The diploma thesis is focused on the socialization and integration of children with autism spectrum disorders. The theoretical part describes the characteristics of the disorders, the process of socialization, the rules of successful inclusion of children in society. It deals with ways of education, especially structured learning. The empirical part focuses on research off socialization and education from the perspective of parents and pedagogical staff. The research is performed through casuistry and interviews.

The aim of this paper was to investigate what principles and approaches can be utilized when helping dyslexic students to improve their reading skills in the English classroom. The structure of this study is narrative research synthesis which means that the paper is based on articles written by others. The results indicate that there are several approaches to make use of, such as the Orton-Gillingham approach, Phonics and Whole language. Many experts support the principle of multisensory structured learning regarding the teaching of dyslexic students. This means that students use all their senses at the same time: visual, auditory and kinaesthetic. In order to further help students improve reading skills, the teacher can create a safe and calm classroom environment to reduce stress.

The thesis called "Mathematics for children with special educational needs in primary school focused on the Asperger´s Syndrome issue" (hereinafter referred to as AS) introduces the problematic of AS and recommends specific teachings methods for pupils having AS. The thesis is divided into four thematic chapters. The first chapter introduces the AS itself. It describes all the typical characteristics, especially problems in the area of communication and establishing social relationships which are substantial at AS. However, all these issues have a solution. This chapter therefore includes a relevant overview of methods and techniques that can help individuals with AS. Then we introduce educational methods of children with AS in primary schools. There are different ways to educate individuals with AS, i.e. individuals with specific educational needs. The major ones are their integration at primary school or their specific education in dedicated classrooms. In this work we also deal with the issue of bullying these children. We note there is number of tutorials on how to prevent existing bullying. The chapter dealing with education of children with AS at primary schools is terminated by a description of methods of teaching these individuals. In the following section we refer to the key part of the thesis which is mathematics. In the thesis we report about the subject matter of mathematics at primary school in detail. The thesis also includes areas investigated in our own research. From all the forms and methods of teaching we are trying to highlight those that are beneficial and convenient for individuals with AS. The practical part includes description of work with two pupils with the AS diagnosis. The research was conducted in two main phases. During the first one the pupils were observed in mathematics lessons, in the second one then they filled in prepared worksheets, whose main aim was to assist them in areas where they are weak or not as good as in other areas of mathematics. We have attached several worksheets to the thesis and with their help we illustrate the results of our work. In the final discussion there are summarized lessons learned and given some practical verification instructions for working with pupils with AS.

Background: There is a need for students to integrate theory with practice and there is an ongoing search for the best learning and teaching models in Primary Health Care settings. The aim of this study was to explore preceptors' and nursing students’ experiences of using peer learning during clinical practice in Primary Health Care. Methods: A qualitative research approach was used based on semi-structured interviews with 7 preceptors and 8 nursing students performed in May 2017. The interviews were transcribed and analyzed by using content analysis based on an inductive reasoning. Results: Preceptors and students perceived peer learning as a pedagogical model beneficial for learning in primary care settings and described the model as stimulating, challenging and developing. All informants were positive of the peer learning experience and students described that they were seen as individuals and not treated as a couple even if they worked in peers. The physical environment was demanding due to telephone counseling, limited opportunities for using computers and small rooms. Conclusion: This study shows that despite the complex learning environment peer learning as a pedagogical model seems to work well in Primary health care setting. However, there is much to improve to facilitate the student's learning process. The students should be given priority and that the assignment with preceptorship should be highlighted Keywords: Learning environment, Peer learning, Physical environment, Primary Health Care, Structured learning activities.

