The pricing and hedging of complex derivative securities via Monte Carlo simulations of stochastic differential equations constitutes an intensive computational task. To achieve "real time" execution, as often required by financial institutions, one needs highly efficient implementations of the multi-point distributed random variables underlying the simulations. In this paper a fast and flexible dedicated hardware solution is proposed. A comparative performance analysis demonstrates that the hardware solution is bottleneck-free and flexible, and significantly increases the computational efficiency of the software solution.
{"title":"A multi-point distributed random variable accelerator for Monte Carlo simulation in finance","authors":"N. Liberati, F. Martini","doi":"10.1109/ISDA.2005.11","DOIUrl":"https://doi.org/10.1109/ISDA.2005.11","url":null,"abstract":"The pricing and hedging of complex derivative securities via Monte Carlo simulations of stochastic differential equations constitutes an intensive computational task. To achieve \"real time\" execution, as often required by financial institutions, one needs highly efficient implementations of the multi-point distributed random variables underlying the simulations. In this paper a fast and flexible dedicated hardware solution is proposed. A comparative performance analysis demonstrates that the hardware solution is bottleneck-free and flexible, and significantly increases the computational efficiency of the software solution.","PeriodicalId":345842,"journal":{"name":"5th International Conference on Intelligent Systems Design and Applications (ISDA'05)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122518202","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, a new functionality of early warning for an online stock trading system is presented. The warning functionality helps to focus traders' attention on specific situations on the stock market. The specific situations relate to the rare circumstances where a trader should be alerted by exceptional raises or drops of share prices, volatilities and market index changes. Usually, these alerts force a trader to make a decision either to buy or sell a share. To discover the warning rules and events, an evolution-based model is proposed. This model also introduces a new function that stores the experimental knowledge by keeping track of all historical alert events-solutions and actions taken by a trader. This model is composed of the three following components, which are integrated with each other: alert rules, pattern clustering and genetic engine. This approach has been tested on real data extracted from the Internet Bourse Expert System and Paris Stock Exchange.
{"title":"Early warning in online stock trading systems","authors":"Piotr Lipiński, J. Korczak","doi":"10.1109/ISDA.2005.42","DOIUrl":"https://doi.org/10.1109/ISDA.2005.42","url":null,"abstract":"In this paper, a new functionality of early warning for an online stock trading system is presented. The warning functionality helps to focus traders' attention on specific situations on the stock market. The specific situations relate to the rare circumstances where a trader should be alerted by exceptional raises or drops of share prices, volatilities and market index changes. Usually, these alerts force a trader to make a decision either to buy or sell a share. To discover the warning rules and events, an evolution-based model is proposed. This model also introduces a new function that stores the experimental knowledge by keeping track of all historical alert events-solutions and actions taken by a trader. This model is composed of the three following components, which are integrated with each other: alert rules, pattern clustering and genetic engine. This approach has been tested on real data extracted from the Internet Bourse Expert System and Paris Stock Exchange.","PeriodicalId":345842,"journal":{"name":"5th International Conference on Intelligent Systems Design and Applications (ISDA'05)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134253372","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 processors that are used in embedded systems become increasingly more powerful, a variety of functions, including multimedia, games and the proper system-operating function, are added to the systems. Despite the fact that embedded systems are becoming increasingly more powerful, system resources remain limited, and system functionalities are being developed in the form of applications running on the middleware such as virtual machines, or in the form of embedded systems' applications. An example of this is virtual machines (VM's) that are in the form of applications in embedded systems. This paper aims to design and implement a structured platform for MVM's (multi-VM's) that enables the middleware and system applications, such as the VM's to manage the system resources and easily share the common functions amongst them.
