S. Schneider, A. Melkumyan, R. Murphy, E. Nettleton
In this paper we use a machine learning algorithm based on Gaussian Processes (GPs) and the Observation Angle Dependent (OAD) covariance function to classify hyper spectral imagery for the first time. This paper demonstrates the potential of the GP-OAD method for use in autonomous mining to identify and map geology and mineralogy on a vertical mine face. We discuss the importance of independent training data (i.e. a spectral library) to map any mine face without a priori knowledge. We compare an independent spectral library to other libraries, based on image data, and evaluate their relative performances to distinguish ore bearing zones from waste. Results show that the algorithm yields high accuracies (90%) and F-scores (77%), the best results are achieved when libraries are combined. We also demonstrate mapping of geology using imagery under different conditions of illumination (e.g. shade).
{"title":"Classification of Hyperspectral Imagery Using GPs and the OAD Covariance Function with Automated Endmember Extraction","authors":"S. Schneider, A. Melkumyan, R. Murphy, E. Nettleton","doi":"10.1109/ICTAI.2011.189","DOIUrl":"https://doi.org/10.1109/ICTAI.2011.189","url":null,"abstract":"In this paper we use a machine learning algorithm based on Gaussian Processes (GPs) and the Observation Angle Dependent (OAD) covariance function to classify hyper spectral imagery for the first time. This paper demonstrates the potential of the GP-OAD method for use in autonomous mining to identify and map geology and mineralogy on a vertical mine face. We discuss the importance of independent training data (i.e. a spectral library) to map any mine face without a priori knowledge. We compare an independent spectral library to other libraries, based on image data, and evaluate their relative performances to distinguish ore bearing zones from waste. Results show that the algorithm yields high accuracies (90%) and F-scores (77%), the best results are achieved when libraries are combined. We also demonstrate mapping of geology using imagery under different conditions of illumination (e.g. shade).","PeriodicalId":332661,"journal":{"name":"2011 IEEE 23rd International Conference on Tools with Artificial Intelligence","volume":"105 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121014123","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}
Marc Bernard, Baptiste Jeudy, Jean-Philippe Peyrache, M. Sebban, F. Thollard
A problem usually encountered in probabilistic automata learning is the difficulty to deal with large training samples and/or wide alphabets. This is partially due to the size of the resulting Probabilistic Prefix Tree (PPT) from which state merging-based learning algorithms are generally applied. In this paper, we propose a novel method to prune PPTs by making use of the H-divergence d_H, recently introduced in the field of domain adaptation. d_H is based on the classification error made by an hypothesis learned from unlabeled examples drawn according to two distributions to compare. Through a thorough comparison with state-of-the-art divergence measures, we provide experimental evidences that demonstrate the efficiency of our method based on this simple and intuitive criterion.
{"title":"Using the H-Divergence to Prune Probabilistic Automata","authors":"Marc Bernard, Baptiste Jeudy, Jean-Philippe Peyrache, M. Sebban, F. Thollard","doi":"10.1109/ICTAI.2011.114","DOIUrl":"https://doi.org/10.1109/ICTAI.2011.114","url":null,"abstract":"A problem usually encountered in probabilistic automata learning is the difficulty to deal with large training samples and/or wide alphabets. This is partially due to the size of the resulting Probabilistic Prefix Tree (PPT) from which state merging-based learning algorithms are generally applied. In this paper, we propose a novel method to prune PPTs by making use of the H-divergence d_H, recently introduced in the field of domain adaptation. d_H is based on the classification error made by an hypothesis learned from unlabeled examples drawn according to two distributions to compare. Through a thorough comparison with state-of-the-art divergence measures, we provide experimental evidences that demonstrate the efficiency of our method based on this simple and intuitive criterion.","PeriodicalId":332661,"journal":{"name":"2011 IEEE 23rd International Conference on Tools with Artificial Intelligence","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128208730","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}
Reading is an integral part of educational development, however, it is frustrating for people who struggle to understand (are not motivated to read, respectively) text documents that are beyond (below, respectively) their readability levels. Finding appropriate reading materials, with or without first scanning through their contents, is a challenge, since there are tremendous amount of documents these days and a clear majority of them are not tagged with their readability levels. Even though existing readability assessment tools determine readability levels of text documents, they analyze solely the lexical, syntactic, and/or semantic properties of a document, which are neither fully-automated, generalized, nor well-defined and are mostly based on observations. To advance the current readability analysis technique, we propose a robust, fully-automated readability analyzer, denoted ReadAid, which employs support vector machines to combine features from the US Curriculum and College Board, traditional readability measures, and the author(s) and subject area(s) of a text document d to assess the readability level of d. ReadAid can be applied for (i) filtering documents (retrieved in response to a web query) of a particular readability level, (ii) determining the readability levels of digitalized text documents, such as book chapters, magazine articles, and news stories, or (iii) dynamically analyzing, in real time, the grade level of a text document being created. The novelty of ReadAid lies on using authorship, subject areas, and academic concepts and grammatical constructions extracted from the US Curriculum to determine the readability level of a text document. Experimental results show that ReadAid is highly effective and outperforms existing state-of-the-art readability assessment tools.
