El reconeixement de patrons és la tasca que pretén distingir objectes entre diferents classes. Quan aquesta tasca es vol solucionar de forma automàtica un pas crucial és el com representar formalment els patrons a l'ordinador. En funció d'aquests formalismes, podem distingir entre el reconeixement estadístic i l'estructural. El primer descriu objectes com un conjunt de mesures col·locats en forma del que s'anomena un vector de característiques. El segon assumeix que hi ha relacions entre parts dels objectes que han de quedar explícitament representades i per tant fa servir estructures relacionals com els grafs per codificar la seva informació inherent. Els espais vectorials són una estructura matemàtica molt flexible que ha permès definir diverses maneres eficients d'analitzar patrons sota la forma de vectors de característiques. De totes maneres, la representació vectorial no és capaç d'expressar explícitament relacions binàries entre parts dels objectes i està restrigida a mesurar sempre, independentment de la complexitat dels patrons, el mateix nombre de característiques per cadascun d'ells. Les representacions en forma de graf presenten la situació contrària. Poden adaptar-se fàcilment a la complexitat inherent dels patrons però introdueixen un problema d'alta complexitat computational, dificultant el disseny d'eines eficients per al procés i l'anàlisis de patrons. Resoldre aquesta paradoxa és el principal objectiu d'aquesta tesi. La situació ideal per resoldre problemes de reconeixement de patrons seria el representar-los fent servir estructures relacionals com els grafs, i a l'hora, poder fer ús del ric repositori d'eines pel processament de dades del reconeixement estadístic. Una solució elegant a aquest problema és la de transformar el domini dels grafs en el domini dels vectors, on podem aplicar qualsevol algorisme de processament de dades. En altres paraules, assignant a cada graf un punt en un espai vectorial, automàticament tenim accés al conjunt d'algorismes del món estadístic per aplicar-los al domini dels grafs. Aquesta metodologia s'anomena graph embedding. En aquesta tesi proposem de fer una associació de grafs a vectors de característiques de forma simple i eficient fixant l'atenció en la informació d'etiquetatge dels grafs. En particular, comptem les freqüències de les etiquetes dels nodes així com de les aretes entre etiquetes determinades. Tot i la seva localitat, aquestes característiques donen una representació prou robusta de les propietats globals dels grafs. Primer tractem el cas de grafs amb etiquetes discretes, on les característiques són sencilles de calcular. El cas continu és abordat com una generalització del cas discret, on enlloc de comptar freqüències d'etiquetes, ho fem de representants d'aquestes. Ens trobem que les representacions vectorials que proposem pateixen d'alta dimensionalitat i correlació entre components, i tractem aquests problems mitjançant algorismes de selecció de característiques. També estudiem com la diversitat de diferents representacions pot ser explotada per tal de millorar el rendiment de classificadors base en el marc d'un sistema de múltiples classificadors. Finalment, amb una extensa evaluació experimental mostrem com la metodologia proposada pot ser calculada de forma eficient i com aquesta pot competir amb altres metodologies per a la comparació de grafs. / Pattern recognition is the task that aims at distinguishing objects among different classes. When such a task wants to be solved in an automatic way a crucial step is how to formally represent such patterns to the computer. Based on the different representational formalisms, we may distinguish between statistical and structural pattern recognition. The former describes objects as a set of measurements arranged in the form of what is called a feature vector. The latter assumes that relations between parts of the underlying objects need to be explicitly represented and thus it uses relational structures such as graphs for encoding their inherent information. Vector spaces are a very flexible mathematical structure that has allowed to come up with several efficient ways for the analysis of patterns under the form of feature vectors. Nevertheless, such a representation cannot explicitly cope with binary relations between parts of the objects and it is restricted to measure the exact same number of features for each pattern under study regardless of their complexity. Graph-based representations present the contrary situation. They can easily adapt to the inherent complexity of the patterns but introduce a problem of high computational complexity, hindering the design of efficient tools to process and analyze patterns. Solving this paradox is the main goal of this thesis. The ideal situation for solving pattern recognition problems would be to represent the patterns using relational structures such as graphs, and to be able to use the wealthy repository of data processing tools from the statistical pattern recognition domain. An elegant solution to this problem is to transform the graph domain into a vector domain where any processing algorithm can be applied. In other words, by mapping each graph to a point in a vector space we automatically get access to the rich set of algorithms from the statistical domain to be applied in the graph domain. Such methodology is called graph embedding. In this thesis we propose to associate feature vectors to graphs in a simple and very efficient way by just putting attention on the labelling information that graphs store. In particular, we count frequencies of node labels and of edges between labels. Although their locality, these features are able to robustly represent structurally global properties of graphs, when considered together in the form of a vector. We initially deal with the case of discrete attributed graphs, where features are easy to compute. The continuous case is tackled as a natural generalization of the discrete one, where rather than counting node and edge labelling instances, we count statistics of some representatives of them. We encounter how the proposed vectorial representations of graphs suffer from high dimensionality and correlation among components and we face these problems by feature selection algorithms. We also explore how the diversity of different embedding representations can be exploited in order to boost the performance of base classifiers in a multiple classifier systems framework. An extensive experimental evaluation finally shows how the methodology we propose can be efficiently computed and compete with other graph matching and embedding methodologies.

