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检索条件"机构=Language Information Processing and Cloud Computing Application"

共 173 条记录，以下是81-90 订阅

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Automatic Verification of Data-Centric Web Service Specifications

Automatic Verification of Data-Centric Web Service Specifica...

引用

2013 International Conference on Electronic Engineering and Computer Science(EECS 2013)

作者： Zhi Fang Lejian Liao Ruoyu Chen Beijing Engineering Research Centre of High Volume Language Information Processing & Cloud Computing Applications School of Computer Science Beijing Institute of Technology

Amodel are proposed for modeling data-centric Web services which are powered by relational databases and interact with users according to logical formulas specifying input constraints,control-flow constraints and state/output/action *** Linear Temporal First-Order Logic(LTL-FO) formulas over inputs,states,outputs and actions are used to express the properties to be *** have proven that automatic verification of LTL-FO properties of data-centric Web services under input-bounded constraints is decidable by reducing Web services to data-centric Web ***,we can verify Web service specifications using existing verifier designed for Web applications.

关键词： Web Service Automatic Verification First-Order Linear Temporal Logic

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Learning Automata-based Adaptive Web Services Composition

Learning Automata-based Adaptive Web Services Composition

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2014 IEEE 5th International Conference on Software Engineering and Service Science

作者： Guoqiang Li Dandan Song Lejian Liao Fuzhen Sun Jianguang Du Beijing Engineering Research Centre of High Volume Language Information Processing & Cloud Computing Applications School of Computer Science Beijing Institute of Technology

Service-oriented computing is a widely adopted paradigm in real *** the continuous evolution of services,adaptive service composition has always been a major *** is a big challenge to adjust the composition to be optimal in *** this paper,a learning automata-based approach is proposed to attack this *** consists of two important components: random environment and a learning *** former can be mapped to the service’s execution *** latter is responsible for the adaptation achievement using reward and penalty functions,while we take the service composition structures into account to compute the usefulness value of all *** last,simulation study has shown that our approach is efficient to find the optimal（sub-optimal） composition.

关键词： web service learning automata self-adaptive web service composition

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A parallel recurrent neural network for language modeling with POS tags 31

A parallel recurrent neural network for language modeling wi...

引用

31st Pacific Asia Conference on language, information and Computation, PACLIC 2017

作者： Su, Chao Huang, Heyan Shi, Shumin Guo, Yuhang Wu, Hao School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications Beijing100081 China Beijing Advanced Innovation Center for Imaging Technology Capital Normal University Beijing100048 China

language models have been used in many natural language processing applications. In recent years, the recurrent neural network based language models have defeated the conventional n-gram based techniques. However, it is difficult for neural network architectures to use linguistic annotations. We try to incorporate part-of-speech features in recurrent neural network language model, and use them to predict the next word. Specifically, we proposed a parallel structure which contains two recurrent neural networks, one for word sequence modeling and another for part-of-speech sequence modeling. The state of part-of-speech network helped improve the word sequence's prediction. Experiments show that the proposed method performs better than the traditional recurrent network on perplexity and is better at reranking machine translation outputs. Copyright © 2017 Chao Su, Heyan Huang, Shumin Shi, Yuhang Guo and Hao Wu

关键词： Recurrent neural networks

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Multi-Features Group Emotion Analysis Based on CNN for Weibo Events

Multi-Features Group Emotion Analysis Based on CNN for Weibo...

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2017 2nd International Conference on Computer Engineering,information Science and Internet Technology(CII 2017)

作者： SHUMIN SHI MENG ZHAO JUN GUAN HEYAN HUANG School of Computer Science and Technology Beijing Institute of Technology Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications

With the development of social network,group emotion analysis on social media such as Facebook,Twitter and Weibo becomes a new trend in recent *** different methods have been proposed for group emotion analysis,including traditional methods like SVM and NB and deep learning methods like RNN and *** paper proposes a CNN model with *** first analyze the characteristic of weibos to collect features including basic features,user-based features and content-based *** introduce these features to our CNN model to analyze emotions for Weibo events,which has been proved in experiment that it is effective to get the accurate sentiment of *** crawl 5,319,687 weibos about 724 events from Sina Weibo and apply several feature matrices to classify them to 4 *** use the new model to analyze 4 kinds of events to get the group *** results suggest that the classification we suggested can capture the emotions within different event group.

