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检索条件"主题词=Pagerank algorithm"

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Effective Reformulation of Query for Code Search using Crowdsourced Knowledge and Extra-Large Data Analytics 34

Effective Reformulation of Query for Code Search using Crowd...

引用

IEEE International Conference on Software Maintenance and Evolution (ICSME)

作者： Rahman, Mohammad Masudur Roy, Chanchal K. Univ Saskatchewan Dept Comp Sci Saskatoon SK Canada

ISBN: (纸本)9781538678701

Software developers frequently issue generic natural language queries for code search while using code search engines (e.g., GitHub native search, Krugle). Such queries often do not lead to any relevant results due to vocabulary mismatch problems. In this paper, we propose a novel technique that automatically identifies relevant and specific API classes from Stack Overflow Q & A site for a programming task written as a natural language query, and then reformulates the query for improved code search. We first collect candidate API classes from Stack Overflow using pseudo-relevance feedback and two term weighting algorithms, and then rank the candidates using Borda count and semantic proximity between query keywords and the API classes. The semantic proximity has been determined by an analysis of 1.3 million questions and answers of Stack Overflow. Experiments using 310 code search queries report that our technique suggests relevant API classes with 48% precision and 58% recall which are 32% and 48% higher respectively than those of the state-of-the-art. Comparisons with two state-of-the-art studies and three popular search engines (e.g., Google, Stack Overflow, and GitHub native search) report that our reformulated queries (1) outperform the queries of the state-of-the-art, and (2) significantly improve the code search results provided by these contemporary search engines.

关键词： Code search query reformulation crowd-sourced knowledge extra-large data analytics Stack Overflow pagerank algorithm Borda count semantic similarity

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Speeding up Simulations of Cascading Blackout in Power Systems by Identifying High Influential Lines

Speeding up Simulations of Cascading Blackout in Power Syste...

引用

2017 IEEE Power & Energy Society General Meeting

作者： Ma, Zhiyuan Liu, Feng Shen, Chen Zhang, Xuemin Mei, Shengwei Gao, Feng Tsinghua Univ Dept Elect Engn State Key Lab Power Syst Beijing Peoples R China State Grid Ningxia Elect Power Co Elect Power Res Inst Yinchuan Peoples R China

ISBN: (纸本)9781538622124

This paper proposes a novel algorithm based on the identification of the most influential lines to speed up the simulation of severe cascading failures in power systems. To this end, we first extend the concept of interaction graph to take into account the impact of hidden failures. Then we devise a pagerank-based algorithm to assess the influence of individual lines, which remarkably facilitates finding such severe cascading failures. Numerical experiments carried out on the New England system show that our algorithm can enhance the efficiency of finding severe blackouts of 1 similar to 2 orders of magnitude.

关键词： cascading blackouts pagerank algorithm power system simulation

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A Method for Test Cases Reduction in Web Application Testing Based on User Session

A Method for Test Cases Reduction in Web Application Testing...

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International Conference on Networking and Network Applications (NaNA)

作者： Wang, Shuyan Wu, Wentao Sun, Jiaze Xian Univ Posts & Telecommun Sch Comp Sci & Technol Xian Shaanxi Peoples R China

ISBN: (纸本)9781538683033

With the development of web application, the internal structure of the web application was more complicated. The software testing method based on user session has been used to verify the quality of web application. However, the amount of real user session data were extremely large. In order to reduce the scale of the test case set for web application testing, we proposed a reduced method of test cases based on user session. First, we cleaned the original log file and identified user session and sorted the user sessions according to the Gini index. Then, we made an "OR" operation on the all user sessions coverage to obtain some user sessions that cover all pages of web application. These user sessions were a set of reduced test cases. Finally, the structure dependency graph of web application was generated according to the user sessions. The pagerank algorithm and the Hamming distance are used to optimize the test cases reduced with the same Gini index. By this means, we obtained the final test cases set for reduction. Through experimental verification, we found that the reduction rate of the test cases reached 85.7% and the fault coverage rate reached 100% by our method. Therefore, the method we proposed in this paper improved effectively the reliability of web application and the effectiveness of software testing and reduced the cost of software testing.

关键词： user session Gini index pagerank algorithm Hamming distance test cases reduction

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Novel Data-Driven Dynamic Network Science Application in algorithmic Trading 48

Novel Data-Driven Dynamic Network Science Application in Alg...

