Through the use of an analog methodology and a factorial experimental design, the current study investigated the effects of counselors' race and multicultural competency on counselor preference for Black participants. Eighty-seven African American college students participated in an online or computer-administered study. The African American multiculturally competent counselor received significantly higher counselor effectiveness ratings than did the White multiculturally competent counselor. Also, the correlations between racial identity ideologies and counselor effectiveness were examined within each condition of race and multicultural competency. Racial Identity was measured with the Multidimensional Inventory of Black Identity (Sellers, Rowley, Chavous, Shelton, & Smith, 1997). The findings from this study may assist counselors and researchers in understanding how potential African American clients perceive counselors based on their race and multicultural competency and how client's racial identity may affect African American clients' perceptions of counselors' effectiveness.

Stimulus Pairing Observational Procedure (SPOP) is an incidental teaching type procedure that has shown success in teaching typically developing children to name, independent tacting in the absence of the verbal community following a history of listener and echoic responses. The purpose of the current study was to compare SPOP and matching to sample procedures to determine the rate of acquisition for correct tact and listener responding , the rate of off task be avior, and the rate of generalization to different settings, stimuli, and instructors. Results of the study indicate that correct responding occurred at higher rates during MTS procedures than SPOP. The average rate of off task behavior was higher during SPOP sessions than during pre test, MTS sessions, or generalization probes for each participant. The results of the generalization probe may indicate that SPOP produces correct responding that increases or maintains during generalization, while MTS procedures produced more variable results.

Les membranes Nafion©, composant essentiel des piles à combustibles (PEMFC), ont des performances liées à leur hydratation. En collaboration avec le CEA, nous avons développé une méthode de mesure de la vitesse ultrasonore et du gonflement de ce type de membranes par insertion-substitution. Conséquence de la faible épaisseur des échantillons devant la longueur d'onde, un phénomène de recouvrement apparait entre les différents échos d'aller-retour dans le matériau. Un algorithme de Matching Pursuit a été adapté au cas des membranes afin de déconvoluer les échos et ainsi de permettre la mesure de la vitesse de propagation ultrasonore et de l'atténuation. Nos mesures nous ont également permis de déterminer l'épaisseur de ces membranes. Une étude sur la robustesse et le domaine de validité de notre méthode de traitement est présentée. Ce manuscrit recense également l'ensemble des résultats sur la caractérisation des membranes Nafion dans différents états d'équilibre hydrique, attestant de la possibilité de caractériser la reprise hydrique de ce polymère par méthode ultrasonore. / No summary available

abstract: A volunteered geographic information system, e.g., OpenStreetMap (OSM), collects data from volunteers to generate geospatial maps. To keep the map consistent, volunteers are expected to perform the tedious task of updating the underlying geospatial data at regular intervals. Such a map curation step takes time and considerable human effort. In this thesis, we propose a framework that improves the process of updating geospatial maps by automatically identifying road changes from user-generated GPS traces. Since GPS traces can be sparse and noisy, the proposed framework validates the map changes with the users before propagating them to a publishable version of the map. The proposed framework achieves up to four times faster map matching performance than the state-of-the-art algorithms with only 0.1-0.3% accuracy loss. / Dissertation/Thesis / Masters Thesis Computer Science 2017

