The population of seniors is growing and will continue to increase in the next decade. Computer technology holds the promise of enhancing the quality of life and independence of older people as it may increase their ability to perform a variety of tasks. This is true for elderly. By the year 2030, people age 65 or older will comprise 22% of the population in the United States. As the population shifts so that a greater percentage are middle-aged and older adults, and as dependence on computer technology increases, it becomes more crucial to understand how to design computer displays for these older age groups. The research has compared various display representation formats to try to find out which is the best way to represent information to seniors in any form of display and the reason for the preferences. The formats compared include high and low density screens for abstract icon representation, concrete icon representation, tabular representation and graphical representation.This research also endeavored to study the effectiveness of remote usability testing as compared to inlab testing for seniors. Results indicated that density of screen is a very important factor affecting the performance of older adults. Density effect showed statistical significance F (1,112)=8.934, p< .05 from further post-hoc analysis that was conducted. Although significant results were not obtained, different formats of display representations may still be an area worth pursuing. Also it was noted that remote usability testing is not as effective as inlab testing for seniors in terms of time taken to conduct the study and the number of user comments collected. Implications, as well as recommendations and conclusions, of the study are presented. / Master of Science

Graphical models are powerful tools when estimating complex dependence structures among large sets of data. This thesis restricts the scope to undirected Gaussian graphical models. An initial predefined sparse precision matrix was specified to generate multivariate normally distributed data. Utilizing the generated data, a simulation study was conducted reviewing accuracy, sensitivity and specificity of the estimated precision matrix. The graphical LASSO was applied using four different packages available in R with seven selection criteria's for estimating the tuning parameter. The findings are mostly in line with previous research. The graphical LASSO is generally faster and feasible in high dimensions, in contrast to stepwise model selection. A portion of the selection methods for estimating the optimal tuning parameter obtained the true network structure. The results provide an estimate of how well each model obtains the true, predefined dependence structure as featured in our simulation. As the simulated data used in this thesis is merely an approximation of real-world data, one should not take the results as the only aspect of consideration when choosing a model.

No description available.

Gaussian graphical model has been a popular tool to investigate conditional dependency between random variables by estimating sparse precision matrices. The estimated precision matrices could be mapped into networks for visualization. For related but different classes, jointly estimating networks by taking advantage of common structure across classes can help us better estimate conditional dependencies among variables. Furthermore, there may exist multilevel structure among variables; some variables are considered as higher level variables and others are nested in these higher level variables, which are called lower level variables. In this dissertation, we made several contributions to the area of joint estimation of Gaussian graphical models across heterogeneous classes: the first is to propose a joint estimation method for estimating Gaussian graphical models across unbalanced multi-classes, whereas the second considers multilevel variable information during the joint estimation procedure and simultaneously estimates higher level network and lower level network. For the first project, we consider the problem of jointly estimating Gaussian graphical models across unbalanced multi-class. Most existing methods require equal or similar sample size among classes. However, many real applications do not have similar sample sizes. Hence, in this dissertation, we propose the joint adaptive graphical lasso, a weighted L1 penalized approach, for unbalanced multi-class problems. Our joint adaptive graphical lasso approach combines information across classes so that their common characteristics can be shared during the estimation process. We also introduce regularization into the adaptive term so that the unbalancedness of data is taken into account. Simulation studies show that our approach performs better than existing methods in terms of false positive rate, accuracy, Mathews correlation coefficient, and false discovery rate. We demonstrate the advantage of our approach using liver cancer data set. For the second one, we propose a method to jointly estimate the multilevel Gaussian graphical models across multiple classes. Currently, methods are still limited to investigate a single level conditional dependency structure when there exists the multilevel structure among variables. Due to the fact that higher level variables may work together to accomplish certain tasks, simultaneously exploring conditional dependency structures among higher level variables and among lower level variables are of our main interest. Given multilevel data from heterogeneous classes, our method assures that common structures in terms of the multilevel conditional dependency are shared during the estimation procedure, yet unique structures for each class are retained as well. Our proposed approach is achieved by first introducing a higher level variable factor within a class, and then common factors across classes. The performance of our approach is evaluated on several simulated networks. We also demonstrate the advantage of our approach using breast cancer patient data. / Ph. D.

