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Electronic Communication and Artificial Intelligence (IWECAI), International Workshop on

作者： Lubin Yu YiXi Peng Xingjian Han Shuqi Liang Peiliang Yang Junbin Liu Shilie He China Electronic Product Reliability and Environmental Testing Research Institute Guangzhou China School of Software Engineering Sun Yat-Sen University Zhuhai China School of Computer Science and Technology Shandong University of Technology Zibo China

ISBN: (数字)9798350386943

ISBN: (纸本)9798350386950

In today's highly integrated and automated aviation field, the stable operation of avionics systems is the foundation for maintaining flight safety and efficient operations. Because of the shortcomings in research on fault classification of avionics modules, this study proposes an Inception-CBAM network model, aiming to improve the accuracy and efficiency of fault diagnosis. This model combines the powerful multi-scale feature extraction capability of the Inception network with the attention mechanism of the CBAM module, which greatly enhances the ability to identify complex fault patterns. Experimental verification shows that the model can effectively classify various faults, enhance the scientificity and systematicness of diagnosis, and contribute important technical support to the formulation of aviation safety maintenance and fault prevention strategies.

关键词： Fault diagnosis Systematics Attention mechanisms Prevention and mitigation Aerospace electronics Feature extraction Data models

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Boosting performance of virtualized desktop infrastructure with physical GPU and SPICE

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science China(Information sciences) 2020年第7期63卷 236-238页

作者： Shupan LI Limin XIAO Chungang SHI Liequan CHE Changyou ZHANG Yuanzhang LI State Key Laboratory of Software Development Environment Beihang University School of Computer Science and Engineering Beihang University Beijing Jinghang Computation and Communication Research Institute Institute of Software Chinese Academy of Sciences School of Computer Science and Technology Beijing Institute of Technology

Dear editor,The virtual desktop infrastructure (VDI) is widely used by enterprises, as it can be easily deployed,has a reduced management cost, improves resource utilization, and can be accessed at any location by the network. Currently, there is a demand for the VDIs to support applications, such as playing 3D games, playing HD videos, and using 3D design-

关键词： visualized desktop infrastructure SPICE physical GPU The virtual machine (VM) virtual machine monitor (VMM)

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Towards Generic and Controllable Attacks Against Object Detection

arXiv

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arXiv 2023年

作者： Li, Guopeng Xu, Yue Ding, Jian Xia, Gui-Song The School of Computer Science Wuhan University Wuhan430079 China The School of Cyber Science and Engineering Wuhan University Wuhan430079 China The State Key Lab LIESMARS Wuhan University Wuhan430079 China The National Engineering Research Center for Multimedia Software School of Computer Science Institute of Artificial Intelligence Wuhan University Wuhan430072 China

Existing adversarial attacks against Object Detectors (ODs) suffer from two inherent limitations. Firstly, ODs have complicated meta-structure designs, hence most advanced attacks for ODs concentrate on attacking specific detector-intrinsic structures, which makes it hard for them to work on other detectors and motivates us to design a generic attack against ODs. Secondly, most works against ODs make Adversarial Examples (AEs) by generalizing image-level attacks from classification to detection, which brings redundant computations and perturbations in semantically meaningless areas (e.g., backgrounds) and leads to an emergency for seeking controllable attacks for ODs. To this end, we propose a generic white-box attack, LGP (local perturbations with adaptively global attacks), to blind mainstream object detectors with controllable perturbations. For a detector-agnostic attack, LGP tracks high-quality proposals and optimizes three heterogeneous losses simultaneously. In this way, we can fool the crucial components of ODs with a part of their outputs without the limitations of specific structures. Regarding controllability, we establish an object-wise constraint that exploits foreground-background separation adaptively to induce the attachment of perturbations to foregrounds. Experimentally, the proposed LGP successfully attacked sixteen state-of-the-art object detectors on MS-COCO and DOTA datasets, with promising imperceptibility and transferability obtained. Codes are publicly released in https://***/liguopeng0923/***. Copyright © 2023, The Authors. All rights reserved.

