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Users today are concerned about how their information is collected, stored and used by Internet sites. Privacy policy languages, such as the Platform for Privacy Preferences (P3P), allow websites to publish their privacy practices and policies in machine readable form. Currently, software agents designed to protect users' privacy follow a "take it or leave it" approach when evaluating these privacy policies. This approach is inflexible and gives the server ultimate control over the privacy of web transactions. Privacy policy negotiation is one approach to leveling the playing field by allowing a client to negotiate with a server to determine how that server collects and uses the client's data. We present a privacy policy negotiation protocol, "Or Best Offer", that includes a formal model for specifying privacy preferences and reasoning about privacy policies. The protocol is guaranteed to terminate within three rounds of negotiation while producing policies that are Pareto-optimal, and thus fair to both parties. That is, it remains fair to both the client and the server.

Cardinality constrained optimization problems (CCOP) are a new class of optimization problems with many applications. In this thesis, we propose a framework called mathematical programs with disjunctive subspaces constraints (MPDSC), a special case of mathematical programs with disjunctive constraints (MPDC), to investigate CCOP. Our method is different from the relaxed complementarity-type reformulation in the literature. The first contribution of this thesis is that we study various stationarity conditions for MPDSC, and then apply them to CCOP. In particular, we recover disjunctive-type strong (S-) stationarity and Mordukhovich (M-) stationarity for CCOP, and then reveal the relationship between them and those from the relaxed complementarity-type reformulation. The second contribution of this thesis is that we obtain some new results for MPDSC, which do not hold for MPDC in general. We show that many constraint qualifications like the relaxed constant positive linear dependence (RCPLD) coincide with their piecewise versions for MPDSC. Based on such result, we prove that RCPLD implies error bounds for MPDSC. These two results also hold for CCOP. All of these disjunctive-type constraint qualifications for CCOP derived from MPDSC are weaker than those from the relaxed complementarity-type reformulation in some sense. / Graduate

no / Predictive toxicology is concerned with the development of models that are able to predict the toxicity of chemicals. A reliable prediction of toxic effects of chemicals in living systems is highly desirable in cosmetics, drug design or food protection to speed up the process of chemical compound discovery while reducing the need for lab tests. There is an extensive literature associated with the best practice of model generation and data integration but management and automated identification of relevant models from available collections of models is still an open problem. Currently, the decision on which model should be used for a new chemical compound is left to users. This paper intends to initiate the discussion on automated model identification. We present an algorithm, based on Pareto optimality, which mines model collections and identifies a model that offers a reliable prediction for a new chemical compound. The performance of this new approach is verified for two endpoints: IGC50 and LogP. The results show a great potential for automated model identification methods in predictive toxicology.

In a developing oil-based economy like Libya the availability of finance is largely affected by the availability of oil revenues which are subjected to disturbances and shocks. Therefore, the decision to save and invest a certain ratio of the country's aggregate output is, to large extent, determined (and affected) by the shocks in the oil markets rather than the requirements of economic development. In this study an attempt is made to determine the optimal rate of saving and investment, both defined as a ratio of the aggregate output, according to the requirements of economic development. For this purpose, a neo-classical Ramsey-type model for Libya is constructed and applied to obtain theoretically and empirically the optimal saving and investment rate during the period (1965-1991). The results reveal that Libya was investing over the optimal level during the oil boom of 1970s and less than the optimal level during the oil crisis of 1980s. In addition, an econometric investigation of the determinants of actual investment by sector (agriculture, non-oil industry, and services) is carried out in order to shed lights on how possible it is for Libya to adjust actual investment towards its optimal level. It is found that, as expected, the most important factor which can be used in this respect is the oil revenues or, generally, the availability of finance. In addition, the study reveals that investment in agriculture is associated, during the period of study, with a very low marginal productivity of capital whereas marginal productivity was higher in both non-oil industry and services. Finally, the study investigates also the future potential saving and investment rates and concludes that the economy, which has already reached its steady state, can be pushed out towards further growth if the economy can be able to increase the level of per worker human capital, proxied by the secondary school enrolment as a percentage of population. / Secretariat of Higher Education in Libya and Libyan Interests Section in London

