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arXiv 2022年

作者： Cui, Leilei Başar, Tamer Jiang, Zhong-Ping The Control and Networks Lab Department of Electrical and Computer Engineering Tandon School of Engineering New York University BrooklynNY11201 United States The Coordinated Science Laboratory University of Illinois Urbana-Champaign UrbanaIL61801 United States

This paper proposes a novel robust reinforcement learning framework for discrete-time linear systems with model mismatch that may arise from the sim-to-real gap. A key strategy is to invoke advanced techniques from control theory. Using the formulation of the classical risk-sensitive linear quadratic Gaussian control, a dual-loop policy optimization algorithm is proposed to generate a robust optimal controller. The dual-loop policy optimization algorithm is shown to be globally and uniformly convergent, and robust against disturbances during the learning process. This robustness property is called small-disturbance input-to-state stability and guarantees that the proposed policy optimization algorithm converges to a small neighborhood of the optimal controller as long as the disturbance at each learning step is relatively small. In addition, when the system dynamics is unknown, a novel model-free off-policy policy optimization algorithm is proposed. Finally, numerical examples are provided to illustrate the proposed algorithm. Copyright © 2022, The Authors. All rights reserved.

关键词： Reinforcement learning

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An embarrassingly simple approach to semi-supervised few-shot learning 22

An embarrassingly simple approach to semi-supervised few-sho...

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Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Xiu-Shen Wei He-Yang Xu Faen Zhang Yuxin Peng Wei Zhou School of Computer Science and Engineering Nanjing University of Science and Technology and State Key Laboratory of Integrated Services Networks Xidian University School of Computer Science and Engineering Nanjing University of Science and Technology Qingdao AInnovation Technology Group Co. Ltd Wangxuan Institute of Computer Technology Peking University CICC Alpha (Beijing) Private Equity

ISBN: (纸本)9781713871088

Semi-supervised few-shot learning consists in training a classifier to adapt to new tasks with limited labeled data and a fixed quantity of unlabeled data. Many sophisticated methods have been developed to address the challenges this problem comprises. In this paper, we propose a simple but quite effective approach to predict accurate negative pseudo-labels of unlabeled data from an indirect learning perspective, and then augment the extremely label-constrained support set in few-shot classification tasks. Our approach can be implemented in just few lines of code by only using off-the-shelf operations, yet it is able to outperform state-of-the-art methods on four benchmark datasets.

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SBPF: Sensitiveness Based Pruning Framework For Convolutional Neural Network On Image Classification

arXiv

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arXiv 2022年

作者： Lu, Yiheng Gong, Maoguo Zhao, Wei Feng, Kai-Yuan Li, Hao State Key Laboratory of Integrated Services Networks School of Computer Science and Technology Xidian University Xi’an710071 China Key Laboratory of Intelligent Perception and Image Understanding School of Electronic Engineering Ministry of Education Xidian University Xi’an710071 China

Pruning techniques are used comprehensively to compress convolutional neural networks (CNNs) on image classification. However, the majority of pruning methods require a well pre-trained model to provide useful supporting parameters, such as C1-norm, BatchNorm value and gradient information, which may lead to inconsistency of filter evaluation if the parameters of the pre-trained model are not well optimized. Therefore, we propose a sensitiveness based method to evaluate the importance of each layer from the perspective of inference accuracy by adding extra damage for the original model. Because the performance of the accuracy is determined by the distribution of parameters across all layers rather than individual parameter, the sensitiveness based method will be robust to update of parameters. Namely, we can obtain similar importance evaluation of each convolutional layer between the imperfect-trained and fully trained models. For VGG-16 on CIFAR-10, even when the original model is only trained with 50 epochs, we can get same evaluation of layer importance as the results when the model is trained fully. Then we will remove filters proportional from each layer by the quantified sensitiveness. Our sensitiveness based pruning framework is verified efficiently on VGG-16, a customized Conv-4 and ResNet-18 with CIFAR-10, MNIST and CIFAR-100, respectively. Copyright © 2022, The Authors. All rights reserved.

