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1st International Conference on Smart Vehicular technology, Transportation, Communication and Applications, VTCA 2017

作者： Mao, Yu Zhang, Fuquan Xu, Lin Zhang, Dakui Yang, Hui School of Computer Science and Technology Beijing Institute of Technology Beijing China School of Software Beijing Institute of Technology Beijing100081 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350121 China Innovative Information Industry Research Institute Fujian Normal University Fuzhou350300 China

ISBN: (纸本)9783319707297

Collaborative filtering is a promising recommendation technique for predicting the preferences of users in recommender systems. The date coming from recommender system is not only big but also sparse. It motivates the need for a more intelligent approach to obtain the tastes of users. In this paper we present a novel bidirectional collaborative filtering method based on EM algorithm. We combine user rating with item rating to build a new rating matrix and calculate a steady result by iterating this strategy. The empirical evaluation demonstrates that our technique have encouraging result contrast to the traditional approaches. © Springer International Publishing AG 2018.

关键词： Collaborative filtering

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Relative influence maximization in competitive social networks

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Science China(information Sciences) 2017年第10期60卷 252-254页

作者： Dingda YANG Xiangwen LIAO Huawei SHEN Xueqi CHENG Guolong CHEN College of Physics and Information Engineering Fuzhou University Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing College of Mathematics and Computer Science Fuzhou University Institute of Computing Technology Chinese Academy of Sciences

In many realistic scenarios,such as political election and viral marketing,two opposite opinions,i.e.,positive opinion and negative opinion,spread simultaneously in the same social networks[1,2].Consequently,to achiev... 详细信息

关键词： net RIM Relative influence maximization in competitive social networks Figure

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Towards real-time eyeblink detection in the wild: Dataset, theory and practices

arXiv

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arXiv 2019年

作者： Hu, Guilei Xiao, Yang Cao, Zhiguo Meng, Lubin Fang, Zhiwen Zhou, Joey Tianyi Yuan, Junsong National Key Laboratory of Science and Technology on Multi-Spectral Information Processing School of Artificial Intelligence and Automation Huazhong University of Science and Technology China Guangdong Provincial Key Laboratory of Medical Image Processing School of Biomedical Engineering Southern Medical University China School of Energy and Mechanicalelectronic Engineering Hunan University of Humanities Science and Technology China Institute of High Performance Computing A∗STAR Singapore Computer Science and Engineering Department of University at Buffalo State University of New York United States

Effective and real-time eyeblink detection is of widerange applications, such as deception detection, drive fatigue detection, face anti-spoofing. Despite previous efforts, most of existing focus on addressing the eyeblink detection problem under constrained indoor conditions with relative consistent subject and environment setup. Nevertheless, towards practical applications, eyeblink detection in the wild is highly preferred, and of greater challenges. In this paper, we shed the light to this research topic. A labelled eyeblink in the wild dataset (i.e., HUST-LEBW) of 673 eyeblink video samples (i.e., 381 positives, and 292 negatives) is first established. These samples are captured from the unconstrained movies, with the dramatic variation on face attribute, head pose, illumination condition, imaging configuration, etc. Then, we formulate eyeblink detection task as a binary spatial-temporal pattern recognition problem. After locating and tracking human eyes using SeetaFace engine and KCF (Kernelized Correlation Filters) tracker respectively, a modified LSTM model able to capture the multi-scale temporal information is proposed to verify eyeblink. A feature extraction approach that reveals the appearance and motion characteristics simultaneously is also proposed. The experiments on HUSTLEBW reveal the superiority and efficiency of our approach. The comparisons with the existing state-of-the-art methods validate the advantages of our manner for eyeblink detection in the wild. Copyright © 2019, The Authors. All rights reserved.

关键词： Long short-term memory

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The Novel Sensor Network Structure for Classification processing Based on the Machine Learning Method of the ACGAN

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Sensors (Switzerland) 2019年第14期19卷 3145页

作者： Chen, Yuantao Tao, Jiajun Wang, Jin Chen, Xi Yang, Kai School of Computer and Communication Engineering & Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportatio Changsha University of Science and Technology Changsha410114 China School of Information Science and Engineering Fujian University of Technology Fuzhou350118 China Hunan Institute of Scientific and Technical Information Changsha410001 China Electronics & Information School Yangtze University Jingzhou434023 China Technical Quality Department Hunan ZOOMLION Heavy Industry Intelligent Technology Corporation Limited Changsha410005 China

