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arXiv 2022年

作者： Chen, Minghao Tian, Yunong Li, Zhishuo Li, En Liang, Zize The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences 95 Zhongguancun East Road Beijing100190 China Yuquan Road Beijing100049 China Engineering Laboratory of Industrial Vision and Intelligent Equipment Technology Chinese Academy of Sciences 95 Zhongguancun East Road Beijing100190 China

Based on multiple instance detection networks (MIDN), plenty of works have contributed tremendous efforts to weakly supervised object detection (WSOD). However, most methods neglect the fact that the overwhelming negative instances exist in each image during the training phase, which would mislead the training and make the network fall into local minima. To tackle this problem, an online progressive instance-balanced sampling (OPIS) algorithm based on hard sampling and soft sampling is proposed in this paper. The algorithm includes two modules: a progressive instance balance (PIB) module and a progressive instance reweighting (PIR) module. The PIB module combining random sampling and IoU-balanced sampling progressively mines hard negative instances while balancing positive instances and negative instances. The PIR module further utilizes classifier scores and IoUs of adjacent refinements to reweight the weights of positive instances for making the network focus on positive instances. Extensive experimental results on the PASCAL VOC 2007 and 2012 datasets demonstrate the proposed method can significantly improve the baseline, which is also comparable to many existing state-of-the-art results. In addition, compared to the baseline, the proposed method requires no extra network parameters and the supplementary training overheads are small, which could be easily integrated into other methods based on the instance classifier refinement paradigm. Copyright © 2022, The Authors. All rights reserved.

关键词： Object detection

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Classifying user connections through social media avatars and users social activities: a case study in identifying sellers on social media

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ENTERPRISE INFORMATION systems 2022年第8-9期16卷 Article: 1856420页

作者： Mao, Yu Zhu, Yifan Liu, Yiping Lin, Qika Lu, Hao Zhang, Fuquan Beijing Inst Technol Sch Comp Sci & Technol Beijing Peoples R China Univ Auckland Sch Comp Sci Auckland New Zealand Xi An Jiao Tong Univ Sch Comp Sci & Technol Xian Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China Minjiang Univ Fujian Prov Key Lab Informat Proc & Intelligent C Fuzhou 350108 Peoples R China

Compilation of users' avatars together with associated online activities strengthens the ability to discover user connections which focuses on understanding users online behaviours. This paper made an initial attempt to identify special type of SNS users by incorporating users avatar information, which proposes a multi-modal user profiling method that examines the relationship between users' avatar and their internet behaviours. Based on investigation and case study, the experiments showed significant differences in behaviour and attributes among avatar categories. The results also proved the feasibility of the proposed method in identifying user groups on a given Internet.

关键词： User profiling avatars in social media user activity suspicious user detection

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Parallel hospital: ACP-based hospital smart operating system 1

Parallel hospital: ACP-based hospital smart operating system

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1st IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2021

作者： Wang, Yongjun Wang, Fei-Yue Wang, Xiao Guan, Zhongjun Ouyang, Liwei Wang, Jiangong Yan, Lan Zheng, Wenbo Zhang, Wenwen Beijing Tiantan Hospital Capital Medical University Beijing Beijing100070 China Institute of Automation Chinese Academy of Sciences The State Key Laboratory for Management and Control of Complex Systems Beijing100190 China Qingdao Academy of Intelligent Industries Qingdao266000 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing 100049 China School of Software Engineering Xi'An Jiaotong University Xi'an710049 China

ISBN: (纸本)9781665433372

This article aims to propose a parallel hospital based on parallel medicine. The parallel hospital takes the parallel idea of virtual and real interaction as its core concept and uses parallel idea's artificial scenarios, computational experiments, and parallel execution (ACP) theory as the guiding theory to construct a new generation of intelligent hospital management system with virtual and real interaction. We conducted a case study of the parallel hospital practice case of Tiantan Hospital-Tiantan Smart Brain, so as to confirm the feasibility and scientificity of the proposed parallel hospital. © 2021 IEEE.

