Fair division is the problem of dividing one or several goods among a set of agents in a way that satisfies a suitable fairness criterion. Traditionally studied in economics, philosophy, and political science, fair division has drawn a lot of attention from the multiagent systems community, since this field is strongly concerned about how a surplus (or a cost) should be divided among a group of agents. Arguably, the Shapley value is the single most important contribution to the problem of fair division. It assigns to each agent a share of the resource equal to the expected marginal contribution of that agent. Thus, it is implicitly assumed that individual marginal contributions can be objectively computed. In this thesis, we propose a game-theoretic model for sharing a joint reward when the quality of individual contributions is subjective. In detail, we consider scenarios where a group has been formed and has accomplished a task for which it is granted a reward, which must be shared among the group members. After observing the contribution of the peers in accomplishing the task, each agent is asked to provide evaluations for the others. Mainly to facilitate the sharing process, agents can also be requested to provide predictions about how their peers are evaluated. These subjective opinions are elicited and aggregated by a central, trusted entity, called the mechanism, which is also responsible for sharing the reward based exclusively on the received opinions. Besides the formal game-theoretic model for sharing rewards based on subjective opinions, we propose three different mechanisms in this thesis. Our first mechanism, the peer-evaluation mechanism, divides the reward proportionally to the evaluations received by the agents. We show that this mechanism is fair, budget-balanced, individually rational, and strategy-proof, but that it can be collusion-prone. Our second mechanism, the peer-prediction mechanism, shares the reward by considering two aspects: the evaluations received by the agents and their truth-telling scores. To compute these scores, this mechanism uses a strictly proper scoring rule. Under the assumption that agents are Bayesian decision-makers, we show that this mechanism is weakly budget-balanced, individually rational, and incentive-compatible. Further, we present approaches that guarantee the mechanism to be collusion-resistant and fair. Our last mechanism, the BTS mechanism, is the only one to elicit both evaluations and predictions from the agents. It considers the evaluations received by the agents and their truth-telling scores when sharing the reward. For computing the scores, it uses the Bayesian truth serum method, a powerful scoring method based on the surprisingly common criterion. Under the assumptions that agents are Bayesian decision-makers, and that the population of agents is sufficiently large so that a single evaluation cannot significantly affect the empirical distribution of evaluations, we show that this mechanism is incentive-compatible, budget-balanced, individually rational, and fair.

<p>Risk factors such as intrauterine and vaginal infection put pregnant women at risk for delivering preterm. Bacterial vaginosis (BV) is a polymicrobial clinical syndrome commonly diagnosed in women of reproductive age, with women of African descent with low socioeconomic status and previous preterm delivery at high risk. Although frequently isolated from healthy women,&nbsp / Gardnerella vaginalis has been most frequently associated with BV. There is limited data available on the prevalence of BV in Southern Africa / therefore, we embarked on a study to determine the&nbsp / prevalence of BV and G. vaginalis in predominantly black communities in the Western Cape, in order to establish the role of G. vaginalis in BV. Women attending various Maternity and Obstetrics&nbsp / units (MOU) in the Cape Peninsula with and without a history of pre-term delivery (PTD) were invited to participate in the study. Several factors were statistically associated with pregnancy history,&nbsp / including location of study population, parity, smoking and presence of clinical symptoms. The presence of G. vaginalis was determined by culture in 51.7% of the preterm delivery group (PTDG)&nbsp / and 44% of the full-term delivery group (FTDG) women. BV was detected in 31.13% of PTDG and 23.67% of FTDG by Gram stained analysis according to Nugent scoring criteria, with age and HIV&nbsp / status posing as risk factors. When comparing PTDG and FTDG for an association between the presence of G. vaginalis and BV, a stronger association was observed in the PTDG but it was not statistically significant. In both PTDG and FTDG, G. vaginalis was isolated significantly more often in women diagnosed with BV at 24.5% (p &lt / 0.05). Antibiogram studies revealed both Metronidazole and Clindamycin resistant strains of G. vaginalis. G. vaginalis Biotype 7 is specifically associated with BV, while Biotype 2 appears to be associated with BV in women with a history&nbsp / of PTD. Accuracy of diagnostic tools were tested and it was determined that Nugent scoring is more sensitive in diagnosing BV (76.04%), but culture for G. vaginalis is more specific (83.21%). Although this study was limited in that we were unable to follow-up pregnancy outcomes, we were able to confirm the perceived role of G. vaginalis in BV.&nbsp / </p>

