202 research outputs found

    A review on probabilistic graphical models in evolutionary computation

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    Thanks to their inherent properties, probabilistic graphical models are one of the prime candidates for machine learning and decision making tasks especially in uncertain domains. Their capabilities, like representation, inference and learning, if used effectively, can greatly help to build intelligent systems that are able to act accordingly in different problem domains. Evolutionary algorithms is one such discipline that has employed probabilistic graphical models to improve the search for optimal solutions in complex problems. This paper shows how probabilistic graphical models have been used in evolutionary algorithms to improve their performance in solving complex problems. Specifically, we give a survey of probabilistic model building-based evolutionary algorithms, called estimation of distribution algorithms, and compare different methods for probabilistic modeling in these algorithms

    A review of estimation of distribution algorithms in bioinformatics

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    Evolutionary search algorithms have become an essential asset in the algorithmic toolbox for solving high-dimensional optimization problems in across a broad range of bioinformatics problems. Genetic algorithms, the most well-known and representative evolutionary search technique, have been the subject of the major part of such applications. Estimation of distribution algorithms (EDAs) offer a novel evolutionary paradigm that constitutes a natural and attractive alternative to genetic algorithms. They make use of a probabilistic model, learnt from the promising solutions, to guide the search process. In this paper, we set out a basic taxonomy of EDA techniques, underlining the nature and complexity of the probabilistic model of each EDA variant. We review a set of innovative works that make use of EDA techniques to solve challenging bioinformatics problems, emphasizing the EDA paradigm's potential for further research in this domain

    Dating Victorians: an experimental approach to stylochronometry

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    A thesis submitted for the degree of Doctor of Philosophy ofthe University of LutonThe writing style of a number of authors writing in English was empirically investigated for the purpose of detecting stylistic patterns in relation to advancing age. The aim was to identify the type of stylistic markers among lexical, syntactical, phonemic, entropic, character-based, and content ones that would be most able to discriminate between early, middle, and late works of the selected authors, and the best classification or prediction algorithm most suited for this task. Two pilot studies were initially conducted. The first one concentrated on Christina Georgina Rossetti and Edgar Allan Poe from whom personal letters and poetry were selected as the genres of study, along with a limited selection of variables. Results suggested that authors and genre vary inconsistently. The second pilot study was based on Shakespeare's plays using a wider selection of variables to assess their discriminating power in relation to a past study. It was observed that the selected variables were of satisfactory predictive power, hence judged suitable for the task. Subsequently, four experiments were conducted using the variables tested in the second pilot study and personal correspondence and poetry from two additional authors, Edna St Vincent Millay and William Butler Yeats. Stepwise multiple linear regression and regression trees were selected to deal with the first two prediction experiments, and ordinal logistic regression and artificial neural networks for two classification experiments. The first experiment revealed inconsistency in accuracy of prediction and total number of variables in the final models affected by differences in authorship and genre. The second experiment revealed inconsistencies for the same factors in terms of accuracy only. The third experiment showed total number of variables in the model and error in the final model to be affected in various degrees by authorship, genre, different variable types and order in which the variables had been calculated. The last experiment had all measurements affected by the four factors. Examination of whether differences in method within each task play an important part revealed significant influences of method, authorship, and genre for the prediction problems, whereas all factors including method and various interactions dominated in the classification problems. Given the current data and methods used, as well as the results obtained, generalizable conclusions for the wider author population have been avoided

    Regularized model learning in EDAs for continuous and multi-objective optimization

