Tese (doutorado)—Universidade de Brasília, Faculdade de Ciências da Saúde, 2007. / Submitted by Luis Felipe Souza (luis_felas@globo.com) on 2008-11-26T17:51:10Z No. of bitstreams: 1 Tese_2007_ClaudiaMaria.pdf: 1191785 bytes, checksum: 2fee6e5e47ef0eeb75b5f7f8f9d3266b (MD5) / Approved for entry into archive by Georgia Fernandes(georgia@bce.unb.br) on 2009-02-10T16:40:43Z (GMT) No. of bitstreams: 1 Tese_2007_ClaudiaMaria.pdf: 1191785 bytes, checksum: 2fee6e5e47ef0eeb75b5f7f8f9d3266b (MD5) / Made available in DSpace on 2009-02-10T16:40:43Z (GMT). No. of bitstreams: 1 Tese_2007_ClaudiaMaria.pdf: 1191785 bytes, checksum: 2fee6e5e47ef0eeb75b5f7f8f9d3266b (MD5) / A proposta deste estudo foi desenvolver, aplicar e avaliar um programa de intervenção das metas de realização em jovens atletas de tênis de mesa de altorendimento do Distrito Federal. O Programa teve como fundamentação teórica a Teoria Sócio-Cognitiva das Metas de Realização, trabalhando com os construtos do clima motivacional, orientação e envolvimento às metas; além do estabelecimento de objetivos e adequação do nível de ativação e ansiedade, pois são fatores determinantes para a manutenção do nível motivacional do atleta. Para a elaboração das tarefas das intervenções, trabalhou-se com ênfase nas habilidades mentais e motoras, buscando a auto-regulação do atleta em momentos críticos do treinamento e de competições. Para avaliar a proposta, foi necessária a realização de um estudo preliminar, o Estudo 1, e para isso, a construção de instrumentos que avaliassem a percepção do clima motivacional de equipe (QPCME), a percepção do nível de ativação do atleta (QPNA), e a tradução de instrumento que avaliasse as orientações às metas dos atletas (TEOSQ). A amostra foi composta por 594 atletas de quatro modalidades (tênis de mesa, voleibol, atletismo e handebol), entre 13 e 18 anos de idade, sendo a média 15,86 e o desvio padrão 0,5, das cinco regiões do pais e representantes do sexo masculino (46,6%) e feminino (53.3%). Na seqüência, foi realizada a validação dos instrumentos, com análise fatorial exploratória e estatística descritiva, os quais se mostraram válidos para aplicação posterior no Programa de Intervenção. O Estudo 2 teve como foco as implicações do estudo da Teoria Motivacional Sócio-Cognitiva Metas de Realização e a intervenção por meio desta teoria. A amostra constituiu-se de 10 atletas de tênis de mesa, que foram avaliados pelos construtos das Metas de Realização e aptidão física. Posteriormente, o Estudo 2 estabeleceu as análises quantitativas do Grupo Experimental e do Grupo Controle da amostra e as análises qualitativas dos atletas Grupo Experimental. Os resultados apresentados demonstraram que desenvolver um Programa Motivacional, com ênfase na elaboração de tarefas que envolvam as habilidades mentais e motoras, leva o atleta a aumentar sua percepção de habilidades, persistência e esforço, a estabelecer metas a curto, médio, longo prazo, e a melhorar seu desempenho e sua auto-regulação. _______________________________________________________________________________________ ABSTRACT / The purpose of this study was to develop, to apply and to assess a goal achievement intervention program into Distrito Federal young athletes of table tennis. To achieve this purpose, the Achievement Goal Theory frameworks like perceived motivational climate, goal orientation and goal involvement was used. Besides, the goal setting, activation and anxiety adequacies, determine the factors for the motivational maintenance athlete level. The intervention tasks emphasis the mental and motor skills, searching the athlete self-regulation at training and competitions critical moments. To assess the proposal, it was necessary the accomplishment of a preliminary study; Study 1, and therefore, the instrument construction to assess the perception of the motivational climate of team (QPCME), the perceived arousal of the athlete (QPNA), and the translation of the instrument that assess the goal orientation of the athletes (TEOSQ). The sample was made of 594 athletes from four different modalities (table tennis, volleyball, athletics and handeball), between 13 and 18 years old, being the average média 15,86 and the diversion standard 0,5, from five regions of the country and representing both male (46,6%) and female (53,3%). After this, the reliability instruments through exploratory factorial analysis the descriptive statistics had applied in the Intervention Program. Later, the Study 2 establishes the quantitative analyses of the Experimental Group and the Group Control of the sample and the qualitative analyses of the athletes Experimental Group. Study 2 partner-cognitive had as focus the implications of the study of the motivational through Goals Achievement Theory. The presented results, having as analysis 3 athletes of the Experimental Group, had demonstrated that to develop a Motivational Program, with emphasis in the elaboration of tasks that involve the mental and motor skills, it takes the athlete to increase its perception of abilities, persistence and effort, to goal setting and to improve its performance and the self-regulation.

