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arXiv 2020年

作者： Wu, Jinting Zhang, Yujia Zhao, Xiaoguang State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China

Hand gesture recognition plays a significant role in human-computer interaction for understanding various human gestures and their intent. However, most prior works can only recognize gestures of limited labeled classes and fail to adapt to new categories. The task of Generalized Zero-Shot Learning (GZSL) for hand gesture recognition aims to address the above issue by leveraging semantic representations and detecting both seen and unseen class samples. In this paper, we propose an end-to-end prototype-based GZSL framework for hand gesture recognition which consists of two branches. The first branch is a prototype-based detector that learns gesture representations and determines whether an input sample belongs to a seen or unseen category. The second branch is a zero-shot label predictor which takes the features of unseen classes as input and outputs predictions through a learned mapping mechanism between the feature and the semantic space. We further establish a hand gesture dataset that specifically targets this GZSL task, and comprehensive experiments on this dataset demonstrate the effectiveness of our proposed approach on recognizing both seen and unseen gestures. Copyright © 2020, The Authors. All rights reserved.

关键词： Palmprint recognition

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Predicting Online News Authorship by an Authorship Embeddings Space Method

Predicting Online News Authorship by an Authorship Embedding...

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IEEE International Conference on Big Data Analysis (ICBDA)

作者： Wanting Wen Qiudan Li Junfeng Li Xu Zhang Daniel Zeng The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China National Internet Emergency Center CNCERT/CC Beijing China

ISBN: (数字)9781728141114

ISBN: (纸本)9781728141121

In this paper, we study the problem of authorship identification in online news data. Most of the existing approaches predict authorship via feature engineering, which cannot focus on important attributes. We designed an authorship identification method named Authorship Embeddings Space model (AES) to predict the online news authorship between online news and authors. First, we propose an authorship space to represent the deep semantic relationship of news content. Second, we use an embedding learning method to perform the relationship between authors and news. Finally, we formulated an authorship prediction algorithm to identify the news authors based on the authorship embeddings. Experimental results on the online news dataset reveal that the AES model outperforms the baseline models.

关键词： Organizations Stock markets Training Semantics Writing Support vector machines

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Faster gradient-based NAS pipeline combining broad scalable architecture with confident learning rate

arXiv

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arXiv 2020年

作者： Ding, Zixiang Chen, Yaran Li, Nannan Zhao, Dongbin State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China

In order to further improve the search efficiency of Neural Architecture Search (NAS), we propose B-DARTS, a novel pipeline combining broad scalable architecture with Confident Learning Rate (CLR). In B-DARTS, Broad Convolutional Neural Network (BCNN) is employed as the scalable architecture for DARTS, a popular differentiable NAS approach. On one hand, BCNN is a broad scalable architecture whose topology achieves two advantages compared with the deep one, mainly including faster single-step training speed and higher memory efficiency (i.e. larger batch size for architecture search), which are all contributed to the search efficiency improvement of NAS. On the other hand, DARTS discovers the optimal architecture by gradient-based optimization algorithm, which benefits from two superiorities of BCNN simultaneously. Similar to vanilla DARTS, B-DARTS also suffers from the performance collapse issue, where those weight-free operations are prone to be selected by the search strategy. Therefore, we propose CLR, that considers the confidence of gradient for architecture weights update increasing with the training time of over-parameterized model, to mitigate the above issue. Experimental results on CIFAR-10 and ImageNet show that 1) B-DARTS delivers state-of-the-art efficiency of 0.09 GPU day using first order approximation on CIFAR-10;2) the learned architecture by B-DARTS achieves competitive performance using state-of-the-art composite multiply-accumulate operations and parameters on ImageNet;and 3) the proposed CLR is effective for performance collapse issue alleviation of both B-DARTS and DARTS. Copyright © 2020, The Authors. All rights reserved.

