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information security audit system based on unlisted words

Journal of Computational information systems 2007年第4期3卷 1489-1494页

作者： Wu, Ronghui Yu, Fei Li, Renfa Xu, Cheng Yue, Guangxue College of Computer and Communication Hunan University Changsha 410082 China Guangdong Province Key Lab. of Electronic Commerce Market Application Technology Guangdong University of Business Studies Guangzhou 510320 China State Key Laboratory of Information Security Graduate School of Chinese Academy of Sciences Beijing 100049 China

This paper proposes a filtering technology based on content. This technology constructs an estimation function, and estimates the candidate properties, then chooses several properties with the highest value which are used by all documents in the document set. At the same time, it can get the characteristics which have low frequency in counter samples but high frequency in consistent samples, and those which have low frequency in consistent samples but high frequency in counter samples by calculating increase of information. And then, it can choose several unlisted words as new keywords which have higher information increase and have not been used as keywords, and audit and filtering of network information at last.

关键词： Security of data

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Securely Fine-tuning Pre-trained Encoders Against Adversarial Examples

arXiv

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arXiv 2024年

作者： Zhou, Ziqi Li, Minghui Liu, Wei Hu, Shengshan Zhang, Yechao Wan, Wei Xue, Lulu Zhang, Leo Yu Yao, Dezhong Jin, Hai National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab. China Cluster and Grid Computing Lab. China Hubei Engineering Research Center on Big Data Security China Hubei Key Laboratory of Distributed System Security China School of Computer Science and Technology Huazhong University of Science and Technology China School of Software Engineering Huazhong University of Science and Technology China School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Information and Communication Technology Griffith University Australia

With the evolution of self-supervised learning, the pre-training paradigm has emerged as a predominant solution within the deep learning landscape. Model providers furnish pre-trained encoders designed to function as versatile feature extractors, enabling downstream users to harness the benefits of expansive models with minimal effort through fine-tuning. Nevertheless, recent works have exposed a vulnerability in pre-trained encoders, highlighting their susceptibility to downstream-agnostic adversarial examples (DAEs) meticulously crafted by attackers. The lingering question pertains to the feasibility of fortifying the robustness of downstream models against DAEs, particularly in scenarios where the pre-trained encoders are publicly accessible to the attackers. In this paper, we initially delve into existing defensive mechanisms against adversarial examples within the pre-training paradigm. Our findings reveal that the failure of current defenses stems from the domain shift between pre-training data and downstream tasks, as well as the sensitivity of encoder parameters. In response to these challenges, we propose Genetic Evolution-Nurtured Adversarial Fine-tuning (Gen-AF), a two-stage adversarial fine-tuning approach aimed at enhancing the robustness of downstream models. Gen-AF employs a genetic-directed dual-track adversarial fine-tuning strategy in its first stage to effectively inherit the pre-trained encoder. This involves optimizing the pre-trained encoder and classifier separately while incorporating genetic regularization to preserve the model’s topology. In the second stage, Gen-AF assesses the robust sensitivity of each layer and creates a dictionary, based on which the top-k robust redundant layers are selected with the remaining layers held fixed. Upon this foundation, we conduct evolutionary adaptability fine-tuning to further enhance the model’s generalizability. Our extensive experiments, conducted across ten self-supervised training methods and six

关键词： Topology

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A simulator for swarm AUVs acoustic communication networking 11

A simulator for swarm AUVs acoustic communication networking

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11th ACM International Conference on Underwater Networks and systems, WUWNet 2016

作者： Li, Guannan Liu, Jun Wang, Xue Xu, Hongli Cui, Jun-Hong Shenyang Institute of Automation CAS University of Chinese Academy of Sciences No. 114 Nanta Street Shenhe District Shenyang China College of Computer Science and Technology Jilin University State Key Laboratory of Robotics Key Laboratory of System Control and Information Processing Ministry of Education of China Changchun China Shenyang Institute of Automation CAS Northeast University No. 114 Nanta Street Shenhe District Shenyang China Shenyang Institute of Automation CAS No. 114 Nanta Street Shenhe District Shenyang China College of Computer Science and Technology Jilin University Changchun China Underwater Sensor Network Lab. University of Connecticut StorrsCT06269 United States

