Text2Emotion: un gestor d'experiències SOA : extracció d'emocions per al suport a la gestió de relació amb clients

Customer Experience Management (CEM) se ha convertido en un factor clave para el éxito de las empresas. CEM gestiona todas las experiencias que un cliente tiene con un proveedor de servicios o productos. Es muy importante saber como se siente un cliente en cada contacto y entonces poder sugerir automáticamente la próxima tarea a realizar, simplificando tareas realizadas por personas. En este proyecto se desarrolla una solución para evaluar experiencias. Primero se crean servicios web que clasifican experiencias en estados emocionales dependiendo del nivel de satisfacción, interés, … Esto es realizado a través de minería de textos. Se procesa y clasifica información no estructurada (documentos de texto) que representan o describen las experiencias. Se utilizan métodos de aprendizaje supervisado. Esta parte es desarrollada con una arquitectura orientada a servicios (SOA) para asegurar el uso de estándares y que los servicios sean accesibles por cualquier aplicación. Estos servicios son desplegados en un servidor de aplicaciones. En la segunda parte se desarrolla dos aplicaciones basadas en casos reales. En esta fase Cloud computing es clave. Se utiliza una plataforma de desarrollo en línea para crear toda la aplicación incluyendo tablas, objetos, lógica de negocio e interfaces de usuario. Finalmente los servicios de clasificación son integrados a la plataforma asegurando que las experiencias son evaluadas y que las tareas de seguimiento son automáticamente creadas.

Aprendizaje y servicio: estudio del grado de satisfacción de estudiantes universitarios

El aprendizaje y servicio es una metodología pedagógica que fomenta el aprendizaje de los estudiantes mediante su participación activa en experiencias asociadas al servicio comunitario. Esta metodología permite al estudiante involucrarse directamente con aquellos a quienes ofrece un servicio, adaptándose a sus necesidades y a una realidad que a menudo es muy diferente de la que vive en el aula. En esto radica uno de sus mayores impactos. Además, este tipo de prácticas contribuye a despertar en los alumnos su interés por la acción colectiva, su formación ciudadana, etc. La investigación que se presenta ha consistido en un estudio diagnóstico- comprensivo con 39 estudiantes, donde a partir del modelo de satisfacción con el que se ha trabajado se han analizado cuatro dimensiones clave (conocimiento e intención atribuida, valoración de la utilidad atribuida, valoración del proceso y proyección social). Estas dimensiones nos han conducido a reflexionar sobre elementos claves del aprendizaje y servicio (aprendizajes ciudadanos, aprendizajes personales, aprendizajes curriculares, procesos de reflexión, etc.). La muestra del estudio ha estado formada por 39 estudiantes y la principal técnica de análisis de la información recogida ha sido el análisis de contenido, mediante la triangulación de técnicas (cuestionario, entrevista y grupos de discusión) e informantes (estudiantes, profesores, coordinadores y miembros de entidades). El análisis de la información muestra un alto grado de satisfacción de los alumnos participantes. La dimensión que ha influido más en este resultado ha sido la valoración de la utilidad atribuida; en concreto, la percepción que tienen los alumnos sobre la adquisición de unos aprendizajes conceptuales, personales y ciudadanos. Atendiendo a lo anterior, cabe señalar que la relación generada entre profesores, entidades y estudiantes, así como la posibilidad de vincular la teoría con la práctica han condicionado los resultados. -------------------------------------------------------------------------------------------- Service learning projects promote student learning through active participation in community service. Thus, the service learning method enables students to engage directly with the people to whom they offer their services and requires them to adapt to other peoples' needs and to a reality that is often very different from the classroom. This is one of the major ways in which service learning has an impact. In addition, service learning helps to awaken students' interest in collective action, citizenship, etc. A comprehensive diagnostic study of 39 students was conducted to analyse four key dimensions (attributed knowledge and intentionality, evaluation of usefulness, evaluation of the process and social impact) on the basis of the chosen satisfaction model. The four dimensions led to reflection on key elements of learning and community service (learning about citizenship, learning about oneself, learning of a curriculum, processes of reflection, etc.). The main data analysis technique was content analysis, involving the triangulation of various techniques (questionnaires, interviews and discussion groups) and informants (students, teachers, coordinators and members of other organisations). Data analysis showed a high degree of satisfaction among the participating students. The dimension that most influenced the results was assessment of the attributed value, more specifically the students' perception of their acquisition of conceptual, personal and citizenship knowledge. It should be cautioned that the results were swayed by the relationship generated among teachers, students and institutions and the possibility of linking theory with practice.

