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arXiv 2019年

作者： Wu, Chenhuang Edemskiy, Vladimir Xu, Chunxiang School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu Sichuan611731 China Provincial Key Laboratory of Applied Mathematics Putian University Putian Fujian351100 China Department of Applied Mathematics and Information Science Novgorod State University Veliky Novgorod173003 Russia

We consider the k-error linear complexity of binary sequences derived from Eluer quotients modulo 2p (p >3 is an odd prime), recently introduced by J. Zhang and C. Zhao. We adopt certain decimal sequences to determine the values of k-error linear complexity for all k > 0. Our results indicate that such sequences have good stability from the viewpoint of cryptography. Copyright © 2019, The Authors. All rights reserved.

关键词： Cryptography

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A Multi-Criteria Band Selection Method of Hyperspectral Images

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Journal of Physics: Conference Series 2020年第1期1651卷

作者： Zhonghua Wang Yulin Zhao Bangsheng He Hao Wang Jiangxi Provincial Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing Nanchang Institute of Technology Nanchang Jiangxi 330099 China Pioneer Software Co. Ltd Nanchang Jiangxi 330029 China School of Information and Engineering Nanchang Hangkong University Nanchang Jiangxi 330063 China

Hyperspectral remote sensing images are characterized by many bands and large amounts of datum, which require the dimensionality reduction, but band selection is one of the basic methods for dimensionality reduction of hyperspectral datum. For this reason, this paper proposes a multi-criteria band selection method. Firstly, the intrinsic dimensionality of hyperspectral image is calculated by virtual dimension, then the subspace is divided according to the band correlation criterion; and secondly, the information criterion is adopted to select the high-quality exponential bands in each subspace; and finally, the most suitable bands in each subspace are selected by class separability criterion to form the optimal band subset. Various experiments show that, compared with the optimum index factor and adaptive band selection methods, the proposed method has better performance in the measurement of information volume and information redundancy.

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Empirical evaluation of speaker adaptation on DNN based acoustic model

arXiv

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arXiv 2018年

作者： Wang, Ke Zhang, Junbo Wang, Yujun Xie, Lei Shaanxi Provincial Key Laboratory of Speech and Image Information Processing School of Computer Science Northwestern Polytechnical University Xi'an China Xiaomi Beijing China

Speaker adaptation aims to estimate a speaker specific acoustic model from a speaker independent one to minimize the mismatch between the training and testing conditions arisen from speaker variabilities. A variety of neural network adaptation methods have been proposed since deep learning models have become the main stream. But there still lacks an experimental comparison between different methods, especially when DNNbased acoustic models have been advanced greatly. In this paper, we aim to close this gap by providing an empirical evaluation of three typical speaker adaptation methods: LIN, LHUC and KLD. Adaptation experiments, with different size of adaptation data, are conducted on a strong TDNN-LSTM acoustic model. More challengingly, here, the source and target we are concerned with are standard Mandarin speaker model and accented Mandarin speaker model. We compare the performances of different methods and their combinations. Speaker adaptation performance is also examined by speaker's accent degree. Copyright © 2018, The Authors. All rights reserved.

关键词： Deep neural networks

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Scene Classification via Learning a Multi-Branch Convolutional Network

Scene Classification via Learning a Multi-Branch Convolution...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Xiaoyong Bian Chunfang Chen Yang Chen Xiongjun Fei Jingshan Tang Intelligent Information Processing and Real-time Industrial Systems Hubei Province Key Laboratory School of Computer Science and Technology Wuhan University of Science and Technology and Institute of Big Data Science and Engineering Wuhan University of Science and Technology Wuhan China School of Information Science and Engineering and Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education Wuhan University of Science and Technology Wuhan China School of Technology Michigan Technological University Michigan USA

Efficient and accurate classification of high-resolution scene remains a challenge of within-class diversity and between-class similarity due to rich image variations in viewpoint, object pose, spatial resolution and background. To address these issues, we propose a multi-branch convolutional neural network (MB-CNN), which focuses on tackling the problem of learning the appropriate representation of a high-resolution scene that is rich enough to discriminate between different semantic classes. First, pyramid scene parsing network (PSPNet) with minor modification is introduced to gather global object information. Then, an attention net is proposed to highlight transformation invariance and key regions for attention feature extraction. Finally, above two branches are fused with original input branch to learn consistently semantic class information and generate powerful predictions. Our approach achieves better performance favorably against state-of-the-arts on two publicly available scene datasets.

