This paper analyzes the global asymptotic stability of delayed Hopfield neural networks by utilizing Lyapunov functional method and a generalized inequality technique. A new sufficient condition ensuring global asymptotic stability of the unique equilibrium point of delayed Hopfield neural networks is obtained. The result is related to the size of delays. The obtained conditions show to be less conservative and restrictive than that reported in the literature. A numerical simulation is given to illustrate the efficiency of our result.
{"title":"A Novel Delay-Dependent Global Stability Criterion of Delayed Hopfield Neural Networks","authors":"Degang Yang, Qun Liu, Yong Wang","doi":"10.1109/GrC.2007.16","DOIUrl":"https://doi.org/10.1109/GrC.2007.16","url":null,"abstract":"This paper analyzes the global asymptotic stability of delayed Hopfield neural networks by utilizing Lyapunov functional method and a generalized inequality technique. A new sufficient condition ensuring global asymptotic stability of the unique equilibrium point of delayed Hopfield neural networks is obtained. The result is related to the size of delays. The obtained conditions show to be less conservative and restrictive than that reported in the literature. A numerical simulation is given to illustrate the efficiency of our result.","PeriodicalId":259430,"journal":{"name":"2007 IEEE International Conference on Granular Computing (GRC 2007)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130059602","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}
Ontology evaluation is one of the most important phases of ontology engineering. Researchers have identified different types of errors that should be catered in ontology evaluation process for fulfillment of the semantic Web vision and classified them in error's taxonomy. We have found that some important errors are missing in the error's taxonomy. We have identified and defined two new incompleteness errors i.e. functional property omission (FPO) for single valued property and inverse-functional property omission (IFPO) for a unique valued property. We have demonstrated the importance of such errors by giving different scenarios where appropriate. We have evaluated different ontologies and presented empirical results.
{"title":"Incompleteness Errors in Ontology","authors":"M. Qadir, Muhammad Fahad, Syed Adnan Hussain Shah","doi":"10.1109/GrC.2007.152","DOIUrl":"https://doi.org/10.1109/GrC.2007.152","url":null,"abstract":"Ontology evaluation is one of the most important phases of ontology engineering. Researchers have identified different types of errors that should be catered in ontology evaluation process for fulfillment of the semantic Web vision and classified them in error's taxonomy. We have found that some important errors are missing in the error's taxonomy. We have identified and defined two new incompleteness errors i.e. functional property omission (FPO) for single valued property and inverse-functional property omission (IFPO) for a unique valued property. We have demonstrated the importance of such errors by giving different scenarios where appropriate. We have evaluated different ontologies and presented empirical results.","PeriodicalId":259430,"journal":{"name":"2007 IEEE International Conference on Granular Computing (GRC 2007)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132981224","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}
Automatic text classification has gained huge popularity with the advancement of information technology. Bayesian method has been found highly appropriate for text classification but it suffers from a number of problems. When there is large number of categories, lack of uniformity in training data becomes a big problem. Some nodes may get less training documents, while other may get a very large number. Therefore, some nodes are biased over others. Besides, presence of noise data or outliers also creates problems. Moreover, when documents are very small, just like a line item describing a product, the problem becomes more difficult. In this paper we describe a method that combines naive Bayesian text classification technique and neural networks to handle these problems. We start with a naive Bayesian classifier, which has the linear separating surfaces. We modify the separating surfaces using neural network to find better separating surfaces and hence better classification accuracy over validation data.