In this thesis, we present a new clustering algorithm we call <em>Significance Feature Clustering</em>, which is designed to cluster text documents. Its central premise is the mapping of raw frequency count vectors to discrete-valued significance vectors which contain values of -1, 0, or 1. These values represent whether a word is <em>significantly negative</em>, <em>neutral</em>, or <em>significantly positive</em>, respectively. Initially, standard tf-idf vectors are computed from raw frequency vectors, then these tf-idf vectors are transformed to significance vectors using a parameter alpha, where alpha controls the mapping -1, 0, or 1 for each vector entry. SFC clusters agglomeratively, with each document's significance vector representing a cluster of size one containing just the document, and iteratively merges the two clusters that exhibit the most similar average using cosine similarity. We show that by using a good alpha value, the significance vectors produced by SFC provide an accurate indication of which words are significant to which documents, as well as the type of significance, and therefore correspondingly yield a good clustering in terms of a well-known definition of clustering quality. We further demonstrate that a user need not manually select an alpha as we develop a new definition of clustering quality that is highly correlated with text clustering quality. Our metric extends the family of metrics known as <em>internal similarity</em>, so that it can be applied to a tree of clusters rather than a set, but it also factors in an aspect of recall that was absent from previous internal similarity metrics. Using this new definition of internal similarity, which we call <em>maximum tree internal similarity</em>, we show that a close to optimal text clustering may be picked from any number of clusterings created by different alpha's. The automatically selected clusterings have qualities that are close to that of a well-known and powerful hierarchical clustering algorithm.

This study is a discourse analysis of media representations of young people’s participation in the summer riots that spread across the UK in August 2011. Drawing on articles published in three UK newspapers The Guardian, The Daily Mail and The Sun this paper critically assesses the ways in which the media identified the behaviour of young people as symptomatic of a general moral decline in British society. Along with the media portrayal of children and young people during these events, the study also highlights the United Nations Convention on the Rights of the Child as a further way of questioning the reporting practices of mainstream media. Articles 2, 12 and 13 will have specific focus in the study, in order to evaluate the media’s recurrent misrepresentation of young people’s participation in decision making on matters concerning their own wellbeing.

In this thesis, we present a new clustering algorithm we call <em>Significance Feature Clustering</em>, which is designed to cluster text documents. Its central premise is the mapping of raw frequency count vectors to discrete-valued significance vectors which contain values of -1, 0, or 1. These values represent whether a word is <em>significantly negative</em>, <em>neutral</em>, or <em>significantly positive</em>, respectively. Initially, standard tf-idf vectors are computed from raw frequency vectors, then these tf-idf vectors are transformed to significance vectors using a parameter alpha, where alpha controls the mapping -1, 0, or 1 for each vector entry. SFC clusters agglomeratively, with each document's significance vector representing a cluster of size one containing just the document, and iteratively merges the two clusters that exhibit the most similar average using cosine similarity. We show that by using a good alpha value, the significance vectors produced by SFC provide an accurate indication of which words are significant to which documents, as well as the type of significance, and therefore correspondingly yield a good clustering in terms of a well-known definition of clustering quality. We further demonstrate that a user need not manually select an alpha as we develop a new definition of clustering quality that is highly correlated with text clustering quality. Our metric extends the family of metrics known as <em>internal similarity</em>, so that it can be applied to a tree of clusters rather than a set, but it also factors in an aspect of recall that was absent from previous internal similarity metrics. Using this new definition of internal similarity, which we call <em>maximum tree internal similarity</em>, we show that a close to optimal text clustering may be picked from any number of clusterings created by different alpha's. The automatically selected clusterings have qualities that are close to that of a well-known and powerful hierarchical clustering algorithm.

