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arXiv 2017年

作者： Hao, You Li, Shirui Mo, Hanlin Li, Hua Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

We present a novel Affine-Gradient based Local Binary pattern (AGLBP) descriptor for texture classification. It is very hard to describe complicated texture using single type information, such as Local Binary pattern (LBP), which just utilizes the sign information of the difference between pixel and its local neighbors. Our descriptor has three characteristics: 1) In order to make full use of the information contained in the texture, the Affine-Gradient, which is different from Euclidean-Gradient and invariant to affine transformation, is incorporated into AGLBP. 2) An improved method is proposed for rotation invariance, which depends on the reference direction calculating respect to local neighbors. 3) Feature selection method, considering both the statistical frequency and the intraclass variance of the training dataset, is also applied to reduce the dimensionality of descriptors. Experiments on three standard texture datasets, Outex12, Outex10 and KTH-TIPS2, are conducted to evaluate the performance of AGLBP. The results show that our proposed descriptor gets better performance comparing to some state-of-the-art rotation texture descriptors in texture classification. Copyright © 2017, The Authors. All rights reserved.

关键词： Feature extraction

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Decoding overlapped time division multiplexing system with Fano algorithm

Decoding overlapped time division multiplexing system with F...

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10th International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM 2014)

作者： Qingshuang Zhang Ke Wang Aijun Liu Daoben Li Xiaofei Pan School of Communications Engineering PLA University of Science and Technology Nanjing China School of Information and Engineering Key Laboratory of Information Processing and Intelligent Technology Beijing University of Posts and Telecommunications China

ISBN: (纸本)9781510804166

OvTDM is a new transmission scheme that can obtain high spectral efficiency with very low threshold signal noise ratio (SNR) by using the inter symbol interference (ISI). Fano algorithm (FA) is researched in this paper to implement fast ML decoding at high spectral efficiency for OvTDM. Numerical results show that the complexity of OvTDM with FA has no relationship with the number of state which is lower than 15% of that of Viterbi algorithm and the bit SNR loss is under 2 dB. Moreover, OvTDM with FA can even outperform Shannon limit when spectral is above 6.7 bits/s/Hz.

关键词： FA ISI MLSD OvTDM maximum likelihood sequence detection Fano algorithm spectral efficiency FAS gene signal to noise ratios ISIS SATELLITES Shannon limit Time division time division multiplexing Viterbi algorithm DECODING

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Modified Image Haze Removal Algorithm Based on Dark Channel Prior

Modified Image Haze Removal Algorithm Based on Dark Channel ...

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

作者： Junpeng Hu Zuoyong Li Xinwei Chen Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China College of Mathematics and Computer Science Fuzhou University Fuzhou China

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

Image defogging/dehazing is an important research hotspot in the field of image processing. The purpose of image defogging is to remove fog and obtain more image details. However, image defogging results based on dark channel prior usually have the problems of color distortion and insufficient brightness, which leads to the loss of partial image information. In order to solve this problem, we proposed a new image defogging algorithm based on dark channel prior. The proposed algorithm first selects the candidate regions of the sky by combining the location priori and brightness characteristics of the image pixel points, and reasonably estimate the atmospheric light value via the pixel points in the candidate regions. Second, the proposed algorithm respectively reduces the RGB (Red, Green, and Blue) channel values of the fog image, and combines each reduced channel and the other two previously unreduced channels to make up three new fog images. Finally, the proposed algorithm uses our image defogging algorithm to get three defogging images, and weights three defogging images to get the restored image. Comparative study and qualitative evaluation indicated that the proposed algorithm can obtain clearer defogging image.

