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The DAO to Metacontrol for MetaSystems in Metaverses:The System of Parallel control Systems for Knowledge Automation and control Intelligence in CPSS

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IEEE/CAA Journal of Automatica Sinica 2022年第11期9卷 1899-1908页

作者： Fei-Yue Wang The State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing100190Chi the Macao Institute of Systems Engineering Macao University of Science and TechnologyMacao999078China

An investigation and outline of Metacontrol and Decontrol in Metaverses for control intelligence and knowledge automation are *** control with prescriptive knowledge and parallel philosophy is proposed as the starting point for the new control philosophy and technology,especially for computational control of metasystems in cyberphysical-social *** argue that circular causality,the generalized feedback mechanism for complex and purposive systems,should be adapted as the fundamental principle for control and management of metasystems with metacomplexity in ***,an interdisciplinary approach is suggested for Metacontrol and Decontrol as a new form of intelligent control based on five control metaverses:MetaVerses,MultiVerses,InterVerses,TransVerse,and DeepVerses.

关键词： Metacontrol MetaSystems Metaverses CPSS DAO Decontrol Prescriptive control Parallel control Computational control Knowledge Automation control Intelligence Parallel Intelligence

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Dynamic Multihead Attention for Enhancing Neural Machine Translation Performance 5th

Dynamic Multihead Attention for Enhancing Neural Machine Tra...

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5th International Conference on Trends in Computational and Cognitive engineering, TCCE 2023

作者： Pandey, Mrinal Pandey, Rashmikiran Nazarov, Alexey Department of Radio Engineering and Cybernetics Moscow Institute of Physics and Technology Moscow Russia Federal Research Center Computer Science and Control Russian Academy of Sciences Moscow Russia

ISBN: (纸本)9789819719228

The research paper delves into the realm of "Attention-Based Neural Machine Translation for Multilingual Communication", focusing on the revolutionary impact of attention mechanisms in enhancing cross-language understanding. Through dynamic allocation of focus to pertinent segments of source sequences, attention mechanisms have significantly improved the precision and coherence of translations, playing a pivotal role in multilingual communication. The paper underscores the importance of this technology in addressing the complexities of aligning words across diverse languages and maintaining grammatical accuracy, crucial for effective cross-lingual interactions. The study also presents a visual flowchart outlining the core components of attention-based NMT, encompassing the encoder, decoder, and attention context. Moreover, the paper extrapolates the future trajectory of attention-based NMT, identifying areas for continued exploration and innovation. Researchers are urged to refine attention models to cater to intricate linguistic structures, potentially unveiling novel mechanisms that strike a balance between capturing long-range dependencies and computational efficiency. Furthermore, the paper anticipates the integration of attention-based NMT with multimodal inputs, unlocking the potential for comprehensive communication by incorporating visual and auditory cues. These advancements hold the promise of revolutionising multilingual communication, shaping a future where language barriers are transcended in various domains, from global diplomacy to education and beyond. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Neural machine translation

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A Temperature Detection Method for Electrical Equipment Based on Infrared Thermal Image 2

A Temperature Detection Method for Electrical Equipment Base...

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2nd International Conference on Cyber-Energy Systems and Intelligent Energy, ICCSIE 2024

作者： Liu, Yangguibo Song, Zhiquan Institutes of Physical Science and Information Technology Anhui University Hefei China Institute of Plasma Physics Chinese Academy of Sciences Power and Control Engineering Division Hefei China

ISBN: (纸本)9798350373318

During the operation of electrical equipment, if there are abnormal situations that cause overheating, it can lead to equipment damage and even serious consequences such as fire. To improve the reliability of electrical equipment design and avoid thermal faults, it is necessary to monitor the temperature of the equipment. The infrared thermal image temperature measurement method has the advantages of rich temperature information, wide detection range, and no equipment contact. The traditional thermal image detection method requires manual designation of the detection area. When the position of the object to be detected changes, the detection area needs to be reassigned. In order to effectively monitor the running state of the equipment, this paper proposes a method for detecting thermal faults in electrical equipment thermal images. This method is based on the improved YOLOv8 algorithm, incorporating Coordinate Attention (CA) into the backbone to enhance the model's learning ability of location information. The Local Intensities Operation (LIO) is used to separate the equipment from the background, and finally, the temperature of the equipment is counted and whether a thermal fault has occurred is calculated. © 2024 IEEE.