L’apprentissage structuré est devenu omniprésent dans le traitement automatique des langues naturelles. De nombreuses applications qui font maintenant partie de notre vie telles que des assistants personnels, la traduction automatique, ou encore la reconnaissance vocale, reposent sur ces techniques. Les problèmes d'apprentissage structuré qu’il est nécessaire de résoudre sont de plus en plus complexes et demandent de prendre en compte de plus en plus d’informations à des niveaux linguistiques variés (morphologique, syntaxique, etc.) et reposent la question du meilleurs compromis entre la finesse de la modélisation et l’exactitude des algorithmes d’apprentissage et d’inférence. L’apprentissage par imitation propose de réaliser les procédures d’apprentissage et d’inférence de manière approchée afin de pouvoir exploiter pleinement des structures de dépendance plus riches. Cette thèse explore ce cadre d’apprentissage, en particulier l’algorithme SEARN, à la fois sur le plan théorique ainsi que ses possibilités d’application aux tâches de traitement automatique des langues, notamment aux plus complexes telles que la traduction. Concernant les aspects théoriques, nous présentons un cadre unifié pour les différentes familles d’apprentissage par imitation, qui permet de redériver de manière simple les propriétés de convergence de ces algorithmes; concernant les aspects plus appliqués, nous utilisons l’apprentissage par imitation d’une part pour explorer l’étiquetage de séquences en ordre libre; d’autre part pour étudier des stratégies de décodage en deux étapes pour la traduction automatique. / Structured learning has become ubiquitousin Natural Language Processing; a multitude ofapplications, such as personal assistants, machinetranslation and speech recognition, to name just afew, rely on such techniques. The structured learningproblems that must now be solved are becomingincreasingly more complex and require an increasingamount of information at different linguisticlevels (morphological, syntactic, etc.). It is thereforecrucial to find the best trade-off between the degreeof modelling detail and the exactitude of the inferencealgorithm. Imitation learning aims to perform approximatelearning and inference in order to better exploitricher dependency structures. In this thesis, we explorethe use of this specific learning setting, in particularusing the SEARN algorithm, both from a theoreticalperspective and in terms of the practical applicationsto Natural Language Processing tasks, especiallyto complex tasks such as machine translation.Concerning the theoretical aspects, we introduce aunified framework for different imitation learning algorithmfamilies, allowing us to review and simplifythe convergence properties of the algorithms. With regardsto the more practical application of our work, weuse imitation learning first to experiment with free ordersequence labelling and secondly to explore twostepdecoding strategies for machine translation.

A plethora of high-impact applications involve predictive modeling of structured data. In various domains, from hospital readmission prediction in the medical realm, though weather forecasting and event detection in power systems, up to conversion prediction in online businesses, the data holds a certain underlying structure. Building predictive models from such data calls for leveraging the structure as an additional source of information. Thus, a broad range of structure-aware approaches have been introduced, yet certain common challenges in many structured learning scenarios remain unresolved. This dissertation revolves around addressing the challenges of scalability, algorithmic stability and temporal awareness in several scenarios of learning from either graphically or sequentially structured data. Initially, the first two challenges are discussed from a structured regression standpoint. The studies addressing these challenges aim at designing scalable and algorithmically stable models for structured data, without compromising their prediction performance. It is further inspected whether such models can be applied to both static and dynamic (time-varying) graph data. To that end, a structured ensemble model is proposed to scale with the size of temporal graphs, while making stable and reliable yet accurate predictions on a real-world application involving gene expression prediction. In the case of static graphs, a theoretical insight is provided on the relationship between algorithmic stability and generalization in a structured regression setting. A stability-based objective function is designed to indirectly control the stability of a collaborative ensemble regressor, yielding generalization performance improvements on structured regression applications as diverse as predicting housing prices based on real-estate transactions and readmission prediction from hospital records. Modeling data that holds a sequential rather than a graphical structure requires addressing temporal awareness as one of the major challenges. In that regard, a model is proposed to generate time-aware representations of user activity sequences, intended to be seamlessly applicable across different user-related tasks, while sidestepping the burden of task-driven feature engineering. The quality and effectiveness of the time-aware user representations led to predictive performance improvements over state-of-the-art models on multiple large-scale conversion prediction tasks. Sequential data is also analyzed from the perspective of a high-impact application in the realm of power systems. Namely, detecting and further classifying disturbance events, as an important aspect of risk mitigation in power systems, is typically centered on the challenges of capturing structural characteristics in sequential synchrophasor recordings. Therefore, a thorough comparative analysis was conducted by assessing various traditional as well as more sophisticated event classification models under different domain-expert-assisted labeling scenarios. The experimental findings provide evidence that hierarchical convolutional neural networks (HCNNs), capable of automatically learning time-invariant feature transformations that preserve the structural characteristics of the synchrophasor signals, consistently outperform traditional model variants. Their performance is observed to further improve as more data are inspected by a domain expert, while smaller fractions of solely expert-inspected signals are already sufficient for HCNNs to achieve satisfactory event classification accuracy. Finally, insights into the impact of the domain expertise on the downstream classification performance are also discussed. / Computer and Information Science

Statistical shift-reduce parsing involves the interplay of representation learning, structured learning, and inexact search. This dissertation considers approaches that tightly integrate these three elements and explores three novel models for shift-reduce CCG parsing. First, I develop a dependency model, in which the selection of shift-reduce action sequences producing a dependency structure is treated as a hidden variable; the key components of the model are a dependency oracle and a learning algorithm that integrates the dependency oracle, the structured perceptron, and beam search. Second, I present expected F-measure training and show how to derive a globally normalized RNN model, in which beam search is naturally incorporated and used in conjunction with the objective to learn shift-reduce action sequences optimized for the final evaluation metric. Finally, I describe an LSTM model that is able to construct parser state representations incrementally by following the shift-reduce syntactic derivation process; I show expected F-measure training, which is agnostic to the underlying neural network, can be applied in this setting to obtain globally normalized greedy and beam-search LSTM shift-reduce parsers.

