Third ACIS Int'l Conference on Software Engineering Research, Management and Applications (SERA'05)最新文献_第3页

Intrusion detection based on clustering a data stream 基于数据流聚类的入侵检测

Third ACIS Int'l Conference on Software Engineering Research, Management and Applications (SERA'05) Pub Date : 2005-08-11 DOI: 10.1007/11836810_30

S. Oh, Jin-Suk Kang, Y. Byun, T. Jeong, W. Lee

引用次数: 34

Requirements for CBD products and process quality CBD产品和工艺质量要求

Third ACIS Int'l Conference on Software Engineering Research, Management and Applications (SERA'05) Pub Date : 2005-08-11 DOI: 10.1109/SERA.2005.57

Haeng-Kon Kim, R. Lee, Hae-Sool Yang

引用次数: 0

A model-driven environment for component deployment 用于组件部署的模型驱动环境

Third ACIS Int'l Conference on Software Engineering Research, Management and Applications (SERA'05) Pub Date : 2005-08-11 DOI: 10.1109/SERA.2005.12

P. Hnetynka

引用次数: 12

A cognitive model for program comprehension 程序理解的认知模型

Third ACIS Int'l Conference on Software Engineering Research, Management and Applications (SERA'05) Pub Date : 2005-08-11 DOI: 10.1109/SERA.2005.2

Shaochun Xu

引用次数: 24

Analysis of secure design patterns: a case study in e-commerce system 安全设计模式分析:以电子商务系统为例

Third ACIS Int'l Conference on Software Engineering Research, Management and Applications (SERA'05) Pub Date : 2005-08-11 DOI: 10.1109/SERA.2005.22

Jing Wang, Yeong-Tae Song, L. Chung

引用次数: 3

Design of SPICE experience factory model for accumulation and utilization of process assessment experience 为积累和利用工艺评价经验，设计SPICE体验工厂模型

Third ACIS Int'l Conference on Software Engineering Research, Management and Applications (SERA'05) Pub Date : 2005-08-11 DOI: 10.1109/SERA.2005.34

Gihan Kim, Minkwang Lee, Jong-Soo Lee, Kyungwhan Lee

{"title":"Design of SPICE experience factory model for accumulation and utilization of process assessment experience","authors":"Gihan Kim, Minkwang Lee, Jong-Soo Lee, Kyungwhan Lee","doi":"10.1109/SERA.2005.34","DOIUrl":"https://doi.org/10.1109/SERA.2005.34","url":null,"abstract":"With growing interest in software process improvement (SPI), many companies are introducing international process models and standards. SPICE is most widely used process assessment model in the SPI work today. In the process of introducing and applying SPICE, practical experiences contribute to enhancing the project performance. The experience helps people to make decisions under uncertainty, and to find better compromises. This paper suggests a SPICE experience factory (SEF) model to use SPICE assessment experience. For this, we collected SPICE assessment results which were conducted in Korea from 1999 to 2004. The collected data does not only contain rating information but also specifies strengths and improvement point for each assessed company and its process. To use this assessment result more efficiently, root words were derived from each result items. And root words were classified into four: 1) measurement, 2) work product, 3) process performance, and 4) process definition and deployment. Database was designed and constructed to save all analyzed data in forms of root words. Database also was designed to efficiently search information the organization needs by strength/improvement point, or root word for each level. This paper describes procedures of SEF model and presents methods to utilize it. By using the proposed SEF model, even organizations which plan to undergo SPICE assessment for the first time can establish the optimal improvement strategies.","PeriodicalId":424175,"journal":{"name":"Third ACIS Int'l Conference on Software Engineering Research, Management and Applications (SERA'05)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122185985","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

Inter-AS session & connection management for QoS-guaranteed DiffServ provisioning 用于qos保证的diffserver配置的as间会话和连接管理

Third ACIS Int'l Conference on Software Engineering Research, Management and Applications (SERA'05) Pub Date : 2005-08-11 DOI: 10.1109/SERA.2005.48

Young-Tak Kim

引用次数: 7

A component-based approach for integrating mobile agents into the existing Web infrastructure 一种基于组件的方法，用于将移动代理集成到现有的Web基础设施中

Third ACIS Int'l Conference on Software Engineering Research, Management and Applications (SERA'05) Pub Date : 2005-08-11 DOI: 10.1109/SERA.2005.5

Haeng-Kon Kim

引用次数: 0

Combining classification improvements by ensemble processing 结合集成处理的分类改进

Third ACIS Int'l Conference on Software Engineering Research, Management and Applications (SERA'05) Pub Date : 2005-08-11 DOI: 10.1109/SERA.2005.30

N. Ishii, Eisuke Tsuchiya, Y. Bao, N. Yamaguchi

{"title":"Combining classification improvements by ensemble processing","authors":"N. Ishii, Eisuke Tsuchiya, Y. Bao, N. Yamaguchi","doi":"10.1109/SERA.2005.30","DOIUrl":"https://doi.org/10.1109/SERA.2005.30","url":null,"abstract":"The k-nearest neighbor (KNN) classification is a simple and effective classification approach. However, improving performance of the classifier is still attractive. Combining multiple classifiers is an effective technique for improving accuracy. There are many general combining algorithms, such as Bagging, Boosting, or Error Correcting Output Coding that significantly improve the classifier such as decision trees, rule learners, or neural networks. Unfortunately, these combining methods developed do not improve the nearest neighbor classifiers. In this paper, first, we present a new approach to combine multiple KNN classifiers based on different distance functions, in which we apply multiple distance functions to improve the performance of the k-nearest neighbor classifier. Second, we develop a combining method, in which the weights of the distance function are learnt by genetic algorithm. Finally, combining classifiers in error correcting output coding, are discussed. The proposed algorithms seek to increase generalization accuracy when compared to the basic k-nearest neighbor algorithm. Experiments have been conducted on some benchmark datasets from the UCI machine learning repository. The results show that the proposed algorithms improve the performance of the k-nearest neighbor classification.","PeriodicalId":424175,"journal":{"name":"Third ACIS Int'l Conference on Software Engineering Research, Management and Applications (SERA'05)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122555545","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: 18

Binary component adaptation technique and supporting tool 二值分量自适应技术及支持工具

Third ACIS Int'l Conference on Software Engineering Research, Management and Applications (SERA'05) Pub Date : 2005-08-11 DOI: 10.1109/SERA.2005.26

Jeong Ah Kim, Kyung-Whan Lee

引用次数: 3