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IEEE Electron Device Letters 2025年第5期46卷 789-792页

作者： Zhang, Guocheng Wang, Hongyu Qin, Shixian Tang, Jianchuan Zeng, Zili Su, Changqiang Yi, Xin Zhang, Xianghong Chen, Huipeng Fujian University of Technology Research Center for Microelectronics Technology Fuzhou350108 China Institute of Optoelectronic Display Fuzhou University Fuzhou350002 China Fudan University State Key Laboratory of Asic and System School of Microelectronics Shanghai200433 China Institute of Optoelectronic Display and Fujian Science and Technology Innovation Laboratory for Optoelectronic Information Fuzhou University Fuzhou350002 China

The hierarchical processing capabilities of the human visual system can significantly enhance the efficiency of data processing in the central nervous system. Volatile and non-volatile devices are key components in simulating the central nervous system. Realizing both volatile and non-volatile functionalities on a single device is ideal;however, challenges such as complex preparation and cumbersome switching persist. In this study, a tunable synaptic transistor with volatile and non-volatile switching capabilities is developed, offering ease of fabrication and convenient switching. It can simulate various forms of synaptic plasticity and exhibits excellent storage performance in non-volatile mode. Finally, we design an image preprocessing and classification system based on visual selective attention, which enables efficient neuromorphic computation through hierarchical data processing. © 1980-2012 IEEE.

关键词： Network security

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security-Enhanced Data Sharing Via Efficient Sanitization for VANETs

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IEEE Transactions on Mobile Computing 2025年第6期24卷 4925-4938页

作者： Zhong, Hong Zhou, Dan Cui, Jie Wang, Li Zhang, Jing Bolodurina, Irina He, Debiao Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education School of Computer Science and Technology Hefei230039 China Anhui University Anhui Engineering Laboratory of IoT Security Technologies Hefei230039 China Orenburg State University Faculty of Mathematics and Information Technologies Orenburg460018 Russia Wuhan University School of Cyber Science and Engineering Wuhan430072 China MatrixElements Technologies Shanghai Key Laboratory of Privacy Preserving Computation Shanghai201204 China

With the widespread deployment of vehicular ad-hoc networks (VANETs), data sharing has garnered considerable attention as a core feature of VANETs. Attribute-based proxy re-encryption (ABPRE) enables fine-grained access control and provides flexible ciphertext updates. The initially authorized vehicle generates the re-encryption key to enable ciphertext-to-ciphertext conversion in the cloud, allowing ciphertext to be shared with new recipients. However, initially authorized vehicles may not always be trustworthy and could share data with malicious receivers. In addition, the computation and communication overhead of ABPRE hinders its widespread application in VANETs. To address these issues, we propose a lightweight sanitizable scheme for edge-assisted VANETs based on ABPRE. In this scheme, the re-encryption key is verified by a sanitizer, to prevent the data from being shared with malicious data receivers. In addition, key-splitting techniques and edge computing are employed to reduce the communication and computation overhead of re-encryption. A comprehensive security analysis and performance evaluation demonstrate that the proposed scheme is efficient and practical. © 2002-2012 IEEE.

关键词： Ciphertext

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Moving Target Defense Meets Artificial Intelligence-Driven Network: A Comprehensive Survey

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IEEE Internet of Things Journal 2025年第10期12卷 13384-13397页

作者： Zhang, Tao Kong, Fanyu Deng, Dongshang Tang, Xiangyun Wu, Xuangou Xu, Changqiao Zhu, Liehuang Liu, Jiqiang Ai, Bo Han, Zhu Deng, Robert H. Beijing Jiaotong University School of Cyberspace Science and Technology Beijing100044 China Anhui Engineering Research Center for Intelligent Applications and Security of Industrial Internet Anhui243002 China Minzu University of China School of Information Engineering Beijing100081 China Chongqing University College of Computer Science Chongqing400044 China Anhui University of Technology School of Computer Science and Technology Anhui Province Key Laboratory of Digital Twin Technology in Metallurgical Industry Anhui243002 China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology China Beijing Institute of Technology School of Cyberspace Science and Technology Beijing100081 China Beijing Jiaotong University School of Electronic and Information Engineering Frontiers Science Center for Smart High-speed Railway System Beijing100044 China University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Singapore Management University Department of Information Systems Singapore Singapore

