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2015 Eighth International Conference on Internet Computing for Science and Engineering (ICICSE)最新文献

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System Dependency Graph Construction Algorithm Based on Equivalent Substitution 基于等价替换的系统依赖图构建算法
2015 Eighth International Conference on Internet Computing for Science and Engineering (ICICSE) Pub Date : 2015-11-06 DOI: 10.1109/ICICSE.2015.29
Yulong Meng, Dong Xu, Ziying Zhang, Wencai Li
{"title":"System Dependency Graph Construction Algorithm Based on Equivalent Substitution","authors":"Yulong Meng, Dong Xu, Ziying Zhang, Wencai Li","doi":"10.1109/ICICSE.2015.29","DOIUrl":"https://doi.org/10.1109/ICICSE.2015.29","url":null,"abstract":"There are many remarkable methods used in software semantics analysis. However, people find some of the methods generally have the common problem of high time complexity or inaccurate results. In order to solve these problems, we propose a program controlled flow algorithm which is based on a control dependency graph and an abstract syntax tree. This algorithm uses object program equivalent substitution and a procedure of dependency graph to replace procedure System Dependency Graph (SDG), which improves the building process of traditional system dependency graph. Experimental results show that the proposed algorithm can effectively reduce the complexity of constructional SDG and increase the rate of program slicing.","PeriodicalId":159836,"journal":{"name":"2015 Eighth International Conference on Internet Computing for Science and Engineering (ICICSE)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124460346","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
SIFT Vector Field Building Algorithm SIFT矢量场构建算法
2015 Eighth International Conference on Internet Computing for Science and Engineering (ICICSE) Pub Date : 2015-11-06 DOI: 10.1109/ICICSE.2015.28
Jing Shen, Haibo Liu, Yanxia Wu, Xingmei Wang
{"title":"SIFT Vector Field Building Algorithm","authors":"Jing Shen, Haibo Liu, Yanxia Wu, Xingmei Wang","doi":"10.1109/ICICSE.2015.28","DOIUrl":"https://doi.org/10.1109/ICICSE.2015.28","url":null,"abstract":"Object detection is an important part of computer vision research, which directly affects the follow-on object identification and tracking, analysis and understanding of the scene. In this paper, based on scale space theory and the SIFT feature matching algorithm, we propose a method to create a SIFT vector field. Through four different application scenarios we demonstrate the value of building a SIFT vector field in our moving object algorithm as the basis of our object detection approach.","PeriodicalId":159836,"journal":{"name":"2015 Eighth International Conference on Internet Computing for Science and Engineering (ICICSE)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128648119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A Method of Social Network Node Preference Evaluation Based on the Topology Potential 基于拓扑势的社会网络节点偏好评价方法
2015 Eighth International Conference on Internet Computing for Science and Engineering (ICICSE) Pub Date : 2015-11-06 DOI: 10.1109/ICICSE.2015.48
Yong Wang, Jing Yang, Jianpei Zhang, Jianchuan Zhang, Hongtao Song, Zhigang Li
{"title":"A Method of Social Network Node Preference Evaluation Based on the Topology Potential","authors":"Yong Wang, Jing Yang, Jianpei Zhang, Jianchuan Zhang, Hongtao Song, Zhigang Li","doi":"10.1109/ICICSE.2015.48","DOIUrl":"https://doi.org/10.1109/ICICSE.2015.48","url":null,"abstract":"This paper reports a hypergraph model for online social networks with an emphasis on the node preference. Some improvements of the model are made in the present study. First, the inherent nodes properties and their links are utilized in the proposed evaluation model. Second, the proposed model contains a topology potential value of node, which is based on cognitive data field in physics. In the calculation of node quality entropy - weight method are used. In way, human interference factors can be obtained for estimating node quality. The calculation of shortest path is based on the Dijkstra hypergraph. Third, a replacing algorithm is employed to account for node preference by modifying deleting algorithm. Then, based on the node preference and the feature that nodes are attracted each other in data field to form a community, we propose a hyper-graph model for the function of social networks community detection. The model is experimented to prove the validity and usability of evaluation results.","PeriodicalId":159836,"journal":{"name":"2015 Eighth International Conference on Internet Computing for Science and Engineering (ICICSE)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117260679","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
The Object Tracking Based on Integral Covariance Matrix 基于积分协方差矩阵的目标跟踪
2015 Eighth International Conference on Internet Computing for Science and Engineering (ICICSE) Pub Date : 2015-11-06 DOI: 10.1109/ICICSE.2015.17
Qian Wang, Xin Gu, Zheng-hao Sun, Zhe Li, Jun Ni