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    Probabilistic modeling is the de�ning characteristic of estimation of distribution algorithms (EDAs) which determines their behavior and performance in optimization. Regularization is a well-known statistical technique used for obtaining an improved model by reducing the generalization error of estimation, especially in high-dimensional problems. `1-regularization is a type of this technique with the appealing variable selection property which results in sparse model estimations. In this thesis, we study the use of regularization techniques for model learning in EDAs. Several methods for regularized model estimation in continuous domains based on a Gaussian distribution assumption are presented, and analyzed from di�erent aspects when used for optimization in a high-dimensional setting, where the population size of EDA has a logarithmic scale with respect to the number of variables. The optimization results obtained for a number of continuous problems with an increasing number of variables show that the proposed EDA based on regularized model estimation performs a more robust optimization, and is able to achieve signi�cantly better results for larger dimensions than other Gaussian-based EDAs. We also propose a method for learning a marginally factorized Gaussian Markov random �eld model using regularization techniques and a clustering algorithm. The experimental results show notable optimization performance on continuous additively decomposable problems when using this model estimation method. Our study also covers multi-objective optimization and we propose joint probabilistic modeling of variables and objectives in EDAs based on Bayesian networks, speci�cally models inspired from multi-dimensional Bayesian network classi�ers. It is shown that with this approach to modeling, two new types of relationships are encoded in the estimated models in addition to the variable relationships captured in other EDAs: objectivevariable and objective-objective relationships. An extensive experimental study shows the e�ectiveness of this approach for multi- and many-objective optimization. With the proposed joint variable-objective modeling, in addition to the Pareto set approximation, the algorithm is also able to obtain an estimation of the multi-objective problem structure. Finally, the study of multi-objective optimization based on joint probabilistic modeling is extended to noisy domains, where the noise in objective values is represented by intervals. A new version of the Pareto dominance relation for ordering the solutions in these problems, namely �-degree Pareto dominance, is introduced and its properties are analyzed. We show that the ranking methods based on this dominance relation can result in competitive performance of EDAs with respect to the quality of the approximated Pareto sets. This dominance relation is then used together with a method for joint probabilistic modeling based on `1-regularization for multi-objective feature subset selection in classi�cation, where six di�erent measures of accuracy are considered as objectives with interval values. The individual assessment of the proposed joint probabilistic modeling and solution ranking methods on datasets with small-medium dimensionality, when using two di�erent Bayesian classi�ers, shows that comparable or better Pareto sets of feature subsets are approximated in comparison to standard methods

    Multivariate Markov networks for fitness modelling in an estimation of distribution algorithm.

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    A well-known paradigm for optimisation is the evolutionary algorithm (EA). An EA maintains a population of possible solutions to a problem which converges on a global optimum using biologically-inspired selection and reproduction operators. These algorithms have been shown to perform well on a variety of hard optimisation and search problems. A recent development in evolutionary computation is the Estimation of Distribution Algorithm (EDA) which replaces the traditional genetic reproduction operators (crossover and mutation) with the construction and sampling of a probabilistic model. While this can often represent a significant computational expense, the benefit is that the model contains explicit information about the fitness function. This thesis expands on recent work using a Markov network to model fitness in an EDA, resulting in what we call the Markov Fitness Model (MFM). The work has explored the theoretical foundations of the MFM approach which are grounded in Walsh analysis of fitness functions. This has allowed us to demonstrate a clear relationship between the fitness model and the underlying dynamics of the problem. A key achievement is that we have been able to show how the model can be used to predict fitness and have devised a measure of fitness modelling capability called the fitness prediction correlation (FPC). We have performed a series of experiments which use the FPC to investigate the effect of population size and selection operator on the fitness modelling capability. The results and analysis of these experiments are an important addition to other work on diversity and fitness distribution within populations. With this improved understanding of fitness modelling we have been able to extend the framework Distribution Estimation Using Markov networks (DEUM) to use a multivariate probabilistic model. We have proposed and demonstrated the performance of a number of algorithms based on this framework which lever the MFM for optimisation, which can now be added to the EA toolbox. As part of this we have investigated existing techniques for learning the structure of the MFM; a further contribution which results from this is the introduction of precision and recall as measures of structure quality. We have also proposed a number of possible directions that future work could take

    Computer vision methods for unconstrained gesture recognition in the context of sign language annotation

