Methods leading to the connection of communicative, linguistic and literary component at primary school

TITLE: Methods leading to the connection of communicative, linguistic and literary component at primary school AUTHOR: Anna Šušmáková DEPARTMENT: Czech literature department SUPERVISOR: PhDr. Ondřej Hausenblas ABSTRACT: The aim of this thesis is to map and suggest procedures leading to connection of communication, linguistic and literary education at lower primary school. I find inspiration in both existing and potential procedures. The work helps in orientation in connections of three educations, makes clear how each education helps the other two and how they in pupil`s natural communication and reading belong together. The work considers possibilities and benefits and identifies demands, which such integrating conception and procedures make on teachers and pupils. In the theoretical part the thesis analyses conception and relation of the three educations in Framework Education Programme (RVP) and explores how differently they are understood and used at some schools and by some teachers. The practical part presents semi- structured interviews with the school representatives. The goal of this was creating an exploratory probe, which reveals how realistic are the proposed outcome and lesson changes. The probe also shows desired changes with teachers and the project itself. Finally, the work proposes initial..

A Deep Learning Model for Automatic Sleep Scoring using Multimodality Time Series

Sleep scoring is a fundamental but time-consuming process in any sleep laboratory. Automatic sleep scoring is crucial and urgent to help address the increasing unmet need for sleep research. Therefore, this paper aims to develop an end-to-end deep learning architecture using raw polysomnographic recordings to automate sleep scoring. The proposed model adopts two-dimensional convolutional neural networks (2D-CNN) to automatically learn features from multi-modality signals, together with a "squeeze and excitation" block for recalibrating channel-wise feature responses. The learnt representations are finally fed to a softmax classifier to generate predictions for each sleep stage. The model performance is evaluated on two public sleep datasets (SHHS and Sleep-EDF) with different available channels. The results have shown that our model achieves an overall accuracy of 85.2% on the SHHS dataset and an accuracy of 85% on the Sleep-EDF dataset. We have also demonstrated that the proposed architecture not only is able to handle various numbers of input channels and several signal modalities from different datasets but also exhibits short runtimes and low computational cost.peerReviewe