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28th International Conference on System Theory Control and Computing

作者： Chirila, Ciprian-Bogdan Sora, Ioana Univ Politehn Comp & Informat Technol Dept Timisoara Romania

ISBN: (纸本)9798350364309;9798350364293

Key classes are deemed as pivotal elements within a software system, serving as focal points for reengineering or documentation endeavors. The identification of these key classes holds significant importance in contemporary practices, with numerous studies dedicated to automating their detection based on representations within class graphs. Research indicates that employing algorithms such as Hyperlink-Introduced Topic Search (HITS) and pagerank (PR) yields optimal precision and recall performance in identifying key classes. However, the runtime execution of these algorithms becomes critical, particularly when operating on graphs with varied weights attributed to class relationships. To address the challenge of runtime execution, we explore parallel implementations of these algorithms utilizing CUDA, invoked from a Java application through JCuda. Specifically, we investigate two approaches: i) employing Java virtual threads and ii) utilizing CUDA threads within the context of the JCuda library. CUDA has fundamentally transformed how we harness GPU acceleration across diverse computational tasks, spanning parallel processing, deep learning, and highperformance computing. Our experiments are conducted on a data set comprising 14 Java projects. Our findings reveal that the hardware parallel CUDA threading model significantly accelerates attribute computation, achieving a runtime reduction of 95% to 97% compared to the virtual threading model.

关键词： CUDA JavaVirtual Threads Hits algorithm pagerank algorithm key classes detection

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Investigations into the Goodness of Posts in Q&A Forums-Popularity Versus Quality 2

Investigations into the Goodness of Posts in Q&A Forums-Popu...

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2nd International Conference on Information Systems Design and Intelligent Applications (INDIA)

作者： Anand, Deepa Ravichandran, Sushma CMR Inst Technol Bangalore Karnataka India

ISBN: (纸本)9788132222477;9788132222460

Q&A forums offer users a platform to gain and showcase their knowledge and facilitate intellectual sharing among community members. An incentive based mechanism is devised as a means to encourage users to contribute to questions posed in the forum by allowing various users to Upvote and Downvote posts and answers. Though this provides the needed impetus to users to contribute quality content, oftentimes the reward mechanism is more biased towards questions on popular topics such as Java, html etc. Moreover simple questions-questions whose answer can be found in most textbooks or on the web-sometimes get a large number of votes due to a number of people finding the answer with the least amount of effort from their side. The downside of this is that quality questions-questions where the user has given thought to the topic, put in effort and which would require an effort from the answerer-is seldom answered or voted up. The investigations in this paper are oriented towards separating popularity from quality. We propose an iterative technique based on link analysis algorithms to separate the quality posts from the low quality ones even if the latter have a high number of upvotes. Such a scoring can be utilized for improving search and for highlighting good posts finding answers to which would enhance the knowledge level of the community as a whole. Experimental evaluations aligning the ranks produced with expert given ranks are promising. In two out of three cases the proposed approach had higher rank correlation with those of the experts as compared to ranks inferred through Stack Overflow assigned scores. Moreover examination of individual posts which refer to common questions illustrate that the proposed techniques have the ability to push down their scores even if Stack Overflow has assigned high scores.

关键词： Stack overflow Q&A forums pagerank algorithm Machine learning

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The p-index: Ranking Scientists using Network Dynamics

The p-index: Ranking Scientists using Network Dynamics

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14th Annual International Conference on Computational Science

作者： Senanayake, U. Piraveenan, M. Zomaya, A. Y. Univ Sydney Ctr Complex Syst Res Sydney NSW 2006 Australia Univ Sydney Ctr Distributed & High Performance Comp Sydney NSW Australia

The indices currently used by scholarly databases, such as Google scholar, to rank scientists, do not attach weights to the citations. Neither is the underlying network structure of citations considered in computing these metrics. This results in scientists cited by well-recognized journals not being rewarded, and may lead to potential misuse if documents are created purely to cite others. In this paper we introduce a new ranking metric, the p-index (pagerank-index), which is computed from the underlying citation network of papers, and uses the pagerank algorithm in its computation. The index is a percentile score, and can potentially be implemented in public databases such as Google scholar, and can be applied at many levels of abstraction. We demonstrate that the metric aids in fairer ranking of scientists compared to h-index and its variants. We do this by simulating a realistic model of the evolution of citation and collaboration networks in a particular field, and comparing h-index and p-index of scientists under a number of scenarios. Our results show that the p-index is immune to author behaviors that can result in artificially bloated h-index values.

关键词： p-index h-index ranking scientists complex networks pagerank algorithm complex systems scholarly databases Google scholar

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Design of Sensor Placement Based on Fault Diagnosability 36

Design of Sensor Placement Based on Fault Diagnosability

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第36届中国控制会议

作者： Xiaoqiang Hu Xiaoli Zhang Xiafu Peng Qi Ding Chengrui Liu Department of Automation Xiamen University Science and Technology on Space Intelligent Control Laboratory Beijing Institute of Control Engineering

ISBN: (纸本)9781538629185

This paper studies the sensor optimization configuration which meets the fault diagnosability of affine nonlinear faulty *** of all,the judgment method of fault diagnosability and the relevant quantitative evaluation indexes based on the differential geometry theory are *** the optimal sensor allocation model is built,and the pagerank algorithm of the web page ranking is introduced into the configuration *** differ from the new intellective heuristic algorithms,the weighted pagerank algorithm shifts the focus from designing an optimal subset search algorithm of better performance out to evaluating the important degree of each measuring point,and makes use of the analytic model more efficiently rather than the direct graph *** the end,a validation example of the point mass satellite system is presented to demonstrate the effectiveness of the proposed approach.

