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3rd International Conference on Machine Learning for Cyber security, ML4CS 2020

作者： Cheng, Xianfu Yao, Yanqing Liu, Ao State Key Laboratory of Software Development Environment Beihang University Beijing100191 China Key Laboratory of Aerospace Network Security Ministry of Industry and Information Technology School of Cyberspace Science and Technology Beihang University Beijing100191 China Beijing Key Laboratory of Network Technology Beihang University Beijing100191 China

ISBN: (纸本)9783030622220

Deep learning techniques based on neural network have made significant achievements in various fields of Artificial Intelligence. However, model training requires large-scale datasets, these datasets are crowd-sourced and model parameters will contain the encoding of private information, resulting in the risk of privacy leakage. With the trend towards sharing pre-trained models, the risk of stealing training datasets through member inference attack and model inversion attack is further heightened. To tackle this problem, we propose an improved Differential Privacy Stochastic Gradient Descent algorithm, using simulated annealing algorithm and denoising mechanism to optimize the allocation method of privacy loss and improve model accuracy. We also analyze privacy cost under random shuffle data batch processing methods in detail within the framework of Subsampled Rényi Differential Privacy. Compared with existing methods, our experiments show that we can train deep neural networks with non-convex objective function more efficiently with moderate privacy budgets. © 2020, Springer Nature Switzerland AG.

关键词： Simulated annealing

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International Conference on Computer Supported Cooperative Work in Design

作者： Wei Ding Luyao Wang Wei Zhang Myung Jin Lee Ling Wang Kwang Woo Nam Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Industry-University Cooperstion Foundation Chungbuk National University Cheongju Korea School of Computer Science Northeast Electric Power University Jilin China School of Computer Science and Engineering Kunsan National University Gunsan Korea

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

In this paper, we propose a new UAV video similarity measurement algorithm to cluster and analyze UAV videos by measuring the similarity between UAV videos and train them in sub-groups, which in turn improves the efficiency of UAV video data mining. Our preliminary work introduces the maximum common view subsequence (LCVS) to compute the similarity between UAV videos, but LCVS needs to frequently compute the common visible region of video frames generating extremely high time cost. In order to solve the problem of high computational cost of LCVS, we propose a similarity algorithm based on minimum tilted outer rectangle clustering (MTORC) for UAV videos. The MTORC algorithm adopts minimum tilted outer rectangle to cluster the field of view of the marked points in each frame, which reduces the time complexity of calculating the common visible region. By comparing experiments with LCSS and LCVS, the experimental results prove that MTORC is better than the present previous research in terms of algorithm accuracy and time cost.

关键词： Training Visualization Costs Accuracy Clustering algorithms Autonomous aerial vehicles Time measurement

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An intelligent file transfer optimization for poor network conditions 6th

An intelligent file transfer optimization for poor network c...

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6th International Symposium on security and Privacy in Social Networks and Big data, SocialSec 2020

作者： Yan, Ming Zhang, Bo Zhao, Gang Kuang, Xiaohui Liu, Lu Zhang, Ruyun School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China Institute of Artificial Intelligence and Blockchain Guangzhou University Guangzhou China Information Center of Ministry of Human Resources and Social Security Beijing100090 China National Key Laboratory of Science and Technology on Information System Security Beijing100091 China Zhejiang Lab HangzhouZhejiang Province China

ISBN: (纸本)9789811590306

File transfer is based on the reliable TCP protocol. However, when the network is of poor quality, TCP-based transmission will still perform less effectively due to some reasons. Existing approaches mainly optimize file transmission by modifying network strategies or optimize transmission mechanism. But when network conditions cannot be selected or modified, a suitable parameter adjustment method would be needed. The experiments in this paper firstly perform TCP-based file transmission tests on a poor network condition and find two problems. Then, multiple tests were performed on multiple platforms to detect the conditions under which these problems occurred. Next, to address the issue and improve the transmission performance, we propose an intelligent optimization scheme. By adjusting the transmission parameters and adding policies, the scheme equips the intermediate parameters with intelligent self-adaptation capabilities. We also test and evaluate the performance of the intelligent scheme. The result shows that the file transfer under the new scheme not only basically avoids the target problems, but also reduces the practical upload time under poor network condition from the perspective of decreasing the number of retransmissions and reducing the failure rate. © Springer Nature Singapore Pte Ltd 2020.

