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42nd IEEE International Conference on Computer Communications, INFOCOM 2023

作者： Gu, Lin Huang, Junhao Huang, Shaoxing Zeng, Deze Li, Bo Jin, Hai Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Wuhan China China University of Geosciences School of Computer Science Wuhan China Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong

ISBN: (纸本)9798350334142

Container based microservices have been widely applied to promote the cloud elasticity. The mainstream Docker containers are structured in layers, which are organized in stack with bottom-up dependency. To start a microservice, the required layers are pulled from a remote registry and stored on its host server, following the layer dependency order. This incurs long microservice startup time and hinders the performance efficiency. In this paper, we discover that, for the first time, the layer pulling order can be adjusted to accelerate the microservice startup. Specifically, we address the problem on microservice layer pulling orchestration for startup time minimization and prove it as NP-hard. We propose a Longest-chain based Out-of-order layer Pulling Orchestration (LOPO) strategy with low computational complexity and guaranteed approximation ratio. Through extensive real-world trace driven experiments, we verify the efficiency of our LOPO and demonstrate that it reduces the microservice startup time by 22.71% on average in comparison with state-of-the-art solutions. © 2023 IEEE.

关键词： Containers

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Scd-Net: An Efficient Detection Network for Pcb Defects Based on Spatial and Channel Reconstruction Convolution and Deformable Convolutions

SSRN

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SSRN 2024年

作者： Wang, Yuanji Wang, Wenyan Pan, Xuejuan Lu, Kun Zhang, Jun Chen, Peng Wang, Bing School of Electrical and Information Engineering Anhui University of Technology Anhui Ma’anshan243032 China School of Electrical Engineering and Automation Anhui University China National Engineering Research Center for Agro-Ecological Big Data Analysis and Application School of Internet Institutes of Physical Science and Information Technology Anhui University Anhui Hefei230601 China School of Chemistry and Chemical Engineering Anhui University of Technology China

The detection of defects on printed circuit boards (PCBs) is crucial for guaranteeing the quality and performance of electronic products. However, due to the size of defects is quite small in high-resolution industrial acquisition images, existing detection models are unable to identify them accurately and effectively. This study introduces a new detection model, SCD-Net, which improves the accuracy, effectiveness and robustness of PCB defect detection systems. By combining the proposed modules CNISC and DCCN with the YOLOv8s network structure, the model can reduce computational costs and improve detection accuracy. Furthermore, replacing the original loss function with a new SIoU function helps the model converge faster to improve performance. In comparison to other PCB detection models, this model achieves a PCB defect detection accuracy of up to 98.0%, a model size of 19.2M, and an detection speed of 79.1 FPS, providing a feasible solution for PCB defect detection. © 2024, The Authors. All rights reserved.

关键词： Convolution

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Quantized stabilization for delayed memristive neural networks

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Neural Computing and Applications 2023年第22期35卷 16473-16486页

作者： Yan, Zhilian Zuo, Dandan Guo, Tong Zhou, Jianping School of Electrical & Information Engineering Anhui University of Technology Ma’anshan China Tencent Cloud Big Data College Ma’anshan University Ma’anshan China School of Computer Science & Technology Anhui University of Technology Ma’anshan China

The issue of $${\mathcal {H}}_{\infty }$$ stabilization for delayed memristive neural networks with dynamic quantization is considered. The aim is to design a quantized sampled-data controller guaranteeing that the closed-loop system is globally asymptotically stable with a prescribed $${\mathcal {H}}_{\infty }$$ disturbance attenuation level. By means of set-valued maps and the differential inclusion theory, the network under consideration is transformed into a dynamic model subject to time-dependent bounded uncertainty. Then, two different time-dependent two-sided loop functionals are constructed for the non-necessarily and necessarily differential time delay situations, respectively. Two sufficient conditions on the stability and $${\mathcal {H}}_{\infty }$$ performance are derived via using these constructed functionals and a few inequality techniques. On the foundation of these conditions, co-designs of the needed feedback gain and dynamic quantization parameter are presented. Finally, three examples are provided to verify the applicability of the quantized sampled-data controller design methods.

