One goal of the present meta-analysis was to assess if attachment-based preventative interventions are effective at fostering attachment security and preventing problems associated with insecure and disorganized attachment. Another goal was to determine what factors are associated with larger effects. Studies were considered eligible if they were a randomized controlled trial, had an attachment-based preventative intervention for children, and had a measure of attachment security, behaviour problems, language development, or emotional regulation. A random effects model was used and a total of 22 studies were included in the meta-analysis. The results of the meta-analysis indicated that attachment-based prevention interventions produced a reliable small to moderate change (d = .37) in children’s attachment security and problems associated with insecure and disorganized attachment. Potential moderating variables were also examined. Total number of sessions and the proportion of single caregivers was associated with a larger effect.

Proefschrift Universiteit van Amsterdam. / Met lit.opg. en samenvatting in het Nederlands.

Diss.--Philosophische Fakultät--Zurich--Universität Zürich, 1986. Titre de soutenance : Sie töten uns - nicht unsere Ideen.

Thesis (Ph. D.)--Florida State University, 2005. / Advisor: Dr. Marcia Rosal, Florida State University, School of Visual Arts and Dance, Dept. of Art Education. Title and description from dissertation home page (viewed June 8, 2005). Document formatted into pages; contains xii, 142 pages. Includes bibliographical references.

Thesis (D.A.)--Illinois State University, 1985. / Title from title page screen, viewed June 30, 2005. Dissertation Committee: Bernard McCarney (chair), Mathew Morey, Ronald Halinski, Alan Dillingham, Anthony Ostrosky. Includes bibliographical references (leaves 162-165) and abstract. Also available in print.

Thesis (M.M.) Florida State University, 2005. / Advisor: Jayne M. Standley, Florida State University, College of Music. Title and description from dissertation home page (viewed 5-16-2007). Document formatted into pages; contains 31 pages. Includes biographical sketch. Includes bibliographical references.

Diss.--Philosophische Fakultät--Zurich--Universität Zürich, 1986. Titre de soutenance : Sie töten uns - nicht unsere Ideen. / Constitue la 2ème partie de la thèse de D. Stump.

PERICINI, Matheus Henrique Machado. Utilização de metaheurísticas para balanceamento de carga em ambientes MapReduce. 2017. 71 f. Dissertação (Mestrado em Ciência da Computação)-Universidade Federal do Ceará, Fortaleza, 2017. / Submitted by Jonatas Martins (jonatasmartins@lia.ufc.br) on 2017-10-19T17:17:01Z No. of bitstreams: 1 2017_dis_mhmpericini.pdf: 2342022 bytes, checksum: 8bfd2d1fee199d87109de3ba41cb73df (MD5) / Approved for entry into archive by Jairo Viana (jairo@ufc.br) on 2017-10-30T17:13:30Z (GMT) No. of bitstreams: 1 2017_dis_mhmpericini.pdf: 2342022 bytes, checksum: 8bfd2d1fee199d87109de3ba41cb73df (MD5) / Made available in DSpace on 2017-10-30T17:13:30Z (GMT). No. of bitstreams: 1 2017_dis_mhmpericini.pdf: 2342022 bytes, checksum: 8bfd2d1fee199d87109de3ba41cb73df (MD5) Previous issue date: 2017 / With the increase in the number of data obtained by large companies, it was necessary to elaborate new strategies for the processing of this data in order to maintain the relevance of the information that they contain. One of the strategies that has been widely used is based on a programming model, called MapReduce, which uses division and conquest to process the data in a cluster of machines. Hadoop is one of the most consolidated implementations of the MapReduce model. But even such a strategy is subject to improvement. In it, the runtime depends on all the machines causing any overloaded machine to generate a delay in the delivery of the result. This overhead is caused by a problem commonly called Data Skew which consists of an unequal division of data, either by the size of the data or by the way it is divided. In order to solve this problem, we have proposed the MALiBU, an improvement of the execution strategy of Hadoop, which partitions the data between the machines using a meta-heuristic among them Simulated Annealing, Local Beam Search or Stochastic Beam Search. Experimental results showed improvements in the performance of Hadoop when using metaheuristics to distribute the data among the processing elements of the model, as well as among the three meta-heuristics evaluated, which has the best results. / Com o aumento do número de dados obtidos por grandes empresas, foi necessário elaborar novas estratégias para o processamento desses dados de modo a manter sua relevância e aproveitar suas informações. Uma das estratégias que tem sido amplamente utilizada tem como base um modelo de programação, chamado MapReduce, que utiliza divisão e conquista para processar os dados em um cluster de máquinas. O Hadoop é uma das implementações mais consolidadas do modelo de MapReduce. Mas mesmo tal estratégia é passível de melhorias. Nela o tempo de execução é dependente de todas as máquinas fazendo com que qualquer máquina sobrecarregada gere um atraso na entrega do resultado. Essa sobrecarga é causada por um problema chamado comumente de Data Skew que consiste em uma divisão desigual dos dados causado pelo tamanho dos dados, o modo como eles são divididos, ou o processamento desigual dos dados. Visando resolver esse problema, propusemos o MALiBU, uma melhoria da estratégia de execução do MapReduce que particiona os dados entre as máquinas usando uma meta-heurística dentre elas Simulated Annealing, Local Beam Search ou Stochastic Beam Search. Resultados experimentais mostraram melhorias no desempenho do MapReduce quando se faz uso de meta-heurística para distribuir os dados entre as máquinas, bem como mostraram, dentre as três meta-heurísticas avaliadas, qual delas melhor balanceia a carga.