This work focuses on statistical methods to understand how frequently rare events occur and what the magnitude of extreme values such as large losses is. It lies in a field called extreme value analysis whose scope is to provide support for scientific decision making when extreme observations are of particular importance such as in environmental applications, insurance and finance. In the univariate case, I propose new techniques to model tails of discrete distributions and illustrate them in an application on word frequency and multiple birth data. Suitably rescaled, the limiting tails of some discrete distributions are shown to converge to a discrete generalized Pareto distribution and generalized Zipf distribution respectively. In the multivariate high-dimensional case, I suggest modeling tail dependence between random variables by a graph such that its nodes correspond to the variables and shocks propagate through the edges. Relying on the ideas of graphical models, I prove that if the variables satisfy a new notion called asymptotic conditional independence, then the density of the joint distribution can be simplified and expressed in terms of lower dimensional functions. This generalizes the Hammersley- Clifford theorem and enables us to infer tail distributions from observations in reduced dimension. As an illustration, extreme river flows are modeled by a tree graphical model whose structure appears to recover almost exactly the actual river network. A fundamental concept when studying limiting tail distributions is regular variation. I propose a new notion in the multivariate case called one-component regular variation, of which Karamata's and the representation theorem, two important results in the univariate case, are generalizations. Eventually, I turn my attention to website visit data and fit a censored copula Gaussian graphical model allowing the visualization of users' behavior by a graph.

The focus of this report is to discuss graphical password systems and how they can contribute to handle security problems that threaten authentication processes. One such threat is shoulder-surfing attacks, which are also reviewed in this report. Three already existing systems that are claimed to be shoulder-surfing resilient are described and a new proposed system is presented and evaluated through a user study. Moreover, the system is compared to the mentioned existing systems to further evaluate the usability, memorability and the time it takes to authenticate. The user study shows that test subjects are able to remember their chosen password one week after having registered and signed in once. It is also shown that the average time to sign in to the system after five minutes of practice is within a range of 3.30 to 5.70 seconds. The participants in the experiments gave the system an average score above 68 on the System Usability Scale, which is the score of an average system.

The goal of this thesis was to create graphical demo in OpenGL, which would appropriately react on music, which is inseparable part of graphical demos. Work consists of two main parts, first is programming of real-time graphical engine, second is creating graphical demo which would run on graphical engine created in first part. First part focues on programming of OpenGL based application, which would render scene in real-time using techniques from computer graphics. Second part focuses mainly on graphical activity, which includes 3D modelling, texturing, creating animation and connection to music (reaction of environment and animations to changes in music)

In the past years, several network reconstruction methods modeled as Gaussian Graphical Model in high dimensional settings where proposed. In this work we will analyze three different methods, the Graphical Lasso (GLasso), Graphical Ridge (GGMridge) and a novel method called LPC, or Local Partial Correlation. The evaluation will be performed in high dimensional data generated from different simulated random graph structures (Erdos-Renyi, Barabasi-Albert, Watts-Strogatz ), using Receiver Operating Characteristic or ROC curve. We will also apply the methods in the reconstruction of genetic co-expression network for the differentially expressed genes in cervical cancer tumors. / Vários métodos tem sido propostos para a reconstrução de redes em alta dimensão, que e tratada como um Modelo Gráfico Gaussiano. Neste trabalho vamos analisar três métodos diferentes, o método Graphical Lasso (GLasso), Graphical Ridge (GGMridge) e um novo método chamado LPC, ou Correlação Parcial Local. A avaliação será realizada em dados de alta dimensão, gerados a partir de grafos aleatórios (Erdos-Renyi, Barabasi-Albert, Watts-Strogatz ), usando Receptor de Operação Característica, ou curva ROC. Aplicaremos também os metidos apresentados, na reconstrução da rede de co-expressão gênica para tumores de câncer cervical.

