Explorações educacionais (educational explorations) : schooling experiences of second-generation Portuguese Canadian students

This study explores the stories and experiences of second-generation Portuguese Canadian secondary school students in Southern Ontario, Canada. The purpose of this research was to understand the educational experiences of students, specifically the successes, challenges, and struggles that the participants faced within the education system. Questions were also asked about identity issues and how participants perceived their identities influencing their educational experiences. Six Portuguese Canadian students in grades 9 to 11 were interviewed twice. The interviews ranged from 45 minutes to 90 minutes in length. Data analysis of qualitative, open-ended interviews, research journals, field notes and curricular documents yielded understandings about the participants' experiences and challenges in the education system. Eight themes emerged from data that explored the realities of everyday life for second-generatiop Portuguese Canadian students. These themes include: influences of part-time work on schooling, parental involvement, the teacher is key, challenges and barriers, the importance of peers, Portuguese Canadian identity, lack of focus on identity in curricul:um content, and the dropout problem. Recommendations in this study include the need for more community-based programs to assist students. Furthermore, teachers are encouraged to utilize strategies and curriculum resources that engage learners and integrate their histories and identities. Educators are encouraged to question power dynamics both inside and outside the school system. There is also a need for further research with Portuguese Canadian students who are struggling in the education system as well as an examination of the number of hours that students work.

Creating a Generation of Upstanders: Curriculum Resource to Help Ontario Educators Build Safe Schools

This project focuses on the bullying found in the 21st century elementary classrooms, more specifically in grades 4-8. These grades were found to have high levels of bullying because of major shifts in a student’s life that may place a student of this age at risk for problems with their peer relationships (Totura et al., 2009). Supporting the findings in the literature review, this handbook was created for Ontario grade 4-8 classroom teachers. The resource educates teachers on current knowledge of classroom bullying, and provides them with information and resources to share with their students so that they can create a culture of upstanders. Upstanders are students who stand up for the victims of bullying, and have the self-esteem and strategies to stand up to classroom bullies. These upstanders, with the support of their classroom teachers and their peers, will be a force strong enough to build the government-mandated Safe School environment.

Creating a Generation of Upstanders: Curriculum Resource to Help Ontario Educators Build Safe Schools

This project focuses on the bullying found in the 21st century elementary classrooms, more specifically in grades 4-8. These grades were found to have high levels of bullying because of major shifts in a student’s life that may place a student of this age at risk for problems with their peer relationships (Totura et al., 2009). Supporting the findings in the literature review, this handbook was created for an Ontario grade 4-8 classroom teachers. The resource educates teachers on current knowledge of classroom bullying, and provides them with information and resources to share with their students so that they can create a culture of upstanders. Upstanders are students who stand up for the victims of bullying, and have the self-esteem and strategies to stand up to classroom bullies. These upstanders, with the support of their classroom teachers and their peers, will be a force strong enough to build the government-mandated Safe School environment.

Automatic Inference of Graph Models for Complex Networks with Genetic Programming

Complex networks can arise naturally and spontaneously from all things that act as a part of a larger system. From the patterns of socialization between people to the way biological systems organize themselves, complex networks are ubiquitous, but are currently poorly understood. A number of algorithms, designed by humans, have been proposed to describe the organizational behaviour of real-world networks. Consequently, breakthroughs in genetics, medicine, epidemiology, neuroscience, telecommunications and the social sciences have recently resulted. The algorithms, called graph models, represent significant human effort. Deriving accurate graph models is non-trivial, time-intensive, challenging and may only yield useful results for very specific phenomena. An automated approach can greatly reduce the human effort required and if effective, provide a valuable tool for understanding the large decentralized systems of interrelated things around us. To the best of the author's knowledge this thesis proposes the first method for the automatic inference of graph models for complex networks with varied properties, with and without community structure. Furthermore, to the best of the author's knowledge it is the first application of genetic programming for the automatic inference of graph models. The system and methodology was tested against benchmark data, and was shown to be capable of reproducing close approximations to well-known algorithms designed by humans. Furthermore, when used to infer a model for real biological data the resulting model was more representative than models currently used in the literature.

Real-Time Automatic Object Classification and Tracking using Genetic Programming and NVIDIA R CUDA TM

Genetic Programming (GP) is a widely used methodology for solving various computational problems. GP's problem solving ability is usually hindered by its long execution times. In this thesis, GP is applied toward real-time computer vision. In particular, object classification and tracking using a parallel GP system is discussed. First, a study of suitable GP languages for object classification is presented. Two main GP approaches for visual pattern classification, namely the block-classifiers and the pixel-classifiers, were studied. Results showed that the pixel-classifiers generally performed better. Using these results, a suitable language was selected for the real-time implementation. Synthetic video data was used in the experiments. The goal of the experiments was to evolve a unique classifier for each texture pattern that existed in the video. The experiments revealed that the system was capable of correctly tracking the textures in the video. The performance of the system was on-par with real-time requirements.