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    Cette thèse porte sur l'étude des méthodes de vision par ordinateur pour la reconnaissance de gestes naturels dans le contexte de l'annotation de la Langue des Signes. La langue des signes (LS) est une langue gestuelle développée par les sourds pour communiquer. Un énoncé en LS consiste en une séquence de signes réalisés par les mains, accompagnés d'expressions du visage et de mouvements du haut du corps, permettant de transmettre des informations en parallèles dans le discours. Même si les signes sont définis dans des dictionnaires, on trouve une très grande variabilité liée au contexte lors de leur réalisation. De plus, les signes sont souvent séparés par des mouvements de co-articulation. Cette extrême variabilité et l'effet de co-articulation représentent un problème important dans les recherches en traitement automatique de la LS. Il est donc nécessaire d'avoir de nombreuses vidéos annotées en LS, si l'on veut étudier cette langue et utiliser des méthodes d'apprentissage automatique. Les annotations de vidéo en LS sont réalisées manuellement par des linguistes ou experts en LS, ce qui est source d'erreur, non reproductible et extrêmement chronophage. De plus, la qualité des annotations dépend des connaissances en LS de l'annotateur. L'association de l'expertise de l'annotateur aux traitements automatiques facilite cette tâche et représente un gain de temps et de robustesse. Le but de nos recherches est d'étudier des méthodes de traitement d'images afin d'assister l'annotation des corpus vidéo: suivi des composantes corporelles, segmentation des mains, segmentation temporelle, reconnaissance de gloses. Au cours de cette thèse nous avons étudié un ensemble de méthodes permettant de réaliser l'annotation en glose. Dans un premier temps, nous cherchons à détecter les limites de début et fin de signe. Cette méthode d'annotation nécessite plusieurs traitements de bas niveau afin de segmenter les signes et d'extraire les caractéristiques de mouvement et de forme de la main. D'abord nous proposons une méthode de suivi des composantes corporelles robuste aux occultations basée sur le filtrage particulaire. Ensuite, un algorithme de segmentation des mains est développé afin d'extraire la région des mains même quand elles se trouvent devant le visage. Puis, les caractéristiques de mouvement sont utilisées pour réaliser une première segmentation temporelle des signes qui est par la suite améliorée grâce à l'utilisation de caractéristiques de forme. En effet celles-ci permettent de supprimer les limites de segmentation détectées en milieu des signes. Une fois les signes segmentés, on procède à l'extraction de caractéristiques visuelles pour leur reconnaissance en termes de gloses à l'aide de modèles phonologiques. Nous avons évalué nos algorithmes à l'aide de corpus internationaux, afin de montrer leur avantages et limitations. L'évaluation montre la robustesse de nos méthodes par rapport à la dynamique et le grand nombre d'occultations entre les différents membres. L'annotation résultante est indépendante de l'annotateur et représente un gain de robustese important.This PhD thesis concerns the study of computer vision methods for the automatic recognition of unconstrained gestures in the context of sign language annotation. Sign Language (SL) is a visual-gestural language developed by deaf communities. Continuous SL consists on a sequence of signs performed one after another involving manual and non-manual features conveying simultaneous information. Even though standard signs are defined in dictionaries, we find a huge variability caused by the context-dependency of signs. In addition signs are often linked by movement epenthesis which consists on the meaningless gesture between signs. The huge variability and the co-articulation effect represent a challenging problem during automatic SL processing. It is necessary to have numerous annotated video corpus in order to train statistical machine translators and study this language. Generally the annotation of SL video corpus is manually performed by linguists or computer scientists experienced in SL. However manual annotation is error-prone, unreproducible and time consuming. In addition de quality of the results depends on the SL annotators knowledge. Associating annotator knowledge to image processing techniques facilitates the annotation task increasing robustness and speeding up the required time. The goal of this research concerns on the study and development of image processing technique in order to assist the annotation of SL video corpus: body tracking, hand segmentation, temporal segmentation, gloss recognition. Along this PhD thesis we address the problem of gloss annotation of SL video corpus. First of all we intend to detect the limits corresponding to the beginning and end of a sign. This annotation method requires several low level approaches for performing temporal segmentation and for extracting motion and hand shape features. First we propose a particle filter based approach for robustly tracking hand and face robust to occlusions. Then a segmentation method for extracting hand when it is in front of the face has been developed. Motion is used for segmenting signs and later hand shape is used to improve the results. Indeed hand shape allows to delete limits detected in the middle of a sign. Once signs have been segmented we proceed to the gloss recognition using lexical description of signs. We have evaluated our algorithms using international corpus, in order to show their advantages and limitations. The evaluation has shown the robustness of the proposed methods with respect to high dynamics and numerous occlusions between body parts. Resulting annotation is independent on the annotator and represents a gain on annotation consistency

