Lately, we hear more and more about Semantic Web and the benefits that it brings to the current Web. But just a few companies are adopting solutions based on semantic web technologies, one reason being that their currently information technology systems are generally based on data stored in relational databases. Replicating a relational database, only with the public information, in a RDF repository, would require additional efforts for the company. This paper presents a solution for mapping a relational database, used to manage the human resources of a company, into a RDF repository, which offers portability and easy integration with other knowledge bases and which can be useful to manage public information about employees. Our approach also highlights the integration of the resulting ontology with PHP to provide a user interface.
{"title":"Mapping a Relational Database into a RDF Repository","authors":"A. Szekely, Anita Hejja, R. Buchmann","doi":"10.1109/SYNASC.2011.49","DOIUrl":"https://doi.org/10.1109/SYNASC.2011.49","url":null,"abstract":"Lately, we hear more and more about Semantic Web and the benefits that it brings to the current Web. But just a few companies are adopting solutions based on semantic web technologies, one reason being that their currently information technology systems are generally based on data stored in relational databases. Replicating a relational database, only with the public information, in a RDF repository, would require additional efforts for the company. This paper presents a solution for mapping a relational database, used to manage the human resources of a company, into a RDF repository, which offers portability and easy integration with other knowledge bases and which can be useful to manage public information about employees. Our approach also highlights the integration of the resulting ontology with PHP to provide a user interface.","PeriodicalId":184344,"journal":{"name":"2011 13th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128144489","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}
Lucian Radu Teodorescu, Razvan Boldizsar, Mihai Alexandru Ordean, Melania Duma, Laura Detesan, Mihaela Ordean
This paper describes a Part of Speech (POS) tagger that has been developed for Romanian Text-to-Speech purposes. In our Text-to-Speech (TTS) system, the Part of Speech tagger is used to disambiguate the pronunciation of some homograph words, determine the semantic links between words, phrase breaks and intonation phrase boundaries and eventually design the intonation curves. The paper focuses on the development and evaluation of the Romanian POS tagger. The findings of this paper show that Naive Bayes models can very well be used for tagging in a hybrid system composed of trained statistical model and a word database. Our experimental results have uncovered an acceptable accuracy and real time performance of the integrated model using a reduced tag set.
{"title":"Part of Speech Tagging for Romanian Text-to-Speech System","authors":"Lucian Radu Teodorescu, Razvan Boldizsar, Mihai Alexandru Ordean, Melania Duma, Laura Detesan, Mihaela Ordean","doi":"10.1109/SYNASC.2011.55","DOIUrl":"https://doi.org/10.1109/SYNASC.2011.55","url":null,"abstract":"This paper describes a Part of Speech (POS) tagger that has been developed for Romanian Text-to-Speech purposes. In our Text-to-Speech (TTS) system, the Part of Speech tagger is used to disambiguate the pronunciation of some homograph words, determine the semantic links between words, phrase breaks and intonation phrase boundaries and eventually design the intonation curves. The paper focuses on the development and evaluation of the Romanian POS tagger. The findings of this paper show that Naive Bayes models can very well be used for tagging in a hybrid system composed of trained statistical model and a word database. Our experimental results have uncovered an acceptable accuracy and real time performance of the integrated model using a reduced tag set.","PeriodicalId":184344,"journal":{"name":"2011 13th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing","volume":"209 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121197496","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}
This paper presents a segregative genetic algorithm for optimizing the variable order in Reduced Ordered Binary Decision Diagrams. The main components are a basic genetic algorithm and two feature functions used to measure the similarity between chromosomes. Many copies of the basic genetic algorithm explore in parallel subpopulations induced in the search space by clustering in the feature space. A communication protocol preserves the similarity within each subpopulation during the evolution process. An associative tabu search memory is used to avoid reexploration of the search space. Extensive experimental evaluation proves the efficiency and stability of the segregative approach, which systematically produces better results than the basic genetic algorithm. The efficiency of the distributed implementation in terms of resource usage and many aspects regarding the communication protocol between different components are thoroughly described. The experiments used classical benchmarks known as very difficult and show that the segregative variant is better than the monopopulation algorithm and the approach using the island model.