De nombreux jeux de données sont publiés sur le web à l’aide des technologies du web sémantique. Ces jeux de données contiennent des données qui représentent des liens vers des ressources similaires. Si ces jeux de données sont liés entre eux par des liens construits correctement, les utilisateurs peuvent facilement interroger des données à travers une interface uniforme, comme s’ils interrogeaient un jeu de données unique. Mais, trouver des liens corrects est très difficile car de nombreuses comparaisons doivent être effectuées. Plusieurs solutions ont été proposées pour résoudre ce problème : (1) l’approche la plus directe est de comparer les valeurs d’attributs d’instances pour identifier les liens, mais il est impossible de comparer toutes les paires possibles de valeurs d’attributs. (2) Une autre stratégie courante consiste à comparer les instances selon les attribut correspondants trouvés par l’alignement d’ontologies à base d’instances, qui permet de générer des correspondances d’attributs basés sur des instances. Cependant, il est difficile d’identifier des instances similaires à travers les ensembles de données car,dans certains cas, les valeurs des attributs en correspondance ne sont pas les mêmes.(3) Plusieurs méthodes utilisent la programmation génétique pour construire des modèles d’interconnexion afin de comparer différentes instances, mais elles souffrent de longues durées d’exécution.Dans cette thèse, une méthode d’interconnexion est proposée pour relier les instances similaires dans différents ensembles de données, basée à la fois sur l’apprentissage statistique et sur l’apprentissage symbolique. L’entrée est constituée de deux ensembles de données, des correspondances de classes sur les deux ensembles de données et un échantillon de liens “positif” ou “négatif” résultant d’une évaluation de l’utilisateur. La méthode construit un classifieur qui distingue les bons liens des liens incorrects dans deux ensembles de données RDF en utilisant l’ensemble des liens d’échantillons évalués. Le classifieur est composé de correspondances d’attributs entre les classes correspondantes et de deux ensembles de données,qui aident à comparer les instances et à établir les liens. Le classifieur est appelé motif d’interconnexion dans cette thèse. D’une part, notre méthode découvre des correspondances potentielles entre d’attributs pour chaque correspondance de classe via une méthode d’apprentissage statistique : l’algorithme de regroupement K-medoids,en utilisant des statistiques sur les valeurs des instances. D’autre part, notre solution s’appuie sur un modèle d’interconnexion par une méthode d’apprentissage symbolique: l’espace des versions, basée sur les correspondances d’attributs potentielles découvertes et l’ensemble des liens de l’échantillon évalué. Notre méthode peut résoudre la tâche d’interconnexion quand il n’existe pas de motif d’interconnexion combiné qui couvre tous les liens corrects évalués avec un format concis.L’expérimentation montre que notre méthode d’interconnexion, avec seulement1% des liens totaux dans l’échantillon, atteint une F-mesure élevée (de 0,94 à 0,99). / There are many data sets being published on the web with Semantic Web technology. The data sets usually contain analogous data which represent the similar resources in the world. If these data sets are linked together by correctly identifying the similar instances, users can conveniently query data through a uniform interface, as if they are connecting a single database. However, finding correct links is very challenging because web data sources usually have heterogeneous ontologies maintained by different organizations. Many existing solutions have been proposed for this problem. (1) One straight-forward idea is to compare the attribute values of instances for identifying links, yet it is impossible to compare all possible pairs of attribute values. (2) Another common strategy is to compare instances with correspondences found by instance-based ontology matching, which can generate attribute correspondences based on overlapping ranges between two attributes, while it is easy to cause incomparable attribute correspondences or undiscovered comparable attribute correspondences. (3) Many existing solutions leverage Genetic Programming to construct interlinking patterns for comparing instances, however the running times of the interlinking methods are usually long. In this thesis, an interlinking method is proposed to interlink instances for different data sets, based on both statistical learning and symbolic learning. On the one hand, the method discovers potential comparable attribute correspondences of each class correspondence via a K-medoids clustering algorithm with instance value statistics. We adopt K-medoids because of its high working efficiency and high tolerance on irregular data and even incorrect data. The K-medoids classifies attributes of each class into several groups according to their statistical value features. Groups from different classes are mapped when they have similar statistical value features, to determine potential comparable attribute correspondences. The clustering procedure effectively narrows the range of candidate attribute correspondences. On the other hand, our solution also leverages a symbolic learning method, called Version Space. Version Space is an iterative learning model that searches for the interlinking pattern from two directions. Our design can solve the interlinking task that does not have a single compatible conjunctive interlinking pattern that covers all assessed correct links with a concise format. The interlinking solution is evaluated with large-scale real-world data from IM@OAEI and CKAN. Experiments confirm that the solution with only 1% of sample links already reaches a high accuracy (up to 0.94-0.99 on F-measure). The F-measure quickly converges improving on other state-of-the-art approaches, by nearly 10 percent of their F-measure values.

