This study explores the international profiles in collaboration and mobility of countries included in the so-called “travel bans” implemented by US President Trump as executive order in 2017. The... Show moreThis study explores the international profiles in collaboration and mobility of countries included in the so-called “travel bans” implemented by US President Trump as executive order in 2017. The objective of this research is to analyze the exchange of knowledge between countries and the relative importance of specific countries in order to inform evidence-based science policy. The work serves as a proof-of-concept of the utility of asymmetry and affinity indexes for collaboration and mobility. Comparative analyses of these indicators can be useful for informing immigration policies and motivating collaboration and mobility relationships—emphasizing the importance of geographic and cultural similarities. Egocentric and relational perspectives are analyzed to provide various lenses on the importance of countries. Our analysis suggests that comparisons of collaboration and mobility from an affinity perspective can identify discrepancies between levels of collaboration and mobility. This approach can inform international immigration policies and, if extended, demonstrate potential partnerships at several levels of analysis (e.g., institutional, sectoral, state/province). Show less
Data mining tools often only use a single type of information. The method proposed in this thesis allows the user to insert relational information into existing data mining tools that are designed... Show moreData mining tools often only use a single type of information. The method proposed in this thesis allows the user to insert relational information into existing data mining tools that are designed for content-based information. It does so by regarding the contents of the neighborhood of an element. In this way, the content variability of elements is reduced by using the homophily in the network. Show less
In this dissertation, I investigate new approaches relevant to content-based image retrieval techniques. First, the MOD paradigm is proposed, a method for detecting salient points in images. These... Show moreIn this dissertation, I investigate new approaches relevant to content-based image retrieval techniques. First, the MOD paradigm is proposed, a method for detecting salient points in images. These salient points are specifically designed to enhance image retrieval accuracy by maximizing distinctiveness. Second, the multi-dimensional maximum likelihood similarity measure is presented, which removes a critical limitation in prior research in this area and provides an improved method of comparing image features. Third, a texture classification method based on low dimensional constructed texture features is introduced which have very low computational complexity and would be suitable for real time video understanding or interactive search of very large image databases. The new approaches are tested on well respected international test sets containing representative imagery. Show less
