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RESEARCH ON UCL-BASED TRUSTED computing MODEL OF WEBPAGE CONTENT

RESEARCH ON UCL-BASED TRUSTED COMPUTING MODEL OF WEBPAGE CON...

引用

2014 ieee 3rd International Conference on Cloud computing and Intelligence systems

作者： Cong Liu Xinyu Zhang Yigang Diao Information Technology Center Tsinghua University Technology Laboratory of Technical and Technology Bureau Xinhua News Agency

ISBN: (纸本)9781467369541

With the development of Internet,the amount of information on the network increases,and the content security issues on the network become more *** the study of web information filtering technology and a variety of implementation methods,combining with UCL(uniform content locator) that is a new innovative technology concept,we discuss and design a new webpage filtering scheme,and propose a UCL-based trusted computing model of webpage content,which is using UCL technology to realize intelligent information filtering based on *** on the UCL semantic vector and Chinese corpus design,the model uses a reliability evaluation algorithm of webpage information content to realize reliability computing based on ***,the model combines with the integrity detection and the source authentication of information,thus forming a collaborative content filtering mechanism which is of high efficiency and can satisfy the filtering accuracy ***,we present the prototype *** have shown that due to less text content in UCL,the model is very suitable for fast processing and filtering of mass data,and has certain references and use values for network content security monitoring and information service mechanisms.

关键词： UCL Trusted computing Information filtering Content security Semantic vector space

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CROSS-DOMAIN SENTIMENT CLASSIFICATION USING DEEP LEARNING APPROACH

CROSS-DOMAIN SENTIMENT CLASSIFICATION USING DEEP LEARNING AP...

引用

2014 ieee 3rd International Conference on Cloud computing and Intelligence systems

作者： Miao Sun Qi Tan Runwei Ding Hong Liu South China Nomal University Peking University Shenzhen Graduate School Peking University

ISBN: (纸本)9781467369541

Deep learning,as a new unsupervised leaning algorithm,has strong capabilities to learn data *** work has shown that new features learned by deep learning algorithm help to improve the accuracy of cross-domain *** this paper,we firstly propose a modified version of marginalized stacked denoising autoencoders(mSDA).We call it mSDA++ algorithm,which can learn excellent and low-dimensional features for training *** addition,we combine mSDA with EASYADAPT algorithm to further improve the accuracy of cross-domain *** we use SVM,mSDA,mSDA++,and EA+mSDA algorithms to do the cross-domain sentiment classification experiments on Amazon benchmark *** results show that EA+mSDA algorithm attains the best ***,the mSDA++ algorithm can accelerate the subsequent calculation and reduce the data storage space.

关键词： Text sentiment classification Deep learning Cross-domain dimension reduction feature augment

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The comparison between IABC with EGARCH in foreign exchange rate forecasting 1

The comparison between IABC with EGARCH in foreign exchange ...

引用

1st Euro-China Conference on intelligent data Analysis and Applications, ECC 2014

作者： Chang, Jui-Fang Tsai, Pei-Wei Chen, Jung-Fang Hsiao, Chun-Tsung Department of International Business National Kaohsiung University of Applied Sciences 415 Chien-Kung Road Kaohsiung City80778 Taiwan Department of Marine Information and Technology National Kaohsiung Marine University 482 Jong-Jhou 3rd Road Kaohsiung City80543 Taiwan Department of Business Administration National Kaohsiung University of Applied Sciences 415 Chien-Kung Road Kaohsiung City80778 Taiwan

ISBN: (纸本)9783319077727

Foreign exchange rate forecasting catches many researchers interests in recent years. Problems of the foreign exchange rate forecasting model selection and the improvement on forecasting accuracy are not easy to be solved. In this paper, the forecasting results obtained by conventional time-series models and by the Inter-active Artificial Bee Colony (IABC), which is a young artificial intelligent meth-od, are compared with each other with 4 years historical data. The sliding win-dow strategy is used in the experiment for both the training and the testing phases. In our experiments, we use continuous previous three days data as the training set, and use the training result to forecast the foreign exchange rate on the fourth day. In addition, we evaluate the forecasting accuracy with three criteria, namely, Mean Square Error (MSE), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). The experimental results indicate that feeding macroeco-nomic factors to IABC as the input data is capable to produce higher accurate data in the foreign exchange rate than the conventional time-series models such as EGARCH. © 2014 Springer International Publishing Switzerland. All rights reserved.

关键词： Mean square error

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Parallelization of ontology construction and fusion based on MapReduce

Parallelization of ontology construction and fusion based on...

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ieee International Conference on Cloud computing and Intelligence systems (CCIS)

作者： Qian Zhang Bin Wu Juan Yang Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia Beijing University of Posts and Telecommunications Beijing China

As a data integration technology, ontology has been widely used in the knowledge systems and domain knowledge representation. An ontology construction and fusion technology for large-scale data is very important. In this paper, we propose a parallel ontology construction and fusion approach adapted to MapReduce framework based on the traditional ontology technology and MapReduce. The method separates the ontology model building process from the repeated calculation processes, and realizes the massive data integration. The evaluations tested on scientific literature data show the feasibility and efficiency of our approach.

关键词： Patents Organizations Ontologies Metadata Boards Snow Computers

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Face recognition based on double complementary space and multi-decision classification

Face recognition based on double complementary space and mul...

