The purpose of this study was to investigate the following relationships: 1. The relationship of personality attributes measured by the Structured-Objective Rorschach Test (SORT) and success in student teaching when the grade point average earned in student teaching was used as a criterion of success. 2. The relationship of SORT attributes and success in student teaching when the college coordinator's ratings of the student teacher were used to measure success.

Structured output learning is the machine learning task of building a classifier to predict structured outputs. Structured outputs arise in several contexts in diverse applications like natural language processing, computer vision, bioinformatics and social networks. Unlike the simple two(or multi)-class outputs which belong to a set of distinct or univariate categories, structured outputs are composed of multiple components with complex interdependencies amongst them. As an illustrative example ,consider the natural language processing task of tagging a sentence with its corresponding part-of-speech tags. The part-of-speech tag sequence is an example of a structured output as it is made up of multiple components, the interactions among them being governed by the underlying properties of the language. This thesis provides efficient solutions for different problems pertaining to structured output learning. The classifier for structured outputs is generally built by learning a suitable model from a set of training examples labeled with their associated structured outputs. Discriminative techniques like Structural Support Vector Machines(Structural SVMs) and Conditional Random Fields(CRFs) are popular alternatives developed for structured output learning. The thesis contributes towards developing efficient training strategies for structural SVMs. In particular, an efficient sequential optimization method is proposed for structural SVMs, which is faster than several competing methods. An extension of the sequential method to CRFs is also developed. The sequential method is adapted to a variant of structural SVM with linear cumulative loss. The thesis also presents a systematic empirical evaluation of various training methods available for structured output learning, which will be useful to the practitioner. To train structural SVMs in the presence of a vast number of training examples without labels, the thesis develops a simple semi-supervised technique based on switching the labels of the components of the structured output. The proposed technique is general and its efficacy is demonstrated using experiments on different benchmark applications. Another contribution of the thesis is towards the design of fast algorithms for sparse structured output learning. Efficient alternating optimization algorithms are developed for sparse classifier design. These algorithms are shown to achieve sparse models faster, when compared to existing methods.

Visual object category recognition is one of the most challenging problems in computer vision. Even assuming that we can obtain a near-perfect instance level representation with the advances in visual input devices and low-level vision techniques, object categorization still remains as a difficult problem because it requires drawing boundaries between instances in a continuous world, where the boundaries are solely defined by human conceptualization. Object categorization is essentially a perceptual process that takes place in a human-defined semantic space. In this semantic space, the categories reside not in isolation, but in relation to others. Some categories are similar, grouped, or co-occur, and some are not. However, despite this semantic nature of object categorization, most of the today's automatic visual category recognition systems rely only on the category labels for training discriminative recognition with statistical machine learning techniques. In many cases, this could result in the recognition model being misled into learning incorrect associations between visual features and the semantic labels, from essentially overfitting to training set biases. This limits the model's prediction power when new test instances are given. Using semantic knowledge has great potential to benefit object category recognition. First, semantic knowledge could guide the training model to learn a correct association between visual features and the categories. Second, semantics provide much richer information beyond the membership information given by the labels, in the form of inter-category and category-attribute distances, relations, and structures. Finally, the semantic knowledge scales well as the relations between categories become larger with an increasing number of categories. My goal in this thesis is to learn discriminative models for categorization that leverage semantic knowledge for object recognition, with a special focus on the semantic relationships among different categories and concepts. To this end, I explore three semantic sources, namely attributes, taxonomies, and analogies, and I show how to incorporate them into the original discriminative model as a form of structural regularization. In particular, for each form of semantic knowledge I present a feature learning approach that defines a semantic embedding to support the object categorization task. The regularization penalizes the models that deviate from the known structures according to the semantic knowledge provided. The first semantic source I explore is attributes, which are human-describable semantic characteristics of an instance. While the existing work treated them as mid-level features which did not introduce new information, I focus on their potential as a means to better guide the learning of object categories, by enforcing the object category classifiers to share features with attribute classifiers, in a multitask feature learning framework. This approach essentially discovers the common low-dimensional features that support predictions in both semantic spaces. Then, I move on to the semantic taxonomy, which is another valuable source of semantic knowledge. The merging and splitting criteria for the categories on a taxonomy are human-defined, and I aim to exploit this implicit semantic knowledge. Specifically, I propose a tree of metrics (ToM) that learns metrics that capture granularity-specific similarities at different nodes of a given semantic taxonomy, and uses a regularizer to isolate granularity-specific disjoint features. This approach captures the intuition that the features used for the discrimination of the parent class should be different from the features used for the children classes. Such learned metrics can be used for hierarchical classification. The use of a single taxonomy can be limited in that its structure is not optimal for hierarchical classification, and there may exist no single optimal semantic taxonomy that perfectly aligns with visual distributions. Thus, I next propose a way to overcome this limitation by leveraging multiple taxonomies as semantic sources to exploit, and combine the acquired complementary information across multiple semantic views and granularities. This allows us, for example, to synthesize semantics from both 'Biological', and 'Appearance'-based taxonomies when learning the visual features. Finally, as a further exploration of more complex semantic relations different from the previous two pairwise similarity-based models, I exploit analogies, which encode the relational similarities between two related pairs of categories. Specifically, I use analogies to regularize a discriminatively learned semantic embedding space for categorization, such that the displacements between the two category embeddings in both category pairs of the analogy are enforced to be the same. Such a constraint allows for a more confusing pair of categories to benefit from a clear separation in the matched pair of categories that share the same relation. All of these methods are evaluated on challenging public datasets, and are shown to effectively improve the recognition accuracy over purely discriminative models, while also guiding the recognition to be more semantic to human perception. Further, the applications of the proposed methods are not limited to visual object categorization in computer vision, but they can be applied to any classification problems where there exists some domain knowledge about the relationships or structures between the classes. Possible applications of my methods outside the visual recognition domain include document classification in natural language processing, and gene-based animal or protein classification in computational biology. / text

