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This study is an evaluation of online annotated readers developed for first grade students enrolled in Chinese immersion. The electronic readers were created to provide additional input to immersion students, who had little time in class for Chinese character reinforcement. The students accessed online readers from their homes and took assessments before and after each reader to test for improved character comprehension. In addition, students were divided into treatment and control groups. The treatment group had annotated electronic readers with audio and games. Conversely, the control group did not have annotations but audio was included. Results demonstrate a significant difference between preliminary and post-assessments, suggesting that students comprehended more characters after reading. No significant differences were detected between the control (non-annotated) and treatment (annotated) groups. Additional data collected from parent surveys provide useful demographics about subjects' socio-cultural and language variables as well as highlight parental desires for more support and help-aides. Computer Assisted Language Learning (CALL) in relationship to young immersion students learning Chinese is also discussed. Results suggest that online annotated readers can be an important resource for students who have limited instructional time in the classroom and little opportunity to receive help at home.

Le Programme de formation de l’école québécoise situe l’élève au cœur de ses apprentissages. L’enseignant peut faciliter le développement des compétences en offrant une rétroaction permettant à l’élève de progresser dans ses apprentissages. Il est difficile pour les enseignants de faire des annotations pertinentes et efficaces en mathématique, car l’accent est mis sur le concept travaillé et non sur la démarche mathématique. C’est pourquoi, nous avons porté notre regard sur l’incidence que peut avoir l’enseignement explicite des stratégies ainsi que sur les annotations faites par l’enseignant sur les copies des élèves en ce qui a trait au développement de leurs compétences à résoudre des problèmes complexes en mathématique. Nous avons opté pour une recherche qualitative et collaborative pour vivre un échange avec l’enseignant et vivre une interinfluence entre le praticien et le chercheur. La qualité des sujets a été favorisée. La technique d’échantillonnage retenue pour le choix de l’enseignant a été celle de cas exemplaires, tandis que celle que nous avons choisie pour les élèves était l’échantillonnage intentionnel critérié. La recherche a duré du mois de novembre au mois de mai de l’année scolaire 2008-2009. Comme instruments de cueillette de données, nous avons opté pour des entrevues avec l’enseignant et des mini-entrevues avec les élèves à deux moments de la recherche. Nous avons consulté les travaux corrigés des élèves dans leur portfolio. Notre étude fait ressortir l’apport de l’enseignement stratégique de la démarche mathématique. Les résultats précisent que les annotations de type méthodologique ont été celles qui ont été les plus utilisées et ont permis une meilleure compréhension chez l’élève. De plus, elles favorisent le transfert d’une situation à l’autre et permettent à l’élève d’obtenir de meilleurs résultats. / The Programme de formation de l’école québécoise (PFEQ) places the student in the center of his learning. The teacher can facilitate the development of the student’s skills by offering a feedback that allows the student to progress in his learning. It is difficult for the teacher’s to make relevant and effective annotations in math, because the emphasis is placed on the concept that was worked on and not on the mathematical process. This is the reason why we decided to concentrate our research on the incidence the teacher’s annotations can have on the development of the student’s mathematical skills. We opted for a qualitative and collaborative research to experiment an exchange with the teacher and live an inter influence between the practitioner and the researcher. The quality of the subjects was favoured. The teacher was chosen according to the sampling of exemplary case techniques and the students were chosen according to the intentional criteria sampling technique. The research lasted from november till may of the school year 2008-2009. Interviews with the teacher and mini interviews with the students at two moments of the research were used to collect data. We also consulted the corrected work placed in the pupil’s portfolios. Our study highlights the contribution of strategic teaching of the mathematical approach. The results specify that methodological annotation was mostly used and aims at a better understanding of the student. Furthermore, this type allows the transfer from a situation to another and allows the student to obtain better results.

