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IEEE International Conference on Software Quality, Reliability and Security (QRS)

作者： Runbing Qin Ningjiang Chen Yihui Huang School of Computer and Electronic Information Guangxi University Nanning China Guangxi Intelligent Digital Services Research Center of Engineering Technology Nanning China Guangxi Colleges and Universities Key Laboratory of Parallel and Distributed Computing Nanning China

Defect detection aims to locate the accurate position of defects in images, which is of great significance to quality inspection in the industrial product manufacturing. Currently, many defect detection methods rely on deep neural networks to extract features. Although the accuracy of these methods is relatively high, it is computationally intensive, making the methods difficult to deploy in resource-limited edge devices. In order to solve these problems, a lightweight defect detection model for the industrial edge environment is proposed, termed the efficient defect detection network (EDDNet). EfficientNet-B0 is used as the feature extraction backbone, extracting feature maps from feature layers of different depths of the network and fusing multilevel features by multilevel feature fusion (MFF). To obtain more information, we redesign the attention mechanism in MBConv blocks, taking the encoding space (ES) attention mechanism as a new module, which solves the problem that the defective image spatial information is ignored. The experimental results on the NEU-DET and DAGM2007 datasets and PCB defect datasets demonstrate the effectiveness of the proposed EDDNet and its possibility for application in industrial edge device.

关键词： Image edge detection Computational modeling Neural networks Software quality Object detection Feature extraction Transformers

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RuleRAG: Rule-Guided Retrieval-Augmented Generation with Language Models for Question Answering

arXiv

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arXiv 2024年

作者： Chen, Zhongwu Xu, Chengjin Wang, Dingmin Huang, Zhen Dou, Yong Jiang, Xuhui Guo, Jian National Key Laboratory of Parallel and Distributed Computing College of Computer Science and Technology National University of Defense Technology China IDEA Research International Digital Economy Academy Department of Computer Science University of Oxford United Kingdom

Retrieval-augmented generation (RAG) has shown promising potential in knowledge intensive question answering (QA). However, existing approaches only consider the query itself, neither specifying the retrieval preferences for the retrievers nor informing the generators of how to refer to the retrieved documents for the answers, which poses a significant challenge to the QA performance. To address these issues, we propose Rule-guided Retrieval-Augmented Generation with LMs, which explicitly introduces rules for in-context learning (RuleRAG-ICL) to guide retrievers to recall related documents in the directions of rules and uniformly guide generators to reason attributed by the same rules. The combination of queries and rules can be used as fine-tuning data to update retrievers and generators, achieving better rule-based instruction-following ability (RuleRAG-FT). Moreover, most existing RAG datasets were constructed without considering rules and Knowledge Graphs (KGs) are recognized as providing high-quality rules. Therefore, we construct five rule-aware RAG benchmarks for QA, RuleQA, based on KGs to stress the significance of retrieval and reasoning with rules. Experiments on RuleQA demonstrate RuleRAG-ICL improves the retrieval quality of +89.2% in Recall@10 and answer accuracy of +103.1% in Exact Match, and RuleRAG-FT yields more enhancement. In addition, experiments on four existing RAG datasets show RuleRAG is also effective by offering rules in RuleQA to them, further proving the generalization of rule guidance in RuleRAG. Code and RuleQA are at https://***/r/RuleRAG. Copyright © 2024, The Authors. All rights reserved.

关键词： Question answering

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FedHybrid: Hierarchical Hybrid Training for High-Performance Federated Learning

FedHybrid: Hierarchical Hybrid Training for High-Performance...

