The paper considers new techniques for clustering and sorting objects that are described with many quantitative and/or qualitative attributes and may exist in several copies with inconsistent and contradictory attributes. Methods are based on the theory of multiset metric spaces. New options for the objectspsila aggregation and features of classes generated are discussed.
{"title":"Clustering and sorting multi-attribute objects in multiset metric space","authors":"A. Petrovsky","doi":"10.1109/IS.2008.4670507","DOIUrl":"https://doi.org/10.1109/IS.2008.4670507","url":null,"abstract":"The paper considers new techniques for clustering and sorting objects that are described with many quantitative and/or qualitative attributes and may exist in several copies with inconsistent and contradictory attributes. Methods are based on the theory of multiset metric spaces. New options for the objectspsila aggregation and features of classes generated are discussed.","PeriodicalId":305750,"journal":{"name":"2008 4th International IEEE Conference Intelligent Systems","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114639730","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}
Information understanding and information valuation have mostly been studied separately. In this paper we propose using information understanding to reach a definition for information value or quality. We demonstrate how information valuation can be predicted and computed as potential causal impact of extractable semantics.
{"title":"Semantics-based information valuation","authors":"Sinan Al-Saffar, G. Heileman","doi":"10.1109/IS.2008.4670438","DOIUrl":"https://doi.org/10.1109/IS.2008.4670438","url":null,"abstract":"Information understanding and information valuation have mostly been studied separately. In this paper we propose using information understanding to reach a definition for information value or quality. We demonstrate how information valuation can be predicted and computed as potential causal impact of extractable semantics.","PeriodicalId":305750,"journal":{"name":"2008 4th International IEEE Conference Intelligent Systems","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114760972","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 family of Ordered Weighted Averaging (OWA) operators, as introduced by Yager, appear to be very useful in multi-criteria decision-making (MCDM) and It provides a general class of parameterized aggregation operators that include the min, max, average operators. In this paper, we present Analog CMOS implementation of OWA operator in current-mode and 0.35 mum CMOS technologies for the first time. The proposed circuit has high flexibility and programmability and accuracy .We simulated our proposed design in 49 level HSPICE simulation software and its results show that this block can be used in analog fuzzy logic controller chip (FLC).
由Yager介绍的有序加权平均(OWA)算子族在多准则决策(MCDM)中非常有用，它提供了一类一般的参数化聚合算子，包括最小、最大、平均算子。在本文中，我们首次提出了OWA算子在电流模式下的模拟CMOS实现和0.35 μ m CMOS技术。该电路具有较高的灵活性、可编程性和精度，并在49级HSPICE仿真软件中进行了仿真，结果表明该电路可用于模拟模糊控制器芯片(FLC)。
{"title":"Analog CMOS implementation of order weight average operator for fuzzy logic controller chip","authors":"K. Gheysari, A. Khoei, K. Hadidi, M. Mokarram","doi":"10.1109/IS.2008.4670401","DOIUrl":"https://doi.org/10.1109/IS.2008.4670401","url":null,"abstract":"The family of Ordered Weighted Averaging (OWA) operators, as introduced by Yager, appear to be very useful in multi-criteria decision-making (MCDM) and It provides a general class of parameterized aggregation operators that include the min, max, average operators. In this paper, we present Analog CMOS implementation of OWA operator in current-mode and 0.35 mum CMOS technologies for the first time. The proposed circuit has high flexibility and programmability and accuracy .We simulated our proposed design in 49 level HSPICE simulation software and its results show that this block can be used in analog fuzzy logic controller chip (FLC).","PeriodicalId":305750,"journal":{"name":"2008 4th International IEEE Conference Intelligent Systems","volume":"112 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117255654","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}
Reinforcement learning, while being a highly popular learning technique for agents and multi-agent systems, has so far encountered difficulties when applying it to more complex domains due to scaling-up problems. This paper focuses on the use of domain knowledge to improve the convergence speed and optimality of various RL techniques. Specifically, we propose the use of high-level STRIPS operator knowledge in reward shaping to focus the search for the optimal policy. Empirical results show that the plan-based reward shaping approach outperforms other RL techniques, including alternative manual and MDP-based reward shaping when it is used in its basic form. We show that MDP-based reward shaping may fail and successful experiments with STRIPS-based shaping suggest modifications which can overcome encountered problems. The STRIPS-based method we propose allows expressing the same domain knowledge in a different way and the domain expert can choose whether to define an MDP or STRIPS planning task. We also evaluate the robustness of the proposed STRIPS-based technique to errors in the plan knowledge.
