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arXiv 2020年

作者： Jiang, Feibo Wang, Kezhi Dong, Li Pan, Cunhua Yang, Kun Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha China Department of Computer and Information Sciences Northumbria University United Kingdom Key Laboratory of Hunan Province for New Retail Virtual Reality Technology Hunan University of Commerce Changsha China School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom School of Computer Sciences and Electrical Engineering University of Essex ColchesterCO4 3SQ United Kingdom University of Electronic Science and Technology of China Chengdu China

An online resource scheduling framework is proposed for minimizing the sum of weighted task latency for all the mobile users, by optimizing offloading decision, transmission power, and resource allocation in the mobile edge computing (MEC) system. Towards this end, a deep reinforcement learning (DRL) method is proposed to obtain an online resource scheduling policy. Firstly, a related and regularized stacked auto encoder (2r-SAE) with unsupervised learning is proposed to perform data compression and representation for high dimensional channel quality information (CQI) data, which can reduce the state space for DRL. Secondly, we present an adaptive simulated annealing based approach (ASA) as the action search method of DRL, in which an adaptive h-mutation is used to guide the search direction and an adaptive iteration is proposed to enhance the search efficiency during the DRL process. Thirdly, a preserved and prioritized experience replay (2p-ER) is introduced to assist the DRL to train the policy network and find the optimal offloading policy. Numerical results are provided to demonstrate that the proposed algorithm can achieve near-optimal performance while significantly decreasing the computational time compared with existing benchmarks. It also shows that the proposed framework is suitable for resource scheduling problem in large-scale MEC networks, especially in the dynamic environment. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

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Camera Abnormal Movement and Foreign Object Invasion Detection Based on Cumulative Edge Distribution Probability Model

Camera Abnormal Movement and Foreign Object Invasion Detecti...

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2018 International Conference on Security, Pattern Analysis, and Cybernetics, SPAC 2018

作者： Yu, Xiangru Cai, Fudong Dou, Yimin Li, Jinping Institute of Pattern Recognition and Intelligent System School of Information Science and Engineering University of Jinan Jinan China Shandong Provincial Key Laboratory of Network Based Intelligent Computing Jinan China Shandong College University Key Laboratory of Information Processing and Cognitive Computing in 13th Five-year University of Jinan Jinan China Shandong Senter Electronic Co. Ltd Zibo China

ISBN: (纸本)9781728105512

In practice, there often exist some occasions where video surveillance can only be realized by using rechargeable batteries due to the high cost of power supply. In order to extend the battery life, images can only be taken at a certain interval. Obviously, image difference is an important method to obtain various changes in this case. Among these changes, foreign invasion is one of the focuses. However, due to bolt shedding, abnormal weather and other factors, the camera may have abnormal movement which may seriously affect the detection effect of image difference method. Therefore, it is an urgent issue to find an effective way to detect the abnormal movement of camera and improve the detection accuracy of foreign invasion. Considering the characteristics of this kind of image sequences, we propose an effective algorithm to detect the camera abnormal movement and foreign object invasion based on a cumulative edge distribution probability model. Since sky region is relatively simple, we only discuss changes in sky region to detect camera abnormal movement. Our algorithm has 6 basic steps: firstly, segment the sky region;secondly, extract the edge information of the current image and the preceding adjacent image in the image sequence;thirdly, determine if the edge information of two adjacent images coincide. If consistent, then go to the next step, otherwise, it indicates that the camera has abnormal movement, then alarm;fourthly, calculate the cumulative edge probability distribution model in the sky region by using the historical image sequence;fifthly, by using adaptive Parzen window, determine if foreign object invasion exists by comparing the probability model of cumulative edge distribution with edge distribution of the current image;sixthly, update the cumulative edge distribution probability model in sky region. The algorithm achieves good results in practical applications. Through the test of thousands of images taken in the wild, the detection accuracy of

关键词： Cameras

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Distributed resource scheduling for large-scale MEC systems: A multi-agent ensemble deep reinforcement learning with imitation acceleration

arXiv

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arXiv 2020年

作者： Jiang, Feibo Dong, Li Wang, Kezhi Yang, Kun Pan, Cunhua Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha China Key Laboratory of Hunan Province for New Retail Virtual Reality Technology Hunan University of Commerce Changsha China Department of Computer and Information Sciences Northumbria University United Kingdom School of Computer Sciences and Electrical Engineering University of Essex ColchesterCO4 3SQ United Kingdom School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom

