Pub Date : 2017-07-29DOI: 10.1109/FSKD.2017.8392942
Binbin Sang, Weihua Xu
Fuzzy relation is the generalization of the classical binary relation. Based on intuitionistic fuzzy information system, a fuzzy equivalence relation is defined in this paper. Thus, fuzzy approximation space is established in intuitionistic fuzzy information system. And rough membership measure is defined by fuzzy equivalence relation. In addition, a few significant properties of the degree of the roughness of membership are proved., after that an demonstrative case is proposed in intuitionistic fuzzy information system.
{"title":"Rough membership measure in intuitionistic fuzzy information system","authors":"Binbin Sang, Weihua Xu","doi":"10.1109/FSKD.2017.8392942","DOIUrl":"https://doi.org/10.1109/FSKD.2017.8392942","url":null,"abstract":"Fuzzy relation is the generalization of the classical binary relation. Based on intuitionistic fuzzy information system, a fuzzy equivalence relation is defined in this paper. Thus, fuzzy approximation space is established in intuitionistic fuzzy information system. And rough membership measure is defined by fuzzy equivalence relation. In addition, a few significant properties of the degree of the roughness of membership are proved., after that an demonstrative case is proposed in intuitionistic fuzzy information system.","PeriodicalId":236093,"journal":{"name":"2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133317807","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}
Pub Date : 2017-07-29DOI: 10.1109/FSKD.2017.8393296
Xiaofang Xu, Joao Amaro, Sam Caulfield, G. Falcão, D. Moloney
With the recent surge in popularity of Convolutional Neural Networks (CNNs), motivated by their significant performance in many classification and related tasks, a new challenge now needs to be addressed: how to accommodate CNNs in mobile devices, such as drones, smartphones, and similar low-power devices? In order to tackle this challenge we exploit the Vision Processing Unit (VPU) that combines dedicated CNN hardware blocks and very high power efficiency. The lack of readily available training data and memory requirements are two of the factors hindering the training and accuracy performance of 3D CNNs. In this paper, we propose a method for generating synthetic 3D point-clouds from realistic CAD scene models (based on the ModelNet10 dataset), in order to enrich the training process for volumetric CNNs. Furthermore, an efficient 3D volumetric object representation (VOLA) is employed. VOLA (Volumetric Accelerator) is a sexaquaternary (power-of-four subdivision) tree-based representation which allows for significant memory saving for volumetric data. Multiple CNN models were trained and the top performing model was ported to the Fathom Neural Compute Stick (NCS). Among the trained CNN models, the maximum test accuracy achieved is 91.3%. After deployment on the Fathom NCS, it takes 11ms (∼ 90 frames per second) to perform inference on each input volume, with a reported power requirement of 1.2W which leads to 75.75 inference per second per Watt.
