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A Forward-Secure Multi-Receiver Signcryption Scheme

Journal of Harbin Institute of Technology(New Series) 2016年第5期23卷 53-61页

作者： Zhimin Yu Zhengjun Jing Hua Yang Chunsheng Gu Key Laboratory of Cloud Computing and Intelligent Information Processing of Changzhou City Jiangsu University of Technology Changzhou Jiangsu 213001 China College of Computer Nanjing University of Posts and Telecommunications Nanjing 210003 China State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing 100093 China

In order to protect historical ciphertext when the private key leaked in the broadcasting system,the forward-secure multi-receiver signcryption scheme is designed based on the generic graded multilinear mapping encoding structure,which effectively prevents illegal access from intruder to the ciphertext in the past time period when the private key in current time period is *** the generalization of the existing multilinear mapping encoding system,it proposes the generic graded multilinear mapping encoding structure and the generic graded decision Diffie-Hellman *** of the generic graded multilinear mapping encoding system adopted,almost all candidate multilinear mapping encoding systems can automatically adapt to our *** the assumption of generic graded decision Diffie-Hellman problem,it has proved that the scheme has the information confidentiality and unforgeability in the current time *** putting forward the security model of forward-secure multi-receiver signcryption scheme,and under the assumption of generic graded decision Diffie-Hellman problem,it has proved that the scheme has the message forward-confidentiality and *** with other forward-secure public key encryption schemes,the relationship between our scheme and time periods is sub-linear,so it is less complex.

关键词： multilinear mapping forward-secure multi-receiver signcryption confidentiality unforgeability

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An attribute reduction framework for inconsistent decision tables from view of the boundary region

An attribute reduction framework for inconsistent decision t...

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2016 International Conference on Machine Learning and Cybernetics, ICMLC 2016

作者： Qian, Jin Chang, Yu-Hui Lv, Ping Key Laboratory of Cloud Computing and Intelligent Information Processing of Changzhou City Jiangsu University of Technology Changzhou213015 China Jiangsu Key Laboratory of Big Data Analysis Technology/B-DAT Nanjing University of Information Science and Technology Nanjing China

ISBN: (纸本)9781509003891

Attribute reduction is one of the key issues for data preprocess in data mining. Many heuristic attribute reduction algorithms based on discernibility matrix have been proposed for inconsistent decision tables. However, these methods are usually computationally time-consuming. To address this issue, the derived consistent decision tables are defined for different definitions of relative reducts. The computations for different reducts of the original inconsistent decision tables are converted into the computations for their corresponding reducts of the derived consistent datasets. The relative discernibility object pair and the more optimal relative discernibility degree from view of the boundary region are designed to accelerate the attribute reduction process. An efficient attribute reduction framework using relative discernibility degree is proposed for large datasets. Experimental results show that our attribute reduction algorithms are effective and feasible for large inconsistent datasets. © 2016 IEEE.

关键词： Decision tables

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Neural network for heterogeneous annotations

Neural network for heterogeneous annotations

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2016 Conference on Empirical Methods in Natural Language processing, EMNLP 2016

作者： Chen, Hongshen Zhang, Yue Liu, Qun Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China Singapore University of Technology and Design Singapore ADAPT Centre School of Computing Dublin City University Ireland

ISBN: (纸本)9781945626258

Multiple treebanks annotated under heterogeneous standards give rise to the research question of best utilizing multiple resources for improving statistical models. Prior research has focused on discrete models, leveraging stacking and multi-view learning to address the problem. In this paper, we empirically investigate heterogeneous annotations using neural network models, building a neural network counterpart to discrete stacking and multi-view learning, respectively, finding that neural models have their unique advantages thanks to the freedom from manual feature engineering. Neural model achieves not only better accuracy improvements, but also an order of magnitude faster speed compared to its discrete baseline, adding little time cost compared to a neural model trained on a single treebank. © 2016 Association for Computational Linguistics

关键词： Forestry

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Design and Realization of MATLAB-based Face Recognition System

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Journal of Physics: Conference Series 2018年第6期1087卷

作者： JianMing Liu College of Mathematics and Computer Science Quanzhou Normal University Quanzhou 362000 Fujian P. R. China Key Laboratory of Intelligent Computing and Information Processing Fujian Province University Quanzhou Normal University Quanzhou 362000 Fujian P. R. China

Through MATLAB, the paper makes a comparison between Principal Component Analysis (PCA)face recognition algorithm and Adaboost recognition algorithm and selects the algorithm with higher recognition rates to develop an auto face recognition system. The paper explicates primary techniques the system adopts and its specific realization process. By downloading face database online, the paper conducts an all-round test to the system, the result of which proves that this face recognition system is completely practical and feasible.

