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arXiv 2021年

作者： Liu, Haiyang Zhang, Hanlin Guo, Li Yu, Jia Lin, Jie College of Computer Science and Technology Qingdao University Qingdao266071 China Institute of Big Data Technology and Smart City Qingdao University Qingdao266071 China State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China School of Electronic and Information Engineering Xian Jiaotong University Xian710049 China

With the rapid development of artificial intelligence and the advent of the 5G era, deep learning has received extensive attention from researchers. Broad Learning System (BLS) is a new deep learning model proposed recently, which shows its effectiveness in many fields, such as image recognition and fault detection. However, the training process still requires vast computations, and therefore cannot be accomplished by some resource-constrained devices. To solve this problem, the resource-constrained device can outsource the BLS algorithm to cloud servers. Nevertheless, some security challenges also follow with the use of cloud computing, including the privacy of the data and the correctness of returned results. In this paper, we propose a secure, efficient, and verifiable outsourcing algorithm for BLS. This algorithm not only improves the efficiency of the algorithm on the client but also ensures that the clients sensitive information is not leaked to the cloud server. In addition, in our algorithm, the client can verify the correctness of returned results with a probability of almost 1. Finally, we analyze the security and efficiency of our algorithm in theory and prove our algorithms feasibility through experiments. Copyright © 2021, The Authors. All rights reserved.

关键词： Outsourcing

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Virtual Reality, Augmented Reality and Mixed Reality on the Marketing of Film and Television Creation Industry 3rd

Virtual Reality, Augmented Reality and Mixed Reality on the ...

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3rd EAI International Conference on 6G for Future Wireless Networks, 6GN 2020

作者： Ge, Rui Chen, Hsuan-Yu Hsiao, Tsung-Chih Chang, Yu-Tzu Wu, Zhen-Yu College of Computer Science and Technology Huaqiao University Quanzhou Fujian China Department of Radiology Tri-Service General Hospital National Defense Medical Center Taipei Taiwan Fujian Key Laboratory of Big Data Intelligence and Security Huaqiao University Quanzhou China Department of Marketing and Logistics Management Chaoyang University of Technology Taichung Taiwan Department of Information Management National Penghu University of Science and Technology Penghu Taiwan

ISBN: (纸本)9783030639402

Virtual reality and augmented reality technology has been on the road to scientific research for many years since the end of the twentieth century, and although the scope of mixed reality application is relatively narrow compared to the first two applications, we have seen the results. Realistic contents also have exploring progress in the field of film and television creation in recent years, such as virtual reality technology can be utilized to make film clips. However, in fact, these technologies can be applied in film and television creation far more than that. I then illustrate some other aspects which these realistic contents can also be applied to in film and television creation in the future and the influence on the industry. © 2020, ICST Institute for computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Augmented reality

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Unsupervised Domain Adaptive Person Re-Identification via Human Learning Imitation

arXiv

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arXiv 2021年

作者： Peng, Yang Liu, Ping Luo, Yawei Zhou, Pan Xu, Zichuan Liu, Jingen Center for Frontier AI Research Agency for Science Technology and Research Singapore Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Hubei Wuhan China College of Computer Science and Technology Zhejiang University Zhejiang Hangzhou China Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province School of Software Dalian University of Technology Liaoning Dalian China

Unsupervised domain adaptive person re-identification has received significant attention due to its high practical value. In past years, by following the clustering and finetuning paradigm, researchers propose to utilize the teacher-student framework in their methods to decrease the domain gap between different person re-identification datasets. Inspired by recent teacher-student framework based methods, which try to mimic the human learning process either by making the student directly copy behavior from the teacher or selecting reliable learning materials, we propose to conduct further exploration to imitate the human learning process from different aspects, i.e., adaptively updating learning materials, selectively imitating teacher behaviors, and analyzing learning materials structures. The explored three components, collaborate together to constitute a new method for unsupervised domain adaptive person re-identification, which is called Human Learning Imitation framework. The experimental results on three benchmark datasets demonstrate the efficacy of our proposed method. Copyright © 2021, The Authors. All rights reserved.