This thesis is concerned with developing methodologies that enable existing models to be effectively reused. Results of this thesis are presented in the framework of Quantitative Structural-Activity Relationship (QSAR) models, but their application is much more general. QSAR models relate chemical structures with their biological, chemical or environmental activity. There are many applications that offer an environment to build and store predictive models. Unfortunately, they do not provide advanced functionalities that allow for efficient model selection and for interpretation of model predictions for new data. This thesis aims to address these issues and proposes methodologies for dealing with three research problems: model governance (management), model identification (selection), and interpretation of model predictions. The combination of these methodologies can be employed to build more efficient systems for model reuse in QSAR modelling and other areas. The first part of this study investigates toxicity data and model formats and reviews some of the existing toxicity systems in the context of model development and reuse. Based on the findings of this review and the principles of data governance, a novel concept of model governance is defined. Model governance comprises model representation and model governance processes. These processes are designed and presented in the context of model management. As an application, minimum information requirements and an XML representation for QSAR models are proposed. Once a collection of validated, accepted and well annotated models is available within a model governance framework, they can be applied for new data. It may happen that there is more than one model available for the same endpoint. Which one to chose? The second part of this thesis proposes a theoretical framework and algorithms that enable automated identification of the most reliable model for new data from the collection of existing models. The main idea is based on partitioning of the search space into groups and assigning a single model to each group. The construction of this partitioning is difficult because it is a bi-criteria problem. The main contribution in this part is the application of Pareto points for the search space partition. The proposed methodology is applied to three endpoints in chemoinformatics and predictive toxicology. After having identified a model for the new data, we would like to know how the model obtained its prediction and how trustworthy it is. An interpretation of model predictions is straightforward for linear models thanks to the availability of model parameters and their statistical significance. For non linear models this information can be hidden inside the model structure. This thesis proposes an approach for interpretation of a random forest classification model. This approach allows for the determination of the influence (called feature contribution) of each variable on the model prediction for an individual data. In this part, there are three methods proposed that allow analysis of feature contributions. Such analysis might lead to the discovery of new patterns that represent a standard behaviour of the model and allow additional assessment of the model reliability for new data. The application of these methods to two standard benchmark datasets from the UCI machine learning repository shows a great potential of this methodology. The algorithm for calculating feature contributions has been implemented and is available as an R package called rfFC. / BBSRC and Syngenta (International Research Centre at Jealott’s Hill, Bracknell, UK).

The standard genetic code (SGC) is the cipher used by nearly all organisms to transcribe information stored in DNA and translate it into its amino acid counterparts. Since the early 1960s, researchers have observed that the SGC is structured so that similar codons encode amino acids with similar physiochemical properties. This structure has been hypothesized to buffer the SGC against transcription or translational error because single nucleotide mutations usually either are silent or impart minimal effect on the containing protein. We herein briefly review different theories for the origin of that structure. We also briefly review different computational experiments designed to quantify buffering capacity for the SGC. We report on computational Monte Carlo simulations that we performed using a computer program that we developed, AGCT. In the simulations, the SGC was ranked against other, hypothetical genetic codes (HGC) for its ability to minimize physiochemical distances between amino acids encoded by codons separated by single nucleotide mutations. We analyzed unappreciated structural aspects and neglected properties in the SGC. We found that error measure type affected SGC ranking. We also found that altering stop codon positions had no effect on SGC ranking, but including stop codons in error calculations improved SGC ranking. We analyzed 49 properties individually and identified conserved properties. Among these, we found that long-range non-bonded energy is more conserved than is polar requirement, which previously was considered to be the most conserved property in the SGC. We also analyzed properties in combinations. We hypothesized that the SGC is organized as a compromise among multiple properties. Finally, we used AGCT to test whether different theories on the origin of the SGC could explain more convincingly the buffering capacity in the SGC. We found that, without accounting for transition/transversion biases, the SGC ranking was modest enough under constraints imposed by the coevolution and four column theories that it could be explained due to constraints associated with either theory (or both theories); however, when transition/transversion biases were included, only the four column theory returned a SGC ranking modest enough that it could be explained due to constraints associated with that theory. / Thesis / Master of Science (MSc) / The standard genetic code (SGC) is the cipher used almost universally to transcribe information stored in DNA and translate it to amino acid counterparts. Since the mid 1960s, researchers have recognized that the SGC is organized so that similar three-nucleotide RNA codons encode amino acids with similar properties; researchers consequently hypothesized that the SGC is structured to minimize effects from transcription or translation errors. This hypothesis has been tested using computer simulation. I briefly review results from those studies, complement them by analyzing unappreciated structural aspects and neglected properties, and test two theories on the origin of the SGC.