This research develops a new approach to the development of training inexperienced journalists in news writing using a web-based platform of instruction delivery. E-training is growing in the world as an instructional setting, and offers not only financial benefits, but also a range of specific advantages over the traditional classroom type of setting. Such advantages include the ability to personalise the content of training to the trainees' current competencies, to facilitate regular multi-faceted monitoring of the changes in these competencies and to combine learning with the immediate practising of what was learned. Two e-training tools have been created and validated in this research: the news text assessment system (NTA) - a comprehensive and effective online scoring rubric, i.e. a matrix describing different levels of competency in several dimensions of the assessed performance - to assess the quality of news writing; and a scoring e-textbook (SET), an asynchronous news writing training tool. The SET is built around the NTA as its core element and contains hundreds of self-learning modules including exercises, examples, instructional texts, and quizzes to be used in a non-linear fashion according to the specific needs of trainees. Both the exercises and the NTA are elements of corrective feedback, which in psychological literature has been shown to be most effective in changing the subsequent performance of trainees. The two tools help both the trainee and the instructor. They assist the instructor to identify and address journalists' weak and absent competencies in news writing and consistently upgrade the learning modules when needed. They help trainees to monitor their progress and to learn from their own mistakes in the short periods of spare time they have during their work or in other time they can spare for the training. To create the NTA, 53 top journalism experts, both practitioners and academics, used the prototype of the assessment rubric with 30 criteria of news writing to assess the quality of several supplied news stories. The results were then subjected to statistical analysis and the NTA rubric was created as a compromise between its comprehensiveness and user-friendliness. To evaluate the NTA and the SET, an experiment was conducted with journalists in one post-Communist country in the form of an action research project, where this researcher was also the instructor. The experiment consisted of four months of training and reflection on its results by both the journalists and the researcher. The results show improvement in news writing competencies to an internationally 'acceptable' news writing standard for most of the trainees. The suggested tools have been well received and the trainees appreciated the interactivity that was provided during the training.

This thesis project examined the generalizabilty of the simplified scoring system for the Clock Drawing Test developed by Lessig and her colleagues in 2008 using a population-based sample. Clock-drawings from 356 participants (80 with dementia, 276 healthy controls) from the Canadian Study on Health and Aging were analyzed using logistic regression and Receiver Operating Characteristic curves. The new scoring system reduced the Lessig system down even further to include five critical errors: missing numbers, repeated numbers, number orientation, extra marks, and number distance, and produced a sensitivity of 81% and a specificity of 68%. The results from this study improve our current state of knowledge concerning the Clock Drawing Task by validating the simplified scoring system proposed by Lessig and her colleagues among a more representative sample and provides further evidence in support of a simple and reliable dementia-screening tool.

