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Malware analysts and researchers around the world are looking for innovative means of malware detection and classification. However, one concept of malware analysis that lacks focus is the rating of malware based on their feature set and capabilities. Malware severity rating is needed in order to prioritize the utilization of resources towards the analysis of a malware by an organization. This thesis proposes the utilization of threat trees for calculating malware severity using a goal oriented approach. This approach is applied to a set of sophisticated malware to study its contribution towards articulation of a useful severity rating.

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Employers rely on judges or raters to accurately rate the potential or performance of candidates through interviews or assessment centre evaluations. As the judgment process places heavy demands on information processing, cognitive ability (of raters) is important to detect and interpret behavioural cues presented by those being rated. A consistent empirical finding is that intelligence is the strongest predictor of rating accuracy, but prior research has largely been based on linear models. However, researchers have yet to investigate whether these variables could be nonlinearly related. By studying nonlinear models in judgment and accuracy, we can not only deepen our understanding of the ‘good judge' in HRM, but we may further enhance methods to select and train raters in applied practice. This secondary research study re-analysed data from a prior published study to evaluate the relationship between rater intelligence and accuracy of interview ratings provided by 146 South African managers. The predictiveness of an ordinary least squares (OLS) linear regression model was compared to two nonlinear models (quadratic and cubic) to determine which statistical approach explained the most variance in rating accuracy scores. Findings provided further support of a linear relationship between intelligence and rating accuracy suggesting no quadratic or cubic interactions. Judges, therefore, produced more accurate ratings at higher levels of intelligence. Possible explanations of the findings include the sample size and task complexity. Study limitations and recommendations for future research are discussed in detail

The growing popularity of multi-agent based approaches towards the formation and operation of virtual organizations (VO) present over the Internet, offer both opportunities and risks. One of the risks involved in such community is in the identification of trustworthy agent partners for transaction. In this paper we aim to describe our trust model which would contribute in measuring trust in the interacting agents. Named as TruMet, the trust metric model works on the basis of the parameters that we have identified as relevant to the features of the community. The model primarily analyses trust value on the basis of the agent¿s reputation, as provided by the agent itself, and the agent¿s aggregate rating as provided by the witness agents. The final computation of the trust value is given by a weighted average of these two components. While computing the aggregate rating, a weight based method has been adopted to discount the contribution of possibly un-fair ratings by the witness agents.

There are over 250,000 reinforced concrete bridges in the U.S. many of which do not have a load rating on record nor the plans required to perform the calculations. The U.S. Army owns and maintains hundreds of these bridges throughout the U.S. This dissertation describes the development of multiple regression models to estimate the load rating of reinforced concrete bridges. An exploratory data analysis of the 2017 NBI data was performed for the selection of a representative data sample. The data was found to have multiple errors and required significant processing in order to extract a reliable sample for modeling. After processing, a data sample of 31,112 bridges remained, providing sufficient sample for model training and testing. A six-variable model (Model A) was determined to provide the best performance while maintaining a desired low level of complexity. The model was tested by comparing the percentage of cases that fell within its 95% prediction interval, which resulted in 94.9% of the real values falling within the prediction interval. Given the concerns that arose of the quality of the 2017 NBI data during its exploration, as built-drawings from 50 slab bridges throughout the U.S. were collected. With these drawings a new data sample was generated by calculating the load rating of each bridge. Availability of the as-built drawings provided the opportunity to investigate other variables not available in the 2017 NBI, most notably the slab thickness. This data sample was significantly smaller than the previous one, therefore a repeated 10old cross-validation approach was taken to evaluate model performance. It was determined that a five-variable model (Model B) provided the best trade-off between complexity and performance. Model B performed significantly better than Model A due to the inclusion of the slab thickness variable. The models presented in this dissertation provide a valuable tool for reinforced concrete bridge owners tasked with the assigning a load rating when no structural plans are available helping.

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