检索结果-内蒙古大学图书馆

39th IEEE/ACM International Conference on Software Engineering (ICSE)

作者： Jiang, He Zhang, Jingxuan Ren, Zhilei Zhang, Tao Dalian Univ Technol Sch Software Dalian Peoples R China Key Lab Ubiquitous Network & Serv Software Liaoni Dalian Peoples R China Wuhan Univ State Key Lab Software Engn Wuhan Hubei Peoples R China Harbin Engn Univ Coll Comp Sci & Technol Harbin Heilongjiang Peoples R China

ISBN: (纸本)9781538638682

Developers increasingly rely on API tutorials to facilitate software development. However, it remains a challenging task for them to discover relevant API tutorial fragments explaining unfamiliar APIs. Existing supervised approaches suffer from the heavy burden of manually preparing corpus-specific annotated data and features. In this study, we propose a novel unsupervised approach, namely Fragment Recommender for APIs with pagerank and Topic model (FRAPT). FRAPT can well address two main challenges lying in the task and effectively determine relevant tutorial fragments for APIs. In FRAPT, a Fragment Parser is proposed to identify APIs in tutorial fragments and replace ambiguous pronouns and variables with related ontologies and API names, so as to address the pronoun and variable resolution challenge. Then, a Fragment Filter employs a set of non-explanatory detection rules to remove non-explanatory fragments, thus address the non-explanatory fragment identification challenge. Finally, two correlation scores are achieved and aggregated to determine relevant fragments for APIs, by applying both topic model and pagerank algorithm to the retained fragments. Extensive experiments over two publicly open tutorial corpora show that, FRAPT improves the state-of-the-art approach by 8.77% and 12.32% respectively in terms of F-Measure. The effectiveness of key components of FRAPT is also validated.

关键词： Application Programming Interface pagerank algorithm Topic Model Unsupervised Approaches

来源：评论

学校读者我要写书评

暂无评论

A Faster Non-Linear Iteration Solver using Graph Computing and its Application in Power Flow Calculation 9th

A Faster Non-Linear Iteration Solver using Graph Computing a...

引用

9th International Conference on Applied Energy (ICAE)

作者： Liu, Kewen Liu, Guangyi Xie, Kai Wang, Zhiwei Global Energy Interconnect Res Inst North Amer San Jose CA 95134 USA Global Energy Interconnect Res Inst Beijing 102209 Peoples R China

To solve large scale fast matrix calculation problems, a Non-Linear Iteration Solver (NLIS) based on a BSP model using a graph database & computing technology is proposed in this paper. A node and edge level parallel iterative algorithm is implemented by using a graph computing algorithm combined with a message passing mechanism. IEEE testing cases and actual operation data of a provincial power system in China are used to verify the method, then expand the MP 10790 to over 100k and 1 million nodes systems for the calculation stress test on a conventional server. (C) 2017 The Authors. Published by Elsevier Ltd.

关键词： Graph theory graph database graph computing power system analysis graph data modeling pagerank algorithm Bulk Synchronous Parallel Computing Model

来源：评论

学校读者我要写书评

暂无评论

A pagerank-BASED COLLABORATIVE FILTERING RECOMMENDATION APPROACH IN DIGITAL LIBRARIES

引用

TEHNICKI VJESNIK-TECHNICAL GAZETTE 2017年第4期24卷 1051-1058页

作者： Guo, Shanshan Zhang, Wenyu Zhang, Shuai Zhejiang Univ Finance & Econ Lib 18 Xueyuan St Hangzhou 310018 Zhejiang Peoples R China Zhejiang Univ Finance & Econ Sch Informat 18 Xueyuan St Hangzhou 310018 Zhejiang Peoples R China

In the current era of big data, the explosive growth of digital resources in Digital Libraries (DLs) has led to the serious information overload problem. This trend demands personalized recommendation approaches to provide DL users with digital resources specific to their individual needs. In this paper we present a personalized digital resource recommendation approach, which combines pagerank and Collaborative Filtering (CF) techniques in a unified framework for recommending right digital resources to an active user by generating and analyzing a time-aware network of both user relationships and resource relationships from historical usage data. To address the existing issues in DL deployment, including unstable user profiles, unstable digital resource features, data sparsity and cold start problem, this work adapts the personalized pagerank algorithm to rank the time-aware resource importance for more effective CF, by searching for associative links connecting both active user and his/her initially preferred resources. We further evaluate the performance of the proposed methodology through a case study relative to the traditional CF technique operating on the same historical usage data from a DL.