Le modèle physique masses interactions est puissant pour la simulation de comportements dynamiques très divers et pour la production de mouvements expressifs, riches et d'une grande complexité. En revanche, une difficulté inhérente à ce type de formalisme pour la production d'images animées réside dans le fait que les masses ponctuelles n'ont pas de spatialité ; il est donc difficile de produire des séquences d'images animées par le rendu direct des masses ponctuelles décrivant le mouvement. D'une manière générale, il est donc nécessaire de développer des méthodes qui étendent la spatialité de ces masses ponctuelles pour compléter la chaîne de production d'images animées par modèle physique particulaire. Une méthode, proposée par le laboratoire ICA, répond à ce type de problématique en permettant d'étendre la spatialité des masses ponctuelles en considérant l'interaction physique entre ces masses et un milieu. Il s'agit d'une métaphore du procédé physique de la gravure. Celle ci a permis de produire des images animées convaincantes de divers phénomènes visuels. Nous présentons dans ce document un élargissement de cette méthode notamment au cas 3D, ainsi qu'à de nouveaux comportements. De plus, l'algorithme de cette méthode a été parallélisé, ce qui nous a permis d'obtenir des simulations calculées en temps réel en utilisant la puissance actuelle des cartes graphiques. Afin de maitriser au mieux les possibilités de la méthode, nous avons développé un logiciel comprenant une interface graphique manipulable et interactive permettant de modéliser avec aisance différents comportements. Cette méthode a été intégrée dans des installations interactives artistiques multi-sensorielles fournissant un comportement dynamique riche et configurable, tout en permettant une interaction en temps réel avec le spectateur. / Mass – Interaction physical modeling is a powerful formalism for the simulation of various dynamic behaviors and for the production of expressive, rich and complex motions. However, there is an inherent matter of this type of formalism for animation production, which resides on the fact that masses have no spatiality. Thus, it is difficult to produce animation sequences directly from rendering mass point describing the movement. It is then necessary to develop methods that extend the masses spatiality in order to complete the animation process. ICA Laboratory addressed the problem with a method based on the physical simulation of interaction between these masses and a dynamic milieu, according to the metaphor of engraving. We present in this document an extension of this method notably towards 3D and other effects. Besides, the parallel implementation on Graphic Cards (GPU) allowed obtaining real time simulation. An interactive graphical interface was also developed to facilitate the creation of different models. We used this process in multi-sensory interactive art installations for its rich and dynamic ability to create shape from motion and interact in real time with spectators.

In the past years, several network reconstruction methods modeled as Gaussian Graphical Model in high dimensional settings where proposed. In this work we will analyze three different methods, the Graphical Lasso (GLasso), Graphical Ridge (GGMridge) and a novel method called LPC, or Local Partial Correlation. The evaluation will be performed in high dimensional data generated from different simulated random graph structures (Erdos-Renyi, Barabasi-Albert, Watts-Strogatz ), using Receiver Operating Characteristic or ROC curve. We will also apply the methods in the reconstruction of genetic co-expression network for the differentially expressed genes in cervical cancer tumors. / Vários métodos tem sido propostos para a reconstrução de redes em alta dimensão, que e tratada como um Modelo Gráfico Gaussiano. Neste trabalho vamos analisar três métodos diferentes, o método Graphical Lasso (GLasso), Graphical Ridge (GGMridge) e um novo método chamado LPC, ou Correlação Parcial Local. A avaliação será realizada em dados de alta dimensão, gerados a partir de grafos aleatórios (Erdos-Renyi, Barabasi-Albert, Watts-Strogatz ), usando Receptor de Operação Característica, ou curva ROC. Aplicaremos também os metidos apresentados, na reconstrução da rede de co-expressão gênica para tumores de câncer cervical.