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Owl: Differential-Based Side-Channel Leakage Detection for CUDA Applications

Owl: Differential-Based Side-Channel Leakage Detection for C...

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International Conference on Dependable Systems and Networks (DSN)

作者： Yu Zhao Wenjie Xue Weijie Chen Weizhong Qiang Deqing Zou Hai Jin National Engineering Research Center for Big Data Technology and System Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Jinyinhu Laboratory Wuhan China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798350341058

ISBN: (纸本)9798350341065

Over the past decade, various methods for detecting side-channel leakage have been proposed and proven to be effective against CPU side-channel attacks. These methods are valuable in assisting developers to identify and patch side-channel vulnerabilities. Nevertheless, recent research has revealed the feasibility of exploiting side-channel vulnerabilities to steal sensitive information from GPU applications, which are beyond the reach of previous side-channel detection methods. Therefore, in this paper, we conduct an in-depth examination of various GPU features and present Owl, a novel side-channel detection tool targeting CUDA applications on NVIDIA GPUs. Owl is designed to detect and locate side-channel leakage in various types of CUDA applications. When tracking the execution of CUDA applications, we design a hierarchical tracing scheme and extend the A-DCFG (Attributed Dynamic Control Flow Graph) to address the massively parallel execution in CUDA, ensuring Owl's detection scalability. After completing the initial assessment and filtering, we conduct statistical tests on the differences in program traces to determine whether they are indeed caused by input variations, subsequently facilitating the positioning of side-channel leaks. We evaluate Owl's capability to detect side-channel leaks by testing it on Libgpucrypto, PyTorch, and nvJPEG. Meanwhile, we verify that our solution effectively handles a large number of threads. Owl has successfully identified hundreds of leaks within these applications. To the best of our knowledge, we are the first to implement side-channel leakage detection for general CUDA applications.

关键词： Target tracking Statistical analysis Filtering Scalability Graphics processing units Side-channel attacks Feature extraction

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A Fast Matrix Completion Method Based on Matrix Bifactorization and QR Decomposition

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Wireless Communications and Mobile Computing 2023年第1期2023卷

作者： Liu, Qing Peng, Chen Yang, Pei Zhou, Xiancun Liu, Zhengyu School of Electronic and Information Engineering West Anhui University Anhui Lu'an237012 China School of Mathematics and Big Data Anhui University of Science and Technology Anhui Huainan232001 China Department of Computer Technology and Application Qinghai University Qinghai Xining810016 China

The problem of recovering the missing values in an incomplete matrix, i.e., matrix completion, has attracted a great deal of interests in the fields of machine learning and signal processing. A matrix bifactorization method, which is abbreviated as MBF, is a fast method of matrix completion that has a better speed than the traditional nuclear norm minimization methods. However, it may become inaccurate and slow when solving matrices of not low rank. In this paper, an improved fast and accurate MBF method based on Qatar Riyal (QR) decomposition is proposed, which can be called FMBF-QR. On one side, the optimization problem of MBF is improved to be an iteratively reweighted L2,1 norm minimization problem to enhance the accuracy of MBF. On the other side, the minimization problem of FMBF-QR is optimized very efficiently by using QR decomposition for improving the speed of MBF. Sufficient experimental results verify that FMBF-QR can converge with a higher accuracy and a faster speed than the traditional matrix completion methods. © 2023 Qing Liu et al.