{"title":"Classify 3D voxel based point-cloud using convolutional neural network on a neural compute stick","authors":"Xiaofang Xu, Joao Amaro, Sam Caulfield, G. Falcão, D. Moloney","doi":"10.1109/FSKD.2017.8393296","DOIUrl":"https://doi.org/10.1109/FSKD.2017.8393296","url":null,"abstract":"With the recent surge in popularity of Convolutional Neural Networks (CNNs), motivated by their significant performance in many classification and related tasks, a new challenge now needs to be addressed: how to accommodate CNNs in mobile devices, such as drones, smartphones, and similar low-power devices? In order to tackle this challenge we exploit the Vision Processing Unit (VPU) that combines dedicated CNN hardware blocks and very high power efficiency. The lack of readily available training data and memory requirements are two of the factors hindering the training and accuracy performance of 3D CNNs. In this paper, we propose a method for generating synthetic 3D point-clouds from realistic CAD scene models (based on the ModelNet10 dataset), in order to enrich the training process for volumetric CNNs. Furthermore, an efficient 3D volumetric object representation (VOLA) is employed. VOLA (Volumetric Accelerator) is a sexaquaternary (power-of-four subdivision) tree-based representation which allows for significant memory saving for volumetric data. Multiple CNN models were trained and the top performing model was ported to the Fathom Neural Compute Stick (NCS). Among the trained CNN models, the maximum test accuracy achieved is 91.3%. After deployment on the Fathom NCS, it takes 11ms (∼ 90 frames per second) to perform inference on each input volume, with a reported power requirement of 1.2W which leads to 75.75 inference per second per Watt.","PeriodicalId":236093,"journal":{"name":"2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)","volume":"140 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123246836","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}
Pub Date : 2017-07-29DOI: 10.1109/FSKD.2017.8392927
Min Li, Zhikang Xiang, Limei Zhang, Z. Lian, Liang Xiao
Segmentation of brain magnetic resonance imaging (MRI) images is greatly significant in neuroscience field. We propose a novel FCM method for segmentation of brain MRI images that makes full use of both the image intensity and spatial feature information. The proposed method can handle images having intensity inhomogeneity and noises by using the regularization that does not only consider the bias field but also takes neighborhood influence into account. Experiment indicates that the novel FCM method achieves more accurate and robust results in segmentation of brain MRI images compared to the expectation-maximization (EM) method and the conventional FCM method.
{"title":"Robust segmentation of brain MRI images using a novel fuzzy c-means clustering method","authors":"Min Li, Zhikang Xiang, Limei Zhang, Z. Lian, Liang Xiao","doi":"10.1109/FSKD.2017.8392927","DOIUrl":"https://doi.org/10.1109/FSKD.2017.8392927","url":null,"abstract":"Segmentation of brain magnetic resonance imaging (MRI) images is greatly significant in neuroscience field. We propose a novel FCM method for segmentation of brain MRI images that makes full use of both the image intensity and spatial feature information. The proposed method can handle images having intensity inhomogeneity and noises by using the regularization that does not only consider the bias field but also takes neighborhood influence into account. Experiment indicates that the novel FCM method achieves more accurate and robust results in segmentation of brain MRI images compared to the expectation-maximization (EM) method and the conventional FCM method.","PeriodicalId":236093,"journal":{"name":"2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123884247","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}
Pub Date : 2017-07-29DOI: 10.1109/FSKD.2017.8393236
Yung Chang Wu, Linfeng, S. Wu
Cloud computing and big data represent a profound change in the trend towards intensive, large-scale, and professional development of the IT area; however, while improving IT capabilities, such changes have brought great impact and challenges to user information asset security and privacy protection; for the large-amount of transfers of the banking industry's commercial activities in the international financial market, a reliable operating environment must be provided to ensure data security. This study explores and analyzes the challenges to technology, standards, supervision, etc. in the field of information security, brought by emerging information areas, such as cloud computing; referring to the cloud computing security framework and the main research content under the framework, it points out that the popularization and application of cloud computing and big data are the major challenges and development opportunities in the field of information security in recent years, which leads to another important technological change in the field of information security. This study adopts the method of empirical study, and collects data through questionnaires, in order to learn about the impact of the research subjects' compliance with information security awareness on the security governance performance and information technology capabilities of banks.