Towards Equitable Access to Postsecondary Education: Learning From the Voices of First Generation Students

This qualitative, phenomenological study investigated first generation students’ perceptions of the challenges they experienced in the process of accessing higher education and the type of school-based support that was received. Particular emphasis was placed on the impact of parental education level on access to postsecondary education (PSE) and how differences in support at the primary and secondary levels of schooling influenced access. Purposeful, homogenous sampling was used to select 6 first generation students attending a postsecondary institution located in Ontario. Analysis of the data revealed that several interrelated factors impact first generation students’ access to postsecondary education. These include familial experiences and expectations, school streaming practices, secondary school teachers’ and guidance counselors’ representations of postsecondary education, and the nature of school-based support that participants received. The implications for theory, research, and practice are discussed and recommendations for enhancing school-based support to ensure equitable access to postsecondary education for first generation students are provided.

Network Similarity Measures and Automatic Construction of Graph Models using Genetic Programming

A complex network is an abstract representation of an intricate system of interrelated elements where the patterns of connection hold significant meaning. One particular complex network is a social network whereby the vertices represent people and edges denote their daily interactions. Understanding social network dynamics can be vital to the mitigation of disease spread as these networks model the interactions, and thus avenues of spread, between individuals. To better understand complex networks, algorithms which generate graphs exhibiting observed properties of real-world networks, known as graph models, are often constructed. While various efforts to aid with the construction of graph models have been proposed using statistical and probabilistic methods, genetic programming (GP) has only recently been considered. However, determining that a graph model of a complex network accurately describes the target network(s) is not a trivial task as the graph models are often stochastic in nature and the notion of similarity is dependent upon the expected behavior of the network. This thesis examines a number of well-known network properties to determine which measures best allowed networks generated by different graph models, and thus the models themselves, to be distinguished. A proposed meta-analysis procedure was used to demonstrate how these network measures interact when used together as classifiers to determine network, and thus model, (dis)similarity. The analytical results form the basis of the fitness evaluation for a GP system used to automatically construct graph models for complex networks. The GP-based automatic inference system was used to reproduce existing, well-known graph models as well as a real-world network. Results indicated that the automatically inferred models exemplified functional similarity when compared to their respective target networks. This approach also showed promise when used to infer a model for a mammalian brain network.

Automatic Preparation of ETD Material from the Internet Archive for the DSpace Repository Platform

A big challenge associated with getting an institutional repository off the ground is getting content into it. This article will look at how to use digitization services at the Internet Archive alongside software utilities that the author developed to automate the harvesting of scanned dissertations and associated Dublin Core XML files to create an ETD Portal using the DSpace platform. The end result is a metadata-rich, full-text collection of theses that can be constructed for little out of pocket cost.

Object-Oriented Genetic Programming for the Automatic Inference of Graph Models for Complex Networks

Complex networks are systems of entities that are interconnected through meaningful relationships. The result of the relations between entities forms a structure that has a statistical complexity that is not formed by random chance. In the study of complex networks, many graph models have been proposed to model the behaviours observed. However, constructing graph models manually is tedious and problematic. Many of the models proposed in the literature have been cited as having inaccuracies with respect to the complex networks they represent. However, recently, an approach that automates the inference of graph models was proposed by Bailey [10] The proposed methodology employs genetic programming (GP) to produce graph models that approximate various properties of an exemplary graph of a targeted complex network. However, there is a great deal already known about complex networks, in general, and often specific knowledge is held about the network being modelled. The knowledge, albeit incomplete, is important in constructing a graph model. However it is difficult to incorporate such knowledge using existing GP techniques. Thus, this thesis proposes a novel GP system which can incorporate incomplete expert knowledge that assists in the evolution of a graph model. Inspired by existing graph models, an abstract graph model was developed to serve as an embryo for inferring graph models of some complex networks. The GP system and abstract model were used to reproduce well-known graph models. The results indicated that the system was able to evolve models that produced networks that had structural similarities to the networks generated by the respective target models.

Educational Attainment of Second-Generation Immigrants in Belgium: an Estimation of the Assimilation Degree of Immigrants Based on Cross Section Analysis.

