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3rd China Intelligent Robotics Annual Conference, CCF CIRAC 2022

作者： Shi, Junfei He, Tiansheng Jin, Haiyan Wang, Hua Xu, Weinan State Key Laboratory of Geo-Information Engineering Shaanxi Xi’an710048 China School of Computer Science and Engineering Xi’an University of Technology Shaanxi Xi’an710048 China Shaanxi Key Laboratory for Network Computing and Security Technology Shaanxi Xi’an710048 China

ISBN: (纸本)9789819903009

Polarimetric Synthetic Aperture Radar (PolSAR) images have attracted much attention with abundant polarimetric scattering information. In recent years, many deep learning models have been proposed and highly expanded to PolSAR image classification. However, how to learn the complex matrix information is an indispensable problem. To address this problem, a Wishart Deeplab network is developed, which can not only learn the polarimetric matrix structure by designing Wishart network level but also learn multi-scale polarimetric scattering features by incorporating dilated convolution. Specifically, the Wishart-Deeplab based PolSAR classification model is constructed by designing the Wishart convolution operation to learn the statistical information of PolSAR data. Then, a deeplabV3 + network is followed to obtain the multi-scale semantic features by dilated convolution. By this way, statistical distrubtion-based and high-level semantic features are adaptively learned to improve the performance of the network. The experiments are conducted on two sets of real PolSAR data and results show that this method can obtain better classification results. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Image classification

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A Fast recognition method for DVS perimeter security intrusion signals based on transfer learning

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Optical Fiber Technology 2025年 93卷

作者： Shen, Yongxu Wang, Chang Sun, Zhihui Zhang, Faxiang Jiang, Shaodong Liu, Xu Gao, Fengxia Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China

In the perimeter security intrusion signal monitoring based on the Distributed Optical Fiber Vibration Sensing (DVS) system, the common approach for identifying intrusion signals in new scenarios is to collect large amounts of data and perform large-scale, long-term retraining of the initial network. However, this process consumes a significant amount of human resources and computational power each time. To address this issue, this paper proposes a transfer learning method. Using 53,000 sample data from six types of events collected in the laboratory, an SE-Densenet model was trained for 300 epochs on three NVIDIA A100 Tensor Core GPUs at the National Supercomputing Center in Jinan. Then, through the transfer learning approach, fine-tuning was performed on the new scenario by freezing 1/2 and 1/4 of the network layers and applying a feature extractor method. The fine-tuning training was carried out on a laptop equipped with 16 GB of memory and an Intel Core i7-12700 CPU. Experimental results show that, after 19.81 min of retraining using 600 data points from the new scenario, the feature extractor method achieved a test accuracy of 97.53 %. Compared to the other two methods, the training time was reduced by 20.87 min and 9.34 min, respectively, while the test accuracy increased by 13.76 % and 3.83 %. Compared to retraining with 6000 sufficient data, the transfer learning method improved training time by 6 to 13 times while achieving similar recognition accuracy. This study provides a new, convenient, and efficient method for perimeter security intrusion signal recognition, enabling quick adaptation to new scenarios and application in common engineering upper-level computer systems. © 2025

关键词： network intrusion

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Non-Deterministic Symmetric Encryption Communication System Based on Generative Adversarial networks

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China Communications 2025年第5期22卷 273-284页

作者： Wu Xuguang Han Yiliang Zhang Minqing Zhu Shuaishuai Li Yu College of Cryptography Engineering Engineering University of People’s Armed PoliceXi’an 710086China Key Laboratory of Network and Information Security Under the People’s Armed Police Xi’an 710086China

Symmetric encryption algorithms learned by the previous proposed end-to-end adversarial network encryption communication systems are *** the same key and same plaintext,the deterministic algorithm will lead to the same *** means that the key in the deterministic encryption algorithm can only be used once,thus the encryption is not *** solve this problem,a nondeterministic symmetric encryption end-to-end communication system based on generative adversarial networks is *** design a nonce-based adversarial neural network model,where a“nonce”standing for“number used only once”is passed to communication participants,and does not need to be ***,we optimize the network structure through adding Batch Normalization(BN)to the CNNs(Convolutional Neural networks),selecting the appropriate activation functions,and setting appropriate CNNs *** of experiments and analysis show that our system can achieve non-deterministic symmetric encryption,where Alice encrypting the same plaintext with the key twice will generate different ciphertexts,and Bob can decrypt all these different ciphertexts of the same plaintext to the correct *** our proposed system has fast convergence and the correct rate of decryption when the plaintext length is 256 or even longer.