Kokemusperäisen tiedon jakaminen työkierrossa

Kokemusperäinen tieto on käytäntöihin sitoutunutta ja siirtyy pitkälti vuorovaikutuksen kautta. Organisaation toiminnalle keskeisenä osatekijänä sen jakamisen tulee olla suunnitelmallista. Tämä tutkimus keskittyy tarkastelemaan kokemusperäisen tiedon jakamista työkierron avulla. Työkierto osaamisen kehittämisen menetelmänä on laajalti organisaatioiden käyttämä, mutta sen tutkiminen kokemusperäisen tiedon osalta on ollut vähäistä. Tutkimuksen teoriaosuus tarkastelee tiedon jakamista ja omaksumista yksilön ja organisaation tasoilla hyödyntäen tietoperustaisen näkemyksen ja organisaation oppimisen teorioita, joiden kautta tutkimuksen viitekehys muotoutui. Tutkimuksen empiriaosuus toteutettiin kvalitatiivisina teemahaastatteluina, joissa haastateltiin kuutta työkierron mentori-oppija –paria. Tutkimuksen tulokset osoittivat työkierron olevan toimiva keino siirtää kokemusperäistä tietoa, johon merkittävimpinä keinoina vaikuttivat vuorovaikutuksellinen yhdessä työskenteleminen, sekä toiminnan organisoinnin suunnitelmallisuus. Tutkimuksen johtopäätöksenä esitettiin yhdenmukaisen työkierron suunnitelman rakentamista, sekä työkierron toteutumisen sitouttamista osaksi työn arviointia.

Adaptive Routing Approaches for Networked Many-Core Systems

Through advances in technology, System-on-Chip design is moving towards integrating tens to hundreds of intellectual property blocks into a single chip. In such a many-core system, on-chip communication becomes a performance bottleneck for high performance designs. Network-on-Chip (NoC) has emerged as a viable solution for the communication challenges in highly complex chips. The NoC architecture paradigm, based on a modular packet-switched mechanism, can address many of the on-chip communication challenges such as wiring complexity, communication latency, and bandwidth. Furthermore, the combined benefits of 3D IC and NoC schemes provide the possibility of designing a high performance system in a limited chip area. The major advantages of 3D NoCs are the considerable reductions in average latency and power consumption. There are several factors degrading the performance of NoCs. In this thesis, we investigate three main performance-limiting factors: network congestion, faults, and the lack of efficient multicast support. We address these issues by the means of routing algorithms. Congestion of data packets may lead to increased network latency and power consumption. Thus, we propose three different approaches for alleviating such congestion in the network. The first approach is based on measuring the congestion information in different regions of the network, distributing the information over the network, and utilizing this information when making a routing decision. The second approach employs a learning method to dynamically find the less congested routes according to the underlying traffic. The third approach is based on a fuzzy-logic technique to perform better routing decisions when traffic information of different routes is available. Faults affect performance significantly, as then packets should take longer paths in order to be routed around the faults, which in turn increases congestion around the faulty regions. We propose four methods to tolerate faults at the link and switch level by using only the shortest paths as long as such path exists. The unique characteristic among these methods is the toleration of faults while also maintaining the performance of NoCs. To the best of our knowledge, these algorithms are the first approaches to bypassing faults prior to reaching them while avoiding unnecessary misrouting of packets. Current implementations of multicast communication result in a significant performance loss for unicast traffic. This is due to the fact that the routing rules of multicast packets limit the adaptivity of unicast packets. We present an approach in which both unicast and multicast packets can be efficiently routed within the network. While suggesting a more efficient multicast support, the proposed approach does not affect the performance of unicast routing at all. In addition, in order to reduce the overall path length of multicast packets, we present several partitioning methods along with their analytical models for latency measurement. This approach is discussed in the context of 3D mesh networks.

