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41st International Conference on Machine Learning, ICML 2024

作者： Zhang, Bin Mao, Hangyu Li, Lijuan Xu, Zhiwei Li, Dapeng Zhao, Rui Fan, Guoliang Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences China SenseTime Research China School of Computer Science and Technology Shandong University China

Asynchronous action coordination presents a pervasive challenge in Multi-Agent Systems (MAS), which can be represented as a Stackelberg game (SG).However, the scalability of existing Multi-Agent Reinforcement Learning (MARL) methods based on SG is severely restricted by network architectures or environmental *** address this issue, we propose the Stackelberg Decision Transformer (STEER).It efficiently manages decision-making processes by incorporating the hierarchical decision structure of SG, the modeling capability of autoregressive sequence models, and the exploratory learning methodology of *** approach exhibits broad applicability across diverse task types and environmental configurations in *** results demonstrate both the convergence of our method towards Stackelberg equilibrium strategies and its superiority over strong baselines in complex scenarios. Copyright 2024 by the author(s)

关键词： Multi agent systems

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Technological Machines Diagnostics of Grain Processing Enterprises as the Object of automation

Technological Machines Diagnostics of Grain Processing Enter...

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2023 International Conference Automatics and Informatics, ICAI 2023

作者： Kalandarov, Palvan I. Ubaydullayeva, Shakhnoza R. Nikolov, Nikola N. Hayitov, Aziz Sharifov, Husen 'Tashkent Institute of Irrigation and Agricultural Mechanization Engineers' National Research University Department 'Automation and Control of Technology Process in Production' Tashkent Uzbekistan Technical University of Varna Faculty of Computer Science and Automation Department of Automation Varna Bulgaria Bukhara Institute of Natural Resource Management of the National Research University 'Tashkent Institute of Irrigation and Agricultural Mechanization Institute' Tashkent Uzbekistan

ISBN: (纸本)9798350312911

The article gives the basic concepts of reliability, performance, durability, considers the issues of changing the technical state of the machine during operation. The models under consideration have the ability to introduce various perturbations in order to simulate physical defects, and numerical simulation on a computer made it possible to obtain solutions in the form of temporary realizations of oscillations for different states of the simulation object. The cooperative use of the full-scale method and mathematical modeling made it possible to reduce the volume of full-scale experiments and apply statistical methods of analysis in the problem of classifying the technical condition by constructing mathematical models of the probability of failure and uptime during their operating time. The content of the main types of machine states is revealed, considerable attention is paid to the issues of identifying and influencing failures and the causes of failures in the machine, the reasons for the loss of performance during operation. © 2023 IEEE.

关键词： Deterioration

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research on underground speech enhancement technology based on generative adversarial network

Research on underground speech enhancement technology based ...

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2022 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2022

作者： Zhang, Zhaozhao Liu, Zhongqi Chen, Xiaolin Sun, Kang Institute of Computer Science and Technology Xi'an University of Science and Technology Ccteg Changzhou Research Research Institute Xi'an China Automation Co. Ltd Changzhou China

ISBN: (数字)9781665469722

ISBN: (纸本)9781665469722

Because of the problems such as speech interaction and speech input difficulty caused by high noise intensity and unclear sources in underground mine, this paper proposes a speech enhancement system based on a Time-domain Generative Adversarial network, which is used in the front end of underground communication or speech recognition to improve the quality of speech information transmission and improve work efficiency. Aiming at the problems of ignoring the feature information between channels and training instability when extracting speech information in a time domain generative adversarial network, this paper introduces channel attention and Relativistic Average Generative Adversarial Network to optimize. The experimental results show that compared with other models, the proposed model can more effectively remove the downhole noise. © 2022 IEEE.

关键词： Generative adversarial networks

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An Adaptive Multi-Stage Evolution Algorithm for High-Dimensional Expensive Problems

An Adaptive Multi-Stage Evolution Algorithm for High-Dimensi...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Zhang, Boyuan Lai, Rui Gong, Guanghong Yuan, Haitao Yang, Jinhong Zhang, Jia School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Cssc Systems Engineering Research Institute Beijing China Southern Methodist University Dept. of Computer Science DallasTX75275 United States

ISBN: (纸本)9781665410205

Recently, many studies have used evolutionary algorithms (EAs) to optimize complex problems across various fields, including mechanical structure design, robotics, and cloud computing. EAs simulate the process of evolution to improve solutions to a given problem iteratively. However, EAs encounter significant challenges when dealing with high-dimensional expensive problems (HEPs). The large solution space and high computing cost of fitness evaluations (FEs) make optimization with limited FEs particularly difficult. To tackle this problem, an Adaptive Multi-stage Evolution Algorithm named AMEA is proposed. In AMEA, an adaptively enhanced teaching-learning-based optimization algorithm is adopted to explore the search space and find potential areas quickly. Then, in the next stage, the Gaussian process surrogate model and a genetic learning particle swarm optimization algorithm are adopted for further exploitation. Besides, this work proposes an adaptive stage switching criterion and an individual screening mechanism to enhance the optimization ability. AMEA demonstrates strong optimization performance when applied to HEPs. We compare AMEA with several state-of-the-art HEP optimization algorithms through seven benchmark functions, and the results show that it performs competitively with other algorithms. Finally, we also validate AMEA's effectiveness with a real-world computation offloading problem. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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Attack-Agnostic Deep Face Anti-Spoofing