关键词： Transmission control protocol

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Scattering modulation of sound waves by underwater oscillating cylinder and surrounding perturbed flow

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Ocean Engineering 2025年 335卷

作者： Chenxuan Jing Shengguo Shi Xuezhi Li Song Li Jiangyi Zhang Desen Yang National Key Laboratory of Underwater Acoustic Technology Harbin Engineering University Harbin 150001 China Key Laboratory of Marine Information Acquisition and Security Ministry of Industry and Information Technology Harbin 150001 China College of Underwater Acoustic Engineering Harbin Engineering University Harbin 150001 China

Numerical and experimental investigations are conducted to analyze the modulation of sound wave scattering caused by the underwater oscillating cylinder and its surrounding perturbed flow. A numerical analysis method is developed, comprising four steps: (1) simulating the perturbed flow field generated by oscillating cylinder; (2) mapping flow data to the acoustic domain via spatial interpolation; (3) simulating the scattered sound field at various times under the quasi-static assumption by solving the convected wave equation; (4) performing temporal interpolation on the time-discretized sound field to analyze time-frequency characteristics. Additionally, the theoretical analysis elucidates the modulation mechanism of acoustic scattering. The theoretical analysis and numerical results demonstrate that the acoustic scattering by the oscillating cylinder and its surrounding perturbed flow induces a modulation effect, with modulation sideband harmonics appearing on both sides of the incident acoustic wave frequency. The modulated sidebands contain the target's oscillation frequency, which is significant for underwater target identification. Experiments conducted in an anechoic water tank validated the scattering modulation effect and the numerical prediction.

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Tailor: Inter-Feature Distinction Filter Fusion Pruningtailor: Inter-Feature Distinction Filter Fusion Pruning

SSRN

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SSRN 2023年

作者： Han, Xuming Chu, Yali Wang, Ke Wang, Limin Yue, Lin Ding, Weiping Gu, Tianlong Weng, Jian College of Information and Science Jinan University Guangzhou510632 China School of Mathematics and Statistics Changchun University of Technology Jilin130000 China Trusted AI Engineering Research Center Ministry of Education Jinan University Guangzhou510632 China School of Artificial Intelligence and Digital Economy Industry Guangdong University of Finance and Economics Guangzhou510320 China University of Newcastle Australia School of Information and Science Nantong University Nantong China Engineering Research Center of Trustworthy AI Ministry of Education Jinan University China Guangdong Key Laboratory of Data Security and Privacy Preserving Jinan University Guangzhou510632 China

Filter pruning is an effective compression method for slimming deep convolutional neural networks by removing the partial structure of the network without sacrificing performance. The pruning criteria directly affect the performance of the network model after compression. Currently, pruning criteria mainly measure the norm of a filter weight matrix (L1 norm and L2 norm)and the matrix rank of a feature map. These criteria are based on the amount of filtered information. We found that inter-feature distinction among filters is also a significant factor in directing filter pruning, in contrast to previous methods. We propose a pruning method that fuses distinction between the output feature maps of filters, namely inTer-feAture distinctIon fiLter fusiOn pRuning (TAILOR). The TAILOR method was implemented by randomly selecting multiple filter combinations in the convolutional layer through the pruning rate and calculating the output feature maps of the next convolutional layer of these combinations. An intelligent distinction optimization scheme was proposed. The approximate optimal solution yields a filter combination, which then supplants the original convolutional layer to facilitate filter pruning. The results indicate that the inter-feature distinctions among filters significantly affect filter pruning. In addition, TAILOR outperforms state-of-the-art filter-pruning methods in terms of model prediction accuracy, floating-point operations, and parameter scale. For example, with VGG-16, we achieve a 70.93% FLOPs reduction by removing 90.05% of the parameters, with a loss of 0.44% in accuracy on CIFAR-10. © 2023, The Authors. All rights reserved.

关键词： Convolution

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Statistical analysis of time-varying channel for underwater acoustic communication and network

Statistical analysis of time-varying channel for underwater ...

