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Achieving Physical Layer Security Against Location Unknown Eavesdroppers via Friendly Jammer

IEEE/CAA Journal of Automatica Sinica 2023年第10期10卷 2038-2040页

作者： Heng Zhang Jianwei Sun Xin Wang Chenglong Gong School of Computer Engineering Jiangsu Ocean UniversityLianyungang 222000China School of Electronic Engineering Jiangsu Ocean UniversityLianyungang 222000China Shandong Provincial Key Laboratory of Computer Networks Shandong Computer Science CenterQilu University of TechnologyJinan 250014China

Dear Editor, This letter is concerning friendly jamming unmanned aerial vehicles(UAVs) to assist in the safe communication of UAV base stations. Due to the openness of UAV wireless communication, it is vulnerable to attacks leading to information disclosure or blockage. To address this issue, friendly jamming UAVs can assist UAV base stations and improve the security of wireless communications.

关键词： communication letter base

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WT-FCTGN:A wavelet-enhanced fully connected time-gated neural network for complex noisy traffic flow modeling

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Chinese Physics B 2024年第7期33卷 652-664页

作者：廖志芳孙轲刘文龙余志武刘承光宋禹成 School of Computer Science and Engineering Central South UniversityChangsha 410083China National Engineering Research Center of High-speed Railway Construction Technology Changsha 410075China School of Civil Engineering Central South UniversityChangsha 410075China School of Traffic&Transportation Engineering Central South UniversityChangsha 410083China

Accurate forecasting of traffic flow provides a powerful traffic decision-making basis for an intelligent transportation system. However, the traffic data's complexity and fluctuation, as well as the noise produced during collecting information and summarizing original data of traffic flow, cause large errors in the traffic flow forecasting results. This article suggests a solution to the above mentioned issues and proposes a fully connected time-gated neural network based on wavelet reconstruction(WT-FCTGN). To eliminate the potential noise and strengthen the potential traffic trend in the data, we adopt the methods of wavelet reconstruction and periodic data introduction to preprocess the data. The model introduces fully connected time-series blocks to model all the information including time sequence information and fluctuation information in the flow of traffic, and establishes the time gate block to comprehend the periodic characteristics of the flow of traffic and predict its flow. The performance of the WT-FCTGN model is validated on the public Pe MS data set. The experimental results show that the WT-FCTGN model has higher accuracy, and its mean absolute error(MAE), mean absolute percentage error(MAPE) and root mean square error(RMSE) are obviously lower than those of the other algorithms. The robust experimental results prove that the WT-FCTGN model has good anti-noise ability.

关键词： traffic flow modeling time-series wavelet reconstruction

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Multi-Task Learning for Alzheimer's Disease Diagnosis and Mini-Mental State Examination Score Prediction

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Big Data Mining and Analytics 2024年第3期7卷 828-842页

作者： Jin Liu Xu Tian Hanhe Lin Hong-Dong Li Yi Pan Hunan Provincial Key Lab on Bioinformatics School of Computer Science and EngineeringCentral South UniversityChangsha 410083China Xinjiang Engineering Research Center of Big Data and Intelligent Software School of SoftwareXinjiang UniversityUrumqi 830091China School of Science and Engineering University of DundeeDundeeDD14HNUK Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China

Accurately diagnosing Alzheimer's disease is essential for improving elderly ***,accurate prediction of the mini-mental state examination score also can measure cognition impairment and track the progression of Alzheimer's ***,most of the existing methods perform Alzheimer's disease diagnosis and mini-mental state examination score prediction separately and ignore the relation between these two *** address this challenging problem,we propose a novel multi-task learning method,which uses feature interaction to explore the relationship between Alzheimer's disease diagnosis and minimental state examination score *** our proposed method,features from each task branch are firstly decoupled into candidate and non-candidate parts for ***,we propose feature sharing module to obtain shared features from candidate features and return shared features to task branches,which can promote the learning of each *** validate the effectiveness of our proposed method on multiple *** Alzheimer's disease neuroimaging initiative 1 dataset,the accuracy in diagnosis task and the root mean squared error in prediction task of our proposed method is 87.86%and 2.5,*** results show that our proposed method outperforms most state-of-the-art *** proposed method enables accurate Alzheimer's disease diagnosis and mini-mental state examination score ***,it can be used as a reference for the clinical diagnosis of Alzheimer's disease,and can also help doctors and patients track disease progression in a timely manner.

