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arXiv 2022年

作者： Wang, Yuntao Su, Zhou Benslimane, Abderrahim Xu, Qichao Dai, Minghui Li, Ruidong School of Cyber Science and Engineering Xi’an Jiaotong University China Laboratory of Computer Sciences Avignon University France School of Mechatronic Engineering and Automation Shanghai University China State Key Laboratory of Internet of Things for Smart City University of Macau Macau China Department of Electrical and Computer Engineering Kanazawa University Japan

The proliferation of unmanned aerial vehicles (UAVs) opens up new opportunities for on-demand service provisioning anywhere and anytime, but it also exposes UAVs to various cyber threats. Low/medium-interaction honeypot is regarded as a promising lightweight defense to actively protect mobile Internet of things, especially UAV networks. Existing works primarily focused on honeypot design and attack pattern recognition, the incentive issue for motivating UAVs’ participation (e.g., sharing trapped attack data in honeypots) to collaboratively resist distributed and sophisticated attacks is still under-explored. This paper proposes a novel game-based collaborative defense approach to address optimal, fair, and feasible incentive mechanism design, in the presence of network dynamics and UAVs’ multi-dimensional private information (e.g., valid defense data (VDD) volume, communication delay, and UAV cost). Specifically, we first develop a honeypot game between UAVs under both partial and complete information asymmetry scenarios. We then devise a contract-theoretic method to solve the optimal VDD-reward contract design problem with partial information asymmetry, while ensuring truthfulness, fairness, and computational efficiency. Furthermore, under complete information asymmetry, we devise a reinforcement learning based distributed method to dynamically design optimal contracts for distinct types of UAVs in the fast-changing network. Experimental simulations show that the proposed scheme can motivate UAV’s collaboration in VDD sharing and enhance defensive effectiveness, compared with existing solutions. Copyright © 2022, The Authors. All rights reserved.

关键词： Reinforcement learning

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Fast Event-based Double Integral for Real-time Robotics

Fast Event-based Double Integral for Real-time Robotics

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IEEE International Conference on Robotics and automation (ICRA)

作者： Shijie Lin Yingqiang Zhang Dongyue Huang Bin Zhou Xiaowei Luo Jia Pan Department of Computer Science The University of Hong Kong Hong Kong SAR China Peng Cheng Laboratory Shenzhen Guangdong China Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong SAR China School of Computer Science and Engineering Beihang University Beijing China Department of Architecture and Civil En-gineering City University of Hong Kong Hong Kong SAR China

Motion deblurring is a critical ill-posed problem that is important in many vision-based robotics applications. The recently proposed event-based double integral (EDI) provides a theoretical framework for solving the deblurring prob-lem with the event camera and generating clear images at high frame-rate. However, the original EDI is mainly designed for offline computation and does not support real-time requirement in many robotics applications. In this paper, we propose the fast EDI, an efficient implementation of EDI that can achieve real-time online computation on single-core CPU devices, which is common for physical robotic platforms used in practice. In experiments, our method can handle event rates at as high as 13 million event per second in a wide variety of challenging lighting conditions. We demonstrate the benefit on multiple downstream real-time applications, including localization, vi-sual tag detection, and feature matching.

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Event-Triggered-Based Privacy-Preserving Finite-Time Flocking Control for Networked Multi-Agent Systems

Event-Triggered-Based Privacy-Preserving Finite-Time Flockin...

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IEEE Conference on Decision and Control

作者： Jiangfeng Yue Kaiyu Qin Jinliang Shao Mengji Shi Weihao Li Wei Xing Zheng School of Aeronautics and Astronautics University of Electronic Science and Technology of China Chengdu China Aircraft Swarm Intelligent Sensing and Cooperative Control Key Laboratory of Sichuan Province Chengdu China School of Automation Engineering University of Electronic Science and Technology of China Chengdu China Laboratory of Electromagnetic Space Cognition and Intelligent Control Beijing China School of Computer Data and Mathematical Sciences Western Sydney University Sydney NSW Australia

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

Drawing inspiration from the dynamics of biological groups, flocking behavior has captivated interest due to its adaptive, self-organizing, and resilient characteristics. However, the presence of numerous agents in an open network environment raises concerns regarding the potential for information leakage during flocking movements. To address this issue, we present a decentralized output mapping function, executed independently by each individual agent, to ensure the maintenance of flocking behavior, including velocity alignment, cohesion and collision avoidance. Additionally, to enhance resource efficiency and convergence speed, an event-triggered mechanism is employed to mitigate the excessive consumption of bandwidth and computation resources in traditional flocking control algorithms. Moreover, the designed finite-time controller facilitates rapid convergence of flocking behavior. Finally, the efficacy of our proposed approach is corroborated through numerical simulation experiments.

