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arXiv 2022年

作者： Xie, Kan Zhang, Zhe Li, Bo Kang, Jiawen Niyato, Dusit Xie, Shengli Wu, Yi The School of Automation Guangdong University of Technology Guangzhou510006 China The Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangzhou510006 China The Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China The 111 Center for Intelligent Batch Manufacturing Based on IoT Technology Guangzhou510006 China The Guangdong Key Laboratory of IoT Information Technology Guangzhou510006 China The School of Data Science and Technology Heilongjiang University Harbin150080 China The Institute for Cryptology & Network Security Heilongjiang University Harbin150080 China School of Computer Science and Engineering Nanyang Technological University Singapore

With the gradual popularization of self-driving, it is becoming increasingly important for vehicles to smartly make the right driving decisions and autonomously obey traffic rules by correctly recognizing traffic signs. However, for machine learning-based traffic sign recognition on the Internet of Vehicles (IoV), a large amount of traffic sign data from distributed vehicles is needed to be gathered in a centralized server for model training, which brings serious privacy leakage risk because of traffic sign data containing lots of location privacy information. To address this issue, we first exploit privacy-preserving federated learning to perform collaborative training for accurate recognition models without sharing raw traffic sign data. Nevertheless, due to the limited computing and energy resources of most devices, it is hard for vehicles to continuously undertake complex artificial intelligence tasks. Therefore, we introduce powerful Spike Neural networks (SNNs) into traffic sign recognition for energy-efficient and fast model training, which is the next generation of neural networks and is practical and well-fitted to IoV scenarios. Furthermore, we design a novel encoding scheme for SNNs based on neuron receptive fields to extract information from the pixel and spatial dimensions of traffic signs to achieve high-accuracy training. Numerical results indicate that the proposed federated SNN outperforms traditional federated convolutional neural networks in terms of accuracy, noise immunity, and energy efficiency as well. Copyright © 2022, The Authors. All rights reserved.

关键词： Traffic signs

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dptCry: an approach to decrypting ransomware WannaCry based on API hooking

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CCF Transactions on networking 2019年第3-4期2卷 207-216页

作者： Guang Cheng Chunsheng Guo Yongning Tang Southeast University Key Lab of Computer Network and Information Integration (Southeast University) Ministry of Education Illinois State University

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Coverage and Rate Analysis for Distributed RISs-Assisted mmWave Communications

arXiv

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arXiv 2024年

作者： Xu, Yuan Huang, Chongwen Li, Wei Zhu, Yongxu Yang, Zhaohui He, Jiguang Yang, Jun Zhang, Zhaoyang Yuen, Chau Debbah, Mérouane College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China Hangzhou310027 China School of Electrical and Electronics Engineering Nanyang Technological University Singapore639798 Singapore National Communications Research Laboratory Southeast University Nanjing210096 China Technology Innovation Institute 9639 Masdar City Abu Dhabi United Arab Emirates State Key Laboratory of Mobile Network and Mobile Multimedia Technology Shenzhen518055 China Wireless Product R&D Institute ZTE Corporation Shenzhen518057 China Department of Electrical Engineering and Computer Science The KU 6G Center Khalifa University Abu Dhabi127788 United Arab Emirates CentraleSupelec University Paris-Saclay Gif-sur-Yvette91192 France

The millimeter wave (mmWave) has received considerable interest due to its expansive bandwidth and high frequency. However, a noteworthy challenge arises from its vulnerability to blockages, leading to reduced coverage and achievable rates. To address these limitations, a potential solution is to deploy distributed reconfigurable intelligent surfaces (RISs), which comprise many low-cost and passively reflected elements, and can facilitate the establishment of extra communication links. In this paper, we leverage stochastic geometry to investigate the ergodic coverage probability and the achievable rate in both distributed RISs-assisted single-cell and multi-cell mmWave wireless communication systems. Specifically, we first establish the system model considering the stochastically distributed blockages, RISs and users by the Poisson point process. Then we give the association criterion and derive the association probabilities, the distance distributions, and the conditional coverage probabilities for two cases of associations between base stations and users without or with RISs. Finally, we use Campbell’s theorem and the total probability theorem to obtain the closed-form expressions of the ergodic coverage probability and the achievable rate. Simulation results verify the effectiveness of our analysis method, and demonstrate that by deploying distributed RISs, the ergodic coverage probability is significantly improved by approximately 50%, and the achievable rate is increased by more than 1.5 times. Copyright © 2024, The Authors. All rights reserved.