关键词： Signal processing

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BDLR:lncRNA identification using ensemble learning

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BIOCELL 2022年第4期46卷 951-960页

作者： LEJUN GONG SHEHAI ZHOU JINGMEI CHEN YONGMIN LI LI ZHANG ZHIHONG GAO Jiangsu Key Lab of Big Data Security&Intelligent Processing School of Computer Science Nanjing University of Posts and TelecommunicationsNanjing210046China Smart Health Big Data Analysis and Location Services Engineering Laboratory of Jiangsu Province Nanjing210046China Zhejiang Engineering Research Center of Intelligent Medicine Wenzhou325035China College of Computer Science and Technology Nanjing Forestry UniversityNanjing210037China

Long non-coding RNAs(lncRNAs)play an important role in many life activities such as epigenetic material regulation,cell cycle regulation,dosage compensation and cell differentiation regulation,and are associated with many human *** are many limitations in identifying and annotating lncRNAs using traditional biological experimental *** the development of high-throughput sequencing technology,it is of great practical significance to identify the lncRNAs from massive RNA sequence data using machine learning *** on the Bagging method and Decision Tree algorithm in ensemble learning,this paper proposes a method of lncRNAs gene sequence identification called *** identification results of this classification method are compared with the identification results of several models including Byes,Support Vector Machine,Logical Regression,Decision Tree and Random *** experimental results show that the lncRNAs identification method named BDLR proposed in this paper has an accuracy of 86.61%in the human test set and 90.34%in the mouse for lncRNAs,which is more than the identification results of the other ***,the proposed method offers a reference for researchers to identify lncRNAs using the ensemble learning.

关键词： lncRNAs High-throughput sequencing Ensemble learning Bagging Decision Tree

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An Approach of Digital Twin System Construction for Underground Locomotive Dispatching Management

An Approach of Digital Twin System Construction for Undergro...

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IEEE International Conference on Networking, Sensing and Control

作者： Geng Wang Huan Fang Shouzheng Zhang College of mathematics and big data anhui university of science and technology Huainan China college of mathematics and big data Anhui university of science and technology Anhui province engineering laboratory for big data analysis and early warning technology of coal mine safety Huainan China

ISBN: (纸本)9781665472449

For addressing the problems that the existing data cannot be effectively integrated with the process control decision-making in the digital mine system, a digital twin system for underground locomotive dispatching management is proposed in this work. Firstly, the designed digital twin system has a four-tier system architecture, while the part of theoretical prior model is established by Petri nets, and the actual dispatching is constructed by integrating simulation software and a three-dimensional (3D) virtual mapping for underground mines. Then, the effectiveness of dispatching algorithm and the consistency of twin information are systematically evaluated through the setting of simulation parameters and the access of twin data. The designed digital twin system of underground locomotive dispatching can not only visualize the dispatching system in two directions, but also provide decision-making basis for the safety of locomotive dispatching, which deepen and widen the application scope of digital twin system in the field of intelligent mine construction.

关键词： Solid modeling Software algorithms Decision making Process control Virtual environments Dispatching Software

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Application of a neural network model with multimodal fusion for fluorescence spectroscopy

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Nuclear Science and Techniques 2024年第10期35卷 135-148页

作者： Lin Tang Shuang Zhou Kai-Bo Shi Hong-Tao Shen Lei You College of Electronic Information and Electrical Engineering Chengdu UniversityChengdu 610106China Guangxi Key Laboratory of Nuclear Physics and Nuclear Technology Guangxi Normal UniversityGuilin 541004China National Engineering Research Center for Agro-Ecological Big Data Analysis&Application Anhui UniversityHefei 230039China School of Electrical and Electronic Engineering Nanyang Technological UniversitySingapore 639798Singapore Vanadium and Titanium Resource Comprehensive Utilization Key Laboratory of Sichuan Province Panzhihua UniversityPanzhihua 617000China Key Laboratory of Interior Layout optimization and Security Institutions of Higher Education of Sichuan ProvinceChengdu Normal UniversityChengdu 611130China