{"title":"ReadAid: A Robust and Fully-Automated Readability Assessment Tool","authors":"Rani Qumsiyeh, Yiu-Kai Ng","doi":"10.1109/ICTAI.2011.87","DOIUrl":"https://doi.org/10.1109/ICTAI.2011.87","url":null,"abstract":"Reading is an integral part of educational development, however, it is frustrating for people who struggle to understand (are not motivated to read, respectively) text documents that are beyond (below, respectively) their readability levels. Finding appropriate reading materials, with or without first scanning through their contents, is a challenge, since there are tremendous amount of documents these days and a clear majority of them are not tagged with their readability levels. Even though existing readability assessment tools determine readability levels of text documents, they analyze solely the lexical, syntactic, and/or semantic properties of a document, which are neither fully-automated, generalized, nor well-defined and are mostly based on observations. To advance the current readability analysis technique, we propose a robust, fully-automated readability analyzer, denoted ReadAid, which employs support vector machines to combine features from the US Curriculum and College Board, traditional readability measures, and the author(s) and subject area(s) of a text document d to assess the readability level of d. ReadAid can be applied for (i) filtering documents (retrieved in response to a web query) of a particular readability level, (ii) determining the readability levels of digitalized text documents, such as book chapters, magazine articles, and news stories, or (iii) dynamically analyzing, in real time, the grade level of a text document being created. The novelty of ReadAid lies on using authorship, subject areas, and academic concepts and grammatical constructions extracted from the US Curriculum to determine the readability level of a text document. Experimental results show that ReadAid is highly effective and outperforms existing state-of-the-art readability assessment tools.","PeriodicalId":332661,"journal":{"name":"2011 IEEE 23rd International Conference on Tools with Artificial Intelligence","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128351711","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}
In this paper we reconsider the computations accomplished in a semantic schema. We reconsider also the computations in a master-slave systems of semantic schemas introduced in [6] as a cooperating system of such structures. We show that a master-slave system is adequate to represent distributed knowledge. To relieve this fact we describe such a system named DiSys implemented in Java by client-server technology.
{"title":"New Computational Aspects in Master-Slave Systems of Semantic Schemas","authors":"N. Tandareanu, Cristina Zamfir","doi":"10.1109/ICTAI.2011.105","DOIUrl":"https://doi.org/10.1109/ICTAI.2011.105","url":null,"abstract":"In this paper we reconsider the computations accomplished in a semantic schema. We reconsider also the computations in a master-slave systems of semantic schemas introduced in [6] as a cooperating system of such structures. We show that a master-slave system is adequate to represent distributed knowledge. To relieve this fact we describe such a system named DiSys implemented in Java by client-server technology.","PeriodicalId":332661,"journal":{"name":"2011 IEEE 23rd International Conference on Tools with Artificial Intelligence","volume":"575 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133321847","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 context-aware computing environment is changing due to recent development of the new computing devices and new concept of services. The systems are developing rapidly, but most of them focus on recognition of the collected information, not on the intelligent capability. In this paper, we defined a new context aware computing environment based on the concept of social intelligence, which implies an ability to share or utilize information by making a relationship, recognizes context by making an inference, and works in collaboration to offer services more efficiently. We have designed and developed a Social Intelligence based Context-Aware Middleware (SI-CAM), under the environment. The SI-CAM provides a service with following functions, multi context-awareness, context based task planning, and grouping intelligent entities for collaboration. The system is developed with blackboard based structure, and tested on virtual environment in the domain of ubiquitous restaurant. The experiment showed some significant results.