{"title":"Static Segregative Genetic Algorithm for Optimizing Variable Ordering of ROBDDs","authors":"O. Brudaru, Cristian Rotaru, I. Furdu","doi":"10.1109/SYNASC.2011.54","DOIUrl":"https://doi.org/10.1109/SYNASC.2011.54","url":null,"abstract":"This paper presents a segregative genetic algorithm for optimizing the variable order in Reduced Ordered Binary Decision Diagrams. The main components are a basic genetic algorithm and two feature functions used to measure the similarity between chromosomes. Many copies of the basic genetic algorithm explore in parallel subpopulations induced in the search space by clustering in the feature space. A communication protocol preserves the similarity within each subpopulation during the evolution process. An associative tabu search memory is used to avoid reexploration of the search space. Extensive experimental evaluation proves the efficiency and stability of the segregative approach, which systematically produces better results than the basic genetic algorithm. The efficiency of the distributed implementation in terms of resource usage and many aspects regarding the communication protocol between different components are thoroughly described. The experiments used classical benchmarks known as very difficult and show that the segregative variant is better than the monopopulation algorithm and the approach using the island model.","PeriodicalId":184344,"journal":{"name":"2011 13th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124609118","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}
Grids became a commonplace for the computation of expensive numerical simulations. This work addresses the problem of searching for relevant simulations and for their results in a grid. This is challenging especially due to the large number of existing simulations, the small semantic differences between them, and the distributed nature of the grid environment. We propose a solution that addresses simultaneously these three challenges by integrating a latent semantic indexing algorithm a linguistic processing module with a grid application framework. This resulted in a novel prototype, ELSIG Explorer, capable of retrieving relevant scenarios computed with Grid SFEA on heterogeneous grids. We evaluated our approach on benchmark datasets from the medical domain and on a set of scenarios for simulating dynamic behavior of biological neural microcircuits in grid.
{"title":"Searching Simulation Scenarios on the Grid with ELSIGExplorer","authors":"I. Muntean, Laura Maria Dansorean","doi":"10.1109/SYNASC.2011.43","DOIUrl":"https://doi.org/10.1109/SYNASC.2011.43","url":null,"abstract":"Grids became a commonplace for the computation of expensive numerical simulations. This work addresses the problem of searching for relevant simulations and for their results in a grid. This is challenging especially due to the large number of existing simulations, the small semantic differences between them, and the distributed nature of the grid environment. We propose a solution that addresses simultaneously these three challenges by integrating a latent semantic indexing algorithm a linguistic processing module with a grid application framework. This resulted in a novel prototype, ELSIG Explorer, capable of retrieving relevant scenarios computed with Grid SFEA on heterogeneous grids. We evaluated our approach on benchmark datasets from the medical domain and on a set of scenarios for simulating dynamic behavior of biological neural microcircuits in grid.","PeriodicalId":184344,"journal":{"name":"2011 13th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing","volume":"143 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125844058","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 article we propose a quantitative approach to a relatively new problem: categorizing text as pragmatically correct or pragmatically incorrect (forcing the notion, coherent/incoherent). The typical text categorization criterions comprise categorization by topic, by style (genre classification, authorship identification), by expressed opinion (opinion mining, sentiment classification), etc. Very few approaches consider the problem of categorizing text by degree of coherence. One example of application of text categorization by its coherence is creating a spam filter for personal e-mail accounts able to cope with one of the new strategies adopted by spamers. This strategy consists of encoding the real message as picture (impossible to directly analyze and reject by the text oriented classical filters) and accompanying it by a text especially designed to surpass the filter. An important question for automatically categorizing texts into coherent and incoherent is: are there features that can be extracted from these texts and be successfully used to categorize them? We propose a quantitative approach that relies on the use of ratios between morphological categories from the texts as discriminant features. We use supervised machine learning techniques on a small corpus of English e-mail messages and let the algorithms extract important features from all the pos ratios. The results are encouraging.
