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arXiv 2024年

作者： Bian, Yiming Pan, Yan Xu, Xuesong Zhao, Liang Li, Yang Huang, Wei Zhang, Lei Yu, Song Zhang, Yichen Xu, Bingjie State Key Laboratory of Information Photonics and Optical Communications Beijing University of Posts and Telecommunications Beijing100876 China School of Electronic Engineering Beijing University of Posts and Telecommunications Beijing100876 China Science and Technology on Communication Security Laboratory Institute of Southwestern Communication Chengdu610041 China School of Integrated Circuits Beijing University of Posts and Telecommunications Beijing100876 China

Quantum key distribution, which ensures information-theoretically secret key generation, is currently advancing through photonic integration to achieve high performance, cost reduction and compact size, thereby facilitating the large-scale deployment. Continuous-variable quantum key distribution is an attractive approach for photonic integrations due to its compatibility with off-the-shelf optical communication devices. However, its chip-based systems have encountered significant limitations primarily related to the shot-noise-limited receiver design, which demands low noise, wide bandwidth, high clearance and well stability. Here, we report the implementation of a real local oscillator continuous-variable quantum key distribution system with an integrated silicon photonic receiver chip. Thanks to the well-designed chip-based homodyne detectors with a bandwidth up to 1.5 GHz and a clearance up to 7.42 dB, the transmission distance of the system has been extended to 28.6 km, achieving a secret key generation rate of Mbps level. This technological advancement enables the quantum key distribution systems with photonic integrated receivers to achieve the coverage in both access network scenarios and short-distance metropolitan interconnections, paving the way for the development of the next-generation quantum key distribution networks on a large scale. Copyright © 2024, The Authors. All rights reserved.

关键词： Quantum cryptography

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Mixed Attention Densely Residual Network for Single Image Super-Resolution

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Computer Systems science & Engineering 2021年第10期39卷 133-146页

作者： Jingjun Zhou Jing Liu Jingbing Li Mengxing Huang Jieren Cheng Yen-Wei Chen Yingying Xu Saqib Ali Nawaz School of Information and Communication Engineering Hainan UniversityHaikou570228China State Key Laboratory of Marine Resource Utilization in the South China Sea Hainan UniversityHaikou570228China Research Center for Healthcare Data Science Zhejiang LabHangzhou311121China School of Computer Science and Cyberspace Security Hainan UniversityHaikou570228China Graduate School of Information Science and Engineering Ritsumeikan University5258577Japan College of Computer Science and Technology Zhejiang UniversityHangzhou311100China

Recent applications of convolutional neural networks(CNNs)in single image super-resolution(SISR)have achieved unprecedented ***,existing CNN-based SISR network structure design consider mostly only channel or spatial information,and cannot make full use of both channel and spatial information to improve SISR performance *** present work addresses this problem by proposing a mixed attention densely residual network architecture that can make full and simultaneous use of both channel and spatial ***,we propose a residual in dense network structure composed of dense connections between multiple dense residual groups to form a very deep *** structure allows each dense residual group to apply a local residual skip connection and enables the cascading of multiple residual blocks to reuse previous features.A mixed attention module is inserted into each dense residual group,to enable the algorithm to fuse channel attention with laplacian spatial attention effectively,and thereby more adaptively focus on valuable feature *** qualitative and quantitative results of extensive experiments have demonstrate that the proposed method has a comparable performance with other stateof-the-art methods.

关键词： Channel attention Laplacian spatial attention residual in dense mixed attention

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Highly stable power control for chip-based continuous-variable quantum key distribution system

arXiv

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arXiv 2024年

作者： Bian, Yiming Li, Yang Xu, Xuesong Zhang, Tao Pan, Yan Huang, Wei Yu, Song Zhang, Lei Zhang, Yichen Xu, Bingjie State Key Laboratory of Information Photonics and Optical Communications Beijing University of Posts and Telecommunications Beijing100876 China School of Electronic Engineering Beijing University of Posts and Telecommunications Beijing100876 China Science and Technology on Communication Security Laboratory Institute of Southwestern Communication Chengdu610041 China School of Integrated Circuits Beijing University of Posts and Telecommunications Beijing100876 China

