Data is one of the most contributing factors for modern business operations. Having bad data could therefore lead to tremendous losses, both financially and for customer experience. This thesis seeks to find anomalies in real-world, complex, structured data, causing an international enterprise to miss out on income and the potential loss of customers. By using graph theory and similarity analysis, the findings suggest that certain countries contribute to the discrepancies more than other countries. This is believed to be an effect of countries customizing their products to match the market’s needs. This thesis is just scratching the surface of the analysis of the data, and the number of opportunities for future work are therefore many.

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This study investigates the effects of authentic materials and contextually-developed role-playing activities on the oral proficiency of Thai undergraduate students. The study was conducted at Prince of Songkla University, Thailand during the first semester (June to September) of 2010. The study consisted of four research instruments and the data were analyzed using Independent Samples t-test to determine whether the authentic materials and contextually-developed role-playing activities had improved the students’ oral fluency and accuracy in the target language. The findings indicated statistically significant differences between the two groups wherein the experimental group performed better on both fluency and accuracy than the control group. Therefore, based on the findings of the current study, it can be concluded that authentic materials and contextually-developed role-playing activities involving a series of sequential events are effective in enhancing learners’ oral proficiency in programs of English as a foreign language in the context of Thailand English education. / English Studies / M.A. (TESOL (Teaching English to Speakers of Other Languages))

This dissertation thesis addresses the area of spatial navigation of the blind. The author theoretically deals with a complex interplay of psychological functions involved in spatial navigation with respect to the specific conditions of the blind. The empirical part of the thesis presents an experimental study in the population of the blind (N=44). This study focuses on effects of the stress recovery phase from shortly increased stress levels on the process of learning a new route. The experiment is placed in real-world settings and overcomes some of the methodological flaws typical for this research domain. The research evidence suggests that the recovery phase from shortly increased stress levels hinders the development of procedural knowledge of the route. However, this deterioration in route knowledge is not associated with the effects of the strsss phase itself, but affects only the recovery phase part of the route in which stress levels are returning to their original baseline levels (not necessarily the level of the resting conditions). Besides its theoretical conclusions, the value of the presented thesis is in its contribution to the advancements of research methods in the given field. The outcomes of this work are practically applicable to the development of navigation aids for the blind....

Au Canada, le cancer du poumon est la cause principale de décès relié au cancer. À l’imagerie médicale, le cancer du poumon peut prendre la forme d’un nodule pulmonaire. La prise en charge menant au diagnostic définitif d’un nodule pulmonaire peut s’avérer complexe. La recherche en oncoprotéomique a permis le développement de nouveaux tests diagnostiques non-invasifs en cancer du poumon. Ceux-ci ont pour objectif d’évaluer le risque de malignité d’un nodule pour guider la prise en charge menant au diagnostic. Toutefois, l’impact économique de tels tests demeure inconnu. L’objectif de ce projet était de mesurer, en milieu de pratique réelle, l’utilisation des ressources en soins de santé pour l’investigation de nodules pulmonaires puis, de développer un modèle générique permettant d’évaluer l’impact économique au Québec des nouveaux tests protéomiques pour l’investigation de ces nodules. Tout d’abord, une revue de dossiers patients a été effectuée dans trois centres hospitaliers du Québec afin de mesurer les ressources en soins de santé et les coûts associés à l’investigation de nodules pulmonaires entre 0,8 et 3,0 cm. Par la suite, une analyse de minimisation de coûts a été effectuée à partir d’un modèle générique développé dans le cadre de ce projet. Ce modèle visait à comparer l’approche courante d’investigation à celle intégrant un test protéomique fictif afin de déterminer l’approche la moins dispendieuse. La revue de dossiers patients a permis de déterminer qu’au Québec, le coût moyen d’investigation