ISBN: (纸本)9781450346375

This paper presents a simulator for swarm operations designed to verify algorithms for a swarm of autonomous underwater robots (AUVs), specifically for constructing an underwater communication network with AUVs carrying acoustic communication devices. This simulator consists of three nodes: a virtual vehicle node (VV), a virtual environment node (VE), and a visual showing node (VS). The modular design treats AUV models as a combination of virtual equipment. An expert acoustic communication simulator is embedded in this simulator, to simulate scenarios with dynamic acoustic communication nodes. The several simulations we have performed demonstrate that this simulator is easy to use and can be further improved.

关键词： Simulators

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Multi-objective Group Decision and Cooperative Planning Based on Preference and Fuzzy Measure 1

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7th Chinese Conference on Swarm Intelligence and Cooperative control, CCSICC 2023

作者： Ma, Xiwen Xie, Wei Yang, Jingsong Zhang, Weidong Lu, Jinhui School of Oceanography Shanghai Jiao Tong University Shanghai China State Key Laboratory of Satellite Ocean Environment Dynamics Second Institute of Oceanography Ministry of Natural Resources Hangzhou China Department of Automation Shanghai Jiao Tong University Shanghai China Zhuhai China School of Information and Communication Engineering Hainan University Hainan Haikou China National Engineering Research Center of Ship and Shipping Control System Shanghai China Crossocean of Suzhou technology No. 218 East Oingdao Road Suzhou China

ISBN: (数字)9789819733323

ISBN: (纸本)9789819733316

To pursue interest balance and improve collab.ration efficiency, this paper proposes a multi-agent cooperation method integrating decision-making and planning. Based on preference and fuzzy measures, this method can balance individual and group benefits under fuzzy uncertain environment. The equilibrium solution of a multi-agent multi-objective game, namely group decision-making (GDM) solution, can be obtained by the Gaussian oscillation particle swarm optimization (GOPSO) algorithm, which overcomes the dilemma of a multi-objective group game and improves the computational efficiency. Considering the smoothness and continuity of the trajectory, a heuristic motion planning (MP) generation model that satisfies the kinematic constraints and minimizes differential thrust is proposed, which improves the cooperation efficiency. To verify the performance of this proposed method, simulation results are presented and some comparative statements are given. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Decision making

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A Hybrid Model for Building-level Day Ahead Power Load Forecasting Based on Gated Recurrent Unit Networks

A Hybrid Model for Building-level Day Ahead Power Load Forec...

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Sustainable Power and Energy Conference (iSPEC)

作者： Hualu Zhang Boya Deng Jie Shi Key Laboratory of Modern Power System Simulation Control and Green Power New Technology of Ministry of Education Northeast Electric Power University Jilin Jilin Nanjing Nanrui Information Communication Technology Co. Ltd. NARI Group Corporation/State Grid Electric Power Research Institute Nanjing

ISBN: (纸本)9781665414401

Accurate forecasting of day-ahead electrical load is a key to optimize the operation of the integrated energy system (IES) and a fundamental function of the cyber-physical system (CPS) of IES. Due to the characteristics of different features, data collected by CPS often have different sampling frequencies, resulting in mining data trends inadequately when making a load forecast. This paper proposes an GRU-based model to combine historical data with different sampling frequencies in forecasting day-ahead building-level load. First, day-level features are clustered by K-means. Hour-levels features are then partitioned according to clustering results and inputted separately into a gated recurrent unit (GRU) based neural network to capture relationships between external features and load fluctuations. The temporal dependencies of minute-level data are captured by the GRU network directly without any external features. Finally, the least square method is used to combine forecasting results at each hour of two models. The simulation results present that the proposed model performs better than a single model when applying to real-world data.

