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International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)

作者： Jin Liu Jingjing Xie Kun Wang Jun Qiang College of Computer and Information Anhui Polytechnic University Wuhu China Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University Nanjing China

ISBN: (数字)9798331507398

ISBN: (纸本)9798331507404

Spectral computed tomography (CT) provides the potential to generate attenuation maps at varying spectral bins, which can subsequently be employed for the resolve of tissue materials. However, the majority of reconstruction algorithms employed for every projection energy bin typically exhibit a considerable degree of noise. Recently, a series of techniques have been designed in spectral CT reconstruction based on traditional iterative models or deep learning (DL) methods. However, these works are independent or simply coupled, often neglecting the dependency relationships of spatial and spectrum. Additionally, the interpretability and generalization capabilities remain as formidable challenges for current methods. To effectively address these challenges, we initially introduce a spatial-spectral convolutional sparse coding (SS-CSC) framework, which aims to jointly represent spatial and spectral features in a unified manner. Subsequently, we devise a novel deep unfolding network, inspired by SS-CSC, for spectral CT image representation. We refer to this component as the SS-CSC module. Furthermore, we expand the iterative reconstruction scheme for constructing an interpretable deep SS-CSC iterative reconstruction network (SS-CSC-Net) model for spectral CT imaging. The SS-CSC-Net is composed of several iteration blocks, with each block containing two modules: image reconstruction and SS-CSC modules. The spectral CT image is updated using a deep learning-based spatial-spectral prior constraint. Experimental results from both qualitative results and quantitative analyses indicate that the proposed SS-CSC-Net excels in noise reduction and in maintaining tissue edge integrity, delivering superior overall performance.

关键词： Convolutional codes Training Statistical analysis Computed tomography Noise reduction Training data Iterative methods Image reconstruction Unsupervised learning Testing

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C-privacy:A social relationship-driven image customization sharing method in cyber-physical networks

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Digital Communications and networks 2025年第2期11卷 563-573页

作者： Dapeng Wu Jian Liu Yangliang Wan Zhigang Yang Ruyan Wang Xinqi Lin School of Communications and Information Engineering Chongqing University of Posts and TelecommunicationsChongqing 400065China Aerospace Tianmu Satellite Technology Company Limited Chongqing401332China School of Cyber Security and Information Law Chongqing University of Posts and TelecommunicationsChongqing400065China College of Computer and Cyber Security Fujian Normal UniversityFujian350117China The Advanced Network and Intelligent Connection Technology Key Laboratory Chongqing400065China Key Laboratory of Ubiquitous Sensing and Networking Chongqing400065China

Cyber-Physical networks(CPN)are comprehensive systems that integrate information and physical domains,and are widely used in various fields such as online social networking,smart grids,and the Internet of Vehicles(IoV).With the increasing popularity of digital photography and Internet technology,more and more users are sharing images on ***,many images are shared without any privacy processing,exposing hidden privacy risks and making sensitive content easily accessible to Artificial Intelligence(AI)*** image sharing methods lack fine-grained image sharing policies and cannot protect user *** address this issue,we propose a social relationship-driven privacy customization protection model for publishers and *** construct a heterogeneous social information network centered on social relationships,introduce a user intimacy evaluation method with time decay,and evaluate privacy levels considering user interest *** protect user privacy while maintaining image appreciation,we design a lightweight face-swapping algorithm based on Generative Adversarial network(GAN)to swap faces that need to be *** proposed method minimizes the loss of image utility while satisfying privacy requirements,as shown by extensive theoretical and simulation analyses.

关键词： Cyber-physical networks Customized privacy Face-swapping Heterogeneous information network Deep fakes

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A community detection model based on hierarchical graph clustering network with probability distribution

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Kongzhi yu Juece/Control and Decision 2025年第6期40卷 1969-1974页

作者： Xu, Sen Liu, Xuan-Qi Chen, Chao-Feng Guo, Nai-Xuan Bian, Xue-Sheng Ma, Fu-Rong Hua, Xiao-Peng Zhou, Tian School of Information Engineering Yancheng Institute of Technology Yancheng224051 China Key Laboratory of Computer Network and Information Integration Southeast University Nanjing210000 China School of Underwater Sound Engineering Harbin Engineering University Harbin150001 China