关键词： Pipelines

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Food-trade-associated COVID-19 outbreak from a contaminated wholesale food supermarket in Beijing

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Journal of Biosafety and Biosecurity 2021年第1期3卷 58-65页

作者： Shan Lu Weijia Wang Yanpeng Cheng Caixin Yang Yifan Jiao Mingchao Xu Yibo Bai Jing Yang Hongbin Song Ligui Wang Jiaojiao Wang Bing Rong Jianguo Xu State Key Laboratory of Infectious Disease Prevention and Control Chinese Center for Disease Control and PreventionNational Institute for Communicable Disease Control and PreventionBeijingPR China Shanghai Public Health Clinical Center Shanghai Institute for Emerging and Re-emerging Infectious DiseasesShanghaiPR China Research Units of Discovery of Unknown Bacteria and Function Chinese Academy of Medical SciencesBeijingPR China University of Connecticut StorrsCTUSA Scholl of Public Health Shanxi Medical UniversityTaiyuanShanxi ProvincePR China Center for Disease Control and Prevention of Chinese People’s Liberation Army Fengtai DistrictBeijingPR China The State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijingPR China Department of Epidemiology Center for Global HealthSchool of Public HealthNanjing Medical UniversityNanjingPR China Institute of Public Health Nankai UniversityTianjingPR China Department of Epidemiology and Biostatistics School of Public HealthNanjing Medical UniversityNanjingPR China

The re-emerging outbreak of COVID-19 in Beijing,China,in the summer of 2020 originated from a SARSCoV-2-infested wholesale food *** postulated that the Xinfadi market outbreak has links with food-trade *** Susceptible to the disease,Infectious,and Recovered coupled Agent Based Modelling(SIR-ABM)analysis for studying the diffusion of SARS-CoV-2 particles suggested that the trade-distancing strategy effectively reduces the reproduction number(R0).The retail shop closure strategy reduced the number of visitors to the market by nearly *** addition,the buy-local policy option reduced the infection by more than 70%in ***,retail closures and buy-local policies could serve as significantly effective strategies that have the potential to reduce the size of the outbreak and prevent probable outbreaks in the future.

关键词： COVID-19 outbreak Transmission mechanism Food distribution network control measures Agent Based Modelling

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Inferring users' usage patterns for drug abuse surveillance 17

Inferring users' usage patterns for drug abuse surveillance

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17th IEEE International Conference on Intelligence and Security Informatics, ISI 2019

作者： Liu, Ruoran Li, Qiudan Dajun Zeng, Daniel State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Institute of Automation Beijing 100190 China University of Chinese Academy of Sciences Beijing China China

ISBN: (纸本)9781728125046

Inferring drug usage patterns includes age of drug abuse and intention of rehabilitation, which is of much importance for drug abuse surveillance. The challenges are how to mine patterns from posts and interaction relationships between users. In this paper, we propose a novel drug usage pattern inference method, which improves the inference accuracy by integrating the semantic features and interaction relationships effectively. Experimental results on a real-world dataset demonstrate the efficacy of the proposed method. © 2019 IEEE.

关键词： Semantics

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Parallel Planning:A New Motion Planning Framework for Autonomous Driving

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IEEE/CAA Journal of Automatica Sinica 2019年第1期6卷 236-246页

作者： Long Chen Xuemin Hu Wei Tian Hong Wang Dongpu Cao Fei-Yue Wang IEEE School of Data and Computer Science Sun Yat-sen University the School of Computer Science and Information Engineering Hubei University Institute of Measurement and Control Systems Karlsruhe Institute of Technology Department of Mechanical and Mechatronics Engineering University of Waterloo the State Key Laboratory of Management and Control for Complex Systems.Institute of Automation Chinese Academy of Sciences the Research Center for Military Computational Experiments and Parallel Systems Technology National University of Defense Technology

Motion planning is one of the most significant technologies for autonomous driving. To make motion planning models able to learn from the environment and to deal with emergency situations, a new motion planning framework called as"parallel planning" is proposed in this paper. In order to generate sufficient and various training samples, artificial traffic scenes are firstly constructed based on the knowledge from the reality.A deep planning model which combines a convolutional neural network(CNN) with the Long Short-Term Memory module(LSTM) is developed to make planning decisions in an end-toend mode. This model can learn from both real and artificial traffic scenes and imitate the driving style of human ***, a parallel deep reinforcement learning approach is also presented to improve the robustness of planning model and reduce the error rate. To handle emergency situations, a hybrid generative model including a variational auto-encoder(VAE) and a generative adversarial network(GAN) is utilized to learn from virtual emergencies generated in artificial traffic scenes. While an autonomous vehicle is moving, the hybrid generative model generates multiple video clips in parallel, which correspond to different potential emergency scenarios. Simultaneously, the deep planning model makes planning decisions for both virtual and current real scenes. The final planning decision is determined by analysis of real observations. Leveraging the parallel planning approach, the planner is able to make rational decisions without heavy calculation burden when an emergency occurs.