关键词： Load forecasting Simulation Neural networks Training data Predictive models Logic gates Data models

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InP-based micro-disc lasers using non-concentric hole as mode control and light extraction mechanism

InP-based micro-disc lasers using non-concentric hole as mod...

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Asia communications and Photonics Conference and Exhibition, ACP 2010

作者： Cai, Xinlun Mezosi, Gabor Wang, Zhuoran Li, Bei Sorel, Marc Zhu, Jiangbo Chi, Nan Yu, Siyuan Dept. of Electronic and Electrical Engineering University of Bristol BS8 1UB Bristol United Kingdom Dept. of Electronics and Electrical Engineering University of Glasgow G12 8LT Glasgow United Kingdom School of Optoelectronic Information University of Electronic Science and Technology of China Shanghai China State Key Lab. of ASIC and System Dept. of Communications Science and Engineering Fudan University Shanghai China

We demonstrate electrically-pumped room-temperature continuous-wave lasing in InP-based micro-disc lasers with threshold current of 6.5mA. A non-concentric hole inside the disc is used to extract the light and control... 详细信息

ISBN: (纸本)9781424471119

关键词： Indium phosphide

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Physical-layer key distribution using synchronous complex dynamics of DBR semiconductor lasers

arXiv

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arXiv 2023年

作者： Wang, Anbang Du, Yicheng Li, Qingtian Wang, Longsheng Jia, Zhiwei Qin, Yuwen Wang, Yuncai Institute of Advanced Photonics Technology School of Information Engineering Guangdong University of Technology Guangzhou51006 China Key Laboratory of Photonic Technology for Integrated Sensing and Communication Ministry of Education of China Guangdong University of Technology Guangzhou51006 China Guangdong Provincial Key Laboratory of Information Photonics Technology Guangdong University of Technology Guangzhou51006 China Key Lab of Advanced Transducers and Intelligent Control System Ministry of Education Taiyuan University of Technology Taiyuan030024 China College of Optoelectronics Taiyuan University of Technology Taiyuan030024 China

Common-signal-induced synchronization of semiconductor lasers with optical feedback inspired a promising physical key distribution with information-theoretic security and potential in high rate. A significant challenge is the requirement to shorten the synchronization recovery time for increasing key rate without sacrificing operation parameter space for security. Here, open-loop synchronization of wavelength-tunable multi-section distributed Bragg reflector (DBR) lasers is proposed as a solution for physical-layer key distribution. Experiments show that the synchronization is sensitive to two operation parameters, i.e., currents of grating section and phase section. Furthermore, fast wavelength-shift keying synchronization can be achieved by direct modulation on one of the two currents. The synchronization recovery time is shortened by one order of magnitude compared to close-loop synchronization. An experimental implementation is demonstrated with a final key rate of 5.98 Mbit/s over 160 km optical fiber distance. It is thus believed that fast-tunable multi-section semiconductor lasers opens a new avenue of high-rate physical-layer key distribution using laser synchronization. Copyright © 2023, The Authors. All rights reserved.

关键词： Synchronization

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Metro train scheduling optimization for virtual coupling based train control

Metro train scheduling optimization for virtual coupling bas...

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Chinese Automation Congress (CAC)

作者： Yuxuan Li Li Zhu Xi Wang Hongli Zhao Xinjun Gao State Key Lab. of Rail Traffic Control and Safety Beijing Jiaotong University Beijing China School of Electronics and Information Engineering Beijing Jiaotong University Beijing P.R China Sciences Signal and Communication Research Institute China Academy of Railway Beijing China

ISBN: (纸本)9781665426480

As China’s economy grows and urbanization intensifies, interchange pressures on urban rail transport are increasing in parallel, and the capacity of the existing CBTC system is saturated. Therefore, this paper uses virtual coupling to reduce passenger pressure during peak periods. A collab.rative optimization model is proposed to balance train utilization, train dwell time, and passenger waiting time to optimize train schedules. We use the CPLEX optimizer to solve the model and generate a train schedule based on the optimal solution. Numerical experiments based on real-world operations verify the validity of the model.

