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Authenticating users by means of passwords is still the dominant form of authentication despite its recognised weaknesses. To solve this, authenticating users with images or pictures (i.e. graphical passwords) is proposed as one possible alternative as it is claimed that pictures are easy to remember, easy to use and has considerable security. Reviewing literature from the last twenty years found that few graphical password schemes have successfully been applied as the primary user authentication mechanism, with many studies reporting that their proposed scheme was better than their predecessors and they normally compared their scheme with the traditional password-based. In addition, opportunities for further research in areas such as image selection, image storage and retrieval, memorability (i.e. the user’s ability to remember passwords), predictability, applicability to multiple platforms, as well as users’ familiarity are still widely possible. Motivated by the above findings and hoping to reduce the aforementioned issues, this thesis reports upon a series of graphical password studies by comparing existing methods, developing a novel alternative scheme, and introducing guidance for users before they start selecting their password. Specifically, two studies comparing graphical password methods were conducted with the specific aims to evaluate users’ familiarity and perception towards graphical methods and to examine the performance of graphical methods in the web environment. To investigate the feasibility of combining two graphical methods, a novel graphical method known as EGAS (Enhanced Graphical Authentication System) was developed and tested in terms of its ease of use, ideal secret combination, ideal login strategies, effect of using smaller tolerances (i.e. areas where the click is still accepted) as well as users’ familiarity. In addition, graphical password guidelines (GPG) were introduced and deployed within the EGAS prototype, in order to evaluate their potential to assist users in creating appropriate password choices. From these studies, the thesis provides an alternative classification for graphical password methods by looking at the users’ tasks when authenticating into the system; namely click-based, choice-based, draw-based and hybrid. Findings from comparative studies revealed that although a number of participants stated that they were aware of the existence of graphical passwords, they actually had little understanding of the methods involved. Moreover, the methods of selecting a series of images (i.e. choice-based) and clicking on the image (i.e. click-based) are actually possible to be used for web-based authentication due to both of them reporting complementary results. With respect to EGAS, the studies have shown that combining two graphical methods is possible and does not introduce negative effects upon the resulting usability. User familiarity with the EGAS software prototype was also improved as they used the software for periods of time, with improvement shown in login time, accuracy and login failures. With the above findings, the research proposes that users’ familiarity is one of the key elements in deploying any graphical method, and appropriate HCI guidelines should be considered and employed during development of the scheme. Additionally, employing the guidelines within the graphical method and not treating them as a separate entity in user authentication is also recommended. Other than that, elements such as reducing predictability, testing with multiple usage scenarios and platforms, as well as flexibility with respect to tolerance should be the focus for future research.

Cette thèse étudie l'inférence de modèles graphiques Gaussiens en grande dimension à partir de données du transcriptome non indépendamment et identiquement distribuées dans l'objectif d'estimer des réseaux de régulation génétique. Dans ce contexte de données en grande dimension, l'hétérogénéité des données peut être mise à profit pour définir des méthodes de régularisation structurées améliorant la qualité des estimateurs. Nous considérons tout d'abord l'hétérogénéité apparaissant au niveau du réseau, fondée sur l'hypothèse que les réseaux biologiques sont organisés, ce qui nous conduit à définir une régularisation l1 pondérée. Modélisant l'hétérogénéité au niveau des données, nous étudions les propriétés théoriques d'une méthode de régularisation par bloc appelée coopérative-Lasso, définie dans le but de lier l'inférence sur des jeux de données distincts mais proches en un certain sens. Pour finir, nous nous intéressons au problème central de l'incertitude des estimations, définissant un test d'homogénéité pour modèle linéaire en grande dimension. / This thesis investigates the inference of high-dimensional Gaussian graphical models from non identically and independently distributed transcriptomic data in the objective of recovering gene regulatory networks. In the context of high-dimensional statistics, data heterogeneity paves the way to the definition of structured regularizers in order to improve the quality of the estimator. We first consider heterogeneity at the network level, building upon the assumption that biological networks are organized, which leads to the definition of a weighted l1 regularization. Modelling heterogeneity at the observation level, we provide a consistency analysis of a recent block-sparse regularizer called the cooperative-Lasso designed to combine observations from distinct but close datasets. Finally we address the crucial question of uncertainty, deriving homonegeity tests for high-dimensional linear regression.

Automated human activity recognition has attracted increasing attention in the past decade. However, the application of machine learning and probabilistic methods for activity recognition problems has been studied only in the past couple of years. For the first time, this thesis explores the application of semi-supervised and active learning in activity recognition. We present a new and efficient semi-supervised training method for parameter estimation and feature selection in conditional random fields (CRFs),a probabilistic graphical model. In real-world applications such as activity recognition, unlabeled sensor traces are relatively easy to obtain whereas labeled examples are expensive and tedious to collect. Furthermore, the ability to automatically select a small subset of discriminatory features from a large pool can be advantageous in terms of computational speed as well as accuracy. We introduce the semi-supervised virtual evidence boosting (sVEB)algorithm for training CRFs — a semi-supervised extension to the recently developed virtual evidence boosting (VEB) method for feature selection and parameter learning. sVEB takes advantage of the unlabeled data via mini-mum entropy regularization. The objective function combines the unlabeled conditional entropy with labeled conditional pseudo-likelihood. The sVEB algorithm reduces the overall system cost as well as the human labeling cost required during training, which are both important considerations in building real world inference systems. Moreover, we propose an active learning algorithm for training CRFs is based on virtual evidence boosting and uses entropy measures. Active virtual evidence boosting (aVEB) queries the user for most informative examples, efficiently builds up labeled training examples and incorporates unlabeled data as in sVEB. aVEB not only reduces computational complexity of training CRFs as in sVEB, but also outputs more accurate classification results for the same fraction of labeled data. Ina set of experiments we illustrate that our algorithms, sVEB and aVEB, benefit from both the use of unlabeled data and automatic feature selection, and outperform other semi-supervised and active training approaches. The proposed methods could also be extended and employed for other classification problems in relational data.