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IEEE Communications Society Conference on Sensor and Ad Hoc Communications and Networks (SECON)

作者： Tao Chang Li Li Meihan Wu Wei Yu Xiaodong Wang National Key Laboratory of Parallel and Distributed Computing National University of Defense Technology China State Key Laboratory of Internet of Things for Smart City University of Macau China The 30th Research Institute of China Electronics Technology Group Corporation China

Federated Learning coordinates multiple devices to train a shared model while preserving data privacy. Despite its potential benefit, the increasing number of participating devices poses new challenges to the deployment in real-world cases. The highly limited amount of data located on each device coupled with significantly unbalanced data across different devices severely impede the performance of the shared model and the overall training progress at the same *** this paper, we propose FedHybrid, a hierarchical hybrid training framework for high-performance Federated Learning on a wide scale. Unlike the existing work that mainly focuses on the statistical challenge, FedHybrid establishes a hierarchical hybrid training framework that effectively utilizes the fragmented and unbalanced data located on the participating devices on a wide scale. Specifically, FedHybrid consists of the following two core components, a global coordinator deployed on the central server and a local coordinator deployed on each participating device. The global coordinator organizes the participating devices into different groups through jointly considering the system heterogeneity and unbalanced training data in order to accelerate the overall training progress while guaranteeing the model performance. Within each group, a novel device-to-device (D2D) sequential training procedure is coordinated by the local coordinator to effectively utilize the fragmented and unbalanced training data in order to intelligently update the local models. At the same time, we provide the theoretical analysis of FedHybrid and conduct extensive experiments to evaluate its effectiveness. The results show that FedHybrid effectively improves model accuracy up to 27% and accelerates the whole training process by 20% on average.

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MANIPULATIONS OF OCTREES AND QUADTREES ON MULTIPROCESSORS

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International Journal of Pattern Recognition and Artificial Intelligence 1994年第2期8卷 439-455页

作者： VIPIN CHAUDHARY K. KUMARI P. ARUNACHALAM J.K. AGGARWAL Parallel and Distributed Computing Laboratory Department of Electrical and Computer Engineering Wayne State University Detroit MI 48202 USA Sun Microsystems 2820 Orchard Parkway San Jose CA 95134 USA Computer and Vision Research Center Department of Electrical and Computer Engineering University of Texas at Austin Austin Texas 78712–1084 USA

Octrees offer a powerful means for representing and manipulating 3-D objects. This paper presents an implementation of octree manipulations using a new approach on a shared memory architecture. Octrees are hierarchical data structures used to model 3-D objects. The manipulation of these data structures involves performing independent computations on each node of the octree. Octrees are much easier to deal with than other forms of representations used to model 3-D objects especially where extensive manipulations are involved. When these operations are distributed among multiple processing elements (PEs) and executed simultaneously, a significant speedup may be achieved. Manipulations such as a complement, a union, an intersection and other operations such as finding the volume and centroid which this paper describes are implemented on the Sequent Balance multiprocessor. In this approach the PEs are allocated dynamically, resulting in a uniform load balancing among them. The experimental results presented illustrate the feasibility of the approach. Although this evaluation has been originally done for shared memory machines, it will provide insight for the evaluation of other architectures.

关键词： Shared memory architecture dynamic allocation union intersection orthogonal views

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MTD: Multi-Timestep Detector for Delayed Streaming Perception

arXiv

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arXiv 2023年

作者： Huang, Yihui Chen, Ningjiang School of Computer and Electronic Information Guangxi University Nanning China Guangxi Intelligent Digital Services Research Center of Engineering Technology Nanning China Key Laboratory of Parallel Distributed and Intelligent Computing Guangxi University Education Department of Guangxi Zhuang Autonomous Region Nanning China

Autonomous driving systems require real-time environmental perception to ensure user safety and experience. Streaming perception is a task of reporting the current state of the world, which is used to evaluate the delay and accuracy of autonomous driving systems. In real-world applications, factors such as hardware limitations and high temperatures inevitably cause delays in autonomous driving systems, resulting in the offset between the model output and the world state. In order to solve this problem, this paper propose the Multi-Timestep Detector (MTD), an end-to-end detector which uses dynamic routing for multi-branch future prediction, giving model the ability to resist delay fluctuations. A Delay Analysis Module (DAM) is proposed to optimize the existing delay sensing method, continuously monitoring the model inference stack and calculating the delay trend. Moreover, a novel Timestep Branch Module (TBM) is constructed, which includes static flow and adaptive flow to adaptively predict specific timesteps according to the delay trend. The proposed method has been evaluated on the Argoverse-HD dataset, and the experimental results show that it has achieved state-of-the-art performance across various delay settings. Copyright © 2023, The Authors. All rights reserved.