Quantum key distribution allows secret key generation with information theoretical security. It can be realized with photonic integrated circuits to benefit the tiny footprints and the large-scale manufacturing capacity. Continuous-variable quantum key distribution is suitable for chip-based integration due to its compatibility with mature optical communication devices. However, the quantum signal power control compatible with the mature photonic integration process faces difficulties on stability, which limits the system performance and causes the overestimation of secret key rate that opens practical security loopholes. Here, a highly stable chip-based quantum signal power control scheme based on a biased Mach-Zehnder interferometer structure is proposed, theoretically analyzed and experimentally implemented with standard silicon photonic techniques. Simulations and experimental results show that the proposed scheme significantly improves the system stability, where the standard deviation of the secret key rate is suppressed by an order of magnitude compared with the system using traditional designs, showing a promising and practicable way to realize highly stable continuous-variable quantum key distribution system on chip. Copyright © 2024, The Authors. All rights reserved.

关键词： Silicon photonics

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Task Offloading and Primary Node Selection in Blockchain, Digital Twin, and MEC Enabled Internet of Vehicles

Task Offloading and Primary Node Selection in Blockchain, Di...

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IEEE Conference on Vehicular technology (VTC)

作者： Jianbo Du Huifang Fang Shulei Li Pengfei Du Ziwen Kong Jiawen Kang Dusit Niyato Shaanxi Key Laboratory of Information Communication Network and Security School of Communications and Information Engineering Xi’an University of Posts and Telecommunications Xi’an China School of Communications Engineering Xidian University Xi’an China Tianyuan Ruixin Communication Technology Co. LTD. Xi’an China Engineering Research Center of Intelligent Airground Integrated Vehicle and Traffic Control Ministry of Education Xihua University Chengdu China School of Automation Guangdong University of Technology China School of Computer Science and Engineering Nanyang Technological University Singapore

ISBN: (数字)9798331517786

ISBN: (纸本)9798331517793

In this paper, we investigate the safe task offloading and primary node selection in blockchain, digital twin (DT) and Multi-access Edge Computing (MEC) enabled Internet of Vehicles (IoV). Edge servers centers provide computing power for task processing for Mobile Vehicles (MVs), while blockchain can provide security guarantees for MVs during task offloading. Based on the above system, we propose a joint optimization scheme for vehicle task offloading decision and the Practical Byzantine Fault Tolerance (PBFT) consensus process. Due to the large number of optimization variables and constraints, the problem becomes more complex. Traditional convex optimization and dynamic programming methods are difficult to effectively solve this problem. To address this issue, we propose a deep reinforcement learning based algorithm that utilizes Proximal Policy Optimization (PPO). The experimental results show that the algorithm proposed in this paper outperforms the benchmark algorithm in terms of convergence and other aspects.

关键词： Fault tolerance Vehicular and wireless technologies Heuristic algorithms Simulation Fault tolerant systems Blockchains Digital twins Servers Optimization Internet of Vehicles

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Act Now: A Novel Online Forecasting Framework for Large-Scale Streaming Data

arXiv

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arXiv 2024年

作者： Liang, Daojun Zhang, Haixia Wang, Jing Yuan, Dongfeng Zhang, Minggao School of Information Science and Engineering Shandong University China School of Control Science and Engineering Shandong University China Ocean College Jiangsu University of Science and Technology China School of Qilu Transportation Shandong University China Shandong Key Laboratory of Intelligent Communication and Sensing-Computing Integration China

In this paper, we find that existing online forecasting methods have the following issues: 1) They do not consider the update frequency of streaming data and directly use labels (future signals) to update the model, leading to information leakage. 2) Eliminating information leakage can exacerbate concept drift and online parameter updates can damage prediction accuracy. 3) Leaving out a validation set cuts off the model’s continued learning. 4) Existing GPU devices cannot support online learning of large-scale streaming data. To address the above issues, we propose a novel online learning framework, Act-Now, to improve the online prediction on large-scale streaming data. Firstly, we introduce a Random Subgraph Sampling (RSS) algorithm designed to enable efficient model training. Then, we design a Fast Stream Buffer (FSB) and a Slow Stream Buffer (SSB) to update the model online. FSB updates the model immediately with the consistent pseudo- and partial labels to avoid information leakage. SSB updates the model in parallel using complete labels from earlier times. Further, to address concept drift, we propose a Label Decomposition model (Lade) with statistical and normalization flows. Lade forecasts both the statistical variations and the normalized future values of the data, integrating them through a combiner to produce the final predictions. Finally, we propose to perform online updates on the validation set to ensure the consistency of model learning on streaming data. Extensive experiments demonstrate that the proposed Act-Now framework performs well on large-scale streaming data, with an average 28.4% and 19.5% performance improvement, respectively. Experiments can be reproduced via https://***/Anoise/Act-Now. © 2024, CC BY.

