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Research Square 2021年

作者： Zhang, Lingling Yu, Baoguo Tang, Chengkai Zhang, Yi Song, Houbing School of Marine Science and Technology Northwestern Polytechnical University Xi’an710072 China The 54th Research Institute of China Electronics Technology Group Corporation Shijiazhuang China School of Electronics and Information Northwestern Polytechnical University Xi’an710072 China Department of Electrical Computer Software and Systems Engineering Embry-Riddle Aeronautical University FL32114 United States

The growing scale of marine exploration requires high-resolution underwater localization, which necessitates cooperation among underwater network nodes, considering the channel complexity and power efficiency. In this paper, we proposed factor graph weight particles aided distributed underwater nodes cooperative positioning algorithm (WP-DUCP). It capitalized on the factor graph and sum-product algorithm to decompose the global optimization to the product of several local optimization functions. Combined with the Gaussian parameters to construct the weighted particles and to realize the belief transfer, it shows low complexity and high efficiency, suitable to the energy-restricted and communication distance-limited underwater networks. In terms of convergence, localization resolution, and computation complexity, we conducted the simulation and real-test with comparison to the existing co-localization methods. The results verified a higher resolution of the proposed method with no extra computation burden. © 2021, CC BY.

关键词： Global optimization

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Communication efficiency in federated learning: Achievements and challenges

arXiv

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arXiv 2021年

作者： Shahid, Osama Pouriyeh, Seyedamin Parizi, Reza M. Sheng, Quan Z. Srivastava, Gautam Zhao, Liang Department of Information Technology Kennesaw State University MariettaGA United States Department of Software Engineering and Game Development Kennesaw State University MariettaGA United States Department of Computing Macquarie University Sydney Australia Department of Math and Computer Science Brandon University Canada Research Centre for Interneural Computing China Medical University Taichung Taiwan

Federated Learning (FL) is known to perform Machine Learning tasks in a distributed manner. Over the years, this has become an emerging technology especially with various data protection and privacy policies being imposed FL allows performing machine learning tasks whilst adhering to these challenges. As with the emerging of any new technology, there are going to be challenges and benefits. A challenge that exists in FL is the communication costs, as FL takes place in a distributed environment where devices connected over the network have to constantly share their updates this can create a communication bottleneck. In this paper, we present a survey of the research that is performed to overcome the communication constraints in an FL setting. Copyright © 2021, The Authors. All rights reserved.

关键词： Machine learning

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Preface

Lecture Notes on Data Engineering and Communications Technol...

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Lecture Notes on Data engineering and Communications Technologies 2024年 222卷 v-viii页

作者： Faure, Emil Tryus, Yurii Vartiainen, Tero Danchenko, Olena Bondarenko, Maksym Bazilo, Constantine Zaspa, Grygoriy Department of Information Security and Computer Engineering Cherkasy State Technological University Cherkasy Ukraine Department of Computer Science and Systems Analysis Cherkasy State Technological University Cherkasy Ukraine Department of Computing Sciences University of Vaasa Vaasa Finland Department of Instrumentation Mechatronics and Computerized Technologies Cherkasy State Technological University Cherkasy Ukraine Department of Software of Automated System Cherkasy State Technological University Cherkasy Ukraine

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Matrix-Variate Restricted Boltzmann Machine Classification Model 11th

Matrix-Variate Restricted Boltzmann Machine Classification M...