关键词： Thermography (imaging)

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An Efficient Fire Detection Algorithm with Dataset Generation Based on Diffusion

An Efficient Fire Detection Algorithm with Dataset Generatio...

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2024 China Automation Congress, CAC 2024

作者： Wang, Gaoyang Zheng, Hongtao Xiao, Duo College of Control science and Engineering Zhejiang University Hangzhou China School of Information & Electrical Engineering Hangzhou City University Hangzhou China The academy of Edge Intelligence Hangzhou City University Hangzhou China

ISBN: (纸本)9798350368604

Faced with increasing urbanization and population growth, the hazard of fires has become a critical issue in urban areas worldwide. Therefore, the focus on how to achieve accurate and rapid fire detection using artificial intelligence has intensified. Currently, fire detection algorithms often struggle to balance precision and processing speed. At the same time, high-quality fire detection datasets are extremely scarce, severely limiting the performance of algorithms. To address these challenges, this paper first proposes an enhanced fire detection algorithm framework based on YOLOv8, aiming to comprehensively improve the accuracy and processing speed of detection. This framework integrates Swin Transformer and Slim Neck to enhance model performance, and utilizes WIoUv3 for network training regularization. Additionally, this paper introduces a novel fire detection dataset generation algorithm: Fire-Generator, to address the scarcity of high-quality training data. This tool utilizes a latent diffusion model to guide image generation, enabling the synthesis of training data from textual descriptions or existing images. Experimental evaluations demonstrate that our method outperforms most currently available fire detection algorithms in terms of performance. The versatility of Fire-Generator extends beyond fire detection, demonstrating potential for generating training data across different domains. Through this comprehensive strategy, our model achieves significant results by leveraging both real and synthesized data. © 2024 IEEE.

关键词： fire Fire-Generator Slim Neck Swin Transformer WIoUv3 YOLOv8

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Empirical Analysis of Vulnerabilities Life Cycle in Golang Ecosystem 24

Empirical Analysis of Vulnerabilities Life Cycle in Golang E...

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44th ACM/IEEE International Conference on Software engineering, ICSE 2024

作者： Hu, Jinchang Zhang, Lyuye Liu, Chengwei Yang, Sen Huang, Song Liu, Yang College of Command and Control Engineering Army Engineering University of Pla Nanjing China Continental-NTU Corporate Lab Nanyang Technological University Singapore Singapore School of Computer Science and Engineering Nanyang Technological University Singapore Singapore Academy of Military Science Nanjing China

ISBN: (纸本)9798400702174

Open-source software (OSS) greatly facilitates program development for developers. However, the high number of vulnerabilities in open-source software is a major concern, including in Golang, a relatively new programming language. In contrast to other commonly used OSS package managers, Golang presents a distinctive feature whereby commits are prevalently used as dependency versions prior to their integration into official releases. This attribute can prove advantageous to users, as patch commits can be implemented in a timely manner before the releases. However, Golang employs a decentralized mechanism for managing dependencies, whereby dependencies are upheld and distributed in separate repositories. This approach can result in delays in the dissemination of patches and unresolved vulnerabilities. To tackle the aforementioned concern, a comprehensive investi-gation was undertaken to examine the life cycle of vulnerability in Golang, commencing from its introduction and culminating with its rectification. To this end, a framework was established by gathering data from diverse sources and systematically amalgamating them with an algorithm to compute the lags in vulnerability patching. It turned out that 66.10% of modules in the Golang ecosystem were affected by vulnerabilities. Within the vulnerability life cycle, we found two kinds of lag impeding the propagation of vulnerability fixing. By analyzing reasons behind non-lagged and lagged vul-nerabilities, timely releasing and indexing patch versions could significantly enhance ecosystem security. © 2024 ACM.