关键词： Convolutional neural networks

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A Framework Based on Generational and Environmental Response Strategies for Dynamic Multi-objective Optimization

arXiv

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arXiv 2022年

作者： Li, Qingya Liu, Xiangzhi Wang, Fuqiang Wang, Shuai Zhang, Peng Wu, Xiaoming Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Shandong Provincial Key Laboratory of Computer Networks Shandong China Heze Branch Qilu University of Technology Shandong Academy of Sciences Biological Engineering Technology Innovation Center of Shandong Province Shandong China

Due to the dynamics and uncertainty of the dynamic multi-objective optimization problems (DMOPs), it is difficult for algorithms to find a satisfactory solution set before the next environmental change, especially for some complex environments. One reason may be that the information in the environmental static stage can not be used well in the traditional framework. In this paper, a novel framework based on generational and environmental response strategies (FGERS) is proposed, in which response strategies are run both in the environmental change stage and the environmental static stage to obtain population evolution information of those both stages. Unlike in the traditional framework, response strategies are only run in the environmental change stage. For simplicity, the feed-forward center point strategy was chosen to be the response strategy in the novel dynamic framework (FGERS-CPS). FGERS-CPS is not only to predict change trend of the optimum solution set in the environmental change stage, but to predict the evolution trend of the population after several generations in the environmental static stage. Together with the feed-forward center point strategy, a simple memory strategy and adaptive diversity maintenance strategy were used to form the complete FGERS-CPS. On 13 DMOPs with various characteristics, FGERS-CPS was compared with four classical response strategies in the traditional framework. Experimental results show that FGERS-CPS is effective for DMOPs. Copyright © 2022, The Authors. All rights reserved.

关键词： Genetic algorithms

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Data Identification Optimal Control for Quantized Linear Systems under DoS Attacks

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IEEE Transactions on Aerospace and Electronic Systems 2025年

作者： Wang, Xianming Li, Liwei Shen, Mouquan Zhao, Xudong Wang, Qing-Guo Zhu, Zheng Hong Nanjing Tech University School of Mechanical and Power Engineering Nanjing211816 China Nanjing Tech University College of Electrical Engineering and Control Science Nanjing211816 China Dalian University of Technology Faculty of Electronic Information and Electrical Engineering Dalian116024 China Beijing Normal University Institute of Ai and Future Networks Zhuhai519087 China BNU-HKBU United International College Guangdong Provincial Key Laboratory Irads Department of Computer Science Zhuhai519087 China York University Department of Mechanical Engineering TorontoONM3J 1P3 Canada

This paper is devoted to data identification optimal control for quantized linear systems under denial of service (DoS) attacks. A least-squares algorithm is provided to identify the unknown system matrices using a data-based representation. An output estimation algorithm utilizing identified matrices is supplied to offset DoS attacks. Consequently, an optimal controller is built on the quantized and attacked data by adaptive dynamic programming. Sufficient conditions are presented to guarantee that the resultant closed-loop system is ultimately uniformly bounded. An example is produced to verify the validity of the proposed approach. © 1965-2011 IEEE.

关键词： Optimal control systems

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Secure the 5G and Beyond networks with Zero Trust and Access Control Systems for Cloud Native Architectures

Secure the 5G and Beyond Networks with Zero Trust and Access...

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ACS/IEEE International Conference on computer Systems and Applications

作者： Hisham A. Kholidy Keven Disen Andrew Karam Elhadj Benkhelifa Mohammad A. Rahman Atta-Ur Rahman Ibrahim Almazyad Ahmed F. Sayed Rakia Jaziri Dept. of Networks and Computer Security College of Engineering SUNY Polytechnic Institute Utica NY USA Dept. of Networks and Comp. Security SUNY Polytechnic Institute Utica NY USA The Air Force Research Laboratory (AFRL RIGB) Rome NY USA Smart Systems AI and Cybersecurity Research Centre Staffordshire Univ. UK Dept of Electrical and Computer Engineering Florida International University USA Dept. of Computer Science Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia Dept. of Electrical and Computer Engineering Univ. of Arizona Tucson AZ USA Transmission Dept. Telecom Egypt Fayoum Egypt Paragraphe Research Lab. Univ. of Paris VIII France