关键词： Hospitals

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IPGAN: Identity-Preservation Generative Adversarial Network for unsupervised photo-to-caricature translation

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KNOWLEDGE-BASED systems 2022年 241卷 108223-108223页

作者： Yan, Lan Zheng, Wenbo Gou, Chao Wang, Fei-Yue Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China Xi An Jiao Tong Univ Sch Software Engn Xian Peoples R China Sun Yat Sen Univ Sch Intelligent Syst Engn Guangzhou Peoples R China

Photo-to-caricature translation is an extremely challenging task because there are not only texture differences between caricatures and photos, but also various spatial deformations in caricatures. Most of existing methods tend to introduce difficult obtained additional information such as precise facial landmarks to guide caricature generation. In addition, identity preservation is a crucial characteristic of caricatures, but unfortunately there seems to be few methods to consider it. Motivated by the aforementioned observations, we propose an Identity-Preservation Generative Adversarial Network (IPGAN) for unsupervised photo-to-caricature translation. In particular, considering the importance of identity retention, we propose a novel identity preservation loss to hold the identity information of original photos and improve the quality of generated caricatures. To capture realistic caricature styles, we design a style differentiation loss to help our model produce caricatures with styles that remarkably differ from photos. Moreover, to learn satisfactory deformations without supervision, our model uses a warp controller to acquire exaggerations automatically that enable to customize diverse exaggerations. As an unsupervised translation method, our IPGAN can also be applied to caricature to-photo translation. Experiments on the WebCaricature dataset suggest that our IPGAN achieves state-of-the-art performance and can generate realistic as well as identity preservation caricatures.(c) 2022 Elsevier B.V. All rights reserved.

关键词： Photo-to-caricature translation Generative adversarial networks Image-to-image translation Style transfer Image warping

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The Emerging intelligent Vehicles and intelligent Vehicle Carriers Collab.rative systems

The Emerging Intelligent Vehicles and Intelligent Vehicle Ca...

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IEEE Symposium on intelligent Vehicle

作者： Zhipeng Shen Chao Huang Hailong Huang Yutong Wang Fei-Yue Wang Abbas Jamalipour Duc Truong Pham Ljubo Vlacic Andrey V. Savkin Department of Aeronautical and Aviation Engineering the Hong Kong Polytechnic University Hong Kong Department of Industrial and Systems Engineering the Hong Kong Polytechnic University Hong Kong State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Institute of Engineering Macau University of Science and Technology Macau China School of Electrical and Information Engineering University of Sydney Sydney Australia Department of Mechanical Engineering University of Birmingham Birmingham UK Institute of Intelligent & Integrated Systems and the School of Engineering and Built Environment Griffith University Brisbane Australia School of Electrical Engineering and Telecommunications University of New South Wales Sydney Australia

ISBN: (数字)9798350348811

ISBN: (纸本)9798350348828

In this paper, we propose the innovative use of intelligent Vehicle Carriers (IVCs) as a key solution to address the energy constraints of small-scale unmanned intelligent Vehicles (IVs). IVCs function as both transporters and charging stations, significantly boosting the operational range and efficiency of IVs. Our research delves into the IV-IVC collab.rative framework, highlighting the existing challenges, exploring potential solutions, and examining a range of applications. This study offers a visionary approach to revolutionizing intelligent transportation systems by leveraging the synergistic relationship between IVs and IVCs.