Generative Part-Based Gabor Object Detector

Object detection is a fundamental task of computer vision that is utilized as a core part in a number of industrial and scientific applications, for example, in robotics, where objects need to be correctly detected and localized prior to being grasped and manipulated. Existing object detectors vary in (i) the amount of supervision they need for training, (ii) the type of a learning method adopted (generative or discriminative) and (iii) the amount of spatial information used in the object model (model-free, using no spatial information in the object model, or model-based, with the explicit spatial model of an object). Although some existing methods report good performance in the detection of certain objects, the results tend to be application specific and no universal method has been found that clearly outperforms all others in all areas. This work proposes a novel generative part-based object detector. The generative learning procedure of the developed method allows learning from positive examples only. The detector is based on finding semantically meaningful parts of the object (i.e. a part detector) that can provide additional information to object location, for example, pose. The object class model, i.e. the appearance of the object parts and their spatial variance, constellation, is explicitly modelled in a fully probabilistic manner. The appearance is based on bio-inspired complex-valued Gabor features that are transformed to part probabilities by an unsupervised Gaussian Mixture Model (GMM). The proposed novel randomized GMM enables learning from only a few training examples. The probabilistic spatial model of the part configurations is constructed with a mixture of 2D Gaussians. The appearance of the parts of the object is learned in an object canonical space that removes geometric variations from the part appearance model. Robustness to pose variations is achieved by object pose quantization, which is more efficient than previously used scale and orientation shifts in the Gabor feature space. Performance of the resulting generative object detector is characterized by high recall with low precision, i.e. the generative detector produces large number of false positive detections. Thus a discriminative classifier is used to prune false positive candidate detections produced by the generative detector improving its precision while keeping high recall. Using only a small number of positive examples, the developed object detector performs comparably to state-of-the-art discriminative methods.

Ohjelmoinnin opetus peruskoulussa: toiminnallisuutta ja graafista ohjelmointikieltä hyödyntävien opetusmenetelmien vertailu

Ohjelmoinnin opettaminen yleissivistävänä oppiaineena on viime aikoina herättänyt kiinnostusta Suomessa ja muualla maailmassa. Esimerkiksi Suomen opetushallituksen määrittämien, vuonna 2016 käyttöön otettavien peruskoulun opintosuunnitelman perusteiden mukaan, ohjelmointitaitoja aletaan opettaa suomalaisissa peruskouluissa ensimmäiseltä luokalta alkaen. Ohjelmointia ei olla lisäämässä omaksi oppiaineekseen, vaan sen opetuksen on tarkoitus tapahtua muiden oppiaineiden, kuten matematiikan yhteydessä. Tämä tutkimus käsittelee yleissivistävää ohjelmoinnin opetusta yleisesti, käy läpi yleisimpiä haasteita ohjelmoinnin oppimisessa ja tarkastelee erilaisten opetusmenetelmien soveltuvuutta erityisesti nuorten oppilaiden opettamiseen. Tutkimusta varten toteutettiin verkkoympäristössä toimiva, noin 9–12-vuotiaille oppilaille suunnattu graafista ohjelmointikieltä ja visuaalisuutta tehokkaasti hyödyntävä oppimissovellus. Oppimissovelluksen avulla toteutettiin alakoulun neljänsien luokkien kanssa vertailututkimus, jossa graafisella ohjelmointikielellä tapahtuvan opetuksen toimivuutta vertailtiin toiseen opetusmenetelmään, jossa oppilaat tutustuivat ohjelmoinnin perusteisiin toiminnallisten leikkien avulla. Vertailututkimuksessa kahden neljännen luokan oppilaat suorittivat samankaltaisia, ohjelmoinnin peruskäsitteisiin liittyviä ohjelmointitehtäviä molemmilla opetus-menetelmillä. Tutkimuksen tavoitteena oli selvittää alakouluoppilaiden nykyistä ohjelmointiosaamista, sitä minkälaisen vastaanoton ohjelmoinnin opetus alakouluoppilailta saa, onko erilaisilla opetusmenetelmillä merkitystä opetuksen toteutuksen kannalta ja näkyykö eri opetusmenetelmillä opetettujen luokkien oppimistuloksissa eroja. Oppilaat suhtautuivat kumpaankin opetusmenetelmään myönteisesti, ja osoittivat kiinnostusta ohjelmoinnin opiskeluun. Sisällöllisesti oppitunneille oli varattu turhan paljon materiaalia, mutta esimerkiksi yhden keskeisimmän aiheen, eli toiston käsitteen oppimisessa aktiivisilla leikeillä harjoitellut luokka osoitti huomattavasti graafisella ohjelmointikielellä harjoitellutta luokkaa parempaa osaamista oppitunnin jälkeen. Ohjelmakoodin peräkkäisyyteen liittyvä osaaminen oli neljäsluokkalaisilla hyvin hallussa jo ennen ohjelmointiharjoituksia. Aiheeseen liittyvän taustatutkimuksen ja luokkien opettajien haastatteluiden perusteella havaittiin koulujen valmiuksien opetussuunnitelmauudistuksen mukaiseen ohjelmoinnin opettamiseen olevan vielä heikolla tasolla.