Based on emerging Artificial Intelligence (AI) tasks, cloud-edge-terminal architecture can provide powerful computing, intelligent interconnection, and real-time response, which can also be regarded as AI-driven network. Unfortunately, multiple network layers in the AI-driven network usually face various types of network threats such as malicious network reconnaissance, side-channel attacks, and Distributed Denial of Service (DDoS). Traditional security solutions respond to network threats after the occurrence of attacks. To solve this problem, the concept of Moving Target Defense (MTD) has been proposed as a proactive defense mechanism that aims to defend against cyber attacks before they occur. In this paper, we firstly provide a thorough analysis of the threats in the cloud-edge-terminal network. Then we conduct a comprehensive survey to discuss the concept, design principles, and main classifications of MTD. Next, we further introduce the development potential in terms of AI-powered MTD on each network layer. Meanwhile, we also explore how MTD improves the security of AI algorithms. Lastly, we describe the existing challenges and research directions of MTD. The aim of this paper is to provide an in-depth understanding for the readers on how to realize the integration between MTD and AI-driven network. © 2014 IEEE.

关键词： Cloud security

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Probing links between climate change and solar variability using wavelet analyses

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Frontiers of Earth science 2025年 1-18页

作者： Wang, Dong Jia, Wenjing State Key Laboratory of Climate System Prediction and Risk Management Nanjing University of Information Science and Technology Nanjing China

Possible links between the Earth’s surface temperature and solar variability are investigated using uni- and bivariate wavelet methods, in an attempt to attribute the fluctuations of the global mean surface temperature (GMST). Time-varying strengths of oscillations on different time scales in time series for several sets of GMST and solar activity indices are evaluated using continuous wavelet transform. Furthermore, possible relationships on different time scales between the GMST and solar activity time series are investigated using cross wavelet transform and wavelet coherence analyses. The results show that for the most prominent oscillations of solar activity on the ∼11-year scale, no statistically significant response is found in any of the temperature time series. Little evidence is found supporting the postulated link on the ∼60-year time scale between surface temperature and solar activity. The ∼60-year oscillations in the climate system likely stem from internal variabilities of the coupled ocean-atmosphere system, especially the variabilities in the North Atlantic and the associated variabilities in the western tropical Pacific, rather than solar variability. The latitudinal differences in temperature response to solar activity depend on time scales. Our findings do not support the notion that solar variability has been playing a dominant role in recent climate change.

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Efficient Design Optimization for Diffractive Deep Neural Networks

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IEEE Transactions on Computer-Aided Design of Integrated Circuits and systems 2025年第3期44卷 1199-1203页

作者： Wu, Kun Liu, Yuncheng Gao, Hui Tao, Jun Xiong, Wei Li, Xin Wuhan University School of Electronic Information Wuhan430072 China Huazhong University of Science and Technology Wuhan National Laboratory for Optoelectronics The School of Optical and Electronic Information Wuhan430074 China Optics Valley Laboratory of Hubei Wuhan430074 China Fudan University State Key Laboratory of Integrated Chips and System School of Microelectronics Shanghai200433 China Duke Kunshan University Data Science Research Center Jiangsu Kunshan215316 China

Since diffractive deep neural network (D2NN) provides a full optical solution to implement deep neural networks (DNNs), it offers ultrafast operation speed and virtually unlimited bandwidth, yielding an alternative-yet-competitive approach for computer-based neural networks. A D2NN is composed of several 3D-printed phase masks as hidden layers and a number of optical detectors at the output. To enable automatic and efficient design of D2NNs, we propose an iterative optimization method to determine the optimal design parameters of D2NNs. During each iteration step, we first optimize the physical parameters for masks (e.g., thicknesses) while fixing the detector parameters (e.g., locations). Next, we exhaustively search the detector parameters with fixed masks. These two steps are repeated until convergence is reached. Our numerical experiments demonstrate that the proposed optimization algorithm can produce a high-performance D2NN achieving 97% accuracy for recognizing handwritten digits. © 2024 IEEE.

