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IEEE International Conference on Image Processing

作者： Xikai Xu Jing Dong Wei Wang Tieniu Tan Center for Research on Intelligent Perception and Computing National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences

ISBN: (纸本)9781479983407

The cover source mismatch problem in steganalysis is a serious problem which keeps current steganalysis from practical use. It is mainly because of the high intra-class variation of cover and stego samples in the feature space, since current steganalytic features are inevitably affected much by the image content, size, quality and many other factors. Small training set often reflects only part of the real data distribution, hence the classifier (steganalyzer) may be undertrained and lack of robustness. In this paper, we propose a scheme to efficiently construct large representative training set for steganalysis. We also scheme out weighted ensemble classifiers which can be adaptive to testing data. Experimental results show that our method can improve the performance and robustness of steganalysis under high intra-class variation.

关键词： Steganography Steganalysis Sample selection Ensemble classifiers Steganography Classifiers Eigenvector Training observational data

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An Empirical Study of Graph Contrastive Learning 35

An Empirical Study of Graph Contrastive Learning

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35th Conference on Neural Information Processing systems - Track on Datasets and Benchmarks, NeurIPS Datasets and Benchmarks 2021

作者： Zhu, Yanqiao Xu, Yichen Liu, Qiang Wu, Shu Center for Research on Intelligent Perception and Computing National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences China School of Computer Science Beijing University of Posts and Telecommunications China

Graph Contrastive Learning (GCL) establishes a new paradigm for learning graph representations without human annotations. Although remarkable progress has been witnessed recently, the success behind GCL is still left somewhat mysterious. In this work, we first identify several critical design considerations within a general GCL paradigm, including augmentation functions, contrasting modes, contrastive objectives, and negative mining strategies. Then, to understand the interplay of different GCL components, we conduct comprehensive, controlled experiments over benchmark tasks on datasets across various domains. Our empirical studies suggest a set of general receipts for effective GCL, e.g., simple topology augmentations that produce sparse graph views bring promising performance improvements;contrasting modes should be aligned with the granularities of end tasks. In addition, to foster future research and ease the implementation of GCL algorithms, we develop an easy-to-use library PyGCL, featuring modularized CL components, standardized evaluation, and experiment management. We envision this work to provide useful empirical evidence of effective GCL algorithms and offer several insights for future research. © 2021 Neural information processing systems foundation. All rights reserved.

关键词： Contrastive Learning

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The Implementation and Analysis of the Monitoring Module based on NETCONF

The Implementation and Analysis of the Monitoring Module bas...

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International Symposium on Information Science and Engineering, ISISE

作者： Rui Wang Bin Zhang Guohui Li Yan Li Xuesong Gao Pattern Recognition and Intelligent System Laboratory Beijing University of Posts and Telecommunications Beijing China HuaWei Beijing Research Institute Beijing China

As network grows in complexity, types of management data become so diverse that the SNMP protocol exceeds the demands of network management. Therefore, the NETCONF protocol appears, which is a network configuration protocol, making use of XML in encoding. Under these circumstances, we have developed an open experimental platform environment for NETCONF protocol, BUPT-NEP (BUPT NETCONF Experimental Platform). In this paper, the monitoring module is focused. In order to get its hands-on experience, find out its advantage and disadvantage, give the direction to improve it, we present our design, implementation and analysis of this module based on our BUPT-NEP platform.

关键词： Subscriptions Transport protocols Statistics Telecommunication network management Encoding Condition monitoring Information science Information analysis pattern analysis pattern recognition

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All-in-Focus Iris Camera With a Great Capture Volume

All-in-Focus Iris Camera With a Great Capture Volume

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IEEE International Joint Conference on Biometrics (IJCB)

作者： Kunbo Zhang Zhenteng Shen Yunlong Wang Zhenan Sun Center for Research on Intelligent Perception and Computing National Lab of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China Tianjin Academy for Intelligent Recognition Technologies Tianjin China