关键词： multi-task learning Alzheimer's disease diagnosis mini-mental state examination score prediction

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Precise control of page cache for containers

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Frontiers of computer science 2024年第2期18卷 17-30页

作者： Kun WANG Song WU Shengbang LI Zhuo HUANG Hao FAN Chen YU Hai JIN National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabCluster and Grid Computing Lab School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China

Container-based virtualization isbecoming increasingly popular in cloud computing due to its efficiency and *** isolation is a fundamental property of *** works have indicated weak resource isolation could cause significant performance degradation for containerized applications and enhanced resource ***,current studies have almost not discussed the isolation problems of page cache which is a key resource for *** leverage memory cgroup to control page cache ***,existing policy introduces two major problems in a container-based ***,containers can utilize more memory than limited by their cgroup,effectively breaking memory ***,the Os kernel has to evict page cache to make space for newly-arrived memory requests,slowing down containerized *** paper performs an empirical study of these problems and demonstrates the performance impacts on containerized *** we propose pCache(precise control of page cache)to address the problems by dividing page cache into private and shared and controlling both kinds of page cache separately and *** do so,pCache leverages two new technologies:fair account(f-account)and evict on demand(EoD).F-account splits the shared page cache charging based on per-container share to prevent containers from using memory for free,enhancing memory *** EoD reduces unnecessary page cache evictions to avoid the performance *** evaluation results demonstrate that our system can effectively enhance memory isolation for containers and achieve substantial performance improvement over the original page cache management policy.

关键词： page cache memory cgroup container isolation cloud computing

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Zero Trust Networks: Evolution and Application from Concept to Practice

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computers, Materials & Continua 2025年第2期82卷 1593-1613页

作者： Yongjun Ren Zhiming Wang Pradip Kumar Sharma Fayez Alqahtani Amr Tolba Jin Wang School of Computer Science School of Cyber Science and EngineeringEngineering Research Center of Digital ForensicsMinistry of EducationNanjing University of Information Science and TechnologyNanjing210044China Department of Computing Science University of AberdeenAberdeenAB243FXUK Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh12372Saudi Arabia Computer Science Department Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Sanya Institute of Hunan University of Science and Technology Sanya572024China

In the context of an increasingly severe cybersecurity landscape and the growing complexity of offensive and defen-sive techniques,Zero Trust Networks(ZTN)have emerged as a widely recognized *** Trust not only addresses the shortcomings of traditional perimeter security models but also consistently follows the fundamental principle of“never trust,always verify.”Initially proposed by John Cortez in 2010 and subsequently promoted by Google,the Zero Trust model has become a key approach to addressing the ever-growing security threats in complex network *** paper systematically compares the current mainstream cybersecurity models,thoroughly explores the advantages and limitations of the Zero Trust model,and provides an in-depth review of its components and key ***,it analyzes the latest research achievements in the application of Zero Trust technology across various fields,including network security,6G networks,the Internet of Things(IoT),and cloud computing,in the context of specific use *** paper also discusses the innovative contributions of the Zero Trust model in these fields,the challenges it faces,and proposes corresponding solutions and future research directions.

关键词： Zero trust cybersecurity software-defined perimeter micro-segmentation internet of things

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SEAformer: frequency domain decomposition transformer with signal enhanced for long-term wind power forecasting

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Neural Computing and Applications 2024年第33期36卷 20883-20906页

作者： Yan, Leiming Wu, Siqi Li, Shaopeng Chen, Xianyi School of Computer Science Nanjing University of Information Science and Technology Nanjing China Engineering Research Center of Digital Forensics Ministry of Education Nanjing University of Information Science and Technology Nanjing China