Attack-Agnostic Deep Face Anti-Spoofing

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2023 IEEE/CVF Conference on computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Liu, Ajian Tan, Zichang Liang, Yanyan Wan, Jun Institute of Automation Chinese Academy of Sciences Mais Beijing China Institute of Deep Learning Baidu Research Beijing China School of Computer Science and Engineering Faculty of Innovation Engineering Macau University of Science and Technology China

ISBN: (纸本)9798350302493

The task of face anti-spoofing (FAS) is to determine whether the captured face from a face recognition system is live or fake. Current methods which are trained with existing fake faces ignore the generalization and perform poorly on unseen attacks. To tackle this problem, a novel Attack-agnostic Face Anti-spoofing framework is proposed. Different from previous methods that can be treated as a defense system, we regard face anti-spoofing as a unified framework with the attack and defense systems and optimize the defense system against unseen attacks via adversarial training with the attack system. Concretely, the attack system consists of two modules: an Adversarial learning-based Attack Pattern Generation (Adv-APG) module and a Supervised learning-based Attack Pattern Drift (Sup-APD) module. The Adv-APG module generates a series of spoofing samples by recombining a live face with known attack patterns in a generative way. The Sup-APD module pulls the generated spoofing samples in a supervised way to an unknown domain that makes the defense system ineffective. The defense system is free to choose and compatible with our attack system. Extensive experiments are conducted by using three different defense architectures to verify that the proposed attack system can improve the performance on both seen- and unseen attacks on multiple datasets. © 2023 IEEE.

关键词： Face recognition

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Low-Latency and Energy-Efficient Task Scheduling for End-Edge-Cloud Collaborative Computing

Low-Latency and Energy-Efficient Task Scheduling for End-Edg...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Li, Jingyao Yuan, Haitao Ma, Yaofei Bi, Jing Yang, Jinhong Zhang, Jia School of Automation Science and Electrical Engineering Beihang University Beijing100191 China College of Computer Beijing University of Technology Beijing100124 China Cssc Systems Engineering Research Institute Beijing China Southern Methodist University Dept. of Computer Science DallasTX75275 United States

ISBN: (纸本)9781665410205

Mobile edge computing (MEC) is a new paradigm that improves the quality of service compared with traditional cloud computing. In MEC, computational tasks are submitted by numerous end users and are partially offloaded to edge servers or a central cloud. However, the characteristics of tasks are different from each other, and the limited resources of computational nodes are also heterogeneous, which brings great challenges to computation offloading and resource allocation for MEC. This work establishes an end-edge-cloud collaborative computing network, which consists of end devices, edge servers, and a central cloud. Task execution location and CPU running frequency determine the execution time and energy consumption to finish the tasks. Considering the aforementioned factors, a multi-objective constrained optimization problem is formulated. To solve the problem, an improved Non-dominated Sorting Genetic Algorithm II (NSGA-II) with self-adaptive crossover and mutation rates is proposed, which is called Improved NSGA-II with _Self-adaptive Crossover and Mutation (INSCM). The total execution time and energy consumption can be jointly minimized with our proposed INSCM. Numerous experiments are carried out to test the performance of INSCM. Simulation results show that INSCM effectively improves the performance of NSGA-II and surpasses random offloading and NSGA-III, which shows practical use in real-life scenarios. © 2024 IEEE.

关键词： Mobile edge computing

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Multiobjective adaptive car-following control of an intelligent vehicle based on receding horizon optimization

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science China(Information sciences) 2023年第4期66卷 301-302页

作者： Hongbo GAO Juping ZHU Fei ZHANG Ruidong YAN Junjie ZHOU Lei JIANG Jianqiang WANG Keqiang LI Department of Automation University of Science and Technology of China Institute of Advanced Technology University of Science and Technology of China Department of Computational Mathematics School of Mathematical Sciences Anhui University School of Traffic and Transportation Beijing Jiaotong University Autonomous Driving Business Unit Chery Automobile School of College of Computer Science and Technology Zhejiang University China North Vehicle Research Institute School of Automotive Engineering Tsinghua University

Dear editor, Vehicle control is one of the key steps of intelligent driving [1]. Algorithms based on receding horizon optimization(RHO) can predict future trajectories and handle multiobjective constraint conditions; therefore, RHO-based methods have attracted considerable attention in the field of vehicle control. Although existing methods use RHO to design controllers, they do not simultaneously meet the multiobjective optimal control performance requirements on tracking, fuel economy, and ride comfort.