关键词： Conferences Cybernetics

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Investigating generative adversarial networks based speech dereverberation for robust speech recognition

arXiv

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arXiv 2018年

作者： Wang, Ke Zhang, Junbo Sun, Sining Wang, Yujun Xiang, Fei Xie, Lei Shaanxi Provincial Key Laboratory of Speech and Image Information Processing School of Computer Science Northwestern Polytechnical University Xi’an China Xiaomi Beijing China

We investigate the use of generative adversarial networks (GANs) in speech dereverberation for robust speech recognition. GANs have been recently studied for speech enhancement to remove additive noises, but there still lacks of a work to examine their ability in speech dereverberation and the advantages of using GANs have not been fully established. In this paper, we provide deep investigations in the use of GAN-based dereverberation front-end in ASR. First, we study the effectiveness of different dereverberation networks (the generator in GAN) and find that LSTM leads to a significant improvement as compared with feed-forward DNN and CNN in our dataset. Second, further adding residual connections in the deep LSTMs can boost the performance as well. Finally, we find that, for the success of GAN, it is important to update the generator and the discriminator using the same mini-batch data during training. Moreover, using reverberant spectrogram as a condition to discriminator, as suggested in previous studies, may degrade the performance. In summary, our GAN-based dereverberation front-end achieves 14%∼19% relative CER reduction as compared to the baseline DNN dereverberation network when tested on a strong multi-condition training acoustic model. Copyright © 2018, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Correction: Study on the influence of meteorological factors on influenza in different regions and predictions based on an LSTM algorithm

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BMC public health 2023年第1期23卷 269页

作者： Hansong Zhu Si Chen Wen Lu Kaizhi Chen Yulin Feng Zhonghang Xie Zhifang Zhang Lingfang Li Jianming Ou Guangmin Chen Emergency Response and Epidemic Management Institute Fujian Center for Disease Control and Prevention Fuzhou 350012 Fujian China. hszhu33@***. Fujian Provincial Key Laboratory of Zoonosis Research Fuzhou 350012 Fujian China. hszhu33@***. The practice base on the school of public health Fujian Medical University Fuzhou 350012 Fujian China. hszhu33@***. Climate Assessment Office of Fujian Climate Center Fuzhou 350007 Fujian China. Shengli Clinical Medical College of Fujian Medical University Department of Health Management of Fujian Provincial Hospital Fuzhou 350001 Fujian China. College of Computer and Data Science Fuzhou University Fuzhou 350108 Fujian China. School of Public Health Fujian Medical University Fuzhou 350108 Fujian China. Emergency Response and Epidemic Management Institute Fujian Center for Disease Control and Prevention Fuzhou 350012 Fujian China. Fujian Provincial Key Laboratory of Zoonosis Research Fuzhou 350012 Fujian China. The practice base on the school of public health Fujian Medical University Fuzhou 350012 Fujian China. Science and Technology Information and Management Fujian Center for Disease Control and Prevention Fuzhou 350012 Fujian China. Emergency Response and Epidemic Management Institute Fujian Center for Disease Control and Prevention Fuzhou 350012 Fujian China. ojm@***. Fujian Provincial Key Laboratory of Zoonosis Research Fuzhou 350012 Fujian China. ojm@***. The practice base on the school of public health Fujian Medical University Fuzhou 350012 Fujian China. ojm@***. Emergency Response and Epidemic Management Institute Fujian Center for Disease Control and Prevention Fuzhou 350012 Fujian China. 231147698@***. Fujian Provincial Key Laboratory of Zoonosis Research Fuzhou 350012 Fujian China. 231147698@***. The practice base on the school of public health Fujian Medical University Fuzhou 350012 Fujian China. 231147698@qq.com.

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Identification of protein hot regions by integrated machine learning algorithm

Identification of protein hot regions by integrated machine ...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Jing Hu Haomin Gan Xiaolong Zhang Nansheng Chen Molecular Biologyl and Biochemistry Simon Fraser University British Columbia Canada School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China Molecular Biology and Biochemistry Simon Fraser University British Columbia Canada

Discovering hot regions in protein-protein interaction is important for understanding the interactions between proteins, while because of the complexity and time-consuming of experimental methods, the computational prediction method can be very helpful to improve the efficiency to predict hot regions. In previous researches, some models are based on a single aspect, such as structure, energy, and sequence, each aspect has its advantage and limitations. In this paper, a new method that combing structure-based classification, energy-based clustering and sequence-based conservation in evolution is proposed. This method makes full use of three aspects of protein information and compensates for the limitations of using one single aspect information. Experimental results show that the proposed method significantly improves the prediction performance of hot regions.