{"title":"Plan-based reward shaping for reinforcement learning","authors":"M. Grzes, D. Kudenko","doi":"10.1109/IS.2008.4670492","DOIUrl":"https://doi.org/10.1109/IS.2008.4670492","url":null,"abstract":"Reinforcement learning, while being a highly popular learning technique for agents and multi-agent systems, has so far encountered difficulties when applying it to more complex domains due to scaling-up problems. This paper focuses on the use of domain knowledge to improve the convergence speed and optimality of various RL techniques. Specifically, we propose the use of high-level STRIPS operator knowledge in reward shaping to focus the search for the optimal policy. Empirical results show that the plan-based reward shaping approach outperforms other RL techniques, including alternative manual and MDP-based reward shaping when it is used in its basic form. We show that MDP-based reward shaping may fail and successful experiments with STRIPS-based shaping suggest modifications which can overcome encountered problems. The STRIPS-based method we propose allows expressing the same domain knowledge in a different way and the domain expert can choose whether to define an MDP or STRIPS planning task. We also evaluate the robustness of the proposed STRIPS-based technique to errors in the plan knowledge.","PeriodicalId":305750,"journal":{"name":"2008 4th International IEEE Conference Intelligent Systems","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124895008","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}
Mathematical definition of a class dynamic systems with entropy operator is considered (DSEO) The main properties of the entropy operator - existing, continuity, boundedness, differentiability are investigated. The stability conditions are obtained for the DSEO with multiplicative and additive flows. The theoretical results are used for mathematical modeling of labor market and in dynamic procedure of the image restoration from projections.
{"title":"Mathematical models of dynamic systems with entropy operator and their applications","authors":"Y. Popkov","doi":"10.1109/IS.2008.4670395","DOIUrl":"https://doi.org/10.1109/IS.2008.4670395","url":null,"abstract":"Mathematical definition of a class dynamic systems with entropy operator is considered (DSEO) The main properties of the entropy operator - existing, continuity, boundedness, differentiability are investigated. The stability conditions are obtained for the DSEO with multiplicative and additive flows. The theoretical results are used for mathematical modeling of labor market and in dynamic procedure of the image restoration from projections.","PeriodicalId":305750,"journal":{"name":"2008 4th International IEEE Conference Intelligent Systems","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122117454","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}
Neural networks have become increasingly important in areas such as medical diagnosis, bio-informatics, intrusion detection, and homeland security. In most of these applications, one major issue is preserving privacy of individualpsilas private information and sensitive data. In this paper, we propose two secure protocols for perceptron learning algorithm when input data is horizontally and vertically partitioned among the parties. These protocols can be applied in both linearly separable and non-separable datasets, while not only data belonging to each party remains private, but the final learning model is also securely shared among those parties. Parties then can jointly and securely apply the constructed model to predict the output corresponding to their target data. Also, these protocols can be used incrementally, i.e. they process new coming data, adjusting the previously constructed network.
{"title":"Privacy-preserving protocols for perceptron learning algorithm in neural networks","authors":"Saeed Samet, Ali Miri","doi":"10.1109/IS.2008.4670499","DOIUrl":"https://doi.org/10.1109/IS.2008.4670499","url":null,"abstract":"Neural networks have become increasingly important in areas such as medical diagnosis, bio-informatics, intrusion detection, and homeland security. In most of these applications, one major issue is preserving privacy of individualpsilas private information and sensitive data. In this paper, we propose two secure protocols for perceptron learning algorithm when input data is horizontally and vertically partitioned among the parties. These protocols can be applied in both linearly separable and non-separable datasets, while not only data belonging to each party remains private, but the final learning model is also securely shared among those parties. Parties then can jointly and securely apply the constructed model to predict the output corresponding to their target data. Also, these protocols can be used incrementally, i.e. they process new coming data, adjusting the previously constructed network.","PeriodicalId":305750,"journal":{"name":"2008 4th International IEEE Conference Intelligent Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130252091","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 hybrid method for training fuzzy cognitive maps is presented. FCMs are based on the knowledge of human experts and may not be accurate enough because of probable mistakes of experts. Thus, some learning methods have been investigated to train FCMs, so that these probable mistakes are covered. Two learning methods, PSO and NHL, and a new hybrid of them are introduced and implemented and tested for a chemical control problem.
