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21st IEEE International Conference on Communication Technology, ICCT 2021

作者： Luo, Jie Bao, Liang Ni, Lvlin The 30th Research Institute of China Electronics Technology Group Corporation Science and Technology on Communication Security Laboratory Sichuan Chengdu China The Third Research Institute of Ministry of Public Security Key Lab of Information Network Security of Ministry of Public Security Shanghai China

ISBN: (纸本)9781665432061

Shadowsocks (R) is a proxy software based on Socks5, which is the collective name of Shadowsocks and Shadowsocks (R). In order to improves the shortcomings of other softwares breaking internet censorship, Shadowsocks (R) is a private protocol without handshake negotiation mechanism, and pays more attention to high camouflage and high concealment of traffic, which makes it difficult to use common methods(fingerprint identification, port filtering)to identify Shadowsocks (R) traffic[1], This paper proposes a method of Shadowsocks (R) traffic identification from the perspective of the protocol, which extracts features from four dimensions, and inputs features into the machine learning model for training. This method performs secondary identification and statistical filtering on the output of the machine learning model, and finally identifys Shadowsocks (R) traffic. This method has been very well validated in the laboratory environment. © 2021 IEEE.

关键词： Machine learning

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Gradient-YOLO: Exploring the integration of gradient architecture into the YOLO network

Gradient-YOLO: Exploring the integration of gradient archite...

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International Joint Conference on Neural networks (IJCNN)

作者： Gang Li Cheng Zhang Delong Han Letian Gao Mingle Zhou 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 SHANDONG SCICOM Information and Economy Research Institute Co. Ltd Jinan China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Object detection is an essential task in the field of computer vision. The one-stage object detection model directly completes object detection through a single forward propagation and has fast real-time object detection capabilities, making it widely used—especially the model of the You Only Look Once (YOLO) series. Improving the detection accuracy of the YOLO model has always been a research topic. The gradient architecture cascades feature information extraction and aggregates all features at the end, which can improve the feature fusion ability of the network. Combining the idea of gradient architecture, this paper proposes a YOLO based on gradient architecture: Gradient-YOLO. Specifically, this paper integrates gradient architecture into the backbone, neck, and bottleneck structures in the YOLO network, obtaining gradient backbone, gradient neck, and gradient bottleneck, respectively. By combining gradient architecture, various network parts in Gradient-YOLO can fully aggregate multi-layer features, reduce feature loss, and thus improve detection accuracy. This paper takes the mature YOLOv5 model and the latest YOLOv8 model as the baseline and combines gradient thinking to obtain Gradient-YOLOv5 and GradientYOLOv8. Moreover, conduct experimental testing on the MS COCO dataset. Regarding the mAP@.5 detection indicator, compared to YOLOv5s, Gradient-YOLOv5s increased by 5.07%. Compared to YOLOv8n, Gradient-YOLOv8n has increased by 4.63%. Therefore, the combination of gradient architecture can improve the detection accuracy of YOLO networks.

关键词： YOLO Accuracy Aggregates Computational modeling computer architecture Feature extraction Real-time systems

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Energy Efficiency Optimization for RF Energy Harvesting Relay System 11th

Energy Efficiency Optimization for RF Energy Harvesting Rela...

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11th EAI International Conference on Broadband Communications, networks, and Systems, BROADNETS 2020

作者： Liang, Guangjun Xin, Jianfang Wang, Qun Xia, Lingling Li, Meng Department of Computer Information and Network Security Jiangsu Police Institute Nanjing China Jiangsu Provincial Public Security Department Key Laboratory of Digital Forensics Nanjing China Electrical Engineering School Anhui Polytechnic University Wuhu China

ISBN: (纸本)9783030687366

This paper conducts research on the RF energy harvesting relay network, and proposes an improved energy harvesting relay protocol, which allows the energy harvesting source node to retransmit data to improve the system diversity gain, and constructs energy harvesting slot allocation, subcarrier pairing, and power Optimized model of distributed system energy efficiency. A resource allocation algorithm based on optimal energy efficiency is further proposed. The Dinkelbach method is used to transform the nonlinear programming problem into a linear programming problem. Then, the Hungarian algorithm and the sub-gradient method are used to obtain the iterative algorithm based on energy efficiency optimization. Simulation shows that the algorithm reduces the complexity of the algorithm and has good global convergence. © 2021, ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Energy harvesting

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On accuracy rate of community detection and pairing in mobile social network

On accuracy rate of community detection and pairing in mobil...

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Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, UIC-ATC

作者： Jinbin Tu Qing Li Yun Wang School of Computer Science and Engineering Southeast University Key Lab of Computer Network and Information Integration Nanjing MOE China

Mobile Social network(MSN) is an opportunity network that considers the social attributes of nodes, and also uses the ”store-carry-forward” model to carry out data transfer between nodes. The community nature of nodes is an important reference in MSN to select messaging path. In this paper, a new community cluster problem based on our observed pickup strategies for car-hailing is proposed, which is involving community detection and community pairing. Then we formulate it by using T-Sorokin mobility model. The Cluster-First algorithm and Pair-First algorithm are proposed based on the defined community dependency metric to maximize the accuracy rate. Simulation experimental results show that both CFA and PFA can effectively solve this problem with an accuracy rate of over 70%. And it is concluded that cluster-first is superior to pair-first for this considered problem.