We consider the optimization of distributed resource scheduling to minimize the sum of task latency and energy consumption for all the Internet of things devices (IoTDs) in a large-scale mobile edge computing (MEC) system. To address this problem, we propose a distributed intelligent resource scheduling (DIRS) framework, which includes centralized training relying on the global information and distributed decision making by each agent deployed in each MEC server. More specifically, we first introduce a novel multi-agent ensemble-assisted distributed deep reinforcement learning (DRL) architecture, which can simplify the overall neural network structure of each agent by partitioning the state space and also improve the performance of a single agent by combining decisions of all the agents. Secondly, we apply action refinement to enhance the exploration ability of the proposed DIRS framework, where the near-optimal state-action pairs are obtained by a novel Lvy flight search. Finally, an imitation acceleration scheme is presented to pre-train all the agents, which can significantly accelerate the learning process of the proposed framework through learning the professional experience from a small amount of demonstration data. Extensive simulations are conducted to demonstrate that the proposed DIRS framework is efficient and outperforms the existing benchmark schemes. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

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Cost Efficient Offloading Strategy for DNN-Based Applications in Edge-Cloud Environment

Cost Efficient Offloading Strategy for DNN-Based Application...

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IEEE International Conference on Big Data and Cloud computing (BdCloud)

作者： Yinhao Huang Bing Lin Yongjie Zheng Junqin Hu Yuchang Mo Xing Chen College of Mathematics and Computer Science Fuzhou University Fuzhou China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou China College of Physics and Energy Fujian Normal University Fuzhou China Fujian Province University Key Laboratory of Computational Science Huaqiao University Quanzhou China

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

Deep Neural Networks (DNNs) are pervasively used in a large number of applications, such as Microsoft Cortana, Apple Siri, and Google Now. The traditional method is to offload them in the cloud, which causes serious data transmission delay and high network resource cost, as well as user privacy leakage. The emergence of Mobile Edge computing(MEC) provides a new solution for the execution of DNN-based applications. With the increase of the computing power of mobile devices and the appearance of edge nodes, offloading partial layers of the DNN-based application to the edge will speed up system response, reduce the burden of cloud center and protect privacy of users. Most of existing researches focus on the optimization of response delay for DNN-based applications in edge computing system, but there is no mature solution to the cost optimization of edge computing system. To effectively reduce the cost of edge computing for offloading DNN-based applications, we propose a offloading strategy based on Discrete Particle Swarm Optimization with Genetic Operators(DPSO-GO). By introducing crossover operator and mutation operator of genetic algorithm, it overcomes the defect that the traditional PSO algorithm is easy to fall into local optimum. The results show that our algorithm can reduce the cost of edge computing system effectively while satisfying the delay requirements of all the DNN-based applications.

关键词： Mobile handsets Task analysis Cloud computing Edge computing Servers Optimization Bandwidth

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Simulating Maxwell-Schrödinger Equations by High-Order Symplectic FDTD Algorithm

arXiv

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arXiv 2019年

作者： Xie, Guoda Huang, Zhixiang Fang, Ming Sha, Wei E.I. Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei230601 China Key Laboratory of Micro-nano Electronic Devices and Smart Systems of Zhejiang Province College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China

—A novel symplectic algorithm is proposed to solve the Maxwell-Schrödinger (M-S) system for investigating light-matter interaction. Using the fourth-order symplectic integration and fourth-order collocated differences, Maxwell-Schrödinger equations are discretized in temporal and spatial domains, respectively. The symplectic finite-difference time-domain (SFDTD) algorithm is developed for accurate and efficient study of coherent interaction between electromagnetic fields and artificial atoms. Particularly, the Dirichlet boundary condition is adopted for modeling the Rabi oscillation problems under the semi-classical framework. To implement the Dirichlet boundary condition, image theory is introduced, tailored to the high-order collocated differences. For validating the proposed SFDTD algorithm, three-dimensional numerical studies of the population inversion in the Rabi oscillation are presented. Numerical results show that the proposed high-order SFDTD(4,4) algorithm exhibits better numerical performance than the conventional FDTD(2,2) approach at the aspects of accuracy and efficiency for the long-term simulation. The proposed algorithm opens up a promising way towards a high-accurate energy-conservation modeling and simulation of complex dynamics in nanoscale light-matter interaction. Copyright © 2019, The Authors. All rights reserved.

