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10th IEEE Smart World Congress, SWC 2024

作者： Wu, Ling Gao, Pingping Guo, Kun Fujian Key Laboratory of Network Computing and Intelligent Information Processing China

ISBN: (纸本)9798331520861

With deep learning's advancement in recent years, heterogeneous graph neural networks (HGNNs) have drawn a lot of interest. They are a group of deep learning models specifically designed for handling heterogeneous graph data, in which nodes and edges are of various types and all nodes are assumed to have either direct or indirect associations. A metaschema, which serves as a thorough and unified blueprint for the heterogeneous graph, elegantly connects varied node types to other heterogeneous nodes via multiple heterogeneous edges with rich sematincs. However, many existing methods that utilize meta-schema to mine heterogeneous information often focus on the different meta-relationships inside the schema, limiting the meta-schema to describing only the local relational semantics. To better utilize meta-schema for efficiently integrating different heterogeneous information, we organize them into a hierarchical structure(i.e., hierarchical meta-schema), with all types of nodes distributed across different layers. The layers are interconnected through heterogeneous relationships. The target type is placed at the final layer and others are arranged hierarchically in the front according to the heterogeneous relationships defined in the meta-schema. During information aggregation, according to the hierarchical meta-schema, the information of each layer is gradually aggregated to the target type on the final layer from far to near via the attention mechanism. Moreover, metapaths regularly begin and end with the same type and help to define complex structures and semantic. Therefore, we also consider the semantic information of neighbors based on metapaths within the proposed hierarchical meta-schema to enhance the complex semantics among nodes of the same-type that may be lacking in the meta-schema. Finally, we proposed a meta-path and meta-schema hierarchical aggregation for heterogeneous graph neural network model, named HGNN-MMHA. And we may allow the meta-schema to not

关键词： Graph neural networks

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High-precision end-to-end adaptive optics aided orbital angular momentum wireless communication with XFTC-Net

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Optics Letters 2025年第10期50卷 3265-3268页

作者： Xu, Panpan Chang, Huan Wang, Fei Gao, Ran Guo, Dong Zhu, Lei Li, Zhipei Zhou, Sitong Xu, Qi Chen, Jing Xin, Xiangjun Beijing100081 China National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing China Beijing100876 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology 250353 China

In this Letter, we demonstrate high-precision end-to-end adaptive optics (AO) technique based on the X-Shape Fusion Transformer-convolutional neural network (XFTC-Net) without an additional probe path to compensate for distortions caused by atmospheric turbulence (AT) in orbital angular momentum (OAM) optical wireless communication links. The experimental results show that the XFTC-Net effectively extracts the intensity distribution features of distorted OAM beams, predicting AT phase screens and identifying OAM modes. After XFTC-Net-aided AO compensation, the bit error rate of the OAM optical communication system remains below 3.8 × 10−3, staying within Cn2 = 1 × 10−15–1 × 10−13 m−2/3. Moreover, the optical power with l= 3 increases from 5.08 dBm to 8.23 dBm at a transmission distance of 800 m. These experimental results indicate that the proposed XFTC-Net AO technique effectively compensates for OAM beam distortion in both single- and multiplexed-channel OAM links, which effectively balances the system complexity and transmission performance of OAM-based free space optical (FSO) communication systems, opening up new avenues for enhancing the performances of high-capacity optical communication. © 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.

关键词： Fiber to the x

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Privacy Preserving Demand Side Management Method via Multi-Agent Reinforcement Learning

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IEEE/CAA Journal of Automatica Sinica 2023年第10期10卷 1984-1999页

作者： Feiye Zhang Qingyu Yang Dou An Faculty of Electronic and Information Engineering Xi’an Jiaotong UniversityXi’an 710049China Key Laboratory for Manufacturing Systems Engineering(SKLMSE) Xi’an Jiaotong UniversityXi’an 710049 MOE Key Laboratory for Intelligent Networks and Network Security Xi’an Jiaotong UniversityXi’an 710049China

