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

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Software Maintenance: Similarity and Inclusion of Rules in Knowledge Extraction 软件维护:知识抽取中规则的相似性和包容性
2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06) Pub Date : 2006-11-13 DOI: 10.1109/ICTAI.2006.106
Marek Reformat, Aashima Kapoor, N. Pizzi
{"title":"Software Maintenance: Similarity and Inclusion of Rules in Knowledge Extraction","authors":"Marek Reformat, Aashima Kapoor, N. Pizzi","doi":"10.1109/ICTAI.2006.106","DOIUrl":"https://doi.org/10.1109/ICTAI.2006.106","url":null,"abstract":"Software maintenance is an important phase in the software life cycle. It focuses on keeping the software fully functional and up to date. Maintenance engineers used different approaches and methods to gain understanding of software systems so maintenance tasks can be performed effectively. A lot of efforts have been put into finding a way to measure maintainability of software. It is a common opinion that software maintainability should be described using a set of measurable software attributes. This paper looks at the issue of rule-based description of attributes of software with different levels of maintainability. Varieties of rules are extracted from a data set that represents human evaluation of maintainability of software objects. Rule similarity and rule inclusion measures are used to identify the most diverse sets of rules representing human evaluation criteria. Additionally, the rules representing all evaluators are analyzed using a rule similarity concept in order to learn more about common evaluation criteria","PeriodicalId":169424,"journal":{"name":"2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06)","volume":"36 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":"128980939","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: 19
A Particle Swarm Algorithm for Multiobjective Design Optimization 多目标设计优化的粒子群算法
2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06) Pub Date : 2006-11-13 DOI: 10.1109/ICTAI.2006.20
Eric Ochlak, B. Forouraghi
{"title":"A Particle Swarm Algorithm for Multiobjective Design Optimization","authors":"Eric Ochlak, B. Forouraghi","doi":"10.1109/ICTAI.2006.20","DOIUrl":"https://doi.org/10.1109/ICTAI.2006.20","url":null,"abstract":"Many engineering design problems are characterized by presence of several conflicting objectives. This requires efficient search of the feasible design region for optimal solutions which simultaneously satisfy multiple design objectives. The search is further complicated in view of the fact that because of inherent manufacturing variations it is often necessary to allocate tolerances to design variables while guaranteeing low variances for product/process performance measures. Particle swarm optimization (PSO) is a powerful search technique with faster convergence rates than traditional evolutionary algorithms. This paper introduces a new PSO-based approach to multiobjective engineering design by incorporating the central quality-control notion of tolerance design. Unlike classical optimization techniques which rely on single-point representation of designs, the modified PSO algorithm allocates tolerances to design variables and flies a swarm of hypercubic particles through the feasible space. To demonstrate the utility of the proposed method, the multiobjective design of an I-beam is presented","PeriodicalId":169424,"journal":{"name":"2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06)","volume":"83 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":"126654339","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
Diagnosability Test for Timed Discrete-Event Systems 定时离散事件系统的可诊断性测试
2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06) Pub Date : 2006-11-13 DOI: 10.1109/ICTAI.2006.50
JiangJing Pan, S. Hashtrudi-Zad
{"title":"Diagnosability Test for Timed Discrete-Event Systems","authors":"JiangJing Pan, S. Hashtrudi-Zad","doi":"10.1109/ICTAI.2006.50","DOIUrl":"https://doi.org/10.1109/ICTAI.2006.50","url":null,"abstract":"In this paper, an algorithm with polynomial time-complexity is presented for testing failure diagnosability in (untimed) discrete-event systems in a state-based framework. Furthermore, an algorithm for testing failure diagnosability in timed discrete-event systems is provided. The test for timed discrete-event systems, in particular, first gathers and complies the information about the timing of events (represented in the timed transition graph of the timed system) in the transition-time function of a reduced model, and then uses this model to verify diagnosability. Sufficient conditions are obtained under which the transition-time sets can be represented as the union of a bounded number of intervals, and the test will have polynomial complexity. This new test, as shown using examples, may significantly reduce the computations of testing diagnosability, compared with other polynomial diagnosability tests (for untimed systems) adapted for timed systems","PeriodicalId":169424,"journal":{"name":"2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06)","volume":"62 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":"124657699","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
Improve Decision Trees for Probability-Based Ranking by Lazy Learners 懒惰学习者改进基于概率排序的决策树
2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06) Pub Date : 2006-11-13 DOI: 10.1109/ICTAI.2006.65
H. Liang, Yuhong Yan