引用

48th Annual IEEE International Computers, Software, and Applications Conference (COMPSAC) - Digital Development for a Better Future

作者： Ranathungage, Thimani Thavaneswaran, Aerambamoorthy Liang, You Thulasiram, Ruppa Paseka, Alex Univ Manitoba Dept Stat Winnipeg MB Canada Toronto Metropolitan Univ Dept Math Toronto ON Canada Univ Manitoba Dept Comp Sci Winnipeg MB Canada Univ Manitoba Dept Accounting & Finance Winnipeg MB Canada

ISBN: (纸本)9798350376975;9798350376968

One of the recent developments in computational finance has been the rise in using graph-based approaches to analyse stock market dynamics systematically. In algo trading literature, stocks for pairs trading and multiple trading are commonly selected by using Engle Granger and Johansen co-integration tests. Identifying all pairs eligible for trading in large datasets, entails a significant computational burden. In this paper, price correlation-based dynamic networks and differenced price series-based correlation networks and their importance ranks are used to pre-select pairs for trading. algorithmic trading profits using commonly used co-integration methods are compared with the profits made by proposed correlation-based financial networks. Unlike the existing work, the novelty of the paper is that it uses data-driven correlation-based financial network approach to propose a stock selection method for pairs trading. In this paper, profit per transaction is used to compare the trading strategies. The superiority of the financial network stock selection method is discussed as well in some detail.

关键词： Dynamic Trading Strategies Financial Networks pagerank algorithm Co-integration

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Combining Hyperlink Structure and Content of Webpage for Personalization of Search Engine 3rd

Combining Hyperlink Structure and Content of Webpage for Per...

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3rd International Conference on Information and Communication Technology for Intelligent Systems (ICTIS)

作者： Anjusha, I. T. Nizar, M. Abdul Coll Engn Trivandrum Thiruvananthapuram Kerala India

ISBN: (纸本)9789811317477;9789811317460

Internet contains huge amount of information, and people explore it for satisfying their information needs. Search engine is the tool used to look for information on the Internet. But generic search engines are not adaptable to user's specific needs and interests. Search engine personalization was introduced as a solution to this problem. Personalization is the process of retrieving important webpages for people with different information goals on the basis of their individual interests and context of their activities. Content-based personalization systems returns webpages that are relevant to user interest profile. But it may not ensure quality of webpage. Hyperlink-based systems return webpages with high quality. But it may also return webpages that are only peripherally related to the user interests. In this paper, we present a proposal for search technique that combines link structure and page contents to produce high-quality, user-relevant webpages. Our experiments reveal that the proposed system produces better search results than generic search engine and content-based personalization system.

关键词： pagerank algorithm Personalization User profiling Webpage re-ranking Search engine

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Multi-document Arabic Text Summarization based on Thematic Annotation 16

Multi-document Arabic Text Summarization based on Thematic A...

引用

16th International Conference on Software Technologies (ICSOFT)

作者： Merniz, Amina Chaibi, Anja Habacha Ben Ghezala, Henda Univ Manouba Natl Sch Comp Sci Manouba Tunisia

ISBN: (纸本)9789897585234

Reduce document(s) by keeping keys and significant sentences from a set of data is called text summarization. It has been around for a long time in natural language processing research, it is improving over the years due to a considerable number of methods and research in this area. The paper suggests Arabic multi-document text summarization. The originality of the approach is that the summary based on thematic annotation such as input documents are analyzed and segmented using LDA. Then segments of each topic are represented by a separate graph because of the redundancy problem in multi-document summarization. In the last step, the proposed approach applies a modified pagerank algorithm that utilizes cosine similarity measure as a weight between edges. Vertices that have high scores are essential. Therefore, they construct the final summary. To evaluate summary systems, researchers develop serval metrics divided into three categories, namely: automatic, semiautomatic and manual. This study research chooses automatic evaluation methods for text summarization, mainly Rouge measure (Rouge-1, Rouge-2, Rouge-L, and Rouge-SU4).

关键词： Text Summarization Multi-document Summarization pagerank algorithm Thematic Annotation

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MULTIPLICATION-AVOIDING VARIANT OF POWER ITERATION WITH APPLICATIONS 47

MULTIPLICATION-AVOIDING VARIANT OF POWER ITERATION WITH APPL...

