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作者： Zhong Chen Xiaomei Tian Tianqi Lei Junyao Chen College of Computer Science and Technology Hengyang Normal University Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Department of Computer Engineering Ajou University

In this paper,synchronization of both non-identical unknown network and identical known network can be *** on Lyapunov stability theory,for the case of non-identical or identical network,

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A Novel Image Encryption Scheme Based on Poker Cross-Shuffling and Fractional Order Hyperchaotic System

A Novel Image Encryption Scheme Based on Poker Cross-Shuffli...

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作者： Zhong Chen Huihuang Zhao Junyao Chen College of Computer Science and Technology Hengyang Normal University Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Department of Computer Engineering Ajou University

A novel digital image encryption scheme based on Poker shuffling and fractional order hyperchaotic system is proposed. Poker intercrossing operation has nonlinearity and periodicity, and can form permutation group. Firstly, the plain image is transformed into block image, and each block is confused by Poker shuffling operation with key. Secondly, bit plane of every pixel in each block is confused. Lastly, the scrambled image is encrypted by fractional order hyperchaotic sequence. This encryption scheme provides a secure and efficient key stream, and guarantees a large key space. The experimental results and security analysis imply that the new encryption method has secure encryption effect and property of resisting common attacks.

关键词： Image encryption algorithm Poker shuffling operation Fractional order hyperchaotic system

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Social Emergency Event Judgement based on BiLSTM-CRF 19

Social Emergency Event Judgement based on BiLSTM-CRF

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19th Chinese National Conference on Computational Linguistic, CCL 2020

作者： Hu, Huijun Wang, Cong Dai, Jianhua Liu, Maofu School 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 Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha410081 China

In recent years, classification and rating of social emergency event have attracted more and more attentions in emergency management. However, most of the current studies adopt the rule-based methods to identify the evidences for event judgement, and have troubles in event judgement due to the complexity of social emergency event composition and the flexibility of language description. Inspired by the idea of event extraction, this paper has proposed the event judgement method via BiLSTM (Bidirectional Long-Short Term Memory) and CRF (Conditional Radom Fields) based on event classification and evidence extraction. The social emergency event classification is carried out firstly, and then the event evidences are extracted based on event type. In the end, the rating of social emergency event is judged with the attention mechanism and the combination of event type and evidence. Experimental results show that the proposed method is more robust than rule-based ones, and effective in the social emergency event judgement. © 2020 China National Conference on Computational Linguistics Published under Creative Commons Attribution 4.0 International License

关键词： Extraction

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Research on Power Attack Comprehensive Experiment Platform Based on SAKURA-G Hardware Circuit

Research on Power Attack Comprehensive Experiment Platform B...

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CENet 2017-the 7th International Conference on Computer Engineering and Networks

作者： Ge Jiao Lang Li Yi Zou College of Computer Science and Technology Hengyang Normal UniversityHunan Provincial Engineering Laboratory for Technology of Traditional Settlements DigitalizationHunan Provincial Key Laboratory of Intelligent Information Processing and Application College of Computer Science and Technology Hengyang Normal University

The power attack of the hardware circuit is going through the steps of algorithm writen in FPGA,power consumption,data processing and *** order to solve the problem of the existing power attack experimental platform because the processing steps are too scattered,feature complex operations and other issues,we've thus developed a higher degree of integration of the experimental *** advanced encryption standard(AES) algorithm is taken as an example to illustrate the whole process of implementing the correlation power analysis(CPA)attack on the experimental *** AES algorithm will be downloaded to the SAKURA-G experiment board,acquire the algorithm runtime power leakage,carry out the energy analysis because related attacks on information may leak location,and restore the AES first round of the first byte of the *** results show that the comprehensive experimental platform will integrate these steps into a platform,simplify the operation process,ensure the accuracy of power acquisition,realize the parallel data processing and improve the accuracy and efficiency of power attack.