{"title":"Improve Decision Trees for Probability-Based Ranking by Lazy Learners","authors":"H. Liang, Yuhong Yan","doi":"10.1109/ICTAI.2006.65","DOIUrl":"https://doi.org/10.1109/ICTAI.2006.65","url":null,"abstract":"Existing work shows that classic decision trees have inherent deficiencies in obtaining a good probability-based ranking (e.g. AUC). This paper aims to improve the ranking performance under decision-tree paradigms by presenting two new models. The intuition behind our work is that probability-based ranking is a relative metric among samples, therefore, distinct probabilities are crucial for accurate ranking. The first model, lazy distance-based tree (LDTree), uses a lazy learner at each leaf to explicitly distinguish the different contributions of leaf samples when estimating the probabilities for an unlabeled sample. The second model, eager distance-based tree (EDTree), improves LDTree by changing it into an eager algorithm. In both models, each unlabeled sample is assigned a set of unique probabilities of class membership instead of a set of uniformed ones, which gives finer resolution to differentiate samples and leads to the improvement of ranking. On 34 UCI sample sets, experiments verify that our models greatly outperform C4.5, C4.4 and other standard smoothing methods designed for better ranking","PeriodicalId":169424,"journal":{"name":"2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06)","volume":"212 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":"115777098","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: 8
Outlier Detection Using Random Walks 使用随机游走的离群值检测
2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06) Pub Date : 2006-11-13 DOI: 10.1109/ICTAI.2006.94
H. Moonesinghe, P. Tan
{"title":"Outlier Detection Using Random Walks","authors":"H. Moonesinghe, P. Tan","doi":"10.1109/ICTAI.2006.94","DOIUrl":"https://doi.org/10.1109/ICTAI.2006.94","url":null,"abstract":"The discovery of objects with exceptional behavior is an important challenge from a knowledge discovery standpoint and has attracted much attention recently. In this paper, we present a stochastic graph-based algorithm, called OutRank, for detecting outlying objects. In our method, a matrix is constructed using the similarity between objects and used as the adjacency matrix of the graph representation. The heart of this approach is the Markov model that is built upon this graph, which assigns an outlier score to each object. Using this framework, we show that our algorithm is more powerful than the existing outlier detection schemes and can effectively address the inherent problems of such schemes. Empirical studies conducted on both real and synthetic data sets show that significant improvements in detection rate and a lower false alarm rate are achieved using our proposed framework","PeriodicalId":169424,"journal":{"name":"2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06)","volume":"6 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":"115807998","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: 86
Constrained Global Optimization by Constraint Partitioning and Simulated Annealing 基于约束划分和模拟退火的约束全局优化
2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06) Pub Date : 2006-11-13 DOI: 10.1109/ICTAI.2006.47
B. Wah, Yixin Chen, Andrew Wan
{"title":"Constrained Global Optimization by Constraint Partitioning and Simulated Annealing","authors":"B. Wah, Yixin Chen, Andrew Wan","doi":"10.1109/ICTAI.2006.47","DOIUrl":"https://doi.org/10.1109/ICTAI.2006.47","url":null,"abstract":"In this paper, we present constraint-partitioned simulated annealing (CPSA), an algorithm that extends our previous constrained simulated annealing (CSA) for constrained optimization. The algorithm is based on the theory of extended saddle points (ESPs). By decomposing the ESP condition into multiple necessary conditions, CPSA partitions a problem by its constraints into subproblems, solves each independently using CSA, and resolves those violated global constraints across the subproblems. Because each subproblem is exponentially simpler and the number of global constraints is very small, the complexity of solving the original problem is significantly reduced. We state without proof the asymptotic convergence of CPSA with probability one to a constrained global minimum in discrete space. Last, we evaluate CPSA on some continuous constrained benchmarks","PeriodicalId":169424,"journal":{"name":"2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06)","volume":"17 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":"129437163","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: 8
Learning Ambiguities Using Bayesian Mixture of Experts 使用贝叶斯混合专家学习歧义
2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06) Pub Date : 2006-11-13 DOI: 10.1109/ICTAI.2006.73
Atul Kanaujia, Dimitris N. Metaxas
{"title":"Learning Ambiguities Using Bayesian Mixture of Experts","authors":"Atul Kanaujia, Dimitris N. Metaxas","doi":"10.1109/ICTAI.2006.73","DOIUrl":"https://doi.org/10.1109/ICTAI.2006.73","url":null,"abstract":"Mixture of Experts (ME) is an ensemble of function approximators that fit the clustered data set locally rather than globally. ME provides a useful tool to learn multi-valued mappings (ambiguities) in the data set. Mixture of Experts training involve learning a multi-category classifier for the gates distribution and fitting a regressor within each of the clusters. The learning of ME is based on divide and conquer which is known to increase the error due to variance. In order to avoid overfitting several researchers have proposed using linear experts. However in the absence of any knowledge of non-linearities existing in the data set, it is not clear how many linear experts could accurately model the data. In this work we propose a Bayesian learning framework for learning Mixture of Experts. Bayesian learning intrinsically embodies regularization and model selection using Occam's razor. In the past Bayesian learning methods have been applied to classification and regression in order to avoid scale sensitivity and orthodox model selection procedure of cross validation. Although true Bayesian learning is computationally intractable, approximations do result in sparser and more compact models","PeriodicalId":169424,"journal":{"name":"2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06)","volume":"80 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":"128645606","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: 10