关键词： Spatio-temporal data

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A Long Synthetic Aperture Passive Localization Method Using Two Planes

A Long Synthetic Aperture Passive Localization Method Using ...

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IEEE International Conference on Radar

作者： Bowei Chen Yuqi Wang Guang-Cai Sun Mengdao Xing Zijing Zhang Xiaoniu Yang National Laboratory of Radar Signal Processing Xidian University Xi'an China Science and Technology on Communication Information Security Control Laboratory Jiaxing China

ISBN: (纸本)9781665468893

A long synthetic aperture passive localization algorithm is proposed in this paper. First of all, the Doppler change rate of signal can be measured by the image contrast and then, grid points are divided within the maximum possible latitude and longitude of the emitter location, the Doppler change rate at each grid point can be calculated by using known position and velocity of the receiver. The grid points at which the calculated Doppler change rate is equal to the value measured by the received signal form an isoline. Two receivers can draw two isolines on the ground and the intersection of the isolines gives estimate of the emitter location.

关键词： Location awareness Passive radar Chirp Estimation Receivers Apertures Position measurement

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A multip-level additive distortion method for security improvement in palette image steganography

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Journal of Visual communication and Image Representation 2025年 110卷

作者： Chen, Yi Wang, Hongxia Cui, Yunhe Shen, Guowei Guo, Chun Liu, Yong Wu, Hanzhou State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China Guizhou Provincial Characteristic Key Laboratory of Software Engineering and Information Security Guizhou University Guiyang550025 China School of Cyber Science and Engineering Sichuan University Chengdu610065 China School of Artificial Intelligence Hubei University of Automotive Technology Shiyan442002 China School of Big Data and Computer Science Guizhou Normal University Guiyang550025 China School of Communication and Information Engineering Shanghai University Shanghai200444 China

With the rapid development of the Internet and communication technology, palette images have become a preferred media for steganography. However, the security of palette image steganography faces a big problem. To address this, we propose a multiple-level additive distortion method for security improvement in palette image steganography. The proposed multiple-level additive distortion method comprises an index-level cost method and a pixel-level cost method. The index-level and the pixel-level costs by the two methods can respectively reflect the relationship changes of adjacent indices and the pixels corresponding to the adjacent indices. Meanwhile, the index-level and the pixel-level costs can also reflect the modification impact of steganography. Therefore, the proposed method can improve the security of palette image steganography. We conducted extensive experiments on three datasets to verify the security improvement. Experiment results have shown our proposed multiple-level distortion method indeed has an advantage in security when compared with four state-of-the-art methods. © 2025 Elsevier Inc.

关键词： Additive costs Color table Index matrix Multiple-level distortions Palette images security Steganography

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A code clone detection algorithm based on graph convolution network with AST tree edge

A code clone detection algorithm based on graph convolution ...

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IEEE International Conference on Software Quality, Reliability and security Companion (QRS-C)

作者： Zhicheng Lu Ruochen Li Huamiao Hu Wen-an Zhou Science and Technology on Communication Information Security Control Laboratory Jiaxing China School of Computer Science Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781665478373

Detecting code cloning will prevent it from bringing risks such as vulnerabilities and intellectual property disputes in complex software systems such as large-scale defense software systems and commercial software systems. In the field of deep code clone detection, neural networks such as Tree-CNN and Tree-LSTM, which extract features from AST (abstract syntax tree), can't collect global information of upper and lower nodes, and information can't flow globally, but graph neural network can avoid this problem. This paper presents a method of edging AST, and uses GCN (Graph Convolutional Network) and GAT(Graph Attention Networks) to extract code feature vector. Finally, the experiment is carried out on BigCloneBench data set, using several common binary classification indexes, and analyzing the time consumption, it is concluded that the effect and time efficiency of using graph neural network for code clone detection are significantly improved, especially for the code fragments with completely different semantics.

