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arXiv 2023年

作者： Hua, Yun Gao, Shang Li, Wenhao Jin, Bo Wang, Xiangfeng Zha, Hongyuan School of Computer Science and Technology East China Normal University Shanghai200092 China School of Data Science The Chinese University of Hong Kong Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Shenzhen China School of Software Engineering Tongji University Shanghai201804 China School of Computer Science and Technology East China Normal University Shanghai200062 China

In multi-agent reinforcement learning, each agent acts to maximize its individual accumulated rewards. Nevertheless, individual accumulated rewards could not fully reflect how others perceive them, resulting in selfish behaviors that undermine global performance. The externality theory, defined as "the activities of one economic actor affect the activities of another in ways that are not reflected in market transactions," is applicable to analyze the social dilemmas in MARL. One of its most profound non-market solutions, "Pigovian Tax", which internalizes externalities by taxing those who create negative externalities and subsidizing those who create positive externalities, could aid in developing a mechanism to resolve MARL's social dilemmas. The purpose of this paper is to apply externality theory to analyze social dilemmas in MARL. To internalize the externalities in MARL, the Learning Optimal Pigovian Tax method (LOPT), is proposed, where an additional agent is introduced to learn the tax/allowance allocation policy so as to approximate the optimal "Pigovian Tax" which accurately reflects the externalities for all agents. Furthermore, a reward shaping mechanism based on the approximated optimal "Pigovian Tax" is applied to reduce the social cost of each agent and tries to alleviate the social dilemmas. Compared with existing state-of-the-art methods, the proposed LOPT leads to higher collective social welfare in both the Escape Room and the Cleanup environments, which shows the superiority of our method in solving social dilemmas. Copyright © 2023, The Authors. All rights reserved.

关键词： Taxation

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Caption Generation from Road Images for Traffic Scene Construction 31

Caption Generation from Road Images for Traffic Scene Constr...

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31st IEEE Intelligent Vehicles Symposium, IV 2020

作者： Wu, Chuan Li, Yaochen Li, Ling Wang, Le Liu, Yuehu School of Software Engineering Xi'an Jiaotong University Xi'an710049 China Xi'an Jiaotong University Suzhou Research School Suzhou215123 China Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an710049 China

In this paper, an image captioning network is proposed for traffic scene modeling, which incorporates element attention into the encoder-decoder mechanism to generate more reasonable scene captions. Firstly, the traffic scene elements are detected and segmented according to their clustered locations. Then, the image captioning network is applied to generate the corresponding caption of each subregion. The static and dynamic traffic elements are appropriately organized to construct a 3D corridor scene model. The semantic relationships between the traffic elements are specified according to the captions. The constructed 3D scene model can be utilized for the offline test of unmanned vehicles. The evaluations and comparisons based on the TSD-max and COCO datasets prove the effectiveness of the proposed framework. © 2020 IEEE.

关键词： Unmanned vehicles

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Can ROS be used securely in industry? Red teaming ROS-Industrial

arXiv

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arXiv 2020年

作者： Mayoral-Vilches, Vctor Pinzger, Martin Rass, Stefan Dieber, Bernhard Gil-Uriarte, Endika group Software Engineering group. Universitt Klagenfurt Austria Alias Robotics Spain Institute for Robotics and Mechatronics Joanneum Research Austria

With its growing use in industry, ROS is rapidly becoming a standard in robotics. While developments in ROS 2 show promise, the slow adoption cycles in industry will push widespread ROS 2 industrial adoption years from now. ROS will prevail in the meantime which raises the question: can ROS be used securely for industrial use cases even though its origins didn’t consider it? The present study analyzes this question experimentally by performing a targeted offensive security exercise in a synthetic industrial use case involving ROS-Industrial and ROS packages. Our exercise results in four groups of attacks which manage to compromise the ROS computational graph, and all except one take control of most robotic endpoints at desire. To the best of our knowledge and given our setup, results do not favour the secure use of ROS in industry today, however, we managed to confirm that the security of certain robotic endpoints hold and remain optimistic about securing ROS industrial deployments. Copyright © 2020, The Authors. All rights reserved.