关键词： Image color analysis Brightness Channel estimation Distortion Image restoration Estimation Atmospheric modeling

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Multi-path SDN route selection subject to multi-constraints

Multi-path SDN route selection subject to multi-constraints

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Third International Conference on Cyberspace Technology (CCT 2015)

作者： Qingfeng Pan Xianghan Zheng Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou 350108 China College of Mathematics and Computer Science Fuzhou University 350108 China

ISBN: (纸本)9781510821873

Multi-constrained optimal routing problem and disjoint path selection are NP-hard problem. The conventional single-path and single constraint routing can not meet people's needs. The reliability of network data transmission and packet loss rate can be decreased when the route or the transmission fails. To solve the problems, we need to study the routing path selection strategies. This paper optimizes the routing path that meets the QoS constraints under the SDN pre planning routing, and proposes a routing mechanism that can solve problems in polynomial time. Firstly, the paper combined with Clustering and Gauss algorithm to simulate the network simulation, obtain the initial experimental data. It can reflect the network status more accurately. Secondly, simplify the multi-objectives based on the AHP, It reduces the complexity of the problem and meets QoS requirements. Finally, select the disjoint communication paths and backup paths with the heuristic algorithms in order to balance the load of network and improve the transmission reliability. The selection mechanism can improve the efficiency of data transmission, and the survival of network communication.

关键词： Analytic Hierarchy Process Disjoint Path Multi constraints Optimal Path QoS Routing

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Application of Deep Learning in Software Security Detection

Application of Deep Learning in Software Security Detection

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2018 International Conference on Computational Science and Engineering(ICCSE 2018)

作者： Lin Li Ying Ding Jiacheng Mao College of Computer Science and Technology Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System

Machine learning is knowledge of learning rules from data through computational models and algorithms. It has been applied in various fields that require mining rules from complex data, and has become one of the most core technologies in the field of artificial intelligence in the broad sense. In recent years, a variety of deep neural networks have made remarkable achievements in a large number of machine learning problems, forming a new branch of machine learning, deep learning, and also raising a new climax of machine learning theory, method and application research. Software security is a highly concerned issue at present. Vulnerability detection technology is an important measure to improve software security. In order to make software application more secure, it is necessary to discuss the application of deep learning in software security detection.

关键词： Deep learning Software security Detection RNN

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Thought expression analysis of english essays combining opinion and sentiment

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Journal of Computers (Taiwan) 2021年第3期32卷 43-56页

作者： Wang, Yabing Huang, Guimin Zhang, Xiaowei Li, Yiqun Li, Maolin Li, Hui Li, Jun Guangxi Key Laboratory of Image and Graphic Intelligent Processing School of Computer Science and Information Security Guilin University of Electronic Technology Guilin541004 China

With the rapid development of science, technology and the gradual information of education, the automatic English essays correction system has received more and more attention. The existing automatic English essays correction system can provide feedback on vocabulary and grammar, but it has a lack of thought expression analysis of English essays. Therefore, it is impossible to comprehensively evaluate whether opinion of English essays is relevant and expression of sentiment is clear. To solve this problem, we propose to analyze the thought expression of English essays by combining sentiment analysis and opinion analysis. Through experiment analysis and verification from the aspects of relevance degree of opinion, sentiment tendency, and thought expression of English essays, the results show the proposed method has a good performance in analyzing thought expression of English essays. © 2021 Computer Society of the Republic of China. All rights reserved.

关键词： Sentiment analysis

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Joint Optimization of the Spectral Coexistence of Radar and Communications System

Joint Optimization of the Spectral Coexistence of Radar and ...

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IEEE National Conference on Radar

作者： Junhui Qian Shuya Zhang Mengchen Lu Fengchun Tian School of Microelectronic and Communication Engineering Chongqing University Chongqing China Chongqing Key Laboratory of Bio-perception and Intelligent Information Processing Chongqing University Chongqing China

ISBN: (数字)9781728189420

ISBN: (纸本)9781728189437

In the present paper, we proposed and analyzed a novel algorithmic framework for the joint design of the spectral coexistence of radar and communications system. The objects of interest in system design are the radar transmit code and the communication system code-book, whereby we state the design problem as a constrained maximization of the communication mutual information (MI) subject to a number of constraints guaranteeing both the radar performance, through a similarity constraint with some standard waveform and the effective power of radar interference. An iterative procedure, whose convergence is analytically proved, based on iterative alternating optimization of the suitably designed subproblems, is proposed and analyzed. Finally, we assess the effectiveness of the proposed approaches by numerical results.