ISBN: (数字)9781728191867

ISBN: (纸本)9781728191874

Imaging volume of an iris recognition system has been restricting the throughput and cooperation convenience in biometric applications. Numerous improvement trials are still impractical to supersede the dominant fixed-focus lens in stand-off iris recognition due to incremental performance increase and complicated optical design. In this study, we develop a novel all-in-focus iris imaging system using a focus-tunable lens and a 2D steering mirror to greatly extend capture volume by spatiotemporal multiplexing method. Our iris imaging depth of field extension system requires no mechanical motion and is capable to adjust the focal plane at extremely high speed. In addition, the motorized reflection mirror adaptively steers the light beam to extend the horizontal and vertical field of views in an active manner. The proposed all-in-focus iris camera increases the depth of field up to 3.9 m which is afactor of 37.5 compared with conventional long focal lens. We also experimentally demonstrate the capability of this 3D light beam steering imaging system in real-time multi-person iris refocusing using dynamic focal stacks and the potential of continuous iris recognition for moving participants.

关键词： Lenses Imaging Iris recognition Mirrors Cameras Optical imaging Optical sensors

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All-in-focus iris camera with a great capture volume

arXiv

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arXiv 2020年

作者： Zhang, Kunbo Shen, Zhenteng Wang, Yunlong Sun, Zhenan Center for Research on Intelligent Perception and Computing National Lab of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China Tianjin Academy for Intelligent Recognition Technologies Tianjin China

Imaging volume of an iris recognition system has been restricting the throughput and cooperation convenience in biometric applications. Numerous improvement trials are still impractical to supersede the dominant fixed-focus lens in stand-off iris recognition due to incremental performance increase and complicated optical design. In this study, we develop a novel all-in-focus iris imaging system using a focus-tunable lens and a 2D steering mirror to greatly extend capture volume by spatiotemporal multiplexing method. Our iris imaging depth of field extension system requires no mechanical motion and is capable to adjust the focal plane at extremely high speed. In addition, the motorized reflection mirror adaptively steers the light beam to extend the horizontal and vertical field of views in an active manner. The proposed all-in-focus iris camera increases the depth of field up to 3.9 m which is a factor of 37.5 compared with conventional long focal lens. We also experimentally demonstrate the capability of this 3D light beam steering imaging system in real-time multi-person iris refocusing using dynamic focal stacks and the potential of continuous iris recognition for moving participants. Copyright © 2020, The Authors. All rights reserved.

关键词： Biometrics

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ANALYSIS AND research OF ASSOCIATION pattern BETWEEN NETWORK PERFORMANCES AND FAULTS IN VOIP NETWORK

ANALYSIS AND RESEARCH OF ASSOCIATION PATTERN BETWEEN NETWORK...

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2009 IEEE International Conference on Network Infrastructure and Digital Content(2009年IEEE网络基础设施与数字内容国际会议 IEEE IC-NIDC2009)

作者： Jun Guo Chuanmei Wang Bin Zhang Guohui Li Jin Zhou Pattern Recognition and Intelligent System Laboratory Beijing University of Posts and Telecommunica IBM China Research Laboratory Beijing University of Posts and Telecommunications Beijing

ISBN: (纸本)9781424448999

The paper focuses on implementation of association pattern between the network performances and the general network faults. We use network simulation tool, OPNET, to accomplish network simulations of different network faults and different situations based on VoIP. Network performance parameters such as end-to-end delay, jitter, traffic dropped, queuing delay, link throughout, link utilization are monitored and collected. The simulation results are processed using data mining software, Weka, to discover the association pattern.

关键词： VoIP Network Performances Network Faults OPNET Data Mining Weka

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Skeleton based action recognition with convolutional neural network 3

Skeleton based action recognition with convolutional neural ...

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3rd IAPR Asian Conference on pattern recognition, ACPR 2015

作者： Du, Yong Fu, Yun Wang, Liang Center for Research on Intelligent Perception and Computing CRIPAC China Center for Excellence in Brain Science and Intelligence Technology CEBSIT China Nat'l Lab of Pattern Recognition Institute of Automation Chinese Academy of Sciences China College of Engineering College of Computer and Information Science Northeastern University United States