关键词： Object detection

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Integrated Content and Network-Based Service Clustering and Web APIs Recommendation for Mashup Development

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IEEE Transactions on Services Computing 2020年第1期13卷 99-113页

作者： Cao, Buqing Liu, Xiaoqing Rahman, M.D. Mahfuzer Li, Bing Liu, Jianxun Tang, Mingdong School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan411201 China Department of Computer Science and Computer Engineering University of Arkansas FayettevilleAR72701 United States International School of Software Wuhan University Wuhan430072 China Department of Computer Science and Engineering Hunan University of Science and Technology Xiangtan411201 China

The rapid growth in the number and diversity of Web APIs, coupled with the myriad of functionally similar Web APIs, makes it difficult to find most suitable Web APIs for users to accelerate and accomplish Mashup development. Even if the existing methods show improvements in Web APIs recommendation, it is still challenging to recommend Web APIs with high accuracy and good diversity. In this paper, we propose an integrated content and network-based service clustering and Web APIs recommendation method for Mashup development. This method, first develop a two-level topic model by using the relationship among Mashup services to mine the latent useful and novel topics for better service clustering accuracy. Moreover, based on the clustering results of Mashups, it designs a collaborative filtering (CF) based Web APIs recommendation algorithm. This algorithm, exploits the implicit co-invocation relationship between Web APIs inferred from the historical invocation history between Mashups clusters and the corresponding Web APIs, to recommend diverse Web APIs for each Mashups clusters. The method is expected to not only find much better matched Mashups with high accuracy, but also diversify the recommendation result of Web APIs with full coverage. Finally, based on a real-world dataset from ProgrammableWeb, we conduct a comprehensive evaluation to measure the performance of our method. Compared with existing methods, experimental results show that our method significantly improves the accuracy and diversity of recommendation results in terms of precision, recall, purity, entropy, DCG and HMD. © 2008-2012 IEEE.

关键词： Collaborative filtering

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Security and privacy in 6G networks:New areas and new challenges

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Digital Communications and Networks 2020年第3期6卷 281-291页

作者： Minghao Wang Tianqing Zhu Tao Zhang Jun Zhang Shui Yu Wanlei Zhou School of Computer Science University of Technology SydneyUltimo2007Australia School of Software and Electrical Engineering Swinburne University of TechnologyAustralia

With the deployment of more and more 5g networks,the limitations of 5g networks have been found,which undoubtedly promotes the exploratory research of 6G networks as the next generation *** investigations include the fundamental security and privacy problems associated with 6G ***,in order to consolidate and solidify this foundational research as a basis for future investigations,we have prepared a survey on the status quo of 6G security and *** survey begins with a historical review of previous networking technologies and how they have informed the current trends in 6G *** then discuss four key aspects of 6G networks–real-time intelligent edge computing,distributed artificial intelligence,intelligent radio,and 3D intercoms–and some promising emerging technologies in each area,along with the relevant security and privacy *** survey concludes with a report on the potential use of *** of the references used in this paper along and further details of several points raised can be found at:***.

关键词： 6G Cyber security Privacy preservation Communication

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Research on Combined Prediction Model based on BP Neural Network Model and Multiple Regression Analysis Model

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Procedia computer science 2025年 262卷 785-794页

作者： Sheng Gou Cheng Gao Guozheng Zhao Siyu Chen Zhongxia Li Dani Wei School of Computer Science and Software Engineering Chengdu Neusoft University Chengdu China School of Information and Business Management Chengdu Neusoft University Chengdu China College of Health and Medical Technology Chengdu Neusoft University Chengdu China Asian-African Language Institute Chengdu International Studies University Chengdu China

This paper proposes a combined prediction model based on BP neural network and multiple regression analysis, aiming to improve the accuracy of prediction tasks in complex systems by integrating the advantages of the two models. The BP neural network is capable of extracting potentially complex patterns from a large amount of data by virtue of its powerful nonlinear fitting ability, while the multiple regression analysis on the other hand, performs well in dealing with linear relationships between multiple independent variables and dependent variable. In the process of model construction, the importance of different factors was first assessed by the random forest algorithm, and the key factors were screened out; then the correlation between each factor and the prediction target was analysed by using the Spearman test and the independent sample t-test. On this basis, a BP neural network is combined to perform nonlinear prediction, and a multiple linear regression model is used to model the relationship between multiple factors and target variables. The experimental results show that the combined model can effectively improve the prediction accuracy with good adaptability and robustness. However, the model suffers from high computational complexity when dealing with large-scale data and is sensitive to hyperparameter selection. Future research will further improve the predictive ability and applicability by optimising the data processing methods, improving architecture and introducing more external factors.