The following thesis explores the impact of the dataset distributional prop- erties on classification performance. We use Gaussian copulas to generate 1000 artificial dataset and train classifiers on them. We train Generalized linear models, Distributed Random forest, Extremely randomized trees and Gradient boosting machines via H2O.ai machine learning platform accessed by R. Classi- fication performance on these datasets is evaluated and empirical observations on influence are presented. Secondly, we use real Australian credit dataset and predict which classifier is possibly going to work best. The predicted perfor- mance for any individual method is based on penalizing the differences between the Australian dataset and artificial datasets where the method performed com- paratively better, but it failed to predict correctly. 1

The purpose of this study is to gain a holistic understanding of the Asian gang phenomenon through the application of a meta-synthesis, which is seldom utilized within the criminal justice and criminology discipline. Noblit and Hare’s (1988) seven step guidelines for synthesizing qualitative research informed this methodology. Through this process, 15 studies were selected for synthesis. The synthesis of these studies not only identified prevalent themes across the sample, but also provided the basis for creating overarching metaphors that captured the collective experience of Asian gang members. Through the interpretive ordering of these metaphors, a line of synthesis argument was developed in which three major inferences about the Asian gang experience were made. First, regardless of ethnic and geographic differences, the experiences of Asian gangs and their members are similar. Second, although extant literature has applied different theories to explain gang membership for individual ethnic gangs (e.g. Chinese, Vietnamese), this synthesis revealed that the dominant theory for explaining the onset and persistence of Asian gangs is Vigil’s (1988) multiple marginality theory. Finally, in comparison to the broader literature, Asian gangs are more similar than they are different to non-Asian gangs because of their overlap in values.

The Gallup Poll and General Social Survey have asked Americans about their religious service attendance since 1939 and 1972, respectively. With remarkable consistency, these two surveys have estimated that just over 40% of the American population regularly attends religious services. Yet, recent research has called this “gold standard” into question, citing three sources of bias in these estimates: (a) ambiguous item wording, (b) an ambiguously specified time frame; and (c) data collection methods that lend themselves to socially desirable responding. Several lines of research have developed to eliminate or minimize these sources of bias, but these efforts have yielded a wide variety of results, with some estimates being half as much as the gold standard! Methodological and psychometric differences are not the only source of variation, however. The characteristics of those sampled into studies also introduces variability. Given that attendance estimates are likely influenced by variations in both methodology and sampling, this study uses meta-analytic techniques to estimate the extent of their influence and to estimate the attendance rate after controlling for their influence. The findings indicate that efforts to reduce socially desirable responding have had the greatest impact on the attendance rate, followed by efforts to overcome the ambiguously specified time-frame. In addition, attendance rates are positively related to the proportion of African Americans, Whites and married respondents sampled, as well as mean years of education. Attendance rates are also negatively related to the proportion of 18 to 30 year-old respondents sampled. After controlling for these methodological and socio-demographic study characteristics, the prevalence of weekly attendance in America was variously estimated as 41.4% for the gold standard items, 43.1% for items measuring attendance in the past week, 27.8% when asking respondents what they did yesterday (i.e., on Sunday via the time-use methodology) and 22.7% when attendance was counted manually.