This study examined teacher pedagogical content knowledge changes through a Middle School Mathematics Program professional development workshop, development of noticing use of student representations, and teacher changes in hypothetical learning trajectories due to noticed aspects of student representation corresponding to the hypothetical learning trajectory model. Using constant comparatives and repertory grid analysis, data was collected in two phases. Phase one, the teacher pre-test, occurred at the beginning of the summer of the 2003 professional development workshop. Phase two, the teacher post-test, occurred at the end of the workshop. Twenty-four teachers supplied data on pre- and post-tests during phases one and two. Eleven teachers were from Texas and 13 from Delaware. Six Texas and eight Delaware teachers worked with the algebraic expression concepts. Five Texas and five Delaware teachers worked with the equivalent fraction concepts. Four mathematics education researchers from Texas, three from Delaware, and two from the American Association for the Advancement of Science participated in facilitating the professional development. The results show that teacher pedagogical content knowledge changes with the help of a professional development partnership. The differences in knowledge can be measured with a hierarchal cluster analysis of the repertory grid by analyzing relationships between constructs and elements. Teacher hypothetical learning trajectories change depending on student representations of what they do and do not know about concepts. The study encourages teachers to use knowledge of students’ representation about a concept to determine what to teach next and how the concept should be taught. Teachers should use different types of representations including formal, imagistic, and action representations in teaching mathematical ideas. This will promote student development in all process standards including reasoning and proof, communication, problem solving, and connection. The findings suggest that teacher pedagogical content knowledge can be redefined during professional development partnerships. Furthermore, teachers’ knowledge of representation is varied and emphasis on the imagistic representation should be explored further. Finally, professional development models that facilitate how to extract what a student does and does not know based on representation, can be the basis for defining hypothetical learning trajectories.

<p>This thesis investigates the possibilities of explaining higher cognition as internal simulations of perception and action at an abstract level. Relatively recent findings in both neuroscience and psychology indicates that both perception and action can be internally simulated by activating sensory and motor areas in the brain in absence of sensory input and without any resulting overt behavior. An investigation was conducted in order to test the hypothesis that perception can be simulated in a mobile robot using abstractions. The result from this investigation showed that this was indeed the case but that the accuracy was limited. The simulations allowed the robot to anticipate long chains of future situations but were not good enough to support any overt behavior. To further improve the results there is a need for better training techniques and/or a more complex architecture.</p>

This study will look into to the concepts of Peripersonal Space, The Body Schema and The Body Image. It examines how the terms are typically used and describes the various views about the concepts found in the literature, as well as the contradictions between these views. In the section “The Difficulty to Differentiate the Concepts” the reader gets a deeper understanding of which criteria researchers use to differentiate the concepts from one another. The fact that there are changes in kineamethic model and sensation in humans when they are using a rake is proposed as support for the idea that also the body schema is involved in tool use. In differentiating the Body schema – Body Image from each other (and other types of body representation) we come to the conclusion that positive definitions about different representations is needed and that researchers should unite their views what the definitions should be. We also mention a problem based on the possibility on infinite body representations and a solution by a Bayesian model is proposed that looks at the input as well as the output in experiments.