关键词： CPA AES Research on Power Attack Comprehensive Experiment Platform Based on SAKURA-G Hardware Circuit

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Traffic Condition Analysis Based on Users Emotion Tendency of Microblog

Advances in Intelligent Systems and Computing

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Advances in intelligent Systems and Computing 2018年 650卷 299-311页

作者： Wang, Shuru Cao, Donglin Lin, Dazhen Chao, Fei Cognitive Science Department Xiamen University Xiamen China Fujian Key Laboratory of Brain-Inspired Computing Technique and Applications Xiamen University Xiamen China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China

ISBN: (纸本)9783319669380

Analysis of traffic condition is of great significance to urban planning and public administration. However, traditional traffic condition analysis approaches mainly rely on sensors, which are high-cost and limit their coverage. To solve these problems, we propose a semi-supervised learning method which uses the social network data instead and analyzes the traffic condition based on user’s emotion tendency. First we train the Gated Recurrent Unit (GRU) model to estimate the sentiment of microblog with traffic information, then using the emotional tendency to predict whether traffic jams happen or not. In order to reduce the data annotated by manpower, we propose a new idea to employ the Conditional Generative Adversarial Networks (CGAN) to generate samples which are as a supplement to the training set of GRU. Finally compared with the GRU model trained by solely the manual annotation data, our method improves the classification accuracy by 4.07%. We also use our model to predict the time and roads of traffic jams in 4 Chinese cities which is proved to be effective. © 2018, Springer International Publishing AG.

关键词： Generative adversarial networks

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A Fast and Effective Multiple Kernel Clustering Method on Incomplete Data

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Computers, Materials & Continua 2021年第4期67卷 267-284页

作者： Lingyun Xiang Guohan Zhao Qian Li Gwang-Jun Kim Osama Alfarraj Amr Tolba Changsha Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation School of Computer and Communication EngineeringChangsha University of Science and TechnologyChangsha410114China Hunan Key Laboratory of Smart Roadway and Cooperative Vehicle-Infrastructure Systems Changsha University of Science and TechnologyChangsha410114China Faculty of Engineering and Information Technology Global Big Data Technologies CentreUniversity of Technology SydneyUltimoNSW2007Australia Department of Computer Engineering Chonnam National UniversityGwangju61186Korea Computer Science Department Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Department of Mathematics and Computer Science Faculty of ScienceMenoua UniversityShebin-El-kom32511Egyp

Multiple kernel clustering is an unsupervised data analysis method that has been used in various scenarios where data is easy to be collected but hard to be ***,multiple kernel clustering for incomplete data is a critical yet challenging *** the existing absent multiple kernel clustering methods have achieved remarkable performance on this task,they may fail when data has a high value-missing rate,and they may easily fall into a local *** address these problems,in this paper,we propose an absent multiple kernel clustering(AMKC)method on incomplete *** AMKC method rst clusters the initialized incomplete ***,it constructs a new multiple-kernel-based data space,referred to as K-space,from multiple sources to learn kernel combination ***,it seamlessly integrates an incomplete-kernel-imputation objective,a multiple-kernel-learning objective,and a kernel-clustering objective in order to achieve absent multiple kernel *** three stages in this process are carried out simultaneously until the convergence condition is *** on six datasets with various characteristics demonstrate that the kernel imputation and clustering performance of the proposed method is signicantly better than state-of-the-art ***,the proposed method gains fast convergence speed.

关键词： Multiple kernel clustering absent-kernel imputation incomplete data kernel k-means clustering

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Sentiment Analysis Model Based on Structure Attention Mechanism

Advances in Intelligent Systems and Computing

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Advances in intelligent Systems and Computing 2018年 650卷 17-27页

作者： Lin, Kai Lin, Dazhen Cao, Donglin Cognitive Science Department Xiamen University Xiamen China Fujian Key Laboratory of Brain-Inspired Computing Technique and Applications Xiamen University Xiamen China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China

ISBN: (纸本)9783319669380

Since the long short-term memory (LSTM) network is a sequential structure, it is difficult to effectively represent the structural level information of the context. Sentiment analysis based on the original LSTM causes a problem of structural level information loss, and its capacity to capture the context information is finite. To address this problem, we proposed a novel structure-attention-based LSTM as a hierarchical structure model. It may capture relevant information in the context as much as possible. We propose HM (ht matrix) to storage the structural information of the context. Furthermore, we introduce the attention mechanism to realize vector selection. Compared with the original LSTM and normal attention-based sentiment classification methods, our model obtains a higher classification precision. It is proved that the structure-attention-based method proposed in this study has an advantage in capturing the potential semantic structure. © 2018, Springer International Publishing AG.

