2017 Brazilian Conference on Intelligent Systems (BRACIS)最新文献

Stochastic Cellular Automata Model to Reduce Rule Space Cardinality Applied to Task Scheduling with Many Processors 降低规则空间基数的随机元胞自动机模型在多处理器任务调度中的应用

2017 Brazilian Conference on Intelligent Systems (BRACIS) Pub Date : 2017-10-01 DOI: 10.1109/BRACIS.2017.27

T. I. D. Carvalho, G. Oliveira

{"title":"Stochastic Cellular Automata Model to Reduce Rule Space Cardinality Applied to Task Scheduling with Many Processors","authors":"T. I. D. Carvalho, G. Oliveira","doi":"10.1109/BRACIS.2017.27","DOIUrl":"https://doi.org/10.1109/BRACIS.2017.27","url":null,"abstract":"Task scheduling consists of allocating the parallel program tasks into the processors of a multiprocessing system. This paper investigates cellular automata (CA) based models for solving the scheduling problem. A standard genetic algorithm (GA) is employed to evolve appropriate CA rules, that is transition rules able to schedule parallel programs. We identified that the state-of-art CA-based schedulers suffer when trying to manage eight or more processors. This difficulty is mainly due to the severe increment in the rule space cardinality when the number of states per cell are increased to represent more processors. We propose a non-standard cellular automata model able to minimize this problem. A new definition of local neighborhood is proposed here, which is denominated as Mapping-Reduce. In addition, the transition rule related to the mapping-reduce neighborhood uses a nondeterministic output, which printed a stochastic characteristic for the new CA model. By using the new CA model we were able to simplify the complexity of the transition rules employed in the proposed CA-based scheduler model. Simulations based on the new model were carried out using a family of four parallel programs that solve equations by Gaussian elimination. Based on the experiments using 4, 8 and 16 processors, it was noted that the results of the CAbased scheduling approach were improved for architectures with a higher number of nodes. Moreover, the evolved rules had shown a better generalization ability when applied to schedule new parallel programs which is a critical point related to the main motivation for the employment of CA in scheduling.","PeriodicalId":202240,"journal":{"name":"2017 Brazilian Conference on Intelligent Systems (BRACIS)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121351100","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}

引用次数: 2

On the Entanglement Dynamics of the Quantum Weightless Neuron 量子失重神经元的纠缠动力学

2017 Brazilian Conference on Intelligent Systems (BRACIS) Pub Date : 2017-10-01 DOI: 10.1109/BRACIS.2017.46

F. M. P. Neto, W. R. Oliveira, A. J. D. Silva, Teresa B Ludermir

引用次数: 2

An Improved Logic-Based Implementation to Ontology Assessment 一种改进的基于逻辑的本体评估实现

2017 Brazilian Conference on Intelligent Systems (BRACIS) Pub Date : 2017-10-01 DOI: 10.1109/BRACIS.2017.22

C. Rodrigues, F. Freitas, R. Azevedo

引用次数: 2

Resolving Resource Incompatibilities in Intelligent Agents 解决智能代理中的资源不兼容问题

2017 Brazilian Conference on Intelligent Systems (BRACIS) Pub Date : 2017-10-01 DOI: 10.1109/BRACIS.2017.28

Mariela Morveli Espinoza, A. Possebom, Cesar Augusto Tacla

引用次数: 0

Online Sequential Learning Based on Extreme Learning Machines for Particulate Matter Forecasting 基于极限学习机的在线顺序学习在颗粒物预测中的应用

2017 Brazilian Conference on Intelligent Systems (BRACIS) Pub Date : 2017-10-01 DOI: 10.1109/BRACIS.2017.25

Andres Bueno, G. P. Coelho, J. R. Bertini

{"title":"Online Sequential Learning Based on Extreme Learning Machines for Particulate Matter Forecasting","authors":"Andres Bueno, G. P. Coelho, J. R. Bertini","doi":"10.1109/BRACIS.2017.25","DOIUrl":"https://doi.org/10.1109/BRACIS.2017.25","url":null,"abstract":"Microscopically small solid particles and liquid droplets suspended in the air, known as particulate matter (PM), may significantly affect not only human health but also urban, natural and agricultural systems. Therefore, it is imperative to keep the concentration levels of these pollutants below harmful thresholds. Forecasting tools based on machine learning have been used to estimate the concentration of PM and other pollutants in the atmosphere. However, PM data are uninterruptedly collected over time, thus producing a stream of data whose distribution may evolve over time. As traditional machine learning techniques do not have mechanisms to handle changes on data distribution at running time, they usually present limited prediction accuracy when facing such scenario. The overall goal of this work is to evaluate whether online sequential learning can improve the estimation accuracy of PM forecasting. To do so, online and offline algorithms based on Extreme Learning Machines (ELM) were compared, in order to evaluate their performance when applied to forecast hourly concentrations of PM. The experiments were performed using real-world data streams of PM concentration from different cities of the State of São Paulo, Brazil. The obtained results show not only that online sequential learning approaches lead to smaller mean squared errors but also that the stability of the results is enhanced when such approaches are combined in ensembles.","PeriodicalId":202240,"journal":{"name":"2017 Brazilian Conference on Intelligent Systems (BRACIS)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129351741","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}

引用次数: 6

Investigating the Impact of Similarity Metrics in an Unsupervised-Based Feature Selection Method 研究基于无监督的特征选择方法中相似性度量的影响

2017 Brazilian Conference on Intelligent Systems (BRACIS) Pub Date : 2017-10-01 DOI: 10.1109/BRACIS.2017.61

Carine A. Dantas, R. Nunes, A. Canuto, J. C. Xavier

引用次数: 4

A Fuzzy Variant for On-Demand Data Stream Classification 按需数据流分类的模糊变体

2017 Brazilian Conference on Intelligent Systems (BRACIS) Pub Date : 2017-10-01 DOI: 10.1109/BRACIS.2017.60

T. P. D. Silva, G. Urban, P. Lopes, H. Camargo

引用次数: 3

A Fast and Robust Max-C Projection Fuzzy Autoassociative Memory with Application for Face Recognition 一种快速鲁棒的Max-C投影模糊自联想记忆及其在人脸识别中的应用

2017 Brazilian Conference on Intelligent Systems (BRACIS) Pub Date : 2017-10-01 DOI: 10.1109/BRACIS.2017.57

A. S. Santos, M. E. Valle

引用次数: 5

Complexity Measures Effectiveness in Feature Selection 复杂性衡量特征选择的有效性

2017 Brazilian Conference on Intelligent Systems (BRACIS) Pub Date : 2017-10-01 DOI: 10.1109/BRACIS.2017.66

L. C. Okimoto, Ricardo Manhães Savii, Ana Carolina Lorena

引用次数: 9

The BinOvNMTF Algorithm: Overlapping Columns Co-clustering Based on Non-negative Matrix Tri-factorization 基于非负矩阵三因子分解的重叠列共聚类算法

2017 Brazilian Conference on Intelligent Systems (BRACIS) Pub Date : 2017-10-01 DOI: 10.1109/BRACIS.2017.80

L. F. Brunialti, S. M. Peres, V. F. Silva, C. Lima

引用次数: 3