关键词： Social media Sentiment analysis Group emotion Deep learning Convolutional neural networks

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Unsupervised Style Control for Image Captioning

Unsupervised Style Control for Image Captioning

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作者： Junyu Tian Zhikun Yang Shumin Shi School of Computer Science and Technology Beijing Institute of Technology Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications

We propose a novel unsupervised image captioning *** captioning involves two fields of deep learning,natural language processing and computer *** excessive pursuit of model evaluation results makes the caption style generated by the model too monotonous,which is difficult to meet people's demands for vivid and stylized image ***,we propose an image captioning model that combines text style transfer and image emotion recognition methods,with which the model can better understand images and generate controllable stylized *** proposed method can automatically judge the emotion contained in the image through the image emotion recognition module,better understand the image content,and control the description through the text style transfer method,thereby generating captions that meet people's *** our knowledge,this is the first work to use both image emotion recognition and text style control.

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Knowledge Base Enhanced Topic Modeling

Knowledge Base Enhanced Topic Modeling

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IEEE International Conference on Big Knowledge (ICBK)

作者： Dandan Song Jingwen Gao Jinhui Pang Lejian Liao Lifei Qin Lab of High Volume language Information Processing & Cloud Computing Applications School of Computer Science & Technology Beijing Institute of Technology Jianpu Technology Inc

ISBN: (数字)9781728181561

ISBN: (纸本)9781728181578

Topic models, such as Latent Dirichlet Allocation (LDA), are successful in learning hidden topics and has been widely applied in text mining. There are many recently developed augmented topic modeling methods to utilize metadata information. However, the effect of topic models is still not comparable to humans. We think one key point is that humans have background knowledge, which is essential for topic understanding. Inspired by this, we propose a knowledge base enhanced topic model in this paper. We take knowledge bases as good presentations of human knowledge, with huge collections of entities and their relations. We assume that documents with related entities tend to have similar topic distributions. Based on this assumption, we compute document similarity information via the linked entities and then use it as a constraint for LDA. More specifically, we embed entities in a low-dimensional space via DeepWalk and use Entity Movers Distance to efficiently and effectively measure the similarities between documents. The results of experiments over two real-world datasets show that our method boosts the LDA model on the document classification while no supervision information is needed.

关键词： Knowledge based systems Task analysis Computational modeling Semantics Optimization Resource management Transforms

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Utilizing Crowdsourcing for the Construction of Chinese-Mongolian Speech Corpus with Evaluation Mechanism

Utilizing Crowdsourcing for the Construction of Chinese-Mong...

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The Third International Conference of Pioneering Computer Scientists, Engineers and Educators,ICPCSEE 2017(originally ICYCSEE)

作者： Rihai Su Shumin Shi Meng Zhao Heyan Huang School of Computer Sciences and Technology Beijing Institute of Technology Beijing Engineering Research Centre of High Volume Language Information Processing and Cloud Computing Applications

Crowdsourcing has been used recently as an alternative to traditional costly annotation by many natural language processing groups. In this paper, we explore the use of Wechat Official Account Platform(WOAP) in order to build a speech corpus and to assess the feasibility of using WOAP followers(also known as contributors) to assemble speech corpus of Mongolian. A Mongolian language qualification test was used to filter out potential non-qualified participants. We gathered natural speech recordings in our daily life, and constructed a ChineseMongolian Speech Corpus(CMSC) of 31472 utterances from 296 native speakers who are fluent in Mongolian, totalling 30.8 h of speech. Then,an evaluation experiment was performed, in where the contributors were asked to choose a correct sentence from a multiple choice list to ensure the high-quality of corpus. The results obtained so far showed that crowdsourcing for constructing CMSC with an evaluation mechanism could be more effective than traditional experiments requiring expertise.