In order to capture the hidden structure of networks and reduce the dependence of community detection models on the choice of initial parameters, this paper proposes a community detection model based on hierarchical graph clustering networks with probability distribution (HGCPD). Firstly, graph convolutional networks are utilized to learn and cache feature representations of nodes in the graph. Then, a hierarchical clustering method based on node pair similarity probability is introduced to recursively construct community structures at different levels. Finally, the problem of model hyperparameter optimization is explored, and the Bayesian optimization methods are designed to automatically adjust parameters, thereby improving the efficiency of the model. Experiments on multiple network datasets of different scales show that the HGCPD model is superior to mainstream methods in terms of the accuracy and effectiveness of community detection, and the model's interpretability is verified through visualization. © 2025 Northeast University. All rights reserved.

关键词： Normal distribution

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Fiber-based quantum secure direct communication without active polarization compensation

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Science China(Physics,Mechanics & Astronomy) 2022年第12期65卷 91-96页

作者： Xin Liu Di Luo Guangshen Lin Zihao Chen Chunfeng Huang Shizhuo Li Chengxian Zhang Zhenrong Zhang Kejin Wei Guangxi Key Laboratory for Relativistic Astrophysics School of Physical Science and TechnologyGuangxi UniversityNanning530004China Guangxi Key Laboratory of Multimedia Communications and Network Technology School of ComputerElectronicsand InformationGuangxi UniversityNanning530004China

Quantum secure direct communication(QSDC)that allows people to directly transmit confidential information through insecure channels is an important branch of quantum *** widespread adoption of the QSDC demands the development of simple and stable ***,most of the existent QSDC systems involve a complex self-alignment process at the initial stage and additional hardware to compensate environmental *** this study,we present a fiber-based QSDC system without active polarization *** system comprises a stable transmitter and a novel Sagnac-Mach-Zehnder interferometer for security *** robust system simplifies the self-alignment and is immune to environmental *** robustness of the system was theoretically and experimentally verified,and low bit error rates in a 12 min continuous operation with an active polarization scrambler were *** addition,we performed a proof-of-principle QSDC demonstration,and a secrecy capacity of 3.43 kbps over a 5 km fiber with a detection bit error rate of 0.85%±0.07%and a quantum bit error rate of 0.42%±0.05%were *** results confirm the viability of the proposed QSDC system for practical applications.

关键词： quantum secure direct communication Sagnac-Mach-Zehnder interferometer without active polarization compensation

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YOLOv8-GR: Gesture Recognition by Improving of YOLOv8 Algorithm

YOLOv8-GR: Gesture Recognition by Improving of YOLOv8 Algori...

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International Conference on Advanced Cloud and Big Data (CBD)

作者： Yi Ding Naixuan Guo Haitao Meng School of Information Engineering Yancheng Institute of Technology Yancheng China Key Laboratory of Computer Network and Information Integration (Southeast University) Ministry of Education

ISBN: (数字)9798331511074

ISBN: (纸本)9798331511081

Gesture recognition has been widely used in many situations, such as human-computer interaction, virtual reality and smart home. To meet the current demands for accuracy and speed in gesture recognition, this paper proposes an improved algorithm based on YOLOv8, referred to as YOLOv8-GR. By replacing the original YOLOv8 detection head with a Dynamic Head, we integrate scale-aware, spatial-aware, and task-aware attention mechanisms within a unified framework. To enhance context information capture, the SPPF (Spatial Pyramid Pooling - Fast) module is optimized to a modified SPPF-LSKA (Large Separable Kernel Attention) module. In addition, we employ the EIoU loss function for more accurate evaluation of object detection results. To evaluate our algorithm, we integrate a gesture dataset in complex and diverse scenarios through network and field shooting annotations. Compared to the original YOLOv8, the proposed YOLOv8-GR achieves a 22.5% improvement in mAP50 and a 10.0% improvement in mAP50-95 on our dataset. We also compare YOLOv8-GR with the MobileNetv2-based SSD, which is a kind of one-stage object detection algorithm. While maintaining similar recognition accuracy, the computational cost of YOLOv8-GR is 9.9 GFLOPs, which is 15.8% of SSD, and our algorithm can be deployed on Raspberry Pi 4B.