The smart grid utilizes the demand side management technology to motivate energy users towards cutting demand during peak power consumption periods, which greatly improves the operation efficiency of the power grid. However, as the number of energy users participating in the smart grid continues to increase, the demand side management strategy of individual agent is greatly affected by the dynamic strategies of other agents. In addition, the existing demand side management methods, which need to obtain users’ power consumption information,seriously threaten the users’ privacy. To address the dynamic issue in the multi-microgrid demand side management model, a novel multi-agent reinforcement learning method based on centralized training and decentralized execution paradigm is presented to mitigate the damage of training performance caused by the instability of training experience. In order to protect users’ privacy, we design a neural network with fixed parameters as the encryptor to transform the users’ energy consumption information from low-dimensional to high-dimensional and theoretically prove that the proposed encryptor-based privacy preserving method will not affect the convergence property of the reinforcement learning algorithm. We verify the effectiveness of the proposed demand side management scheme with the real-world energy consumption data of Xi’an, Shaanxi, China. Simulation results show that the proposed method can effectively improve users’ satisfaction while reducing the bill payment compared with traditional reinforcement learning(RL) methods(i.e., deep Q learning(DQN), deep deterministic policy gradient(DDPG),QMIX and multi-agent deep deterministic policy gradient(MADDPG)). The results also demonstrate that the proposed privacy protection scheme can effectively protect users’ privacy while ensuring the performance of the algorithm.

关键词： Centralized training and decentralized execution demand side management multi-agent reinforcement learning privacy preserving

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An Empirical Application of User-Guided Program Analysis

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China Communications 2024年第7期21卷 325-333页

作者： Wang Jigang Cheng Shengyu Cao Jicheng He Meihua State Key Laboratory of Mobile Network and Mobile Multimedia Shenzhen 518055China Department of Cyberspace Security ZTE CorporationShenzhen 518057China

Although static program analysis methods are frequently employed to enhance software quality,their efficiency in commercial settings is limited by their high false positive *** EUGENE tool can effectively lower the false positive ***,in continuous integration(CI)environments,the code is always changing,and user feedback from one version of the software cannot be applied to a subsequent ***,people find it difficult to distinguish between true positives and false positives in the analytical *** this study,we developed the EUGENE-CI technique to address the CI problem and the EUGENE-rank lightweight heuristic algorithm to rate the reports of the analysis output in accordance with the likelihood that they are true *** the three projects ethereum,go-cloud,and kubernetes,we assessed our *** to the trial findings,EUGENE-CI may drastically reduce false positives while EUGENE-rank can make it much easier for users to identify the real positives among a vast number of *** paired our techniques with GoInsight~1 and discovered a *** also offered a patch to the community.

关键词： continuous integration report ranking static program analysis

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MarkNeRF:Watermarking for Neural Radiance Field

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Computers, Materials & Continua 2024年第7期80卷 1235-1250页

作者： Lifeng Chen Jia Liu Wenquan Sun Weina Dong Xiaozhong Pan Cryptographic Engineering Department Institute of Cryptographic EngineeringEngineering University of PAPXi’an710086China Key Laboratory of Network and Information Security of PAP Xi’an710086China

This paper presents a novel watermarking scheme designed to address the copyright protection challenges encountered with Neural radiation field(NeRF)*** employ an embedding network to integrate the watermark into the images within the training ***,theNeRFmodel is utilized for *** copyright verification,a secret image is generated by inputting a confidential viewpoint into *** this basis,design an extraction network to extract embedded watermark images fromconfidential *** the event of suspicion regarding the unauthorized usage of NeRF in a black-box scenario,the verifier can extract the watermark from the confidential viewpoint to authenticate the model’s *** experimental results demonstrate not only the production of visually appealing watermarks but also robust resistance against various types of noise attacks,thereby substantiating the effectiveness of our approach in safeguarding NeRF.