关键词： Codes Semantics Cloning Software quality Syntactics Feature extraction Software systems

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An Enhanced Resource Selection Scheme for Efficient Intra-Platoon Message Delivery

An Enhanced Resource Selection Scheme for Efficient Intra-Pl...

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IEEE Conference on Global communications (GLOBECOM)

作者： Bingying Wang Jun Zheng Cheng Li School of Electronics and Information Engineering Nanjing University of Information Science and Technology Nanjing China National Mobile Communications Research Laboratory Frontiers Science Center for Mobile Information Communication and Security Southeast University Nanjing China Purple Mountain Laboratories Nanjing China School of Engineering Science Simon Fraser University Burnaby Canada Department of Electrical and Computer Engineering Memorial University St. John's Canada

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

This paper proposes an enhanced resource selection (eInP-RS) scheme for efficient intra-platoon message delivery of cooperative awareness messages (CAMs) and decentralized environmental notification messages (DENMs). To achieve this goal, the eInP-RS scheme allows each vehicle to transmit CAM and DENM packets on a C-V2X channel (CH1) and an 802.11p channel (CH2), separately, and introduces four mechanisms to enhance the standardized sensing-based SPS scheme. A contention window (CW) size adjustment mechanism is introduced to enable a vehicle to adjust its CW size according to the information it collects on CH1 in order to avoid potential packet collisions on CH2; a resource partition mechanism is introduced to divide frequency-time resources in a selection window into two sets in order for vehicles moving in opposite directions to select different resources and thus avoid potential merging collisions on CH1; an intra-platoon cooperation mechanism is introduced to enable the PL of a platoon to know the resource and channel occupation information of the platoon's hidden nodes on CH1 and CH2; and a packet collision detection mechanism is used to enable a non-platoon vehicle to detect packet collisions occurring on both channels after a lane-changing maneuver to avoid potential merging collisions. Simulation results show that the proposed eInP-RS scheme outperforms the standardized sensing-based SPS scheme in terms of the intra-platoon CAM/DENM delivery ratio and the average intra-platoon DENM delivery delay.

关键词： Simulation Merging Frequency conversion Dynamic scheduling Sensors Cams Delays Global communication Vehicle dynamics Collision avoidance

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IR Net: information restriction and information recovery for accurate binary neural networks

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Neural Computing and Applications 2023年第19期35卷 14449-14464页

作者： Xue, Ping Lu, Yang Chang, Jingfei Wei, Xing Wei, Zhen School of Computer Science and Information Engineering Hefei University of Technology Hefei China Anhui Mine IOT and Security Monitoring Technology Key Laboratory Hefei China Engineering Research Center of Safety Critical Industrial Measurement and Control Technology Ministry of Education Hefei University of Technology Hefei China Intelligent Manufacturing Institute of Hefei University of Technology Hefei China

Weight and activation binarization can efficiently compress deep neural networks and accelerate model inference, but they cause severe accuracy degradation. Existing optimization methods for binary neural networks (BNNs) focus on fitting full-precision networks to reduce quantization errors and suffer from a tradeoff between accuracy and efficiency. In contrast, considering information loss and the mismatch between model capacity and input information quantity caused by network binarization, we propose information Restriction and information Recovery Network (IR $$^2$$ Net) to stimulate the potential of BNNs and achieve improved network accuracy by restricting the input information and recovering feature information. The proposed approach includes (1) information restriction, which evaluates the feature information extracted from the input by a BNN, discards some of the information it cannot focus on, and reduces the amount of the input information to match the model capacity; and (2) information recovery: due to the information loss incurred during forward propagation, the extracted feature information of the network is not sufficient for supporting accurate classification. Shallow feature maps with richer information are selected, and these feature maps are fused with the final feature maps to recover the extracted feature information and further enhance the model capacity to match the amount of input information. In addition, the computational cost is reduced by streamlining the information recovery method to strike a better tradeoff between accuracy and efficiency. Experimental results demonstrate that our approach still achieves comparable accuracy even with a $$\sim$$ 10x floating-point operations (FLOPs) reduction for ResNet-18. The models and code are available at https://***/pingxue-hfut/IR2Net .

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