关键词： Couplings Rails Schedules Automation Collab.ration Numerical models Optimization

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AI-aided power electronic converters automatic online real-time efficiency optimization method

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Fundamental Research 2023年第3期5卷 1111-1116页

作者： Tang, Yuanhong Cao, Di Xiao, Jian Jiang, Chenying Huang, Qi Li, Yunwei Chen, Zhe Blaabjerg, Frede Hu, Weihao Power System Wide-area Measurement and Control Sichuan Provincial Key Laboratory School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu 611731 China MORNSUN Guangzhou Science & Technology Co. Ltd. Guangzhou 510663 China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu 611731 China Yangtze Delta Region Institute (Quzhou) University of Electronic Science and Technology of China Quzhou 324000 China Department of Chengdu State Grid Corporation of China Chengdu 610599 China Southwest University of Science and Technology Mianyang 621010 China Department of Electrical and Computer Engineering University of Alberta Edmonton AB T6G 2V4 Canada Department of Energy Technology Aalborg University Aalborg 9220 Denmark

Energy losses during the conversion and supply of electric power are considered a significant issue and cannot be estimated. Improvement in the efficiency of energy conversion systems is highly restricted because of their internal nonlinearity and complexity. Thus, inspired by the successful utilization of robotic chemists, we demonstrate a pioneering concept of artificial intelligence (AI)-aided automatic online real-time optimization of a power electronics converter using a dual active bridge (DAB) converter as an example. An optimal modulation strategy was obtained through repeated automatic exploration experiments on a practical DAB converter platform. Specifically, the DAB experimental platform operated autonomously around the clock for approximately 71 h. It performed 120,000 consecutive experiments (12,000 episodes) within a six-variable experimental space driven by a deep deterministic policy gradient (DDPG) algorithm. The proposed AI-aided automatic online real-time optimization method achieved significantly improved efficiency of power conversion and supply. Consequently, zero carbon emissions may be obtained in the future. © 2023

关键词： Artificial intelligence Automatic online real-time exploration DC-DC converter Dual active bridge converter Energy conversion system Power electronics

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JSRNN: Joint Sampling and Reconstruction Neural Networks for High Quality Image Compressed Sensing

arXiv

引用

arXiv 2022年

作者： Zeng, Chunyan Ye, Jiaxiang Wang, Zhifeng Zhao, Nan Wu, Minghu Hubei Key Lab. for High-efficiency Utiliz. of Solar Ener. and Oper. Control of Energy Storage System Hubei University of Technology Wuhan430068 China School of Educational Information Technology Hubei Research Center for Educational Informationization Central China Normal University Wuhan430079 China

Most Deep Learning (DL) based Compressed Sensing (DCS) algorithms adopt a single neural network for signal reconstruction, and fail to jointly consider the influences of the sampling operation for reconstruction. In this paper, we propose unified framework, which jointly considers the sampling and reconstruction process for image compressive sensing based on welldesigned cascade neural networks. Two sub-networks, which are the sampling sub-network and the reconstruction sub-network, are included in the proposed framework. In the sampling sub-network, an adaptive full connected layer instead of the traditional random matrix is used to mimic the sampling operator. In the reconstruction sub-network, a cascade network combining stacked denoising autoencoder (SDA) and convolutional neural network (CNN) is designed to reconstruct signals. The SDA is used to solve the signal mapping problem and the signals are initially reconstructed. Furthermore, CNN is used to fully recover the structure and texture features of the image to obtain better reconstruction performance. Extensive experiments show that this framework outperforms many other state-ofthe-art methods, especially at low sampling rates. Copyright © 2022, The Authors. All rights reserved.

关键词： Compressed sensing

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