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Two Layer Symmetric Cryptography Algorithm for Protecting Data from Attacks

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computers, Materials & Continua 2023年第2期74卷 2625-2640页

作者： Muhammad Nadeem Ali Arshad Saman Riaz Syeda Wajiha Zahra Shahab SBand Amir Mosavi Department of Computer Science Abasyn UniversityIslamabad44000Pakistan Department of Computer Science National University of TechnologyIslamabad44000Pakistan Future Technology Research Center National Yunlin University of Science and TechnologyDouliuYunlin64002Taiwan Institute of Information Society University of Public ServiceBudapest1083Hungary John Von Neumann Faculty of Informatics Obuda UniversityBudapestHungary Institute of Information Engineering Automation and MathilticsSlovak University of Technology in BratislavaSlovakia

Many organizations have insisted on protecting the cloud server from the outside,although the risks of attacking the cloud server are mostly from the *** are many algorithms designed to protect the cloud server from attacks that have been able to protect the cloud server ***,the attackers have designed even better mechanisms to break these security *** cryptography is the best data protection algorithm that exchanges data between authentic *** this article,one symmetric cryptography algorithm will be designed to secure cloud server data,used to send and receive cloud server data securely.A double encryption algorithm will be implemented to send data in a secure ***,the XOR function will be applied to plain text,and then salt technique will be ***,a reversing mechanism will be implemented on that data to provide more data *** decrypt data,the cipher text will be reversed,salt will be removed,andXORwill be *** the end of the paper,the proposed algorithm will be compared with other algorithms,and it will conclude how much better the existing algorithm is than other algorithms.

关键词： Cryptography symmetric algorithm encryption decryption cipher text cloud security asymmetric algorithm

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Preventing Cloud Network from Spamming Attacks Using Cloudflare and KNN

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computers, Materials & Continua 2023年第2期74卷 2641-2659页

作者： Muhammad Nadeem Ali Arshad Saman Riaz SyedaWajiha Zahra Muhammad Rashid Shahab S.Band Amir Mosavi Department of Computer Science Abasyn UniversityIslamabad44000Pakistan Department of Computer Science National University of TechnologyIslamabad44000Pakistan Future Technology Research Center National Yunlin University of Science and TechnologyDouliuYunlin64002Taiwan Institute of Information Society University of Public ServiceBudapest1083Hungary John von Neumann Faculty of Informatics Obuda UniversityBudapestHungary Institute of Information Engineering Automation and MathematicsSlovak University of Technology in BratislavaSlovakia

Cloud computing is one of the most attractive and cost-saving models,which provides online services to *** computing allows the user to access data directly from any *** nowadays,cloud security is one of the biggest issues that *** types of malware are wreaking havoc on the *** on the cloud server are happening from both internal and external *** paper has developed a tool to prevent the cloud server from spamming *** an attacker attempts to use different spamming techniques on a cloud server,the attacker will be intercepted through two effective techniques:Cloudflare and K-nearest neighbors(KNN)*** will block those IP addresses that the attacker will use and prevent spamming ***,the KNN classifiers will determine which area the spammer belongs *** the end of the article,various prevention techniques for securing cloud servers will be discussed,a comparison will be made with different papers,a conclusion will be drawn based on different results.

关键词： Intrusion prevention system spamming KNN classification spam cyber security botnet

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Instance Segmentation of Ice-Covered Transmission Line in Unmanned Aerial Vehicle Images 5

Instance Segmentation of Ice-Covered Transmission Line in Un...

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5th International Seminar on Artificial Intelligence, Networking and Information Technology, AINIT 2024

作者： Pan, Nianyi Sun, Jiacheng Zhou, Xinghong Li, Xiaomao School of Computer Engineering and Science Shanghai University Shanghai China School of Future Technology Shanghai University Shanghai China Research Institute of Usv Engineering School of Mechatronic Engineering and Automation Shanghai University Shanghai China

ISBN: (纸本)9798350385557

Unmanned aerial vehicle (UAV) detection of ice-covered transmission lines (ITL) is a crucial task for winter power grid maintenance. Instance segmentation offers robustness in handling complex scene variations in UAV images while providing instance-level information. However, the quality of instance segmentation heavily relies on precise pixel-level annotations, while predictions from low-cost annotations tend to be coarse. Therefore, this paper proposes a soft label mask optimization (SLMO) method to achieve high-quality segmentation results of ITL with only coarse annotations. Furthermore, to alleviate loss of feature details during multi-scale feature fusion, a pixel shuffle module (PSM) is introduced to replace the conventional interpolation sampling method. Combining these methods, this paper proposes a SLMO Network (SLMONet) based on the efficient SparseInst framework. Extensive experiments on our ITL datasets validate the effectiveness of SLMONet in segmenting ITL. © 2024 IEEE.

关键词： Electric lines

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