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15th International Conference on intelligent Computing, ICIC 2019

作者： Jiao, Zhihao Zhang, Lijun Yuan, Chang-An Qin, Xiao Shang, Li Institute of Machine Learning and Systems Biology School of Electronics and Information Engineering Tongji University Shanghai China Collaborative Innovation Center of Intelligent New Energy Vehicle Shanghai China School of Automotive Studies Tongji University Shanghai China Science Computing and Intelligent Information Processing of GuangXi Higher Education Key Laboratory Nanning Normal University NanningGuangxi China Department of Communication Technology College of Electronic Information Engineering Suzhou Vocational University SuzhouJiangsu215104 China

ISBN: (纸本)9783030267629

Plants play an important role in human life, Identifying and protecting plants has far-reaching implications for the sustainable development of the ecological environment. Plant leaves can often reflect important characteristics of plants, so it is scientific and feasible to effectively identify plant species through plant leaves. With the rapid development of deep learning in recent years, it has been widely applied in plant leaf recognition. Compared with the traditional method, deep learning based plant leaf recognition algorithm can extract plant leaf features more effectively and can greatly improve the performance. Based on the detailed analysis of the structural characteristics of three classical convolutional neural network models, this paper comprehensively compares the recognition performance of three convolutional neural network models on ICL plant leaf datasets. The experimental results show that the Conditional Generative Adversarial Net with optimized output layer has better recognition results on the plant leaf dataset than other convolutional neural network models. © 2019, Springer Nature Switzerland AG.

关键词： Neural network models

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A nonlinear impulsive control system with impulse time windows and un-fixed coefficient of impulsive intensity 6

A nonlinear impulsive control system with impulse time windo...

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6th International Conference on Information, Cybernetics, and Computational Social Systems, ICCSS 2019

作者： Feng, Yuming Wang, Zitao Liu, Xiaoyu Zhang, Wei Chongqing Engineering Research Center of Internet of Things Intelligent Control Technology Chongqing Three Gorges University Wanzhou Chongqing404100 China School of Computer Science and Engineering Chongqing Three Gorges University Wanzhou Chongqing404100 China Key Laboratory of Intelligent Information Processing and Control Chongqing Three Gorges University Wanzhou Chongqing404100 China School of Three Gorges Artificial Intelligence Chongqing Three Gorges University Wanzhou Chongqing404100 China

ISBN: (纸本)9781728158570

Based on the fact that there always exists error in the impulsive intensity, which leads to the fact that the coefficient is un-fixed, and in the occurrence of the impulses, we propose a new model, which is called nonlinear impulsive control system with impulse time windows and un-fixed coefficient of impulsive intensity. We find sufficient conditions for ensuring its stability by using Lypunov's method. We choose chaotic Lorenz system and Chua's system as numerical examples to show the effectiveness of the results, by employing such method, the systems are controlled. © 2019 IEEE.

关键词： Numerical methods

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Improved Siamese Network for Video-Based Person Re-identification

Improved Siamese Network for Video-Based Person Re-identific...

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International Conference on Networking and Network Applications (NaNA)

作者： Zhongmin Wang Lanlan Cai Na Duan Lin Fan Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications Xi'an China School of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi'an China

ISBN: (数字)9781728126296

ISBN: (纸本)9781728126302

In the field of human-computer interaction and visual surveillance, person re-identification is undoubtedly a key task. In this paper, we propose improved Siamese network for video-based person re-identification, which combines the verification model and the classification model, in a way that all the supervision information of the samples is fully utilized. The key frame extraction is able to select informative frames over the sequence, which removes occluded frames according to optical flow vector and measures the similarity of two adjacent frames' contents in terms of the block mutual information entropy. A convolutional network and long short-term memory joint training is used as a feature extractor to re-identify using the improved Siamese network framework. Experimental data on the iLIDS-VID and PRID-2011 datasets show that this approach is superior to existing video-based re-identification methods.

关键词： Feature extraction Optical flow Mutual information Data mining Entropy Logic gates Cameras

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A hybrid filtering-based network document recommendation system in cloud storage

A hybrid filtering-based network document recommendation sys...

