Several technologies and languages exist for the development and implementation of distributed systems. Furthermore, several models for teaching computer programming and teaching programming in a distance-based educational environment exist. Limited literature, however, is available on models for teaching distributed computing in a distance-based educational environment. The focus of this study is to examine how distributed computing should be taught in a distance-based educational environment so as to ensure effective and quality learning for students. The required effectiveness and quality should be comparable to those for students exposed to laboratories, as commonly found in residential universities. This leads to an investigation of the factors that contribute to the success of teaching distributed computing and how these factors can be integrated into a distance-based teaching model. The study consisted of a literature study, followed by a comparative study of available tools to aid in the learning and teaching of distributed computing in a distance-based educational environment. A model to accomplish this teaching and learning is then proposed and implemented. The findings of the study highlight the requirements and challenges that a student of distributed computing in a distance-based educational environment faces and emphasises how the proposed model can address these challenges. This study employed qualitative research, as opposed to quantitative research, as qualitative research methods are designed to help researchers to understand people and the social and cultural contexts within which they live. The research methods employed are design research, since an artefact is created, and a case study, since “how” and “why” questions need to be answered. Data collection was done through a survey. Each method was evaluated via its own well-established evaluation methods, since evaluation is a crucial component of the research process. / Computing / M. Sc. (Computer Science)

We present a theory for constructing linear, black box approximations to face recognition algorithms and empirically demonstrate that a surprisingly diverse set of face recognition approaches can be approximated well using a linear model. The construction of the linear model to a face recognition algorithm involves embedding of a training set of face images constrained by the distances between them, as computed by the face recognition algorithm being approximated. We accomplish this embedding by iterative majorization, initialized by classical multi-dimensional scaling (MDS). We empirically demonstrate the adequacy of the linear model using six face recognition algorithms, spanning both template based and feature based approaches on standard face recognition benchmarks such as the Facial Recognition Technology (FERET) and Face Recognition Grand Challenge (FRGC) data sets. The experimental results show that the average Error in Modeling for six algorithms is 6.3% at 0.001 False Acceptance Rate (FAR), for FERET fafb probe set which contains maximum number of subjects among all the probe sets. We demonstrate the usefulness of the linear model for algorithm dependent indexing of face databases and find that it results in more than 20 times reduction in face comparisons for Bayesian Intra/Extra-class person classifier (BAY), Elastic Bunch Graph Matching algorithm (EBGM), and the commercial face recognition algorithms. We also propose a novel paradigm to reconstruct face templates from match scores using the linear model and use the reconstructed templates to explore the security breach in a face recognition system. We evaluate the proposed template reconstruction scheme using three, fundamentally different, face recognition algorithms: Principal Component Analysis (PCA), Bayesian Intra/Extra-class person classifier (BAY), and a feature based commercial algorithm. With an operational point set at 1% False Acceptance Rate (FAR) and 99% True Acceptance Rate (TAR) for 1196 enrollments (FERET gallery), we show that at most 600 attempts (score computations) are required to achieve 73%, 72% and 100% chance of breaking in as a randomly chosen target subject for the commercial, BAY and PCA based face recognition system, respectively. We also show that the proposed reconstruction scheme has 47% more probability of breaking in as a randomly chosen target subject for the commercial system as compared to a hill climbing approach with the same number of attempts.

Several technologies and languages exist for the development and implementation of distributed systems. Furthermore, several models for teaching computer programming and teaching programming in a distance-based educational environment exist. Limited literature, however, is available on models for teaching distributed computing in a distance-based educational environment. The focus of this study is to examine how distributed computing should be taught in a distance-based educational environment so as to ensure effective and quality learning for students. The required effectiveness and quality should be comparable to those for students exposed to laboratories, as commonly found in residential universities. This leads to an investigation of the factors that contribute to the success of teaching distributed computing and how these factors can be integrated into a distance-based teaching model. The study consisted of a literature study, followed by a comparative study of available tools to aid in the learning and teaching of distributed computing in a distance-based educational environment. A model to accomplish this teaching and learning is then proposed and implemented. The findings of the study highlight the requirements and challenges that a student of distributed computing in a distance-based educational environment faces and emphasises how the proposed model can address these challenges. This study employed qualitative research, as opposed to quantitative research, as qualitative research methods are designed to help researchers to understand people and the social and cultural contexts within which they live. The research methods employed are design research, since an artefact is created, and a case study, since “how” and “why” questions need to be answered. Data collection was done through a survey. Each method was evaluated via its own well-established evaluation methods, since evaluation is a crucial component of the research process. / Computing / M. Sc. (Computer Science)

The master thesis focuses on further education of secondary-grammar-school geography teachers in the Karlovy Vary region. The topic has been studied in detail using a qualitative research in the form of multiple case studies of semi-structured interviews of persons relevant to further education in the studied region. The qualitative research conducted brings a wide range of data relevant to the approaches taken by management of different schools and geography teachers to further education, opportunities for further education in the Karlovy Vary region, and the subsequent transfer of its results to the geography curriculum. Based on the data collected from the persons asked, a proposal for a possible expansion of the further education of secondary-grammar-school geography teachers in the Karlovy Vary region was drafted. This proposal represents a motivation for improvement of the quality of the geography curriculum in the region.

This thesis issues with possible methods for face detection and localization according to the state of the art. It describes various approaches and it is aimed at localization by neural networks and at necessary operations that have to be done before localization and after that for correct results representation. This project contains implementation of few approaches to neural netwok based face localization with emphasis on eigenfaces based face localization as well as implementation of simple classifier using distance of reconstructed face to the original one. Detailed description of implemented system, achieved results and dependecy of system performance on it's inner settings is also provided.