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EeCA: A Novel Approach for Energy Conservation in MEC via NDN-Based Content Caching

EeCA: A Novel Approach for Energy Conservation in MEC via ND...

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Asia-Pacific network Operations and Management Symposium (APNOMS)

作者： Jiaxin Xu Huiling Shi Haoxiang Chu Wei Zhang 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

In the context of escalating demands on wireless networks owing to the widespread application of 5G and the Internet of Things (IoT), enhancing network performance and user experience through the convergence of Multi-access Edge Computing (MEC) and information-Centric networking (ICN) has become a critical research focus. This paper proposes a novel Energy-efficient Caching Algorithm (EeCA) based on Named Data networking (NDN), a specific implementation of ICN. This NDN-based caching strategy extends local caching in MEC networks and enhances the overall network performance by considering the multitude of repeated computational tasks and the residual energy of low-energy devices. The EeCA aims to reduce redundant offloading of tasks across the entire network, contributing to energy savings, and reducing overall system costs. Simulations reveal superior performance of EeCA compared to four other caching methods, specifically in terms of cache hit ratio and energy consumption, making it a promising solution for network performance enhancement and energy conservation in MEC networks.

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Long and Recent Preference Learning with Recent-k Items Distribution for Recommender System

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IEEE Transactions on Multimedia 2025年

作者： Gao, Yongbiao Niu, Sijie Lv, Guohua Ling, Miaogen Geng, Xin Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Jinan China Southeast University Ministry of Education Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Application China University of Jinan Shandong Provincial Key Laboratory of Ubiquitous Intelligent Computing School of Information Science and Engineering China Nanjing University of Information Science and Technology School of Computer Science China Southeast University School of Computer Science and Engineering China

Reinforcement learning (RL) aims to formulate the recommendation task as a Markov decision process (MDP) and trains an agent to automatically learn the optimal recommendation policy from interaction trajectories through trial-and-error and reward mechanisms. However, most existing RL-based approaches overlook the correlation between items and the dynamics of user interests implied in temporally close interactions. Therefore, in this paper, we propose a reinforcement learning method that incorporates a "recent-k items"distribution to capture users' local preferences. Specifically, we model the output layer as two distinct branches. The "recent-k items"branch, formulated with a Kullback-Leibler divergence loss, learns the recent interests of users, whereas the other branch utilizes a one-step temporal difference error to capture long-term preferences. The proposed structure is integrated into deep Q-learning and actor-critics, resulting in two enhanced methods named R k Q and R k AC, respectively. Furthermore, a novel soft inter-reward is carefully designed to enhance the proposed method, and we theoretically prove the convergence of the proposed algorithm. We perform extensive experiments on two large real-world datasets and conduct further analysis of the influences of different action sequences, time intervals, and enhancement capabilities for state-of-the-art models. The experimental results demonstrate the efficacy of our proposed methods. © 1999-2012 IEEE.

关键词： Deep reinforcement learning

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Research on YOLOv7-tiny Improved Algorithm for Compli-ance Detection of Production Personnel Wearing

Research on YOLOv7-tiny Improved Algorithm for Compli-ance D...

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2023 International Conference on Communication network and Machine Learning, CNML 2023

作者： Shen, Qiao Huang, Hanming Wang, Pengfei Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guangxi Guilin541004 China Guangxi Key Lab of Multi-Source Information Mining and Security Guangxi Normal University Guangxi Guilin541004 China School of Computer Science and Engineering Guangxi Normal University Guangxi Guilin541004 China Hunan Aerospace Industry General Corp Aircraft Technology Branch Hunan Changsha410006 China

ISBN: (纸本)9798400716683

Proposed is an improved production personnel wearing target detection algorithm based on YOLOv7, aiming to address the issues of false positives and false negatives caused by occlusion and detection of small to medium-sized objects in complex backgrounds. The algorithm introduces a Transformer module into the Backbone to better capture global feature information of images. The spatial pyramid pooling layer (SPPCSP) is enhanced to improve the model's detection speed. Additionally, the detection head of the model is replaced with the decoupled YOLOX detection head to enhance detection accuracy. Experiments were conducted on the publicly available safety helmet dataset SHWD, with the addition of two classes of targets: reflective clothing and people wearing reflective clothing. The experimental results demonstrate that the improved algorithm achieves a detection accuracy of 89.6% on this dataset, which is an improvement of 1.5 percentage points compared to the original algorithm. The detection speed reaches 85 FPS, showing good accuracy and real-time performance. Furthermore, compared with some mainstream target detection algorithms, the proposed improved algorithm also exhibits certain advantages. © 2023 ACM.