关键词： Neural radiation field 3D watermark robustness black box

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A Traceable Privacy-Preserving Transaction Protocol With Evolutionary Threshold Authentication

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IEEE Internet of Things Journal 2025年第12期12卷 20787-20799页

作者： Xie, Ninghai Li, Jiguo Lin, Chao Zhang, Yichen Shen, Jian Fujian Normal University College of Computer and Cyber Security Fuzhou350117 China School of Mathematics and Statistics Key Laboratory of Analytical Mathematics and Applications Ministry of Education Fujian Fuzhou350117 China Fujian Provincial Key Laboratory of Network Security and Cryptology Fuzhou350117 China Fujian Normal University Fujian Provincial Key Laboratory of Network Security and Cryptology College of Computer and Cyber Security Fuzhou350117 China Guangzhou University Guangdong Key Laboratory of Blockchain Security Guangzhou510006 China Zhejiang Sci-Tech University School of Information Science and Engineering Hangzhou310018 China

Decentralized payment systems, such as Bitcoin, enable immutable and transparent payments in a distributed manner. To address the issue of user privacy leakage due to transaction transparency, some efforts have enhanced privacy protection in decentralized payment systems. However, the lack of regulatory functions in these systems allows malicious users to engage in illegal activities. To balance user privacy protection and the regulation of malicious users, some efforts have attempted to introduce decentralized agencies. However, they have not considered the issue of agency corruption. In this paper, we propose a new traceable privacy-preserving transaction protocol, which tracks the addresses and transaction amounts of anonymous parties through decentralized institutions. To address the problem of committee corruption, we present a traceable privacy-preserving protocol with evolving threshold authentication based on a distributed random beacon (TPETA-to-DRB), enabling committee member updates. Furthermore, we prove that the protocol is secure under the random oracle model and conduct a comprehensive performance evaluation. © 2014 IEEE.

关键词： Authentication Protocol

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OSE: On-Site State-of-Health Estimation for Li-Ion Battery Using Real-Time Field Data

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IEEE Transactions on Transportation Electrification 2025年第3期11卷 7452-7462页

作者： Chen, Yanwen Zhao, Cong Yang, Shusen Zhao, Peng Ren, Xuebin Han, Qing Yang, Yuqian Wu, Shuaijun Xi’an Jiaotong University National Engineering Laboratory for Big Data Analytics Shaanxi Xi’an710049 China Xi’an Jiaotong University National Engineering Laboratory for Big Data Analytics Ministry of Education Key Laboratory for Intelligent Networks and Network Security Shaanxi Xi’an710049 China

On-site lithium-ion batteries’ state-of-health (SoH) estimation is of crucial importance for reliable operations of electric vehicles (EVs). Yet, due to the low quality of unlabeled real-time field data, diverse operating environments of in-service EVs, and limited computational capability of onboard devices, existing techniques established on data from well-controlled experimental environments are not practical for real-world EVs’ SoH estimation. Accurate and rapid SoH estimation based on field data of in-service EVs still remains quite challenging. To tackle this challenge, we present an on-site SoH estimation (OSE) method using in-service EV field data through a new knowledge-embedded deep transfer learning (DTL) model. Initially, a universal data preprocessing approach integrating mechanism knowledge is designed to process low-quality data under diverse operating environments. Then, we develop a domain-adaptive hybrid deep neural network (DAHDNN) model suitable for unlabeled field data, which can be deployed via an edge cloud collaborative framework to meet actual computational capability. We demonstrate the superiority of our method across four real datasets, where OSE’s estimation error is decreased by up to 78.5% compared with the state-of-the-art methods. The results indicate that the proposed method has good generalizability and reliability for SoH estimation on real-time field data. © 2015 IEEE.