关键词： Human computer interaction Solid modeling Accuracy Convolution Heuristic algorithms Gesture recognition Object detection Virtual reality Smart homes Kernel

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Defending Against Poisoning Attacks in Federated Prototype Learning on Non-IID Data 18th

Defending Against Poisoning Attacks in Federated Prototype ...

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18th International Conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

作者： Zhang, Jie Zhang, Hongliang Wang, Guijuan Dong, Anming Jinan250353 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan250353 China

ISBN: (纸本)9783031714665

Federated learning (FL) is an emerging distributed machine learning paradigm that enables participants to cooperatively train learning tasks without revealing the raw data. However, the distributed nature of FL makes it susceptible to poisoning attacks, especially when the local data of participants are non-independent and identically diatributed (non-IID). Although several defense methods have been proposed to mitigate poisoning attacks, their effectiveness is limited by the specific assumptions about the data distribution. In this work, we propose a new defense strategy, FedAPA (Federated Prototype Learning Against Poisoning Attacks). Specifically, we use abstract class prototypes to communicate between the clients and server, thus effectively alleviating the impact of non-IID data. Moreover, we propose a new abnormal client detection method that aims to mitigate the impact of malicious clients while distinguishing between malicious and benign clients, thereby effectively defending against poisoning attacks. Extensive experiments on different datasets show that FedAPA can effectively resist the poisoning attacks under various data distributions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

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Multi-View Graph Embedding Learning for Image Co-Segmentation and Co-Localization

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IEEE Transactions on Circuits and Systems for Video Technology 2024年第6期34卷 4942-4956页

作者： Huang, Aiping Li, Lijian Zhang, Le Niu, Yuzhen Zhao, Tiesong Lin, Chia-Wen Fuzhou University Fujian Key Laboratory for Intelligent Processing and Wireless Transmission of Media Information College of Physics and Information Engineering Fuzhou350108 China Fuzhou University Fujian Key Laboratory of Network Computing and Intelligent Information Processing College of Computer and Data Science Fuzhou350108 China University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China Fuzhou University Fujian Key Laboratory for Intelligent Processing and Wireless Transmission of Media Information College of Physics and Information Engineering The Fujian Science and Technology Innovation Laboratory for Optoelectronic Information Fuzhou350108 China Institute of Communications Engineering National Tsing Hua University Department of Electrical Engineering Hsinchu30013 Taiwan

Image co-segmentation and co-localization exploit inter-image information to identify and extract foreground objects with a batch mode. However, they remain challenging when confronted with large object variations or complex backgrounds. This paper proposes a multi-view graph embedding (MV-Gem) learning scheme which integrates diversity, robustness and discernibility of object features to alleviate this phenomenon. To encourage the diversity, the deep co-information containing both low-layer general representations and high-layer semantic information is generated to form a multi-view feature pool for comprehensive co-object description. To enhance the robustness, a multi-view adaptive weighted learning is formulated to fuse the deep co-information for feature complementation. To ensure the discernibility, the graph embedding and sparse constraint are embedded into the fusion formulation for feature selection. The former aims to inherit important structures from multiple views, and the latter further selects important features to restrain irrelevant backgrounds. With these techniques, MV-Gem gradually recovers all co-objects through optimization iterations. Extensive experimental results on real-world datasets demonstrate that MV-Gem is capable of locating and delineating co-objects in an image group. © 1991-2012 IEEE.

关键词： Semantics

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Position Measurement Based Slave Torque Feedback Control for Teleoperation Systems With Time-Varying Communication Delays

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IEEE/CAA Journal of Automatica Sinica 2023年第2期10卷 388-402页

作者： Xian Yang Jing Yan Changchun Hua Xinping Guan the Institute of Information Science and Engineering Yanshan University the Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao 066004China IEEE the Institute of Electrical Engineering Yanshan UniversityQinhuangdao 066004China the Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China

Bilateral teleoperation system is referred to as a promising technology to extend human actions and intelligence to manipulating objects *** the tracking control of teleoperation systems,velocity measurements are necessary to provide feedback ***,due to hardware technology and cost constraints,the velocity measurements are not always *** addition,the time-varying communication delay makes it challenging to achieve tracking *** paper provides a solution to the issue of real-time tracking for teleoperation systems,subjected to unavailable velocity signals and time-varying communication *** order to estimate the velocity information,immersion and invariance(I&I)technique is employed to develop an exponential stability velocity *** the proposed velocity observer,a linear relationship between position and observation state is constructed,through which the need of solving partial differential and certain integral equations can be ***,the mean value theorem is exploited to separate the observation error terms,and hence,all functions in our observer can be analytically *** the estimated velocity information,a slave-torque feedback control law is presented.A novel Lyapunov-Krasovskii functional is constructed to establish asymptotic tracking *** particular,the relationship between the controller design parameters and the allowable maximum delay values is ***,simulation and experimental results reveal that the proposed velocity observer and controller can guarantee that the observation errors and tracking error converge to zero.