关键词： Autonomous driving artificial traffic scene deep learning emergencies motion planning parallel planning

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The Architectural Unit Setting up and Architectural Characteristics of Néré, <i>Parkia biglobosa</i>, Jack, R. Br. (Fabaceae)

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American Journal of Plant Sciences 2022年第1期13卷 109-136页

作者： Beda Innocent Adji Doffou Sélastique Akaffou Véronique Letort Mengzhen Kang Xiujuan Wang Marc Jaeger Philippe De Reffye Kouadio Henri Kouassi Yao Patrice Houphouet Jerôme Duminil Yves Caraglio Sylvie Sabatier Université Jean Lorougnon Guédé UFR Agroforesterie Daloa C&#244 te d’Ivoire CIRAD UMR-botAnique et Modélisation de l’Architecture des Plantes et des végétations (AMAP) Montpellier France MICS CentraleSupélec-Université Paris-Saclay Gif-sur-Yvette France Key Laboratory of Management and Control for Complex Systems (CASIA-LMCCS) Institute of Automation Chinese Academy of Sciences Beijing China IRD UMR-DIADE Montpellier France

Parkia biglobosa is a much-loved and over-exploited African savannah species for its socio-economic importance. Knowing and taking into account its architectural unit, which is the basis for diagnosing phenology, prod... 详细信息

Parkia biglobosa is a much-loved and over-exploited African savannah species for its socio-economic importance. Knowing and taking into account its architectural unit, which is the basis for diagnosing phenology, productivity and tree health, could provide a new perspective on its sustainable management. The aim of this study is to establish the architectural development in Parkia biglobosa by retrospective analysis. To achieve this objective, 390 individuals of all sizes ranging from seedlings to senescent trees were observed and analysed under various soil and climatic conditions in Côte d’Ivoire. The results showed that Parkia biglobosa is a light plant but shading tolerant. It is a mixed vegetative axis plant, the stem is orthotropic* in its proximal part and plagiotropic* (collapsing) in its distal part in young stage. The tree then transitions to an adult and old stage into a tree with a plagiotropic* axis in the proximal and distal parts, the trunk is built up by superimposing collapsed relay axes that gradually straighten, branching is sympodial*, growth is defined and sexuality is terminal and lateral. The ontogeny takes place in three phases: initiation of development and establishment of the crown (young), then flowering and establishment of the architectural unity (adult) and finally the death of secondary axes in the crown, duplication of the architecture by a series of partial and total reiterations (old). The level of organisation is 5: the phytomere, the module or growth unit, the axis, the architectural unit and the reiterated complex. Retrospective analysis of the modules showed that the dimensions of the growth units are indicators of morphological variation and species adaptation to a changing climate (P < 0.05). However, the equations generated by the morphological and habitat dimension linkage models are not significant (R² and

关键词： Parkia biglobosa Architectural Development Architectural Unit Côte d’Ivoire

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Guided Crowd Evacuation:Approaches and Challenges

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IEEE/CAA Journal of Automatica Sinica 2019年第5期6卷 1081-1094页

作者： Min Zhou Hairong Dong Petros A.Ioannou Yanbo Zhao Fei-Yue Wang the State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong UniversityBeijing 100044China the Department of Electrical Engineering University of Southern CaliforniaLos AngelesCA 90089-2560 USA the Center for Advanced Transportation Technologies University of Southern CaliforniaLos AngelesCA 90089 USA the State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 the Research Center of Computational Experiments and Parallel Systems the National University of Defense TechnologyChangsha 410073China

Evacuation leaders and/or equipment provide route and exit information for people and guide them to the expected destinations, which could make crowd evacuation more efficient in case of emergency. The purpose of this paper is to provide an overview of recent advances in guided crowd evacuation. Different guided crowd evacuation approaches are classified according to guidance approaches and technologies. A comprehensive analysis and comparison of crowd evacuation with static signage, dynamic signage, trained leader, mobile devices, mobile robot and wireless sensor networks are presented based on a single guidance mode perspective. In addition, the different evacuation guidance systems that use high-tech means such as advanced intelligent monitoring techniques, AI techniques, computer technology and intelligent inducing algorithms are reviewed from a system’s perspective. Future researches in the area of crowd evacuation are also discussed.