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47th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Pan, Hongyi Badawi, Diaa Miao, Runxuan Koyuncu, Erdem Cetin, Ahmet Enis Univ Illinois Dept Elect & Comp Engn Chicago IL 60607 USA

ISBN: (纸本)9781665405409

Power iteration is a fundamental algorithm in data analysis. It extracts the eigenvector corresponding to the largest eigenvalue of a given matrix. Applications include ranking algorithms, principal component analysis (PCA), among many others. Certain use cases may benefit from alternate, non-linear power methods with low complexity. In this paper, we introduce multiplication-avoiding power iteration (MAPI). MAPI replaces the standard l(2) inner products that appear at the regular power iteration (RPI) with multiplication-free vector products, which are Mercer-type kernels that induce the l(1) norm. For an n x n matrix, MAPI requires n multiplications, while RPI needs n(2) multiplications per iteration. Therefore, MAPI provides a significant reduction of the number of multiplication operations, which are known to be costly in terms of energy consumption. We provide applications of MAPI to PCA-based image reconstruction as well as to graph-based ranking algorithms. When compared to RPI, MAPI not only typically converges much faster, but also provides superior performance.

关键词： Power iteration multiplication-free algorithms principal component analysis pagerank algorithm

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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

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Dynamic Hidden Markov Model for Metropolitan Traffic Flow Prediction 92

Dynamic Hidden Markov Model for Metropolitan Traffic Flow Pr...

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92nd IEEE Vehicular Technology Conference (IEEE VTC-Fall)

作者： Li, Zihan Chen, Cailian Min, Yang He, Jianping Yang, Bo Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China Minist Educ China Key Lab Syst Control & Informat Proc Shanghai 200240 Peoples R China

ISBN: (纸本)9781728194844

Traffic flow prediction is one of the core technologies in Intelligent Transportation System (ITS) to improve traffic management. However, in metropolitan circumstances, the complex traffic road networks and numerous unpredictable traffic anomalies are still tough problems, which bring challenges of leveraging topological and anomalies information to accurate traffic flow prediction. In this paper, we propose a Dynamic Hidden Markov Model (DHMM) based on global pagerank algorithm to overcome these challenges. The global pagerank algorithm is more applicable than traditional algorithm for traffic scenarios, through which the pagerank metric is calculated to measure the accumulation of traffic anomalies at intersections. By incorporating the pagerank metric, DHMM leverages topological and anomalies information to dynamically model the traffic variations. Experiments on real-world dataset demonstrate that the pagerank metric can describe the degree of traffic anomalies intuitively, and the proposed model has superior traffic flow prediction performance both under normal and abnormal traffic conditions.

关键词： pagerank algorithm Hidden Markov Model traffic flow prediction

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Research on the Nodes Importance in Complex Networks Based on Community Partition 19

Research on the Nodes Importance in Complex Networks Based o...

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2nd International Conference on Big Data Technologies (ICBDT) / 3rd International Conference on Business Information Systems Workshop (ICBIS)

作者： Chen, Feng Huang, Yajing Beijing Forestry Univ Sch Sci China 35 Tsinghua East Rd Beijing Peoples R China

ISBN: (纸本)9781450371926

In complex network analysis, the importance of nodes has always been one of the main research fields of graph theory and the most common calculation method is the pagerank algorithm. The existing research is mainly based on the whole network level to rank all nodes, which will bring a huge amount of computation when the network is quite large. In fact, a node is more likely to be affected by the nodes that are closely connected to it, rather than by the nodes that have the highest importance in the entire network. So, it is necessary to decompose the networks into communities, whose main feature is that the nodes within a community are closely connected, while the nodes between different communities are sparsely connected. In this paper, the classical Louvain algorithm will be applied to achieve community partition, for the further study of nodes importance. Firstly, the statistical characteristics of pagerank values of nodes between different communities are studied for four different types of networks: regular network, random network, small-world network and scale-free network. Secondly, a real enterprise investment network is studied based on a large number of Chinese enterprise investment data. The nodes in the same community are extracted to form a sub-network and a new set of pagerank values will be calculated for these nodes. Comparison results of two sets of pagerank values of the same group of nodes show that the rank of nodes importance in the whole network is highly consistent with that in the sub-network. For the network model established by big data, community partition method can greatly reduce the computational load of nodes important and simplify the research process. The conclusion of this paper is applicable to large-scale complex networks in reality, and has vital practical significance.

关键词： Complex network Community partition Node importance Louvain algorithm pagerank algorithm

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