关键词： sensor optimization configuration fault diagnosability pagerank algorithm

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The attack efficiency of pagerank and HITS algorithms on complex networks

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INTERNATIONAL JOURNAL OF EMBEDDED SYSTEMS 2019年第3期11卷 306-314页

作者： Su, Yangqian Yi, Yunfei Qin, Jun Guangxi Teachers Educ Univ Coll Comp & Informat Engn Nanning 530023 Peoples R China Hechi Univ Coll Comp & Informat Engn Yizhou 546300 Peoples R China South Cent Univ Nationalities Coll Comp Sci Wuhan 430074 Hubei Peoples R China

With the growing scale of networks, network attack strategies with high attack efficiency and computational efficiency are becoming more and more important. Various attack strategies with various sorting methods have been proposed. However most of them neglected the computational efficiency. Inspired by the high computational efficiency of pagerank and HITS algorithms that used in web pages network, this paper introduces those two algorithms to the network attack separately and explores the feasibility of those two new strategies. The initial experiments choose degree and BC attack as the contrast group, and compare the attack efficiency in six virtual networks. The results indicate that considering the computational efficiency and attack efficiency simultaneously, pagerank strategy has a better attack performance than other compared strategies. Initial discussions about the results are also given.

关键词： pagerank algorithm HITS algorithm attack strategy

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Hybridization of B-Tree and HashMap for Optimized Search Engine Indexing

Hybridization of B-Tree and HashMap for Optimized Search Eng...

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International Conference On Smart Technologies For Smart Nation (SmartTechCon)

作者： Jantkal, Bhagyashri Anand Deshpande, Santosh L. VTU Belagavi Dept PG Studies CNE Belagavi India

ISBN: (纸本)9781538605691

Performance of a Search engines depend on indexing schemes for fast retrieval of results. Hash Map based indexing schemes is one the most popular indexing scheme. Although it has a good best case performance, it has poor performance with increase in number of documents as many keys hash to the same value and the search complexity rises [4]. A balanced multi-way B-tree based solution is used as an attempt to find whether document contains search word and then Hash Map is used to retrieve the occurrence of the word in a document. It reduces the number of look ups on Hash Map. The advantages of both the algorithms are combined to produce faster results in constant time, Ranking of web pages is done by analyzing the content. The document containing higher frequency of occurrence of query word is listed on top and the decreasing order of results is displayed. The proposed solution provides a logarithmic retrieval time even with increasing number of documents.

关键词： B-tree HashMap Content-based ranking Stop-word filtering pagerank algorithm

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ProRank: A Method for Detecting Protein Complexes 12

ProRank: A Method for Detecting Protein Complexes

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14th International Conference on Genetic and Evolutionary Computation Conference (GECCO)

作者： Zaki, Nazar Berengueres, Jose Efimov, Dmitry UAEU Fac Info Technol Al Ain 17551 U Arab Emirates

ISBN: (纸本)9781450311779

Detecting protein complexes from protein-protein interaction (PPI) network is becoming a difficult challenge in computational biology. Observations show that genes causing the same or similar diseases tend to lie close to one another in a network of protein-protein or functional interactions. This paper introduces a novel method for detecting protein-complexes from PPI by using a protein ranking algorithm (ProRank) and incorporating an evolutionary relationships between proteins in the network. The method successfully predicted 57 out of 81 benchmarked protein complexes created from the Munich Information Center for Protein Sequence (MIPS). The level of the accuracy achieved using ProRank in comparison to other recent methods for detecting protein complexes is a strong argument in favor of our proposed method. Datasets, programs and results are available at http://***/nzaki/***

关键词： protein-protein interaction protein complex detection pairwise similarity pagerank algorithm

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Adaptively Weighted Graph Fusion for Image Retrieval 16

Adaptively Weighted Graph Fusion for Image Retrieval

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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

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An Unsupervised Approach for Discovering Relevant Tutorial Fragments for APIs 17

An Unsupervised Approach for Discovering Relevant Tutorial F...

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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

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The Research of Spam Web Page Detection Method Based on Web Page Differentiation and Concrete Cluster Centers 13th

The Research of Spam Web Page Detection Method Based on Web ...

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13th International Conference on Wireless algorithms, Systems, and Applications (WASA)

作者： Yu, Mei Zhang, Jie Wang, Jianrong Gao, Jie Xu, Tianyi Yu, Ruiguo Tianjin Univ Sch Comp Sci & Technol Tianjin Peoples R China Tianjin Univ Tianjin Int Engn Inst Tianjin Peoples R China Tianjin Key Lab Adv Networking TANK Lab Tianjin Peoples R China Tianjin Key Lab Cognit Comp & Applicat Tianjin Peoples R China

ISBN: (纸本)9783319942681;9783319942674

To improve the pagerank algorithm's disadvantage of assigning link weights evenly and ignoring the authority of web page, we propose an improved pagerank algorithm based on web page differentiation (DPR) which evaluate pages authority according it's links' numbers and assign corresponding weights according to its authoritativeness when assigning PR values. To improve the cluster's stability and accuracy of the K-Means algorithm, we combine DPR with K-Means, design a differentiation page-based K-Means (DPK-Means) algorithm. This algorithm will sort the pages according to the PR value obtained by the DPR algorithm and then concrete cluster centers according to the current sorting result. Experiments show that in spam detection, the DPR is superior to pagerank in terms of pages numbers, recall rate, accuracy, and F-Measure value and DPK-Means has better performance than the K-Means.

关键词： Web page differentiation Concrete cluster center Spam web page detection pagerank algorithm K-Means algorithm

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