d’un nodule pulmonaire est de 7 354$. Selon les résultats de l’analyse, si le coût du test protéomique est fixé en-deçà de 3 228,70$, l’approche intégrant celui-ci serait moins dispendieuse que l’approche courante. La présente analyse suggère que l’utilisation d’un test diagnostique protéomique non-invasif en début d’investigation pour un nodule de 0,8 à 3,0 cm, permettrait d’engendrer des économies pour le système de santé au Québec. / In Canada, lung cancer is the leading cause of death among cancer patients. Imaging technologies, such as computed tomography, allows the detection of potential lung cancers in the form of pulmonary nodules. The clinical pathway leading to the definitive diagnostic of a pulmonary nodule can be complex. Research in oncoproteomics has led to the development of novel noninvasive diagnostic tests in lung cancer. These tests aim to evaluate the risk of malignancy of a nodule in order to guide the clinical pathway leading to a diagnostic. However, the economic impact of such tests remains unknown. The objective of this project was to measure, in a real-life setting, health care resource utilization for the investigation of pulmonary nodules and then, develop a generic model to assess the economic impact in the province of Quebec of new proteomic tests for the investigation of these nodules. Firstly, a medical chart review was performed in three hospitals in Quebec to measure health care resource utilization for the investigation of pulmonary nodules of 0,8 to 3,0 cm. Then, a cost minimization analysis was performed by using a generic model developed for this project. This model compared the usual care to the approach integrating a fictive proteomic test in order to identify the less expensive approach. As per the medical chart review, the average cost for the investigation of a pulmonary nodule was $7,354. According to the results of the analysis, if the cost of the test is below $3,228.70, the approach integrating a proteomic test would be less expensive then the current approach. This study tends to demonstrate that the use of a noninvasive proteomic diagnostic test at the beginning of the investigation of a pulmonary nodule from 0,8 to 3,0 cm could generate savings for the health care system in Quebec.

Advances in unsupervised learning and deep neural networks have led to increased performance in a number of domains, and to the ability to draw strong comparisons between the biological method of self-organization conducted by the brain and computational mechanisms. This thesis aims to use real-world data to tackle two areas in the domain of computer vision which have biological equivalents: feature detection and motion tracking. The aforementioned advances have allowed efficient learning of feature representations directly from large sets of unlabeled data instead of using traditional handcrafted features. The first part of this thesis evaluates such representations by comparing regularization and preprocessing methods which incorporate local neighbouring information during training on a single-layer neural network. The networks are trained and tested on the Hollywood2 video dataset, as well as the static CIFAR-10, STL-10, COIL-100, and MNIST image datasets. The induction of topography or simple image blurring via Gaussian filters during training produces better discriminative features as evidenced by the consistent and notable increase in classification results that they produce. In the visual domain, invariant features are desirable such that objects can be classified despite transformations. It is found that most of the compared methods produce more invariant features, however, classification accuracy does not correlate to invariance. The second, and paramount, contribution of this thesis is a biologically-inspired model to explain the emergence of motion tracking behaviour in early development using unsupervised learning. The model’s self-organization is biased by an original concept called retinal constancy, which measures how similar visual contents are between successive frames. In the proposed two-layer deep network, when exposed to real-world video, the first layer learns to encode visual motion, and the second layer learns to relate that motion to gaze movements, which it perceives and creates through bi-directional nodes. This is unique because it uses general machine learning algorithms, and their inherent generative properties, to learn from real-world data. It also implements a biological theory and learns in a fully unsupervised manner. An analysis of its parameters and limitations is conducted, and its tracking performance is evaluated. Results show that this model is able to successfully follow targets in real-world video, despite being trained without supervision on real-world video.