ISBN: (纸本)9781479961009

Temporal dynamics of postures over time is crucial for sequence-based action recognition. Human actions can be represented by the corresponding motions of articulated skeleton. Most of the existing approaches for skeleton based action recognition model the spatial-temporal evolution of actions based on hand-crafted features. As a kind of hierarchically adaptive filter banks, Convolutional Neural Network (CNN) performs well in representation learning. In this paper, we propose an end-to-end hierarchical architecture for skeleton based action recognition with CNN. Firstly, we represent a skeleton sequence as a matrix by concatenating the joint coordinates in each instant and arranging those vector representations in a chronological order. Then the matrix is quantified into an image and normalized to handle the variable-length problem. The final image is fed into a CNN model for feature extraction and recognition. For the specific structure of such images, the simple max-pooling plays an important role on spatial feature selection as well as temporal frequency adjustment, which can obtain more discriminative joint information for different actions and meanwhile address the variable-frequency problem. Experimental results demonstrate that our method achieves the state-of-art performance with high computational efficiency, especially surpassing the existing result by more than 15 percentage on the challenging ChaLearn gesture recognition dataset. © 2015 IEEE.

关键词： Neural networks

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Deep Audio-visual Learning:A Survey

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International Journal of Automation and computing 2021年第3期18卷 351-376页

作者： Hao Zhu Man-Di Luo Rui Wang Ai-Hua Zheng Ran He Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and TechnologyAnhui UniversityHefei 230601China Center for Research on Intelligent Perception and Computing(CRIPAC)and National Laboratory of Pattern Recognition(NLPR) Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Artificial Intelligence University of the Chinese Academy of SciencesBeijing 100049China Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of SciencesShanghai 200031China

Audio-visual learning,aimed at exploiting the relationship between audio and visual modalities,has drawn considerable attention since deep learning started to be used *** tend to leverage these two modalities to improve the performance of previously considered single-modality tasks or address new challenging *** this paper,we provide a comprehensive survey of recent audio-visual learning *** divide the current audio-visual learning tasks into four different subfields:audiovisual separation and localization,audio-visual correspondence learning,audio-visual generation,and audio-visual representation ***-of-the-art methods,as well as the remaining challenges of each subfield,are further ***,we summarize the commonly used datasets and challenges.

关键词： Deep audio-visual learning audio-visual separation and localization correspondence learning generative models representation learning

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The comparison and analysis of tree data model and table-like data model based on NETCONF

The comparison and analysis of tree data model and table-lik...

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International Asia Conference on Informatics in Control, Automation and Robotics, CAR

作者： Yanxiang Gao Bin Zhang Guohui Li Yan Li Xuesong Gao Pattern Recognition and Intelligent System Lab Beijing University of Posts and Telecommunications Beijing China HuaWei Beijing Research Institute HuaWei Beijing Research Institute China

ISBN: (纸本)9781424451920

NETCONF is a new standard network configuration protocol. For further research we have developed an open experimental platform for NETCONF protocol, BUPT-NEP (BUPT NETCONF Experimental Platform). Moreover, based on the experimental platform some significant technologies, such as data filtering, data storage and so on, are considered. This paper gives a detailed introduction of the tree data model and the table-like data model, which both could be used to organize and store the network facilities' information, and highlights the comparison of them in two aspects of the space occupancy as well as the filtering performance. From the comparison results it comes to a conclusion that the table-like data model could keep more information; however, the tree data model is easy to understand and shows better filtering performance.

关键词： Data models Data analysis Protocols Information filtering Information filters Routing Robotics and automation intelligent robots Informatics Automatic control

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Fitting a functional structural plant model based on global sensitivity analysis

Fitting a functional structural plant model based on global ...

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2012 IEEE International Conference on Automation Science and Engineering: Green Automation Toward a Sustainable Society, CASE 2012

作者： Lin, Yubin Kang, Mengzhen Hua, Jing National Key Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing 100190 China State Key Laboratory of Management and Control for Complex Systems Beijing Engineering Research Center of Intelligent Systems and Technology Chinese Academy of Sciences Beijing 100190 China

ISBN: (纸本)9781467304283

Calibration of a functional structural plant model is a challenging task because of the complexity of model structure. Parameter estimation through gradient-based optimization technique was highly dependent on initial parameter values. This motivated the use of global sensitivity analysis technique to choose parameter subset in fitting the data sequence. Global sensitivity indices were computed using the source sink ratio as the output of interest, which regulates all organ growth. By fitting on chrysanthemum data from nine sampling dates, it is shown that sensitivity analysis method helps to identify the influential parameters for a given sampling date. As a result, fitting process is less dependent on the initial parameter values. Current work provides a new method of calibrating a plant growth model with multiple outputs. © 2012 IEEE.

关键词： Sensitivity analysis

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