If we are to understand how we can build machines capable of broad purpose learning and reasoning, we must first aim to build systems that can represent, acquire, and reason about the kinds of commonsense knowledge that we humans have about the world. This endeavor suggests steps such as identifying the kinds of knowledge people commonly have about the world, constructing suitable knowledge representations, and exploring the mechanisms that people use to make judgments about the everyday world. In this work, I contribute to these goals by proposing an architecture for a system that can learn commonsense knowledge about the properties and behavior of objects in the world. The architecture described here augments previous machine learning systems in four ways: (1) it relies on a seven dimensional notion of context, built from information recently given to the system, to learn and reason about objects' properties; (2) it has multiple methods that it can use to reason about objects, so that when one method fails, it can fall back on others; (3) it illustrates the usefulness of reasoning about objects by thinking about their similarity to other, better known objects, and by inferring properties of objects from the categories that they belong to; and (4) it represents an attempt to build an autonomous learner and reasoner, that sets its own goals for learning about the world and deduces new facts by reflecting on its acquired knowledge. This thesis describes this architecture, as well as a first implementation, that can learn from sentences such as ``A blue bird flew to the tree'' and ``The small bird flew to the cage'' that birds can fly. One of the main contributions of this work lies in suggesting a further set of salient ideas about how we can build broader purpose commonsense artificial learners and reasoners.

Upplevd likhet med målpersonen har ansetts vara viktig för empati och viss forskning har visat att empatin ökar med upplevd etnisk sam-hörighet. Denna studies primära syfte var att experimentellt undersöka om svenska och icke-svenska deltagare kände olika mycket empati beroende på målpersonens etnicitet samt upplevd likhet med mål-personen. Majoriteten av de 160 deltagarna rekryterades från Mälar-dalens högskola, varav 102 var svenska och 84 var kvinnliga. Resultatet visade två signifikanta disordinala interaktioner där svenska deltagare kände signifikant mer empati och upplevd likhet med en svensk än icke-svensk målperson, medan icke-svenska inte visade signifikant mer empati eller upplevd likhet med en icke-svensk än svensk målperson. Ingen signifikant skillnad i empati fanns mellan äldre och yngre deltagare. Män uppvisade signifikant lägre empati än kvinnor och inget av könen väckte mer empati. Orsaken till de disordinala interaktionerna diskuterades i termer av social kategorisering. Vidare forskning med en annan definition av begreppet etnicitet föreslogs.

Measures of semantic distance have received a great deal of attention recently in the field of computational lexical semantics. Although techniques for approximating the semantic distance of two concepts have existed for several decades, the introduction of the WordNet lexical database and improvements in corpus analysis have enabled significant improvements in semantic distance measures. <br /><br /> In this study we investigate a special kind of semantic distance, called <em>semantic relatedness</em>. Lexical semantic relatedness measures have proved to be useful for a number of applications, such as word sense disambiguation and real-word spelling error correction. Most relatedness measures rely on the observation that the shortest path between nodes in a semantic network provides a representation of the relationship between two concepts. The strength of relatedness is computed in terms of this path. <br /><br /> This dissertation makes several significant contributions to the study of semantic relatedness. We describe a new measure that calculates semantic relatedness as a function of the shortest path in a semantic network. The proposed measure achieves better results than other standard measures and yet is much simpler than previous models. The proposed measure is shown to achieve a correlation of <em>r</em> = 0. 897 with the judgments of human test subjects using a standard benchmark data set, representing the best performance reported in the literature. We also provide a general formal description for a class of semantic distance measures &mdash; namely, those measures that compute semantic distance from the shortest path in a semantic network. Lastly, we suggest a new methodology for developing path-based semantic distance measures that would limit the possibility of unnecessary complexity in future measures.

This is an expository thesis which addresses the requirements for an operator algebra to be similar to a <em>C</em>*-algebra. It has been conjectured that this similarity condition is equivalent to either amenability or total reductivity; however, the problem has only been solved for specific types of operators. <br /><br /> We define amenability and total reductivity, as well as present some of the implications of these properties. For the purpose of establishing the desired result in specific cases, we describe the properties of two well-known types of operators, namely the compact operators and quasitriangular operators. Finally, we show that if A is an algebra of compact operators or of triangular operators then A is similar to a <em>C</em>* algebra if and only if it has the total reduction property.

Consumer confusion, caused by product similarity, choice and/or information overload, and the presence of ambiguous information, can negatively affect consumersʼ decision making, and thereby also companiesʼ profitability. The purpose of this quantitative study was to investigate how the three variables (i.e. similarity, overload, ambiguity) of Walsh et al.ʼs (2007) consumer confusion proneness model affect consumersʼ decision postponement and brand loyalty, concerning low involvement products. A conceptual framework based on consumer behavior- and consumer confusion literature, was utilized to form six hypotheses predicting the causality between the different variables. After validating and adapting the scale to data gathered through a survey, regarding Swedish studentsʼ purchasing habits of laundry detergent, two standard multiple regressions revealed that one hypothesis was supported; overload confusion proneness decreases brand loyalty in a low involvement product category. All implications were then discussed from practitionersʼ and researchersʼ points of view, concluding with possible limitations and further research.