关键词： robotics

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Label Self-Adaption Hashing for Image Retrieval

Label Self-Adaption Hashing for Image Retrieval

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International Conference on Pattern Recognition

作者： Jianglin Lu Zhihui Lai Jingxu Lin Qinghong Lin Jie Zhou Institute of Computer Mathematics and Information Technologies Kazan Federal University Kazan Russia SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China College of Computer Science and Software Engineering Shenzhen Univeristy Shenzhen China

Hashing has attracted widespread attention in image retrieval because of its fast retrieval speed and low storage cost. Compared with supervised methods, unsupervised hashing methods are more reasonable and suitable for large-scale image retrieval since it is always difficult and expensive to collect true labels of the massive data. Without label information, however, unsupervised hashing methods can not guarantee the quality of learned binary codes. To resolve this dilemma, this paper proposes a novel unsupervised hashing method called Label Self-Adaption Hashing (LSAH), which contains effective hashing function learning part and self-adaption label generation part. In the first part, we utilize anchor graph to keep the local structure of the data and introduce joint sparsity into the model to extract effective features for high-quality binary code learning. In the second part, a self-adaptive cluster label matrix is learned from the data under the assumption that the nearest neighbor points should have a large probability to be in the same cluster. Therefore, the proposed LSAH can make full use of the potential discriminative information of data to guide the learning of binary codes. It is worth noting that LSAH can learn effective binary codes, hashing function and cluster labels simultaneously in a unified optimization framework. To solve the resulting optimization problem, an Augmented Lagrange Multiplier based iterative algorithm is elaborately designed. Extensive experiments on three large-scale data sets indicate the promising performance of the proposed LSAH.

关键词： Training Image resolution Image retrieval Binary codes Feature extraction Data models Data mining

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A novel channel model and optimal power control schemes for mobile mmWave two-tier networks

TechRxiv

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TechRxiv 2022年

作者： Ghosh, Joydev Haci, Huseyin Kumar, Neeraj Al-Utaibi, Khaled A. Sait, Sadiq M. So-In, Chakchai School of Computer Science and Robotics Tomsk Polytechnic University Tomsk Russia Department of Electrical and Electronics Engineering Near East University Mersin 10 Turkey Department of Computer Science & Engineering Thapar Institute of Engineering & Technology Punjab India Computer Science and Software Engineering Department University of Ha’il Saudi Arabia Department of Computer Engineering King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia Department of Computer Science College of Computing Khon Kaen University Thailand

We present a unified system model and framework for the analytical performance study of two heterogeneous and physically-distinct, but coexisting, networks that work harmoniously at the same time, space, and frequency domains. The two-tier network model considered in this paper is an overlaying of femtocells on a macrocell. Overlaying femtocells improves the performance by offloading traffic from macrocells and providing spatial diversity. The mmWave channel model employed considers the number of clusters and rays within each cluster to vary due to the end-user mobility. This is a new and different model compared to the widely used channel models for mmWave two-tier networks. Optimal power control is formulated as a sum-rate maximization problem for downlink and uplink transmissions at two-tier networks and a power allocation scheme is proposed by following Shannon-Hartley theorem. A comprehensive and interesting performance investigation is provided, where it is shown that the upper bound on the number of admitted secondary users has a linear relationship with the outage probability threshold, logarithmic relationship with SINR and exponential relationship with channel gain factors. Simulation results show that the proposed scheme with sub-channel iterative Lagrange multipliers search algorithm is very effective at managing the cross-tier interference and can outperform a competitive scheme from literature that is based on cognitive radio technology. The computational complexity analysis of proposed algorithms are also given, since the complexity of second algorithm can be a performance-complexity trade-off issue for systems with limited computation power and time requirements. © 2022, CC BY.

关键词： Femtocell

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Unlimited Neighborhood Interaction for Heterogeneous Trajectory Prediction

Unlimited Neighborhood Interaction for Heterogeneous Traject...