Hierarchical Language Models for Expert Finding in Enterprise Corpora 面向企业语料库专家搜索的层次语言模型
2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06) Pub Date : 2006-11-13 DOI: 10.1142/S0218213008003741
D. Petkova, W. Bruce Croft
{"title":"Hierarchical Language Models for Expert Finding in Enterprise Corpora","authors":"D. Petkova, W. Bruce Croft","doi":"10.1142/S0218213008003741","DOIUrl":"https://doi.org/10.1142/S0218213008003741","url":null,"abstract":"Enterprise corpora contain evidence of what employees work on and therefore can be used to automatically find experts on a given topic. We present a general approach for representing the knowledge of a potential expert as a mixture of language models from associated documents. First we retrieve documents given the expert's name using a generative probabilistic technique and weight the retrieved documents according to expert-specific posterior distribution. Then we model the expert indirectly through the set of associated documents, which allows us to exploit their underlying structure and complex language features. Experiments show that our method has excellent performance on TREC 2005 expert search task and that it effectively collects and combines evidence for expertise in a heterogeneous collection","PeriodicalId":169424,"journal":{"name":"2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06)","volume":"7 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":"122085812","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: 188
Managing Deceitful Arguments with X-Logics 用x逻辑管理欺骗性论证
2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06) Pub Date : 2006-11-13 DOI: 10.1109/ICTAI.2006.78
Geoffroy Aubry, V. Risch
{"title":"Managing Deceitful Arguments with X-Logics","authors":"Geoffroy Aubry, V. Risch","doi":"10.1109/ICTAI.2006.78","DOIUrl":"https://doi.org/10.1109/ICTAI.2006.78","url":null,"abstract":"In most works on negotiation dialogues, agents are supposed to be ideally honest. However, there are many situations where such a behaviour cannot always be expected from the agents (e.g. advertising, political negotiation, etc.). The aim of this paper is to reconsider the role of deceitful arguments in argumentation frameworks. We propose a logical tool for representing and handling deceitful arguments in a dialogue between two formal agents having to face their respective knowledge and trying to convince each other. X-logics, a nonmonotonic extension of classical propositional logics, is used as the background formalism for representing the reasoning of the agents on arguments. Starting from a previous work dedicated to the generation of new arguments, we propose to define the notion of lie as a new kind of possible agent's answer. Finally, we describe the way an agent may trick and how the other agent may detect it","PeriodicalId":169424,"journal":{"name":"2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06)","volume":"46 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":"122567146","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
Mathematical Analysis of Diabetes Related Proteins Having High Sequence Complexity 具有高序列复杂性的糖尿病相关蛋白的数学分析
2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06) Pub Date : 2006-11-13 DOI: 10.1109/ICTAI.2006.79
A. A. Rao, Bhremeramba, G. Sridhar
{"title":"Mathematical Analysis of Diabetes Related Proteins Having High Sequence Complexity","authors":"A. A. Rao, Bhremeramba, G. Sridhar","doi":"10.1109/ICTAI.2006.79","DOIUrl":"https://doi.org/10.1109/ICTAI.2006.79","url":null,"abstract":"We have searched for proteins affecting diabetes and we also found in which common species these proteins were more prevalent and have performed protein composition analysis of those having high sequence complexity. About 90% of rat genes have counterparts in the mouse and human genomes and this is the reason to find proteins common among the three different species (Rat Genome Sequencing Consortium 2004, www.ratbehaviour.org/Ratsmice.htm). The distribution pattern of the protein variates was examined and bivariate plots were further drawn. The bivariate plots show a similar clustering for Rattus norvegicus and Mus musculus but show some variation in Homo sapiens indicating that the plots are correct as Rattus norvegicus and Mus musculus are relatively close in the phylogenetic tree (Sridhar, GR. et al) hence having a similar clustering. The proteins which are away from the cluster are outliers due to the reason that they are having different compositional characteristics","PeriodicalId":169424,"journal":{"name":"2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06)","volume":"214 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120860805","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
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