关键词： Millimeter waves

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Secrecy Outage Probability Analysis Based on Cognitive Decode-and-Forward Relaying

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computers, Materials & Continua 2020年第3期62卷 1387-1395页

作者： Ruoyu Su Xiaojun Sun Fei Ding Dengyin Zhang Hongbo Zhu M.I.M.Wahab Jiangsu Key Laboratory of Wireless Communications Nanjing University of Posts and Tele-CommunicationsNanjing210003China Key Laboratory of Broadband Wireless Communication and Sensor Network Technology Ministry of EducationNanjing University of Posts and TelecommunicationsNanjing210003China School of Information Science and Engineering Southeast UniversityNanjing210018China Department of Electrical and Computer Engineering Faculty of Engineering and Applied ScienceMemorial University of NewfoundlandSt.John’sA1B 3X5Canada Department of Mechanical and Industrial Engineering Ryerson UniversityToronto M5B 2K3Canada

Wireless communications have to face to several different security issues in practice due to the nature of *** information theory is well known to provide efficient approaches to address security issues in wireless communications,which attracts much attention in both industry and academia in recent *** this paper,inspired by information theory,we study the outage probability of the opportunistic relay selection based on cognitive decode-and-forward relaying with the secrecy ***,the closed-form expression of the outage probability is ***,the asymptotic performance evaluation on the basis of the analytical results is *** simulation results show that the relay selection can reduce the outage probability in accordance with our theoretical analysis.

关键词： Secure communication average secrecy rate relay selection probability density function

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Neural topic modeling with bidirectional adversarial training

arXiv

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arXiv 2020年

作者： Wang, Rui Hu, Xuemeng Zhou, Deyu He, Yulan Xiong, Yuxuan Ye, Chenchen Xu, Haiyang School of Computer Science and Engineering Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University China Department of Computer Science University of Warwick United Kingdom AI Labs Didi Chuxing Co. Ltd. Beijing China

Recent years have witnessed a surge of interests of using neural topic models for automatic topic extraction from text, since they avoid the complicated mathematical derivations for model inference as in traditional topic models such as Latent Dirichlet Allocation (LDA). However, these models either typically assume improper prior (e.g. Gaussian or Logistic Normal) over latent topic space or could not infer topic distribution for a given document. To address these limitations, we propose a neural topic modeling approach, called Bidirectional Adversarial Topic (BAT) model, which represents the first attempt of applying bidirectional adversarial training for neural topic modeling. The proposed BAT builds a two-way projection between the document-topic distribution and the document-word distribution. It uses a generator to capture the semantic patterns from texts and an encoder for topic inference. Furthermore, to incorporate word relatedness information, the Bidirectional Adversarial Topic model with Gaussian (Gaussian-BAT) is extended from BAT. To verify the effectiveness of BAT and Gaussian-BAT, three benchmark corpora are used in our experiments. The experimental results show that BAT and Gaussian-BAT obtain more coherent topics, outperforming several competitive baselines. Moreover, when performing text clustering based on the extracted topics, our models outperform all the baselines, with more significant improvements achieved by Gaussian-BAT where an increase of near 6% is observed in accuracy. Copyright © 2020, The Authors. All rights reserved.