Probability is a useful tool for reasoning when faced with uncertainty. Bayesian networks offer a compact representation of a probabilistic problem, exploiting independence amongst variables that allows a factorization of the joint probability into much smaller local probability distributions.<p>The standard approach to probabilistic inference in Bayesian networks is to compile the graph into a join­tree, and perform computation over this secondary structure. While join­trees are among the most time­efficient methods of inference in Bayesian networks, they are not always appropriate for certain applications. The memory requirements of join­tree can be prohibitively large. The algorithms for computing over join­trees are large and involved, making them difficult to port to other systems or be understood by general programmers without Bayesian network expertise. <p>This thesis proposes a different method for probabilistic inference in Bayesian networks. We present a data structure called a conditioning graph, which is a run­time representation of Bayesian network inference. The structure mitigates many of the problems of join­tree inference. For example, conditioning graphs require much less space to store and compute over. The algorithm for calculating probabilities from a conditioning graph is small and basic, making it portable to virtually any architecture. And the details of Bayesian network inference are compiled away during the construction of the conditioning graph, leaving an intuitive structure that is easy to understand and implement without any Bayesian network expertise. <p>In addition to the conditioning graph architecture, we present several improvements to the model, that maintain its small and simplistic style while reducing the runtime required for computing over it. We present two heuristics for choosing variable orderings that result in shallower elimination trees, reducing the overall complexity of computing over conditioning graphs. We also demonstrate several compile and runtime extensions to the algorithm, that can produce substantial speedup to the algorithm while adding a small space constant to the implementation. We also show how to cache intermediate values in conditioning graphs during probabilistic computation, that allows conditioning graphs to perform at the same speed as standard methods by avoiding duplicate computation, at the price of more memory. The methods presented also conform to the basic style of the original algorithm. We demonstrate a novel technique for reducing the amount of required memory for caching. <p>We demonstrate empirically the compactness, portability, and ease of use of conditioning graphs. We also show that the optimizations of conditioning graphs allow competitive behaviour with standard methods in many circumstances, while still preserving its small and simple style. Finally, we show that the memory required under caching can be quite modest, meaning that conditioning graphs can be competitive with standard methods in terms of time, using a fraction of the memory.

The most common computer authentication method is to use alphanumerical usernames and passwords. This method has been shown to have significant drawbacks. For example, users tend to pick passwords that can be easily guessed. On the other hand, if a password is hard to guess, then it is often hard to remember. To address this problem, some researchers have developed authentication methods that use pictures as passwords. In this paper, I conduct a comprehensive survey of the existing graphical password techniques. I classify these techniques into two categories: recognition-based and recall-based approaches. I discuss the strengths and limitations of each method and point out the future research directions in this area. I also developed three new techniques against the common problem exists in the present graphical password techniques. In this thesis, the scheme of each new technique will be proposed; the advantages of each technique will be discussed; and the future work will be anticipated.

Probability is a useful tool for reasoning when faced with uncertainty. Bayesian networks offer a compact representation of a probabilistic problem, exploiting independence amongst variables that allows a factorization of the joint probability into much smaller local probability distributions.<p>The standard approach to probabilistic inference in Bayesian networks is to compile the graph into a join­tree, and perform computation over this secondary structure. While join­trees are among the most time­efficient methods of inference in Bayesian networks, they are not always appropriate for certain applications. The memory requirements of join­tree can be prohibitively large. The algorithms for computing over join­trees are large and involved, making them difficult to port to other systems or be understood by general programmers without Bayesian network expertise. <p>This thesis proposes a different method for probabilistic inference in Bayesian networks. We present a data structure called a conditioning graph, which is a run­time representation of Bayesian network inference. The structure mitigates many of the problems of join­tree inference. For example, conditioning graphs require much less space to store and compute over. The algorithm for calculating probabilities from a conditioning graph is small and basic, making it portable to virtually any architecture. And the details of Bayesian network inference are compiled away during the construction of the conditioning graph, leaving an intuitive structure that is easy to understand and implement without any Bayesian network expertise. <p>In addition to the conditioning graph architecture, we present several improvements to the model, that maintain its small and simplistic style while reducing the runtime required for computing over it. We present two heuristics for choosing variable orderings that result in shallower elimination trees, reducing the overall complexity of computing over conditioning graphs. We also demonstrate several compile and runtime extensions to the algorithm, that can produce substantial speedup to the algorithm while adding a small space constant to the implementation. We also show how to cache intermediate values in conditioning graphs during probabilistic computation, that allows conditioning graphs to perform at the same speed as standard methods by avoiding duplicate computation, at the price of more memory. The methods presented also conform to the basic style of the original algorithm. We demonstrate a novel technique for reducing the amount of required memory for caching. <p>We demonstrate empirically the compactness, portability, and ease of use of conditioning graphs. We also show that the optimizations of conditioning graphs allow competitive behaviour with standard methods in many circumstances, while still preserving its small and simple style. Finally, we show that the memory required under caching can be quite modest, meaning that conditioning graphs can be competitive with standard methods in terms of time, using a fraction of the memory.

Thesis (Ph.D.)--York University, 2008. Graduate Programme in Mathematics and Statistics. / Typescript. Includes bibliographical references (leaves 81-83). Also available on the Internet. MODE OF ACCESS via web browser by entering the following URL: http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&res_dat=xri:pqdiss&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&rft_dat=xri:pqdiss:NR39029