Made available in DSpace on 2016-08-29T15:33:17Z (GMT). No. of bitstreams: 1 tese_5121_.pdf: 735975 bytes, checksum: aeffd7d6fc81e4f73c1f18fb633dc4e1 (MD5) Previous issue date: 2012-07-30 / Agrupamento de dados é uma tarefa recorrente em mineração de dados. Com o passar do tempo, vem se tornando mais importante o agrupamento de bases cada vez maiores. Contudo, aplicar heurísticas de agrupamento tradicionais em grandes bases não é uma tarefa fácil. Essas técnicas geralmente possuem complexidades pelo menos quadráticas no número de pontos da base, tornando o seu uso inviável pelo alto tempo de resposta ou pela baixa qualidade da solução final. A solução mais comumente utilizada para resolver o problema de agrupamento em bases de dados grandes é usar algoritmos especiais, mais fracos no ponto de vista da qualidade. Este trabalho propõe uma abordagem diferente para resolver esse problema: o uso de algoritmos tradicionais, mais fortes, em um sub-conjunto dos dados originais. Esse sub-conjunto dos dados originais é obtido com uso de um algoritmo co-evolutivo que seleciona um sub-conjunto de pontos difícil de agrupar.

This doctoral thesis will present the results of work into optimisation algorithms. We first give a detailed exploration of the problems involved in comparing optimisation algorithms. In particular we provide extensions and refinements to no free lunch results, exploring algorithms with arbitrary stopping conditions, optimisation under restricted metrics, parallel computing and free lunches, and head-to-head minimax behaviour. We also characterise no free lunch results in terms of order statistics. We then ask what really constitutes understanding of an optimisation algorithm. We argue that one central part of understanding an optimiser is knowing its Bayesian prior and cost function. We then pursue a general Bayesian framing of optimisation, and prove that this Bayesian perspective is applicable to all optimisers, and that even seemingly non-Bayesian optimisers can be understood in this way. Specifically we prove that arbitrary optimisation algorithms can be represented as a prior and a cost function. We examine the relationship between the Kolmogorov complexity of the optimiser and the Kolmogorov complexity of it’s corresponding prior. We also extended our results from deterministic optimisers to stochastic optimisers and forgetful optimisers, and we show that uniform randomly selecting a prior is not equivalent to uniform randomly selecting an optimisation behaviour. Lastly we consider what the best way to go about gaining a Bayesian understanding of real optimisation algorithms is. We use the developed Bayesian framework to explore the affects of some common approaches to constructing meta-heuristic optimisation algorithms, such as on-line parameter adaptation. We conclude by exploring an approach to uncovering the probabilistic beliefs of optimisers with a “shattering” method.