关键词： Asymptotic stability bilateral teleoperation time-varying delay velocity observer

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Early Prediction of Heart Disease via LSTM-XGBoost 23

Early Prediction of Heart Disease via LSTM-XGBoost

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9th International Conference on Computing and Artificial Intelligence, ICCAI 2023

作者： Zang, Xiaodong Du, Jin Song, Yuansheng School of Cyber Science and Engineering Qufu Normal University Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University Nanjing211189 China School of Cyber Science and Engineering Qufu Normal University China

ISBN: (纸本)9781450399029

With the development of information and technology, especially with the boom in big data, healthcare support systems are becoming much better. However, an early diagnosis is not an easy task because it is hard to find out the hidden patterns in the disease. The symptoms of heart failure are usually non-specific. Therefore, this study aims to devise an early prediction model for incident heart disease based on machine learning algorithms. In this study, we compared several powerful models, including support vector machines (SVM), k-nearest neighbor (KNN), decision tree, extreme gradient boosting (XGBoost), random forest, gradient boosting decision tree (GBDT), categorical boosting (CatBoost), light gradient boosting machine (LightGBM), and choose two best models, namely, XGBoost and LSTM as our prediction model. The XGBoost algorithm was used to extract a subset from the data of patients, which will be fed into the LSTM-XGBoost model for training. An experiment comparison is conducted based on the data from Kaggle. Experimental results demonstrate that the LSTM-XGBoost model can operate effectively with a prediction accuracy achieving 0.9896 and a loss rate achieving 0.0105, which is much better than XGBoost or LSTM, respectively. © 2023 Copyright held by the owner/author(s).

关键词： Forecasting

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Endogenous Security of FQn networks: Adaptive System-Level Fault Self-Diagnosis

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IEEE Transactions on Reliability 2024年第3期73卷 1659-1668页

作者： Lin, Yuhang Lin, Limei Huang, Yanze Xu, Li Hsieh, Sun-Yuan Fujian Normal University College of Computer and Cyber Security Fujian Provincial Key Laboratory of Network Security and Cryptology Fuzhou350117 China Fujian University of Technology School of Computer Science and Mathematics Fuzhou350118 China National Cheng Kung University Department of Computer Science and Information Engineering Tainan701 Taiwan

Endogenous security has the ability to discover, eliminate, and solve internal security problems and hidden dangers within the network, and is a superior technology to ensure future network security. The t/k-diagnosis strategy, as a strong and adaptive self-diagnosis strategy, is an important part for ensuring endogenous security. Moreover, the folded hypercube (FQn) as a data transmission network (e.g., optical network) topology offers new potential for the construction of large-scale, high data throughput, and low-latency systems, such as computing network, human-cyber-physical systems, and smart grid. However, there are few studies on endogenous security based on FQn networks. Therefore, this article designs an adaptive system-level fault self-diagnosis strategy, namely Fast t/k-Diagnosis Under Maeng-Malek Model (Ftk-DIAG-MM*) to diagnosis the faulty vertices in FQn network under the Maeng-Malek model (MM∗ mod). Then, we provide a proof of the algorithm correctness theoretically by the fault tolerance of FQn network. It is derived by theoretical derivation that the t/k-diagnosability inherent to the FQn network is (n+1)(k+1)-k(k+3)/2. The simulation experiments demonstrate that the designed Ftk-DIAG-MM∗ strategy can correctly diagnose all vertices within the range allowed by the diagnosability, and still has a great performance when it exceeds the range allowed by the diagnosability. It greatly enhances the fault diagnosis capability of FQn network in the circumstance of misdiagnosing a few vertices, which provides an important theoretical basis for the reliability and endogenous security of FQn networks. © 1963-2012 IEEE.

关键词： Topology

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