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International Journal of Computational Science and Engineering

作者： Wu, Yuezhong Liu, Qin Li, Changyun Wang, Guojun School of Information Science and Engineering Central South University Changsha410083 China School of Computer Science Hunan University of Technology Zhuzhou412007 China College of Computer Science and Electronic Engineering Hunan University Changsha410082 China Intelligent Information Perception and Processing Technology Hunan Province Key Laboratory School of Computer Science Hunan University of Technology Zhuzhou412007 China School of Computer Science and Educational Software Guangzhou University Guangzhou510006 China

Since the key requirement of users is to efficiently obtain personalised services from mass network document resources, a hybrid filtering-based network document recommendation system is designed with the method of incorporating the content-based recommendation and collaborative filtering recommendation based on the powerful and extensible storage and computing power in cloud storage. The proposed system realises the main service module on Hadoop and Mahout platforms, and processes the documents containing the information of user interests by applying AHP-based attribute weighted fusion method. Based on the network interaction, the proposed system not only has advantages on the extensible storage space and high recommendation precision but also has an essential role in realising network resources sharing and personalised recommendation. Copyright © 2019 Inderscience Enterprises Ltd.

关键词： Storage as a service (STaaS) Collaborative filtering Information retrieval systems Recommender systems Cloud storages Collaborative filtering recommendations Content based recommendation Document recommendation Network interaction Personalised recommendations Recommendation precision User interest model

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A hybrid NSGA-II for matching biomedical ontology 14th

A hybrid NSGA-II for matching biomedical ontology

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14th International Conference on intelligent Information Hiding and Multimedia Signal processing, IIH-MSP 2018

作者： Xue, Xingsi Chen, Jie Chen, Junfeng Chen, Dongxu College of Information Science and Engineering Fujian University of Technology FuzhouFujian350118 China Intelligent Information Processing Research Center Fujian University of Technology FuzhouFujian350118 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology FuzhouFujian350118 China Fujian Key Lab for Automotive Electronics and Electric Drive Fujian University of Technology FuzhouFujian350118 China College of IOT Engineering Hohai University ChangzhouJiangsu China Fujian Medical University Union Hospital FuzhouFujian350001 China

ISBN: (纸本)9783030037444

Over the recent years, ontologies are widely used in the biomedical domains. However, biomedical ontology heterogeneity problem hamper the cooperation between intelligent applications based on biomedical ontologies. It is crucial to establish correspondences between the heterogeneous biomedical concepts in different ontologies, which is so-called biomedical ontology matching. Approaches based on Multi-Objective Evolutionary Algorithm (MOEA), such as NSGA-II, are emerging as a new methodology to solve the ontology matching problem. In this paper, to further improve the quality of biomedical ontology alignments, a hybrid NSGA-II is proposed, which modifies the knee solutions in the Pareto front by using a local search method. Experiment utilizes two biomedical ontology matching tracks provided by Ontology Alignment Evaluation Initiative (OAEI 2017). The experimental results show that our approach outperforms the participants of OAEI 2017 and NSGA-II based ontology matching technique. © Springer Nature Switzerland AG 2019.

关键词： Ontology

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Synchronization stability and circuit experiment of hyperchaos with time delay using impulse control

arXiv

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arXiv 2019年

作者： Ren, Hai-Peng Tian, Kun Bai, Chao Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xian University of Technology Xian710048 China School of Mechatronics Engineering Xi’an Technological University Xian710048 China

Secure communication using hyperchaos has a better potential performance, but hyperchaotic impulse circuits synchronization is a challenging task. In this paper, an impulse control method is proposed for the synchronization of two hyperchaotic Chen circuits. The sufficient conditions for the synchronization of hyperchaotic systems using the impulse control are given. The upper bound of the impulse interval is derived to assure the synchronization error system to be asymptotically stable. Simulation and circuit experiment show the correctness of the analysis and feasibility of the proposed method. Copyright © 2019, The Authors. All rights reserved.