关键词： Students

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The (t, k)-diagnosability of shuffle-cubes under PMC model

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International Journal of computer Mathematics: computer Systems Theory 2019年第2期4卷 111-126页

作者： Li, Jin'e Lin, Limei Huang, Yanze Yu, Hui Chen, Riqing Faculty of Computer Science and Information Technology Digital Fujian Institute of Big Data for Agriculture and Forestry Fujian Agriculture and Forestry University Fuzhou China College of Mathematics and Computer Science Fujian Normal University Fuzhou China School of Mathematics and Physics Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China

System-level diagnosis aims to identify faulty processors in a multiprocessor system by analysing the test results among the processors. The (Formula presented.) -diagnosis requires to identify and repair at least k faulty processors in each iteration until that all t faulty processors are found where (Formula presented.). It is one of the most important system-level diagnosis strategies. Meanwhile, shuffle-cube (Formula presented.), a variant of hypercube for multiprocessor systems, has some good combinatorial properties and fault-tolerant properties. In this paper, we propose a (Formula presented.) -diagnosis algorithm for n-dimensional shuffle-cube (Formula presented.), and then we show that (Formula presented.) is (Formula presented.) -diagnosable. Moreover, we prove that the proposed algorithm can identify at most (Formula presented.) faulty nodes under the condition that it can identify at least n faulty nodes in each iteration. © 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group.

关键词： aggregate Diagnosability fault diagnosis reliability shuffle-cube

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A zero-shot based fingerprint presentation attack detection system

arXiv

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arXiv 2020年

作者： Liu, Haozhe Zhang, Wentian Liu, Guojie Liu, Feng National Engineering Laboratory for Big Data System Computing Technology Guangdong Key Laboratory of IntelligentInformation Processing College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China

With the development of presentation attacks, Automated Fingerprint Recognition Systems(AFRSs) are vulnerable to presentation attack. Thus, numerous methods of presentation attack detection(PAD) have been proposed to ensure the normal utilization of AFRS. However, the demand of large-scale presentation attack images and the low-level generalization ability always astrict existing PAD methods' actual performances. Therefore, we propose a novel Zero-Shot Presentation Attack Detection Model to guarantee the generalization of the PAD model. The proposed ZSPAD-Model based on generative model does not utilize any negative samples in the process of establishment, which ensures the robustness for various types or materials based presentation attack. Different from other auto-encoder based model, the Fine-grained Map architecture is proposed to refine the reconstruction error of the auto-encoder networks and a task-specific gaussian model is utilized to improve the quality of clustering. Meanwhile, in order to improve the performance of the proposed model, 9 confidence scores are discussed in this article. Experimental results showed that the ZSPAD-Model is the state of the art for ZSPAD, and the MS-Score is the best confidence score. Compared with existing methods, the proposed ZSPAD-Model performs better than the feature-based method and under the multi-shot setting, the proposed method overperforms the learning based method with little training data. When large training data is available, their results are similar. Copyright © 2020, The Authors. All rights reserved.

关键词： Palmprint recognition

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Volatile organic compounds exposure, biological ageing, and increased sarcopenia risk: Association, interaction, and mechanistic exploration

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Journal of hazardous materials 2025年 495卷 138929页

作者： Le Hong Qing Liu Wei Liu Jiahao Song Shuhui Wan Zhiying Huo Yongfang Zhang Da Shi Ruyi Liang Bingdong Chen Yani Xiong Feifei Wang Pengfei Chen Weihong Chen Bin Wang Department of Occupational and Environmental Health School of Public Health Tongji Medical College Huazhong University of Science and Technology Wuhan Hubei 430030 China Key Laboratory of Environment and Health Ministry of Education & Ministry of Environmental Protection and State Key Laboratory of Environmental Health (Incubating) School of Public Health Tongji Medical College Huazhong University of Science and Technology Wuhan Hubei 430030 China. Medical Big Data and Bioinformatics Research Centre First Affiliated Hospital of Gannan Medical University Ganzhou 341000 China School of Public Health and Health Management Gannan Medical University Ganzhou Jiangxi 341000 China. Faculty of Agricultural Life and Environmental Sciences University of Alberta Edmonton Alberta T6G 2P5 Canada. Key Laboratory of Environment and Health Ministry of Education & Ministry of Environmental Protection and State Key Laboratory of Environmental Health (Incubating) School of Public Health Tongji Medical College Huazhong University of Science and Technology Wuhan Hubei 430030 China. Electronic address: wchen@***. Key Laboratory of Environment and Health Ministry of Education & Ministry of Environmental Protection and State Key Laboratory of Environmental Health (Incubating) School of Public Health Tongji Medical College Huazhong University of Science and Technology Wuhan Hubei 430030 China. Electronic address: gentwong@***.