Accurate and reliable wind power forecasting is of great importance for stable grid operation and advanced dispatch planning. Due to the complex, non-stationary, and highly volatile nature of wind power data, Transformer-based methods find it difficult to capture long-term trend features and incur high computational costs. To address these challenging problems, we propose a frequency domain decomposition Transformer architecture with signal enhanced attention mechanism (SEAformer). Firstly, we devise a frequency domain-based trend decomposition structure that enables the Transformer to extract more effective long-term trend features, thereby further improving the long-term prediction accuracy of the model. Secondly, in response to the large fluctuations and instability of wind power data, we design an internal signal enhanced substructure combined with an attention mechanism in the Transformer, which filters out high-frequency noise signals and reduces the computational cost of the Transformer. We conduct extensive experiments on the benchmark dataset, the experimental analysis demonstrates that SEAformer outperforms the baseline methods (Transformer-based, MLP-based, and Traditional methods) in both multivariate and univariate prediction tasks, exhibiting the best prediction performance. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Wind forecasting

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ViF-SD2E: a robust weakly-supervised framework for neural decoding

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Neural Computing and Applications 2025年第9期37卷 6645-6661页

作者： Feng, Jingyi Luo, Yong Song, Shuang Hu, Han National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan430072 China School of Information and Electronics Beijing Institute of Technology Beijing100081 China

Neural decoding plays a vital role in the interaction between the brain and the outside world. Our task in this paper is to decode the movement track of a finger directly based on the neural data. Existing neural decoding solutions primarily perform some preprocessing operations on neural data before feeding them into existing models (such as LSTM) for decoding. However, these solutions either are prone to overfitting or cannot well exploit the spatial and temporal information. In our previous observations, there is a symmetrical phenomenon between the unsupervised decoded trajectory and the ground truth trajectory within the activity space. This precisely motivates us to propose (or derive) a robust weakly-supervised framework (or model structure), called ViF-SD2E, for neural decoding. In particular, it consists of a space-division (SD) module and an exploration–exploitation (2E) strategy, to effectively exploit both the spatial information of the outside world and the temporal information of neural activity, where the SD2E output is analogized with the weak 0/1 vision feedback (ViF) label for training. Extensive experiments demonstrate the effectiveness of our method, which can sometimes be comparable to supervised counterparts. Therefore, we redirect our attention to the information (hidden in data) ViF-SD2E conveys to us. In other words, we believe that the advantage of ViF-SD2E lies in the fact that its processing steps are objectively determined by the inherent attributes (i.e., symmetry) of the neural data, or rather, the model structure is fixed. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Space division multiple access

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Deep Learning Approaches for Detecting and Predicting Knee Osteoarthritis Severity 2

Deep Learning Approaches for Detecting and Predicting Knee O...

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2nd IEEE International Conference on Recent Advances in Information Technology for Sustainable Development, ICRAIS 2024

作者： Karegoudra, Shilpa Veeresha, R.K. Yendapalli, Vamsidhar Department of Computer Science and Engineering Udupi574110 India Department of Robotics and Artificial Intelligence Udupi574110 India GITAM University GITAM School of Technology Department of Computer Science and Engineering Karnataka Bengaluru India

ISBN: (纸本)9798350354461

Knee osteoarthritis stands as one of the most prevalent musculoskeletal ailments globally. Its initial signs include joint stiffness, discomfort, and limitations in functionality, marking it as a significant joint disorder. Older adults are more likely to have arthritis, and it normally gets worse with age. If there are no early, effective therapies used, it can lead to impairment in older people. Therapy must be started as soon as possible;otherwise, a full knee replacement may be necessary. The very subjective nature of the standard procedures creates a barrier to early illness progression detection. This study involves in-depth exploration and examination of the application of deep learning techniques for knee osteoarthritis detection and severity prediction. In this work EfficientNet-B6 was chosen for its superior performance in balancing accuracy, speed, and resource efficiency. Unlike models such as MobileNet or ResNet, X-ray images are used to train the model to predict the severity of knee osteoarthritis. The model classifies the data with an accuracy of 87%. A website for OA prediction is developed, the user needs to drag and drop an X-ray image in the dialog box given. The predicted output along with the severity will be shown within a few seconds. This website is very easy to use. © 2024 IEEE.