To address the problem of unstable training and poor accuracy in image classification algorithms based on generative adversarial networks (GAN), a novel sensor network structure for classification processing using auxiliary classifier generative adversarial networks (ACGAN) is proposed in this paper. Firstly, the real/fake discrimination of sensor samples in the network has been canceled at the output layer of the discriminative network and only the posterior probability estimation of the sample tag is outputted. Secondly, by regarding the real sensor samples as supervised data and the generative sensor samples as labeled fake data, we have reconstructed the loss function of the generator and discriminator by using the real/fake attributes of sensor samples and the cross-entropy loss function of the label. Thirdly, the pooling and caching method has been introduced into the discriminator to enable more ekective extraction of the classification features. Finally, feature matching has been added to the discriminative network to ensure the diversity of the generative sensor samples. Experimental results have shown that the proposed algorithm (CP-ACGAN) achieves better classification accuracy on the MNIST dataset, CIFAR10 dataset and CIFAR100 dataset than other solutions. Moreover, when compared with the ACGAN and CNN classification algorithms, which have the same deep network structure as CP-ACGAN, the proposed method continues to achieve better classification effects and stability than other main existing sensor solutions. © Sensors (Switzerland) 2019. All Rights Reserved.

关键词： Generative adversarial networks

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Cloud computing data security protection strategy 4th

Cloud computing data security protection strategy

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4th International Conference on Cloud Computing and Security, ICCCS 2018

作者： Wei, Yu Zhang, Yongsheng School of Information Science and Engineering Shandong Normal University Jinan250358 China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan250358 China

ISBN: (纸本)9783030000059

Cloud computing is a computing model that has emerged in recent years. It is of great significance for promoting massive information data calculations. We store large amounts of data in the cloud and use algorithms to make huge computations on large-scale data centers on the Internet. Big data cloud computing resources have a huge amount of information, which can provide convenient and fast computing while being a new type of storage. Due to the large amount of data that needs to be processed by cloud computing, many information processing methods are involved, and customer privacy data is difficult to be effectively protected in the cloud computing environment. Therefore, the protection of information security in the cloud computing environment becomes more important. Based on the analysis of the data security problems in the big data cloud computing environment, this paper analyzes the security protection of big data cloud computing data, which is of great significance for promoting the establishment of a complete data security system, and can also be further promoted the useful of cloud computing. © Springer Nature Switzerland AG 2018.

关键词： Data privacy

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Efficient trajectory data privacy protection scheme based on laplace's differential privacy

Efficient trajectory data privacy protection scheme based on...

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作者： Gu, Ke Yang, Lihao Liu, Yongzhi Yin, Bo School of Computer and Communication Engineering Changsha University of Science and Technology Changsha410114 China Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and Technology Changsha410114 China School of Information Science and Engineering Central South University Changsha410083 China

Now many applications of trajectory (location) data have facilitated people's daily life. However, publishing trajectory data may divulge individual sensitive information so as to influence people's normal life. On the other hand, if we cannot mine and share trajectory data information, trajectory data will lose its value to serve our society. Currently, because the records of trajectory data are discrete in database, some existing privacy protection schemes are difficult to protect trajectory data. In this paper, we propose a trajectory data privacy protection scheme based on Laplace's differential privacy mechanism. In the proposed scheme, the algorithm first selects the protected points from the user's trajectory data;secondly, the algorithm builds the polygons according to the protected points and the adjacent and high frequent accessed points selected from the accessed point database, then the algorithm calculates the polygon centroids;finally, the noises are added to the polygon centroids by the Laplace's differential privacy method, and the new polygon centroids are used to replace the protected points, and then the algorithm constructs and issues the new trajectory data. The experiments show that the running time of the proposed algorithms is fast, the privacy protection of the scheme is effective and the data usability of the scheme is higher. © 2018 Slovene Society Informatika. All rights reserved.