This study investigates the effects of authentic materials and contextually-developed role-playing activities on the oral proficiency of Thai undergraduate students. The study was conducted at Prince of Songkla University, Thailand during the first semester (June to September) of 2010. The study consisted of four research instruments and the data were analyzed using Independent Samples t-test to determine whether the authentic materials and contextually-developed role-playing activities had improved the students’ oral fluency and accuracy in the target language. The findings indicated statistically significant differences between the two groups wherein the experimental group performed better on both fluency and accuracy than the control group. Therefore, based on the findings of the current study, it can be concluded that authentic materials and contextually-developed role-playing activities involving a series of sequential events are effective in enhancing learners’ oral proficiency in programs of English as a foreign language in the context of Thailand English education. / English Studies / M.A. (TESOL (Teaching English to Speakers of Other Languages))

[ES] ¿Cómo es la atribución en un entorno de omnicanal? Se puede determinar una distinción importante en contraste con la atribución en un entorno multicanal. Además de proporcionar el proceso de análisis de marketing, una especificación del proceso estándar intersectorial para la minería de datos (CRISP¿DM), se utiliza un enfoque de método mixto secuencial para analizar la cuestión principal de la investigación. En el primer paso de esta investigación se analizan las características y los requisitos de atribución eficiente en un entorno omnicanal. A partir de entrevistas semiestructuradas con expertos y de un proceso de investigación bibliográfica holística estructurada, se identifica claramente la falta de un enfoque de atribución omnicanal. Los enfoques de atribución existentes se identifican mediante la realización de un proceso estructurado de revisión de la literatura. Estos enfoques identificados se evalúan aplicando los resultados de las entrevistas semiestructuradas con expertos, es decir, los requisitos y características de una atribución omnicanal eficiente. Ninguno de los enfoques de atribución identificados cumple con la mayoría de los requisitos de omnicanal analizados. Al tener la brecha de investigación ¿ la falta de un enfoque de atribución de omnicanales ¿ claramente identificada, se desarrolla un enfoque de atribución de omnicanales en la segunda parte de esta investigación presentada. Utilizando la metodología MAP, la principal laguna de investigación se llena proporcionando el Holistic Customer Journey (HCJ): una base de datos lista para el omni¿canal y un enfoque de atribución de omni¿canal correspondiente. Entre otras cosas, el enfoque de atribución desarrollado consiste en una clasificación de aprendizaje automático. Esta investigación presentada es la primera en utilizar información de casi 240.000.000 de conjuntos de datos de interacción, que contienen información entre dispositivos y entre plataformas. Todas las fuentes de datos subyacentes son proporcionadas por una de las plataformas inmobiliarias más grandes de Alemania. / [CAT] Com és l'atribució en un entorn de omnicanal? Es pot determinar una distinció important en contrast amb l'atribució en un entorn multicanal. A més de proporcionar el procés d'anàlisi de màrqueting, una especificació del procés estàndard intersectorial per a la mineria de dades (CRISP¿DM), s'utilitza un enfocament de mètode mixt seqüencial per analitzar la qüestió principal de la investigació. En el primer pas d'aquesta investigació s'analitzen les característiques i els requisits d'atribució eficient en un entorn omnicanal. A partir d'entrevistes semiestructurades amb experts i d'un procés de recerca bibliogràfica holística estructurada, s'identifica clarament la falta d'un enfocament d'atribució omnicanal. Els enfocaments d'atribució existents s'identifiquen mitjançant la realització d'un procés estructurat de revisió de la literatura. Aquests enfocaments identificats s'avaluen aplicant els resultats de les entrevistes semiestructurades amb experts, és a dir, els requisits i característiques d'una atribució omnicanal eficient. Cap dels enfocaments d'atribució identificats compleix amb la majoria dels requisits de omnicanal analitzats. En tenir la bretxa de recerca ¿ la manca d'un enfocament d'atribució de omnicanales ¿ clarament identificada, es desenvolupa un enfocament d'atribució de omnicanales a la segona part d'aquesta investigació presentada. Utilitzant la metodologia MAP, la principal llacuna de recerca s'omple proporcionant el Holistic Customer Journey (HCJ): una base de dades a punt per