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International Conference on Computer Vision (ICCV)

作者： Fang Zheng Le Wang Sanping Zhou Wei Tang Zhenxing Niu Nanning Zheng Gang Hua School of Software Engineering Xi’an Jiaotong University Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University University of Illinois at Chicago School of Computer Science and Technology Xidian University Wormpex AI Research

ISBN: (纸本)9781665428132

Understanding complex social interactions among agents is a key challenge for trajectory prediction. Most existing methods consider the interactions between pairwise traffic agents or in a local area, while the nature of interactions is unlimited, involving an uncertain number of agents and non-local areas simultaneously. Besides, they treat heterogeneous traffic agents the same, namely those among agents of different categories, while neglecting people’s diverse reaction patterns toward traffic agents in different categories. To address these problems, we propose a simple yet effective Unlimited Neighborhood Interaction Network (UNIN), which predicts trajectories of heterogeneous agents in multiple categories. Specifically, the proposed unlimited neighborhood interaction module generates the fused-features of all agents involved in an interaction simultaneously, which is adaptive to any number of agents and any range of interaction area. Meanwhile, a hierarchical graph attention module is proposed to obtain category-to-category interaction and agent-to-agent interaction. Finally, parameters of a Gaussian Mixture Model are estimated for generating the future trajectories. Extensive experimental results on benchmark datasets demonstrate a significant performance improvement of our method over the state-of-the-art methods.

关键词： Computer vision Benchmark testing Trajectory Gaussian mixture model

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Precise Facial Landmark Detection by Reference Heatmap Transformer

arXiv

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arXiv 2023年

作者： Wan, Jun Liu, Jun Zhou, Jie Lai, Zhihui Shen, Linlin Sun, Hang Xiong, Ping Min, Wenwen The School of Information and Safety Engineering Zhongnan University of Economics and Law Wuhan430073 China The Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore487372 Singapore The College of Computer Science and Software Engineering Shen zhen University Shenzhen518060 China The Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518060 China College of Computer and Information Technology China Three Gorges University HuBei Yichang China The School of Information Science and Engineering Yunnan University Yunnan Kunming650091 China

Most facial landmark detection methods predict landmarks by mapping the input facial appearance features to landmark heatmaps and have achieved promising results. However, when the face image is suffering from large poses, heavy occlusions and complicated illuminations, they cannot learn discriminative feature representations and effective facial shape constraints, nor can they accurately predict the value of each element in the landmark heatmap, limiting their detection accuracy. To address this problem, we propose a novel Reference Heatmap Transformer (RHT) by introducing reference heatmap information for more precise facial landmark detection. The proposed RHT consists of a Soft Transformation Module (STM) and a Hard Transformation Module (HTM), which can cooperate with each other to encourage the accurate transformation of the reference heatmap information and facial shape constraints. Then, a Multi-Scale Feature Fusion Module (MSFFM) is proposed to fuse the transformed heatmap features and the semantic features learned from the original face images to enhance feature representations for producing more accurate target heatmaps. To the best of our knowledge, this is the first study to explore how to enhance facial landmark detection by transforming the reference heatmap information. The experimental results from challenging benchmark datasets demonstrate that our proposed method outperforms the state-of-the-art methods in the literature. Copyright © 2023, The Authors. All rights reserved.

关键词： Face recognition

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Efficient Random Subspace Decision Forests with A Simple Probability Dimensionality Setting Scheme

SSRN

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SSRN 2022年

作者： Wang, Quan Wang, Fei Li, Zhongheng Jiang, Peilin Ren, Fuji Nie, Feiping Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Shaanxi Xi’an710049 China Beijing Key Laboratory of Intelligent Space Robotic Systems Technology and Applications Beijing Institute of Spacecraft System Engineering Beijing100094 China School of Software Engineering Xi’an Jiaotong University Shaanxi Xi’an710049 China School of Computer Science and Engineering University of Electronic Science and Technology of China Sichuan Chengdu611731 China Northwestern Polytechnical University Shaanxi Xi’an710072 China