关键词： Autonomous vehicles

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Optical interconnection as an IP macro of a CMOS library

Optical interconnection as an IP macro of a CMOS library

引用

Hot Interconnects

作者： T. Yoshikawa I. Hatakeyama K. Miyoshi K. Kurata J. Sasaki N. Kami T. Sugimoto M. Fukaishi K. Nakamura K. Tanaka H. Nishi T. Kudoh Real World Computing Partnership Optical interconnection NEC Laboratory Japan Photonic and Wireless Device Research Laboratories NEC Corporation Limited Tsukuba Ibaraki Japan Silicon System Research Laboratories NEC Corporation Limited Sagamihara Japan Production Technology Research Laboratories NEC Corporation Limited Kawasaki Japan Parallel and Distributed Architecture Group Real World Computing Partnership Tsukuba Ibaraki Japan

With the aim of producing a switch LSI with several-hundred-Gb/s-class throughput, we first developed LAN-LSIs with 10-Gb/s-class optical input/output (I/O) buffer. The optical I/O buffer was supplied as a set consisting of an optical-electrical package, soft or hard macros of the optical interface, and library files for a targeted CMOS technology. Using such library files, system LSI designers can implement an optical interface in their chips in a conventional design manner.

关键词： Libraries Optical buffering High speed optical techniques Optical attenuators Optical design Optical interconnections Integrated circuit interconnections Large scale integration CMOS logic circuits Packaging

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Knowledge Distillation with Source-free Unsupervised Domain Adaptation for BERT Model Compression

Knowledge Distillation with Source-free Unsupervised Domain ...

引用

International Conference on Computer Supported Cooperative Work in Design

作者： Jing Tian Juan Chen Ning Jiang Chen Lin Bai Suqun Huang School of Computer and Electronic Information Guangxi University Nanning China Guangxi Intelligent Digital Services Research Center of Engineering Technology Nanning China Education Department of Guangxi Zhuang Autonomous Region Key Laboratory of Parallel Distributed and Intelligent Computing (Guangxi University) Nanning China

The pre-training language model BERT has brought significant performance improvements to a series of natural language processing tasks, but due to the large scale of the model, it is difficult to be applied in many practical application scenarios. With the continuous development of edge computing, deploying the models on resource-constrained edge devices has become a trend. Considering the distributed edge environment, how to take into account issues such as data distribution differences, labeling costs, and privacy while the model is shrinking is a critical task. The paper proposes a new BERT distillation method with source-free unsupervised domain adaptation. By combining source-free unsupervised domain adaptation and knowledge distillation for optimization and improvement, the performance of the BERT model is improved in the case of cross-domain data. Compared with other methods, our method can improve the average prediction accuracy by up to around 4% through the experimental evaluation of the cross-domain sentiment analysis task.

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MMDSSE: Multi-client and Multi-keyword Dynamic Searchable Symmetric Encryption for Cloud Storage

MMDSSE: Multi-client and Multi-keyword Dynamic Searchable Sy...