In this article, we combine two so far separate strands of the economic literature and argue that democratization leads to a real exchange rate appreciation. We test this hypothesis empirically for a sample of countries observed from 1980 to 2007 by combining a difference-in-difference approach with propensity score matching estimators. Our empirical results reveal a strong and significant finding: democratization causes real exchange rates to appreciate. Consequently, the ongoing process of democratization observed in many parts of the world is likely to reduce exchange rate distortions.

When two distributed parties want to share information stored in ontologies, they have to make sure that they refer to the same concepts. This is done matching the ontologies. This thesis will show the implementation of a method for automatic ontology matching based on the representation of polygons. The method is used to compare two ontologies and determine the degree of similarity between them. The first of the ontologies will be taken as the standard, while the other will be compared to it by analyzing the elements in both. According to the degrees of similarity obtained from the comparison of elements, a set of polygons is represented for the standard ontology and another one for the second ontology. Comparing the polygons we obtain the final result of the similarity between the ontologies. With that result it is possible to determine if two ontologies handle information referred to the same concept.

Context: In this thesis subimage matching in historical handwritten documents using SIFT (Scale-Invariant Feature Transform) keypoints was tested. SIFT features are invariant to scale and rotation and have gained a lot of interest in the research community. The historical documents used in this thesis orignates from 16th century and forward. The following steps have been executed; binarization, word segmentation, feature identification and clustering. The binarization step converts the images into binary images. The word segmentation separates the different words into individual subimages. In the feature identification SIFT keypoints was found and descriptors was computed. The last step was to cluster the images based on the distances between the set of image features identified. Objectives: The main objectives are to find a good configuration for the binarization step, implement a good word segmentation, identify image features and lastly to cluster the images based on their similarity. The context from subimages are matched to each other rather than trying to predict what the context of a subimage is, simply because the data that has been used is unlabeled. Methods: Implementation were the main methodology used combined with experimentation. Measurements were taken throughout the development and accuracy of word segmentation and the clustering is measured. Results: The word segmentation got an average accuracy of 89\% correct segmentation which is comparable to other word segmentating results. The clustering however matched 0% correctly.Conclusions: The conclusions that have been drawn from this study is that SIFT keypoints are not very well suited for this type of problem which includes a lot of handwritten text. The descriptors were not discriminative enough and different keypoints were found in different images with the same handwritten text, which lead to the bad clustering results.

Essay 1: Swedish micro and macro data on internal migration indicate that home-owners on average have a higher propensity to migrate to other labour market areas in response to higher unemployment and job vacancy rates than renters and tenant-owners. This is evidence that owning your home does not constrain labour mobility across labour market areas in comparison to other forms of housing tenure. The response to high local job vacancy rates indicates that migration in general is driven by differences in matching efficiency in local labour market areas rather than a pure response to high local unemployment. A third finding is that at higher levels of aggregation high unemployment is associated to high levels of home-ownership as previous researchers have found. The suggested explanation for this correlation, i.e. that home-ownership constrains mobility of individuals, however, seems falsified in Sweden by our results. Essay 2: Swedish micro and macro data on internal migration indicate that interregional migration responds to regional labour market conditions and individual unemployment. Migration is found to go from relatively high unemployment and high job vacancy to low unemployment and low job vacancy regions contrary to earlier research. The response to job vacancy rates indicate that migration responds to differences in local Beveridge curves and migration goes towards better matching efficiency. Unemployment on the individual level is found to increases the propensity for interregional migration.

The purpose was to measure any statistically significant differences in pitch-matching skills among three classes of third grade students when using either adult male voice, piano, or resonator bells as melodic models for rote instruction of classroom singing. Each class was randomly assigned one of the three melodic models for a ten week treatment phase. Results indicated no significant differences in pitch matching skills between any of the three groups. No significant differences in pitch matching skills were found according to gender of subjects or among class piano students and non-piano students. Findings indicated overall improvement in pitch matching skills of subjects from pre-test to post-test phase.