Rapport de recherche

AutoFACT: An Automatic Functional Annotation and Classification Tool

Affiliation: Centre Robert-Cedergren de l'Université de Montréal en bio-informatique et génomique & Département de biochimie, Université de Montréal

Algorithmes de recommandation musicale

Ce mémoire est composé de trois articles qui s’unissent sous le thème de la recommandation musicale à grande échelle. Nous présentons d’abord une méthode pour effectuer des recommandations musicales en récoltant des étiquettes (tags) décrivant les items et en utilisant cette aura textuelle pour déterminer leur similarité. En plus d’effectuer des recommandations qui sont transparentes et personnalisables, notre méthode, basée sur le contenu, n’est pas victime des problèmes dont souffrent les systèmes de filtrage collaboratif, comme le problème du démarrage à froid (cold start problem). Nous présentons ensuite un algorithme d’apprentissage automatique qui applique des étiquettes à des chansons à partir d’attributs extraits de leur fichier audio. L’ensemble de données que nous utilisons est construit à partir d’une très grande quantité de données sociales provenant du site Last.fm. Nous présentons finalement un algorithme de génération automatique de liste d’écoute personnalisable qui apprend un espace de similarité musical à partir d’attributs audio extraits de chansons jouées dans des listes d’écoute de stations de radio commerciale. En plus d’utiliser cet espace de similarité, notre système prend aussi en compte un nuage d’étiquettes que l’utilisateur est en mesure de manipuler, ce qui lui permet de décrire de manière abstraite la sorte de musique qu’il désire écouter.

Espaces de timbre générés par des réseaux profonds convolutionnels

Il est avant-tout question, dans ce mémoire, de la modélisation du timbre grâce à des algorithmes d'apprentissage machine. Plus précisément, nous avons essayé de construire un espace de timbre en extrayant des caractéristiques du son à l'aide de machines de Boltzmann convolutionnelles profondes. Nous présentons d'abord un survol de l'apprentissage machine, avec emphase sur les machines de Boltzmann convolutionelles ainsi que les modèles dont elles sont dérivées. Nous présentons aussi un aperçu de la littérature concernant les espaces de timbre, et mettons en évidence quelque-unes de leurs limitations, dont le nombre limité de sons utilisés pour les construire. Pour pallier à ce problème, nous avons mis en place un outil nous permettant de générer des sons à volonté. Le système utilise à sa base des plug-ins qu'on peut combiner et dont on peut changer les paramètres pour créer une gamme virtuellement infinie de sons. Nous l'utilisons pour créer une gigantesque base de donnée de timbres générés aléatoirement constituée de vrais instruments et d'instruments synthétiques. Nous entrainons ensuite les machines de Boltzmann convolutionnelles profondes de façon non-supervisée sur ces timbres, et utilisons l'espace des caractéristiques produites comme espace de timbre. L'espace de timbre ainsi obtenu est meilleur qu'un espace semblable construit à l'aide de MFCC. Il est meilleur dans le sens où la distance entre deux timbres dans cet espace est plus semblable à celle perçue par un humain. Cependant, nous sommes encore loin d'atteindre les mêmes capacités qu'un humain. Nous proposons d'ailleurs quelques pistes d'amélioration pour s'en approcher.

Decarboxylative Generation of Carbenes for the Synthesis of N-Heterocyclic Carbene Copper(I) Complexes - Applications in the Oxidative Coupling of 2-Naphthols

Le présent mémoire décrit la synthèse et l’utilité de complexes Cu-NHC. En premier lieu, la synthèse de complexes de cuivre porteurs de ligand(s) de type carbène-N-hétérocyclique (NHC) via une génération décarboxylative de carbènes sera présentée. En effet, de précédents rapports font état de l’utilisation de carboxylates d’imidazol(in)ium en tant que précurseurs carbéniques sous conditions thermolytiques. Ainsi, la présente étude montre l’utilisation de ces espèces zwitterioniques pour la synthèse de complexes de cuivre(I) mono- et bis-NHC comportant divers substituants et contre-ions. Une seconde partie du projet se concentrera sur l’évaluation de complexes Cu-NHC en tant que catalyseurs pour la synthèse de 2,2’-binaphtols via une réaction de couplage oxydatif de naphtols. L’objectif de ce projet de recherche est d’étudier les effets de variations structurales de différents complexes Cu-NHC afin de construire un processus catalytique plus efficace. Les effets de la structure du catalyseur sur la réaction de couplage ont été évalués en variant son contre-ion, le nombre de ligands NHC se coordonnant au cuivre, ainsi que la nature des substituants du ligand.