The purpose of this thesis is to introduce a reader with existed commercial applications and control the FITkit using QDevKit. The main goal of the thesis is proposed and implemeted modul for application QDevKit which makes easy proposal and progress applications for platform FITkit.

Tato práce je zaměřena na multiplatformní grafická uživatelské rozhraní a jejich hardwarovou akceleraci. Popisuje, co to uživatelské rozhraní jsou a srovnává nástroje na jejich tvorbu  a způsoby jejich realizace. Hlavním bodem je vlastní návrh a implementace nástroje na tvorbu multiplatformních hardwarově akcelerovaných grafických uživatelských rozhraní. Srovnává vlastní koncept s existujícími řešeními, a uvádí ho do praxe na projektu s externí firmou.

Orientador: João Peres Vieira / Resumo: A Programação Linear é usada na Pesquisa Operacional para resolução de problemas cujo objetivo é encontrar a melhor solução para aqueles problemas que tenham seus modelos representados por expressões lineares. A Álgebra Linear vai ser a ferramenta para a Programação Linear, resolvendo problemas de maximização ou minimização. Vamos utilizar o Método Simplex e, no caso de duas variáveis, apresentaremos também o método gráfico. / Abstract: Linear Programming is used in Operational Research to solve problems resolution whose goal is to find the best solution for those problems that have their models represented by linear expressions. Linear Algebra will be the tool for Linear Programming, solving maximization or minimization problems. We will use the Simplex Method and, in the case of two variables, we will also present the graphical method. / Mestre

For this study three questions are being studied. Question one: “How does the audio compressionaffect the perceived video quality?” Question two is somewhat reversed: “How does the texturedetail affect the perceived audio quality?” The final question follows: “How do the audiocompression and texture detail affect the perceived overall quality?” The experiment was done on 36different untrained listeners, whereof 2 didn’t give complete answers and were therefore notincluded in the results. In total the result was based on 34 test subjects. Subjects participating in theexperiment had to play through three levels, one level for each research question. In each levelsubjects evaluated the game’s different qualities. In one level only the texture detail changed (LevelVV), in the other only audio compression rate changed (Level AA) and in the third both audiocompression and texture detail changed (Level AV). The texture detail ranged from low to medium tohigh setting, while the audio compression had four different levels ranging from 49 kbit/s to 150kbit/s, in the compressed format of ogg vorbis. The result shows that subjects did not perceive animprovement in quality in either of the single quality tests, i.e. levels AA and VV. In the last level withmultiple changing stimuli (AV), subjects could identify the lower video quality from medium and high.Subjects also showed a significant difference in perceived overall quality in the AV level, subjectsshowed that they could identify a difference in audio quality better, when video quality was on thelow setting.

Gemeinsamer Workshop von Universitaetsrechenzentrum und Professur Rechnernetze und verteilte Systeme der Fakultaet fuer Informatik der TU Chemnitz. Möglichkeiten der Accountverwaltung und Authentifizierung in der zukünftigen Windowsplattform.

Embodied agents, be they animals or robots, acquire information about the world through their senses. Embodied agents, however, do not simply lose this information once it passes by, but rather process and store it for future use. The most general theory of how an agent can combine stored knowledge with new observations is Bayesian inference. In this dissertation I present a theory of how embodied agents can learn to implement Bayesian inference with neural networks. By neural network I mean both artificial and biological neural networks, and in my dissertation I address both kinds. On one hand, I develop theory for implementing Bayesian inference in deep generative models, and I show how to train multilayer perceptrons to compute approximate predictions for Bayesian filtering. On the other hand, I show that several models in computational neuroscience are special cases of the general theory that I develop in this dissertation, and I use this theory to model and explain several phenomena in neuroscience. The key contributions of this dissertation can be summarized as follows: - I develop a class of graphical model called nth-order harmoniums. An nth-order harmonium is an n-tuple of random variables, where the conditional distribution of each variable given all the others is always an element of the same exponential family. I show that harmoniums have a recursive structure which allows them to be analyzed at coarser and finer levels of detail. - I define a class of harmoniums called rectified harmoniums, which are constrained to have priors which are conjugate to their posteriors. As a consequence of this, rectified harmoniums afford efficient sampling and learning. - I develop deep harmoniums, which are harmoniums which can be represented by hierarchical, undirected graphs. I develop the theory of rectification for deep harmoniums, and develop a novel algorithm for training deep generative models. - I show how to implement a variety of optimal and near-optimal Bayes filters by combining the solution to Bayes' rule provided by rectified harmoniums, with predictions computed by a recurrent neural network. I then show how to train a neural network to implement Bayesian filtering when the transition and emission distributions are unknown. - I show how some well-established models of neural activity are special cases of the theory I present in this dissertation, and how these models can be generalized with the theory of rectification. - I show how the theory that I present can model several neural phenomena including proprioception and gain-field modulation of tuning curves. - I introduce a library for the programming language Haskell, within which I have implemented all the simulations presented in this dissertation. This library uses concepts from Riemannian geometry to provide a rigorous and efficient environment for implementing complex numerical simulations. I also use the results presented in this dissertation to argue for the fundamental role of neural computation in embodied cognition. I argue, in other words, that before we will be able to build truly intelligent robots, we will need to truly understand biological brains.