关键词： end-to-end communication systems generative adversarial networks symmetric encryption

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Super-resolution reconstruction of single image for latent features

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Computational Visual Media 2024年第6期10卷 1219-1239页

作者： Xin Wang Jing-Ke Yan Jing-Ye Cai Jian-Hua Deng Qin Qin Yao Cheng School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu 610000 China State Key Laboratory of Rail Transit Vehicle System Southwest Jiaotong UniversityChengdu 610000China School of Marine Engineering Guilin University of Electronic TechnologyGuilin 541000China School of Computer and Information Security Guilin University of Electronic TechnologyGuilin 541000China

Single-image super-resolution(SISR)typically focuses on restoring various degraded low-resolution(LR)images to a single high-resolution(HR)***,during SISR tasks,it is often challenging for models to simultaneously maintain high quality and rapid sampling while preserving diversity in details and texture *** challenge can lead to issues such as model collapse,lack of rich details and texture features in the reconstructed HR images,and excessive time consumption for model *** address these problems,this paper proposes a Latent Feature-oriented Diffusion Probability Model(LDDPM).First,we designed a conditional encoder capable of effectively encoding LR images,reducing the solution space for model image reconstruction and thereby improving the quality of the reconstructed *** then employed a normalized flow and multimodal adversarial training,learning from complex multimodal distributions,to model the denoising *** so boosts the generative modeling capabilities within a minimal number of sampling *** comparisons of our proposed model with existing SISR methods on mainstream datasets demonstrate that our model reconstructs more realistic HR images and achieves better performance on multiple evaluation metrics,providing a fresh perspective for tackling SISR tasks.

关键词： image superresolution reconstruction denoising diffusion probabilistic model normalized flow adversarial neural network variational auto-encoder

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FedFLD: Heterogeneous Federated Learning via Forget-Less Distillation

FedFLD: Heterogeneous Federated Learning via Forget-Less Dis...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Yi, Xiaoyang Zhang, Jian Chen, Jing Bao, Yuru Xing, Lingkai College of Cyber Science Nankai University China College of Computer Science Nankai University China Tianjin Key Laboratory of Network and Data Security Technology Tianjin China

ISBN: (纸本)9798350368741

Federated learning, as a distributed machine learning paradigm, enhances privacy protection but faces the challenge of heterogeneity. Data-free knowledge distillation (DFKD) methods attempt to overcome this challenge by using a generator to synthesize samples for fine-tuning a global model. However, these methods often suffer from significant shifts in output distribution, leading to catastrophic forgetting. To tackle these issues, we propose FedFLD, a novel federated forget-less distillation framework that mitigates catastrophic forgetting in DFKD while addressing the heterogeneity challenge. Specifically, FedFLD guides the generator's training from three key aspects and constrains the output distribution with an elastic weight consolidation penalty term. By synthesizing diverse samples from different perspectives through additional generator updates, FedFLD facilitates effective knowledge distillation from local models to the global model. Additionally, the global model is further optimized via the heterogeneity fine-tuning process, mitigating the bias from heterogeneity and resulting in a more expressive and robust model. © 2025 IEEE.

关键词： Catastrophic Forgetting Data-free Knowledge Distillation Federated learning Heterogeneity

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Research and Implementation of Electric Equipment Connectivity Data Analysis Model Based on Graph Database 1

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4th International Conference on 3D Imaging Technologies- Multidimensional Signal Processing and Deep Learning, 3D IT-MSP and DL 2022

作者： Qiao, Junfeng Peng, Lin Zhou, Aihua Zhu, Lipeng Yang, Pei Pan, Sen State Grid Key Laboratory of Information & Network Security State Grid Smart Grid Research Institute Co. Ltd Nanjing210014 China

ISBN: (数字)9789819911455

ISBN: (纸本)9789819911448

Effective description of electric data resources is an important basis for electric data modeling. With the access of large-scale distributed new energy, electric allocation data resources are more complex and changeable, and existing data description methods are difficult to accurately describe. Aiming at the above problems, the research on hierarchical description method of electric distribution and distributed new energy data resources based on complex network is carried out. Based on the complex network theory, the hierarchical description method of data resources is formed by comprehensively describing the external characteristics, internal elements, organizational structure, and relationships between data resources. The holographic modeling technology of electric distribution and distributed new energy data resources based on graphic computing is studied. Data modeling is an important basis for data fusion and sharing. The production, operation, control, measurement, and other business links in the field of electric distribution and distributed new energy maintain a certain degree of independence from each other, and there is an "information island" problem in the data resources of each link. In view of the above problems, research on holographic modeling technology of electric distribution and distributed new energy data resources based on graphic computing is carried out. Based on graphic computing technology, equipment is taken as the core, covering resources, assets, measurement, topology, graphics, and other all-round information, to build a holographic data model of electric distribution and distributed new energy data resources, and support the deepening integration and sharing of electric distribution data. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Complex networks

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Design and implementation of cooperative multi-agent based on MAPPO 5

Design and implementation of cooperative multi-agent based o...