First degree relatives (FDRs) of subjects with early onset of coronary heart disease (CHD) have higher risk of developing cardiovascular disease. We verified early CHD by angiography in the index patients and extensively phenotyped their FDRs to investigate the relationship of traditional and non-traditional cardiovascular risk factors to carotid ultrasound and coronary calcium scoring findings. B-mode carotid ultrasound was used to assess the combined intima-media thickness and plaque burden in 111 FDRs. The biochemical and anthropometrical characteristics of the FDRs were compared with those of healthy controls matched for sex, age, ethnicity and BMI. Odds ratios indicate that FDRs are more likely to have positive carotid ultrasound findings compared to controls; 2.23 (95% CI 1.14 – 4.37) for intima-media thickness and 2.3 (95% CI 1.22 - 4.35) for average total thickness. In multivariate analysis positive carotid ultrasound findings were higher in FDRs independent of age, gender, total cholesterol over HDL-c ratio, systolic blood pressure and smoking but not homocysteine which had higher values in FDRs compared to controls. In conclusion FDRs of patients with angiographically confirmed CHD have higher burden of subclinical atherosclerosis even when considered in the context of traditional risk factors. Coronary artery calcium scoring (CAC), assessed by 64-slice multi-detector computed tomography (MDCT), was used to assess burden of subclinical atherosclerosis in 57 FDRs compared to controls. FDRs have a two-fold increase in risk of having CAC positive findings; odds ratios for the 75th percentile was 1.96 (95%CI 1.04 – 3.67, p<0.05) while for the 90th percentile odds ratio was 2.59 (95% 1.232 – 5.473, p<0.05). In summary, the risk of significant CAC findings, measured by 64-slice MDCT, is two-fold higher in FDRs than controls. These findings correlate highly with carotid ultrasound findings in the same cohort. Different thresholds for CAC may be appropriate when assessing male versus female FDRs. Together increased carotid ultrasound findings and CAC scoring results in FDRs of patients with validated early onset of CHD suggest these imaging techniques as potentially useful tools in cardiovascular risk assessment that will go above and beyond the current diagnostic algorithms.

Zugl.: Wuppertal, Univ., Diss., 2008

Cisco 2014 Annual Security Report clearly outlines the evolution of the threat landscape and the increase of the number of attacks. The UK government in 2012 recognised the cyber threat as Tier-1 threat since about 50 government departments have been either subjected to an attack or a direct threat from an attack. The cyberspace has become the platform of choice for businesses, schools, universities, colleges, hospitals and other sectors for business activities. One of the major problems identified by the Department of Homeland Security is the lack of clear security metrics. The recent cyber security breach of the US retail giant TARGET is a typical example that demonstrates the weaknesses of qualitative security, also considered by some security experts as fuzzy security. High, medium or low as measures of security levels do not give a quantitative representation of the network security level of a company. In this thesis, a method is developed to quantify the security risk level of known and unknown attacks in an enterprise network in an effort to solve this problem. The identified vulnerabilities in a case study of a UK based company are classified according to their severity risk levels using common vulnerability scoring system (CVSS) and open web application security project (OWASP). Probability theory is applied against known attacks to create the security metrics and, detection and prevention method is suggested for company network against unknown attacks. Our security metrics are clear and repeatable that can be verified scientifically.

Master of Agribusiness / Department of Agricultural Economics / Christine Wilson / Credit scoring is a tool used to make lending decisions. AgChoice Farm Credit has a dealer financing program called Farm Credit Express that makes lending decisions based on a scoring model. Farm Credit Express is a dealer financing option for farm equipment purchases. AgChoice has generated significant loan volume with this program but has also experienced challenges with loan delinquencies as field staff must service loans that they did not originate. This thesis evaluates loan delinquencies within AgChoice Farm Credit’s Farm Credit Express (“FCE”) program. The thesis develops a regression model that includes delinquencies as the dependent variable and Total AgChoice Borrowing, Original Loan Amount, Farming Segment, CBI Score, AgScore, and FCE Only as the independent variables. The model provides an examination of AgChoice’s Farm Credit Express delinquencies and evaluates the variables mentioned above and their ability to predict delinquencies. The results showed that Total AgChoice Borrowing, Original Loan Amount, CBI Score and FCE only were statistically significant independent variables. Based on results of the model, recommendations were made to potentially reduce future delinquencies in the Farm Credit Express loan portfolio.