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Frontiers of information technology (FIT)

作者： Honglu Yan Tianlong Ma Chenyu Pan Yanan Liu Songzuo Liu Acoustic Science and Technology Laboratory Harbin Engineering University Harbin China Ministry of Industry and Information Technology Key Laboratory of Marine Information Acquisition and Security (Harbin Engineering University) Harbin China College of Underwater Acoustic Engineering Harbin Engineering University Harbin China

ISBN: (纸本)9781665408318

The spatial-temporal random variation characteristics of underwater acoustic channel make the difference among the underwater acoustic communication network link channels, which make network performance difficult to predict. In order to better understand the fluctuation and difference of network link channel, we analyze the measured channel data of five links in the Qiandao Lake underwater acoustic communication network experiment. This paper first estimates impulse response, spread function, power delay profile and Doppler power spectrum of the time-varying channel in a short detection time, and compares the time-frequency energy distribution characteristics of each link channel. Then, we statistically analyze the discreteness of the signal to noise ratio, multipath spread and Doppler spread parameter distributions for a total of145 channels over a long observation period. The results show that energy distribution structure and fading fluctuation scale of each link channel in underwater acoustic communication network are obviously different.

关键词： Fading channels Time-frequency analysis Fluctuations Time-varying channels Acoustic measurements Time measurement Communication networks

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Secure and Privacy-preserving data-sharing Framework based on blockchain technology for Al-Najaf/Iraq Oil Refinery 19

Secure and Privacy-preserving Data-sharing Framework based o...

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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and Computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable Computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy Computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Umran, Samir M. Lu, SongFeng Abduljabbar, Zaid Ameen Lu, Zhi Feng, Bingyan Zheng, Lu Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan430074 China Iraqi Cement State Company Ministry of Industry and Minerals Baghdad10011 Iraq Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen518057 China University of Basrah College of Education for Pure Sciences Iraq Al-Kunooze University College Technical Computer Engineering Department Basrah Iraq Huazhong University of Science and Technology School of Cyber Science and Engineering Wuhan430074 China Industrial Internet Research Institute Wuhan Huazhong Numerical Control Co. Ltd Wuhan430074 China South-Central University for Nationalities College of Computer Science Wuhan430074 China

ISBN: (纸本)9798350346558

The Industrial Internet of Things or industry 4.0 efficiently enhances the manufacturing process in terms of raising productivity, system performance, cost reduction, and building large-scale systems. It enables the connection of numerous heterogeneous devices and sensors into the internet network through the utilization of revolutionary techniques that transfer the manufacturing paradigm to intelligent processes. The traditional industrial internet of things adopts centralized architectures and complex encryption algorithms that suffer from several cyber-security threats and lead to data leakage, privacy disclosure, and high computational and communicational costs. In our work, we proposed a decentralized, efficient, low-power, scalable, secure, privacy-preserving, and trusting architecture based on a private blockchain network/smart contract and interplanetary file system technology for the Al-NajafyIraq oil refinery factory. That enables autonomous working and provides a high level of security authentication and privacy preservation, P2P communication, remote access, immutability and information backtracking. Traditional blockchain technology has storage limitation issues, most recent works adopted external servers or the cloud for storage purposes. Our proposed scheme addresses the limitations of blockchain data storage by adopting an interplanetary file system (IPFS) network;provides an efficient data encryption mechanism at the perceptual layer by adopting a lightweight encryption algorithm, high-performance, and ultra-low-power consumption ARM Cortex-M microcontroller. In addition, our architecture succeeds in merging blockchain technology, IoTs, and an IPFS with the oil refinery as an industrial application area. Moreover, it provides an efficient framework that resists common cyber-security attacks and realizes cyber-security requirements represented by data availability, integrity, and confidentiality. © 2022 IEEE.