For both randomized clinical trials and prospective cohort studies, the Cox regression model is a powerful tool for evaluating the effect of a treatment or an explanatory variable on time-to-event outcome. This method assumes proportional hazards over time. Systematic approaches to efficiently evaluate non-proportionality and to model data in the presence of non-proportionality are investigated. Six graphical methods are assessed to verify the proportional hazards assumption based on characteristics of the survival function, cumulative hazard, or the feature of residuals. Their performances are empirically evaluated with simulations by checking their ability to be consistent and sensitive in detecting proportionality or non-proportionality. Two-sample data are generated in three scenarios of proportional hazards and five types of alternatives (that is, non-proportionality). The usefulness of these graphical assessment methods depends on the event rate and type of non-proportionality. Three numerical (statistical testing) methods are compared via simulation studies to investigate the proportional hazards assumption. In evaluating data for proportionality versus non-proportionality, the goal is to test a non-zero slope in a regression of the variable or its residuals on a specific function of time, or a Kolmogorov-type supremum test. Our simulation results show that statistical test performance is affected by the number of events, event rate, and degree of divergence of non-proportionality for a given hazards scenario. Determining which test will be used in practice depends on the specific situation under investigation. Both graphical and numerical approaches have benefits and costs, but they are complementary to each other. Several approaches to model and summarize non-proportionality data are presented, including non-parametric measurements and testing, semi-parametric models, and a parametric approach. Some illustrative examples using simulated data and real data are also presented. In summary, we present a systemic approach using both graphical and numerical methods to identify non-proportionality, and to provide numerous modeling strategies when proportionality is violated in time-to-event data.

Multi-factor authentication (MFA) offers a wide range of methods and techniques available today. The security benefits of using MFA are almost indisputable, however, users are reluctant to adopt the technology. While many new MFA solutions are being proposed, there is a lack of consideration for user sentiment in the early stages of development. In an attempt to balance security and usability, this report investigates the feasibility of a new authentication mechanism that uses browser fingerprinting, graphical passwords, and honeytokens. This was evaluated by conducting a limited literature review, producing a prototype, interviews with test users, and security experts, as well as ensuring feasibility through a requirements checklist. The results of this research provides evidence that this mechanism is feasible, and appealing to end users. However, more investigation is required in order to ensure the mechanism's viability in a real-world deployment.

This thesis consists of two papers studying structure learning in probabilistic graphical models for both undirected graphs anddirected acyclic graphs (DAGs). Paper A, presents a novel family of graph theoretical algorithms, called the junction tree expanders, that incrementally construct junction trees for decomposable graphs. Due to its Markovian property, the junction tree expanders are shown to be suitable for proposal kernels in a sequential Monte Carlo (SMC) sampling scheme for approximating a graph posterior distribution. A simulation study is performed for the case of Gaussian decomposable graphical models showing efficiency of the suggested unified approach for both structural and parametric Bayesian inference. Paper B, develops a novel prior distribution over DAGs with the ability to express prior knowledge in terms of graph layerings. In conjunction with the prior, a search and score algorithm based on the layering property of DAGs, is developed for performing structure learning in Bayesian networks. A simulation study shows that the search and score algorithm along with the prior has superior performance for learning graph with a clearly layered structure compared with other priors. / <p>QC 20160111</p>