关键词： Trajectories

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Image Fusion via Domain and Feature Transfer

Image Fusion via Domain and Feature Transfer

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

作者： Yicheng Yang Huabing Zhou Wei Zhang Changcai Yang Zhenghong Yu Computer Science and Engineering School Hubei Provincial Key Laboratory of Intelligent Robot Wuhan Institute of Technology Wuhan China College of Computer and Information Sciences College of Mechanical and Electrical Engineering Fujian Agriculture and Forestry University FuZhou China

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

The infrared and visible images fusion technique is designed to simultaneously preserve the thermal radiation information of the infrared image and the detailed texture information of the visible image. Therefore, the fused image with rich detail information and a clear target area in order to find it more efficiently. Inspired by the powerful GANs technology in recent years, we proposed a novel method for fusing infrared and visible images that based on domain and feature transfer. First, it is designated as an optimization problem with a latent encoding which can be mapped into a pixel intensity consistent image on the latent image. We employ generative adversarial network, which can capture content characteristics of one image dataset and figure out how these characteristics could be translated into the domain of another image dataset, to transfer the appearance of an image from visible image to infrared one. And then we use the feature consistent constrains to enhance the features of the fused image. Moreover, by adding gradient constraints to preserve the details of visible image, therefore we combine the fused image with a similar gradient to the visible image. Qualitative and quantitative comparisons on public datasets demonstrate our proposed method is superior to the state-of-the-art technology. The fused image we have generated is more like a high-resolution enhanced infrared image, which is more efficient for discovering the target and tracking.

关键词： Image fusion Generative adversarial networks Feature extraction Gallium nitride Imaging Convolutional neural networks Manifolds

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Design of Distributed Multi-Parameter Measurement Control System Based On CAN Bus 3

Design of Distributed Multi-Parameter Measurement Control Sy...

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2018 3rd International Conference on Insulating Materials, Material Application and Electrical Engineering, IMMAEE 2018

作者： Guo, Hong Hu, Wei College of Computer Science and Technology Wuhan University of Science and Technology Wuhan430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan430065 China

This paper introduces a multi-parameter distributed measurement control system based on CAN bus network and uses C8051F040 MCU as measurement control node, and corresponding hardware and software design to realize simultaneous detection of multiple measurement nodes. This paper described the overall structure of the system, introduced the hardware design and system software design of the data acquisition and control processing module based on C8051F040 microcontroller, and verified the correctness and feasibility of the system design by experimental tests. © 2018 Published under licence by IOP Publishing Ltd.

关键词： Software design

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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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One-pass multi-task networks with cross-task guided attention for brain tumor segmentation

arXiv

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arXiv 2019年

作者： Zhou, Chenhong Ding, Changxing Wang, Xinchao Lu, Zhentai Tao, Dacheng School of Electronic and Information Engineering South China University of Technology Guangzhou510000 China Department of Computer Science Stevens Institute of Technology Hoboken United States Guangdong Provincial Key Laboratory of Medical Image Processing School of Biomedical Engineering Southern Medical University Guangzhou China UBTech Sydney Artificial Intelligence Centre School of Information Technologies Faculty of Engineering and Information Technologies University of Sydney DarlingtonNSW2008 Australia

Class imbalance has emerged as one of the major challenges for medical image segmentation. The model cascade (MC) strategy, a popular scheme, significantly alleviates the class imbalance issue via running a set of individual deep models for coarse-to-fine segmentation. Despite its outstanding performance, however, this method leads to undesired system complexity and also ignores the correlation among the models. To handle these flaws in the MC approach, we propose in this paper a light-weight deep model, i.e., the One-pass Multi-task Network (OM-Net) to solve class imbalance better than MC does, while requiring only one-pass computation for brain tumor segmentation. First, OM-Net integrates the separate segmentation tasks into one deep model, which consists of shared parameters to learn joint features, as well as task-specific parameters to learn discriminative features. Second, to more effectively optimize OM-Net, we take advantage of the correlation among tasks to design both an online training data transfer strategy and a curriculum learning-based training strategy. Third, we further propose sharing prediction results between tasks, which enables us to design a cross-task guided attention (CGA) module. By following the guidance of the prediction results provided by the previous task, CGA can adaptively recalibrate channel-wise feature responses based on the category-specific statistics. Finally, a simple yet effective post-processing method is introduced to refine the segmentation results of the proposed attention network. Extensive experiments are conducted to demonstrate the effectiveness of the proposed techniques. Most impressively, we achieve state-of-the-art performance on the BraTS 2015 testing set and BraTS 2017 online validation set. Using these proposed approaches, we also won joint third place in the BraTS 2018 challenge among 64 participating teams. The code is publicly available at https://***/chenhong-zhou/OM-Net. Copyright © 2019, The Author

关键词： Magnetic resonance imaging

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