关键词： Digital storage

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Automated classification of humpback whale calls in four regions using convolutional neural networks and multi scale deep feature aggregation (MSDFA)

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Measurement 2025年 255卷

作者： Ali Raza Sun Zongxin Gang Qiao Mubeen Javed Muhammad Bilal Habib Hussain Zuberi Muhammad Mohsin Acoustic Science and Technology Laboratory Harbin Engineering University Harbin 150001 China Key Laboratory of Marine Information Acquisition and Security Harbin Engineering University Ministry of Industry and Information Technology Harbin 150001 China College of Underwater Acoustic Engineering Harbin Engineering University Harbin 150001 China

Classifying humpback whale calls plays a vital role in understanding the ecological behavior and social communication patterns of marine mammals in diverse underwater environments. This study explores the impact of regional acoustic variations in humpback whale calls on deep learning-based classification performance. Recordings were collected from four distinct regions: Tortola (British Virgin Islands), St. David’s Island, Argus Island, and Castle Harbour (Bermuda). These vocal differences, often referred to as dialects, reflect regional adaptations in communication patterns and are essential to this classification task. A novel Multi-scale Deep Feature Aggregation (MSDFA) model was developed and evaluated alongside CNN, Residual-Net, and Dense-Net architectures. Preprocessing involved bandpass filtering and spectral subtraction-based denoising, followed by MFCC-based feature extraction. The MSDFA model achieved a classification accuracy of 95 %. To ensure model robustness and reliability, K-fold cross-validation, comprehensive error analysis, and advanced data augmentation techniques are employed. The results demonstrate how regional vocal characteristics influence classification accuracy and provide insights into the geographic diversity of humpback whale communication.

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Target detection algorithm in side-scan sonar image based on pixel importance value measurement

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Chinese Journal of Acoustics 2020年第3期39卷 329-340页

作者： BIAN Hongyu CHEN Yiming ZHANG Zhigang WEI Mingzhe Acoustic Science and Technology Laboratory Harbin Engineering UniversityHarbin 150001 Peng Cheng Laboratory Shenzhen 518000 Key Laboratory of Marine Information Acquisition and Security(Harbin Engineering University) Ministry of Industry and Information TechnologyHarbin 150001 College of Underwater Acoustic Engineering Harbin Engineering UniversityHarbin 150001 Intelligence and Information Engineering College Tangshan UniversityTangshan 063000

Side-scan sonar detection application always combines with unstable results.A two-stage novel pixel importance value measurement algorithm is proposed to stabilize the detection ability and false alarm probability *** first stage of the algorithm,a new feature defined as pixel importance value(PIV)is proposed in terms of distances between the target pixel and each other *** measurement of current pixel is defined as the weighted sum of all remaining segmented *** weighted part refers to Gaussian kernel,which means closer pixels gets higher ***,targets with higher PIV can be *** the second stage,we use convolutional neural network as classifier to eliminate the dot-like false *** experiment data is obtained by autonomous underwater vehicle,where we demonstrate superior performance of our algorithm over the state-of-the-art sonar detection algorithms in terms of 90.39% recall rate and 2.39% false alarm probability.

关键词： algorithm false value

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DE-RSTC: A rational secure two-party computation protocol based on direction entropy

arXiv

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arXiv 2021年

作者： Chen, Yuling Ma, Juan Wang, Xianmin Zhang, Xinyu Zhou, Huiyu State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China Guangxi Key Laboratory of Cryptography and Information Security Guilin University Of Electronic Technology Guilin541000 China Institute of Artificial Intelligence and Blockchain Guangzhou University Guangzhou510000 China University of Leicester LeicesterLe1 7RH United Kingdom

Rational secure multi-party computation (RSMC) means two or more rational parties to complete a function on private inputs. In the process, the rational parties choose strategies to maximize utility, which will cause players to maliciously execute the protocol and undermine the fairness and correctness of the protocol. To solve this problem, we leverage game theory to propose the direction entropy-based solution. First, we utilize the direction vector of the direction entropy to examine the player's strategy uncertainty and quantify its strategy from different dimensions. Specifically, when parties choose a cooperation strategy, the direction vector is positive, and the information transmitted is positive, conversely, it is negative information. Then, we provide mutual information to construct new utility functions for the players. What's more, we measure the mutual information of players to appraise their strategies. Finally, we prove in detail the protocol we gave, and the result show that the fairness problem in rational secure two-party computation. We also prove that the proposed protocol reaches the Nash equilibrium. Furthermore, we conduct experiments using mutual information to construct utility, and the results show that the utility obtained when the player is honest will be higher. Copyright © 2021, The Authors. All rights reserved.

关键词： Entropy

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