In Model-Based Testing (MBT) the main goal is to test a system by designing models which describe the functionality of the system to test. Subsequently, test cases are obtained from the model, and these test cases can be executed automatically. Experience has shown that the learning curve for learning MBT can be steep - especially for people who do not have previous programming experience. This is because the language used to design models uses programming language concepts. In this thesis we describe a tool which automatically generates models, given an initial set of requirements. The advantage of this tool is that users do not need to learn a model-based testing language to design models, but instead they must learn to use a high-level of abstraction and a Graphical User Interface to specify their test cases. We demonstrate the value of the tool by using it to design models that generate test cases for telecommunications system, but show that this tool can be adapted for use in testing similar systems. The application of this tool can facilitate traditional phase-based software development methods, by saving a considerable amount of time and resources. In addition, when applied to agile software development, the reduced time required for testing because of the use of our tool helps shortening the feedback loops between designing and testing, thus increasing team efficiency within every iteration. / I Model-Based Testing (MBT) är det huvudsakliga målet att testa ett system genom modeller som beskriver systemets unktionalitet för att testa. Därefter erhålls testfall från modellen, och dessa testfall kan utföras automatiskt. Erfarenheten har visat att inlärningskurvan för lärande MBT kan vara branta - särskilt för personer som inte har tidigare erfarenhet av programmering. Detta beror på det språk som används för användning programmeringsspråk begrepp. I denna avhandling beskriver vi ett verktyg som automatiskt genererar modellerna, med tanke på en första uppsättning krav. Fördelen med detta verktyg är att användarna inte behöver lära sig en modellbaserad testning av språket att konstruera modeller, utan de måste lära sig att använda en hög nivåav abstraktion och ett grafiskt användargränssnitt för att ange sina testfall. Vi demonstrerar värdet av verktyget genom att använda den för att konstruera modeller som genererar testfall för telekommunikationssystem, men visar att detta verktyg kan anpassas för användning vid testning av liknande system. Tillämpningen av detta verktyg kan underlätta traditionella fas-baserade metoder mjukvaruutveckling, genom att spara en avsevärd tid och resurser. Dessutom, när det tillämpas på Agile Software utveckling, minskade tid som krävs för att testa på grund av användningen av vårt verktyg hjälper förkorta återkopplingar mellan design och testning, vilket ökar teamet effektiviteten inom varje iteration. / En Model-Based Testing (MBT), el objetivo principal es testear un sistema mediante el diseño de modelos que describan su funcionalidad. En consecuencia, estos modelos generan test cases que pueden ser ejecutados automáticamente en dicho sistema. La experiencia nos muestra que la curva de aprendizaje en el caso de MBT puede ser pronunciada, especialmente para aquellos sin ninguna experiencia previa en programación. Esto se debe a que los lenguajes usados para diseñar modelos usan conceptos intrínsecos a los lenguajes de programación. En este Proyecto Fin de Carrera, describimos una herramienta que genera automáticamente modelos, dado un conjunto de requisitos inicial. La ventaja que ofrece esta herramienta es que los usuarios no requieren el aprendizaje de ninguno lenguaje de modelado a la hora de diseñar modelos, sino que tan solo deben aprender a utilizar una Interfaz de Usuario Gráfica (GUI), a un alto nivel de abstracción, para especificar sus test cases. Demostramos el valor de esta herramienta mediante su aplicación en un nuevo sistema de telecomunicaciones en fase de pruebas de Ericsson, mostrando al mismo tiempo que puede ser utilizada en el testeo de sistemas similares. La aplicación de esta herramienta puede facilitar los métodos de desarrollo de software tradicionales mediante el ahorro de una cantidad considerable de tiempo y recursos. Además, aplicada a métodos de desarrollo ágil de software, el tiempo reducido requerido para el testing a causa del uso de esta herramienta ayuda a acortar los plazos entre diseño y testing, y en consecuencia, incrementando la eficiencia del equipo en cada iteración.