{"title":"A hybrid method using PSO and NHL algorithms to train Fuzzy Cognitive Maps","authors":"M. N. Yazdi, C. Lucas","doi":"10.1109/IS.2008.4670458","DOIUrl":"https://doi.org/10.1109/IS.2008.4670458","url":null,"abstract":"In this paper a new hybrid method for training fuzzy cognitive maps is presented. FCMs are based on the knowledge of human experts and may not be accurate enough because of probable mistakes of experts. Thus, some learning methods have been investigated to train FCMs, so that these probable mistakes are covered. Two learning methods, PSO and NHL, and a new hybrid of them are introduced and implemented and tested for a chemical control problem.","PeriodicalId":305750,"journal":{"name":"2008 4th International IEEE Conference Intelligent Systems","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130232282","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}
Enhanced network flow methods have been proposed in the logical operations frame aiming at efficient methods and algorithms in the analysis and synthesis of various logical structures. Quantitative truth maintenance methods have been combined or replaced by qualitative optimization calculations. The method is widely applicable in intelligent systems.
{"title":"Network flow interpretation of logical structures in decision support systems","authors":"V. Sgurev, M. Hadjiski, V. Jotsov","doi":"10.1109/IS.2008.4670448","DOIUrl":"https://doi.org/10.1109/IS.2008.4670448","url":null,"abstract":"Enhanced network flow methods have been proposed in the logical operations frame aiming at efficient methods and algorithms in the analysis and synthesis of various logical structures. Quantitative truth maintenance methods have been combined or replaced by qualitative optimization calculations. The method is widely applicable in intelligent systems.","PeriodicalId":305750,"journal":{"name":"2008 4th International IEEE Conference Intelligent Systems","volume":"127 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122498623","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 considers the problem of finding facial features in frontal color images by offering a method for efficient detection. For a given image, our method firstly detects the skin regions on by color segmentation. This segmentation is performed in a 2D projection of the selected color space (HSV, YES, YCrCb or normalized RGB). After that face candidates are found. Then we apply fuzzy mathematical morphology methods to locate precisely the right eyebrow (if exists). Thus we obtain a meaningful feature. A computer implementation in MATLAB is made for experimentation. The visual results of the experiments show the plausibility of proposed techniques.
{"title":"A new approach for finding face features in color images","authors":"A. Popov, D. Dimitrova","doi":"10.1109/IS.2008.4670517","DOIUrl":"https://doi.org/10.1109/IS.2008.4670517","url":null,"abstract":"This paper considers the problem of finding facial features in frontal color images by offering a method for efficient detection. For a given image, our method firstly detects the skin regions on by color segmentation. This segmentation is performed in a 2D projection of the selected color space (HSV, YES, YCrCb or normalized RGB). After that face candidates are found. Then we apply fuzzy mathematical morphology methods to locate precisely the right eyebrow (if exists). Thus we obtain a meaningful feature. A computer implementation in MATLAB is made for experimentation. The visual results of the experiments show the plausibility of proposed techniques.","PeriodicalId":305750,"journal":{"name":"2008 4th International IEEE Conference Intelligent Systems","volume":"132 2-4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120918033","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 objective of this paper is to propose a framework of ERP systems implementation success that helps project managers to deal with large and complex ERP systems implementation projects. The framework is designed to evaluate and to select activities or alternatives to improvement the degree of performance by the success factors by using the analytic network process (ANP) and the priority matrix (PM). The experience with the application of ANP in a framework of ERP systems implementation success suggests that it offers guidelines in the evaluation and selection of alternatives.
{"title":"Application of the Analytic Network Process (ANP) in a framework of ERP systems implementation success","authors":"V. Dimitrova","doi":"10.1109/IS.2008.4670468","DOIUrl":"https://doi.org/10.1109/IS.2008.4670468","url":null,"abstract":"The objective of this paper is to propose a framework of ERP systems implementation success that helps project managers to deal with large and complex ERP systems implementation projects. The framework is designed to evaluate and to select activities or alternatives to improvement the degree of performance by the success factors by using the analytic network process (ANP) and the priority matrix (PM). The experience with the application of ANP in a framework of ERP systems implementation success suggests that it offers guidelines in the evaluation and selection of alternatives.","PeriodicalId":305750,"journal":{"name":"2008 4th International IEEE Conference Intelligent Systems","volume":"160 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132633944","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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