{"title":"Design and Development of a Social Intelligence Based Context-Aware Middleware Using BlackBoard","authors":"Joohee Suh, Chong-woo Woo","doi":"10.1109/ICTAI.2011.151","DOIUrl":"https://doi.org/10.1109/ICTAI.2011.151","url":null,"abstract":"The context-aware computing environment is changing due to recent development of the new computing devices and new concept of services. The systems are developing rapidly, but most of them focus on recognition of the collected information, not on the intelligent capability. In this paper, we defined a new context aware computing environment based on the concept of social intelligence, which implies an ability to share or utilize information by making a relationship, recognizes context by making an inference, and works in collaboration to offer services more efficiently. We have designed and developed a Social Intelligence based Context-Aware Middleware (SI-CAM), under the environment. The SI-CAM provides a service with following functions, multi context-awareness, context based task planning, and grouping intelligent entities for collaboration. The system is developed with blackboard based structure, and tested on virtual environment in the domain of ubiquitous restaurant. The experiment showed some significant results.","PeriodicalId":332661,"journal":{"name":"2011 IEEE 23rd International Conference on Tools with Artificial Intelligence","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128844407","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}
Computing all diagnoses of an inconsistent ontology is important in ontology-based applications. However, the number of diagnoses can be very large. It is impractical to enumerate all diagnoses before identifying the target one to render the ontology consistent. Hence, we propose to represent all diagnoses by multiple sets of partial diagnoses, where the total number of partial diagnoses can be small and the target diagnosis can be directly retrieved from these partial diagnoses. We also propose methods for computing the new representation of all diagnoses in an OWL DL ontology. Experimental results show that computing the new representation of all diagnoses is much easier than directly computing all diagnoses.
{"title":"A Decomposition-Based Approach to OWL DL Ontology Diagnosis","authors":"Jianfeng Du, G. Qi, Jeff Z. Pan, Yi-Dong Shen","doi":"10.1109/ICTAI.2011.104","DOIUrl":"https://doi.org/10.1109/ICTAI.2011.104","url":null,"abstract":"Computing all diagnoses of an inconsistent ontology is important in ontology-based applications. However, the number of diagnoses can be very large. It is impractical to enumerate all diagnoses before identifying the target one to render the ontology consistent. Hence, we propose to represent all diagnoses by multiple sets of partial diagnoses, where the total number of partial diagnoses can be small and the target diagnosis can be directly retrieved from these partial diagnoses. We also propose methods for computing the new representation of all diagnoses in an OWL DL ontology. Experimental results show that computing the new representation of all diagnoses is much easier than directly computing all diagnoses.","PeriodicalId":332661,"journal":{"name":"2011 IEEE 23rd International Conference on Tools with Artificial Intelligence","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117148724","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}
W. T. Adrian, Szymon Bobek, G. J. Nalepa, K. Kaczor, Krzysztof Kluza
Semantic wikis constitute an increasingly popular class of systems for collaborative knowledge engineering. We developed Loki, a semantic wiki that uses a logic-based knowledge representation. It is compatible with semantic annotations mechanism as well as Semantic Web languages. We integrated the system with a rule engine called Heart that supports inference with production rules. Several modes for modularized rule bases, suitable for the distributed rule bases present in a wiki, are considered. Embedding the rule engine enables strong reasoning and allows to run production rules over semantic knowledge bases. In the paper, we demonstrate the system concepts and functionality using an illustrative example.
{"title":"How to Reason by HeaRT in a Semantic Knowledge-Based Wiki","authors":"W. T. Adrian, Szymon Bobek, G. J. Nalepa, K. Kaczor, Krzysztof Kluza","doi":"10.1109/ICTAI.2011.71","DOIUrl":"https://doi.org/10.1109/ICTAI.2011.71","url":null,"abstract":"Semantic wikis constitute an increasingly popular class of systems for collaborative knowledge engineering. We developed Loki, a semantic wiki that uses a logic-based knowledge representation. It is compatible with semantic annotations mechanism as well as Semantic Web languages. We integrated the system with a rule engine called Heart that supports inference with production rules. Several modes for modularized rule bases, suitable for the distributed rule bases present in a wiki, are considered. Embedding the rule engine enables strong reasoning and allows to run production rules over semantic knowledge bases. In the paper, we demonstrate the system concepts and functionality using an illustrative example.","PeriodicalId":332661,"journal":{"name":"2011 IEEE 23rd International Conference on Tools with Artificial Intelligence","volume":"159 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115264064","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}
Feature selection is an effective technique to reduce the dimensionality of a data set and to select relevant features for the domain problem. Recently, stability of feature selection methods has gained increasing attention. In fact, it has become a crucial factor in determining the goodness of a feature selection algorithm besides the learning performance. In this work, we conduct an extensive experimental study using verity of data sets and different well-known feature selection algorithms in order to study the behavior of these algorithms in terms of the stability.