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A java code protection scheme via dynamic recovering runtime instructions 18th

A java code protection scheme via dynamic recovering runtime...

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18th International Conference on Algorithms and Architectures for Parallel processing, ICA3PP 2018

作者： Jiajia, Sun Jinbao, Gao Yu-an, Tan Yu, Zhang Xiao, Yu School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China School of Electrical and Information Engineering Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and Architecture Beijing100044 China China University of Mining and Technology Beijing100083 China Department of Computer Science and Technology Shandong University of Technology ZiboShandong255022 China

ISBN: (纸本)9783030050627

As Android operating system and applications on the device play important roles, the security requirements of Android applications increased as well. With the upgrade of Android system, Android runtime mode (ART mode) has gradually become the mainstream architecture of the Android operating system. ART introduces several improvements in Android, but it also introduces new ways to enhance malicious activities. This paper proposed a confidential finer granularity protection scheme for application programs under ART mode of ROOT Android devices. Taking Java method as the protection granularity, the protection scheme increased the accuracy of protecting targets. In addition, the protection scheme provided a more thorough protection for applications by combining dynamic loading technology and encryption technology in ART mode, and improved the security of Android applications. Experiments showed that the proposed protection scheme is effective. © Springer Nature Switzerland AG 2018.

关键词： Android (operating system)

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Fast Semantic Preserving Hashing for Large-Scale Cross-Modal Retrieval

Fast Semantic Preserving Hashing for Large-Scale Cross-Modal...

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IEEE International Conference on Data Mining (ICDM)

作者： Xingzhi Wang Xin Liu Shujuan Peng Yiu-ming Cheung Zhikai Hu Nannan Wang Dept. of Comput. Sci. & Fujian Key Lab. of Big Data Intelligence and Security Huaqiao University Xiamen China State Key Lab. of Integrated Services Networks & School of Telecommun. Eng. Xidian University Xi’an China Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou China Dept. of Comput. Sci. and Institute of Research and Continuing Education HK Baptist University Hong Kong SAR China

Most Cross-modal hashing methods do not sufficiently exploit the discrimination power of semantic information when learning hash codes, while often involving time-consuming training procedures for large-scale dataset. To tackle these issues, we first formulate the learning of similarity-preserving hash codes in terms of orthogonally rotating the semantic data to hamming space, and then propose a novel Fast Semantic Preserving Hashing (FSePH) approach to large-scale cross-modal retrieval. Specifically, FSePH introduces an orthonormal basis to regress the targeted hash codes of training examples to their corresponding reasonably relaxed class labels, featuring significantly reducing the quantization error. Meanwhile, an effective optimization algorithm is derived for modality-specific projection function learning and an efficient closed-form solution for hash code learning, which are computationally tractable. Extensive experiments have shown that the proposed FSePH approach runs sufficiently fast, and also significantly improves the retrieval performances over the state-of-the-arts.

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A novel bimodal emotion database from physiological signals and facial expression

A novel bimodal emotion database from physiological signals ...

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作者： Yan, Jingjie Wang, Bei Liang, Ruiyu Jiangsu Provincial Key Laboratory of Image Processing and Image Communication College of Telecommunications and Information Engineering Nanjing University of Posts and Telecommunications Nanjing210003 China Research Center for Learning Science Southeast University Nanjing210096 China School of Communication Engineering Nanjing Institute of Technology Nanjing211167 China

In this paper, we establish a novel bimodal emotion database from physiological signals and facial expression, which is named as PSFE. The physiological signals and facial expression of the PSFE database are respectively recorded by the equipment of the BIOPAC MP 150 and the Kinect for Windows in the meantime. The PSFE database altogether records 32 subjects which include 11 women and 21 man, and their age distribution is from 20 to 25. Moreover, the PSFE database records three basic emotion classes containing calmness, happiness and sadness, which respectively correspond to the neutral, positive and negative emotion state. The general sample number of the PSFE database is 288 and each emotion class contains 96 samples. © Copyright 2018 The Institute of Electronics information and Communication Engineers.

关键词： Database systems

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