This thesis examines the development of communication skills of children with developmental dysphasia, with the use of augmentative and alternative communication. The work is made up of theoretical and practical part. The research focuses on the influence of elements augmentative communication intervention in children with developmental dysphasia in preschool facilities. The research part is formed by empirical research based on a case study of a boy with developmental dysphasia, case reports describing the progress of special education intervention in preschool institutions, research part is completed by the conclusions of the investigation and recommendations for practice. The aim is to show the positive influence the course of education of children with developmental dysphasia support structured learning and noticeable advances in communication development of children with the support of the graphic expression.

The topic of this thesis is "Pupil with Autism Spectrum Disorder in a Mainstream School - Case Study of a Pupil in Elementary School and Secondary School". The first, theoretical, part of the thesis deals with autism spectrum disorders (ASD) and diagnostics; it will also address current legislative changes of the educational system in the Czech Republic as well as the counseling system, school counseling centers, and school counseling facilities. The second, practical, part will deal with the process of educating pupils with ASD in mainstream elementary schools and secondary schools. The aim of my research was to determine how pupils with ASD were able to integrate into a standard primary school and secondary. I focused on approaches and methods used in the educational process, especially on structured learning, as well as on the teachers' readiness to integrate pupils with special educational needs. The research was conducted in České Budějovice, at the J. Š. Baar Elementary School and Kindergarten, and at the J. V. Jirsík Gymnasium. I used the observation method, interviews with the legal representatives of both pupils, teachers and teachers' assistants, and direct work with autistic pupils; I also had the opportunity to teach the pupil at the gymnasium during my teaching practice as part of my...

Introdução: O número de estudos relacionados à Síndrome Metabólica (SM) vem aumentando nos últimos anos, muitas vezes motivados pelo aumento do número de casos de sobrepeso/obesidade e diabetes Tipo II levando ao desenvolvimento de doenças cardiovasculares e, como consequência, infarto agudo do miocárdio e AVC, dentre outros desfechos desfavoráveis. A SM é uma doença multifatorial composta de cinco características, porém, para que um indivíduo seja diagnosticado com ela, possuir pelo menos três dessas características torna-se condição suficiente. Essas cinco características são: Obesidade visceral, caracterizada pelo aumento da circunferência da cintura, Glicemia de jejum elevada, Triglicérides aumentado, HDL-colesterol reduzido, Pressão Arterial aumentada. Objetivo: Estabelecer a rede de associações entre os fenótipos que compõem a Síndrome Metabólica através do aprendizado de estruturas de dependência, decompor a rede em componentes de correlação genética e ambiental e avaliar o efeito de ajustes por covariáveis e por variantes genéticas exclusivamente relacionadas à cada um dos fenótipos da rede. Material e Métodos: A amostra do estudo corresponderá a 79 famílias da cidade mineira de Baependi, composta por 1666 indivíduos. O aprendizado de estruturas de redes será feito por meio da Teoria de Grafos e Modelos de Equações Estruturais envolvendo o modelo linear misto poligênico para determinar as relações de dependência entre os fenótipos que compõem a Síndrome Metabólica / Introduction: The number of studies related to Metabolic Syndrome (MetS) has been increasing in the last years, encouraged by the increase on the overweight / obesity and Type II Diabetes cases, leading to the development of cardiovascular disease and, therefore, acute myocardial infarction and stroke, and others unfavorable outcomes. MetS is a multifactorial disease containing five characteristics, however, for an individual to be diagnosed with MetS, he/she may have at least three of them. These characteristics are: Truncal Obesity, characterized by increasing on the waist circumference, increasing on Fasting Blood Glucose, increasing on Triglycerides, decreasing on HDL cholesterol and increasing on Blood Pressure. Aims: Establish the best association network between MetS phenotypes through structured dependency learning between phenotypes considering genetic variants exclusively related to each phenotype. Materials and Methods: The study sample is composed of 79 families, 1666 individuals of a city in a rural area of Brazil, called Beapendi. Structured learning will use graph theory and Structural Equations Models to establish the dependency relations between MetS phenotypes