关键词： Auxiliary equipment evacuation guidance system (EGS) guided crowd evacuation leader signage

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Semi-autonomous greenhouse environment control by combining expert knowledge and machine learning

Semi-autonomous greenhouse environment control by combining ...

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Chinese automation Congress (CAC)

作者： Mengzhen Kang Yuchen Weng Haitong Pang Liangde Li Xing-Rong Fan Jing Hua Fangle Chang Xiujuan Wang Longhua Ma The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Ningbo Institute of Technology Zhejiang University Ningbo China The School of Computer Science and Information Engineering Chongqing Technology and Business University Chongqing China The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Qingdao AgriTech Co. Ltd Qingdao Qingdao China The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing Engineering Research Center of Intelligent Systems and Technology Beijing China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

The concept of "autonomous greenhouse" hints the automatic setting of control target, and giving orders on facilities to reach these targets (set point). Information and communication technologies are widely used, but how to integrate common sense or knowledge in decision-making is still challenging. Semi-autonomous greenhouse control is to set rules with joint advantages of experienced grower and powerful machinery, with the support of knowledge graph and semantic analysis. The contribution of each side is dependent on the intelligence level of computers, the availability of data, etc. In this work, the hybrid system between human and machinery is shown with the temperature control in greenhouse. The target is set mainly by expert knowledge, while the temperature controlled by vent opening is decided by an algorithm. Result shows that the target is better reached compared to that of the experience-based approach. This example shows the feasibility of human-machine hybrid intelligence system for greenhouse environment control. Although this approach is tested for temperature control, it can be extended to control multiple factors simultaneously (temperature, light intensity, humidity, etc.) by driving multiple facilities (heating, lightening, venting, etc.).

关键词： Green products Temperature measurement Monitoring Temperature sensors Vents Agriculture Temperature distribution

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Deep Rank-Consistent Pyramid Model for Enhanced Crowd Counting

arXiv

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arXiv 2022年

作者： Gao, Jiaqi Huang, Zhizhong Lei, Yiming Shan, Hongming Wang, James Z. Wang, Fei-Yue Zhang, Junping The Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai200433 China The Institute of Science and Technology for Brain-inspired Intelligence MOE Frontiers Center for Brain Science Fudan University Shanghai200433 China The Shanghai Center for Brain Science and Brain-Inspired Technology Shanghai201210 China The College of Information Sciences and Technology The Pennsylvania State University University ParkPA16802 United States The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China The Institute of Systems Engineering Macau University of Science and Technology China Qingdao Academy of Intelligent Industries Qingdao266109 China

Most conventional crowd counting methods utilize a fully-supervised learning framework to establish a mapping between scene images and crowd density maps. They usually rely on a large quantity of costly and time-intensive pixel-level annotations for training supervision. One way to mitigate the intensive labeling effort and improve counting accuracy is to leverage large amounts of unlabeled images. This is attributed to the inherent self-structural information and rank consistency within a single image, offering additional qualitative relation supervision during training. Contrary to earlier methods that utilized the rank relations at the original image level, we explore such rank-consistency relation within the latent feature spaces. This approach enables the incorporation of numerous pyramid partial orders, strengthening the model representation capability. A notable advantage is that it can also increase the utilization ratio of unlabeled samples. Specifically, we propose a Deep Rank-consistEnt pyrAmid Model (DREAM), which makes full use of rank consistency across coarse-to-fine pyramid features in latent spaces for enhanced crowd counting with massive unlabeled images. In addition, we have collected a new unlabeled crowd counting dataset, FUDAN-UCC, comprising 4,000 images for training purposes. Extensive experiments on four benchmark datasets, namely UCF-QNRF, ShanghaiTech PartA and PartB, and UCF-CC-50, show the effectiveness of our method compared with previous semi-supervised methods. The codes are available at https://***/bridgeqiqi/DREAM. Copyright © 2022, The Authors. All rights reserved.

关键词： Image enhancement

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