关键词： intelligent vehicles Collab.ration Charging stations Boosting

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A bioinspired retinal neural network for accurately extracting small-target motion information in cluttered backgrounds

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IMAGE AND VISION COMPUTING 2021年 114卷 104266-104266页

作者： Huang, Xiao Qiao, Hong Li, Hui Jiang, Zhihong Beijing Inst Technol Adv Innovat Ctr Intelligent Robots & Syst Key Lab Biomimet Robots & Syst Chinese Minist EducSch Mechatron Engn Beijing 100081 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

Robust and accurate detection of small moving targets in cluttered moving backgrounds is a significant and challenging problem for robotic visual systems to perform search and tracking tasks. Inspired by the neural circuitry of elementary motion vision in the mammalian retina, this paper proposes a bioinspired retinal neural network based on a new neurodynamics-based temporal filtering and multiform 2-D spatial Gabor filtering. This model can estimate motion direction accurately via only two perpendicular spatiotemporal filtering signals, and respond to small targets of different sizes and velocities through adjusting the dendrite field size of spatial filter. Meanwhile, an algorithm of directionally selective inhibition is proposed to suppress the target-like features in the moving background, which can reduce the influence of background motion effectively. Extensive synthetic and real-data experiments show that the proposed model works stably for small targets of a wider size and velocity range, and has better detection performance than other bioinspired models. Additionally, it can also extract the information of motion direction and motion energy accurately and rapidly. (c) 2021 Elsevier B.V. All rights reserved.

关键词： Bioinspiration Small-target motion detector Robotic visual perception Spatiotemporal energy model

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Research on Attention-Based Predictive Model in Multi-Agent Reinforcement Learning

Research on Attention-Based Predictive Model in Multi-Agent ...

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Chinese Automation Congress (CAC)

作者： Weixin Hong Huaiyu Wu Zhen Shen Gang Xiong Bin Hu Xisong Dong Fei-Yue Wang The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China The Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing China The Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan China

Agent actions planning is a challenging problem in multi-agent reinforcement learning. Recent methods typically build their predictive models by full connection layers, but the shortage of utilizing action and observation information in complex multi-agent environment limits the performance of those methods. To address this issue, we propose a novel predictive model based on self-attention mechanism which can effectively use information in the environment. Specifically, we use the MADDPG algorithm to make a dataset where the input is the observations and actions and the output is the predicted observations and rewards. We also introduce the multi-head attention mechanism into the predictive model so that the predictive model can better combine different information for prediction. We perform extensive experiments to assess the proposed method and compare it with the representative baseline approach. Results demonstrate that our method outperforms competitors with higher accuracy in different tasks. Our implementation will be publicly availab.e.

关键词： Automation Reinforcement learning Predictive models Prediction algorithms Planning Task analysis

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3D-RVP: A method for 3D object reconstruction from a single depth view using voxel and point

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NEUROCOMPUTING 2021年 430卷 94-103页

作者： Zhao, Meihua Xiong, Gang Zhou, MengChu Shen, Zhen Wang, Fei-Yue Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci Beijing Engn Res Ctr Intelligent Syst & Technol Inst Automat Beijing 100190 Peoples R China Chinese Acad Sci Guangdong Engn Res Ctr 3D Printing & Intelligent Cloud Comp Ctr Dongguan 523808 Peoples R China New Jersey Inst Technol Helen & John C Hartmann Dept Elect & Comp Engn Newark NJ 07102 USA Qingdao Acad Intelligent Ind Intelligent Mfg Ctr Qingdao 266109 Peoples R China Macau Univ Sci & Technol Inst Syst Engn Macau Peoples R China

Three-dimensional object reconstruction technology has a wide range of applications such as augment reality, virtual reality, industrial manufacturing and intelligent robotics. Although deep learning-based 3D object reconstruction technology has developed rapidly in recent years, there remain important problems to be solved. One of them is that the resolution of reconstructed 3D models is hard to improve because of the limitation of memory and computational efficiency when deployed on resource-limited devices. In this paper, we propose 3D-RVP to reconstruct a complete and accurate 3D geometry from a single depth view, where R, V and P represent Reconstruction, Voxel and Point, respectively. It is a novel two-stage method that combines a 3D encoder-decoder network with a point prediction network. In the first stage, we propose a 3D encoder-decoder network with residual learning to output coarse prediction results. In the second stage, we propose an iterative subdivision algorithm to predict the lab.ls of adaptively selected points. The proposed method can output high-resolution 3D models by increasing a small number of parameters. Experiments are conducted on widely used benchmarks of a ShapeNet dataset in which four categories of models are selected to test the performance of neural networks. Experimental results show that our proposed method outperforms the state-of-the-arts, and achieves about 2:7% improvement in terms of the intersection-over-union metric. (c) 2020 Elsevier B.V. All rights reserved.