{"title":"Short Text Categorization via Coherence Constraints","authors":"Anca Dinu","doi":"10.1109/SYNASC.2011.33","DOIUrl":"https://doi.org/10.1109/SYNASC.2011.33","url":null,"abstract":"In this article we propose a quantitative approach to a relatively new problem: categorizing text as pragmatically correct or pragmatically incorrect (forcing the notion, coherent/incoherent). The typical text categorization criterions comprise categorization by topic, by style (genre classification, authorship identification), by expressed opinion (opinion mining, sentiment classification), etc. Very few approaches consider the problem of categorizing text by degree of coherence. One example of application of text categorization by its coherence is creating a spam filter for personal e-mail accounts able to cope with one of the new strategies adopted by spamers. This strategy consists of encoding the real message as picture (impossible to directly analyze and reject by the text oriented classical filters) and accompanying it by a text especially designed to surpass the filter. An important question for automatically categorizing texts into coherent and incoherent is: are there features that can be extracted from these texts and be successfully used to categorize them? We propose a quantitative approach that relies on the use of ratios between morphological categories from the texts as discriminant features. We use supervised machine learning techniques on a small corpus of English e-mail messages and let the algorithms extract important features from all the pos ratios. The results are encouraging.","PeriodicalId":184344,"journal":{"name":"2011 13th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116676311","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}
Many real world applications contain a decision making process which can be regarded as a pattern classification stage. Various pattern classification techniques have been introduced in the literature ranging from heuristic methods to intelligent soft computing techniques. In this paper, we focus on the latter and in particular on fuzzy rule-based classification algorithms.We show how an effective classifier employing fuzzy if-then rules can be generated from training data, and highlight how the introduction of class weights can be used for costsensitive classification. We also show how a training algorithm can be applied to tune the classification performance and how genetic algorithms can be used to extract a compact fuzzy rule base. We also give pointers to various applications where these methods have been employed successfully.
{"title":"Hybrid Fuzzy Rule-Based Classification","authors":"G. Schaefer","doi":"10.1109/SYNASC.2011.61","DOIUrl":"https://doi.org/10.1109/SYNASC.2011.61","url":null,"abstract":"Many real world applications contain a decision making process which can be regarded as a pattern classification stage. Various pattern classification techniques have been introduced in the literature ranging from heuristic methods to intelligent soft computing techniques. In this paper, we focus on the latter and in particular on fuzzy rule-based classification algorithms.We show how an effective classifier employing fuzzy if-then rules can be generated from training data, and highlight how the introduction of class weights can be used for costsensitive classification. We also show how a training algorithm can be applied to tune the classification performance and how genetic algorithms can be used to extract a compact fuzzy rule base. We also give pointers to various applications where these methods have been employed successfully.","PeriodicalId":184344,"journal":{"name":"2011 13th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129056290","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}
As computation increasingly pervades our environment and every aspect of our activity, there is the need of finding new paradigms of computation that support flexible interactions among humans, autonomous software components and objects enhanced with information, in a common shared environment. In this paper, we make a review of three important paradigms that promote autonomy, flexibility and dynamics of the supported applications, namely self-organizing systems, multi-agent systems, and context aware systems. We argue that the combination of these paradigms leads to the development of adaptable systems and applications that can face the current demands of users and requirements of modern digital environments and devices. We also show that self organizing context aware agent systems are fitted to develop ambient intelligence applications.
{"title":"Self-Organizing Context Aware Agent Systems","authors":"A. Florea","doi":"10.1109/SYNASC.2011.66","DOIUrl":"https://doi.org/10.1109/SYNASC.2011.66","url":null,"abstract":"As computation increasingly pervades our environment and every aspect of our activity, there is the need of finding new paradigms of computation that support flexible interactions among humans, autonomous software components and objects enhanced with information, in a common shared environment. In this paper, we make a review of three important paradigms that promote autonomy, flexibility and dynamics of the supported applications, namely self-organizing systems, multi-agent systems, and context aware systems. We argue that the combination of these paradigms leads to the development of adaptable systems and applications that can face the current demands of users and requirements of modern digital environments and devices. We also show that self organizing context aware agent systems are fitted to develop ambient intelligence applications.","PeriodicalId":184344,"journal":{"name":"2011 13th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125683285","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}
Outlier detection is a relevant problem for many domains, among which for detection of fraudulent behavior. Exploratory Data Analysis techniques are known to be very useful for highlighting patterns and deviations in data through visual representations. Less explored is the feasibility of using them to build learning models for outlier detection, which can be applied automatically to classify data without human intervention. In this paper we propose a method that uses one Exploratory Data Analysis technique -- Andrews curves -- in order to produce a classifier, which we applied to a real dataset, extracted from an online auction site, obtaining positive results regarding elicitation of fraudulent behavior.