In this master thesis, we compare three different types of funding alternatives from a Swedish municipality's point of view, with the main focus on analysing a Nikkei-linked loan. We do this by analysing the resulting interest rate and the expected exposures, taking collateral into consideration. We conclude, with certainty, that there are many alternatives for funding and that they each need to be analysed and compared on many levels to be able to make a correct decision as to which ones to choose. An important part of this is to consider the implications of the newest regulations and risk exposure, as it might greatly influence the final price for contracts. Between the cases that we considered, the SEK bond was the one with the lowest resulting spread, and the one which is the simplest considering the collateral involved. While other alternatives might be better depending on how profitable it is for the municipality to receive collateral, the SEK bond is the most transparent one and with least risk involved.

Typescript (photocopy). / Digitized by Kansas Correctional Industries

Computing / (M.Sc. (Information Systems))

The hypothesis that a structured, sequenced, approach to mathematics learning, based on the application of learnt facts, decreases mathematics anxiety and increases mathematics achievement is tested. A literature study and an empirical investigation were conducted with respect to the relationships between maths anxiety, maths achievement and teaching methods. A qualitative research design which focussed on the cross-case analysis of different case studies was used. The qualitative case study involves multiple methods such as interviews, observations and a pretest, posttest design. It analyses and compares the effects of the Kuman method, used as the intervention programme, on maths anxiety and maths achievement of an experimental group and a control group. The results of this research indicate that learners on the intervention programme who showed a decrease in anxiety, showed an increase in achievement. This has implications for the teaching methods used in South Africa. / Teacher Education / M. Ed. (Specialisation in Guidance and Counselling)

The focus of this research study is on establishing emotional awareness in the child in her middle childhood that finds herself in a children's home. Emphasis is placed on the effect that structured Gestalt group therapy has on the development of emotional awareness in the child in a children's home and how it can be utilized to positively support the process of emotional awareness. The main aim of the study is to investigate, evaluate and describe the impact of structured gestalt group therapy on the emotional awareness of the child in a children's home, for play therapists to use within this context. / SOCIAL WORK / MDIAC (PLAY THERAPY)

This study will focus on explaining the dynamics of Saudi Arabia’s national security. In explaining these dynamics, the study will consider two of Buzan’s frameworks for analysing national security. Further enhancement will be given by conceptualising specific assumptions about Saudi Arabia’s national security – these will be based on the manner in which certain features are utilised within the Saudi state. Semistructured interviews will be utilised to examine the findings from the adapted frameworks. By studying the state’s domestic, regional and international concerns, as well as the specific threats that each level pose with regards to several security sectors (including the: social, political, economic, militant and environmental), this study will provide a distinctive analysis of national security within the Saudi state. Initially, this study acknowledges that only a few studies have been conducted into Saudi Arabia’s national security; furthermore, these have focused on the internal perspective by considering Saudi national security in terms of its military and strategic partnerships. Secondly, the study proposes that Saudi Arabia is unique (and unlike any other state) as it holds various important social and religious aspects that are not fully understood by external sources. Consequently, this study conceptualises Saudi national security from the internal perspective by considering the Saudi state’s specific features.

The issue of standards for household latrines is complex because discussions related to standards for latrines in literature from the water, sanitation and hygiene (WASH) sector tend to focus on the negative aspects of standards and highlights cases where the miss-application of standards in the past has caused problems. However, despite concerns about the constraints that standards can seemingly impose, there is an acknowledgement that standards can play a more positive role in supporting efforts to increase access to household latrines. The World Health Organisation has long established and widely recognised standards for water supply quality and quantity but there are no equivalent standards for sanitation services and there is currently no guidance that deals with the topic of standards for household latrines. Household latrines are a small component of the wider sanitation system in a country and by considering how standards for household latrines operate within this wider sanitation system the aim of this research is to understand what influences standards can have on household latrines and explore how the negative perceptions about standards and latrine building can be overcome. The development of guidance on how to develop well written standards is the core focus of this research. This research explores the factors that can influence the development and use of a standard for household latrines in Rwanda using three data collection methods. Document analysis using 66 documents, including policies and strategies, design manuals and training guides from 17 countries throughout Sub-Saharan Africa was used in conjunction with the Delphi Method involving an expert panel of 27 from Rwanda and 38 semi-structured interviews. The research concludes that perceptions about standards for household latrines are fragmented and confused with little consensus in Rwanda on what need a standard should meet and what role it should play. The study has found that the need for a standard must be considered in the context of the wider sanitation system otherwise it can lead to duplication of efforts and increased confusion for all stakeholders. The study also found that there is an assumed link between standards and enforcement of standards through regulation and punishments which creates the negative perceptions about standards in Rwanda. However, despite this aversion to standards, there are still intentions to promote the standardisation of latrine technologies and designs, led by national government in Rwanda and in other Sub-Saharan African countries. The contribution to knowledge of this research includes a decision process presented at the end of the study which can be used by decision makers who are interested in developing a standard for household latrines. The decision process acts as a tool for outlining how a standard can operate within the national sanitation system. This understanding provides decision makers with the basis for continuing the debate on what a well written standard looks like in the national context and supports the development of a standard that is fit for purpose and provides a positive contribution to the sector.