关键词： Communication systems Simulation Radar Optimization System analysis and design Standards Convergence

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Exploring the role of visual content in fake news detection

arXiv

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arXiv 2020年

作者： Cao, Juan Qi, Peng Sheng, Qiang Yang, Tianyun Guo, Junbo Li, Jintao Key Laboratory of Intelligent Information Processing Center for Advanced Computing Research Institute of Computing Technology CAS China University of Chinese Academy of Sciences China

The increasing popularity of social media promotes the proliferation of fake news, which has caused significant negative societal effects. Therefore, fake news detection on social media has recently become an emerging research area of great concern. With the development of multimedia technology, fake news attempts to utilize multimedia content with images or videos to attract and mislead consumers for rapid dissemination, which makes visual content an important part of fake news. Despite the importance of visual content, our understanding about the role of visual content in fake news detection is still limited. This chapter presents a comprehensive review of the visual content in fake news, including the basic concepts, effective visual features, representative detection methods and challenging issues of multimedia fake news detection. This chapter can help readers to understand the role of visual content in fake news detection, and effectively utilize visual content to assist in detecting multimedia fake news. Copyright © 2020, The Authors. All rights reserved.

关键词： Social networking (online)

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An improved Chinese text multi-label classification method based on CNN 13

An improved Chinese text multi-label classification method b...

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2020 13th International Conference on Computer and Electrical Engineering, ICCEE 2020

作者： Xin, Yuanxia Zhang, Zhi Collage of Computer Science and Technology Wuhan University of Science and Technology WuhanHubei430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System WuhanHubei430065 China Big Data Science and Engineering Research Institute Wuhan University of Science and Technology WuhanHubei430065 China

Text multi-label classification technology can accurately and quickly classify text information into related categories or topics, and help people quickly locate the required content in massive information resources, which is of great significance in application. As the traditional classification algorithm is faced with the problems of low classification accuracy due to the low correlation of data labels, unbalanced label data and few short text feature words, this paper firstly performs hierarchical pre-processing on label data to transform multi-label classification into hierarchical text multi-classification. At the same time, an improved multi-label classification algorithm Multi-label Convolutional Neural Networks (ML-CNN) is proposed. Based on the TensorFlow framework, a CNN model is designed and different training models are constructed for each level of label classification. According to the number of classification levels, the output of the upper level label is stitched to the original input tail as the next level of input. Experiments on the description information of 500,000 Chinese products with labels, show that the improved algorithm will significantly improve the classification accuracy and the accuracy of each level can reach more than 88%, which proves the feasibility and effectiveness of the algorithm. © Published under licence by IOP Publishing Ltd.

关键词： Classification (of information)

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Nonparametric regression for the short-term traffic flow forecasting

Nonparametric regression for the short-term traffic flow for...

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International Conference on Mechanic Automation and Control Engineering (MACE)

作者： Tao Zhang Lifang Hu Zhixin Liu Yuejie Zhang School of information Management and Engineering Shanghai University of Finance and Economics Shanghai China School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai China

ISBN: (纸本)9781424477371

This paper uses the K-NN based nonparametric regression to forecast the short term traffic flow, applies the prediction interval calculated by K to forecast during unconventional road condition, and improves the forecasting results. Finally, nonparametric regression's advantages of high accuracy and strong transplant ability are showed while being compared with neural network.

关键词： Telecommunication traffic Economic forecasting Traffic control Predictive models Nearest neighbor searches Databases Runtime Technology forecasting Weather forecasting Finance

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