关键词： Crowdsourcing Chinese-Mongolian corpus Speech corpus WOAP Evaluation mechanism

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Effective Fuzzing Based on Dynamic Taint Analysis

Effective Fuzzing Based on Dynamic Taint Analysis

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International Conference on Computational Intelligence and Security

作者： Guangcheng Liang Lejian Liao Xin Xu Jianguang Du Guoqiang Li Henglong Zhao Beijing Engineering Research Center of High Volume Language Information Processing & Cloud Computing Applications Beijing Institute of Technology Beijing China DISECC Beijing China

In this paper we present a new vulnerability-targeted black box fuzzing approach to effectively detect errors in the program. Unlike the standard fuzzing techniques that randomly change bytes of the input file, our approach remarkably reduces the fuzzing range by utilizing an efficient dynamic taint analysis technique. It locates the regions of seed files that affect the values used at the hazardous points. Thus it enables to pay more attention to deep errors in the core of the program. Because our approach is directly targeted to the specific potential vulnerabilities, most of the detected errors are with vulnerability signatures. Besides, this approach does not need the information of the input file format in advance. So it is especially appropriate for testing applications with complex and highly structured input file formats. We design and implement a prototype, Taint Fuzz, to realize this approach. The experiments demonstrate that Taint Fuzz can effectively expose more errors with much lower time cost and much smaller number of input samples compared with the standard fuzzer.

关键词： Software Performance analysis Standards Security Instruments Testing Memory management

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Named entity recognition based on bilingual co-training

Named entity recognition based on bilingual co-training

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14th Workshop on Chinese Lexical Semantics, CLSW 2013

作者： Li, Yegang Huang, Heyan Zhao, Xingjian Shi, Shumin School of Computer Science and Technology Beijing Institute of Technology Beijing China Beijing Eng. Applic. Res. Center of High Volume Language Information Processing and Cloud Computing Beijing Institute of Technology Beijing China Department of Computer Science and Technology Shandong University of Technology Zibo Shandong China

ISBN: (纸本)9783642451843

Named entity recognition (NER) is a very important task in natural language processing (NLP). In this paper we present a semi-supervised approach to extract bilingual named entity, starting from a bilingual corpus where the named entities are extracted independently for each language. Then a bilingual co-training algorithm is used to improve the named entity annotation quality, and iterative process is applied to extract named entity pairs with higher bilingual conformity ratio. This leads to a significant improvement of the monolingual named entity annotation quality for both languages. Experimental result shows that the annotation quality of Chinese NE is improved from 87.17 to 88.28, and improved 80.37 to 81.76 of English NE in F-measure. © 2013 Springer-Verlag.

关键词： Iterative methods

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Microblog Keyphrase Extraction Based on Similarity Features

Microblog Keyphrase Extraction Based on Similarity Features

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2013 International Conference on Advanced Computer Science and Electronics information(ICACSEI 2013)

作者： Lizi Liao Heyan Huang Beijing Engineering Applications Research Center of High Volume Language Information Processing and Cloud Computing (BIT) School of Computer Science and Technology Beijing Institute of Technology

This paper proposes to extract keyphrases from microblog based on similarity features. We analyze a large number of microblogs and find an interesting phenomenon that people use various nugget phrases to express the same factoid while many of these nugget phrases show similarity relationships. We propose a similarity features based context-sensitive topical PageRank method for keyphrase ranking. We evaluate our proposed methods on a large microblog dataset. Experiments show that our system is very effective for keyphrase extraction.

关键词： Keyphrase extraction, Similarity features, Graph- based model

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