关键词： Privacy Event detection System performance Bandwidth Numerical simulation Stability analysis Trajectory Optimization Numerical stability Convergence

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A Learning-based Honeypot Game for Collaborative Defense in UAV Networks

A Learning-based Honeypot Game for Collaborative Defense in ...

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GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Yuntao Wang Zhou Su Abderrahim Benslimane Qichao Xu Minghui Dai Ruidong Li School of Cyber Science and Engineering Xi'an Jiaotong University China Laboratory of Computer Sciences Avignon University France School of Mechatronic Engineering and Automation Shanghai University China State Key Laboratory of Internet of Things for Smart City University of Macau Macau China Department of Electrical and Computer Engineering Kanazawa University Japan

ISBN: (纸本)9781665435413

The proliferation of unmanned aerial vehicles (UAVs) opens up new opportunities for on-demand service provisioning anywhere and anytime, but it also exposes UAVs to various cyber threats. Low/medium-interaction honeypot is regarded as a promising lightweight defense to actively protect mobile Internet of things, especially UAV networks. Existing works primarily focused on honeypot design and attack pattern recognition, the incentive issue for motivating UAVs' participation (e.g., sharing trapped attack data in honeypots) to collaboratively resist distributed and sophisticated attacks is still under-explored. This paper proposes a novel game-based collaborative defense approach to address optimal, fair, and feasible incentive mechanism design, in the pres-ence of network dynamics and UAVs' multi-dimensional private information (e.g., valid defense data (VDD) volume, communication delay, and UAV cost). Specifically, we first develop a honeypot game between UAVs under both partial and complete information asymmetry scenarios. We then devise a contract-theoretic method to solve the optimal VDD-reward contract design problem with partial information asymmetry, while ensuring truthfulness, fair-ness, and computational efficiency. Furthermore, under complete information asymmetry, we devise a reinforcement learning based distributed method to dynamically design optimal contracts for distinct types of UAVs in the fast-changing network. Experimental simulations show that the proposed scheme can motivate UAV's collaboration in VDD sharing and enhance defensive effectiveness, compared with existing solutions.

关键词： Costs Heuristic algorithms Collaboration Games Resists Reinforcement learning Autonomous aerial vehicles

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6G-Enabled Edge AI for Metaverse:Challenges, Methods,and Future Research Directions

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Journal of Communications and Information Networks 2022年第2期7卷 107-121页

作者： Luyi Chang Zhe Zhang Pei Li Shan Xi Wei Guo Yukang Shen Zehui Xiong Jiawen Kang Dusit Niyato Xiuquan Qiao Yi Wu School of Data Science and Technology Heilongjiang UniversityHarbin 150080China Institute for Cryptology and Network Security Heilongjiang UniversityHarbin 150080China SenseTime Group Limited Shenzhen 518000China Pillar of Information Systems Technology and Design Singapore University of Technology and Design.Singapore 487372Singapore School of Automation.Guangdong University of Technology and Center for Intelligenl Batch Manufacturing based on IoT TechnologyGuangzhou 510006China School of Computer Science and Engineering Nanyang Technological UniversitySingapore 639798.Singapore State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications.Beijing 100876China

Sixth generation(6G)enabled edge intelligence opens up a new era of Internet of everything and makes it possible to interconnect people-devices-cloud anytime,*** and more next-generation wireless network smart service applications are changing our way of life and improving our quality of *** the hottest new form of next-generation Internet applications,Metaverse is striving to connect billions of users and create a shared world where virtual and reality ***,limited by resources,computing power,and sensory devices,Metaverse is still far from realizing its full vision of immersion,materialization,and *** this end,this survey aims to realize this vision through the organic integration of 6G-enabled edge artificial intelligence(AI)and ***,we first introduce three new types of edge-Metaverse architectures that use 6G-enabled edge AI to solve resource and computing constraints in *** we summarize technical challenges that these architectures face in Metaverse and the existing ***,we explore how the edge-Metaverse architecture technology helps Metaverse to interact and share digital ***,we discuss future research directions to realize the true vision of Metaverse with 6G-enabled edge AI.