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ieee International Conference on Cloud computing and Intelligence systems (CCIS)

作者： Fuji Ren Yanqiu Li Liangfeng Xu Min Hu Xiaohua Wang School of Computer and Information Hefei University of Technology Anhui China Affective Computing and Advanced Intelligent Machines Anhui Key Laboratory Faculty of Engineering The University of Tokushima Tokushima Japan

ISBN: (纸本)9781467369541

In order to overcome the one-sidedness and limitations of a single subspace in feature extraction and classification, we propose a face recognition method that extracts features in double complementary space and utilize multi-decision for classification. In the feature extraction stage, we use ICA and LPE algorithm as the first layer in complementary space to extract global and local features of the face images. Then, for the purpose of solving the problem that features extracted by ICA are lack of classification information, we further extract classification information from the independent features extracted by ICA on the condition that the FLDA and DCV algorithm are used as the second layer in the complementary space. In the classification stage, the test samples are firstly projected to the independent subspace. After that, projected the samples which are difficult to identify to the LPE space and reclassified them. Finally, the results are been determined comprehensively. Experimental results on ORL database show that the proposed method can effectively improve the recognition rate.

关键词： Feature extraction Classification algorithms Face recognition Face data mining Entropy databases

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Using hybrid Neural Network to address Chinese Named Entity Recognition

Using hybrid Neural Network to address Chinese Named Entity ...

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ieee International Conference on Cloud computing and Intelligence systems (CCIS)

作者： Guoyu Wang Yongquan Cai Fujiang Ge Beijing University of Technology Beijing China Lenovo Cloud/Big Data and Intelligent Computing Lab China

Many machine learning methods have been applied on Named Entity Recognition (NER). Such methods generally build on a large manually-annotated training set. However, the training set is usually limited as human labeling is costly and time consuming. Compare to the training set, the unlabeled corpus is usually much bigger and contains rich information about language. In this paper, a hybrid Deep Neural Network (DNN) is proposed to take advantage of the implicit information embedded in the un-labeled corpus. The experiments show that F1-score is improved from 85% to 90% (person name), from 75% to 81% (location name), and from 74% to 78% (organization name), compared with Conditional Random Fields (CRFs).

关键词： Training Neurons Neural networks Semantics Computational modeling Hidden Markov models Dictionaries

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RESEARCH AND DESIGN OF intelligent NAVIGATION data PLATFORM

RESEARCH AND DESIGN OF INTELLIGENT NAVIGATION DATA PLATFORM

引用

2014 ieee 3rd International Conference on Cloud computing and Intelligence systems

作者： Yiming Shen Yu Wu Rui Wan Guangdong Televehicle Information Technology Service Co. ltd

After studying the structure and model of existing navigation map data,this paper presented a design scheme of intelligent navigation data *** described the data model and data organization which was used by data platform,and designed conceptual model,logical model,physical model in *** paper achieved the construction of intelligent navigation data platform and provided strong support for various applications of telematics and intelligent tourism.

关键词： intelligent navigation,data Platform,Spatial data

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IMAGE RESTORATION METHOD SELF-ADAPTIVE TO THE DIELECTRIC LAYER COLOR

IMAGE RESTORATION METHOD SELF-ADAPTIVE TO THE DIELECTRIC LAY...

引用

2014 ieee 3rd International Conference on Cloud computing and Intelligence systems

作者： Tian Chen Xin Yi Dandan Shen Fuji Ren Wei Wang Bingdong Yang Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine Hefei University of Technology School of Computer and Information Hefei University of Technology The University of Tokushima 770-8506Japan

This paper put forwards a new single image restoration method which is self-adaptive to the dielectric layer *** method is used to solve the problem that existing single image visibility restoration method has limited adaptability for dielectric layer color in image acquisition ***,the method can remove influence caused by propagation medium layer from input image,realizing visibility restoration of the input *** show hazy image restoration method proposed by this paper has better adaption ability for dielectric layer color and recover an image.

关键词： Image Restoration Self-Adaptive Dielectric Layer Color Defogging

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A cloud-based Cyber-Physical System for environmental monitoring

A cloud-based Cyber-Physical System for environmental monito...

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Mediterranean Conference on Embedded computing (MECO)

作者： Teodora Sanislav George Mois Silviu Folea Liviu Miclea Giulio Gambardella Paolo Prinetto Faculty of Automation and Computer Science Technical University of Cluj-Napoca Cluj-Napoca Romania Dipartimento di Automatica e Informatica Politecnico di Torino Turin Italy

Cyber-Physical systems (CPSs) represent a new generation of digital systems, where cyber entities and physical devices cooperate towards a set of common goals. The research presented in this paper aims to contribute to the development of CPSs by proposing an open architecture applicable in environmental monitoring, consisting of three layers. The paper provides a detailed description of each one of the CPS's main components. The bottom layer, composed of wireless sensor nodes, allows the acquisition of data and their transfer to the upper layers. The top and middle layers, composed of a measurements database, a knowledge base, a multi-agent society and web services, assure the proper operation of the CPS based on decision rules and complex data analyses. The proposed architecture provides a high degree of flexibility and scalability.

关键词： Wireless sensor networks Computer architecture Environmental monitoring Web services ieee 802.11 Standards Wireless communication

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PARALLELIZATION OF ONTOLOGY CONSTRUCTION AND FUSION BASED ON MAPREDUCE

PARALLELIZATION OF ONTOLOGY CONSTRUCTION AND FUSION BASED ON...

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2014 ieee 3rd International Conference on Cloud computing and Intelligence systems

作者： Qian Zhang Bin Wu Juan Yang Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia Beijing University of Posts and Telecommunications

As a data integration technology,ontology has been widely used in the knowledge systems and domain knowledge *** ontology construction and fusion technology for large-scale data is very *** this paper,we propose a parallel ontology construction and fusion approach adapted to MapReduce framework based on the traditional ontology technology and *** method separates the ontology model building process from the repeated calculation processes,and realizes the massive data *** evaluations tested on scientific literature data show the feasibility and efficiency of our approach.

关键词： Information fusion Ontology MapReduce Parallelization

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