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11th EAI International Conference on Simulation Tools and Techniques, SIMUTools 2019

作者： Li, Jinghua Tian, Pengyu Kong, Dehui Wang, Lichun Wang, Shaofan Yin, Baocai Beijing Key Laboratory of Multimedia and Intelligent Software Technology Faculty of Information Technology Beijing University of Technology Beijing100124 China College of Computer Science and Technology Faculty of Electronic Information and Electrical Engineering Dalian University of Technology Dalian116620 China

ISBN: (纸本)9783030322151

Recently, Restricted Boltzmann Machine (RBM) has demonstrated excellent capacity of modelling vector variable. A variant of RBM, Matrix-variate Restricted Boltzmann Machine (MVRBM), extends the ability of RBM and is able to model matrix-variate data directly without vectorized process. However, MVRBM is still an unsupervised generative model, and is usually used to feature extraction or initialization of deep neural network. When MVRBM is used to classify, additional classifiers are necessary. This paper proposes a Matrix-variate Restricted Boltzmann Machine Classification Model (ClassMVRBM) to classify 2D data directly. In the novel ClassMVRBM, classification constraint is introduced to MVRBM. On one hand, the features extracted by MVRBM are more discriminative, on the other hand, the proposed model can be directly used to classify. Experiments on some publicly available databases demonstrate that the classification performance of ClassMVRBM has been largely improved, resulting in higher image classification accuracy than conventional unsupervised RBM, its variants and Restricted Boltzmann Machine Classification Model (ClassRBM). © ICST Institute for computer sciences, Social Informatics and Telecommunications engineering 2019.

关键词： Deep neural networks

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Impossible meet-in-the-middle fault analysis on the LED lightweight cipher in VANETs

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science China(information sciences) 2018年第3期61卷 118-130页

作者： Wei LI Vincent RIJMEN Zhi TAO Qingju WANG Hua CHEN Yunwen LIU Chaoyun LI Ya LIU School of Computer Science and Technology Donghua University imec-COSIC KU Leuven Shanghai Key Laboratory of Integrate Administration Technologies for Information Security Department of Computer Science and Engineering Shanghai Jiao Tong University Department of Applied Mathematics and Computer Science Technical University of Denmark Trusted Computing and Information Assurance Laboratory Institute of SoftwareChinese Academy of Sciences College of Science National University of Defense Technology Department of Computer Science and Engineering University of Shanghai for Science and Technology

With the expansion of wireless technology, vehicular ad-hoc networks(VANETs) are emerging as a promising approach for realizing smart cities and addressing many serious traffic problems, such as road safety, convenience, and efficiency. To avoid any possible rancorous attacks, employing lightweight ciphers is most effective for implementing encryption/decryption, message authentication, and digital signatures for the security of the VANETs. Light encryption device(LED) is a lightweight block cipher with two basic keysize variants: LED-64 and LED-128. Since its inception, many fault analysis techniques have focused on provoking faults in the last four rounds to derive the 64-bit and 128-bit secret keys. It is vital to investigate whether injecting faults into a prior round enables breakage of the LED. This study presents a novel impossible meet-in-the-middle fault analysis on a prior round. A detailed analysis of the expected number of faults is used to uniquely determine the secret key. It is based on the propagation of truncated differentials and is surprisingly reminiscent of the computation of the complexity of a rectangle attack. It shows that the impossible meet-in-the-middle fault analysis could successfully break the LED by fault injections.

关键词： VANETs LED lightweight cipher impossible meet-in-the-middle fault analysis

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A sensor attack detection method in intelligent vehicle with multiple sensors 2

A sensor attack detection method in intelligent vehicle with...

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2nd IEEE International Conference on Industrial Internet Cloud, ICII 2019

作者： Wang, Rui Song, Houbing Jing, Yu Yang, Kang Guan, Yong Sun, Jiaguang College of Information Engineering Capital Normal University Beijing China Electrical Engineering and Computer Science Embry-Riddle Aeronautical University Florida United States School of Software Tsinghua University Beijing China Academy for Multidisciplinary Studies Capital Normal University Beijing China