关键词： collaborative filtering digital library pagerank algorithm recommendation approach social network

来源：评论

学校读者我要写书评

暂无评论

Novel pagerank algorithm based on topic and link weighted

引用

Huazhong Keji Daxue Xuebao (Ziran Kexue Ban)/Journal of Huazhong University of Science and Technology (Natural Science Edition) 2012年第SUPPL.1期40卷 300-303页

作者： Yang, Gelan Tu, Li Department of Computer Science Hunan City University Yiyang 413000 Hunan China

Based on the analysis of pagerank algorithm and link weighted, a topic weighted pagerank algorithm was porposed. Firstly, any chain randomly selected a page, then, the improved pagerank value was obtained through the topic relevance evaluation algorithm. The experiment shows that this algorithm improves the query speed and accuracy with good stability and expandability.

关键词： Link weighted pagerank algorithm Sorting Topic weighted Web search World Wide Web

来源：评论

学校读者我要写书评

暂无评论

A Faster Non-Linear Iteration Solver using Graph Computing and its Application in Power Flow Calculation

引用

Energy Procedia 2017年 142卷 2534-2540页

作者： Kewen Liu Guangyi Liu Kai Xie Zhiwei Wang Global Energy Interconnection Research Institute 102209 Beijing China Global Energy Interconnection Research Institute North Amercia 95134 San Jose CA United States

关键词： Graph theory graph database graph computing power system analysis graph data modeling pagerank algorithm Bulk Synchronous Parallel Computing Model

来源：评论

学校读者我要写书评

暂无评论

Opinion Representative of Cosmetic Products 20

Opinion Representative of Cosmetic Products

引用

20th International Computer Science and Engineering Conference (ICSEC)

作者： Thessrimuang, Nipaporn Chaowalit, Orawan Silpakorn Univ Fac Sci Dept Comp Nakhon Pathom Thailand

ISBN: (纸本)9781509044207

The purpose of this research is to summarize customer reviews of cosmetic products that were written in Thai by using sentiment analysis technics. We expect that text summarization system will help refine the results from customer review. The consumer will then be able to select the products that meet their needs while the cosmetic company will also have a chance to improve the quality of the products to meet the customers' need. We used the data from popular web sites, which has cosmetic products reviews. In this paper, the Naive Bayes algorithm is used to classify the products while we classify the data as positive or negative polarity through opinion words used. Then Cosine similarity was used to find similar phrases. They were then ranked by pagerank algorithm and Hopfield Network algorithm to find the most important phrase. The performance of the program was measured with ROUGE and can be concluded that the data which were tested in the Hopfield algorithm has the highest accuracy at 0.82040, from ROUGE-1 performance measurement.

关键词： Text Summarization Sentiment Analysis Naive Bayes Cosine similarity pagerank algorithm Hopfield Networks algorithm

来源：评论

学校读者我要写书评

暂无评论

Adaptively Weighted Graph Fusion for Image Retrieval 16

Adaptively Weighted Graph Fusion for Image Retrieval

引用

8th International Conference on Internet Multimedia Computing and Service (ICIMCS)

作者： Wang, Xuya Zhou, Wengang Tian, Qi Li, Houqiang Univ Sci & Technol China CAS Key Lab Technol Geospatial Informat Proc & Ap Hefei Peoples R China Univ Texas San Antonio Dept Comp Sci San Antonio TX USA

ISBN: (纸本)9781450348508

In content-based image retrieval, feature representation is a fundamental issue and the selection of features significantly impacts the retrieval performance. Generally, the performance of a single visual feature is limited and the fusion with multiple complementary features is a preferable solution to boost the accuracy of image retrieval. In this paper, we propose an adaptively weighted graph fusion method to boost the retrieval accuracy. Given a query image, for each feature, one graph is constructed based on the nearest-neighborhood of database images. In order to evaluate the quality of this feature, we firstly use the pagerank algorithm to analyse the linked graph and get the corresponding pagerank value of each relevant image, which can be regarded as a conditional probability. Then, the weight of each feature is determined by the distribution of the above probability. We evaluate our method on two public datasets and the experimental results demonstrate that our method improves the retrieval performance consistently.