关键词： Finite difference time domain method

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First-principle calculation of Chern number in gyrotropic photonic crystals

arXiv

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arXiv 2020年

作者： Zhao, Ran Xie, Guo-Da Chen, Menglin L.N. Lan, Zhihao Huang, Zhixiang Sha, Wei E.I. Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei230039 China Key Laboratory of Electromagnetic Environmental Sensing Department of Education of Anhui Province Hefei230039 China Department of Electrical and Electronic Engineering University of Hong Kong Hong Kong Department of Electronic and Electrical Engineering University College London United Kingdom Key Laboratory of Micro-nano Electronic Devices and Smart Systems of Zhejiang Province College of Information Science Electronic Engineering Zhejiang University Hangzhou310027 China

As an important figure of merit for characterizing the quantized collective behaviors of the wavefunction, Chern number is the topological invariant of quantum Hall insulators. Chern number also identifies the topological properties of the photonic topological insulators (PTIs), thus it is of crucial importance in PTI design. In this paper, we develop a first principle computatioal method for the Chern number of 2D gyrotropic photonic crystals (PCs), starting from the Maxwell’s equations. Firstly, we solve the Hermitian generalized eigenvalue equation reformulated from the Maxwell’s equations by using the full-wave finite-difference frequency-domain (FDFD) method. Then the Chern number is obtained by calculating the integral of Berry curvature over the first Brillouin zone. Numerical examples of both transverse-electric (TE) and transverse-magnetic (TM) modes are demonstrated, where convergent Chern numbers can be obtained using rather coarse grids, thus validating the efficiency and accuracy of the proposed method. Copyright © 2020, The Authors. All rights reserved.

关键词： Photonic crystals

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Understanding metric-related pitfalls in image analysis validation

arXiv

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arXiv 2023年

作者： Reinke, Annika Tizabi, Minu D. Baumgartner, Michael Eisenmann, Matthias Heckmann-Nötzel, Doreen Kavur, A. Emre Rädsch, Tim Sudre, Carole H. Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Benis, Arriel Blaschko, Matthew B. Buettner, Florian Cardoso, M. Jorge Cheplygina, Veronika Chen, Jianxu Christodoulou, Evangelia Cimini, Beth A. Collins, Gary S. Farahani, Keyvan Ferrer, Luciana Galdran, Adrian van Ginneken, Bram Glocker, Ben Godau, Patrick Haase, Robert Hashimoto, Daniel A. Hoffman, Michael M. Huisman, Merel Isensee, Fabian Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kenngott, Hannes Kleesiek, Jens Kofler, Florian Kooi, Thijs Kopp-Schneider, Annette Kozubek, Michal Kreshuk, Anna Kurc, Tahsin Landman, Bennett A. Litjens, Geert Madani, Amin Maier-Hein, Klaus Martel, Anne L. Mattson, Peter Meijering, Erik Menze, Bjoern Moons, Karel G.M. Müller, Henning Nichyporuk, Brennan Nickel, Felix Petersen, Jens Rafelski, Susanne M. Rajpoot, Nasir Reyes, Mauricio Riegler, Michael A. Rieke, Nicola Saez-Rodriguez, Julio Sánchez, Clara I. Shetty, Shravya Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. van Calster, Ben Varoquaux, Gaël Yaniv, Ziv R. Jäger, Paul F. Maier-Hein, Lena Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Intelligent Medical Systems Germany NCT Heidelberg A Partnership Between DKFZ University Medical Center Heidelberg Germany Heidelberg Division of Medical Image Computing Germany Heidelberg Division of Intelligent Medical Systems Germany MRC Unit for Lifelong Health and Ageing UCL Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom School of Biomedical Engineering and Imaging Science King’s College London London United Kingdom Instituto de Cálculo CONICET – Universidad de Buenos Aires Buenos Aires Argentina Centre for Medical Image Computing University College London London United Kingdom McGill University Montreal Canada Division of Computational Pathology Dept of Pathology & Laboratory Medicine Indiana University School of Medicine IU Health Information and Translational Sciences Building Indianapolis United States University of Pennsylvania Richards Medical Research Laboratories FL7 PhiladelphiaPA United States Department of Digital Medical Technologies Holon Institute of Technology Holon Israel European Federation for Medical Informatics Le Mont-sur-Lausanne Switzerland Center for Processing Speech and Images Department of Electrical Engineering KU Leuven Leuven Belgium partner site Frankfurt/Mainz a partnership between DKFZ and UCT Frankfurt Marburg Germany Heidelberg Germany Goethe University Frankfurt Department of Medicine Germany Goethe University Frankfurt Department of Informatics Germany and Frankfurt Cancer Insititute Germany Department of Computer Science IT University of Copenhagen Copenhagen Denmark Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V. Dortmund Germany Imaging Platform Broad Institute of MIT and Harvard CambridgeMA United States Centre for Statistics in Medicine University of Oxford Oxford United Kingdom Center for Biomedical In

Validation metrics are key for tracking scientific progress and bridging the current chasm between artificial intelligence (AI) research and its translation into practice. However, increasing evidence shows that particularly in image analysis, metrics are often chosen inadequately. While taking into account the individual strengths, weaknesses, and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers. Based on a multi-stage Delphi process conducted by a multidisciplinary expert consortium as well as extensive community feedback, the present work provides the first reliable and comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. While focused on biomedical image analysis, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy. The work serves to enhance global comprehension of a key topic in image analysis validation. © 2023, CC BY.