al omni¿canal i un enfocament d'atribució de omni¿canal corresponent. Entre altres coses, l'enfocament d'atribució desenvolupat consisteix en una classificació d'aprenentatge automàtic. Aquesta investigació presentada és la primera a utilitzar informació de gairebé 240.000.000 de conjunts de dades d'interacció, que contenen informació entre dispositius i entre plataformes. Totes les fonts de dades subjacents són proporcionades per una de les plataformes immobiliàries més grans d'Alemanya. / [EN] What does attribution in an omni¿channel environment look like? A major distinction can be determined in contrast to attribution in a multi¿channel environment. Besides providing the Marketing Analytics Process, a specification of the Cross¿industry standard process for data mining (CRISP¿DM), a sequential mixed method approach is utilized to analyze the main research question. Within the first step of this presented research characteristics, and requirements of efficient attribution in an omni¿channel environment are analyzed. Based on semi¿structured expert interviews and a holistic structured literature research process, the lack of an omni¿channel attribution approach is clearly identified. Existing attribution approaches are identified by conducting the structured literature review process. Those identified approaches are evaluated by applying the results of the semi¿structured expert interviews - the requirements and characteristics of efficient omni¿channel attribution. None of the identified attribution approaches fulfill a majority of the analyzed omni¿channel requirements. By having the research gap - the lack of an omni¿channel attribution approach - clearly identifed, an omni¿channel attribution approach is developed in the second part of this presented research. Utilizing the MAP methodology, the main research gap is filled by providing the Holistic Customer Journey (HCJ): an omni¿channel ready data foundation and a corresponding omni¿channel attribution approach. Among other things the developed attribution approach consists of a machine learning classification. This presented research is the first to utilize information from almost 240.000.000 interaction data sets, containing crossdevice and cross¿platform information. All underlying data sources are provided by one of Germany's largest real¿estate platforms. / Nass, O. (2019). Optimizing Online Marketing Efficiency By Analyzing the Mutual Influence of Online Marketing Channels with Respect to Different Devices [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/122296 / TESIS

Für viele Krankheitsbilder und Indikationen ist ein breites Spektrum an Arzneimitteln und Arzneimittelkombinationen verfügbar. Darüber hinaus stellen Therapieziele oft Kompromisse zwischen medizinischen Zielstellungen und Präferenzen und Erwartungen von Patienten dar, um Zufriedenheit und Adhärenz zu gewährleisten. Die Auswahl der optimalen Therapieoption kann daher eine große Herausforderung für den behandelnden Arzt darstellen. Klinische Entscheidungsunterstützungssysteme, die Wirksamkeit oder Risiken unerwünschter Arzneimittelwirkung für Behandlungsoptionen vorhersagen, können diesen Entscheidungsprozess unterstützen und \linebreak Leitlinien-basierte Empfehlungen ergänzen, wenn Leitlinien oder wissenschaftliche Literatur fehlen oder ungeeignet sind. Bis heute sind keine derartigen Systeme verfügbar. Im Rahmen dieser Arbeit wird die Anwendung von Methoden aus der Domäne der Recommender Systems (RS) und des Maschinellen Lernens (ML) in solchen Unterstützungssystemen untersucht. Aufgrund ihres erfolgreichen Einsatzes in anderen Empfehlungssystemen und der einfachen Interpretierbarkeit werden zum einen Nachbarschafts-basierte Collaborative Filter (CF) an die besonderen Anforderungen und Herausforderungen der Therapieempfehlung angepasst. Zum anderen werden ein Modell-basierter CF-Ansatz (SLIM) und ein ML Algorithmus (GBM) erprobt. Alle genannten Ansätze werden anhand eines exemplarischen Therapieempfehlungssystems evaluiert, das auf die Behandlung der Autoimmunkrankheit Psoriasis abzielt. Um das Risiko der Empfehlung kontraindizierter oder gar gesundheitsgefährdender Medikamente zu reduzieren, werden Regeln aus evidenzbasierten Leitlinien und Expertenempfehlungen implementiert, um solche Therapieoptionen aus den Empfehlungslisten herauszufiltern. Insbesondere die Nachbarschafts-basierten CF-Algorithmen zeigen insgesamt kleine durchschnittliche Abweichungen zwischen geschätztem und tatsächlichem Therapie-Outcome. Auch die aus den Outcome-Schätzungen abgeleiteten Empfehlungen zeigen eine hohe Übereinstimmung mit der tatsächlich angewandten Behandlung. Die Modell-basierten Ansätze sind den Nachbarschafts-basierten Ansätzen insgesamt unterlegen, was auf den begrenzten Umfang