{"title":"Structured platform for multiple virtual machines","authors":"ChanSung Jung, Seman Oh","doi":"10.1109/ISDA.2005.86","DOIUrl":"https://doi.org/10.1109/ISDA.2005.86","url":null,"abstract":"As processors that are used in embedded systems become increasingly more powerful, a variety of functions, including multimedia, games and the proper system-operating function, are added to the systems. Despite the fact that embedded systems are becoming increasingly more powerful, system resources remain limited, and system functionalities are being developed in the form of applications running on the middleware such as virtual machines, or in the form of embedded systems' applications. An example of this is virtual machines (VM's) that are in the form of applications in embedded systems. This paper aims to design and implement a structured platform for MVM's (multi-VM's) that enables the middleware and system applications, such as the VM's to manage the system resources and easily share the common functions amongst them.","PeriodicalId":345842,"journal":{"name":"5th International Conference on Intelligent Systems Design and Applications (ISDA'05)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130368675","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}
Formal concept analysis (FCA) is a method for deriving conceptual structures out of data that are represented as objects with features. FCA discovers dependencies within the data based on the relation among objects and features. However, not every pair of objects and features defines a concept. Concept approximation is to find the best or closest concept(s) to approximate a pair of objects and features. Concept approximation is significant in that under the circumstances that we can not find a concept, using concept approximation will give the best or most possible solution. In this paper, we evaluate three approaches through experiments in the application of document retrieval. We provide analysis of these approaches and give our concluding remarks.
{"title":"Comparative evaluation on concept approximation approaches","authors":"J. Deogun, Liying Jiang","doi":"10.1109/ISDA.2005.35","DOIUrl":"https://doi.org/10.1109/ISDA.2005.35","url":null,"abstract":"Formal concept analysis (FCA) is a method for deriving conceptual structures out of data that are represented as objects with features. FCA discovers dependencies within the data based on the relation among objects and features. However, not every pair of objects and features defines a concept. Concept approximation is to find the best or closest concept(s) to approximate a pair of objects and features. Concept approximation is significant in that under the circumstances that we can not find a concept, using concept approximation will give the best or most possible solution. In this paper, we evaluate three approaches through experiments in the application of document retrieval. We provide analysis of these approaches and give our concluding remarks.","PeriodicalId":345842,"journal":{"name":"5th International Conference on Intelligent Systems Design and Applications (ISDA'05)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115510677","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}
Genetic algorithms (GAs) are very popular optimization tool, although efficient applications of GAs requires users have problem with setting their parameters and used genetic operators to obtain satisfactory solution in acceptable time. We propose an intelligent agent as a control mechanism for a group of cooperating genetic algorithms. A core of the system is a family of cooperating genetic algorithms. Manager, a kind of fuzzy expert system, is responsible for control of GAs to assure effectiveness of the search process. It requires providing knowledge concerning influence of some parameters of GAs on tempo and mode of evolution. Such knowledge in the form of fuzzy inference rules should be included into Manager. Analyser - the third part of the system, will gather knowledge concerning progress and actual state of a working system. This knowledge will be used in the form of facts to fire suitable rules. The simulation study of efficiency of developed system is presented and discussed.
{"title":"Managing of cooperative genetic algorithms by intelligent agent","authors":"H. Kwasnicka, Magdalena Gierusz","doi":"10.1109/ISDA.2005.61","DOIUrl":"https://doi.org/10.1109/ISDA.2005.61","url":null,"abstract":"Genetic algorithms (GAs) are very popular optimization tool, although efficient applications of GAs requires users have problem with setting their parameters and used genetic operators to obtain satisfactory solution in acceptable time. We propose an intelligent agent as a control mechanism for a group of cooperating genetic algorithms. A core of the system is a family of cooperating genetic algorithms. Manager, a kind of fuzzy expert system, is responsible for control of GAs to assure effectiveness of the search process. It requires providing knowledge concerning influence of some parameters of GAs on tempo and mode of evolution. Such knowledge in the form of fuzzy inference rules should be included into Manager. Analyser - the third part of the system, will gather knowledge concerning progress and actual state of a working system. This knowledge will be used in the form of facts to fire suitable rules. The simulation study of efficiency of developed system is presented and discussed.","PeriodicalId":345842,"journal":{"name":"5th International Conference on Intelligent Systems Design and Applications (ISDA'05)","volume":"271 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122076650","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}
B. Berg, C. Tattersall, J. Janssen, F. Brouns, Hub Kurvers, R. Koper
Open and distance learning (ODL) gives learners freedom of time, place and pace of study, putting learner self-direction centre-stage. However, increased responsibility should not come at the price of over-burdening or abandonment of learners as they progress along their learning journey. This paper introduces an approach to recommending the sequencing of e-learning modules for distance learners based on self-organization theory. It describes an architecture which supports the recording, processing and presentation of collective learner behavior designed to create a feedback loop informing learners of successful paths towards the attainment of learning goals. The article includes initial results from a large-scale experiment designed to validate the approach.