In energy-dispersive X-ray fluorescence spectroscopy,the estimation of the pulse amplitude determines the accuracy of the spectrum *** error generated by the amplitude estimation of the pulse output distorted by the measurement system leads to false peaks in the measured *** eliminate these false peaks and achieve an accurate estimation of the distorted pulse amplitude,a composite neural network model is proposed,which embeds long and short-term memory(LSTM)into the UNet *** UNet network realizes the fusion of pulse sequence features and the LSTM model realizes pulse amplitude *** model is trained using simulated pulse datasets with different amplitudes and distortion *** the pulse height estimation,the average relative error of the trained model on the test set was approximately 0.64%,which is 27.37% lower than that of the traditional trapezoidal shaping *** processing of a standard iron source further validated the pulse height estimation performance of the UNet-LSTM *** estimating the amplitude of the distorted pulses using the model,the false peak area was reduced by approximately 91% over the full spectrum and was corrected to the characteristic peak region of interest(ROI).The corrected peak area accounted for approximately 1.32%of the characteristic peak ROI *** results indicate that the model can accurately estimate the height of distorted pulses and has substantial corrective effects on false peaks.

关键词： UNet Long-and short-term memory Pulse distortion Pulse height estimation Fluorescent spectroscopy

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The Research on Clustering Ensembles Selection Algorithm based on Semi-supervised K-means Clustering 2

The Research on Clustering Ensembles Selection Algorithm bas...

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2nd International Seminar on Computer Science and engineering technology, SCSET 2020

作者： Wu, Xiaoxuan Guo, Changjian Hu, Tanglei School of Artificial Intelligence and Big Data Hefei University Hefei China Anhui Engineering Lab of Big Data Technology Application for Urban Infrastructure Hefei University Hefei China Institute of Business Intelligence Hefei University of Technology Hefei China

Selective clustering ensemble algorithm can eliminate the inferior quality clustering member's influence and can achieve a better clustering solution relative to the clustering ensemble algorithm. For high dimensional data clustering, in this paper, a novel selective ensemble algorithm based on semi-supervised K-means clustering is proposed. In this paper, through a large number of experiments to verify the validity of the proposed algorithm for dealing with high dimensional data clustering. The new algorithm can achieve statistically significant performance improvement over other clustering algorithms. © Published under licence by IOP Publishing Ltd.

关键词： K-means clustering

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xGCN: An Extreme Graph Convolutional Network for Large-scale Social Link Prediction 23

xGCN: An Extreme Graph Convolutional Network for Large-scale...

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2023 World Wide Web Conference, WWW 2023

作者： Song, Xiran Lian, Jianxun Huang, Hong Luo, Zihan Zhou, Wei Lin, Xue Wu, Mingqi Li, Chaozhuo Xie, Xing Jin, Hai Huazhong University of Science and Technology Wuhan China Microsoft Research Asia Beijing China Microsoft Gaming Redmond United States National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China

ISBN: (纸本)9781450394161

Graph neural networks (GNNs) have seen widespread usage across multiple real-world applications, yet in transductive learning, they still face challenges in accuracy, efficiency, and scalability, due to the extensive number of trainable parameters in the embedding table and the paradigm of stacking neighborhood aggregations. This paper presents a novel model called xGCN for large-scale network embedding, which is a practical solution for link predictions. xGCN addresses these issues by encoding graph-structure data in an extreme convolutional manner, and has the potential to push the performance of network embedding-based link predictions to a new record. Specifically, instead of assigning each node with a directly learnable embedding vector, xGCN regards node embeddings as static features. It uses a propagation operation to smooth node embeddings and relies on a Refinement neural Network (RefNet) to transform the coarse embeddings derived from the unsupervised propagation into new ones that optimize a training objective. The output of RefNet, which are well-refined embeddings, will replace the original node embeddings. This process is repeated iteratively until the model converges to a satisfying status. Experiments on three social network datasets with link prediction tasks show that xGCN not only achieves the best accuracy compared with a series of competitive baselines but also is highly efficient and scalable. © 2023 ACM.

关键词： Graph neural networks

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