{"title":"The Effect of the Characteristics of the Dataset on the Selection Stability","authors":"Salem Alelyani, Huan Liu, Lei Wang","doi":"10.1109/ICTAI.2011.167","DOIUrl":"https://doi.org/10.1109/ICTAI.2011.167","url":null,"abstract":"Feature selection is an effective technique to reduce the dimensionality of a data set and to select relevant features for the domain problem. Recently, stability of feature selection methods has gained increasing attention. In fact, it has become a crucial factor in determining the goodness of a feature selection algorithm besides the learning performance. In this work, we conduct an extensive experimental study using verity of data sets and different well-known feature selection algorithms in order to study the behavior of these algorithms in terms of the stability.","PeriodicalId":332661,"journal":{"name":"2011 IEEE 23rd International Conference on Tools with Artificial Intelligence","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115281852","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}
Research on link based object ranking attracts increasing attention these years, which also brings computer science research and business marketing brand-new concepts, opportunities as well as a great deal of challenges. With prosperity of web pages search engine and widely use of social networks, recent graph-theoretic ranking approaches have achieved remarkable successes although most of them are focus on homogeneous networks studying. Previous study on co-ranking methods tries to divide heterogeneous networks into multiple homogeneous sub-networks and ties between different sub-networks. This paper proposes an efficient topic biased ranking method for bringing order to co-effecting heterogeneous networks among authors, papers and accepted institutions (journals/conferences) within one single random surfer. This new method aims to update ranks for different types of objects (author, paper, journals/conferences) at each random walk.
{"title":"Ranking in Co-effecting Multi-object/Link Types Networks","authors":"Bo Zhou, Manna Wu, Xin Xia, Chao Wu","doi":"10.1109/ICTAI.2011.84","DOIUrl":"https://doi.org/10.1109/ICTAI.2011.84","url":null,"abstract":"Research on link based object ranking attracts increasing attention these years, which also brings computer science research and business marketing brand-new concepts, opportunities as well as a great deal of challenges. With prosperity of web pages search engine and widely use of social networks, recent graph-theoretic ranking approaches have achieved remarkable successes although most of them are focus on homogeneous networks studying. Previous study on co-ranking methods tries to divide heterogeneous networks into multiple homogeneous sub-networks and ties between different sub-networks. This paper proposes an efficient topic biased ranking method for bringing order to co-effecting heterogeneous networks among authors, papers and accepted institutions (journals/conferences) within one single random surfer. This new method aims to update ranks for different types of objects (author, paper, journals/conferences) at each random walk.","PeriodicalId":332661,"journal":{"name":"2011 IEEE 23rd International Conference on Tools with Artificial Intelligence","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127341193","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}
In statistical machine translation, the number of sentence pairs in the bilingual corpus is very important to the quality of translation. However, when the quantity reaches some extent, enlarging corpus has less effect on the translation, whereas increasing greatly the time and space complexity to building translation systems, which hinders the development of statistical machine translation. In this paper, we propose several ranking approaches to measure the quantity of information of each sentence pair, and apply them into a graph-based bilingual corpus selection framework to form an improved corpus selection approach, which now considers the difference of the initial quantities of information between the sentence pairs. Our experiments in a Chinese-English translation task show that, selecting only 50% of the whole corpus via the graph-based selection approach as training set, we can obtain the near translation result with the one using the whole corpus, and we obtain better results than the baselines after using the IDF-related ranking approach.
{"title":"Improved Graph-Based Bilingual Corpus Selection with Sentence Pair Ranking for Statistical Machine Translation","authors":"Wen-Han Chao, Zhoujun Li","doi":"10.1109/ICTAI.2011.73","DOIUrl":"https://doi.org/10.1109/ICTAI.2011.73","url":null,"abstract":"In statistical machine translation, the number of sentence pairs in the bilingual corpus is very important to the quality of translation. However, when the quantity reaches some extent, enlarging corpus has less effect on the translation, whereas increasing greatly the time and space complexity to building translation systems, which hinders the development of statistical machine translation. In this paper, we propose several ranking approaches to measure the quantity of information of each sentence pair, and apply them into a graph-based bilingual corpus selection framework to form an improved corpus selection approach, which now considers the difference of the initial quantities of information between the sentence pairs. Our experiments in a Chinese-English translation task show that, selecting only 50% of the whole corpus via the graph-based selection approach as training set, we can obtain the near translation result with the one using the whole corpus, and we obtain better results than the baselines after using the IDF-related ranking approach.","PeriodicalId":332661,"journal":{"name":"2011 IEEE 23rd International Conference on Tools with Artificial Intelligence","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125992505","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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