Solving a sequence of high-dimensional, nonconvex, but potentially similar optimization problems poses a significant computational challenge in various engineering applications. This thesis presents the first meta-learning framework that leverages the shared structure among sequential tasks to improve the computational efficiency and sample complexity of derivative-free optimization. Based on the observation that most practical high-dimensional functions lie on a latent low-dimensional manifold, which can be further shared among problem instances, the proposed method jointly learns the meta-initialization of a search point and a meta-manifold. This novel approach enables the efficient adaptation of the optimization process to new tasks by exploiting the learned meta-knowledge. Theoretically, the benefit of meta-learning in this challenging setting is established by proving that the proposed method achieves improved convergence rates and reduced sample complexity compared to traditional derivative-free optimization techniques. Empirically, the effectiveness of the proposed algorithm is demonstrated in two high-dimensional reinforcement learning tasks, showcasing its ability to accelerate learning and improve performance across multiple domains. Furthermore, the robustness and generalization capabilities of the meta-learning framework are explored through extensive ablation studies and sensitivity analyses. The thesis highlights the potential of meta-learning in tackling complex optimization problems and opens up new avenues for future research in this area. / Master of Science / Optimization problems are ubiquitous in various fields, from engineering to finance, where the goal is to find the best solution among a vast number of possibilities. However, solving these problems can be computationally challenging, especially when the search space is high-dimensional and the problem is nonconvex, meaning that there may be multiple locally optimal solutions. This thesis introduces a novel approach to tackle these challenges by leveraging the power of meta-learning, a technique that allows algorithms to learn from previous experiences and adapt to new tasks more efficiently. The proposed framework is based on the observation that many real-world optimization problems share similar underlying structures, even though they may appear different on the surface. By exploiting this shared structure, the meta-learning algorithm can learn a low-dimensional representation of the problem space, which serves as a guide for efficiently searching for optimal solutions in new, unseen problems. This approach is particularly useful when dealing with a sequence of related optimization tasks, as it allows the algorithm to transfer knowledge from one task to another, thereby reducing the computational burden and improving the overall performance. The effectiveness of the proposed meta-learning framework is demonstrated through rigorous theoretical analysis and empirical evaluations on challenging reinforcement learning tasks. These tasks involve high-dimensional search spaces and require the algorithm to adapt to changing environments. The results show that the meta-learning approach can significantly accelerate the learning process and improve the quality of the solutions compared to traditional optimization methods.

ITC/USA 2008 Conference Proceedings / The Forty-Fourth Annual International Telemetering Conference and Technical Exhibition / October 27-30, 2008 / Town and Country Resort & Convention Center, San Diego, California / Typically, FTI projects utilise acquisition hardware from multiple vendors. There are at least three ways of facilitating their integration. The first option is to implement a series of ad hoc mechanisms customised to the software interfaces provided by each specific FTI vendor. The second option is to define a meta-data format that can be used to define hardware setup and configuration in a common way. The final option is to define a common software architecture that prescribes a set of interfaces and services through which vendor hardware can be configured, and measurement data retrieved. This paper discusses the pros and cons of each approach and outlines the level of difficulty associated with each.