关键词： edge artificial intelligence artificial intelli-gence 6G metaverse federated learning

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Multi-Objective Optimized Generative Adversarial Networks For Video Anomaly Detection

Multi-Objective Optimized Generative Adversarial Networks Fo...

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Chinese automation Congress (CAC)

作者： Zhexiao Cao Yao Fu Tian Wang Deyuan Liu Jian Wang Hichem Snoussi Institute of Artificial Intelligence Beihang University Beijing China Fine Mechanics and Physics Chinese Academy of Sciences Changchun Institute of Optics Changchun China Zhongguancun Laboratory Institute of Artificial Intelligence SKLSDE Beihang University Beijing China School of Automation Science and Electrical Engineering Beihang University Beijing China School of Computer Science Wuhan University Wuhan China Laboratory of Computer Science and Digital Society University of Technology of Troyes Troyes France

The objective of video anomaly detection is to distinguish events within videos that deviate from expected normal behavior. An effective anomaly detection model requires strong spatio-temporal feature extraction capabilities to capture both appearance and motion information from the video. We utilize a generative adversarial networks model to perform anomaly detection by predicting future frame. The model is trained with multi-objective loss function for appearance and motion constraints. We introduce multi-objective optimization algorithm to guarantee the convergence of training objectives, including intensity loss, gradient loss for appearance constraints, optical flow loss for motion constraints and adversarial loss for adversarial training. By ensuring corresponding generative outcome for both normal events to confirm to expectation and anomaly events not to do so, we obtain an anomaly detection model with satisfying experimental result on video anomaly detection datasets, showing the success of the proposed training strategy.

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Abstraction-Based Verification of Approximate Pre-Opacity for Control Systems

arXiv

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arXiv 2022年

作者： Hou, Junyao Liu, Siyuan Yin, Xiang Zamani, Majid Department of Automation Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University China Department of Electrical and Computer Engineering Technical University of Munich Germany Department of Computer Science LMU Munich Germany Department of Computer Science University of Colorado Boulder United States

In this paper, we consider the problem of verifying pre-opacity for discrete-time control systems. Preopacity is an important information-flow security property that secures the intention of a system to execute some secret behaviors in the future. Existing works on pre-opacity only consider non-metric discrete systems, where it is assumed that intruders can distinguish different output behaviors precisely. However, for continuous-space control systems whose output sets are equipped with metrics (which is the case for most real-world applications), it is too restrictive to assume precise measurements from outside observers. In this paper, we first introduce a concept of approximate pre-opacity by capturing the security level of control systems with respect to the measurement precision of the intruder. Based on this new notion of pre-opacity, we propose a verification approach for continuous-space control systems by leveraging abstraction-based techniques. In particular, a new concept of approximate pre-opacity preserving simulation relation is introduced to characterize the distance between two systems in terms of preserving pre-opacity. This new system relation allows us to verify pre-opacity of complex continuous-space control systems using their finite abstractions. We also present a method to construct pre-opacity preserving finite abstractions for a class of discrete-time control systems under certain stability assumptions. Copyright © 2022, The Authors. All rights reserved.

关键词： Opacity

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Sliding Mode Control for Quadrotor-Slung Load Transportation System with State Constraints*

Sliding Mode Control for Quadrotor-Slung Load Transportation...

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IEEE International Conference on Cyborg and Bionic Systems (CBS)

作者： Feng Ding Chong Sun Yong Ai Jian Huang School of Computer Science South-Central Minzu University Hubei Provincial Engineering Research Center for Intelligent Management of Manufacturing Enterprises Wuhan Hubei China Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and automation Huazhong University of Science and Technology Wuhan Hubei China

Quadrotor-slung load transportation is an important way to transport goods, but the swing of the load bring a great deal of challenges to the stability of the system. In this paper, a barrier Lyapunov function based sliding mode controller is proposed to limit the load swing, which also satisfies the speed limitation of the quadrotor. An inner-outer loop control strategy is proposed, where the outer one is the virtual position controller which guarantees the stability of the quadrotor while eliminating the load swing, and the inner one is the attitude controller which driver the actual force to the position controller to ensure the effectiveness of the system. Specifically, the dynamics of quadrotor-slung load transportation system is reconstructed as a cascade form. Then, a barrier Lyapunov function based sliding mode controller is designed to ensure the trajectory tracking errors uniformly bounded with state constraints valid. Finally, comparative simulation results illustrate the effectiveness of the proposed method.