关键词： Gaussian distribution

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Towards 6G wireless communication networks:vision, enabling technologies, and new paradigm shifts

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Science China(information Sciences) 2021年第1期64卷 5-78页

作者： Xiaohu YOU Cheng-Xiang WANG Jie HUANG Xiqi GAO Zaichen ZHANG Mao WANG Yongming HUANG Chuan ZHANG Yanxiang JIANG Jiaheng WANG Min ZHU Bin SHENG Dongming WANG Zhiwen PAN Pengcheng ZHU Yang YANG Zening LIU Ping ZHANG Xiaofeng TAO Shaoqian LI Zhi CHEN Xinying MA Chih-Lin I Shuangfeng HAN Ke LI Chengkang PAN Zhimin ZHENG Lajos HANZO Xuemin (Sherman) SHEN Yingjie Jay GUO Zhiguo DING Harald HAAS Wen TONG Peiying ZHU Ganghua YANG Jun WANG Erik G.LARSSON Hien Quoc NGO Wei HONG Haiming WANG Debin HOU Jixin CHEN Zhe CHEN Zhangcheng HAO Geoffrey Ye LI Rahim TAFAZOLLI Yue GAO H.Vincent POOR Gerhard P.FETTWEIS Ying-Chang LIANG National Mobile Communications Research Laboratory School of Information Science and EngineeringSoutheast University Purple Mountain Laboratories Shanghai Institute of Fog Computing Technology (SHIFT) ShanghaiTech University Research Center for Network Communication Peng Cheng Laboratory State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications National Engineering Laboratory for Mobile Network Technologies Beijing University of Posts and Telecommunications National Key Laboratory of Science and Technology on Communications University of Electronic Science and Technology of China (UESTC) China Mobile Research Institute School of Electronics and Computer Science University of Southampton Department of Electrical and Computer Engineering University of Waterloo Global Big Data Technologies Centre (GBDTC) University of Technology Sydney School of Electrical and Electronic Engineering The University of Manchester LiFi Research and Development Centre Institute for Digital CommunicationsSchool of EngineeringThe University of Edinburgh Huawei Technologies Canada Co. Ltd. Huawei Technologies Department of Electrical Engineering (ISY) Link?ping University Institute of Electronics Communications & Information Technology (ECIT)Queen's University Belfast State Key Laboratory of Millimeter Waves School of Information Science and EngineeringSoutheast University School of Electrical and Computer Engineering Georgia Institute of Technology 5G Innovation Centre University of Surrey Princeton University Vodafone Chair Mobile Communications Systems Technische Universit?t Dresden Center for Intelligent Networking and Communications (CINC) University of Electronic Science and Technology of China (UESTC)

The fifth generation(5G) wireless communication networks are being deployed worldwide from 2020 and more capabilities are in the process of being standardized, such as mass connectivity, ultra-reliability,and guaranteed low latency. However, 5G will not meet all requirements of the future in 2030 and beyond, and sixth generation(6G) wireless communication networks are expected to provide global coverage, enhanced spectral/energy/cost efficiency, better intelligence level and security, etc. To meet these requirements, 6G networks will rely on new enabling technologies, i.e., air interface and transmission technologies and novel network architecture, such as waveform design, multiple access, channel coding schemes, multi-antenna technologies, network slicing, cell-free architecture, and cloud/fog/edge computing. Our vision on 6G is that it will have four new paradigm shifts. First, to satisfy the requirement of global coverage, 6G will not be limited to terrestrial communication networks, which will need to be complemented with non-terrestrial networks such as satellite and unmanned aerial vehicle(UAV) communication networks, thus achieving a space-airground-sea integrated communication network. Second, all spectra will be fully explored to further increase data rates and connection density, including the sub-6GHz, millimeter wave(mmWave), terahertz(THz),and optical frequency bands. Third, facing the big datasets generated by the use of extremely heterogeneous networks, diverse communication scenarios, large numbers of antennas, wide bandwidths, and new service requirements, 6G networks will enable a new range of smart applications with the aid of artificial intelligence(AI) and big data technologies. Fourth, network security will have to be strengthened when developing 6G networks. This article provides a comprehensive survey of recent advances and future trends in these four aspects. Clearly, 6G with additional technical requirements beyond those of 5G will enable f