Made available in DSpace on 2019-03-30T00:01:20Z (GMT). No. of bitstreams: 0 Previous issue date: 2017-09-22 / Some software systems must respond to thousands or millions of concurrent requests. These systems must be properly tested to ensure that they can function correctly under the expected load. Performance degradation and consequent system failures usually arise in stressed conditions. Stress testing subjects the program to heavy loads. Stress tests di¿er from other kinds of testing in that the system is executed on its breakpoints, forcing the application or the supporting infrastructure to fail. The search for the longest execution time is seen as a discontinuous, nonlinear, optimization problem, with the input domain of the system under test as a search space. In this context, search-based testing is viewed as a promising approach to verify timing constraints. Search-based software testing is the application of metaheuristic search techniques to generate software tests. The test adequacy criterion is transformed into a ¿tness function and a set of solutions in the search space is evaluated with respect to the ¿tness functionusingametaheuristic. Search-basedstresstestinginvolves¿ndingthebest-andworst-case executiontimestoascertainwhethertimingconstraintsareful¿lled. ServiceLevelAgreements (SLAs) are documents that specify realistic performance guarantees as well as penalties for non-compliance. SLAsaremadebetweenprovidersandcustomersthatincludeservicequality, resourcescapability,scalability,obligations,andconsequencesincaseofviolations. Satisfying SLAisofgreatimportanceandachallengingissue. Themainmotivationofthisthesisisto¿nd theadequateresponsetimeofSLAsusingStressTesting. Thisthesisaddressesthreeapproaches insearch-basedstresstests. First,HybridmetaheuristicusesTabuSearch,SimulatedAnnealing, andGeneticAlgorithmsinacollaborativemanner. Second,anapproachcalledHybridQusesa reinforcementlearningtechniquetooptimizethechoiceofneighboringsolutionstoexplore,reducingthetimeneededtoobtainthescenarioswiththelongestresponsetimeintheapplication. The best solutions found by HybridQ were on average 5.98% better that achieved by the Hybrid approach without Q-learning. Third, the thesis investigates the use of the multi-objective NSGA-II,SPEA2,PAESandMOEA/Dalgorithms. MOEA/Dmetaheuristicsobtainedthebest hypervolume value when compared with other approaches. The collaborative approach using MOEA/D and HybridQ improves the hypervolume values obtained and found more relevant workloadsthanthepreviousexperiments. AtoolnamedIAdapter,aJMeterpluginforperformingsearch-basedstresstests,wasdevelopedandusedtoconductalltheexperiments. Keywords: Search-based Testing, Stress Testing, Multi-objective metaheuristics, Hybrid metaheuristics,ReinforcementLearning. / Alguns sistemas de software devem responder a milhares ou milhões de requisições simultâneos. Tais sistemas devem ser devidamente testados para garantir que eles possam funcionar corretamente sob uma carga esperada. Normalmente, a degradação do desempenho e consequentes falhas do sistema geralmente ocorrem em condições de estresse. No teste de estresse o sistema é submetido a cargas de trabalho acima dos resquistos não funcionais estabelecidos. Os testes de estresse diferem de outros tipos de testes em que o sistema é executado em seus pontos de interrupção, forçando o aplicativo ou a infra-estrutura de suporte a falhar. Testes de estresse podem ser vistos como um problema de otimização descontínuo, não-linear, comodomínio de entrada do sistema em test ecomo espaço de busca. Neste contexto,ostestes baseados em busca (search-based tests) são vistos como uma abordagem promissora para veri¿car as restrições de tempo. O teste de software baseado em busca é a aplicação de técnicas de pesquisa metaheurística para gerar testes de software. O critério de adequação do teste é transformado em uma função objetivo e um conjunto de soluções no espaço de busca é avaliado em relação à função objetivo usando uma metaheurística. Otestedeestressebaseadoembusca envolve encontrar os tempos de execução melhores e piores para veri¿car se as restrições de tempo são cumpridas. Os acordos de nível de serviço (SLA) são documentos que especi¿cam garantias de desempenho realistas, bem como penalidades por incumprimento. Os SLAs são feitos entre provedores e clientes que incluem qualidade do serviço, capacidade de recursos, escalabilidade, obrigações e consequencias em caso de violação. Satisfazer o SLA é de grande importância e um problema desa¿ador. A principal motivação desta tese é encontrar o tempo de resposta adequado dos SLAs usando teste de estresse. Esta tese apresenta três abordagens em testes de estresse baseados em busca. Primeiro, a metaheurística híbrida usa Tabu Search, Simulated Annealing e Algoritmos Genéticos de forma colaborativa. Em segundo lugar, uma abordagem chamada HybridQ usa uma técnica de aprendizado de reforço para otimizar a escolha de soluções vizinhas para explorar, reduzindo o tempo necessário para obter os cenários com o tempo de resposta mais longo na aplicação. As melhores soluções encontradas pelo HybridQ foram em média 5,98 % melhores que alcançadas pela abordagem híbrida sem Qlearning. Em terceiro lugar, a tese investiga o uso dos algoritmos multi-objetivos NSGA-II, SPEA2, PAES e MOEA/D. A metaheurística MOEA/D obteve o melhor valor de hipervolume quando comparada com outras abordagens. A abordagem colaborativa usand oMOEA/DeHybridQ melhora os valores de hipervolume obtidos e encontrou workloads mais relevantes do que as experiências anteriores. Uma ferramenta chamada IAdapter, um plugin JMeter para realizar testes de esforço baseados em busca, foi desenvolvida e usada para realizar todas as experiências. Palavras-chave: Search-basedTesting,StressTesting,Multi-objective metaheuristics,Hybridmetaheuristics,ReinforcementLearning