关键词： Deep neural networks

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A MIMO Neural Network Integrated with Maximum Likelihood Phase Recovery for Transoceanic Coherent Transmission

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Journal of Lightwave technology 2025年

作者： Wang, Kaihui Wang, Chen Zheng, Tianqi Long, Jianyu Sang, Bohan Zhou, Wen Yu, Jianjun Fudan University State Key Laboratory of ASIC and System School of Information Science and Technology Shanghai200433 China

We proposed a low-complexity multi-input multi-output neural network integrated with a maximum likelihood phase recovery algorithm (MIMO-NN-BMLPR), which is adopted in long-haul coherent optical communication. Neural network based equalization can effectively overcome the nonlinear distortions induced during long-haul transmission. However, the forward neural network equalizer is difficult to track the phase of coherent optical signals. We integrate an additional maximum likelihood phase recovery layer with the MIMO-NN structure so that the phase noise can be compensated. In this work, 8-channel 100-GBaud dual-polarization (DP) PS-16QAM signals are successfully transmitted over 6400km and satisfy the normalized generalized mutual information (NGMI) threshold at 0.86. The proposed MIMO-NN-BMLPR integrates the low-complexity phase estimation with the NNE to improve the performance. Compared with the traditional DSP structure, the proposal prolongs the distance by 48.84% with a 39.80% complexity reduction. © 1983-2012 IEEE.

关键词： Optical communication

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High-frequency enhanced ultrafast compressed active photography

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Opto-Electronic Advances 2025年第1期8卷 32-43页

作者： Yizhao Meng Yu Lu Pengfei Zhang Yi Liu Fei Yin Lin Kai Qing Yang Feng Chen State Key Laboratory for Manufacturing System Engineering and Shaanxi Key Laboratory of Photonics Technology for Information School of Electronic Science and EngineeringXi’an Jiaotong UniversityXi’an 710049China School of Mechanical Engineering Xi’an Jiaotong UniversityXi’an 710049China

Single-shot ultrafast compressed imaging(UCI)is an effective tool for studying ultrafast dynamics in physics,chemistry,or material science because of its excellent high frame rate and large frame ***,the random code(Rcode)used in traditional UCI will lead to low-frequency noise covering high-frequency information due to its uneven sampling interval,which is a great challenge in the fidelity of large-frame ***,a high-frequency enhanced compressed active photography(H-CAP)is *** uniformizing the sampling interval of R-code,H-CAP capture the ultrafast process with a random uniform sampling *** sampling mode makes the high-frequency sampling energy dominant,which greatly suppresses the low-frequency noise blurring caused by R-code and achieves high-frequency information of image *** superior dynamic performance and large-frame reconstruction ability of H-CAP are verified by imaging optical self-focusing effect and static object,*** applied H-CAP to the spatial-temporal characterization of double-pulse induced silicon surface ablation dynamics,which is performed within 220 frames in a single-shot of 300 ps.H-CAP provides a high-fidelity imaging method for observing ultrafast unrepeatable dynamic processes with large frames.

关键词： ultrafast compressed imaging high-frequency enhanced sampling spectral-temporal transform transient processes high-fidelity reconstruction

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A secure quantum homomorphic encryption ciphertext retrieval scheme

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Soft Computing 2025年第3期29卷 1497-1509页

作者： Cheng, Zhen-Wen Chen, Xiu-Bo Xu, Gang Chang, Yan Miao, Li-Hua Yang, Yi-Xian Wang, Ya-Lan School of Data and Computer Science Shandong Women’s University Jinan250300 China Information Security Center State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China School of Information Science and Technology North China University of Technology Beijing100144 China School of Cybersecurity Chengdu University of Information Technology Chengdu610225 China Huawei Technologies Co. Ltd Shenzhen518129 China Department of Computer Science Faculty of Engineering and Physical Sciences University of Surrey Surrey GuildfordGU2 7XH United Kingdom