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Today's modern aerospace systems exhibit strong interdisciplinary coupling and require a multidisciplinary, collaborative approach. Analysis methods that were once considered feasible only for advanced and detailed design are now available and even practical at the conceptual design stage. This changing philosophy for conducting conceptual design poses additional challenges beyond those encountered in a low fidelity design of aircraft. This thesis takes some steps towards bridging the gaps in existing technologies and advancing the state-of-the-art in aircraft design. The first part of the thesis proposes a new Pareto front approximation method for multiobjective optimization problems. The method employs a hybrid optimization approach using two derivative free direct search techniques, and is intended for solving blackbox simulation based multiobjective optimization problems with possibly nonsmooth functions where the analytical form of the objectives is not known and/or the evaluation of the objective function(s) is very expensive (very common in multidisciplinary design optimization). A new adaptive weighting scheme is proposed to convert a multiobjective optimization problem to a single objective optimization problem. Results show that the method achieves an arbitrarily close approximation to the Pareto front with a good collection of well-distributed nondominated points. The second part deals with the interdisciplinary data communication issues involved in a collaborative mutidisciplinary aircraft design environment. Efficient transfer, sharing, and manipulation of design and analysis data in a collaborative environment demands a formal structured representation of data. XML, a W3C recommendation, is one such standard concomitant with a number of powerful capabilities that alleviate interoperability issues. A compact, generic, and comprehensive XML schema for an aircraft design markup language (ADML) is proposed here to provide a common language for data communication, and to improve efficiency and productivity within a multidisciplinary, collaborative environment. An important feature of the proposed schema is the very expressive and efficient low level schemata. As a proof of concept the schema is used to encode an entire Convair B58. As the complexity of models and number of disciplines increases, the reduction in effort to exchange data models and analysis results in ADML also increases. / Ph. D.

In this thesis, we investigate several inferential issues regarding the lifetime data from exponential distribution under different censoring schemes. For reasons of time constraint and cost reduction, censored sampling is commonly employed in practice, especially in reliability engineering. Among various censoring schemes, progressive Type-I censoring provides not only the practical advantage of known termination time but also greater flexibility to the experimenter in the design stage by allowing for the removal of test units at non-terminal time points. Hence, we first consider the inference for a progressively Type-I censored life-testing experiment with k uniformly spaced intervals. For small to moderate sample sizes, a practical modification is proposed to the censoring scheme in order to guarantee a feasible life-test under progressive Type-I censoring. Under this setup, we obtain the maximum likelihood estimator (MLE) of the unknown mean parameter and derive the exact sampling distribution of the MLE through the use of conditional moment generating function under the condition that the existence of the MLE is ensured. Using the exact distribution of the MLE as well as its asymptotic distribution and the parametric bootstrap method, we discuss the construction of confidence intervals for the mean parameter and their performance is then assessed through Monte Carlo simulations. Next, we consider a special class of accelerated life tests, known as step-stress tests in reliability testing. In a step-stress test, the stress levels increase discretely at pre-fixed time points and this allows the experimenter to obtain information on the parameters of the lifetime distributions more quickly than under normal operating conditions. Here, we consider a k-step-stress accelerated life testing experiment with an equal step duration τ. In particular, the case of progressively Type-I censored data with a single stress variable is investigated. For small to moderate sample sizes, we introduce another practical modification to the model for a feasible k-step-stress test under progressive censoring, and the optimal τ is searched using the modified model. Next, we seek the optimal τ under the condition that the step-stress test proceeds to the k-th stress level, and the efficiency of this conditional inference is compared to the preceding models. In all cases, censoring is allowed at each change stress point iτ, i = 1, 2, ... , k, and the problem of selecting the optimal Tis discussed using C-optimality, D-optimality, and A-optimality criteria. Moreover, when a test unit fails, there are often more than one fatal cause for the failure, such as mechanical or electrical. Thus, we also consider the simple stepstress models under Type-I and Type-II censoring situations when the lifetime distributions corresponding to the different risk factors are independently exponentially distributed. Under this setup, we derive the MLEs of the unknown mean parameters of the different causes under the assumption of a cumulative exposure model. The exact distributions of the MLEs of the parameters are then derived through the use of conditional moment generating functions. Using these exact distributions as well as the asymptotic distributions and the parametric bootstrap method, we discuss the construction of confidence intervals for the parameters and then assess their performance through Monte Carlo simulations. / Thesis / Doctor of Philosophy (PhD)