关键词： Synchronization

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Joint Spatial-Temporal Trajectory Clustering Method for Mobile Social Networks

Joint Spatial-Temporal Trajectory Clustering Method for Mobi...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Ji Tang Linfeng Liu Jiagao Wu Jian Zhou Yang Xiang School of Computer Science and Technology Nanjing University of Posts and Telecommunications Nanjing China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (纸本)9781728125848

As an important issue in the trajectory mining task, the trajectory clustering technique has attracted lots of the attention in the field of data mining. Trajectory clustering technique identifies the similar trajectories (or trajectory segments) and classifies them into the several clusters which can reveal the potential movement behaviors of nodes. At present, most of the existing trajectory clustering methods focus on some spatial properties of trajectories (such as geographic locations, movement directions), while the spatial-temporal properties (especially the combination of spatial distances and semantic distances) are ignored, and thus some vital information regarding the movement behaviors of nodes is probably lost in the trajectory clustering results. In this paper, we propose a Joint Spatial-Temporal Trajectory Clustering Method (JSTTCM), where some spatialtemporal properties of the trajectories are exploited to cluster the trajectory segments. Finally, the number of clusters and the silhouette coefficient are observed through simulations, and the results show that JSTTCM can cluster the trajectory segments appropriately.

关键词： Trajectory clustering Spatial-temporal properties Spatial distances Semantic distances trajectory Social Networks Clustering methods Social Media Ganglia

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Contrary description logic: Gentzen deduction system

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Science China(Information Sciences) 2017年第11期60卷 171-179页

作者： Wei LI Yuefei SUI Jie LUO Bo CHEN State Key Laboratory of Software Development Environment Beihang University Key Laboratory of Intelligent Information Processing Institute of Computing TechnologyChinese Academy of Sciences

Traditional description logics are based on complementary rather than contrary concepts. This work proposes a contrary description logic based on the contrary concept constructors ～, ?(instead of ?).A Gentzen-type deduction system is applied to make the system sound and complete with the three-valued semantics of contrary description logic.

关键词： description logics contradictory contrary soundness completeness

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An improved reversible data hiding in encrypted images using parametric binary tree labeling

arXiv

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arXiv 2019年

作者： Wu, Youqing Xiang, Youzhi Guo, Yutang Tang, Jin Yin, Zhaoxia The School of Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei230601 China The School of Computer Science and Technology Hefei Normal University Hefei230601 China

This work proposes an improved reversible data hiding scheme in encrypted images using parametric binary tree labeling(IPBTL-RDHEI), which takes advantage of the spatial correlation in the entire original image but not in small image blocks to reserve room for hiding data. Then the original image is encrypted with an encryption key and the parametric binary tree is used to label encrypted pixels into two different categories. Finally, one of the two categories of encrypted pixels can embed secret information by bit replacement. According to the experimental results, compared with several state-of-the-art methods, the proposed IPBTL-RDHEI method achieves higher embedding rate and outperforms the competitors. Due to the reversibility of IPBTL-RDHEI, the original plaintext image and the secret information can be restored and extracted losslessly and separately. Copyright © 2019, The Authors. All rights reserved.

关键词： Binary trees

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A Survey of Low Resource Neural Machine Translation

A Survey of Low Resource Neural Machine Translation

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International Conference on Mechanical, Control and Computer Engineering (ICMCCE)

作者： Ding Liu Ning Ma Fangtao Yang Xuebin Yang Key Laboratory of China's Ethnic Languages and Information Technology of Ministry of Education Northwest Minzu University Lanzhou Gansu China Key Laboratory of China's Ethnic Languages and Intelligent Processing of Gansu Province Northwest Minzu University Lanzhou Gansu China

ISBN: (数字)9781728146898

ISBN: (纸本)9781728146904

Machine translation is one of the important research directions in the field of artificial intelligence and natural language. It has important scientific research value and practical value. Neural machine translation (NMT) is a new machine translation model which uses neural network to realize the transformation from source language to target language. In recent years, neural machine translation method based on sequence to sequence has major breakthrough in machine translation task, in most of the language of the translation effect beyond the effects of statistical machine translation, but for the translation under the condition of scarce resources, neural machine translation has yet to achieve the desired effect, this paper mainly introduces several effective methods and models under low resource conditions, as well as their problems and challenges, and the existing problems and challenges. Then look forward to the future research direction and development trend.

关键词： Handheld computers Conferences Recurrent neural networks Strain Dictionaries

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