关键词： Combinatorial Prediction Models BP Neural Networks Multiple Regression Analysis Feature Importance Assessment Prediction Accuracy Enhancement

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Boost Decentralized Federated Learning in Vehicular Networks by Diversifying Data Sources

arXiv

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arXiv 2022年

作者： Su, Dongyuan Zhou, Yipeng Cui, Laizhong College of Computer Science and Software Engineering Shenzhen University Shenzhen China School of Computing Faculty of Science and Engineering Macquarie University Sydney Australia

Recently, federated learning (FL) has received intensive research because of its ability in preserving data privacy for scattered clients to collaboratively train machine learning models. Commonly, a parameter server (PS) is deployed for aggregating model parameters contributed by different clients. Decentralized federated learning (DFL) is upgraded from FL which allows clients to aggregate model parameters with their neighbours directly. DFL is particularly feasible for vehicular networks as vehicles communicate with each other in a vehicle-to-vehicle (V2V) manner. However, due to the restrictions of vehicle routes and communication distances, it is hard for individual vehicles to sufficiently exchange models with others. Data sources contributing to models on individual vehicles may not diversified enough resulting in poor model accuracy. To address this problem, we propose the DFL-DDS (DFL with diversified Data Sources) algorithm to diversify data sources in DFL. Specifically, each vehicle maintains a state vector to record the contribution weight of each data source to its model. The Kullback-Leibler (KL) divergence is adopted to measure the diversity of a state vector. To boost the convergence of DFL, a vehicle tunes the aggregation weight of each data source by minimizing the KL divergence of its state vector, and its effectiveness in diversifying data sources can be theoretically proved. Finally, the superiority of DFL-DDS is evaluated by extensive experiments (with MNIST and CIFAR-10 datasets) which demonstrate that DFL-DDS can accelerate the convergence of DFL and improve the model accuracy significantly compared with state-of-the-art baselines. Copyright © 2022, The Authors. All rights reserved.

关键词： Vehicles

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Boost Decentralized Federated Learning in Vehicular Networks by Diversifying Data Sources

Boost Decentralized Federated Learning in Vehicular Networks...

引用

International Conference on Network Protocols

作者： Dongyuan Su Yipeng Zhou Laizhong Cui College of Computer Science and Software Engineering Shenzhen University Shenzhen China Faculty of Science and Engineering School of Computing Macquarie University Sydney Australia

ISBN: (数字)9781665482349

ISBN: (纸本)9781665482356

Recently, federated learning (FL) has received intensive research because of its ability in preserving data privacy for scattered clients to collaboratively train machine learning models. Commonly, a parameter server (PS) is deployed for aggregating model parameters contributed by different clients. Decentralized federated learning (DFL) is upgraded from FL which allows clients to aggregate model parameters with their neighbours directly. DFL is particularly feasible for vehicular networks as vehicles communicate with each other in a vehicle-to-vehicle (V2V) manner. However, due to the restrictions of vehicle routes and communication distances, it is hard for individual vehicles to sufficiently exchange models with others. Data sources contributing to models on individual vehicles may not diversified enough resulting in poor model accuracy. To address this problem, we propose the DFL-DDS (DFL with diversified Data Sources) algorithm to diversify data sources in DFL. Specifically, each vehicle maintains a state vector to record the contribution weight of each data source to its model. The Kullback–Leibler (KL) divergence is adopted to measure the diversity of a state vector. To boost the convergence of DFL, a vehicle tunes the aggregation weight of each data source by minimizing the KL divergence of its state vector, and its effectiveness in diversifying data sources can be theoretically proved. Finally, the superiority of DFL-DDS is evaluated by extensive experiments (with MNIST and CIFAR-10 datasets) which demonstrate that DFL-DDS can accelerate the convergence of DFL and improve the model accuracy significantly compared with state-of-the-art baselines.