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Annual Conference on Privacy, Security and Trust, PST

作者： Panyu Wu Zhenfu Cao Jiachen Shen Xiaolei Dong Yihao Yang Jun Zhou Liming Fang Zhe Liu Chunpeng Ge Chunhua Su Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai China Research Center for Basic Theories of Intelligent Computing Research Institute of Basic Theories Zhejiang Lab Hangzhou China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Science and Technology on Parallel and Distributed Processing Laboratory (PDL) Changsha China Shandong University Jinan China University of Aizu Fukushima Japan

Since data outsourcing poses privacy concerns with data leakage, searchable symmetric encryption (SSE) has emerged as a powerful solution that enables clients to perform query operations on encrypted data while preserving their privacy. Dynamic SSE schemes have been proposed to handle update operations. However, it is shown that updates might increase the risk of information leakage. Meanwhile, to meet the requirement of real-world applications, it is desirable to have the searchable encryption scheme which supports both multiple clients and multi-keyword queries. To address these issues, this paper proposes MMDSSE, a multi-client forward secure dynamic SSE scheme that supports multi-keyword queries. MMDSSE allows the clients narrow down the results by providing an arbitrary subset of the entire archive, and thus suitable for cloud storage environment. Security analysis and experimental evaluations show that MMDSSE is secure and efficient.

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A Fault Classification Prediction Model Based on Loose-Type Wavelet Neural Network for Server Health Management

A Fault Classification Prediction Model Based on Loose-Type ...

引用

Cloud computing, Big Data Applications and Software Engineering (CBASE), International Conference on

作者： Yi Wei Yingxiong Nong Zhe Li Jian Pan Zhenyu Yang Xiaoqi Lu Ningjiang Chen China Tobacco Guangxi Industrial Co. Ltd Nanning China School of Computer Electronics and Information Guangxi University Nanning China School of Computer Electronics and Information Guangxi University Guangxi Intelligent Digital Services Research Center of Engineering Technology Key Laboratory of Parallel Distributed and Intelligent Computing (Guangxi University) Nanning China

To prevent the serious impact on production efficiency caused by service interruption, data loss, and other issues resulting from server faults, this paper proposes a server fault prediction model named Wavelet Packet Transform Probabilistic Neural Network(WPT-PNN). WPT-PNN loosely mixes wavelet packet transform and probabilistic neural network to achieve quick fault localization and signal denoising effects. The proposed model is validated using server operational data gathered. The experimental results suggest that the WPT-PNN model can effectively manage the challenges of complicated, non-stationary, and noisy signals in the feature extraction stage and extract signal features reliably. In the fault classification prediction stage, our method improves fault prediction accuracy to 81%, and limits the error range within [-2, 4], better matching the requirements for precise and low false-positive fault prediction in servers.

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A Deep Belief Networks Intrusion Detection Method Based on Generative Adversarial Networks

A Deep Belief Networks Intrusion Detection Method Based on G...

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Artificial Intelligence, Networking and Information Technology (AINIT), International Seminar on

作者： Yi Wei Yingxiong Nong Zhe Li Jian Pan Zhenyu Yang Zezhang Zheng Ningjiang Chen China Tobacco Guangxi Industrial Co. Ltd Nanning China School of Computer Electronics and Information Guangxi University Nanning China Guangxi Intelligent Digital Services Research Center of Engineering Technology Key Laboratory of Parallel Distributed and Intelligent Computing School of Computer Electronics and Information Guangxi University (Guangxi University) Nanning China

ISBN: (数字)9798350385557

ISBN: (纸本)9798350385564

In response to the substantial threat that Internet attacks pose to data center network security, researchers have proposed several deep learning-based methods for detecting network intrusions. However, while algorithms are constantly improving in terms of accuracy, their stability in the face of insufficient attack samples is a major obstacle. To solve the issues of insufficient attack samples and low detection accuracy in network intrusion detection, this paper proposes a deep confidence network intrusion detection method G-DBN based on GAN. The model is based on the malicious sample extension of the generative adversarial network, and it can produce adversarial samples using malicious network flows as original samples. Furthermore, this paper uses deep belief network technology to create and assess the efficacy of the G-DBN model in detecting network attacks, comparing it to standard DBN models and other network intrusion detection techniques. Experimental results show that compared to the standard three-layer DBN method, the G-DBN method described in this paper improves the detection accuracy of attack samples by 6.46% and better meets the performance requirements of current practical applications.

关键词： Seminars Learning systems Data centers Accuracy Network intrusion detection Network security Generative adversarial networks

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