Evidence on the impacts of volatile organic compounds (VOCs), a class of ubiquitous environmental toxics, on sarcopenia, a major age-related global health issue, remains scarce. data from 3007 adults in the National Health and Nutrition Examination Survey (2011-2018) were analyzed to assess the individual and mixture effects of 16 VOCs metabolites (VOCMs) on sarcopenia using multivariate logistic regression, weighted quantile sum (WQS), and Bayesian kernel machine regression (BKMR) models. Network pharmacology was employed to explore potential underlying mechanisms. The phenotypic age (PhenoAge), a biomarker of biological ageing, was used to evaluate its interaction with VOCMs. Several VOCMs, including 2-aminothiazoline-4-carboxylic acid (ATCA; odds ratio: 1.30, 95 % confidence interval: 1.07-1.57), N-acetyl-S-(2-carboxyethyl)-l-cysteine (CEMA; 1.40, 1.01-1.94), N-acetyl-S-(3,4-dihydroxybutyl)-l-cysteine (DHBMA; 2.35, 1.45-3.82), and 3-methylhippuric acid & 4-methylhippuric acid (3,4MHA; 1.32, 1.05-1.64), were associated with an increased risk of sarcopenia. The mixture of 16 VOCMs was linked to a higher sarcopenia risk in both BKMR and WQS (1.79, 1.30-2.49) models. The tumor necrosis factor (TNF) signaling pathway was identified as a key mechanism underlying the VOCs-sarcopenia relationship. PhenoAge was associated with an elevated sarcopenia risk (1.05, 1.01-1.09) and intensified the adverse effects of VOCMs (P<0.05). This study is the first to demonstrate that VOCs exposure, in interaction with biological ageing, may elevate sarcopenia risk, potentially through the TNF signaling pathway, offering novel insights into sarcopenia prevention in the context of VOCs exposure.

关键词： Environmental health Mixture effect Network pharmacology Phenotypic age TNF signaling pathway

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LNAD: Towards Lightweight Network Anomaly Detection in Software-Defined Networking

LNAD: Towards Lightweight Network Anomaly Detection in Softw...

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IEEE International Conference on High Performance Computing and Communications (HPCC)

作者： Yunhe Cui Qing Qian Huanlai Xing Saifei Li State Key Laboratory of Public Big Data School of Computer Science and Technology Guizhou University Guiyang China School of Information Guizhou University of Finance and Economics Guiyang China School of Information Science and Technology Southwest Jiaotong University Chengdu China

ISBN: (纸本)9781728176505

As an emerging architecture, Software-Defined Networking (SDN) is suffering from a lot of security issues. Network anomaly detection technology plays a crucial role in addressing SDN security issues. In SDN, the network status can be denoted by the packets or flow entries. Hence, the existing network anomaly detection methods are commonly designed based on inspecting packets or flow entries. Subsequently, these anomaly detection methods have to collect packets or flow entries from the underlying switches. More seriously, some anomaly detection methods even need to add extra modules to the switches. Unlike these existing network anomaly detection methods, this work proposes a lightweight network anomaly detection method. The main design principle of the proposed method is mining the inherent OpenFlow messages in SDN to represent the network status and further detect the network anomaly. The proposed method does not need to collect any additional messages from the underlying switches or add any additional modules to the underlying switches. The evaluation results demonstrate that the proposed network anomaly detection method can afford high detection accuracy and reduce the overhead of SDN controller.