ISBN: (纸本)9781728129778

With the rapid development of intelligent vehicles, more and more researchers are paying attention to their security issues. This paper considers a vehicle system with multiple sensors measuring the same physical variable. Some of these sensors may be maliciously attacked, resulting in the system being unable to work properly. This paper mainly addresses the detection and identification of malicious attacks on sensors in the presence of transient faults. Although there are some solutions at present, the existing methods can hardly capture attacks when a professional attacker manipulates the sensor output very slightly or infrequently. To address this problem, we design a resilient sensor attack detection algorithm. This algorithm adds an additional virtual sensor to the system and builds a fault model for each sensor, and using the pairwise inconsistencies between the sensors to detect the attack. To improve the detection rate in the presence of transient faults, we consider the system dynamics model and incorporates historical measurements into the detection algorithm. In addition, we propose a method to select transient fault model parameters to obtain a more accurate sensor fault model in a dynamic environment. Finally, this paper obtains real experimental data from the EV3 platform to verify the algorithm. Experimental results show that the proposed method is more robust and can detect and identify more attacks. Especially for stealth attacks which are extremely difficult to detect, the detection rate and recognition rate are increased by about 90%. © 2019 IEEE.

关键词： Intelligent vehicle highway systems

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Data-driven Surplus Material Prediction in Steel Coil Production

Data-driven Surplus Material Prediction in Steel Coil Produc...

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International Conference on Wireless and Optical Communications ( WOCC)

作者： Ziyan Zhao Xiaoyue Yong Shixin Liu Mengchu Zhou State Key Laboratory of Synthetical Automation for Process Industries College of Information Science and Engineering Northeastern University Shenyang China Shanghai Baosight Software Co. Ltd Shanghai China Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark USA

ISBN: (数字)9781728161242

ISBN: (纸本)9781728161259

A steel enterprise is currently trying to avoid the presence of surplus materials since they can greatly increase its operational cost. The complicated production process of steel products makes it difficult to find the causes of surplus materials. In this work, we propose a surplus material prediction problem and solve it based on statistical analysis and machine learning methods. In the concerned problem, we predict whether there are surplus materials under a given group of production parameters. The dataset used in this work is from a real-world three-month steel coil production process. First, data cleaning is conducted to standardize the industrial dataset. Then, the production parameters highly correlated with surplus material prediction results are selected by a series of feature selection methods. Finally, two prediction models based on extreme gradient boosting and logistic regression are presented according to the selected features. The experimental results reveal that the proposed prediction models have similar effectiveness. A visible regression function makes the logistic regression method more suitable for practical application.

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A Data Replication Placement Strategy for the Distributed Storage System in Cloud-Edge-Terminal Orchestrated Computing Environments

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IEEE Internet of Things Journal 2025年

作者： Chen, Peng Zheng, Mengke Du, Xin Bilal, Muhammad Lu, Zhihui Duan, Qiang Xu, Xiaolong Nanjing University of Information Science and Technology School of Software and Technology Nanjing210044 China Fudan University School of Computer Science Shanghai200433 China Zhejiang University School of Software Technology Zhejiang 310027 China Zhejiang University State Key Lab of Brain-Machine Intelligence Zhejiang 310027 China Lancaster University School of Computing and Communications LancasterLA1 4WA United Kingdom Shanghai Blockchain Engineering Research Center Shanghai200433 China Pennsylvania State University School of Information Sciences and Technology State CollegePA16802 United States

Cloud-edge-terminal orchestrated computing, as an expansion of cloud computing, has sunk resources to the edge nodes and terminal equipment, which can provide high-quality services for delay-sensitive applications and reduce the cost of network communication. Due to the high volume of data generated by Internet of Things (IoT) devices and the limited storage capacities of edge nodes, a significant number of terminal devices are now being considered for utilization as storage nodes. However, because of the heterogeneous storage capacity and reliability of these hardware devices and the different data requirements of user services, the performance and storage reliability of applications deployed in cloud-edge-terminal orchestrated computing environments have become urgent problems to be solved. Especially, for a distributed storage system in these environments, it is required to ensure reliable storage of the generated data and its’ replications. In this paper, we first implement a distributed storage system and construct a data replication placement model. Then, based on the constructed model, we formulate the data replication placement problem and design a data replication placement strategy called DRPS to solve it. The DRPS covers a ranks-based replication storage node selection algorithm and a greedy load balancing algorithm, which can select appropriate hardware devices for different data requirements of services and is implemented in the data storage system to store replications and balance loads. We design extensive experiments to verify the effectiveness of DRPS. The results indicate that the proposed strategy outperforms other state-of-the-art algorithms in terms of system delay reduction by 39.9%, an increase of 43.3% in the replication numbers, a 27.5% improvement in memory utilization, and a reduction of unreliability rate by 82.0%. © 2014 IEEE.