{"title":"Knowledge Based Neural Network for Text Classification","authors":"R. D. Goyal","doi":"10.1109/GrC.2007.108","DOIUrl":"https://doi.org/10.1109/GrC.2007.108","url":null,"abstract":"Automatic text classification has gained huge popularity with the advancement of information technology. Bayesian method has been found highly appropriate for text classification but it suffers from a number of problems. When there is large number of categories, lack of uniformity in training data becomes a big problem. Some nodes may get less training documents, while other may get a very large number. Therefore, some nodes are biased over others. Besides, presence of noise data or outliers also creates problems. Moreover, when documents are very small, just like a line item describing a product, the problem becomes more difficult. In this paper we describe a method that combines naive Bayesian text classification technique and neural networks to handle these problems. We start with a naive Bayesian classifier, which has the linear separating surfaces. We modify the separating surfaces using neural network to find better separating surfaces and hence better classification accuracy over validation data.","PeriodicalId":259430,"journal":{"name":"2007 IEEE International Conference on Granular Computing (GRC 2007)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132023799","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}
Detecting unaffected races is important for debugging MPI parallel programs, because unaffected races can cause the occurrence of affected races which do not need to be debugged. However, the previous techniques can not discern unaffected races from affected races so that programmers will be easily overwhelmed by the vast information of race detection. In this paper, we present a new visualization which lets programmers know which race is affected or not. For this, our technique checks whether any message racing toward a race is affected or not based on happen- before relation, and also checks which process influences a race during an execution. After the execution, it visualizes the affect-relations of the detected races. Therefore, our visualization helps for programmers to effectively distinguish unaffected races from affected races, and to debug MPI parallel programs.
{"title":"Visualization of Affect-Relations of Message Races for Debugging MPI Programs","authors":"Mi-Young Park, S. Kim, Hyuk-Ro Park","doi":"10.1109/GrC.2007.120","DOIUrl":"https://doi.org/10.1109/GrC.2007.120","url":null,"abstract":"Detecting unaffected races is important for debugging MPI parallel programs, because unaffected races can cause the occurrence of affected races which do not need to be debugged. However, the previous techniques can not discern unaffected races from affected races so that programmers will be easily overwhelmed by the vast information of race detection. In this paper, we present a new visualization which lets programmers know which race is affected or not. For this, our technique checks whether any message racing toward a race is affected or not based on happen- before relation, and also checks which process influences a race during an execution. After the execution, it visualizes the affect-relations of the detected races. Therefore, our visualization helps for programmers to effectively distinguish unaffected races from affected races, and to debug MPI parallel programs.","PeriodicalId":259430,"journal":{"name":"2007 IEEE International Conference on Granular Computing (GRC 2007)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133189494","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}
Extraction of concepts and identification of their semantic classes are useful in applications such as automatic instantiation of ontologies and construction of information extraction systems. Even though various techniques exist for the extraction of domain specific concepts from unstructured texts, very little concentration is in the semantic class labeling for concepts. In this paper we propose the semantic class labeling (SCL) problem and differentiate it from the named entity classification (NEC) problem. We also present a Naive Bayes solution to SCL. Experiments suggest that Naive Bayes learning method with specified features achieves high classification accuracy. Empirical and statistical evaluation on the significance of attributes for SCL is also presented.
{"title":"Learning for Semantic Classification of Conceptual Terms","authors":"Janardhana Punuru, Jianhua Chen","doi":"10.1109/GrC.2007.75","DOIUrl":"https://doi.org/10.1109/GrC.2007.75","url":null,"abstract":"Extraction of concepts and identification of their semantic classes are useful in applications such as automatic instantiation of ontologies and construction of information extraction systems. Even though various techniques exist for the extraction of domain specific concepts from unstructured texts, very little concentration is in the semantic class labeling for concepts. In this paper we propose the semantic class labeling (SCL) problem and differentiate it from the named entity classification (NEC) problem. We also present a Naive Bayes solution to SCL. Experiments suggest that Naive Bayes learning method with specified features achieves high classification accuracy. Empirical and statistical evaluation on the significance of attributes for SCL is also presented.","PeriodicalId":259430,"journal":{"name":"2007 IEEE International Conference on Granular Computing (GRC 2007)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122999981","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}
Social network analysis is a methodology used extensively in social and behavioral sciences, as well as in political science, economics, organization theory, and industrial engineering. Positional analysis of a social network aims to find similarities between actors in the network. One of the the most studied notions in the positional analysis of social networks is regular equivalence. According to Borgatti and Everett, two actors are regularly equivalent if they are equally related to equivalent others. In recent years, fuzzy social networks have also received considerable attention because they can represent both the qualitative relationship and the degrees of interaction between actors. In this paper, we generalize the notion of regular equivalence to fuzzy social networks based on two alternative definitions of regular equivalence. While these two definitions are equivalent for social networks, they induce different generalizations for fuzzy social networks. The first generalization, called regular similarity, is based on the characterization of regular equivalence as an equivalence relation that commutes with the underlying social relations. The regular similarity is then a fuzzy binary relation that specifies the degree of similarity between actors in the social network. The second generalization, called generalized regular equivalence, is based on the definition of role assignment or coloring. A role assignment (resp. coloring) is a mapping from the set of actors to a set of roles (resp. colors). The mapping is regular if actors assigned to the same role have the same roles in their neighborhoods. Consequently, generalized regular equivalence is an equivalence relation that can determine the role partition of the actors in a fuzzy social network.