{"title":"A study on the impact of regulatory compliance awareness on security management performance and information technology capabilities","authors":"Yung Chang Wu, Linfeng, S. Wu","doi":"10.1109/FSKD.2017.8393236","DOIUrl":"https://doi.org/10.1109/FSKD.2017.8393236","url":null,"abstract":"Cloud computing and big data represent a profound change in the trend towards intensive, large-scale, and professional development of the IT area; however, while improving IT capabilities, such changes have brought great impact and challenges to user information asset security and privacy protection; for the large-amount of transfers of the banking industry's commercial activities in the international financial market, a reliable operating environment must be provided to ensure data security. This study explores and analyzes the challenges to technology, standards, supervision, etc. in the field of information security, brought by emerging information areas, such as cloud computing; referring to the cloud computing security framework and the main research content under the framework, it points out that the popularization and application of cloud computing and big data are the major challenges and development opportunities in the field of information security in recent years, which leads to another important technological change in the field of information security. This study adopts the method of empirical study, and collects data through questionnaires, in order to learn about the impact of the research subjects' compliance with information security awareness on the security governance performance and information technology capabilities of banks.","PeriodicalId":236093,"journal":{"name":"2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124808520","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}
Pub Date : 2017-07-29DOI: 10.1109/FSKD.2017.8393033
Hao Sun, Bing Li, Bo Han
Fast-growing technologies like cloud-computing, big data, mobile Internet, artificial intelligence, etc. have driven the emergences of a lot of new phrases. In this paper, we propose a novel keyphrases extraction method with two steps by combining FP-growth algorithm and Latent Dirichlet Allocation (LDA) topic modeling. In the first step, we apply FP-growth algorithm to obtain frequent neighborhood words co-occurring frequently as candidate phrases. In the second step, we extract significant keyphrases by LDA models. Our experiments on two datasets CVE-2015 and 20-newsgroups have shown that the proposed approach can extract significant keyphrases and these phrases can help improve the text classification accuracy.
{"title":"A novel keyphrase extraction method by combining FP-growth and LDA","authors":"Hao Sun, Bing Li, Bo Han","doi":"10.1109/FSKD.2017.8393033","DOIUrl":"https://doi.org/10.1109/FSKD.2017.8393033","url":null,"abstract":"Fast-growing technologies like cloud-computing, big data, mobile Internet, artificial intelligence, etc. have driven the emergences of a lot of new phrases. In this paper, we propose a novel keyphrases extraction method with two steps by combining FP-growth algorithm and Latent Dirichlet Allocation (LDA) topic modeling. In the first step, we apply FP-growth algorithm to obtain frequent neighborhood words co-occurring frequently as candidate phrases. In the second step, we extract significant keyphrases by LDA models. Our experiments on two datasets CVE-2015 and 20-newsgroups have shown that the proposed approach can extract significant keyphrases and these phrases can help improve the text classification accuracy.","PeriodicalId":236093,"journal":{"name":"2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)","volume":"125 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124881745","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}
Pub Date : 2017-07-29DOI: 10.1109/FSKD.2017.8393008
Lietao Fang, Hong Jiang, Shuqi Cui
As a classical data mining algorithm, decision tree has a wide range of application areas. Most of the researches on decision tree are based on ID3 and its derivative algorithms, which are all based on information entropy. In this paper, as the most important key point of the decision tree, the metric of the split attribute is studied. The mutual information is introduced into decision tree classification. The results show that the decision tree classification model based on mutual information is a better classifier. Compared with the ID3 classifier based on information entropy, it is verified that the accuracy of the decision tree algorithm based on mutual information has been greatly improved, and the construction of the classifier is more rapid.