der verfügbaren Trainingsdaten zurückzuführen ist und die Generalisierungsfähigkeit der Modelle erschwert. Im Vergleich mit menschlichen Experten sind alle untersuchten Algorithmen jedoch hinsichtlich Übereinstimmung mit der tatsächlich angewandten Therapie unterlegen. Eine objektive und effiziente Bewertung des Behandlungserfolgs kann als Voraussetzung für ein erfolgreiches ``Krankheitsmanagement'' angesehen werden. Daher wird in weiteren Untersuchungen für ausgwählten klinische Anwendungen der Einsatz von ML Methoden zur automatischen Quantifizierung von Gesunheitszustand und Therapie-Outcome erprobt. Zusätzlich, als weitere Quelle für Informationen über Therapiewirksamkeiten, wird der Einsatz von Sentiment Analysis Methoden zur Extraktion solcher Informationen aus Medikamenten-Bewertungen untersucht. / Under most medical conditions and indications, a great variety of pharmaceutical drugs and drug combinations are available. Beyond that, trade-offs need to be found between the medical requirements and the patients' preferences and expectations in order to support patients’ satisfaction and adherence to treatments. As a consequence, the selection of an optimal therapy option for an individual patient poses a challenging task to prescribers. Clinical Decision Support Systems (CDSSs), which predict outcome as effectiveness and risk of adverse effects for available treatment options, can support this decision-making process and complement guideline-based decision-making where evidence from scientific literature is missing or inappropriate. To date, no such systems are available. Within this work, the application of methods from the Recommender Systems (RS) domain and Machine Learning (ML) in such decision support systems is studied. Due to their successful application in other recommender systems and good interpretability, neighborhood-based CF algorithms are transferred to the medical domain and are adapted to meet the requirements and challenges of the therapy recommendation task. Moreover, a model-based CF method (SLIM) and a state of the art ML algorithm (GBM) are employed. All algorithms are evaluated in an exemplary therapy recommender system, targeting the treatment of the autoimmune skin disease Psoriasis. In order to reduce the risk of recommending contraindicated or even health-endangering drugs, rules derived from evidence-based guidelines and expert recommendations are implemented to filter such options from the recommendation lists. Especially the neighborhood-based CF algorithms show small average errors between estimated and observed outcome. Also, the recommendations derived from outcome estimates show high agreement with the ground truth. The performance of both model-based approaches is inferior to the neighborhood-based recommender. This is primarily assumed to be due to the limited training data sizes, which renders generalizability of the learned models difficult. Compared with recommendations provided by various experts, all proposed approaches are, however, inferior in terms of agreement with the ground truth. An objective and efficient assessment of treatment response can be regarded a prerequisite for successful ``disease management''. Therefore, the use of ML methods for the automatic quantification of health status and therapy outcome for selected clinical applications is investigated in further experiments. Moreover, as additional source of information about drug effectiveness, the use of Sentiment Analysis, in order to extract such information from drug reviews, is investigated.

This project proposes and uses co-creation design methods, a design approach based on allowing users to play a design role; by creating a project. The design process comprises design specialists and participants from various specialties and ages, then finding common ground and interests to develop a future work plan. Collective creation designers can provide tools and workshops to support and develop a fledgling community initiative that works within design and change. After the Syrians were exposed to a movement towards world countries, forming a diaspora condition within their families and host societies. This project was implemented in January 2021, with two collaborating students of the Bachelor of Design + Change at Linnaeus International University in Sweden, titled ‘’Syrians of The Diaspora’’. The project deals with collective creation in addressing issues to which immigrants are exposed, several issues that cause feelings of despair, and loss of creative value, influenced by their neglected skills and life experiences. To create a ‘’vocational cultural knowledgeable club’’ in the host country that employs their skills and presents them to the host community, facilitating integration plans.