关键词： Personnel

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DRSC: Dual-Reweighted Siamese Contrastive Learning network for Cross-Domain Rotating Machinery Fault Diagnosis With Multi-Source Domain Imbalanced Data

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IEEE Internet of Things Journal 2025年

作者： Bi, Yuanguo Fu, Rao Jiang, Cunyu Zhang, Xiaoling Li, Fengyun Zhao, Liang Han, Guangjie Northeastern University School of Computer Science and Engineering Shenyang110169 China Ministry of Education Engineering Research Center of Security Technology of Complex Network System Shenyang110169 China Shenyang University of Technology Institute of Artificial Intelligence Shenyang110870 China Shenyang Aerospace University School of Computer Science Shenyang110135 China Hohai University Key Laboratory of Maritime Intelligent Network Information Technology Ministry of Education China

To enhance the reliability of rotating machinery, cross-domain fault diagnosis becomes vital for detecting faults under unknown operating conditions. However, multi-source domain imbalanced data present significant challenges, as divergent label distributions across domains cause complex domain-class shifts and degrade the performance of cross-domain fault diagnosis. Moreover, diagnostic models often struggle to learn features from minority classes due to label imbalance within each domain, which may degrade the performance in diagnosing these minority classes. To address these challenges, we propose a Dual-Reweighted Siamese Contrastive learning network (DRSC) for cross-domain fault diagnosis with multi-source domain imbalanced data. In DRSC, we design a Siamese feature extractor based on a wide-kernel convolutional neural network to capture short-term characteristics and leverage the convenience in extracting domain-invariant features. Subsequently, to alleviate domain-class shifts, we design a reweighted contrastive domain-class alignment mechanism that strategically pulls domain-class pairs together while pushing other health conditions away. Finally, to enable the diagnostic model to learn from minority health conditions, a reweighted health condition classifier is developed by assigning higher weights to the minority classes. Evaluation results on two public datasets illustrate DRSC outperforms comparison models in cross-domain fault diagnosis. © 2014 IEEE.

关键词： Convolutional neural networks

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Two-phase Unsourced Random Access in Massive MIMO: Performance Analysis and Approximate Message Passing Decoder

arXiv

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arXiv 2023年

作者： Jiang, Jia-Cheng Wang, Hui-Ming The School of Information and Communications Engineering Xi'an Jiaotong University Shaanxi Xi’an710049 China The Ministry of Education Key Lab for Intelligent Networks and Network Security Shaanxi Xi’an710049 China

In this paper, we design a novel two-phase unsourced random access (URA) scheme in massive multiple input multiple output (MIMO). In the first phase, we collect a sequence of information bits to jointly acquire the user channel state information (CSI) and the associated information bits. In the second phase, the residual information bits of all the users are partitioned into sub-blocks with a very short length to exhibit a higher spectral efficiency and a lower computational complexity than the existing transmission schemes in massive MIMO URA. By using the acquired CSI in the first phase, the sub-block recovery in the second phase is cast as a compressed sensing (CS) problem. From the perspective of the statistical physics, we provide a theoretical framework for our proposed URA scheme to analyze the induced problem based on the replica method. The analytical results show that the performance metrics of our URA scheme can be linked to the system parameters by a single-valued free entropy function. An AMP-based recovery algorithm is designed to achieve the performance indicated by the proposed theoretical framework. Simulations verify that our scheme outperforms the most recent counterparts. © 2023, CC0.

关键词： Statistical Physics

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Hey! You Photographed My Screen without Approved: A Practical Screen Content Protection Scheme Based on Visual Cryptography

Hey! You Photographed My Screen without Approved: A Practica...

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2022 International Conference on networking and network Applications, NaNA 2022

作者： Liu, Kui Wang, Bin Shi, Ziwei Chen, Jiadong Zhang, Zhiwei School of Computer Science and Technology Xidian University Shaanxi Xi’an710071 China Network and Information Security Laboratory Hangzhou Hikvision Digital Technology Co. LTD. Hangzhou China Zhejiang Key Laboratory of Multi-dimensional Perception Technology Application and Cybersecurity Hangzhou310053 China

ISBN: (数字)9781665461313

ISBN: (纸本)9781665461313

Nowadays , with the continuous development of digital media, electronic screens have become the most direct tool for people to interact with information. Various styles of electronic screens provide vivid information exchange methods for people's lives, and at the same time make information leakage more likely to happen. Electronic screens are susceptible to stealing photographs when sharing information, which leads to information leakage and security issues. Under the unavoidable situation of such leaks, various protection schemes have been proposed before, but these schemes cannot effectively protect the screen content. Therefore, in this paper, we propose an effective a screen content protection scheme, based on Visual Cryptography(VC) for image encryption, combined with Scale-Invariant Feature Transform(SIFT) to deal with screen stealing scenes. In addition, our experiments show that our scheme is superior to existing schemes in terms of convenience and security. © 2022 IEEE.

关键词： Digital storage

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