关键词： 6G vision network architecture air interface and transmission technologies space-air-ground-sea integrated network all spectra artificial intelligence network security

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A PSO Algorithm with Multi-Grouping and Two-Layer Structure

A PSO Algorithm with Multi-Grouping and Two-Layer Structure

引用

International Conference on Data Science and Business Analytics (ICDSBA)

作者： Tiaojun Zeng Chuanjian Wang Zhangliang Wei Shihezi University Key Laboratory of Computer Network and Information Integration (Southeast University) Ministry of Education

ISBN: (数字)9781728146447

ISBN: (纸本)9781728146454

A PSO (particle swarm optimization) algorithm with multiple groups and two-layer structure(MTPSO) is proposed. First, the whole particle swarm is divided into several groups, each one is optimized. For enhance efficiency in the optimization process, each particle adopts a mixed algorithm of negative gradient. Secondly, according to the function value of each particle solution, the particle group is divided into two structures: the elite layer and the ordinary layer. In the algorithm initialization phase, the particles with the best value in each group are selected into the elite layer. The particles in the elite layer are relatively fixed and regularly selected (similar to the parliamentary mechanism in human society), that is, each elite layer as a whole. After several iterations of optimization calculations in the same way as the normal layer, the new elite layer is formed again according to the previou method. The optimal solution calculated by the elite layer is added as a global optimal solution to the position update formula of each particle. Similarly, the optimal solution for the common layer is also added to the update formula for each particle in the elite layer. It reflects the upper and lower particle's mutual penetration and mutual influence of. Simulation experiments show the proposed algorithm is able to avoid premature and powerful global optimization ability and fast convergence, PSO's computing efficiency.

关键词： Optimization Particle swarm optimization Clustering algorithms Convergence Gradient methods Quantization (signal) Nickel

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DRTNet: Dual-route transformer network for thyroid ultrasound segmentation based on Bbox-supervised learning

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Knowledge-Based Systems 2025年 324卷

作者： Hui Bi Chengjie Cai Jiawei Sun Shihao Ge Huazhong Shu Xinye Ni School of Computer Science and Artificial Intelligence Changzhou University Changzhou 213164 China The Affiliated Changzhou NO.2 People’s Hospital of Nanjing Medical University Changzhou 213003 China Key Laboratory of Computer Network and Information Integration Southeast University Nanjing 211096 China Jiangsu Province Engineering Research Center of Medical Physics Changzhou 213003 China Center of Medical Physics Nanjing Medical University Changzhou 213003 China Laboratory of Image Science and Technology Southeast University Nanjing 210096 China Centre de Recherche en Information Biomédicale Sino-Français Rennes F-35000 France

Background and Objective : Accurate nodule delineation plays a significant role in the intelligent diagnosis of thyroid disease. However, the labels accessing is difficult since it is time-consuming and laborious. To mitigate the over-dependence of the segmentation accuracy on the labels, we proposed a dual-route transformer network (DRTNet) based on Bbox-supervised learning that only requires the rough bounding rectangle as labels instead of a precise boundary for thyroid ultrasound segmentation. Methods : DRTNet incorporates double-branch foreground class activation mappings (CAMs) into Transformers to combine key areas. Meanwhile, double-branch architecture dynamically adjusts the feature distribution of nodules of different sizes in frequency channels and spatial dimensions, effectively addressing the localization of nodules of different sizes. Moreover, ultrasound prior background-aware pooling (UPBAP) is proposed in both branches to deal with the ambiguous boundary of thyroid nodules. Finally, adaptive uncertainty estimate multi-scale consistency (AUEMC) is proposed to help mitigate the risk of excessive over-fitting because of pseudo annotations, which further guarantees consistency among nodules with diverse resolutions. Results : Substantial improvement of segmentation accuracy is shown on the public thyroid dataset of TN3k and DDTI dataset with Dice similarity coefficient (DSC) of 84.94% and 83.98%, with 95% of the asymmetric Hausdorff distance (HD95) of 27.69 and 29.18, respectively. And our private dataset has a DSC of 84.39% and HD95 of 14.53. Conclusions : The proposed DRTNet used rectangular box labeled for thyroid ultrasound images based on Bbox-supervised learning. The experimental results show that the DRTNet is comparable to these fully supervised methods. Code is available at https://***/ccjcv/DRTNet .