关键词： High performance computing Design methodology Conferences Security Communication networks Anomaly detection

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Construction and optimization of a file distribution model for all-to-all comparison of big dataset

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Ingenierie des Systemes d'Information 2019年第3期24卷 337-342页

作者： Li, Leixiao Gao, Jing Wang, Hui Deng, Dan Lin, Hao College of Data Science and Application Inner Mongolia University of Technology Hohhot010080 China Inner Mongolia Autonomous Region Engineering and Technology Research Center of Big Data Based Software Service Hohhot010080 China College of Computer and Information Engineering Inner Mongolia Agricultural University Hohhot010018 China Inner Mongolia Autonomous Region Key Laboratory of big data research and application for agriculture and animal husbandry Hohhot010018 China

This paper probes into the all-to-all comparison of large dataset, and gives a formal mathematical description of the problem. Then, a multi-objective file distribution model was constructed based on the LP, aiming to localize the data, balance node storage and loads, minimize the storage occupation, and control the occupied storage within the storage limit of each node. To save storage space, the established model was further optimized, and the file distribution algorithm was designed for the distributed environment. Experimental results show that our model and algorithm successfully balanced the storage occupation and loads between computing nodes, and minimized the occupation of node storage. © 2019 International Information and Engineering technology Association. All rights reserved.

关键词： Linear programming

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Learning the Compositional Visual Coherence for Complementary Recommendations

arXiv

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arXiv 2020年

作者： Li, Zhi Wu, Bo Liu, Qi Wu, Likang Zhao, Hongke Mei, Tao Anhui Province Key Laboratory of Big Data Analysis and Application School of Data Science University of Science and Technology of China Columbia University School of Computer Science and Technology University of Science and Technology of China College of Management and Economics Tianjin University JD AI Research

Complementary recommendations, which aim at providing users product suggestions that are supplementary and compatible with their obtained items, have become a hot topic in both academia and industry in recent years. Existing work mainly focused on modeling the co-purchased relations between two items, but the compositional associations of item collections are largely unexplored. Actually, when a user chooses the complementary items for the purchased products, it is intuitive that she will consider the visual semantic coherence (such as color collocations, texture compatibilities) in addition to global impressions. Towards this end, in this paper, we propose a novel Content Attentive Neural Network (CANN) to model the comprehensive compositional coherence on both global contents and semantic contents. Specifically, we first propose a Global Coherence Learning (GCL) module based on multi-heads attention to model the global compositional coherence. Then, we generate the semantic-focal representations from different semantic regions and design a Focal Coherence Learning (FCL) module to learn the focal compositional coherence from different semantic-focal representations. Finally, we optimize the CANN in a novel compositional optimization strategy. Extensive experiments on the large-scale real-world data clearly demonstrate the effectiveness of CANN compared with several state-of-the-art methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Textures

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A traffic prediction method of bicycle-sharing based on long and short term memory network

Journal of Network Intelligence

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Journal of Network Intelligence 2019年第2期4卷 17-29页

作者： Zhang, Shun-Miao Su, Xin Jiang, Xin-Hua Chen, Ming-Long Wu, Tsu-Yang College of Information Science and Engineering Fujian University of Technology Fuzhou350118 China Fujian Provincial Key Laboratory of Big Data Mining and Applications & College of Information Science and Engineering Fujian University of Technology Fuzhou350118 China Fujian Key Laboratory for Automotive Electronics and Electric Drive Fujian University of Technology Fuzhou350118 China National Demonstration Center for Experimental Electronic Information and Electrical Technology Education Fujian University of Technology Fuzhou350118 China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China

The prediction of bicycle-sharing traffic flow plays an important role in man-agement of the service stations for bicycle-sharing. In this paper, based on an improved long and short term memory network we propose a traffic prediction method of bicycle-sharing. Firstly, the bicycle-sharing area is divided by the probability threshold clustering algorithm. Secondly, based on the long and short term memory network the bicycle-sharing traffic model is established. In order to increase the accuracy of prediction, we optimize the related parameters by differential evolution algorithm. Finally, we adopt the traffic data set of bicycle-sharing in Yancheng city, Jiangsu province, China in our experiment. With compared to the existed methods, the experimental results are shown that our proposed method has a better effectivity and is suitable for the traffic prediction of bicycle-sharing. © 2019, Taiwan Ubiquitous Information. All rights reserevd.

关键词： Optimization

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