2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06)最新文献_第10页

Variables Interaction for Mining Negative and Positive Quantitative Association Rules 基于变量交互的正、负量化关联规则挖掘

2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06) Pub Date : 2006-11-13 DOI: 10.1109/ICTAI.2006.119

L. N. Alachaher, S. Guillaume

引用次数: 4

Automated AI Planning and Code Pattern Based Code Synthesis 自动人工智能规划和基于代码模式的代码合成

2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06) Pub Date : 2006-11-13 DOI: 10.1109/ICTAI.2006.37

Jicheng Fu, F. Bastani, I. Yen

{"title":"Automated AI Planning and Code Pattern Based Code Synthesis","authors":"Jicheng Fu, F. Bastani, I. Yen","doi":"10.1109/ICTAI.2006.37","DOIUrl":"https://doi.org/10.1109/ICTAI.2006.37","url":null,"abstract":"The past decade has seen great progress in the development of embedded real-time systems, which are playing increasingly important roles in various application domains. However, the growing complexity of these systems has revealed the urgent need to develop advanced techniques to reduce the time-to-market as well as the overall system development cost. One method for achieving both of these goals is automated code synthesis combined with component based software development (CBSD). This enables the synthesizer to focus on generating the glue code needed to assemble an application from existing components. The main challenge is how to enable the synthesizer to recognize and generate complex conditional or loop statements. In this paper, a pattern-based code synthesis approach is enhanced to enable it to synthesize new loop statements. Specifically, we use an extended version of Graphplan to help the synthesizer to recognize and generate new loop statements. The paper proposes a planning domain model for code patterns and an automated code synthesis system, which integrates the enhanced AI planner with the code pattern integration system (CPIS) to fully automate the code synthesis process","PeriodicalId":169424,"journal":{"name":"2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132471073","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: 9

Multi-Criterion Active Learning in Conditional Random Fields 条件随机场中的多准则主动学习

2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06) Pub Date : 2006-11-13 DOI: 10.1109/ICTAI.2006.90

Christopher T. Symons, N. Samatova, R. Krishnamurthy, Byung-Hoon Park, Tarik Umar, David J. Buttler, T. Critchlow, D. Hysom

引用次数: 23

Preserving Patterns in Bipartite Graph Partitioning 二部图划分中的模式保持

2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06) Pub Date : 2006-11-13 DOI: 10.1109/ICTAI.2006.97

Tianming Hu, Chao Qu, C. Tan, S. Sung, Wenjun Zhou

引用次数: 5

Toward Human-Level Machine Intelligence 迈向人类水平的机器智能

2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06) Pub Date : 2006-11-13 DOI: 10.1109/ICTAI.2006.114

L. Zadeh

引用次数: 9

The Culebra Algorithm for Path Planning and Obstacle Avoidance in Kat-5 基于cullebra算法的Kat-5路径规划与避障

2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06) Pub Date : 2006-11-13 DOI: 10.1109/ICTAI.2006.110

J. Nagel, P. G. Trepagnier, C. Koutsougeras, P. Kinney, M. Dooner

引用次数: 7

Robust Controllability of Temporal Constraint Networks under Uncertainty 不确定条件下时间约束网络的鲁棒可控性

2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06) Pub Date : 2006-11-13 DOI: 10.1109/ICTAI.2006.100

H. Lau, Jia Li, R. Yap

引用次数: 14

UNPCC: A Novel Unsupervised Classification Scheme for Network Intrusion Detection UNPCC:一种新的网络入侵检测无监督分类方案

2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06) Pub Date : 2006-11-13 DOI: 10.1109/ICTAI.2006.115

Zongxing Xie, Thiago Quirino, M. Shyu, Shu‐Ching Chen, LiWu Chang

{"title":"UNPCC: A Novel Unsupervised Classification Scheme for Network Intrusion Detection","authors":"Zongxing Xie, Thiago Quirino, M. Shyu, Shu‐Ching Chen, LiWu Chang","doi":"10.1109/ICTAI.2006.115","DOIUrl":"https://doi.org/10.1109/ICTAI.2006.115","url":null,"abstract":"The development of effective classification techniques, particularly unsupervised classification, is important for real-world applications since information about the training data before classification is relatively unknown. In this paper, a novel unsupervised classification algorithm is proposed to meet the increasing demand in the domain of network intrusion detection. Our proposed UNPCC (unsupervised principal component classifier) algorithm is a multiclass unsupervised classifier with absolutely no requirements for any a priori class related data information (e.g., the number of classes and the maximum number of instances belonging to each class), and an inherently natural supervised classification scheme, both which present high detection rates and several operational advantages (e.g., lower training time, lower classification time, lower processing power requirement, and lower memory requirement). Experiments have been conducted with the KDD Cup 99 data and network traffic data simulated from our private network testbed, and the promising results demonstrate that our UNPCC algorithm outperforms several well-known supervised and unsupervised classification algorithms","PeriodicalId":169424,"journal":{"name":"2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114323893","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

QoS Adaptive ISHM Systems QoS自适应ISHM系统

2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06) Pub Date : 2006-11-13 DOI: 10.1109/ICTAI.2006.99

Yansheng Zhang, Jicheng Fu, I. Yen, F. Bastani, A. Tai, S. Chau, F. Vatan, A. Fijany

{"title":"QoS Adaptive ISHM Systems","authors":"Yansheng Zhang, Jicheng Fu, I. Yen, F. Bastani, A. Tai, S. Chau, F. Vatan, A. Fijany","doi":"10.1109/ICTAI.2006.99","DOIUrl":"https://doi.org/10.1109/ICTAI.2006.99","url":null,"abstract":"Embedded systems are becoming highly complex and increasingly being used in critical applications. Integrated system health management (ISHM) techniques have therefore been developed to ensure the proper operation of these systems. However, some ISHM systems are relatively complex and may consume a significant amount of resources. In some situations, activating the full ISHM system may cause resource contention and prevents the target system from timely completing critical tasks. Thus, it is imperative to introduce the notion of adaptivity into ISHM systems. This paper systematically discusses the issues that need to be addressed in an adaptive ISHM system with a focus on adaptation in terms of QoS aspects. A novel model, adaptive diagnosis quality-oriented system model (ADQSM), is proposed to model the QoS specification and fault diagnosis quality measurement issues as well as the abstraction of the adaptation problem. We then present the method to evaluate various diagnosability attributes based on a modified fault signature matrix. We further map the ADQSM model to the particle swarm optimization (PSO) problem model and use PSO for rapid configuration decision making","PeriodicalId":169424,"journal":{"name":"2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124608119","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: 1

Sustained Emerging Spatio-Temporal Co-occurrence Pattern Mining: A Summary of Results 持续新兴时空共现模式挖掘:结果综述

2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06) Pub Date : 2006-11-13 DOI: 10.1109/ICTAI.2006.108

Mete Celik, S. Shekhar, James P. Rogers, J. Shine

引用次数: 30