关键词： Cloud storage

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Weakly Supervised Fine-Grained Image Classification via Guassian Mixture Model Oriented Discriminative Learning

Weakly Supervised Fine-Grained Image Classification via Guas...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Zhihui Wang Shijie Wang Shuhui Yang Haojie Li Jianjun Li Zezhou Li International School of Information Science & Engineering Dalian University of Technology China Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province China School of Computer Science and Technology Hangzhou Dianzi University China Shanghai Pinlan Data Technology

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

Existing weakly supervised fine-grained image recognition (WFGIR) methods usually pick out the discriminative regions from the high-level feature maps directly. We discover that due to the operation of stacking local receptive filed, Convolutional Neural Network causes the discriminative region diffusion in high-level feature maps, which leads to inaccurate discriminative region localization. In this paper, we propose an end-to-end Discriminative Feature-oriented Gaussian Mixture Model (DF-GMM), to address the problem of discriminative region diffusion and find better fine-grained details. Specifically, DF-GMM consists of 1) a low-rank representation mechanism (LRM), which learns a set of low-rank discriminative bases by Gaussian Mixture Model (GMM) to accurately select discriminative details and filter more irrelevant information in high-level semantic feature maps, 2) a low-rank representation reorganization mechanism (LR2M) which resumes the space information of low-rank discriminative bases to reconstruct the low-rank feature maps. By recovering the low-rank discriminative bases into the same embedding space of highlevel feature maps, LR2M alleviates the discriminative region diffusion problem in high-level feature map and discriminative regions can be located more precisely on the new low-rank feature maps. Extensive experiments verify that DF-GMM yields the best performance under the same settings with the most competitive approaches, in CUBBird, Stanford-Cars datasets, and FGVC Aircraft.

关键词： Gaussian mixture model Correlation Feature extraction Kernel Image recognition Semantics

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Fault Diagnosis of Motor in Frequency Domain Signal by Stacked De-noising Auto-encoder

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computers, Materials & Continua 2018年第11期57卷 223-242页

作者： Xiaoping Zhao Jiaxin Wu Yonghong Zhang Yunqing Shi Lihua Wang School of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044China School of Information and Control Nanjing University of Information Science and TechnologyNanjing210044China Electrical and Computer Engineering New Jersey Institute of TechnologyNewark07102USA

With the rapid development of mechanical equipment,mechanical health monitoring field has entered the era of big *** learning has made a great achievement in the processing of large data of image and speech due to the powerful modeling capabilities,this also brings influence to the mechanical fault diagnosis ***,according to the characteristics of motor vibration signals(nonstationary and difficult to deal with)and mechanical‘big data’,combined with deep learning,a motor fault diagnosis method based on stacked de-noising auto-encoder is *** frequency domain signals obtained by the Fourier transform are used as input to the *** method can extract features adaptively and unsupervised,and get rid of the dependence of traditional machine learning methods on human extraction features.A supervised fine tuning of the model is then carried out by *** Asynchronous motor in Drivetrain Dynamics Simulator system was taken as the research object,the effectiveness of the proposed method was verified by a large number of data,and research on visualization of network output,the results shown that the SDAE method is more efficient and more intelligent.

关键词： Big data deep learning stacked de-noising auto-encoder fourier transform

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