{"title":"The Object Tracking Based on Integral Covariance Matrix","authors":"Qian Wang, Xin Gu, Zheng-hao Sun, Zhe Li, Jun Ni","doi":"10.1109/ICICSE.2015.17","DOIUrl":"https://doi.org/10.1109/ICICSE.2015.17","url":null,"abstract":"The object tracking by using single feature is possible to generate errors and easy to lose the target if the illumination and object size scale are changed. We propose a particle-filter-object-tracking algorithm. The proposed algorithm is based on a covariance region descriptor (CRD). The CRD can fuse different features of a targeted object region while handling various complex backgrounds. Hence, the robustness of tracking algorithm is achieved. Moreover, the integral covariance matrix computation is an extension to Bayesian tracking framework, which makes the tracking more efficiency and for handling high performance tracking in real-time. The comparative experiments show that the proposed algorithm is more robust and its efficiency of computation of tracking is higher performed than the one uses traditional the object tracking algorithm with only consideration of single feature.","PeriodicalId":159836,"journal":{"name":"2015 Eighth International Conference on Internet Computing for Science and Engineering (ICICSE)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129485053","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Research on Program Execution Time Assessment of LLVM IR Program LLVM IR程序执行时间评估研究
2015 Eighth International Conference on Internet Computing for Science and Engineering (ICICSE) Pub Date : 2015-11-06 DOI: 10.1109/ICICSE.2015.13
Shuyong Liu, Yanxia Wu, Tianxiang Sun, Guoyin Zhang
{"title":"Research on Program Execution Time Assessment of LLVM IR Program","authors":"Shuyong Liu, Yanxia Wu, Tianxiang Sun, Guoyin Zhang","doi":"10.1109/ICICSE.2015.13","DOIUrl":"https://doi.org/10.1109/ICICSE.2015.13","url":null,"abstract":"Program performance assessment is one of important methods to optimize a system design. The assessment of program execution time is always key topic in computer structure. A good assessment can provide important measure basis for hardware/software automatic partitioning in reconfigurable computer compiler. This paper analyzes the characteristics of IR program and proposes an IR layer program classification algorithm for assessing the program execution time. The method can provide an accurate measure and can be constructed into a BP neural network assessment system. The experimental results show that the proposed assessment model has lower assessment deviation compared to other models under the same conditions. The BP neural network model can be trained.","PeriodicalId":159836,"journal":{"name":"2015 Eighth International Conference on Internet Computing for Science and Engineering (ICICSE)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129615032","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Association Rules for Auditing Systems 审计系统关联规则
2015 Eighth International Conference on Internet Computing for Science and Engineering (ICICSE) Pub Date : 2015-11-06 DOI: 10.1109/ICICSE.2015.15
Linshan Shen, Shaobin Huang, Xiangke Mao, Junjun Fan, Jianghua Li
{"title":"Association Rules for Auditing Systems","authors":"Linshan Shen, Shaobin Huang, Xiangke Mao, Junjun Fan, Jianghua Li","doi":"10.1109/ICICSE.2015.15","DOIUrl":"https://doi.org/10.1109/ICICSE.2015.15","url":null,"abstract":"In this paper, we apply the association rules in data mining to an auditing system in order to mine the characteristics of audit data. The approach as a new mining technology can be used by an auditor to better interpret vast amounts of audit data. Association rules based algorithm is an outstanding methodology with which people can discover the hidden correlation relationships among dataset. It is applicable to mining of huge data which were difficult to start with. Because audit data usually contain a large number of rare data with different distribution characteristics, we hereby propose a multiple supports-based framework for digging data pattern from the rare data. We use all-confidence method to deal with crossing platform supports. In this paper we propose the MSAC_Apriori algorithm with generalized association rules, which helps establish the relationships during quantitative association analysis. Experimental results on practical datasets show that the proposed approach improves the performance by decreasing the number of frequent items without missing rare items.","PeriodicalId":159836,"journal":{"name":"2015 Eighth International Conference on Internet Computing for Science and Engineering (ICICSE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116645852","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A Review of Uncertain Data Stream Clustering Algorithms 不确定数据流聚类算法综述
2015 Eighth International Conference on Internet Computing for Science and Engineering (ICICSE) Pub Date : 2015-11-06 DOI: 10.1109/ICICSE.2015.30
Yue Yang, Zhuo Liu, Zhidan Xing
{"title":"A Review of Uncertain Data Stream Clustering Algorithms","authors":"Yue Yang, Zhuo Liu, Zhidan Xing","doi":"10.1109/ICICSE.2015.30","DOIUrl":"https://doi.org/10.1109/ICICSE.2015.30","url":null,"abstract":"Because of data uncertainty and time and space limitations in clustering process, clustering uncertain data stream becomes a very challenging task. A lot of processing algorithms has been proposed continuously. This paper reviews clustering algorithms of uncertain data stream and dissects the advantages and disadvantages of these algorithms for future development.","PeriodicalId":159836,"journal":{"name":"2015 Eighth International Conference on Internet Computing for Science and Engineering (ICICSE)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115148195","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 5