关键词： Open source software

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A new ChatGPT-empowered,easy-to-use machine learning paradigm for environmental science

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Eco-Environment & Health 2024年第2期3卷 131-136页

作者： Haoyuan An Xiangyu Li Yuming Huang Weichao Wang Yuehan Wu Lin Liu Weibo Ling Wei Li Hanzhu Zhao Dawei Lu Qian Liu Guibin Jiang State Key Laboratory of Environmental Chemistry and Toxicology Research Center for Eco-Environmental SciencesChinese Academy of SciencesBeijing 100085China Biomedical Engineering Institute School of Control Science and EngineeringShandong UniversityJinan 250061China

The quantity and complexity of environmental data show exponential growth in recent ***-quality big data analysis is critical for performing a sophisticated characterization of the complex network of environmental *** learning(ML)has been employed as a powerful tool for decoupling the complexities of environmental big data based on its remarkable fitting ***,due to the knowledge gap across different subjects,ML concepts and algorithms have not been well-popularized among researchers in environmental *** this context,we introduce a new research paradigm—“ChatGPT t ML t Environment”,providing an unprecedented chance for environmental researchers to reduce the difficulty of using ML *** instance,each step involved in applying ML models to environmental sustainability,including data preparation,model selection and construction,model training and evaluation,and hyper-parameter optimization,can be easily performed with guidance from *** also discuss the challenges and limitations of using this research paradigm in the field of environmental ***,we highlight the importance of“secondary training”for future application of“ChatGPT t ML t Environment”.

关键词： Machine learning Environmental application ChatGPT Secondary training

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Occurrence, distribution and risk assessment of antibiotics at various aquaculture stages in typical aquaculture areas surrounding the Yellow Sea

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Journal of Environmental sciences 2023年第4期126卷 621-632页

作者： Jiachao Zhang Xuanrui Zhang Yang Zhou Qianfan Han Xiaoli Wang Chao Song Shuguang Wang Shan Zhao Shandong Key Laboratory of Water Pollution Control and Resource Reuse School of Environmental Science and EngineeringShandong UniversityQingdao 266237China Water Science and Environmental Engineering Research Center College of Chemical and Environmental EngineeringShenzhen UniversityShenzhen 518060China Qingdao Municipal Bureau of Ecology and Environment Qingdao 266003China Analysis and Test Center Qilu University of Technology(Shandong Academy of Sciences)Jinan 250014China

The pollution of antibiotics commonly existed throughout the entire aquaculture process,but the residues of antibiotics at different aquaculture stages have rarely been *** study investigated the occurrence,distribution and risk assessment of antibiotics at different aquaculture stages (the non-aquaculture stage,the early aquaculture stage,the middle aquaculture stage,and the late aquaculture stage) in two typical marine aquaculture areas(Mahegang River and Dingzi Bay) surrounding the Yellow *** and tetracyclines were commonly used antibiotics in the aquaculture of these areas with high detection frequencies (17%to 83%).Compared among four aquaculture stages,the highest concentration of antibiotics (9032.08 ng/L) in aquaculture ponds was detected at the late aquaculture *** the antibiotic pollution level of natural water was directly related to the aquaculture ***,at the aquaculture stages,the detection frequency of antibiotics in sediments was higher than that at the non-aquaculture *** on the correlation analysis,the concentration of main antibiotics in water showed a positive correlation with total nitrogen (p<0.05) and chlorophyll a (p<0.01),while it showed a negative correlation with salinity (p<0.01) in coastal water of the Dingzi *** to the risk assessment,with the development of aquaculture stages,the selection pressure of fluoroquinolones and tetracyclines on resistant bacteria had *** the ecological risks caused by sulfonamides and tetracyclines to aquatic organisms had also increased ***,this study may provide a reference for formulating regulatory policies regarding antibiotic use at different aquaculture stages.

关键词： Antibiotics Aquaculture stages Correlation analysis Risk assessment

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Deep Learning-based Moving Object Segmentation:Recent Progress and Research Prospects

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Machine Intelligence Research 2023年第3期20卷 335-369页

作者： Rui Jiang Ruixiang Zhu Hu Su Yinlin Li Yuan Xie Wei Zou College of Information Engineering Shanghai Maritime UniversityShanghai201306China School of Computer Science and Technology East China Normal UniversityShanghai200062China Research Center of Precision Sensing and Control Institute of AutomationChinese Academy of SciencesBeijing100190China State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing100190China