关键词： State of charge

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DE-DFKD: diversity enhancing data-free knowledge distillation

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Multimedia Tools and Applications 2024年 1-28页

作者： Liu, Yanni Ye, Ayong Chen, Qiulin Zhang, Yuexin Chen, Jianwei College of Computer and Cyber Security Fujian Normal University Fuzhou350117 China Fujian Provincial Key Laboratory of Network Security and Cryptology Fuzhou350117 China

Data-Free Knowledge Distillation (DFKD) can be used to train students using synthetic data, when the original dataset of the teacher network is not accessible. However, existing studies mainly focus on how to use the prior knowledge of the teacher network to synthesize data, ignoring the lack of diversity of synthesized data, which leads to the inability of the student network to learn the real data distribution and low robustness. In this paper, we propose a Diversity-Enhanced Data-Free Knowledge Distillation (DE-DFKD) method based on the idea of generative image modelling, which introduces conditional generative networks and metric learning to solve the problem of class imbalance and single intra-class data distribution in synthetic datasets. The experimental results show that DE-DFKD synthesizes better quality data on MNIST, CIFAR-10, and CIFAR-100 datasets with Frechet Inception Distance (FID) values of 51.79, 60.25, and 50.1, respectively, and higher accuracy of student networks compared with existing schemes. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Generative adversarial networks

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Artificial Immune Detection for network Intrusion Data Based on Quantitative Matching Method

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Computers, Materials & Continua 2024年第2期78卷 2361-2389页

作者： CaiMing Liu Yan Zhang Zhihui Hu Chunming Xie School of Electronic Information and Artificial Intelligence Leshan Normal UniversityLeshan614000China Intelligent Network Security Detection and Evaluation Laboratory Leshan Normal UniversityLeshan614000China Internet Natural Language Intelligent Processing Key Laboratory of Education Department of Sichuan Province Leshan Normal UniversityLeshan614000China

Artificial immune detection can be used to detect network intrusions in an adaptive approach and proper matching methods can improve the accuracy of immune detection *** paper proposes an artificial immune detection model for network intrusion data based on a quantitative matching *** proposed model defines the detection process by using network data and decimal values to express features and artificial immune mechanisms are simulated to define immune ***,to improve the accuracy of similarity calculation,a quantitative matching method is *** model uses mathematical methods to train and evolve immune elements,increasing the diversity of immune recognition and allowing for the successful detection of unknown *** proposed model’s objective is to accurately identify known intrusions and expand the identification of unknown intrusions through signature detection and immune detection,overcoming the disadvantages of traditional *** experiment results show that the proposed model can detect intrusions *** has a detection rate of more than 99.6%on average and a false alarm rate of 0.0264%.It outperforms existing immune intrusion detection methods in terms of comprehensive detection performance.

关键词： Immune detection network intrusion network data signature detection quantitative matching method

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Abnormal-trajectory detection method based on sub-trajectory classification and outlier-factor acquisition

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Journal of intelligent and Fuzzy Systems 2024年第4期46卷 8477-8496页

作者： Xu, Dongsheng Chen, Chuanming Jin, Qi Zheng, Ming Ni, Tianjiao Yu, Qingying School of Computer and Information Anhui Normal University Anhui Wuhu China Anhui Provincial Key Laboratory of Network and Information Security Anhui Wuhu China

Abnormal-trajectory detection can be used to detect fraudulent behavior of taxi drivers transporting passengers. Aiming at the problem that existing methods do not fully consider abnormal fragments of trajectories, this paper proposes an abnormal-trajectory detection method based on sub-trajectory classification and outlier-factor acquisition, which effectively detects abnormal sub-trajectories and further detects abnormal trajectories. First, each trajectory is reconstructed using the turning angles and is divided into multiple sub-trajectories according to the turning angle threshold and trajectory point original acceleration. The sub-trajectories are then classified according to the extracted directional features. Finally, the multivariate distances between angular adjacent segments are calculated to obtain the outlier factor, and abnormal sub-trajectories are detected. The sum of the lengths of the abnormal sub-trajectories is used to calculate the outlier score and identify abnormal trajectories. Based on experimental results using real trajectory datasets, it has been found that the proposed method performs better at detecting abnormal trajectories than other similar methods. © 2024 – IOS Press. All rights reserved.

关键词： Trajectories

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