{"title":"Using Exploratory Data Analysis for Fraud Elicitation through Supervised Learning","authors":"Vinicius Almendra, B. Roman","doi":"10.1109/SYNASC.2011.35","DOIUrl":"https://doi.org/10.1109/SYNASC.2011.35","url":null,"abstract":"Outlier detection is a relevant problem for many domains, among which for detection of fraudulent behavior. Exploratory Data Analysis techniques are known to be very useful for highlighting patterns and deviations in data through visual representations. Less explored is the feasibility of using them to build learning models for outlier detection, which can be applied automatically to classify data without human intervention. In this paper we propose a method that uses one Exploratory Data Analysis technique -- Andrews curves -- in order to produce a classifier, which we applied to a real dataset, extracted from an online auction site, obtaining positive results regarding elicitation of fraudulent behavior.","PeriodicalId":184344,"journal":{"name":"2011 13th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128875120","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}
Solving a system of nonlinear equations is a common operation in many practical applications such as analyzing physics experiments or running simulations of analog electronic circuits. Applications become more and more complex, both in terms of variables and number of involved equations, severely limiting the applicability of the sequential algorithms. As both the processing power and the available bandwidth in modern network increase, the distributing solution becomes more and more appealing. This paper presents a parallel algorithm for solving systems nonlinear of equations based on the Newton-Raphson method. The core of this algorithm is the Gaussian reduction. Our implementation attempts to minimize the overall amount of data to be transferred during both the Gauss pivoting operation and each Newton-Raphson iteration.
{"title":"A Distributed Approach for Solving Systems of Nonlinear Equations","authors":"A. Mocanu, N. Tapus","doi":"10.1109/SYNASC.2011.30","DOIUrl":"https://doi.org/10.1109/SYNASC.2011.30","url":null,"abstract":"Solving a system of nonlinear equations is a common operation in many practical applications such as analyzing physics experiments or running simulations of analog electronic circuits. Applications become more and more complex, both in terms of variables and number of involved equations, severely limiting the applicability of the sequential algorithms. As both the processing power and the available bandwidth in modern network increase, the distributing solution becomes more and more appealing. This paper presents a parallel algorithm for solving systems nonlinear of equations based on the Newton-Raphson method. The core of this algorithm is the Gaussian reduction. Our implementation attempts to minimize the overall amount of data to be transferred during both the Gauss pivoting operation and each Newton-Raphson iteration.","PeriodicalId":184344,"journal":{"name":"2011 13th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133393561","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 paper provides a series of results concerning the learning from data a linear regressive model in a multivariate framework. The parameter estimates of the regressive model are determined using the maximum likelihood principle and the adaptive learning algorithms are derived using the gradient ascent technique. The predicted output is expressed as the sum of a linear combination of the entries of the input and the random vector that represents the effects of the unobservable factors and noise. In the second section of the paper the mathematical arguments for the estimation scheme based exclusively on a finite size set of observations is provided. The third section of the paper is focused on experimental evaluation of the quality of the resulted learning scheme in order to establish conclusions concerning their accuracy and generalization capacities, the evaluation being performed in terms of metric, probabilistic and informational criterion functions. The final section of the paper contains a series of conclusions and suggestions for further work.
{"title":"A Probabilistic Model-Free Approach in Learning Multivariate Noisy Linear Systems","authors":"L. State, Iuliana Paraschiv-Munteanu","doi":"10.1109/SYNASC.2011.14","DOIUrl":"https://doi.org/10.1109/SYNASC.2011.14","url":null,"abstract":"The paper provides a series of results concerning the learning from data a linear regressive model in a multivariate framework. The parameter estimates of the regressive model are determined using the maximum likelihood principle and the adaptive learning algorithms are derived using the gradient ascent technique. The predicted output is expressed as the sum of a linear combination of the entries of the input and the random vector that represents the effects of the unobservable factors and noise. In the second section of the paper the mathematical arguments for the estimation scheme based exclusively on a finite size set of observations is provided. The third section of the paper is focused on experimental evaluation of the quality of the resulted learning scheme in order to establish conclusions concerning their accuracy and generalization capacities, the evaluation being performed in terms of metric, probabilistic and informational criterion functions. The final section of the paper contains a series of conclusions and suggestions for further work.","PeriodicalId":184344,"journal":{"name":"2011 13th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115934343","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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