{"title":"An improved decision tree algorithm based on mutual information","authors":"Lietao Fang, Hong Jiang, Shuqi Cui","doi":"10.1109/FSKD.2017.8393008","DOIUrl":"https://doi.org/10.1109/FSKD.2017.8393008","url":null,"abstract":"As a classical data mining algorithm, decision tree has a wide range of application areas. Most of the researches on decision tree are based on ID3 and its derivative algorithms, which are all based on information entropy. In this paper, as the most important key point of the decision tree, the metric of the split attribute is studied. The mutual information is introduced into decision tree classification. The results show that the decision tree classification model based on mutual information is a better classifier. Compared with the ID3 classifier based on information entropy, it is verified that the accuracy of the decision tree algorithm based on mutual information has been greatly improved, and the construction of the classifier is more rapid.","PeriodicalId":236093,"journal":{"name":"2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)","volume":"160 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121086457","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}
Pub Date : 2017-07-29DOI: 10.1109/FSKD.2017.8393116
A. Saleh, L. Weigang
The majority of text summarization techniques in literature depend, in one way or another, on language dependent pre-structured lexicons, databases, taggers and/or parsers. Such techniques require a prior knowledge of the language of the text being summarized. In this paper we propose an extractive text summarization tool, UnB Language Independent Text Summarizer (UnB-LITS), which is capable of performing text summarization in a language independent manner. The new model depends on intrinsic characteristics of the text being summarized rather than its language and thus eliminates the need for language dependent lexicons, databases, taggers or parsers. Within this tool, we develop an innovative way of coding the shapes of text elements (words, n-grams, sentences and paragraphs), in addition to proposing language independent algorithms that is capable of normalizing words and performing relative stemming or lemmatization. The proposed algorithms and Shape-Coding routine enable the UnB-LITS tool to extract intrinsic features of document elements and score them statistically to extract a representative extractive summary independent of the document language. In this paper we focused on single document summarization of western European languages. The tool was tested on hundreds of documents written in English, Portuguese, French and Spanish and showed better performance as compared with the results obtained in literature as well as from commercial summarizers.
{"title":"Language independent text summarization of western European languages using shape coding of text elements","authors":"A. Saleh, L. Weigang","doi":"10.1109/FSKD.2017.8393116","DOIUrl":"https://doi.org/10.1109/FSKD.2017.8393116","url":null,"abstract":"The majority of text summarization techniques in literature depend, in one way or another, on language dependent pre-structured lexicons, databases, taggers and/or parsers. Such techniques require a prior knowledge of the language of the text being summarized. In this paper we propose an extractive text summarization tool, UnB Language Independent Text Summarizer (UnB-LITS), which is capable of performing text summarization in a language independent manner. The new model depends on intrinsic characteristics of the text being summarized rather than its language and thus eliminates the need for language dependent lexicons, databases, taggers or parsers. Within this tool, we develop an innovative way of coding the shapes of text elements (words, n-grams, sentences and paragraphs), in addition to proposing language independent algorithms that is capable of normalizing words and performing relative stemming or lemmatization. The proposed algorithms and Shape-Coding routine enable the UnB-LITS tool to extract intrinsic features of document elements and score them statistically to extract a representative extractive summary independent of the document language. In this paper we focused on single document summarization of western European languages. The tool was tested on hundreds of documents written in English, Portuguese, French and Spanish and showed better performance as compared with the results obtained in literature as well as from commercial summarizers.","PeriodicalId":236093,"journal":{"name":"2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126927909","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}
Pub Date : 2017-07-29DOI: 10.1109/FSKD.2017.8393387
Ying Gao, Yanhai Gan, Junyu Dong, Lin Qi, Huiyu Zhou
The majority of studies on texture analysis focus on classification and generation, and few works concern perceptual similarity between textures, which is one of the fundamental problems in the field of texture analysis. Previous methods for perceptual similarity learning were mainly assisted by psychophysical experiments and computational feature extraction. However, the calculated similarity matrix is always seriously biased from human observation. In this paper, we propose a novel method for similarity prediction, which is based on convolutional neural networks (CNNs) and stacked sparse auto-encoder (SSAE). The experimental results show that the predicted similarity matrixes are more perceptually consistent with psychophysical experiments compared to other predicting methods.