{"title":"Swarm-based sequencing recommendations in e-learning","authors":"B. Berg, C. Tattersall, J. Janssen, F. Brouns, Hub Kurvers, R. Koper","doi":"10.1109/ISDA.2005.88","DOIUrl":"https://doi.org/10.1109/ISDA.2005.88","url":null,"abstract":"Open and distance learning (ODL) gives learners freedom of time, place and pace of study, putting learner self-direction centre-stage. However, increased responsibility should not come at the price of over-burdening or abandonment of learners as they progress along their learning journey. This paper introduces an approach to recommending the sequencing of e-learning modules for distance learners based on self-organization theory. It describes an architecture which supports the recording, processing and presentation of collective learner behavior designed to create a feedback loop informing learners of successful paths towards the attainment of learning goals. The article includes initial results from a large-scale experiment designed to validate the approach.","PeriodicalId":345842,"journal":{"name":"5th International Conference on Intelligent Systems Design and Applications (ISDA'05)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124248293","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 extends the analysis of a statistical methodology for source detection and localization (SDL) in a network of randomly distributed wireless nodes equipped with homogeneous and omni-directional sensors. The investigations are focused on SDL with respect to the nearest sensor node and are based on the observed source (phenomenon) energy. In this framework, the SDL algorithms are viewed as classification problems which are solved using pattern recognition techniques. In the presented approach: (i) sensors are randomly distributed and little is known about their exact locations; and (ii) a self-calibrating mechanism is proposed for creating the dataset whose feature vectors constitute the reference points for sensor locations in the space of sensor readings. The performance of the proposed algorithms is evaluated through Monte Carlo simulations and is demonstrated to be robust in the presence of noise and changes in the propagation environments.
{"title":"Pattern recognition based detection and localization in a network of randomly distributed sensor nodes","authors":"H. Al-Hertani, J. Ilow","doi":"10.1109/ISDA.2005.77","DOIUrl":"https://doi.org/10.1109/ISDA.2005.77","url":null,"abstract":"This paper extends the analysis of a statistical methodology for source detection and localization (SDL) in a network of randomly distributed wireless nodes equipped with homogeneous and omni-directional sensors. The investigations are focused on SDL with respect to the nearest sensor node and are based on the observed source (phenomenon) energy. In this framework, the SDL algorithms are viewed as classification problems which are solved using pattern recognition techniques. In the presented approach: (i) sensors are randomly distributed and little is known about their exact locations; and (ii) a self-calibrating mechanism is proposed for creating the dataset whose feature vectors constitute the reference points for sensor locations in the space of sensor readings. The performance of the proposed algorithms is evaluated through Monte Carlo simulations and is demonstrated to be robust in the presence of noise and changes in the propagation environments.","PeriodicalId":345842,"journal":{"name":"5th International Conference on Intelligent Systems Design and Applications (ISDA'05)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128572227","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 aim of this paper is to present some remarks on computing maximal consistent extensions of dynamic information systems. Dynamic information systems can be used as a tool for description of concurrent systems. In this case, they include the knowledge about global states and transitions between them observed in given concurrent systems. The task of creating a maximal consistent extension is to find all global states and all transitions between them which are consistent with the knowledge included in data tables representing a dynamic information system. A method for computing such an extension is given. The method presented here seems to be more efficient than methods presented earlier in the literature. Our approach is based on the rough set theory.