关键词： Bbox-supervised segmentation DRTNet Thyroid nodule segmentation Transformer-based network Ultrasound image segmentation

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Binary relevance for multi-label learning： an overview

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Frontiers of computer Science 2018年第2期12卷 191-202页

作者： Min-Ling ZHANG Yu-Kun LI Xu-Ying LIU Xin GENG School of Computer Science and Engineering Southeast University Nanjing 210096 China Key Laboratory of Computer Network and Information Integration (Southeast University) Ministry of Education China Collaborative Innovation Center for Wireless Communications Technology Nanjing 211100 China

Multi-label learning deals with problems where each example is represented by a single instance while being associated with multiple class labels simultaneously. Binary relevance is arguably the most intuitive solution for learning from multi-label examples. It works by decomposing the multi-label learning task into a number of independent binary learning tasks （one per class label）. In view of its potential weakness in ignoring correlations between labels, many correlation-enabling extensions to binary relevance have been proposed in the past decade. In this paper, we aim to review the state of the art of binary relevance from three perspectives. First, basic settings for multi-label learning and binary relevance solutions are briefly summarized. Second, representative strategies to provide binary relevance with label correlation exploitation abilities are discussed. Third, some of our recent studies on binary relevance aimed at issues other than label correlation exploitation are introduced. As a conclusion, we provide suggestions on future research directions.

关键词： machine learning multi-label learning binary relevance label correlation class-imbalance relative labeling-importance

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Blockchain Assisted Federated Learning over Wireless Channels: Dynamic Resource Allocation and Client Scheduling

arXiv

引用

arXiv 2021年

作者： Deng, Xiumei Li, Jun Ma, Chuan Wei, Kang Shi, Long Ding, Ming Chen, Wen Poor, H. Vincent The School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing210094 China Zhejiang Lab Hangzhou China Nanjing University of Science and Technology Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education China Data61 CSIRO KensingtonWA6152 Australia The Department of Electronics Engineering Shanghai Jiao Tong University Shanghai200240 China Department of Electrical and Computer Engineering Princeton University NJ08544 United States

The blockchain technology has been extensively studied to enable distributed and tamper-proof data processing in federated learning (FL). Most existing blockchain assisted FL (BFL) frameworks have employed a third-party blockchain network to decentralize the model aggregation process. However, decentralized model aggregation is vulnerable to pooling and collusion attacks from the third-party blockchain network. Driven by this issue, we propose a novel BFL framework that features the integration of training and mining at the client side. To optimize the learning performance of FL, we propose to maximize the long-term time average (LTA) training data size under a constraint of LTA energy consumption. To this end, we formulate a joint optimization problem of training client selection and resource allocation (i.e., the transmit power and computation frequency at the client side), and solve the long-term mixed integer non-linear programming based on a Lyapunov technique. In particular, the proposed dynamic resource allocation and client scheduling (DRACS) algorithm can achieve a trade-off of [O(1/V), O(√V)] to balance the maximization of the LTA training data size and the minimization of the LTA energy consumption with a control parameter V. Our experimental results show that the proposed DRACS algorithm achieves better learning accuracy than benchmark client scheduling strategies with limited time or energy consumption. Copyright © 2021, The Authors. All rights reserved.

关键词： Blockchain

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