Modelos de regressão para variáveis resposta binárias são muito comuns em diversas áreas do conhecimento. O modelo mais utilizado nessas situações é o modelo de regressão logística, que assume que o logito da probabilidade de ocorrência de um dos valores da variável resposta é uma função linear das variáveis preditoras. Quando essa suposição não é razoável, algumas possíveis alternativas são: realizar transformação das variáveis preditoras e/ou inserir termos quadráticos ou cúbicos no modelo. O problema dessa abordagem é que ela dificulta bastante a interpretação dos parâmetros do modelo e, em algumas áreas, é fundamental que eles sejam interpretáveis. Assim, uma abordagem muitas vezes utilizada é a categorização das variáveis preditoras quantitativas do modelo. Sendo assim, este trabalho tem como objetivo propor duas novas classes de métodos de categorização de variáveis contínuas em modelos de regressão para variáveis resposta binárias. A primeira classe de métodos é univariada e busca maximizar a associação entre a variável resposta e a covariável categorizada utilizando medidas de associação para variáveis qualitativas. Já a classe de métodos multivariada tenta incorporar a estrutura de dependência entre as covariáveis do modelo através da categorização conjunta de todas as variáveis preditoras. Para avaliar o desempenho, aplicamos as classes de métodos propostas e quatro métodos de categorização existentes em 3 bases de dados relacionadas à área de risco de crédito e a dois cenários de dados simulados. Os resultados nas bases reais sugerem que a classe univariada proposta têm um desempenho superior aos métodos existentes quando comparamos o poder preditivo do modelo de regressão logística. Já os resultados nas bases de dados simuladas sugerem que ambas as classes propostas possuem um desempenho superior aos métodos existentes. Em relação ao desempenho computacional, o método multivariado mostrou-se inferior e o univariado é superior aos métodos existentes. / Regression models for binary response variables are very common in several areas of knowledge. The most used model in these situations is the logistic regression model, which assumes that the logit of the probability of a certain event is a linear function of the predictors variables. When this assumption is not reasonable, it is common to make some changes in the model, such as: transformation of predictor variables and/or add quadratic or cubic terms to the model. The problem with this approach is that it hinders parameter interpretation, and in some areas it is fundamental to interpret the parameters. Thus, a common approach is to categorize the quantitative covariates. This work aims to propose two new classes of categorization methods for continuous variables in binary regression models. The first class of methods is univariate and seeks to maximize the association between the response variable and the categorized covariate using measures of association for qualitative variables. The second class of methods is multivariate and incorporates the predictor variables correlation structure through the joint categorization of all covariates. To evaluate the performance, we applied the proposed methods and four existing categorization methods in 3 credit scoring databases and in two simulated cenarios. The results in the real databases suggest that the proposed univariate class of categorization methods performs better than the existing methods when we compare the predictive power of the logistic regression model. The results in the simulated databases suggest that both proposed classes perform better than the existing methods. Regarding computational performance, the multivariate method is inferior and the univariate method is superior to the existing methods.