Despite interest in the influence of religion on economic activity by early economists like Adam Smith, modern economists have done little research on the subject. In light of the apparent religious fervour in many parts of the global economy, economists' seeming lack of interest in studying how religious cultures enhance or retard the globalization of economic activity is especially surprising. In general, trade theories have given less weight towards the reason for trade explanation on demand side. As a contrary to H-O theory Linder had proposed a theoretically sound and empirically consistent trade theory with a new claim for the reasons why countries trade on the demand side. To fill this gap, I use international survey data on religiosity for a broad panel of countries trading with US to investigate the effects of church attendance and religious beliefs on trade. The beliefs are, in turn, the principal output of the religion sector, and the believer alignment to a specific denomination measures the inputs to this sector. Hence, I used an extended gravity model of international trade to control for a variety of factors that determine trade, and I used two regression methods, OLS and WLS, to exploit the model to its fullest. I find that the sharing of same religious cultures by people in different countries has a significantly positive influence on bilateral trade, all other things being equal. These results accord with a perspective in which religious beliefs influence individual traits that enhance trade and economic performance in general. And my attempt to magnify religion as a means to trade is only a derivation of Linder’s overlapping demand theory.

Measures of semantic distance have received a great deal of attention recently in the field of computational lexical semantics. Although techniques for approximating the semantic distance of two concepts have existed for several decades, the introduction of the WordNet lexical database and improvements in corpus analysis have enabled significant improvements in semantic distance measures. <br /><br /> In this study we investigate a special kind of semantic distance, called <em>semantic relatedness</em>. Lexical semantic relatedness measures have proved to be useful for a number of applications, such as word sense disambiguation and real-word spelling error correction. Most relatedness measures rely on the observation that the shortest path between nodes in a semantic network provides a representation of the relationship between two concepts. The strength of relatedness is computed in terms of this path. <br /><br /> This dissertation makes several significant contributions to the study of semantic relatedness. We describe a new measure that calculates semantic relatedness as a function of the shortest path in a semantic network. The proposed measure achieves better results than other standard measures and yet is much simpler than previous models. The proposed measure is shown to achieve a correlation of <em>r</em> = 0. 897 with the judgments of human test subjects using a standard benchmark data set, representing the best performance reported in the literature. We also provide a general formal description for a class of semantic distance measures &mdash; namely, those measures that compute semantic distance from the shortest path in a semantic network. Lastly, we suggest a new methodology for developing path-based semantic distance measures that would limit the possibility of unnecessary complexity in future measures.

This is an expository thesis which addresses the requirements for an operator algebra to be similar to a <em>C</em>*-algebra. It has been conjectured that this similarity condition is equivalent to either amenability or total reductivity; however, the problem has only been solved for specific types of operators. <br /><br /> We define amenability and total reductivity, as well as present some of the implications of these properties. For the purpose of establishing the desired result in specific cases, we describe the properties of two well-known types of operators, namely the compact operators and quasitriangular operators. Finally, we show that if A is an algebra of compact operators or of triangular operators then A is similar to a <em>C</em>* algebra if and only if it has the total reduction property.

Inter-document similarity is used for clustering, classification, and other purposes within information retrieval. In this thesis, we investigate several aspects of document similarity. In particular, we investigate the quality of several measures of inter-document similarity, providing a framework suitable for measuring and comparing the effectiveness of inter-document similarity measures. We also explore areas of research related to novelty and diversity in information retrieval. The goal of diversity and novelty is to be able to satisfy as many users as possible while simultaneously minimizing or eliminating duplicate and redundant information from search results. In order to evaluate the effectiveness of diversity-aware retrieval functions, user query logs and other information captured from user interactions with commercial search engines are mined and analyzed in order to uncover various informational aspects underlying queries, which are known as subtopics. We investigate the suitability of implicit associations between document content as an alternative to subtopic mining. We also explore subtopic mining from document anchor text and anchor links. In addition, we investigate the suitability of inter-document similarity as a measure for diversity-aware retrieval models, with the aim of using measured inter-document similarity as a replacement for diversity-aware evaluation models that rely on subtopic mining. Finally, we investigate the suitability and application of document similarity for requirements traceability. We present a fast algorithm that uncovers associations between various versions of frequently edited documents, even in the face of substantial changes.

Complex wavelet structural similarity (CW-SSIM) index has been recognized as a novel image similarity measure of broad potential applications due to its robustness to small geometric distortions such as translation, scaling and rotation of images. Nevertheless, how to make the best use of it in image classification problems has not been deeply investi- gated. In this study, we introduce a series of novel image classification algorithms based on CW-SSIM and use handwritten digit and face image recognition as examples for demonstration, including CW-SSIM based nearest neighbor method, CW-SSIM based k means method, CW-SSIM based support vector machine method (SVM) and CW-SSIM based SVM using affinity propagation. Among the proposed approaches, the best compromise between accuracy and complexity is obtained by the CW-SSIM support vector machine algorithm, which combines an unsupervised clustering method to divide the training images into clusters with representative images and a supervised learning method based on support vector machines to maximize the classification accuracy. Our experiments show that such a conceptually simple image classification method, which does not involve any registration, intensity normalization or sophisticated feature extraction processes, and does not rely on any modeling of the image patterns or distortion processes, achieves competitive performance with reduced computational cost.