Movement and memory are two of the most fundamental components of our existence. From the moment of birth, we rely on a variety of movements to interact with people and objects around us, and as we grow, we continuously form new motor memories to improve the fidelity of these interactions by exploring and learning more about our environment, especially in unfamiliar situations, ultimately becoming better equipped to handle novel and unknown environments. In this dissertation, we explore four facets of motor memory associated with voluntary movement and postural control in the upper limbs: (1) Optimal motor memory formation via sensorimotor integration. We ask whether the motor system combines prior memories with new sensory information to produce statistically-optimal weight estimates. We find that the weight estimate that the motor system makes in order to re-stabilize one’s arm posture when an object is rapidly removed from the hand that supports it, reflected information integration in a Bayesian, statistically-optimal fashion. Remarkably, we demonstrate that when experiencing the well-known size-weight illusion, the motor and perceptual system’s weight estimates are biased in opposite directions, suggesting two divergent modes for information integration within the central nervous system. (2) Movement features important for the learning and generalization of motor memories. We show that, velocity-dependent adaptation generalizes across different movements, even from discrete straight point-to-point to continuous circular movements, however the amount of generalization is limited and context-dependent. In a series of experiments, we quantified the contributions of different movement features to the elicited adaptation transfer. In particular, we show that other movement states (i.e. position and acceleration) make only minor contributions whereas, the contexts provided by movement geometry and movement continuity are critical. (3) Internal representation of motor memories in intrinsic-extrinsic coordinates. We show that motor memories are based not on fully intrinsic or extrinsic representations but on a gain-field (multiplicative) combination the two. This gain-field representation generalizes between actions by effectively computing movement similarity based on the Mahalanobis distance across both intrinsic and extrinsic coordinates, in line with neural recordings showing mixed intrinsic-extrinsic representations in motor and parietal cortices. (4) Motor memories with local and global generalization. We demonstrate the existence of two distinct components of motor memory displaying different generalization footprints: One generalizes only locally, around the trained movement direction and with the trained end-effector, whereas the other generalizes broadly across both., We proceed to show that broad generalization results from a rapidly-learning adaptive process, dominates on easier-to-learn tasks, and performs high-level processing, producing adaptation vectors that integrate multiple sources of information, in line with a recent theory for perceptual learning. / Engineering and Applied Sciences

To investigate the role of action representations in the identification of upright and rotated objects, we examined the time course of their evocation. Across five experiments, subjects made vertically or horizontally oriented reach and grasp actions primed by images of handled objects that were depicted in upright or rotated orientations, at various Stimulus Onset Asynchronies: -250 ms (action cue preceded the prime), 0 ms, and +250 ms. Congruency effects between action and object orientation were driven by the object's canonical (upright) orientation at the 0 ms SOA, but by its depicted orientation at the +250 ms SOA. Alignment effects between response hand and the object's handle appeared only at the +250 ms SOA, and were driven by the depicted orientation. Surprisingly, an attempt to replicate this finding with improved stimuli (Experiment 3) did not show significant congruency effects at the 0 ms SOA; a further examination of the 0 ms SOA in Experiments 4 and 5 also failed to reach significance. However, a meta-analysis of the latter three experiments showed evidence for the congruency effect, suggesting that the experiments might just have been underpowered. We conclude that subjects initially evoke a conceptually-driven motor representation of the object, and that only after some time can the depicted form become prominent enough to influence the elicited action representation. / Graduate / 0633 / ragavk@uvic.ca