A modelagem das interações entre macromoléculas e ligantes ainda se depara com diversos desafios na área de desenho de fármacos assistidos por computador. Apesar do crescimento da área, temas como a flexibilidade do receptor, funções de pontuação e solvatação ainda têm sido alvo de intensa investigação na comunidade científica. Com o objetivo de analisar a interação em milhares ou milhões de complexos, é imprescindível uma boa harmonização entre o custo computacional e a acurácia dos métodos computacionais que permitem a classificação de ligantes de acordo com a energia de interação. O LiBELa (Ligand Binding Energy Landscape) é um programa de docagem molecular com abordagem híbrida, ou seja, utiliza informações do ligante e do receptor durante o processo de docagem. Inicialmente, as características estéricas e eletrostáticas de um ligante de referência (cristalográfico, por exemplo) são utilizadas nos cálculos de similaridade e sobreposição, obtendo assim uma conformação inicial pré-otimizada do ligante testado. Em seguida, a energia de interação é minimizada no sítio ativo de receptor a partir de potenciais energéticos. Quatro funções de pontuação baseadas em campo de força foram testadas e otimizadas, compostas por potenciais de van der Waals, de Coulomb, e uma função empírica de solvatação denominada função de Stouten-Verkhivker (SV). A flexibilidade do sistema foi tratada através da geração de confôrmeros que amostram os graus de liberdade dos ligantes descritos como semi-rígidos e através de potenciais atenuados que suavizam a superfície de energia de interação, permitindo interações em distâncias interatômicas antes repulsivas. Como ponto de partida, os métodos implementados no programa LiBELa demonstraram resultados satisfatórios nos testes de cross- e self-docking, mostrando ser uma ferramenta eficiente em encontrar os modos de ligação cristalográficos de forma equivalente ou até melhor às dos programas comparados. Através de testes de enriquecimento nos conjuntos de dados DUD, DUDE e CM-DUD, foram otimizadas de forma sistemática as constantes dielétrica, do termo de solvatação, e dos termos de atenuação. Também foi realizado um paralelo entre as funções de pontuação, incluindo a atenuação e o termo de solvatação. Estes mesmos testes mostraram resultados superiores do LiBELa de 39% e 15% em comparação com um programa baseado puramente no receptor (DOCK 6.6), relativo à média da área sob a curva em escala semi-logarítmica nas bases de dados DUDE e DUD respectivamente. Apesar da função de solvatação SV implementada no LiBELa apresentar boa correlação com dados experimentais (r=0,72) e com o modelo Zou GB de solvatação (r=0,88), não apresentou correlação significativa com os métodos GB e PB implementados no pacote de programas disponível no AmberTools. Comparadas às funções de pontuação do LiBELa, as funções com correção para solvatação apresentaram pior enriquecimento, salvo alguns alvos específicos. Por fim, foram realizados ensaios de docagem molecular utilizando como alvo uma enzima &beta;-galactosidase da família GH42, cuja estrutura fora resolvida em nosso grupo. Os resultados permitiram conclusões acerca de como o modo de ligação interfere na preferência de ligação entre dissacarídeos de ligações glicosídicas distintas, consistentes com dados experimentais de ensaios cinéticos de ligação. / Modeling the interactions between macromolecules and ligands still faces several challenges in the computer-aided drug design area. Despite the growth in the area, subjects such as receptor flexibility, scoring functions and solvation still have been widely explored in the scientific community. In order to analyze the interaction for thousands or millions of complexes, a good harmonization between the computational cost and the accuracy of the calculation methods in molecular docking programs is essential. LiBELa (Ligand Binding Energy Landscape) is a hybrid approach program that uses both ligand and receptor information for ligand docking. Initially, the steric and electrostatic characteristics from a reference binder (crystallographic, for example) are used to similarity and overlay calculations, thus obtaining an initial conformation of the ligand tested. Then, within the receptor´s active site, the interaction energy is minimized using energetic potentials. Four force field-based scoring functions were tested and optimized, composed of van der Waals and Coulomb potentials and an empirical solvation function called Stouten-Verkhivker (SV). Concerning the system flexibility, besides the confomers generation that sample the degrees of freedom for semi-rigid ligands, attenuated potentials smooth the energy surface allowing interactions between previously repulsive interatomic distances. As a starting point, LiBELa performed satisfactorily in the cross- and self-docking tests, showing that is an eficient tool to reproduce crystallographic binding modes equivalently to or even better than reference programs. Through enrichment of DUD, DUDE and CM-DUD datasets, the dielectric constant, solvation and softening terms were systematically optimized. It also allowed a parallel between scoring functions, including attenuation and solvation term. Finally, it revealed the LiBELa showed an enhancement of 39% and 15% as compared to the purely receptor-based program DOCK 6.6, relative to the mean of the area under the curve on a semi-logarithmic scale in the DUDE and DUD databases respectively. Although the SV solvation function implemented in LiBELa showed good correlations with experimental data (r = 0.72) and with the Zou GB / SA solvation method implemented in DOCK6 (r = 0.88), it did not show significant correlation with the GB/SA and PB/SA methods implemented in AmberTools. Comparing all the LiBELa tested scoring functions, those including solvation correction showed worse enrichments, except for some specific targets. Finally, molecular docking experiments using LiBELa were conducted with a &beta;-galactosidase from GH42 family, whose structure was solved in our group. The results allowed conclusions concerning how the binding mode interferes the preference for some disaccharides of distinct glycosidic bonds, consistent with experimental data from kinetic assays.