{"title":"Some remarks on computing consistent extensions of dynamic information systems","authors":"Z. Suraj, K. Pancerz","doi":"10.1109/ISDA.2005.84","DOIUrl":"https://doi.org/10.1109/ISDA.2005.84","url":null,"abstract":"The aim of this paper is to present some remarks on computing maximal consistent extensions of dynamic information systems. Dynamic information systems can be used as a tool for description of concurrent systems. In this case, they include the knowledge about global states and transitions between them observed in given concurrent systems. The task of creating a maximal consistent extension is to find all global states and all transitions between them which are consistent with the knowledge included in data tables representing a dynamic information system. A method for computing such an extension is given. The method presented here seems to be more efficient than methods presented earlier in the literature. Our approach is based on the rough set theory.","PeriodicalId":345842,"journal":{"name":"5th International Conference on Intelligent Systems Design and Applications (ISDA'05)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131081190","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 term visual arts data mining defines a framework for Data Mining techniques applied to learn and discover patterns in visual arts collections. Its results can be widely used by visual arts market, museums and art galleries. This paper proposes a multi-label voting algorithm to identify similar visual arts objects studied using neuro-fuzzy classifiers. The algorithm integrates predictions of experts trained on clusters of heterogeneous collections of data. It combines predictions of the modular ensemble of classifiers by identifying hierarchical votes for most similar classes. Experimental results show better performances than individual global models. Relationships between some visual arts patterns are inferred. We also compare the results obtained for few fusion versions of our algorithm with other methods applied on IRIS and Glass benchmarks. The results show that our algorithm has at least similar performance to other schemes on all data sets and adds flexibility to cases where classifiers' expertise overlaps on unions of disjunctive sets of the universe of discourse.
{"title":"A multi-label voting algorithm for neuro-fuzzy classifier ensembles with applications in visual arts data mining","authors":"D. Neagu, Shuai Zhang, C. Balescu","doi":"10.1109/ISDA.2005.10","DOIUrl":"https://doi.org/10.1109/ISDA.2005.10","url":null,"abstract":"The term visual arts data mining defines a framework for Data Mining techniques applied to learn and discover patterns in visual arts collections. Its results can be widely used by visual arts market, museums and art galleries. This paper proposes a multi-label voting algorithm to identify similar visual arts objects studied using neuro-fuzzy classifiers. The algorithm integrates predictions of experts trained on clusters of heterogeneous collections of data. It combines predictions of the modular ensemble of classifiers by identifying hierarchical votes for most similar classes. Experimental results show better performances than individual global models. Relationships between some visual arts patterns are inferred. We also compare the results obtained for few fusion versions of our algorithm with other methods applied on IRIS and Glass benchmarks. The results show that our algorithm has at least similar performance to other schemes on all data sets and adds flexibility to cases where classifiers' expertise overlaps on unions of disjunctive sets of the universe of discourse.","PeriodicalId":345842,"journal":{"name":"5th International Conference on Intelligent Systems Design and Applications (ISDA'05)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127393066","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 the paper the experimental study of the influence of parameters on the final results of the rule extraction method from neural network for classification problem is described. The method is based on evolutionary approach, where for each class evolves separate population. The paper starts on the presentation of the basic concepts of the method. Next, the results of experiments are described. They examine the influence of genetic parameters. Then, the parameters that affect the rule extraction efficiency are tested. All experiments are made with using UCI data sets. At the end, some general conclusion concerning the role of the parameters and their influence on the final results are formulated.
{"title":"The influence of parameters in evolutionary based rule extraction method from neural network","authors":"Urszula Markowska-Kaczmar","doi":"10.1109/ISDA.2005.91","DOIUrl":"https://doi.org/10.1109/ISDA.2005.91","url":null,"abstract":"In the paper the experimental study of the influence of parameters on the final results of the rule extraction method from neural network for classification problem is described. The method is based on evolutionary approach, where for each class evolves separate population. The paper starts on the presentation of the basic concepts of the method. Next, the results of experiments are described. They examine the influence of genetic parameters. Then, the parameters that affect the rule extraction efficiency are tested. All experiments are made with using UCI data sets. At the end, some general conclusion concerning the role of the parameters and their influence on the final results are formulated.","PeriodicalId":345842,"journal":{"name":"5th International Conference on Intelligent Systems Design and Applications (ISDA'05)","volume":"258 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121348127","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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