The random subspace concept is widely used in decision forests. However, there is not a reasonable approach to specify the appropriate number of randomly selected features. Previous random subspace decision forests simply specify the random subspace dimensionality d s to a preset function value such as [log 2 d ] or [√ d ] where d is the number of the total feature dimensions. Actually, this preset value may be a relatively poor choice for high dimensional data sets. In this paper, a novel framework named Efficient Random Subspace decision forest (ERS) is proposed which pays attention to how to set the random subspace dimensionality for each decision tree of decision forests. Specifically, a simple discrete uniform distribution is employed to set with probability the number of randomly selected features for each tree in random subspace decision forests. The proposed Half Range Discrete Uniform distribution-based Varied Dimensionality setting (HRDUVD) method removes the hesitation of how to preset an appropriate d s for different data sets but also results in good enough classification performance along with relatively short running time. Plenty of experiments on public benchmark data sets have demonstrated the effectiveness and the efficiency of the proposed ERS. © 2022, The Authors. All rights reserved.

关键词： Decision trees

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Group-wise Inhibition based Feature Regularization for Robust Classification

Group-wise Inhibition based Feature Regularization for Robus...

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International Conference on Computer Vision (ICCV)

作者： Haozhe Liu Haoqian Wu Weicheng Xie Feng Liu Linlin Shen Computer Vision Institute College of Computer Science and Software Engineering SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society National Engineering Laboratory for Big Data System Computing Technology Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China

The convolutional neural network (CNN) is vulnerable to degraded images with even very small variations (e.g. corrupted and adversarial samples). One of the possible reasons is that CNN pays more attention to the most discriminative regions, but ignores the auxiliary features when learning, leading to the lack of feature diversity for final judgment. In our method, we propose to dynamically suppress significant activation values of CNN by group-wise inhibition, but not fixedly or randomly handle them when training. The feature maps with different activation distribution are then processed separately to take the feature independence into account. CNN is finally guided to learn richer discriminative features hierarchically for robust classification according to the proposed regularization. Our method is comprehensively evaluated under multiple settings, including classification against corruptions, adversarial attacks and low data regime. Extensive experimental results show that the proposed method can achieve significant improvements in terms of both robustness and generalization performances, when compared with the state-of-the-art methods. Code is available at https://***/LinusWu/TENET_Training.

关键词： Training Computer vision Codes Heuristic algorithms Robustness Classification algorithms Convolutional neural networks

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State and disturbance observers-based chattering-free fixed-time sliding mode control for a class of high-order nonlinear systems

Advanced Control for Applications: Engineering and Industria...

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Advanced Control for Applications: engineering and Industrial Systems 2021年第3期3卷

作者： Alinaghi Hosseinabadi, Pooyan Ordys, Andrzej Soltani Sharif Abadi, Ali Mekhilef, Saad Pota, Hemanshu Roy School of Engineering and Information Technology The University of New South Wales CanberraACT Australia The Institute of Automatic Control and Robotics Faculty of Mechatronics Warsaw University of Technology Warsaw Poland Faculty of Engineering University of Malaya Kuala Lumpur Malaysia School of Software and Electrical Engineering Swinburne University of Technology MelbourneVIC Australia

It is essential to consider chattering alleviation of Sliding Mode Control (SMC) design along with providing the system convergence regardless of initial states utilizing the fixed-time stability notion. Unknown states and disturbance are two major issues in practical applications, which can be effectively solved by using sliding mode observers. This paper deals with state and disturbance Observers-based Chattering-Free Fixed-time SMC (OCFFSMC) design for a class of high-order nonlinear systems with unknown disturbance, while only the first state is measured physically. A new form of the combined observer-controller is designed to provide estimated data of unknown disturbance and unmeasured states in the control law. The designed disturbance observer-based sliding mode controller is not only capable of estimating unknown disturbance but also capable of alleviating the chattering problem in the control signal. Based on defining a new form of the sliding surface, a new control law is designed to alleviate chattering problem and achieve trajectory tracking in a fixed time independently of initial conditions. The fixed-time stability proof of the closed-loop system is obtained using Lyapunov stability theory. The validity of the proposed control scheme, OCFFSMC, is verified by applying two examples and simulating in Simulink/MATLAB. © 2021 John Wiley & Sons Ltd.

关键词： Sliding mode control

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