关键词： 3D object reconstruction Encoder-decoder network Machine learning Point prediction network

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Collab.rative Scheduling of Computing Tasks for Edge Computing

Collaborative Scheduling of Computing Tasks for Edge Computi...

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Chinese Automation Congress (CAC)

作者： Yue Liu Gang Xiong Fenghua Zhu Shichao Chen Liguo Zhang Xudong Liu School of Artificial Intelligence and Automation Beijing University of Technology The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation and the Cloud Computing Center Chinese Academy of Sciences Beijng China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation and Qingdao Academy of Intelligent Industries Chinese Academy of Sciences Beijng China Faculty of Information and Technology Beijing University of Technology Beijing China

ISBN: (纸本)9781665426480

In the face of the rapid development of the IoT, the traditional cloud computing, while providing services for mobile intelligent devices, also brings problems such as network bandwidth occupation and communication delay. Edge computing can solve these problems to some extent. But too many offloaded tasks may result in an edge server becoming overloaded and execution delay of tasks increasing, the computing resources of idle edge devices are also not fully utilized. In order to make full use of the computing resources of idle edge devices in the local network and reduce the execution delay for computing tasks, this paper takes the average delay time for all end devices as the optimization objective and proposes an end-edge cooperative scheduling (EECS) model to schedule tasks for edge computing. The scheduling algorithm used in EECS model is improved based on MCT algorithm. We use the EdgeCloudSim to verily the EECS model and scheduling algorithm. The simulation results show that the average completion time of computing tasks in EECS model, compared with only single edge server (SEE) task computing model, can be reduced by up to three times. In addition, the system load can be balanced to a certain extent in local network.

关键词： Analytical models Scheduling algorithms Computational modeling Collab.ration Scheduling Delays Servers

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A novel framework of collab.rative early warning for COVID-19 based on blockchain and smart contracts

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INFORMATION SCIENCES 2021年 570卷 124-143页

作者： Ouyang, Liwei Yuan, Yong Cao, Yumeng Wang, Fei-Yue Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Renmin Univ China Sch Math Beijing 100872 Peoples R China Minist Educ Engn Res Ctr Finance Computat & Digital Engn Beijing 100872 Peoples R China Qingdao Acad Intelligent Ind Qingdao 266109 Peoples R China Macau Univ Sci & Technol Inst Syst Engn Macau 999078 Peoples R China

Early warning is a vital component of emergency response systems for infectious diseases. However, most early warning systems are centralized and isolated, thus there are potential risks of single evidence bias and decision-making errors. In this paper, we tackle this issue via proposing a novel framework of collab.rative early warning for COVID-19 based on blockchain and smart contracts, aiming to crowdsource early warning tasks to distributed channels including medical institutions, social organizations, and even individuals. Our framework supports two surveillance modes, namely, medical federation surveillance based on federated learning and social collab.ration surveillance based on the learning markets approach, and fuses their monitoring results on emerging cases to alert. By using our framework, medical institutions are expected to obtain better federated surveillance models with privacy protection, and social participants without mutual trusts can also share verified surveillance resources such as data and models, and fuse their surveillance solutions. We implemented our proposed framework based on the Ethereum and IPFS plat-forms. Experimental results show that our framework has advantages of decentralized decision-making, fairness, auditability, and universality. It also has potential guidance and reference value for the early warning and prevention of unknown infectious diseases. (c) 2021 Elsevier Inc. All rights reserved.

关键词： Blockchain Smart contracts Federated learning Learning markets Collab.rative early warning

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