5G networks are highly distributed, built on an open service-based architecture that requires multi-vendor hardware and software development environments, all of which create a high attack surface in the 5G networks than other proprietary fixed-function networks. Besides that, cloud-native architectures also present new security challenges. Cloud-native separates monolithic virtual machines into microservice pods, resulting in higher volumes of signaling and communication flowing through and between microservices. In addition, secure connections in monolithic applications have been replaced by untrusted communication between microservice pods, requiring additional cybersecurity capabilities. Access control systems were created to provide reliability and limit access to an organization’s assets. However, due to technology's constant evolution and dynamicity, these conventional security systems lack the security to protect an organization’s information because they were created to address access control for known users. For 5G based cloud native technology, these access controls need to be taken further by implementing a Zero Trust model to secure one’s essential assets for all users within the system. Zero Trust is implemented in an access control system under the concept "Never Trust, Always Verify". In this paper, we implement zero trust as a factor within access control systems by combining the principles of access control systems and zero-trust security by factoring in the user’s historical behavior and recommendations into the mix.

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Unlocking Mode Programming with Multi-Plane Light Conversion Using computer-Generated Hologram Optimisation

arXiv

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arXiv 2024年

作者： Rothe, Stefan Barbosa, Fabio Czarske, Jürgen W. Ferreira, Filipe M. Department of Applied Physics Yale University New HavenCT06520 United States Optical Networks Dept Electronic & Electrical Eng University College London United Kingdom TU Dresden Faculty of Electrical and Computer Engineering Laboratory of Measurement and Sensor System Technique Dresden01062 Germany TU Dresden Faculty of Physics Dresden01062 Germany

Programmable optical devices provide performance enhancement and flexibility to spatial multiplexing systems enabling transmission of tributaries in high-order eigenmodes of spatially-diverse transmission media, like multimode fibre (MMF). Wavefront shaping with spatial light modulators (SLMs) facilitates scalability of the transmission media by allowing for channel diagonalization and quasi-single-input single-output operation. Programmable mode multiplexing configurations like multi-plane light conversion (MPLC) utilise the SLM and offer the potential to simultaneously launch an arbitrary subset of spatial tributaries in any N-mode MMF. Such programmable optical processor would enable the throughput of space-division multiplexing (SDM) systems to be progressively increased by addressing a growing number of tributaries over one MMF and in this way meet a growing traffic demand – similarly to the wavelength-division multiplexing evolution path. Conventionally, MPLC phasemasks are calculated using the wavefront matching algorithm (WMA). However, this method does not exploit the full potential of programmable mode multiplexers. We show, that computer-generated hologram algorithms like direct search enable significant improvement compared to the traditional WMA-approach. Such gains are enabled by tailored cost functions with dynamic constraints concerning insertion loss as well as mode extinction ratio. We show that average mode extinction ratio can be greatly improved by as much as ≈ 15 dB at the expense of insertion loss deterioration of © 2024, CC BY.

关键词： Light modulators

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Research on Security of Key Algorithms in Intelligent Driving System

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Chinese Journal of Electronics 2019年第1期28卷 29-38页

作者： LIU Shangdong WU Ye JI Yimu CHEN Chen BI Qiang JIAO Zhipeng GONG Jian WANG Ruchuan School of Computer Science and Engineering Southeast University School of Computer Science Nanjing University of Posts and Telecommunications Jiangsu Provincial Key Laboratory of Computer Network Technology Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks

With the rapid development of the smart driving technology, the security of smart driving algorithms is becoming more and more important. Four core smart driving algorithms are determined by studying the architecture of smart driving algorithm. These algorithms comprise local path planning, pedestrian detection, lane detection and obstacle detection. The security issues of these algorithms are investigated by closely examining the work carried out by the algorithms. We found that there are vulnerabilities in all four algorithms. These vulnerabilities can cause abnormality and even road accidents for the smart cars. The final experiment shows that the vulnerabilities of these algorithms do exist under certain circumstances and therefore have high security risks. This study will lay a foundation to improve the security of the smart driving system.