Pork quality is an important issue for the whole meat chain, from producers, abattoirs, retailers through to costumers and is affected by a web of multi-factorial actions that occur throughout the pork production chain. A vast amount of information is available on how these diverse factors influence different pork quality traits. However, results derived from individual studies often vary and are in some cases even contradictory due to different experimental designs or different pork quality assessment techniques or protocols. Also, individual influencing factors are often studied in isolation, ignoring interacting effects. A suitable method is therefore required to account for a range of interacting factors, to combine the results from different experiments and to derive generic response-laws. The aim of this thesis was to use meta-analyses to produce quantitative, predictive models that describe how diverse factors affect pork quality over a range of experimental conditions.

The purpose of this study was to quantitatively evaluate the overall effectiveness of eating disorder prevention programs and to investigate potential moderating variables that may influence the magnitude of intervention effects. Meta-analysis was used to conduct a comprehensive and systematic analysis of data across 46 studies. Effect size estimates were grouped into outcome sets based on the following variables: knowledge, general eating pathology, dieting, thin-ideal internalization, body dissatisfaction, negative affect, and self-esteem. Q statistics were used to analyze the distribution of effect size estimates within each outcome set and to explore the systematic influence of moderating variables. Results revealed large effects on the acquisition of knowledge and small net effects on reducing maladaptive eating attitudes and behaviors at posttest and follow-up. These programs were not found to produce significant effects on negative affect, and there were inconsistent effects on self-esteem across studies. Population targeted was the sole moderator that could account for variability in effect size distributions. There was a tendency toward greater benefits for studies targeting participants considered to be at a relatively higher risk for developing an eating disorder. Previous assumptions regarding the insufficiency of "one-shot" interventions and concerns about the iatrogenic effects of including information about eating disorders in an intervention were not supported by the data. These findings challenge negative conclusions drawn in previous review articles regarding the inability of eating disorder prevention programs to demonstrate behavioral improvements. Although these findings have implications for the prevention of eating disorders, it was argued that a clear link between intervention efficacy and a decreased incidence of eating disorders was not demonstrated. Rather, only direct information was offered about the ability to influence eating disorder related knowledge, attitudes, and behaviors. Specific recommendations related to intervention content, reasonable goals/expectations, and outcome criteria were offered for improving research in this area.

Functional Communication Training (FCT) is an intervention that involves teaching a communicative response to decrease the occurrence of challenging behavior in individuals with disabilities. FCT is a two step intervention in which the interventionist first determines the function, or purpose, of the challenging behavior and then teaches a communicative response that will provide the same function as the challenging behavior. This meta-analysis addressed the following questions: (a) Is FCT more effective with a complete or brief functional analysis? (b) Is FCT differentially more effective for one communication mode versus another (unaided augmentative and alternative communication, aided augmentative and alternative communication, or verbal)? (c) Is FCT more effective when implemented in natural or contrived contexts? (d) Is FCT more effective for different functions of challenging behavior (attention, tangible, escape and multiple)? (e) How effective is FCT with individuals with challenging behavior, across different age ranges? (f) How effective is FCT with individuals with challenging behavior, across different disability categories? A thorough search was performed to find all articles related to FCT. The articles were then reviewed to ensure that they met the inclusion criteria. Data were extracted from the graphs within each study and then analyzed using Robust Improvement Rate Difference (IRD). Forest plots were also created to aid in visual analysis to determine statistical significance and consistency of the results. A variable was determined to moderate the effectiveness of FCT if there was a statistically significant difference between the levels within each variable. Thirty nine studies were included in this meta-analysis. Over-all FCT has a Robust IRD score of .86 (confidence intervals = .85 - .87). Based on the findings of this meta-analysis FCT is most effective with brief functional analysis and verbal communication. FCT was equally effective in natural and contrived settings. FCT appears to be most effective when an individual's behavior serves as attention seeking or an attempt to gain access to a tangible item. FCT appears to be more effective with school age individuals rather than adults. Finally, FCT may be more effective with individuals with autism spectrum disorder than intellectual disabilities or other disabilities.