Ce mémoire de thèse de doctorat présente, discute et propose des outils de fouille automatique de mégadonnées dans un contexte de classification supervisée musical.L'application principale concerne la classification automatique des thèmes musicaux afin de générer des listes de lecture thématiques.Le premier chapitre introduit les différents contextes et concepts autour des mégadonnées musicales et de leur consommation.Le deuxième chapitre s'attelle à la description des bases de données musicales existantes dans le cadre d'expériences académiques d'analyse audio.Ce chapitre introduit notamment les problématiques concernant la variété et les proportions inégales des thèmes contenus dans une base, qui demeurent complexes à prendre en compte dans une classification supervisée.Le troisième chapitre explique l'importance de l'extraction et du développement de caractéristiques audio et musicales pertinentes afin de mieux décrire le contenu des éléments contenus dans ces bases de données.Ce chapitre explique plusieurs phénomènes psychoacoustiques et utilise des techniques de traitement du signal sonore afin de calculer des caractéristiques audio.De nouvelles méthodes d'agrégation de caractéristiques audio locales sont proposées afin d'améliorer la classification des morceaux.Le quatrième chapitre décrit l'utilisation des caractéristiques musicales extraites afin de trier les morceaux par thèmes et donc de permettre les recommandations musicales et la génération automatique de listes de lecture thématiques homogènes.Cette partie implique l'utilisation d'algorithmes d'apprentissage automatique afin de réaliser des tâches de classification musicale.Les contributions de ce mémoire sont résumées dans le cinquième chapitre qui propose également des perspectives de recherche dans l'apprentissage automatique et l'extraction de caractéristiques audio multi-échelles. / This doctoral dissertation presents, discusses and proposes tools for the automatic information retrieval in big musical databases.The main application is the supervised classification of musical themes to generate thematic playlists.The first chapter introduces the different contexts and concepts around big musical databases and their consumption.The second chapter focuses on the description of existing music databases as part of academic experiments in audio analysis.This chapter notably introduces issues concerning the variety and unequal proportions of the themes contained in a database, which remain complex to take into account in supervised classification.The third chapter explains the importance of extracting and developing relevant audio features in order to better describe the content of music tracks in these databases.This chapter explains several psychoacoustic phenomena and uses sound signal processing techniques to compute audio features.New methods of aggregating local audio features are proposed to improve song classification.The fourth chapter describes the use of the extracted audio features in order to sort the songs by themes and thus to allow the musical recommendations and the automatic generation of homogeneous thematic playlists.This part involves the use of machine learning algorithms to perform music classification tasks.The contributions of this dissertation are summarized in the fifth chapter which also proposes research perspectives in machine learning and extraction of multi-scale audio features.

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Nous présentons dans cette thèse un système de correction automatique d'annotations (tags) fournies par des utilisateurs qui téléversent des vidéos sur des sites de partage de documents multimédia sur Internet. La plupart des systèmes d'annotation automatique existants se servent principalement de l'information textuelle fournie en plus de la vidéo par les utilisateurs et apprennent un grand nombre de "classifieurs" pour étiqueter une nouvelle vidéo. Cependant, les annotations fournies par les utilisateurs sont souvent incomplètes et incorrectes. En effet, un utilisateur peut vouloir augmenter artificiellement le nombre de "vues" d'une vidéo en rajoutant des tags non pertinents. Dans cette thèse, nous limitons l'utilisation de cette information textuelle contestable et nous n'apprenons pas de modèle pour propager des annotations entre vidéos. Nous proposons de comparer directement le contenu visuel des vidéos par différents ensembles d'attributs comme les sacs de mots visuels basés sur des descripteurs SIFT ou des motifs fréquents construits à partir de ces sacs. Nous proposons ensuite une stratégie originale de correction des annotations basées sur la fréquence des annotations des vidéos visuellement proches de la vidéo que nous cherchons à corriger. Nous avons également proposé des stratégies d'évaluation et des jeux de données pour évaluer notre approche. Nos expériences montrent que notre système peut effectivement améliorer la qualité des annotations fournies et que les motifs fréquents construits à partir des sacs de motifs fréquents sont des attributs visuels pertinents / This thesis presents a new system for video auto tagging which aims at correcting the tags provided by users for videos uploaded on the Internet. Most existing auto-tagging systems rely mainly on the textual information and learn a great number of classifiers (on per possible tag) to tag new videos. However, the existing user-provided video annotations are often incorrect and incomplete. Indeed, users uploading videos might often want to rapidly increase their video’s number-of-view by tagging them with popular tags which are irrelevant to the video. They can also forget an obvious tag which might greatly help an indexing process. In this thesis, we limit the use this questionable textual information and do not build a supervised model to perform the tag propagation. We propose to compare directly the visual content of the videos described by different sets of features such as SIFT-based Bag-Of-visual-Words or frequent patterns built from them. We then propose an original tag correction strategy based on the frequency of the tags in the visual neighborhood of the videos. We have also introduced a number of strategies and datasets to evaluate our system. The experiments show that our method can effectively improve the existing tags and that frequent patterns build from Bag-Of-visual-Words are useful to construct accurate visual features

This work focuses on scientific area called Music Information Retrieval, more precisely it’s subdivision focusing on the recognition of emotions in music called Music Emotion Recognition. The beginning of the work deals with general overview and definition of MER, categorization of individual methods and offers a comprehensive view of this discipline. The thesis also concentrates on the selection and description of suitable parameters for the recognition of emotions, using tools openSMILE and MIRtoolbox. A freely available DEAM database was used to obtain the set of music recordings and their subjective emotional annotations. The practical part deals with the design of a static dimensional regression evaluation system for numerical prediction of musical emotions in music recordings, more precisely their position in the AV emotional space. The thesis publishes and comments on the results obtained by individual analysis of the significance of individual parameters and for the overall analysis of the prediction of the proposed model.