Research on Communities Detection in Social Network 社交网络中的社区检测研究
2015 Eighth International Conference on Internet Computing for Science and Engineering (ICICSE) Pub Date : 2015-11-06 DOI: 10.1109/ICICSE.2015.46
Hongbin Wang, Guisheng Yin, Yue Fu, Lu Wang, Wenqian Xu
{"title":"Research on Communities Detection in Social Network","authors":"Hongbin Wang, Guisheng Yin, Yue Fu, Lu Wang, Wenqian Xu","doi":"10.1109/ICICSE.2015.46","DOIUrl":"https://doi.org/10.1109/ICICSE.2015.46","url":null,"abstract":"During the evolution of social network, there is a social network phenomenon that small communities also become important. Generally, each community has its own characteristics of internal correlation and relation. Accurate division of whole social networks into multiple small communities may help improve the quality of social network services as whole. With the comparison among substantial community detection algorithms, we present a Label Propagation Algorithm (LPA), which has proven to be more efficient for large scale community detection and widely used. Random (node) access orders within the algorithm severely hamper its robustness, consequently, and the stability of the identified community structure. In this paper, we propose a Precedential Label Propagation Algorithm (PLPA) which counteracts for the introduced randomness by increasing propagation preference. The experiment results verify the PLP is more robust than LP.","PeriodicalId":159836,"journal":{"name":"2015 Eighth International Conference on Internet Computing for Science and Engineering (ICICSE)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123071760","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Hadoop-Based Distributed Computing Algorithms for Healthcare and Clinic Data Processing 基于hadoop的医疗保健和临床数据处理分布式计算算法
2015 Eighth International Conference on Internet Computing for Science and Engineering (ICICSE) Pub Date : 2015-11-06 DOI: 10.1109/ICICSE.2015.41
Jun Ni, Ying Chen, Jie Sha, Minghuan Zhang
{"title":"Hadoop-Based Distributed Computing Algorithms for Healthcare and Clinic Data Processing","authors":"Jun Ni, Ying Chen, Jie Sha, Minghuan Zhang","doi":"10.1109/ICICSE.2015.41","DOIUrl":"https://doi.org/10.1109/ICICSE.2015.41","url":null,"abstract":"There exit a huge demand on utilizing big data technology to process healthcare related patients data for healthcare information extraction and medical knowledge discovery. In this paper, we briefly review the demands and application potentials using big data technology with an emphasis on common challenges. After briefly addressing the Hadoop/MapReduce code components and modules, we use a simple clinic data to demonstrate how to map and reduce on small dataset with illustrated workflow. We give simple scenario of using other MapReduce calculation modules for counting and classification. This serves as a basic step into future utilization of big data to healthcare domain.","PeriodicalId":159836,"journal":{"name":"2015 Eighth International Conference on Internet Computing for Science and Engineering (ICICSE)","volume":"144 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122708858","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 12
Design of Mobile Micro-English Vocabulary System Based on the Ebbinghaus Forgetting Theory 基于艾宾浩斯遗忘理论的移动微英语词汇系统设计
2015 Eighth International Conference on Internet Computing for Science and Engineering (ICICSE) Pub Date : 2015-11-06 DOI: 10.1109/ICICSE.2015.51
Weiyong Zheng
{"title":"Design of Mobile Micro-English Vocabulary System Based on the Ebbinghaus Forgetting Theory","authors":"Weiyong Zheng","doi":"10.1109/ICICSE.2015.51","DOIUrl":"https://doi.org/10.1109/ICICSE.2015.51","url":null,"abstract":"Learning English requires a large number of vocabulary memories and needs numerous practices of using grammatical structures. In order to improve the learning efficiency, we propose a mobile-based micro system to help quickly master vocabulary uses. The system is based the vocabulary classification with consideration of complexity. The system features lie in monitoring practical vocabulary usages in real time, hence to quickly increase students learning ability. The technology deployed in the system is based on the Ebbinghaus Forgetting Theory (EFT). The core of this system focuses on the vocabularies at the easily-forgettable pools (nodes). The nodes are generated based on the Ebbinghaus curving. The system highlights the importance of personalized learning curving and study procedure. The system helps learners to progressively increase the level of vocabulary, so they can handle difficult linguistic cases. The system allows learners to look over learning achievement and significantly improve vocabulary learning efficiency.","PeriodicalId":159836,"journal":{"name":"2015 Eighth International Conference on Internet Computing for Science and Engineering (ICICSE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130855062","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 4
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