{"title":"Positional Analysis in Fuzzy Social Networks","authors":"T. Fan, C. Liau, T. Lin","doi":"10.1109/GrC.2007.9","DOIUrl":"https://doi.org/10.1109/GrC.2007.9","url":null,"abstract":"Social network analysis is a methodology used extensively in social and behavioral sciences, as well as in political science, economics, organization theory, and industrial engineering. Positional analysis of a social network aims to find similarities between actors in the network. One of the the most studied notions in the positional analysis of social networks is regular equivalence. According to Borgatti and Everett, two actors are regularly equivalent if they are equally related to equivalent others. In recent years, fuzzy social networks have also received considerable attention because they can represent both the qualitative relationship and the degrees of interaction between actors. In this paper, we generalize the notion of regular equivalence to fuzzy social networks based on two alternative definitions of regular equivalence. While these two definitions are equivalent for social networks, they induce different generalizations for fuzzy social networks. The first generalization, called regular similarity, is based on the characterization of regular equivalence as an equivalence relation that commutes with the underlying social relations. The regular similarity is then a fuzzy binary relation that specifies the degree of similarity between actors in the social network. The second generalization, called generalized regular equivalence, is based on the definition of role assignment or coloring. A role assignment (resp. coloring) is a mapping from the set of actors to a set of roles (resp. colors). The mapping is regular if actors assigned to the same role have the same roles in their neighborhoods. Consequently, generalized regular equivalence is an equivalence relation that can determine the role partition of the actors in a fuzzy social network.","PeriodicalId":259430,"journal":{"name":"2007 IEEE International Conference on Granular Computing (GRC 2007)","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124578868","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}
Reasoning algorithm of single-value fuzzy causality diagram (SFCD) cannot directly apply to multi-value fuzzy causality diagram (MFCD). So it is necessary to study reasoning algorithm of MFCD. Firstly, with the discussing of reasoning problem of MFCD in this paper, a guideline to solve the problem is introduced, and a normalization method of fuzzy probability of event state is proposed to preprocess data. Secondly, a reasoning algorithm of MFCD based on unitizing coefficient is proposed to deal with the reasoning of MFCD. Lastly, an example about fault diagnosis of a steam generator in the nuclear power plant demonstrates the effect of the reasoning algorithm of MFCD, and the result is coincident with the fact. The research shows that the reasoning algorithm of MFCD is so effective to solve the problem of MFCD for fault analysis and reasoning, its reasoning process is rigorous, and the result coincides with the reality.
{"title":"Reasoning Algorithm of Multi-Value Fuzzy Causality Diagram Based on Unitizing Coefficient","authors":"Xinyuan Liang","doi":"10.1109/GrC.2007.142","DOIUrl":"https://doi.org/10.1109/GrC.2007.142","url":null,"abstract":"Reasoning algorithm of single-value fuzzy causality diagram (SFCD) cannot directly apply to multi-value fuzzy causality diagram (MFCD). So it is necessary to study reasoning algorithm of MFCD. Firstly, with the discussing of reasoning problem of MFCD in this paper, a guideline to solve the problem is introduced, and a normalization method of fuzzy probability of event state is proposed to preprocess data. Secondly, a reasoning algorithm of MFCD based on unitizing coefficient is proposed to deal with the reasoning of MFCD. Lastly, an example about fault diagnosis of a steam generator in the nuclear power plant demonstrates the effect of the reasoning algorithm of MFCD, and the result is coincident with the fact. The research shows that the reasoning algorithm of MFCD is so effective to solve the problem of MFCD for fault analysis and reasoning, its reasoning process is rigorous, and the result coincides with the reality.","PeriodicalId":259430,"journal":{"name":"2007 IEEE International Conference on Granular Computing (GRC 2007)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114729072","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}
When faced with large volumes of information, it is natural to adopt a granular approach by grouping together related items. Frequently, this is extended to a granular hierarchy, with progressively finer division as one moves down the hierarchy. The widespread use of hierarchical organisation shows that this is a natural approach for humans, as is the use of fuzzy granules rather than inflexible category specifications. Care is needed when information systems use fuzzy sets in this way - they are not disjunctive possibility distributions, but must be interpreted conjunctively. We clarify this distinction and show how an extended mass assignment framework can be used to extract relations between granules. These relations are association rules and are useful when integrating multiple information sources categorised according to different hierarchies. Our association rules do not suffer from problems associated with use of fuzzy cardinalities.