Κύριος στόχος αυτής της διπλωματικής εργασίας είναι η εξέταση της δυνατότητας δημιουργίας ενός project μικρής διάρκειας με την χρήση κατάλληλων μαθησιακών αντικειμένων, καθώς και η μελέτη των μαθησιακών αποτελεσμάτων του. Πιο συγκεκριμένα, αξιοποιήθηκε το ιστορικό πλαίσιο για την μέτρηση του χρόνου από το βιβλίο « Time for Science Education. How Teaching the History and Philosophy of pendulum Motion Can Contribute to Science Literacy» του Matthews M., σχετικά με τις εργασίες του Γαλιλαίου και του Χόιχενς πάνω στο απλό εκκρεμές και του Χουκ πάνω στο ελατήριο και κατασκευάσαμε δυo ιστορικές αφηγήσεις. Με βάση αυτές αναπτύχθηκαν δυο βίντεο με την βοήθεια του προγράμματος Microsoft Movie Maker, του λογισμικού ζωγραφικής και ηχογράφησης των windows, αντλώντας κείμενα και εικόνες από βιβλία, περιοδικά και άλλες διαδικτυακές πηγές. Ο διδακτικός σχεδιασμός του project, που περιλάμβανε μαθησιακά αντικείμενα, οδήγησε στη δημιουργία ενός δυναμικού περιβάλλοντος μάθησης. Βασίστηκε στην ιστορία της επιστήμης και αξιοποίησε τόσο την τεχνολογία, όσο και πραγματικές εργαστηριακές δραστηριότητες για την μελέτη των ταλαντώσεων. Υιοθετήθηκε μια εξερευνητική αρχιτεκτονική σχεδιασμού, η οποία εστιάζει στην εμπλοκή των μαθητών μέσω διαδικασιών αναζήτησης, εφαρμόζοντας τις αρχές και τα στάδια της Συνεργατικής Έρευνας Δράσης. Σε όλη την διαδικασία εφαρμογής του project, ακολουθήσαμε τις αρχές της θεωρίας του Κοινωνικού Εποικοδομισμού (social constructivism) σε συνδυασμό με τις αρχές της βιωματικής διδασκαλίας. Η υλοποίησή του project έγινε σε σχολικές τάξεις Γ’ γυμνασίου αποτελούμενες από 66 μαθητές, ηλικίας 14-16 ετών που συμμετείχε κατά κανόνα στις μαθησιακές δραστηριότητες. Ποικίλα δεδομένα συλλέχτηκαν τόσο μέσα από παρατήρηση και φύλλα εργασίας, πριν και μετά τη διδασκαλία, όσο και μέσα από μαγνητοφωνήσεις συζητήσεων στις ομάδες. Υιοθετήθηκε η επαγωγική ανάλυση δεδομένων και τόσο κατά την ανάλυση, όσο και για την παρουσίαση, χρησιμοποιήθηκε η τεχνική των συστημικών δικτύων. Η ανάλυση έγινε σε έναν αριθμό ερωτημάτων που απάντησαν οι μαθητές. Πίνακες και γραφήματα παρουσιάζουν συγκεντρωτικά τα ευρήματα της μελέτης. Η υλοποίηση του project με τα μαθησιακά αντικείμενα που χρησιμοποιήθηκαν, τα ιστορικά βίντεο, το applet, οι συσκευές και τα όργανα του πραγματικού εργαστηρίου και τα φύλλα εργασίας που χρησιμοποιήθηκαν, φαίνεται ότι προκάλεσε το ενδιαφέρον των μαθητών και οδήγησε στην αποτελεσματική εμπλοκή με τις δραστηριότητες. Οι μαθητές μέσα στο ιστορικό πλαίσιο ήρθαν σε επαφή με τους προβληματισμούς ιστορικών προσώπων, όπως του Γαλιλαίου και του Hooke, σχεδίασαν και στη συνέχεια μελέτησαν τις ταλαντώσεις και τις εμπλεκόμενες παραμέτρους και με πραγματικές εργαστηριακές δραστηριότητες και με προσομοιώσεις τύπου applet. Οι διάφορες πλευρές στις απαντήσεις τους αποκαλύπτουν ότι οι μαθητές κατά την προσωπική εμπλοκή τους εμβάθυναν στην μελέτη των φαινομένων και τα μαθησιακά αποτελέσματα κρίνονται ως ικανοποιητικά Η ανταπόκριση των μαθητών στις διάφορες φάσεις του project συνηγορεί στη συμβολή της βιωματικής διδακτικής προσέγγισης στην αύξηση των κινήτρων των μαθητών για πειραματική διερεύνηση και αναζήτηση της επιστημονικής γνώσης. Οι εμπειρίες και τα ευρήματα της μελέτης αυτής αναδεικνύουν την αξία ανάπτυξης μικρών project στο πλαίσιο των διδακτικών ενοτήτων με μαθησιακούς στόχους. Η αξιοποίηση της ιστορίας και οι αφηγηματικές μορφές διδασκαλίας δίνουν τη δυνατότητα για δημιουργία αυθεντικών πλαισίων μάθησης και λειτουργούν ως κίνητρα για προσωπική εμπλοκή των μαθητών, για ομαδική συνεργασία και ενασχόλησή τους με επιστημονικές πτυχές, σημαντικές στην κατανόηση του θεματικού πεδίου. / --