关键词： Knee prostheses

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Fast UAV path planning in urban environments based on three-step experience buffer sampling DDPG

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Digital Communications and Networks 2024年第4期10卷 813-826页

作者： Shasha Tian Yuanxiang Li Xiao Zhang Lu Zheng Linhui Cheng Wei She Wei Xie College of Computer Science South-Central Minzu UniversityWuhan430074China Hubei Provincial Engineering Research Center for Intelligent Management of Manufacturing Enterprises Wuhan430074China School of Computer Science Wuhan UniversityWuhan430072China School of Mathematics&Statistics South-Central Minzu UniversityWuhan430074China

The path planning of Unmanned Aerial Vehicle(UAV)is a critical issue in emergency communication and rescue operations,especially in adversarial urban *** to the continuity of the flying space,complex building obstacles,and the aircraft's high dynamics,traditional algorithms cannot find the optimal collision-free flying path between the UAV station and the ***,in this paper,we study the fast UAV path planning problem in a 3D urban environment from a source point to a target point and propose a Three-Step Experience Buffer Deep Deterministic Policy Gradient(TSEB-DDPG)*** first build the 3D model of a complex urban environment with buildings and project the 3D building surface into many 2D geometric *** transformation,we propose the Hierarchical Learning Particle Swarm Optimization(HL-PSO)to obtain the empirical ***,to ensure the accuracy of the obtained paths,the empirical path,the collision information and fast transition information are stored in the three experience buffers of the TSEB-DDPG algorithm as dynamic guidance *** sampling ratio of each buffer is dynamically adapted to the training ***,we designed a reward mechanism to improve the convergence speed of the DDPG algorithm for UAV path *** proposed TSEB-DDPG algorithm has also been compared to three widely used competitors experimentally,and the results show that the TSEB-DDPG algorithm can archive the fastest convergence speed and the highest *** also conduct experiments in real scenarios and compare the real path planning obtained by the HL-PSO algorithm,DDPG algorithm,and TSEB-DDPG *** results show that the TSEBDDPG algorithm can archive almost the best in terms of accuracy,the average time of actual path planning,and the success rate.

关键词： Unmanned aerial vehicle Path planning Deep deterministic policy gradient Three-step experience buffer Particle swarm optimization

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Streaming Histogram Publication over Weighted Sliding Windows Under Differential Privacy

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Tsinghua science and Technology 2024年第6期29卷 1674-1693页

作者： Xiujun Wang Lei Mo Xiao Zheng Zhe Dang School of Computer Science and Technology Anhui University of TechnologyMa’anshan 243032China Institute for Artificial Intelligence Hefei Comprehensive National Science CenterHefei 230088China Baosight Software(Anhui)Co.Ltd. Ma’anshan 243000China School of Electrical Engineering and Computer Science Washington State UniversityPullman 99164WAUSA

Continuously publishing histograms in data streams is crucial to many real-time applications,as it provides not only critical statistical information,but also reduces privacy leaking *** the importance of elements usually decreases over time in data streams,in this paper we model a data stream by a sequence of weighted sliding windows,and then study how to publish histograms over these windows *** existing literature can hardly solve this problem in a real-time way,because they need to buffer all elements in each sliding window,resulting in high computational overhead and prohibitive storage *** this paper,we overcome this drawback by proposing an online algorithm denoted by Efficient Streaming Histogram Publishing(ESHP)to continuously publish histograms over weighted sliding ***,our method first creates a novel sketching structure,called Approximate-Estimate Sketch(AESketch),to maintain the counting information of each histogram interval at every time instance;then,it creates histograms that satisfy the differential privacy requirement by smartly adding appropriate noise values into the sketching *** experimental results and rigorous theoretical analysis demonstrate that the ESHP method can offer equivalent data utility with significantly lower computational overhead and storage costs when compared to other existing methods.

关键词： differential privacy randomized algorithm streaming data publication weighted sliding window approximate statistics data usability computational complexity

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