{"title":"Perceptual texture similarity learning using deep neural networks","authors":"Ying Gao, Yanhai Gan, Junyu Dong, Lin Qi, Huiyu Zhou","doi":"10.1109/FSKD.2017.8393387","DOIUrl":"https://doi.org/10.1109/FSKD.2017.8393387","url":null,"abstract":"The majority of studies on texture analysis focus on classification and generation, and few works concern perceptual similarity between textures, which is one of the fundamental problems in the field of texture analysis. Previous methods for perceptual similarity learning were mainly assisted by psychophysical experiments and computational feature extraction. However, the calculated similarity matrix is always seriously biased from human observation. In this paper, we propose a novel method for similarity prediction, which is based on convolutional neural networks (CNNs) and stacked sparse auto-encoder (SSAE). The experimental results show that the predicted similarity matrixes are more perceptually consistent with psychophysical experiments compared to other predicting methods.","PeriodicalId":236093,"journal":{"name":"2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115352115","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}
Pub Date : 2017-07-29DOI: 10.1109/FSKD.2017.8392943
Fatigue Life Durability, Fatigue Behaviour
For composites static strength, fatigue damage and durability demonstrate a scatter factor of results larger than for isotropic materials. To characterize it the fuzzy set approach is proposed. Two different mechanical descriptions of fatigue life are used in order to describe the uncertainty and randomness of parameters characterizing the fatigue damage and finally the fatigue durability. The theoretical predictions representing the lower and upper bounds of a fatigue life are compared with experimental data. In general, the present analysis shows that the fuzzy set description allows us to take into account much more parameters than classical deterministic or statistical methods.
{"title":"Fuzzy approach to fatigue problems in composite materials and structures","authors":"Fatigue Life Durability, Fatigue Behaviour","doi":"10.1109/FSKD.2017.8392943","DOIUrl":"https://doi.org/10.1109/FSKD.2017.8392943","url":null,"abstract":"For composites static strength, fatigue damage and durability demonstrate a scatter factor of results larger than for isotropic materials. To characterize it the fuzzy set approach is proposed. Two different mechanical descriptions of fatigue life are used in order to describe the uncertainty and randomness of parameters characterizing the fatigue damage and finally the fatigue durability. The theoretical predictions representing the lower and upper bounds of a fatigue life are compared with experimental data. In general, the present analysis shows that the fuzzy set description allows us to take into account much more parameters than classical deterministic or statistical methods.","PeriodicalId":236093,"journal":{"name":"2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114733054","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}
Pub Date : 2017-07-29DOI: 10.1109/FSKD.2017.8392972
Bruno M. P. Moura, G. Schneider, A. Yamin, R. Reiser, M. Pilla
Scheduling tasks is a known NP-Hard problem. As grow the number of variables such as computational power and network metrics, even heuristic-based schedulers start to become overwhelmed by the underlying complexity. Computational Grids (CGs) are known for their heterogeneity of resources and interconnections, and as these resources may be deployed throughout the world, it is not possible to have a single, centralized, precise view of the system at any given moment. This paper provides a new approach with Fuzzy Type-2 logics to treat uncertainties and dynamic behavior for scheduling tasks in grid environments, named Int-fGrid. The scheduler was validated through simulations in the SimGrid framework with a model of the GridRS architecture. Our results show that the Fuzzy Type-2 approach provides makespans up to 18.5 times better than the best alternative tested scheduler XSufferage.
{"title":"Int-fGrid: A type-2 fuzzy approach for scheduling tasks of computational grids","authors":"Bruno M. P. Moura, G. Schneider, A. Yamin, R. Reiser, M. Pilla","doi":"10.1109/FSKD.2017.8392972","DOIUrl":"https://doi.org/10.1109/FSKD.2017.8392972","url":null,"abstract":"Scheduling tasks is a known NP-Hard problem. As grow the number of variables such as computational power and network metrics, even heuristic-based schedulers start to become overwhelmed by the underlying complexity. Computational Grids (CGs) are known for their heterogeneity of resources and interconnections, and as these resources may be deployed throughout the world, it is not possible to have a single, centralized, precise view of the system at any given moment. This paper provides a new approach with Fuzzy Type-2 logics to treat uncertainties and dynamic behavior for scheduling tasks in grid environments, named Int-fGrid. The scheduler was validated through simulations in the SimGrid framework with a model of the GridRS architecture. Our results show that the Fuzzy Type-2 approach provides makespans up to 18.5 times better than the best alternative tested scheduler XSufferage.","PeriodicalId":236093,"journal":{"name":"2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115072938","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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