关键词： Data privacy Protocols Federated learning Soft sensors Aggregates Vehicular ad hoc networks Data models

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WakeMint: Detecting Sleepminting Vulnerabilities in NFT Smart Contracts

WakeMint: Detecting Sleepminting Vulnerabilities in NFT Smar...

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IEEE International Conference on software Analysis, Evolution and Reengineering (SANER)

作者： Lei Xiao Shuo Yang Wen Chen Zibin Zheng School of Computer Science Sun Yat-sen University Guangzhou China School of Software Engineering Sun Yat-sen University Zhuhai China Energy Development Research Institute China Southern Power Grid Company Limited Guangzhou China

ISBN: (数字)9798331535100

ISBN: (纸本)9798331535117

The non-fungible tokens (NFTs) market has evolved over the past decade, with NFTs serving as unique digital iden-tifiers on a blockchain that certify ownership and authenticity. The trading attributes of NFTs have drawn many users and investors. However, their high value also attracts attackers who exploit vulnerabilities in NFT smart contracts for illegal profits, thereby harming the NFT ecosystem. One notable vulnerability in NFT smart contracts is sleep minting, which allows attackers to illegally transfer others' tokens. Although some research has been conducted on sleepminting, these studies are basically qualitative analyses or based on historical transaction data. There is a lack of understanding from the contract code perspective, which is crucial for identifying such issues and preventing attacks before they occur. To address this gap, in this paper, we categorize the sleep-minting issue and find four distinct types of sleepminting in NFT smart contracts. Each type is accompanied by a comprehensive definition and illustrative code examples to provide a clear understanding of how these vulnerabilities manifest within the contract code. Furthermore, to help detect the defined defects before the sleepminting problem occurrence, we propose a tool named WakeMint, which is built on a symbolic execution framework. WakeMint is designed to be compatible with both high and low versions of Solidity, ensuring broad applicability across various smart contracts. The tool also employs a pruning strategy to shorten the detection period. Additionally, WakeMint gathers some key information, such as the owner of an NFT and emissions of events related to the transfer of the NFT's ownership during symbolic execution. Then, it analyzes the features of the transfer function based on this information so that it can judge the existence of sleepminting. We ran WakeMint on 11,161 real-world NFT smart contracts and evaluated the results. We found 115 instances of sleep minting issues

关键词： Codes Source coding Smart contracts Ecosystems Transfer functions software Nonfungible tokens Security software measurement Research and development

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Cross-modal Search Method of Technology Video based on Adversarial Learning and Feature Fusion

Cross-modal Search Method of Technology Video based on Adver...

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IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS)

作者： Xiangbin Liu Junping Du Meiyu Liang Ang Li Beijing Key Laboratory of Intelligent Communication Software and Multimedia School of Computer Science (National Pilot Software Engineering School) Beijing University of Posts and Telecommunications Beijing

ISBN: (纸本)9781665456579

Technology videos contain rich multi-modal information. In cross-modal information search, the data features of different modalities cannot be compared directly, so the semantic gap between different modalities is a key problem that needs to be solved. To address the above problems, this paper proposes a novel Feature Fusion based Adversarial Cross-modal Retrieval method (FFACR) to achieve text-to-video matching, ranking and searching. The proposed method uses the framework of adversarial learning to construct a video multimodal feature fusion network and a feature mapping network as generator, a modality discrimination network as discriminator. Generator and discriminator are trained alternately based on adversarial learning, so that the data obtained by the feature mapping network is semantically consistent with the original data and the modal features are eliminated. Experimental results demonstrate that the proposed method performs better in text-to-video search on the self-built datasets of technology videos.

关键词： Cloud computing Semantics Search problems Adversarial machine learning Generators Intelligent systems Videos

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