In recent years, there has been a significant increase in the usage of audience engagement platforms, which have allowed for engaging interactions between presenters and their audiences. The increased popularity of the platforms comes from the fact that engaging and interactive presentations have been shown to improve learning outcomes and create positive presentation experiences. However, using the platforms does not guarantee that your audience is engaged and participating. Given that the added value of engaging presentations only applies if the audience is actually engaged, it increases the need to know if and how engaged your audience is. The usage of audience engagement platforms has allowed for new ways of engagement to be studied. By utilizing the data gathered from the interactive presentation sessions, engagement can be studied and quantified through the modeling of the data. As the usage of audience engagement platforms and the study of presentation engagement is relatively new, there exists a limited amount of labeled data quantifying the level of engagement during presentations. To model the data, machine learning models should therefore be trained to generalize by being exposed to a limited number of presentation samples. This technique of training machine learning models is also referred to as few-shot learning. Distance-based machine learning models are defined in this study as models that make classifications and inferences by calculating distances between observations or observation class representations. Distance-based models have previously shown relatively good performance in few-shot learning applications, and interest therefore lies in expanding their application areas. This study presents a comparative evaluation of distance- and non-distance-based machine learning models given the problem of classifying presentations as being engaged or non-engaged, and estimating presentation class likelihoods in a few-shot learning context. A presentation-level dataset was gathered from the interactive presentation sessions, and each presentation observation was labeled as being engaged or non-engaged. The machine learning models were then trained to model the data and evaluated in terms of how well they were able to generalize to unseen testing samples by being exposed to a limited number of training observations. In particular, their classification and class likelihood estimation performances were evaluated. The results conclude that the distance-based models outperformed the non-distance-based models artificial neural network and relevance-vector machine given the presentation class likelihood estimation problem. The metric learning nearest neighbor classifier was the only distance-based model that outperformed all the non-distance-based models given both the presentation classification and class likelihood estimation problems. / Under de senaste åren har det skett en betydande ökning av användningen av plattformar för publikengagemang, vilket har möjliggjort engagerande interaktioner mellan presentatörer och deras publik. Plattformarnas ökade popularitet kommer från det faktum att engagerande och interaktiva presentationer har visat sig förbättra läranderesultat och skapa positiva presentationsupplevelser. Att använda plattformarna garanterar dock inte att din publik är engagerad och deltagande. Med tanke på att mervärdet av engagerande presentationer bara gäller om publiken faktiskt är engagerad, ökar det behovet av att veta om och hur engagerad din publik är. Användningen av plattformar för publikengagemang har gjort det möjligt att på nya sätt studera engagemang. Genom att använda data som samlats in från de interaktiva presentationssessionerna kan engagemang studeras och kvantifieras genom modellering av data. Eftersom användandet av plattformar för publikengagemang och studien av presentationsengagemang är relativt nytt, finns det en begränsad mängd märkt data som kvantifierar nivån av engagemang under presentationerna. För att modellera datan så bör maskininlärningsmodeller tränas att generalisera genom att utsättas för ett begränsad antal presentations observationer. Denna teknik för att träna inlärningsmodeller kallas också few-shot lärande. Distans-baserade maskininlärningsmodeller definieras i denna studie som modeller som gör klassificeringar genom att beräkna avstånd mellan observationer eller observationsklass representationer. Distans-baserade modeller har tidigare visat relativt goda resultat i few-shot inlärning problem, och intresset ligger därför i att utöka deras tillämpningsområden. Denna studie presenterar en jämförande utvärdering av distans- och icke-distans baserade maskininlärningsmodeller givet problemet med att klassificera presentationer som engagerade eller icke-engagerade, och uppskattning av presentation klasssannolikheter i ett few-shot inlärnings sammanhang. Ett dataset på presentationsnivå samlades in från de interaktiva presentationssessionerna, och varje presentation märktes som engagerad eller icke-engagerad. Maskininlärningsmodellerna tränades sedan för att modellera data och utvärderades i termer av hur väl de kunde generalisera till osedda testobservationer givet att de exponeras mot ett begränsat antal träningsobservationer. I synnerhet utvärderades deras klassificering och uppskattning av klasssannolikheter. Resultaten visade att alla distans-baserade modeller var bättre än de icke-distansbaserade modellerna artificial neural network och relevence-vector machine givet problemet med uppskattning av klasssannolikheter. Den distans-baserade metric learning nearest neighbor klassificeraren var den enda avståndsbaserade modellen som överträffade alla icke-distansbaserade modeller givet problemen med presentations klassificering och klasssannolikhets uppskattning.

Our intention is to find similarity among the time series expressions of the genes in microarray experiments. It is hypothesized that at a given time point the concentration of one gene’s mRNA is directly affected by the concentration of other gene’s mRNA, and may have biological significance. We define dissimilarity between two time-series data set as the variance of Euclidean distances of each time points. The large numbers of gene expressions make the calculation of variance of distance in each point computationally expensive and therefore computationally challenging in terms of execution time. For this reason we use autoregressive model which estimates nineteen points gene expression to a three point vector. It allows us to find variance of difference between two data sets without point-to-point matching. Previous analysis from the microarray experiments data found that 62 genes are regulated following EGF (Epidermal Growth Factor) and HRG (Heregulin) treatment of the MCF-7 breast cancer cells. We have chosen these suspected cancer-related genes as our reference and investigated which additional set of genes has similar time point expression profiles. Keeping variance of difference as a measure of distance, we have used several methods for clustering the gene expression data, such as our own maximum clique finding heuristics and hierarchical clustering. The results obtained were validated through a text mining study. New predictions from our study could be a basis for further investigations in the genesis of breast cancer. Overall in 84 new genes are found in which 57 genes are related to cancer among them 35 genes are associated with breast cancer.