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5th International Conference on Telecommunications, Optics, and computer Science, TOCS 2024

作者： Wang, Haolong Fei, Rong Chen, Ruizhu School of Computer Science and Engineering Xi'an University of Technology Xi'an China Shannxi Key Laboratory for Network Computing and Security Technology Xi'an University of Technology Xi'an China

ISBN: (纸本)9781510691667

In the past few years, with the continuous development of deep reinforcement learning, the application of deep reinforcement has been set off within the realms of self-driving vehicle technology, game games, and natural language processing, and the proximal policy optimization algorithm has emerged as a focal point in the sphere of deep reinforcement learning research. In this paper, the Proximal Policy Optimization (PPO) algorithm and the Multi-Agent Proximal Policy Optimization (MAPPO) algorithm are studied, and a cooperative multi-agent system is designed and implemented based on the MAPPO algorithm in the field of StarCraft game, which is compared with the popular Q-Value Mixing (QMIX) reinforcement learning algorithm in recent years, and proposes a direction for subsequent system improvement. In the proximal policy optimization algorithm, the traditional policy gradient algorithm, natural policy gradient algorithm, trust domain policy optimization algorithm, proximal policy optimization algorithm, and independent proximal policy optimization algorithm are studied. In the multi-agent proximal policy optimization algorithm, the centralized training decentralized execution architecture, athlete critic method, value standardization, experience playback, and GAE (GAE) adopted by the MAPPO algorithm are studied. Designing and implementing a cooperative multi-agent system, introducing the StarCraft environmental campaign. Regarding the specifics of the training process, the policy gradient algorithm is used to update the policy network, the time series difference algorithm is improved, and the generalized advantage estimation is used to update the value network. Compared with the QMIX learning algorithm, the model trained by the MAPPO algorithm has the advantages of faster convergence and better stability. © 2025 SPIE.

关键词： Deep reinforcement learning

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Prediction of miRNA family based on class-incremental learning

Prediction of miRNA family based on class-incremental learni...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Liu, Yajun Qiu, Lulu Li, Aimin Fei, Rong Li, Junhuai Wu, Fang-Xiang Xi'an University of Technology Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an China Division of Biomedical Engineering Department of Mechanical Engineering Department of Computer Science Saskatoon Canada

ISBN: (纸本)9798350386226

With the development of deep sequencing, recent studies indicate that a miRNA precursor can generate multiple miRNA isoforms (isomiRs). The family prediction of canonical miRNAs and isomiRs could provide a basis for miRNA functional research. In this study, we propose a novel method for family identification of canonical miRNA and isomiRs based on incremental learning. First, a benchmark dataset is constructed by processing data based on miRNA sequences and their family annotation. Moreover, sequence embedding and RNN are used for capturing essential features and inherent dependencies within a sequence. Finally, incremental learning is applied to accommodate the continuous influx of miRNA sequencing data, enabling RNN to stay relevant and effective over time. Comparative experiments and ablation studies illustrate the effectiveness of our model, which can help to comprehensively understand miRNA's function. © 2024 IEEE.

关键词： Contrastive Learning

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Efficient Wildcard Searchable Symmetric Encryption with Forward and Backward security 1st

Efficient Wildcard Searchable Symmetric Encryption with For...

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1st International Conference on Artificial Intelligence security and Privacy, AIS and P 2023

作者： Zhang, Xi Su, Ye Wei, Zhongkai Shen, Wenting Qin, Jing College of Computer and Cyber Security Hebei Normal University Shijiazhuang050024 China Hebei Provincial Key Laboratory of Network and Information Security Hebei Normal University Shijiazhuang050024 China School of Information Science and Engineering Shandong Normal University Jinan250358 China School of Mathematics Shandong University Jinan250100 China College of Computer Science and Technology Qingdao University Qingdao266071 China

ISBN: (纸本)9789819997848

Wildcard Searchable Symmetric Encryption can achieve flexibility and pattern matching while protecting data privacy. There is a promising future for combining secure wildcard search with emerging technologies such as AI. However, there are challenges in reducing the communication cost and improving security. In this paper, we propose an efficient wildcard searchable symmetric encryption with forward and backward security. The complexity of communication costs in the search protocol is O(1), independent of the number of characters of wildcard keywords, or files in the search result. It is achieved by a double-compressed index in which the character set and file identifiers are encoded simultaneously. Then, the double-compressed index provides a possibility to achieve oblivious keyword query and update, so that the proposed scheme only reveals the query type (search or update) without anything else. It achieves forward and backward security using the distributed multi-point function and an additively homomorphic symmetric encryption scheme. Detailed security proof and the theoretical comparison show the improvement in security and efficiency. The acceptable overheads of the proposed scheme are presented by the extensive performance evaluation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Pattern matching

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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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