关键词： Attitude control Trajectory tracking Simulation Force Dynamics Transportation Stability analysis

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GSLB: the graph structure learning benchmark 23

GSLB: the graph structure learning benchmark

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Zhixun Li Liang Wang Xin Sun Yifan Luo Yanqiao Zhu Dingshuo Chen Yingtao Luo Xiangxin Zhou Qiang Liu Shu Wu Jeffrey Xu Yu Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Center for Research on Intelligent Perception and Computing State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences and School of Artificial Intelligence University of Chinese Academy of Sciences and Department of Automation University of Science and Technology of China Department of Automation University of Science and Technology of China School of Cyberspace Security Beijing University of Posts and Telecommunications Department of Computer Science University of California Los Angeles Center for Research on Intelligent Perception and Computing State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences and School of Artificial Intelligence University of Chinese Academy of Sciences Heinz College of Information Systems and Public Policy Machine Learning Department School of Computer Science Carnegie Mellon University

Graph Structure Learning (GSL) has recently garnered considerable attention due to its ability to optimize both the parameters of Graph Neural Networks (GNNs) and the computation graph structure simultaneously. Despite the proliferation of GSL methods developed in recent years, there is no standard experimental setting or fair comparison for performance evaluation, which creates a great obstacle to understanding the progress in this field. To fill this gap, we systematically analyze the performance of GSL in different scenarios and develop a comprehensive Graph Structure Learning Benchmark (GSLB) curated from 20 diverse graph datasets and 16 distinct GSL algorithms. Specifically, GSLB systematically investigates the characteristics of GSL in terms of three dimensions: effectiveness, robustness, and complexity. We comprehensively evaluate state-of-the-art GSL algorithms in node- and graph-level tasks, and analyze their performance in robust learning and model complexity. Further, to facilitate reproducible research, we have developed an easy-to-use library for training, evaluating, and visualizing different GSL methods. Empirical results of our extensive experiments demonstrate the ability of GSL and reveal its potential benefits on various downstream tasks, offering insights and opportunities for future research. The code of GSLB is available at: https://***/GSL-Benchmark/GSLB.

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Few-Shot Learning Based on Information Geometry

Few-Shot Learning Based on Information Geometry

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Chinese automation Congress (CAC)

作者： Jiacheng Chen Yao Fu Tian Wang Deyuan Liu Jian Wang Hichem Snoussi Institute of Artificial Intelligence Beihang University Beijing China Changchun Institute of Optics Fine Mechanics and Physics Chinese Academy of Sciences Changchun China Zhongguancun Laboratory Institute of Artificial Intelligence SKLSDE Beihang University Beijing China School of Automation Science and Electrical Engineering Beihang University Beijing China School of Computer Science Wuhan University Wuhan China Laboratory of Computer Science and Digital Society University of Technology of Troyes Troyes France

Few-shot classification, as the name suggests, is a very challenging task where only a few training examples are available for each category. Extensive research has been done to solve the problem of the high cost of labeling a large number of samples. A recent research strategy is to find similarity measures between query sets and support sets for judgment. Recently, information geometry techniques have been applied in deep learning research, which explores the relationships and properties between probability distributions by treating them as geometric objects and using geometric tools. Based on information geometry, this paper characterizes differences in the geometric properties of images as differences in probability distributions. Assume that the data satisfies a multivariate Gaussian distribution and that different images are considered as different points on the manifold according to the parameters that characterize the probability distribution of the images. We measure the similarity between different images by the geodesic and validate three methods for estimating the geodesic on a neural network model with 64 neurons at four levels and improve the model performance by Firth bias. The GM, GMF, KM, and KMF methods are validated in the resnet-12 and resnet-18 frameworks using a meta-learning approach on the miniImageNet and CUB datasets, respectively. The experiments show that the models trained by the above four algorithms have good performance and have advantages over the classical algorithms.

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