关键词： Smart driving algorithm Local path planning Pedestrian detection Lane line detection Obstacle detection

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Semantic Communication-Aware End-to-End Routing in Large-Scale LEO Satellite networks

Semantic Communication-Aware End-to-End Routing in Large-Sca...

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Metaverse Computing, Networking and Applications (MetaCom), IEEE International Conference on

作者： Binquan Guo Zehui Xiong Bo Wang Tony Q. S. Quek Zhu Han State Key Laboratory of Integrated Service Networks Xidian University Xi’an P. R. China Tianjin Artificial Intelligence Innovation Center (TAIIC) Tianjin P. R. China Pillar of ISTD Singapore University of Technology and Design Singapore Department of Electrical and Computer Engineering University of Houston Houston TX USA

ISBN: (数字)9798331515997

ISBN: (纸本)9798331516000

Enhanced by inter-satellite links, large-scale satellite networks (SNs) hold promise to deliver low-latency services globally. However, given the scarcity of available spectrum and the capacity limitations of Shannon’s classic information theory, supporting the ever-growing multimedia communication services poses a challenge for SNs. A promising direction is to employ semantic communication (SC), which utilizes artificial intelligence (AI) to extract and transmit the "meaning" of raw data, thereby reducing bandwidth consumption. We identify that SC requires the raw data to be transmitted to a satellite equipped with the AI encoder supporting the same knowledge base for successful decoding at the destination. This advanced functionality renders traditional routing schemes such as contact graph routing (CGR) not seamlessly suitable. Hence, we develop the SC-aware routing (SCR) strategy aimed at optimizing SC’s delay and bandwidth consumption. Firstly, we define the problem using integer linear programming (ILP) and observe its intractability in practice. Then, we use the temporal graph to represent diverse knowledge bases, semantic encoders and transmission resources of SNs, and design SCR based on it. Simulations conducted on the Starlink constellation show that SCR runs much faster than the ILP solver. Additionally, we verify that SC can bring significant performance gains in terms of higher data throughput and lower average end-to-end delay when compared to CGR.

关键词： Satellites Spectral efficiency Knowledge based systems Bandwidth Performance gain Routing Throughput Polynomials Delays Multimedia communication

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Extended $T$T: Learning With Mixed Closed-Set and Open-Set Noisy Labels

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2022年第3期45卷 3047-3058页

作者： Xiaobo Xia Bo Han Nannan Wang Jiankang Deng Jiatong Li Yinian Mao Tongliang Liu Trustworthy Machine Learning Lab School of Computer Science Faculty of Engineering University of Sydney Camperdown NSW Australia Department of Computer Science Hong Kong Baptist University Hong Kong State Key Laboratory of Integrated Services Networks School of Telecommunications Engineering Xidian University Xi'an Shaanxi China Department of Computing Imperial College London London U.K. Meituan Beijing China

The noise transition matrix $T$ , reflecting the probabilities that true labels flip into noisy ones, is of vital importance to model label noise and build statistically consistent classifiers. The traditional transition matrix is limited to model closed-set label noise, where noisy training data have true class labels within the noisy label set. It is unfitted to employ such a transition matrix to model open-set label noise, where some true class labels are outside the noisy label set. Therefore, when considering a more realistic situation, i.e., both closed-set and open-set label noises occur, prior works will give unbelievable solutions. Besides, the traditional transition matrix is mostly limited to model instance-independent label noise, which may not perform well in practice. In this paper, we focus on learning with the mixed closed-set and open-set noisy labels. We address the aforementioned issues by extending the traditional transition matrix to be able to model mixed label noise, and further to the cluster-dependent transition matrix to better combat the instance-dependent label noise in real-world applications. We term the proposed transition matrix as the cluster-dependent extended transition matrix. An unbiased estimator (i.e., extended $T$ -estimator) has been designed to estimate the cluster-dependent extended transition matrix by only exploiting the noisy data. Comprehensive experiments validate that our method can better cope with realistic label noise, following its more robust performance than the prior state-of-the-art label-noise learning methods.

关键词： Noise measurement Training Training data Data models Search engines Face recognition computer science

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