In a recent paper (Gong et al. Quantum Inf Process 19:3, 2020), a novel ciphertext retrieval scheme based on the Grover algorithm and quantum homomorphic encryption was presented. In this scheme, when the server performs the operation of marking the solution on the user’s encrypted state in the Grover iteration, it needs to remove many gate-errors generated in the homomorphic evaluation of the T gate. And the server could judge this specific solution from the quantum circuit of marking the solution. It makes this scheme unable to achieve the low-cost and secure ciphertext retrieval. Therefore, we improve the Gong et al.’s scheme and propose a secure quantum homomorphic encryption ciphertext retrieval scheme. In our scheme, the trusted third party is introduced to cooperate with the server to execute the Grover algorithm. In each Grover iteration, the trusted third party can quickly mark the solution on the plaintext state, encrypt the marked state, and transmit it to the server. Then the server performs the remaining operations of this Grover iteration on the encrypted state. The trusted third party finally decrypts the iterated state. This cooperative approach ensures that the number of auxiliary qubits required and extra quantum gates executed in our scheme are lower than the Gong et al.’s scheme. By analyzing the security of our scheme, we confirm that the server and the trusted third party will not be informed of this solution. Thus, our scheme realizes the secure ciphertext retrieval with low computational overhead. We utilize IBM’s Qiskit framework to simulate our scheme, and the experimental result shows that our scheme is correct. It is worth noting that the low-cost and secure ciphertext retrieval will play a crucial role in modern information security and privacy protection. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Ciphertext

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Fake News Detection Across Multiple Domains Using Fuzzy Association Rules 24th

Fake News Detection Across Multiple Domains Using Fuzzy Asso...

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Xu, Xiaofeng Zhang, Weisha Huang, Yuanyuan Lu, Hongyu Lu, Jiazhong School of Cybersecurity Xin Gu Industrial College Chengdu University of Information Technology Chengdu610225 China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu610225 China SUGON Industrial Control and Security Center Chengdu610225 China School of Foreign Languages University of Electronic Science and Technology of China Chengdu611731 China

ISBN: (纸本)9789819615247

The detection of fake news is not confined to a single domain, and due to domain transfer, the reliance on detection methods specific to a single domain becomes inadequate when dealing with multi-domain issues. When a multi-domain detection occurs, labels from one domain focus exclusively on the news characteristics of that particular domain, ignoring the possibility that part of the news may include functions from more than one domain. To address these challenges, we propose a multi-domain fake news detection model based on fuzzy rules, incorporating a fuzzy mechanism and an optimization module for fuzzy association rules. In this model, we first introduce a fuzzy mechanism that applies fuzzy processing to domains through neural networks, constructing fuzzy domain labels for each news domain. Subsequently, we incorporate fuzzy association rules to extract a broader range of sample features, thereby enhancing detection effectiveness. Experiments conducted on a fake news dataset demonstrate that our model achieves commendable detection performance with a relatively low training cost, reaching an F1 score of over 97.13% for sample detection. This model successfully overcomes the limitations of single-domain detection in fake news identification and improves the precision of feature extraction through fuzzy rules, showing superior performance in handling multi-domain fake news detection. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： fake news detection fuzzy rules multi-domain social media

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Mining User–Item Interactions via Knowledge Graph for Recommendation

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ACM Transactions on Recommender systems 2025年第3期3卷 1-19页

作者： Shenghao Liu Lingyun Lu Bang Wang Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Hubei Key Laboratory of Smart Internet Technology School of Electronic Information and Communications Huazhong University of Science and Technology (HUST) Wuhan China

Introducing a Knowledge Graph (KG) to facilitate a recommender system has become a tendency in recent years. Many existing methods leverage KGs to obtain side information of items to promote item representation learning for enhancing recommendation performance. However, they ignore that KGs also may contribute to better user representation learning. To solve this issue, we propose a novel algorithm, the KIGR (Knowledge-aware Interaction Graph for Recommendation), to mine user–item interactions via KGs for assisting user representation learning. Specifically, a user–item interaction is encoded by attentively summing up the relation embedding about the item in the KG. Then, an unsupervised learning method is used to group the user–item interactions into different latent types. Further, a user–item interaction graph is divided into several subgraphs, which is referred to as a Knowledge-aware Interaction Graph, making each subgraph only contain one latent type of interaction. Finally, user representation is the fusion of user interest embedding, which is learned on the knowledge-aware interaction graph, whereas item representation is learned on the KG. Experimental results on MovieLens, LastFM and Amazon-Book validate that the proposed KIGR has a superior performance compared with the state-of-the-art algorithms.

关键词： Recommendation system knowledge graph graph neural network

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