The number of experimentally resolved protein structures in the Protein Data Bank has been growing fast in the last 20 years, which motivates the develop- ment of many computational tools for protein-ligand binding sites prediction. Binding sites prediction from protein 3D structure has many important applica- tions; it is an essential step in the complex process of rational drug design, it helps to infer the side-effects of drugs, it provides insight into proteins biological functions and it is helpful in many other fields, such as protein-ligand docking and molecular dynamics. As far as we know, there has not been a study that would systematically investigate general properties of known ligand binding sites on a large scale. In this thesis, we examine these properties using existing experimen- tal and predicted residue-level annotations of protein sequence and structure. We present an automated pipeline for statistical analysis of these annotations, based on hypothesis testing and effect size estimation. It is implemented in Python and it is easily extensible by user-defined annotations. The usage is demonstrated on 33 existing annotations and 4 different datasets. The practical significance of the results is tested with P2Rank prediction method. We hope that the results as well as the pipeline...

<p>Nowadays, Dynamic Geometry Software (DGS) is widely accepted as a tool for creating and presenting visually rich interactive teaching and learning materials, called dynamic drawings. Dynamic drawings are specified by writing expressions in functional domain-specific languages. Due to wide acceptance of DGS, there has arisen a need for their extensibility, by adding new semantics and visual objects (visuals). We have developed a programming framework for the Dynamic Geometry Software, SLGeometry, with a genericized functional language and corresponding expression evaluator that act as a framework into which specific semantics is embedded in the form of code annotated with metadata. The framework transforms an ordinary expression tree evaluator into an object-oriented one, and provide guidelines and examples for creation of interactive objects with dynamic properties, which participate in evaluation optimization at run-time. Whereas other DGS are based on purely functional expression evaluators, our solution has advantages of being more general, easy to implement, and providing a natural way of specifying object properties in the user interface, minimizing typing and syntax errors.LGeometry is implemented in C# on the .NET Framework. Although attributes are a preferred mechanism to provide association of declarative information with C# code, they have certain restrictions which limit their application to representing complex structured metadata. By developing a metadata infrastructure which is independent of attributes, we were able to overcome these limitations. Our solution, presented in this&nbsp; dissertation, provides extensibility to simple and complex data types, unary and binary operations, type conversions, functions and visuals, thus enabling developers to seamlessly add new features to SLGeometry by implementing them as C# classes annotated with metadata. It also provides insight into the way a domain specific functional language of dynamic geometry software can be genericized and customized for specific needs by extending or restricting the set of types, operations, type conversions, functions and visuals.Furthermore, we have conducted&nbsp; experiments with several groups of students of mathematics and high school pupils, in order to test how our approach compares to the existing practice. The experimental subjects tested mathematical games using interactive visual controls (UI controls) and sequential behavior controllers. Finally, we present a new evaluation algorithm, which was compared to the usual approach employed in DGS and found to perform well, introducing advantages while maintaining the same level of performance.</p> / <p>U dana&scaron;nje vreme softver za dinamičku geometriju (DGS) je &scaron;iroko prihvaćen kao alat za kreiranje i prezentovanje vizuelno bogatih interaktivnih nastavnih materijala i materijala za samostalno učenje, nazvanih dinamičkim crtežima. Kako je raslo prihvatanje softvera za dinamičku geometriju, tako je i rasla potreba da se oni pro&scaron;iruju, dodajući im novu semantiku i vizualne objekte. Razvili smo programsko okruženje za softver za dinamičku geometriju, SLGeometry, sa generičkim&nbsp; funkcionalnim jezikom i odgovarajućim evaluatorom izraza koji čini okruženje u kom su ugrađene specifične semantike u obliku koda označenog metapodacima. Ovo okruženje pretvara uobičajen evaluator stabla izraza u objektno orijentiran, te daje uputstva i primere za stvaranje interaktivnih objekata sa dinamičkim osobinama, koji sudeluju u optimizaciji izvr&scaron;enja tokom izvođenja. Dok se drugi DGS-ovi temelje na čisto funkcionalnim evaluatorima izraza, na&scaron;e rje&scaron;enje ima prednosti jer je uop&scaron;tenije, lako za implementaciju i pruža prirodan način navođenja osobina objekta u korisničkom interfejsu, minimizirajući kucanje i sintaksne gre&scaron;ke. SLGeometry je implementirana u jeziku C# .NET Framework-a. Iako su atributi preferiran mehanizam, koji povezuje C# k&ocirc;d sa deklarativnim informacijama, oni imaju određena ograničenja koja limitiraju njihovu primenu za predstavljanje složenih strukturiranih metapodataka. Razvijanjem infrastrukture metapodataka koja je nezavisna od atributa, uspeli smo prevladati ta ograničenja. Na&scaron;e re&scaron;enje, predstavljeno u ovoj disertaciji, pruža pro&scaron;irivost: jednostavnim i složenim vrstama podataka, unarnim i binarnim operacijama, konverzijama tipova, funkcijama i vizuelnim objektima, omogućavajući&nbsp; time programerima da neprimetno dodaju nove osobine u SLGeometry&nbsp; implementirajući ih kao C# klase označene metapodacima.</p>

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