    Para além de pressupostos psicométricos : como desenvolver novas medidas psicológicas

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    Tese (doutorado)—Universidade de Brasília, Instituto de Psicologia, Programa de Pós-graduação em Psicologia Social, do Trabalho e das Organizações, 2019.O que define uma boa medida? Na presente tese, argumentamos e mostramos que definir uma boa medida pode ser muito mais complexo do que simplesmente executar uma análise fatorial ou uma análise usando a teoria da resposta ao item. O objetivo geral desta dissertação é apresentar três principais pressupostos da medida psicométrica e desenvolver alternativas para a medida psicológica tradicional. A tese está dividida em quatro estudos. O primeiro é um estudo teórico no qual são apresentados três pressupostos centrais comuns à teoria psicométrica e à prática psicométrica, e no qual é mostrado como alternativas às abordagens psicométricas tradicionais podem ser usadas para melhorar a medição psicológica. Essas alternativas foram desenvolvidas adaptando cada um desses três pressupostos: (1) o pressuposto de validade estrutural; (2) o pressuposto do processo; e (3) o pressuposto de construto. O pressuposto de validade estrutural refere-se à implementação de modelos matemáticos. O pressuposto de processo implica que um processo subjacente específico está gerando os dados observados. O pressuposto de construto infere que os dados observados por si só não constituem uma medida, mas que as medidas são as variáveis latentes que originam os dados observados. Vários exemplos de abordagens psicométricas alternativas já existentes são apresentados no primeiro estudo. O segundo estudo se refere ao pressuposto de validade estrutural e teve como objetivo desenvolver dois novos modelos de resposta aos itens para itens politômicos e binários que não assumem uma distribuição normal dos escores verdadeiros. O primeiro modelo desenvolvido, o Modelo de resposta ao item condicional (CIRM), assume uma distribuição beta- binomial. O segundo novo modelo é uma implementação Bayesiana do procedimento de escore ótimo (OS-IRM). Ambos os novos modelos foram comparados com o modelo tradicional de Rasch: os resultados indicam que os dois modelos desenvolvidos melhoram vários aspectos do modelo de Rasch. O terceiro estudo foi derivado do pressuposto do processo e tinha três objetivos. Primeiro, desenvolver uma implementação Bayesiana do framework de análise da função de otimização situacional (SOFA). Segundo, comparar essa implementação Bayesiana do SOFA com outros três modelos baseados em Máxima Verossimilhança, usados para estimar escores verdadeiros. O terceiro objetivo foi mostrar como a modelagem conjunta pode ser usada para pesquisas de validade. Uma das principais vantagens do framework SOFA em comparação com a abordagem psicométrica tradicional é que o SOFA depende de dados experimentais, melhorando a validade das medidas. O quarto e último estudo foi derivado do pressuposto de construto e seu principal objetivo era desenvolver um procedimento de aprendizado de estrutura de gráficos de cadeia de potência (PCGs). Um PCG é um tipo de gráfico que representa relações causais entre grupos de variáveis. Pode ser pensado como uma versão exploratória completa da modelagem de equações estruturais, bem como um modelo psicométrico que não depende de variáveis latentes. Esses quatro estudos pretendem mostrar que a modelagem psicométrica não deve se restringir ao uso de modelos tradicionais de mensuração, mas também deve considerar a adaptação desses modelos tradicionais de acordo com o uso pretendido e os processos teóricos que originam as medidas observadas.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES).What defines a good measurement? In the present dissertation we argue, and show, that defining a good measurement can be much more complex than simply performing a factor analysis or an analysis using item response theory. The overall objective of this dissertation is to present three principal assumptions of psychometric measurement, and to develop alternatives for traditional psychological measurement. The dissertation is divided in four studies. The first one is a theoretical study in which three central assumptions common to psychometric theory and psychometric practice are presented, and in which is shown how alternatives to traditional psychometric approaches can be used to improve psychological measurement. These alternatives were developed by adapting each of these three assumptions: (1) the assumption of structural validity; (2) the process assumption; and, (3) the construct assumption. The structural validity assumption relates to the implementation of mathematical models. The process assumption implies that a specific underlying process is generating the observed data. The construct assumption infers that the observed data on its own do not constitute a measurement, but the measure are the latent variables that originate the observed data. Several examples of already existing alternative psychometric approaches are presented in the first study. The second study relates to the structural validity assumption and aimed to develop two new item response models for polytomous and binary items that do not assume a normal distribution of the true scores. The first model that was developed, the Conditional Item Response Model (CIRM), assumes a beta-binomial distribution. The second new model is a Bayesian implementation of the optimal score procedure (OS-IRM). Both new models were compared with the traditional Rasch model: the results indicate that the two developed models improve various aspects of the Rasch model. The third study was derived from the process assumption and had three objectives. First, to develop a Bayesian implementation of the situational optimization function analysis (SOFA) framework. Second, to compare this Bayesian implementation of SOFA with three other Maximum Likelihood-based models that are used to estimate true scores. The third objective was to show how joint modeling can be used for validity research. One of the main advantages of the SOFA framework compared to the traditional psychometric approach is that SOFA relies on experimental data, improving the validity of the measures. The fourth and final study was derived from the construct assumption and its main objective was to develop a procedure of structure learning of power chain graphs (PCGs). A PCG is a type of graph that represents causal relations between groups of variables. It can be thought as a full exploratory version of structural equation modeling, as well as a psychometric model that is not dependent on latent variables. These four studies intend to show that psychometric modeling should not be restricted to the use of traditional measurement models, but should also consider adapting these traditional models in accordance with the intended use and theoretical processes that originate the observed measures

