehicular networks, onboard devices face the challenge of limited storage, and computational resources constrain their processing and storage capabilities. This limitation is particularly significant for applications that require complex computations and real-time responses. Additionally, limited storage capacity reduces the range of cacheable data, which can impact the immediate availability of data and the continuity of services. Therefore, improving cache utilization and meeting vehicles’ real-time data demands pose significant challenges. Deep reinforcement learning can optimize the issues arising from agents’ continuously changing state and action spaces due to increasing request demands. However, training the network may encounter instability and convergence difficulties in dynamic and complex environments or situations with sparse rewards. In response to these issues, this paper proposes a Priority-based Deep Reinforcement Learning Collaborative Cache Management method (PDRL-CM). PDRL-CM first designs a lightweight cache admission strategy that leverages data’s inherent and combined attributes. It then makes cache admission decisions Using Monte Carlo sampling and a max-value search strategy combined with a feedforward neural network. Secondly, the method considers minimizing system latency and reducing vehicle energy consumption as joint optimization problems. An improved deep reinforcement learning algorithm solves this problem and makes cache-sharding decisions. A prioritized experience replay mechanism is incorporated to adjust the network prediction model quickly and accelerate the convergence process. Experimental results indicate that, compared to existing DRL-based caching methods, PDRL-CM offers faster data processing efficiency and higher cache hit rates under varying vehicle density, storage capacity, and content volume conditions.

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Exploiting contention and congestion aware switch allocation in network-on-chips 17

Exploiting contention and congestion aware switch allocation...

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50th ACM Turing Conference - China, ACM TUR-C 2017

作者： Li, Cunlu Dongy, Dezun Liao, Xiangke National Laboratory for Parallel and Distributed Processing Collaborative Innovation Center of High Performance Computing College of Computer National University of Defense Technology Changsha Hunan410073 China

ISBN: (纸本)9781450348737

Network-on-chip system plays an important role to improve the performance of chip multiprocessor systems. As the complexity of the network increases, congestion problem has become the major performance bottleneck and seriously influence the performance of NoCs. Prior works have focused on designing effective routing algorithm based on collecting congestion and contention information to load balance the traffic. However, most prior works do not consider balancing the traffic load during switch allocation. Due to the lack of congestion information in switch allocation stage, switch allocator performs allocation only based on packet requests and thus aggravates the congestion in the ports of switch. In this paper, we propose to add the congestion and contention information into the switch allocation process and design an efficient on-chip switching strategy which utilizes the contention and congestion information to load balance the traffic and achieve efficient switch allocation. To further enhance the performance of our design, we carefully design our switch allocation strategy to balance the trade-off between traffic load balance and the matching efficiency in switch allocation. We evaluate our design under synthetic traffic and trace of parsec benchmarks. Our evaluations show this mechanism achieves optimal latency compared to best previous switch allocation strategies. © 2017 ACM.

关键词： Network-on-chip

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A fission technique enabling parallelization of imperfectly nested loops

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6th International Conference on High Performance computing, HiPC 1999

作者： Ju, Jialin Chaudhary, Vipin Pacific Northwest National Laboratory Richland WA 99352 United States Parallel and Distributed Computing Laboratory Wayne State University Detroit MI 48202 United States

ISBN: (纸本)3540441271

This paper addresses the issue of parallelizing imperfectly nested loops. Current parallelizing compilers or transformations would either only parallelize the inner-most loop (which is more like vectorization than parallelization), or not parallelize the loops at *** present an approach that transforms an imperfectly nested loop into at most three fully parallel perfectly nested loops. The transformed loops can be parallelized by any parallelizing compiler. The advantage of our technique is the simplicity of the transformed loops and low synchronization overhead. The feasibility of this approach was tested using several types of loops including those from the Eispack math library and from Linpack benchmark on different multi-processor platforms and performance was compared with Sun's MP C and Cray's autotasking. The results show that our method is very effective. © 1999 Springer-Verlag Berlin Heidelberg.

关键词： Benchmarking

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Computation and communication efficient graph processing with distributed immutable view 14

Computation and communication efficient graph processing wit...