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Multifractal temporally weighted detrended cross-correlation analysis to quantify power-law cross-correlation and its application to stock markets

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Chaos 2017年第6期27卷 063111-063111页

作者： Wei, Yun-Lan Yu, Zu-Guo Zou, Hai-Long Anh, Vo Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Hunan Key Laboratory for Computation and Simulation in Science and Engineering Xiangtan University Xiangtan Hunan 411105 China School of Mathematical Sciences Queensland University of Technology GPO Box 2434 Brisbane Q4001 Australia

A new method-multifractal temporally weighted detrended cross-correlation analysis (MF-TWXDFA)-is proposed to investigate multifractal cross-correlations in this paper. This new method is based on multifractal temporally weighted detrended fluctuation analysis and multifractal cross-correlation analysis (MFCCA). An innovation of the method is applying geographically weighted regression to estimate local trends in the nonstationary time series. We also take into consideration the sign of the fluctuations in computing the corresponding detrended cross-covariance function. To test the performance of the MF-TWXDFA algorithm, we apply it and the MFCCA method on simulated and actual series. Numerical tests on artificially simulated series demonstrate that our method can accurately detect long-range cross-correlations for two simultaneously recorded series. To further show the utility of MF-TWXDFA, we apply it on time series from stock markets and find that power-law cross-correlation between stock returns is significantly multifractal. A new coefficient, MF-TWXDFA cross-correlation coefficient, is also defined to quantify the levels of cross-correlation between two time series. © 2017 Author(s).

关键词： CORRELATION (Statistics) STOCK price indexes REGRESSION analysis TIME series analysis COEFFICIENTS (Statistics)

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Is Local Window Essential for Neural Network based Chinese Word Segmentation?

Is Local Window Essential for Neural Network based Chinese W...

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第十五届全国计算语言学学术会议(CCL2016)暨第四届基于自然标注大数据的自然语言处理国际学术研讨会(NLP-NABD-2016)

作者： Jinchao Zhang Fandong Meng Mingxuan Wang Daqi Zheng Wenbin Jiang Qun Liu Key Laboratory of Intelligent Information Processing Institute of Computing TechnologyChinese Academy of Sciences

Neural network based Chinese Word Segmentation(CWS)approaches can bypass the burdensome feature engineering comparing with the conventional *** previous neural network based approaches rely on a local window in character sequence labelling *** can hardly exploit the outer context and may preserve indifferent inner ***,the size of local window is a toilsome manual-tuned hyper-parameter that has significant influence on model *** are wondering if the local window can be discarded in neural network based *** this paper,we present a window-free Bi-directional Long Short-term Memory(Bi-LSTM)neural network based Chinese word segmentation *** model takes the whole sentence under consideration to generate reasonable word *** experiments show that the Bi-LSTM can learn sufficient context for CWS without the local window.

关键词： Chinese Word Segmentation Neural Network Window

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COMRANCE: A rapid method for Network-on-Chip design space exploration 7

COMRANCE: A rapid method for Network-on-Chip design space ex...

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7th International Green and Sustainable computing Conference, IGSC 2016

作者： Zhang, Mingzhe Shi, Yangguang Zhang, Fa Liu, Zhiyong Beijing Key Laboratory of Mobile Computing and Pervasive Device ICT CAS Beijing China University of Chinese Academy of Sciences Beijing China Key Lab of Intelligent Information Processing ICT CAS Beijing China

ISBN: (纸本)9781509051175

As the communication sub-system that connecting various on-chip components, Network-on-Chip (NoC) has a great influence on the performance of multi-/many-core processors. Because of NoC model contains a large number of parameters, the design space exploration (DSE) for NoC is a critical problem for the architects. Similar to the core design, existing DSE process mainly depends on iteratively time-consuming simulations. To lower the time budget, many previous studies focus on reducing the simulations. However, most of the proposed works based on regression or machine learning techniques, whose accuracy will be significantly affected by the scale of training set. It still needs a lot of simulations to build the training set. © 2016 IEEE.