关键词： Computer vision

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Attributed rhetorical structure grammar for domain text summarization

arXiv

引用

arXiv 2019年

作者： Lu, Ruqian Hou, Shengluan Wang, Chuanqing Huang, Yu Fei, Chaoqun Zhang, Songmao Key Laboratory of MADIS Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China

This paper presents a new approach of automatic text summarization which combines domain oriented text analysis (DoTA) and rhetorical structure theory (RST) in a grammar form: the attributed rhetorical structure grammar (ARSG), where the non-terminal symbols are domain keywords, called domain relations, while the rhetorical relations serve as attributes. We developed machine learning algorithms for learning such a grammar from a corpus of sample domain texts, as well as parsing algorithms for the learned grammar, together with adjustable text summarization algorithms for generating domain specific summaries. Our practical experiments have shown that with support of domain knowledge the drawback of missing very large training data set can be effectively compensated. We have also shown that the knowledge based approach may be made more powerful by introducing grammar parsing and RST as inference engine. For checking the feasibility of model transfer, we introduced a technique for mapping a grammar from one domain to others with acceptable cost. We have also made a comprehensive comparison of our approach with some others. Copyright © 2019, The Authors. All rights reserved.

关键词： Text processing

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Tongue image segmentation via color decomposition and thresholding

Tongue image segmentation via color decomposition and thresh...

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作者： Li, Zuoyong Yu, Zhaochai Liu, Weixia Xu, Yong Zhang, Daoqiang Cheng, Yong Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350121 China The Fujian College's Research Base of Humanities and Social Science for Internet Innovation Research Center Minjiang University Fuzhou350121 China Straits Institute Minjiang University Fuzhou350121 China Bio-Computing Research Center Shenzhen Graduate School Harbin Institute of Technology Shenzhen518055 China Department of Computer Science and Engineering Nanjing University of Aeronautics and Astronautics Nanjing210016 China School of Communication Engineering Nanjing Institute of Technology Nanjing211167 China

Tongue image segmentation is a crucial step in developing an automatic tongue diagnosis system. After exploring characteristics of image thresholding in different color spaces, we propose a simple and effective tongue image segmentation method. Specifically, the proposed method first extracts an initial tongue body region by performing image thresholding on the transformed hue component in HSI (Hue, Saturation, and Intensity) color space. Then, one of two image thresholding results on the red component of an original tongue image is adaptively selected to find the gap region between the tongue body root and the upper lip. Finally, the initial tongue body region is refined by removing fake object regions such as the upper lip to obtain final tongue image segmentation result. Experimental results on a tongue image dataset containing 200 subjects demonstrate the effectiveness of the proposed method. © 2018 John Wiley & Sons, Ltd.

关键词： Image segmentation

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Identifying High Potential Talent: A Neural Network Based Dynamic Social Profiling Approach

Identifying High Potential Talent: A Neural Network Based Dy...

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IEEE International Conference on Data Mining (ICDM)

作者： Yuyang Ye Hengshu Zhu Tong Xu Fuzhen Zhuang Runlong Yu Hui Xiong Anhui Province Key Lab of Big Data Analysis and Application University of Science and Technology of China Baidu Talent Intelligence Center Baidu Inc Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology University of Chinese Academy of Sciences

How to identify high-potential talent (HIPO) earlier in their career always has strategic importance for human resource management. While tremendous efforts have been made in this direction, most existing approaches are still based on the subjective selection of human resource experts. This could lead to unintentional bias and inconsistencies. To this end, in this paper, we propose a neural network based dynamic social profiling approach for quantitatively identifying HIPOs from the newly-enrolled employees by modeling the dynamics of their behaviors in organizational social networks. A basic assumption is that HIPOs usually perform more actively and have higher competencies than their peers to accumulate their social capitals during their daily work practice. Along this line, we first propose to model the social profiles of employees with both Graph Convolutional Network (GCN) and social centrality analysis in a comprehensive way. Then, an adaptive Long Short Term Memory (LSTM) network with global attention mechanism is designed to capture the profile dynamics of employees in the organizational social networks during their early career. Finally, extensive experiments on real-world data clearly validate the effectiveness of our approach as well as the interpretability of our results.

关键词： Social networking (online) Companies Artificial neural networks Germanium Engineering profession

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