{"title":"Finding Soft Relations in Granular Information Hierarchies","authors":"T. Martin, Yun Shen, B. Azvine","doi":"10.1109/GrC.2007.30","DOIUrl":"https://doi.org/10.1109/GrC.2007.30","url":null,"abstract":"When faced with large volumes of information, it is natural to adopt a granular approach by grouping together related items. Frequently, this is extended to a granular hierarchy, with progressively finer division as one moves down the hierarchy. The widespread use of hierarchical organisation shows that this is a natural approach for humans, as is the use of fuzzy granules rather than inflexible category specifications. Care is needed when information systems use fuzzy sets in this way - they are not disjunctive possibility distributions, but must be interpreted conjunctively. We clarify this distinction and show how an extended mass assignment framework can be used to extract relations between granules. These relations are association rules and are useful when integrating multiple information sources categorised according to different hierarchies. Our association rules do not suffer from problems associated with use of fuzzy cardinalities.","PeriodicalId":259430,"journal":{"name":"2007 IEEE International Conference on Granular Computing (GRC 2007)","volume":"153 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124272230","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}
Text classification algorithms, such SVM, and Naive Bayes, have been developed to build up search engines and construct spam email filters. As a simple yet powerful sample of Bayesian theorem, naive Bayes shows advantages in text classification yielding satisfactory results. In this paper, a spam email detector is developed using naive Bayes algorithm. We use pre-classified emails (priory knowledge) to train the spam email detector. With the model generated from the training step, the detector is able to decide whether an email is a spam email or an ordinary email.
{"title":"Naïve Bayes Text Classifier","authors":"Haiyi Zhang, Di Li","doi":"10.1109/GrC.2007.40","DOIUrl":"https://doi.org/10.1109/GrC.2007.40","url":null,"abstract":"Text classification algorithms, such SVM, and Naive Bayes, have been developed to build up search engines and construct spam email filters. As a simple yet powerful sample of Bayesian theorem, naive Bayes shows advantages in text classification yielding satisfactory results. In this paper, a spam email detector is developed using naive Bayes algorithm. We use pre-classified emails (priory knowledge) to train the spam email detector. With the model generated from the training step, the detector is able to decide whether an email is a spam email or an ordinary email.","PeriodicalId":259430,"journal":{"name":"2007 IEEE International Conference on Granular Computing (GRC 2007)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121371339","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}
The year 2007 marks the 10th anniversary of the introduction of granular computing research. We have experienced the emergence and growth of granular computing research in the past ten years. It is essential to explore and review the progress made in the field of granular computing. We use two popular databases, ISI's Web of Science and IEEE Digital Library to conduct our research. We study the current status, the trends and the future direction of granular computing and identify prolific authors, impact authors, and the most impact papers in the past decade.
{"title":"A Ten-year Review of Granular Computing","authors":"Jingtao Yao","doi":"10.1109/GrC.2007.11","DOIUrl":"https://doi.org/10.1109/GrC.2007.11","url":null,"abstract":"The year 2007 marks the 10th anniversary of the introduction of granular computing research. We have experienced the emergence and growth of granular computing research in the past ten years. It is essential to explore and review the progress made in the field of granular computing. We use two popular databases, ISI's Web of Science and IEEE Digital Library to conduct our research. We study the current status, the trends and the future direction of granular computing and identify prolific authors, impact authors, and the most impact papers in the past decade.","PeriodicalId":259430,"journal":{"name":"2007 IEEE International Conference on Granular Computing (GRC 2007)","volume":"92 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115342881","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}
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