关键词： Long short-term memory

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Three-dimensional spin Hall effect of light in tight focusing

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Physical Review A 2020年第2期101卷 023819-023819页

作者： Weixing Shu Chuyang Lin Jun Wu Shizhen Chen Xiaohui Ling Xinxing Zhou Hailu Luo Shuangchun Wen Key Laboratory for Micro/Nano Optoelectronic Devices of Ministry of Education & Hunan Provincial Key Laboratory of Low-Dimensional Structural Physics and Devices School of Physics and Electronics Hunan University Changsha 410082 China Key Laboratory of Low Dimensional Quantum Structures and Quantum Control of Ministry of Education Hunan Normal University Changsha 410081 China College of Computer Science and Electronic Engineering Hunan University Changsha 410082 China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application College of Physics and Electronic Engineering Hengyang Normal University Hengyang 421002 China

We theoretically propose the realization of a three-dimensional (3D) spin Hall effect (SHE) of light by tightly focusing a linearly polarized light. Owing to the spin-orbital interaction in focusing, the left and right circularly polarized components of light are partly converted into each other, acquiring opposite vortex phases. As the superposition of the vortex and the initial vortex-free light, the two polarized components exhibit spiral intensity patterns which rotate oppositely around the propagation axis. Consequently, the two spin components are distributed alternatively in both the azimuthal and the longitudinal directions, leading to a 3D array of split spin components, and the transverse intensity is also spit asynchronously. Moreover, that array is rotatable by the incident polarization, and the longitudinal spacing in the array can be controlled by the numerical aperture of the lens. The 3D SHE of light finds an additional degree of freedom available to control the spin state of the photon, which may be valuable in optical manipulation and quantum information.

关键词： Classical optics

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A New Ontology Meta-Matching Technique with a Hybrid Semantic Similarity Measure 15th

A New Ontology Meta-Matching Technique with a Hybrid Semanti...

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15th International Conference on intelligent information Hiding and Multimedia Signal processing, IIH-MSP 2019, held in conjunction with the 12th International Conference on Frontiers of information Technology, applications and Tools, FITAT 2019

作者： Lu, Jiawei Xue, Xingsi Lin, Guoxiang Huang, Yikun College of Information Science and Engineering Fujian University of Technology Fuzhou China Intelligent Information Processing Research Center Fujian University of Technology Fuzhou China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China Fujian Key Lab for Automotive Electronics and Electric Drive Fujian University of Technology Fuzhou China Concord University College Fujian Normal University Fuzhou China

ISBN: (纸本)9789811397097

Ontology is the kernel technique of semantic web, which can be used to describe the concepts and their relationships in a particular domain. However, different domain experts would construct the ontologies according to different requirements, and there exists a heterogeneity problem among the ontologies, which hinders the interaction between ontology-based intelligent systems. Ontology matching technique can determine the links between heterogeneous concepts, which is an effective method for solving this problem. Semantic similarity measure is a function to calculate to what extent two concepts are similar to each other, which is the key component of ontology matching technique. Generally, multiple semantic similarity measures are used together to improve the accuracy of the concept recognition. How to combine these semantic similarity measures, i.e., the ontology meta-matching problem, is a challenge in the ontology matching domain. To address this challenge, this paper proposes a new ontology meta-matching technique, which applies a novel combination framework to aggregate two broad categories of similarity measures. The experiment uses the famous benchmark provided by the Ontology Alignment Evaluation Initiative (OAEI). Comparing results with the participants of OAEI shows the effectiveness of the proposal. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Ontology

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Geo-spatial ontology matching through compact evolutionary algorithm 2nd

Geo-spatial ontology matching through compact evolutionary a...

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2nd International Conference on Smart Vehicular Technology, Transportation, Communication and applications, VTCA 2018

作者： Xue, Xingsi Liu, Jianhua 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

ISBN: (纸本)9783030045845

Geo-spatial ontologies can provide a formal description of concepts, relationships, activities, features and rules in GIS domain. However, simply use them only allows to partially solve semantic conflicts, and does not completely solve heterogeneity issues that are caused by themselves. Geo-spatial ontology matching technique can find the correspondences between semantic identical entities, and solve the heterogeneous problem between two geo-spatial ontologies. Be inspired by the successful application of Evolutionary Algorithm (EA) in instance matching domain, in this paper, it is utilized to match the heterogeneous geo-spatial ontologies. To reduce the runtime and memory consumption required by EA, a compact version of it is presented, which does not work on the whole population but a probability representation on it. In addition, a geo-spatial similarity measure is presented to determine the identical geo-spatial entities, and an optimal model is constructed for geo-spatial ontology matching problem. The experimental results show that cEA-based geo-spatial ontology matching technique can efficiently determine the alignment. © Springer Nature Switzerland AG 2019.

关键词： Evolutionary algorithms

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