关键词： Image retrieval graph fusion adaptive weight pagerank algorithm nearest-neighborhood

来源：评论

学校读者我要写书评

暂无评论

A novel method for expert finding in online communities based on concept map and pagerank

引用

HUMAN-CENTRIC COMPUTING AND INFORMATION SCIENCES 2015年第1期5卷 1页

作者： Rafiei, Majid Kardan, Ahmad A. Amirkabir Univ Technol Dept Comp Engn & IT Adv E Learning Lab Tehran Iran Amirkabir Univ Technol Comp Engn & IT Dept Tehran Iran

An online community is a virtual community where people can express their opinions and their knowledge freely. There are a great deal of information in online communities, however there is no way to determine its authenticity. Thus the knowledge which has been shared in online communities is not reliable. By determining expertise level of users and finding experts in online communities the accuracy of posted comments can be evaluated. In this study, a hybrid method for expert finding in online communities is presented which is based on content analysis and social network analysis. The content analysis is based on concept map and the social network analysis is based on pagerank algorithm. To evaluate the proposed method java online community was selected and then correlation between our results and scores prepared by java online community was calculated. Based on obtained results Spearman correlation for 11 subcategories of java online community using this method is 0.904, which is highly an acceptable value.

关键词： Expert finding Online community Concept map Dijkstra's algorithm Knowledge sharing pagerank algorithm

来源：评论

学校读者我要写书评

暂无评论

Semistability of switched linear systems with application to pagerank algorithms

引用

EUROPEAN JOURNAL OF CONTROL 2014年第3期20卷 132-140页

作者： Shen, Jinglai Hu, Jianghai Hui, Qing Univ Maryland Baltimore Cty Dept Math & Stat Baltimore MD 21250 USA Purdue Univ Sch Elect & Comp Engn W Lafayette IN 47907 USA Texas Tech Univ Dept Mech Engn Lubbock TX 79409 USA

This paper investigates semistability and its computation for discrete-time, switched linear systems under both deterministic and random switching policies. The notion of semistability pertains to a continuum of initial state dependent equilibria, and finds wide applications in multi-agent and distributed network systems. It is shown in this paper that exponential semistability on a common equilibrium space is equivalent to output exponential stability of a reduced switched linear system with a suitably defined output, under arbitrary and random switchings. Besides, their convergence rates are shown to be identical. A generating function based approach is proposed to compute convergence rates of the reduced switched systems under these switching rules. The obtained semistability results are applied to performance analysis of pagerank algorithms for distributed web-page systems subject to topology switching. The iteration processes of these algorithms are formulated as switched linear systems. Their equilibrium properties are studied, and convergence rates are characterized via the semistability techniques and the generating function approach. (C) 2014 European Control Association. Published by Elsevier Ltd. All rights reserved.

关键词： Semistability Switched systems Stability Generating function pagerank algorithm

来源：评论

学校读者我要写书评

暂无评论

A Probability PPV Model for Social Network Influence Maximization Problem

A Probability PPV Model for Social Network Influence Maximiz...

引用

2016 2nd International Conference on Artificial Intelligence and Industrial Engineering (AIIE2016)

作者： Yunjia Ge Dong Wang Shanghai Jiaotong University

ISBN: (纸本)9781510835368

For Influence Maximization(IM) problem based on social network,effective and personalized probability learning method was still not theoretical *** this paper,we proposed a PPV probability model based on IM problem,which effectively learnt influence probabilities and personal-ized influence for each node *** clustering user groups and analyzing similarity of users from both offline action log and online social network topological structure,we estimated reliable parameters of our probability model,differed user's influence with different features on its neighbors,and improved probability learning *** Rank algorithm and fuzzy cognitive map concept help to validate our model in PPV *** show that our approach outperforms the state-of-the-art *** preference property g and transition property p,which is called PPV in our model,explains features of influence between user pairs,accuracy of personalized influence probability is improved.

关键词： influence maximization viral marketing pagerank algorithm personalization

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：