关键词： Network-on-chip

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Identification of pre-microRNAs by characterizing their sequence order evolution information and secondary structure graphs

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BMC bioinformatics 2018年第SUPPL 19期19卷 521页

作者： Yuanlin Ma Zuguo Yu Guosheng Han Jinyan Li Vo Anh Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education and Hunan Key Laboratory for Computation and Simulation in Science and Engineering Xiangtan University Hunan 411105 China. Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education and Hunan Key Laboratory for Computation and Simulation in Science and Engineering Xiangtan University Hunan 411105 China. yuzuguo@***. School of Electrical Engineering and Computer Science Queensland University of Technology GPO Box 2434 Brisbane Q4001 Australia. yuzuguo@***. Advanced Analytics Institute Faculty of Engineering & IT University of Technology Sydney P.O Box 123 Broadway NSW 2007 Australia. School of Mathematical Sciences Queensland University of Technology GPO Box 2434 Brisbane Q4001 Australia.

BACKGROUND:Distinction between pre-microRNAs (precursor microRNAs) and length-similar pseudo pre-microRNAs can reveal more about the regulatory mechanism of RNA biological processes. Machine learning techniques have been widely applied to deal with this challenging problem. However, most of them mainly focus on secondary structure information of pre-microRNAs, while ignoring sequence-order information and sequence evolution information. RESULTS:We use new features for the machine learning algorithms to improve the classification performance by characterizing both sequence order evolution information and secondary structure graphs. We developed three steps to extract these features of pre-microRNAs. We first extract features from PSI-BLAST profiles and Hilbert-Huang transforms, which contain rich sequence evolution information and sequence-order information respectively. We then obtain properties of small molecular networks of pre-microRNAs, which contain refined secondary structure information. These structural features are carefully generated so that they can depict both global and local characteristics of pre-microRNAs. In total, our feature space covers 591 features. The maximum relevance and minimum redundancy (mRMR) feature selection method is adopted before support vector machine (SVM) is applied as our classifier. The constructed classification model is named MicroRNA -NHPred. The performance of MicroRNA -NHPred is high and stable, which is better than that of those state-of-the-art methods, achieving an accuracy of up to 94.83% on same benchmark datasets. CONCLUSIONS:The high prediction accuracy achieved by our proposed method is attributed to the design of a comprehensive feature set on the sequences and secondary structures, which are capable of characterizing the sequence evolution information and sequence-order information, and global and local information of pre-microRNAs secondary structures. MicroRNA -NHPred is a valuable method for pre-microRNAs iden

关键词： Hibert-Huang transform Network PSI-BLAST profiles Pre-microRNA SVM mRMR

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A mobility model for opportunistic networks in commercial areas

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International Journal of Networking and Virtual Organisations 2016年第4期16卷 402-416页

作者： Yang, Wanchun Peng, Shiyao Department of College of Information Engineering Key Laboratory of Intelligent Computing and Information Processing of Education Ministry Xiangtan University Xiangtan Hunan China

In this paper, we propose a mobility model for opportunistic networks in a commercial area (MMCA). The commercial area is divided into two parts: internal and external areas. We modified the traditional susceptible-infected-removed (SIR) model for predicting the performance of the message forwarding in MMCA. Using the opportunistic networks environment (ONE) simulation platform, we validated this modified SIR by comparing the theoretical results and modelled data from ONE. We further analyse the performances of MMCA with the number of nodes, walking speeds, communication ranges and the size of areas. © 2016 Inderscience Enterprises Ltd.

关键词： Routing algorithms

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Browsing regularities in hedonic content systems 25

Browsing regularities in hedonic content systems

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25th International Joint Conference on Artificial Intelligence, IJCAI 2016

作者： Luo, Ping Zhou, Ganbin Tang, Jiaxi Chen, Rui Yu, Zhongjie He, Qing Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China School of Computing Science Simon Fraser University Canada School of Information University of Michigan United States Deparment of Statistics and Finance University of Science and Technology of China China

Various hedonic content systems (e.g. mobile apps for video, music, news, jokes, pictures, social networks etc.) increasingly dominates people's daily spare life. This paper studies common regularities of browsing behaviors in these systems, based on a large data set of user logs. We found that despite differences in visit time and user types, the distribution over browsing length for a visit can be described by the inverse Gaussian form with a very high precision. It indicates that the choice threshold model of decision making on continuing browsing or leave does exist. Also, We found that the stimulus intensity, in terms of the amount of recent enjoyed items, affects the probability of continuing browsing in a curve of inverted-U shape. We discuss the possible origin of this curve based on a proposed Award-Aversion Contest model. This hypothesis is supported by the empirical study, which shows that the proposed model can successfully recover the original inverse Gaussian distribution for the browsing length. These browsing regularities can be used to develop better organization of hedonic content, which helps to attract more user dwell time in these systems.

关键词： Decision making

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