    LIPIcs, Volume 277, GIScience 2023, Complete Volume

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    LIPIcs, Volume 277, GIScience 2023, Complete Volum

    Drivers And Impacts Of The Invasive Round Goby (neogobius Melanostomus) In Michigan Tributaries To The Great Lakes

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    The abundance and persistence of the invasive round goby (Neogobius melanostomus) has often resulted in antagonistic interactions between the invasive and its native competitors. In this study, I sought to quantify the consequences and environmental context of these interactions in Great Lakes tributaries. Specifically, I aimed to identify changes in feeding and reproductive behavior in a native competitor in response to round goby invasion, identify potential solutions to increase regular stream monitoring by tapping into citizen science programs, and quantify the environmental context associated with successful goby invasion. Surveys of fish communities were conducted over three years in seven Michigan tributaries to the Great Lakes. Each site was evaluated for fish assemblage composition, round goby abundance, and habitat quality. Individual round goby and a native competitor, the Johnny darter (Etheostoma nigrum), were dissected for a diet comparison and to identify investment in reproduction to illustrate changes in feeding and reproductive behavior by the native species. To inform better practices for stream management and invasion detection, a quality assessment of two citizen science programs in the area was completed. Citizen data was directly compared to traditional research focused sampling methods to verify the validity of the data and its potential inclusion in ecological research. Finally, a model was developed to identify the environmental context common to sites invaded by round goby. Results suggest that Johnny darter diet diversity decreases, trophic position increases, and reproductive timing changes when goby are present. Citizen science may provide a way to monitor stream degradation which can facilitate these negative interactions. Despite differences in sampling methodology, qualitative citizen data reached similar conclusions about site quality as quantitative research methods. As identified by the environmental context model, altered riparian land use and decreased native species diversity are common characteristics of sites invaded by round goby. Regular monitoring for these characteristics may help identify locations vulnerable to round goby invasion so prevention and mitigation resources can be efficiently allocated. This research provides background on round goby invasion that can be utilized to better manage native species and ecosystems to increase resistance to and reduce the impacts of invasion
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