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23rd ACM Symposium on High-Performance parallel and distributed computing, HPDC 2014

作者： Chen, Rong Ding, Xin Wang, Peng Chen, Haibo Zang, Binyu Guan, Haibing Shanghai Key Laboratory of Scalable Computing and Systems Institute of Parallel and Distributed Systems Shanghai Jiao Tong University China Shanghai Key Laboratory of Scalable Computing and Systems Department of Computer Science Shanghai Jiao Tong University China

ISBN: (纸本)9781450327480

Cyclops is a new vertex-oriented graph-parallel framework for writing distributed graph analytics. Unlike existing distributed graph computation models, Cyclops retains simplicity and computation-efficiency by synchronously computing over a distributed immutable view, which grants a vertex with read-only access to all its neighboring vertices. The view is provided via readonly replication of vertices for edges spanning machines during a graph cut. Cyclops follows a centralized computation model by assigning a master vertex to update and propagate the value to its replicas unidirectionally in each iteration, which can significantly reduce messages and avoid contention on replicas. Being aware of the pervasively available multicore-based clusters, Cyclops is further extended with a hierarchical processing model, which aggregates messages and replicas in a single multicore machine and transparently decomposes each worker into multiple threads ondemand for different stages of computation. We have implemented Cyclops based on an open-source Pregel clone called Hama. Our evaluation using a set of graph algorithms on an in-house multicore cluster shows that Cyclops outperforms Hama from 2.06X to 8.69X and 5.95X to 23.04X using hash-based and Metis partition algorithms accordingly, due to the elimination of contention on messages and hierarchical optimization for the multicore-based clusters. Cyclops (written in Java) also has comparable performance with PowerGraph (written in C++) despite the language difference, due to the significantly lower number of messages and avoided contention. Copyright © 2014 ACM.

关键词： Iterative methods

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RScam: Cloud-based anti-malware via reversible sketch 11th

RScam: Cloud-based anti-malware via reversible sketch

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11th International Conference Security and Privacy in Communication Networks, SecureComm 2015

作者： Sun, Hao Wang, Xiaofeng Su, Jinshu Chen, Peixin College of Computer National University of Defense Technology Changsha China National Key Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China

ISBN: (纸本)9783319288642

Cybercrime caused by malware becomes a persistent and damaging threat which makes the trusted security solution urgently demanded, especially for resource-constrained ends. The existing industry and academic approaches provide available anti-malware systems based on different perspectives. However, it is hard to achieve high performance detection and data privacy protection simultaneously. This paper proposes a cloud-based anti-malware system, called RScam, which provides fast and trusted security service for the resource-constrained ends. In RScam, we present suspicious bucket filtering, a novel signature-based detection mechanism based on the reversible sketch structure, which provides retrospective and accurate orientations of malicious signature fragments. Then we design a lightweight client which utilizes the digest of signature fragments to sharply reduce detection range. Finally, we design balanced interaction mechanism, which transmits sketch coordinates of suspicious file fragments and transformation of malicious signature fragments between the client and cloud server to protect data privacy and reduce traffic volume. We evaluate the performance of RScam with campus suspicious traffic and normal files. The results demonstrate validity and veracity of the proposed mechanism. Our system can outperform other existing systems with less time and traffic consumption. © Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2015.

关键词： Malware

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Concurrent and consistent virtual machine introspection with hardware transactional memory

Concurrent and consistent virtual machine introspection with...

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20th IEEE International Symposium on High Performance Computer Architecture, HPCA 2014

作者： Liu, Yutao Xia, Yubin Guan, Haibing Zang, Binyu Chen, Haibo Shanghai Key Laboratory of Scalable Computing and Systems Institute of Parallel and Distributed Systems Shanghai Jiao Tong University China Shanghai Key Laboratory of Scalable Computing and Systems Department of Computer Science Shanghai Jiao Tong University China

ISBN: (纸本)9781479930975

Virtual machine introspection, which provides tamperresistant, high-fidelity 'out of the box' monitoring of virtual machines, has many prominent security applications including VM-based intrusion detection, malware analysis and memory forensic analysis. However, prior approaches are either intrusive in stopping the world to avoid race conditions between introspection tools and the guest VM, or providing no guarantee of getting a consistent state of the guest VM. Further, there is currently no effective means for timely examining the VM states in question. In this paper, we propose a novel approach, called TxIntro, which retrofits hardware transactional memory (HTM) for concurrent, timely and consistent introspection of guest VMs. Specifically, TxIntro leverages the strong atomicity of HTM to actively monitor updates to critical kernel data structures. Then TxIntro can mount introspection to timely detect malicious tampering. To avoid fetching inconsistent kernel states for introspection, TxIntro uses HTM to add related synchronization states into the read set of the monitoring core and thus can easily detect potential inflight concurrent kernel updates. We have implemented and evaluated TxIntro based on Xen VMM on a commodity Intel Haswell machine that provides restricted transactional memory (RTM) support. To demonstrate the effectiveness of TxIntro, we implemented a set of kernel rootkit detectors using TxIntro. Evaluation results show that TxIntro is effective in detecting these rootkits, and is efficient in adding negligible performance overhead. © 2014 IEEE.

关键词： Malware

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