Moving object segmentation(MOS),aiming at segmenting moving objects from video frames,is an important and challenging task in computer vision and with various *** the development of deep learning(DL),MOS has also entered the era of deep models toward spatiotemporal feature *** paper aims to provide the latest review of recent DL-based MOS methods proposed during the past three ***,we present a more up-to-date categorization based on model characteristics,then compare and discuss each category from feature learning(FL),and model training and evaluation *** FL,the methods reviewed are divided into three types:spatial FL,temporal FL,and spatiotemporal FL,then analyzed from input and model architectures aspects,three input types,and four typical preprocessing subnetworks are *** terms of training,we discuss ideas for enhancing model *** terms of evaluation,based on a previous categorization of scene dependent evaluation and scene independent evaluation,and combined with whether used videos are recorded with static or moving cameras,we further provide four subdivided evaluation setups and analyze that of reviewed *** also show performance comparisons of some reviewed MOS methods and analyze the advantages and disadvantages of reviewed MOS methods in terms of ***,based on the above comparisons and discussions,we present research prospects and future directions.

关键词： Moving object segmentation(MOS) change detection background subtraction deep learning(DL) video understanding

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Rethinking Guidance Information to Utilize Unlabeled Samples: A Label Encoding Perspective 41

Rethinking Guidance Information to Utilize Unlabeled Samples...

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41st International Conference on Machine Learning, ICML 2024

作者： Zhang, Yulong Yao, Yuan Chen, Shuhao Jin, Pengrong Zhang, Yu Jin, Jian Lu, Jiangang Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China College of Control Science and Engineering Zhejiang University Hangzhou China Beijing Teleinfo Technology Company Ltd. China Academy of Information and Communications Technology Beijing China Research Institute of Industrial Internet of Things China Academy of Information and Communications Technology Beijing China

Empirical Risk Minimization (ERM) is fragile in scenarios with insufficient labeled samples.A vanilla extension of ERM to unlabeled samples is Entropy Minimization (EntMin), which employs the soft-labels of unlabeled samples to guide their ***, EntMin emphasizes prediction discriminability while neglecting prediction *** alleviate this issue, in this paper, we rethink the guidance information to utilize unlabeled *** analyzing the learning objective of ERM, we find that the guidance information for labeled samples in a specific category is the corresponding label *** by this finding, we propose a Label-Encoding Risk Minimization (LERM).It first estimates the label encodings through prediction means of unlabeled samples and then aligns them with their corresponding ground-truth label *** a result, the LERM ensures both prediction discriminability and diversity, and it can be integrated into existing methods as a ***, we analyze the relationships between LERM and ERM as well as ***, we verify the superiority of the LERM under several label insufficient *** codes are available at https://***/zhangyl660/LERM. Copyright 2024 by the author(s)

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Wind tunnel simulation of wind erosion and dust emission processes,and the influences of soil texture

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International Soil and Water Conservation Research 2024年第2期12卷 455-466页

作者： Xiaofeng Zuo Chunlai Zhang Xiaoyu Zhang Rende Wang Jiaqi Zhao Wenping Li State Key Laboratory of Earth Surface Processes and Resource Ecology MOE Engineering Research Center of Desertification and Blown-sand ControlFaculty of Geographical ScienceBeijing Normal UniversityBeijing100875PR China Hebei Engineering Research Center for Geographic Information Application Institute of Geographical SciencesHebei Academy of SciencesShijiazhuang050011PR China

Dust emission caused by wind erosion of soil is an important surface process in arid and semi-arid ***,existing dust emission models pay insufficient attention to the impacts of aerodynamic entrainment of *** addition,studies of wind erosion processes do not adequately account for the dynamics of wind erosion rates and dust emission fluxes,or for the impact of soil texture on dust *** wind tunnel simulations of wind erosion and dust emission showed that the soil texture,wind erosion duration,and shear velocity are major factors that affect the dynamics of wind erosion and dust *** of the limited supply of surface sand and the change in surface erosion resistance caused by surface coarsening during erosion,the wind erosion rate and the flux of particles smaller than 10μm(PM_(10))caused by aerodynamic entrainment decreased rapidly with increasing erosion duration,which suggests that surface wind erosion and dust emission occur primarily during the initial stage of wind *** PM_(10) emission efficiency decreased with increasing shear velocity following a power function,and finer textured sandy loam soils had greater PM_(10) emission efficiency than loamy sand soils.

关键词： Soil texture Wind erosion rate Dust emission efficiency Dynamic process Wind tunnel experiment Shear velocity

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