Émiliano Renaud (1875-1932) : premier pianiste-virtuose du Québec : interprète-pédagogue-compositeur

La version intégrale de ce mémoire est disponible uniquement pour consultation individuelle à la Bibliothèque de musique de l'Université de Montréal (http://www.bib.umontreal.ca/MU).

Amorçage cognitif pour amélioration de l’acquisition de la connaissance dans un système tutoriel intelligent

Cette thèse vise à définir une nouvelle méthode d’enseignement pour les systèmes tutoriels intelligents dans le but d’améliorer l’acquisition des connaissances. L’apprentissage est un phénomène complexe faisant intervenir des mécanismes émotionnels et cognitifs de nature consciente et inconsciente. Nous nous intéressons à mieux comprendre les mécanismes inconscients du raisonnement lors de l’acquisition des connaissances. L’importance de ces processus inconscients pour le raisonnement est bien documentée en neurosciences, mais demeure encore largement inexplorée dans notre domaine de recherche. Dans cette thèse, nous proposons la mise en place d’une nouvelle approche pédagogique dans le domaine de l’éducation implémentant une taxonomie neuroscientifique de la perception humaine. Nous montrons que cette nouvelle approche agit sur le raisonnement et, à tour de rôle, améliore l’apprentissage général et l’induction de la connaissance dans un environnement de résolution de problème. Dans une première partie, nous présentons l’implémentation de notre nouvelle méthode dans un système tutoriel visant à améliorer le raisonnement pour un meilleur apprentissage. De plus, compte tenu de l’importance des mécanismes émotionnels dans l’apprentissage, nous avons également procédé dans cette partie à la mesure des émotions par des capteurs physiologiques. L’efficacité de notre méthode pour l’apprentissage et son impact positif observé sur les émotions a été validée sur trente et un participants. Dans une seconde partie, nous allons plus loin dans notre recherche en adaptant notre méthode visant à améliorer le raisonnement pour une meilleure induction de la connaissance. L’induction est un type de raisonnement qui permet de construire des règles générales à partir d’exemples spécifiques ou de faits particuliers. Afin de mieux comprendre l’impact de notre méthode sur les processus cognitifs impliqués dans ce type de raisonnement, nous avons eu recours à des capteurs cérébraux pour mesurer l’activité du cerveau des utilisateurs. La validation de notre approche réalisée sur quarante-trois volontaires montre l’efficacité de notre méthode pour l’induction de la connaissance et la viabilité de mesurer le raisonnement par des mesures cérébrales suite à l’application appropriée d’algorithmes de traitement de signal. Suite à ces deux parties, nous clorons la thèse par une discussion applicative en décrivant la mise en place d’un nouveau système tutoriel intelligent intégrant les résultats de nos travaux.

Éléments nécessaires à une recherche évaluative de programmes de formation et de soutien aux préceptrices en sciences infirmières

Travail dirigé présenté à la Faculté des sciences infirmières en vue de l’obtention du grade de Maître ès sciences (M.Sc.) en sciences infirmières option formation

Validating Co-Training Models for Web Image Classification

Co-training is a semi-supervised learning method that is designed to take advantage of the redundancy that is present when the object to be identified has multiple descriptions. Co-training is known to work well when the multiple descriptions are conditional independent given the class of the object. The presence of multiple descriptions of objects in the form of text, images, audio and video in multimedia applications appears to provide redundancy in the form that may be suitable for co-training. In this paper, we investigate the suitability of utilizing text and image data from the Web for co-training. We perform measurements to find indications of conditional independence in the texts and images obtained from the Web. Our measurements suggest that conditional independence is likely to be present in the data. Our experiments, within a relevance feedback framework to test whether a method that exploits the conditional independence outperforms methods that do not, also indicate that better performance can indeed be obtained by designing algorithms that exploit this form of the redundancy when it is present.

O fio de Ariadne : a formação contínua do PNEP e desenvolvimento da competência da leitura no 1.º CEB

Afirmando-se como uma necessidade premente, a qualidade da formação contínua de professores tem evoluído nos últimos anos. Esta urgência surge a partir das exigências que cada vez mais se sentem, quer a nível nacional, quer internacional, de um ensino e aprendizagem que acompanhe as mudanças sociais, tecnológicas e multiculturais. O Programa Nacional de Ensino do Português (PNEP), que surgiu pela necessidade de aumentar os níveis de literacia dos alunos portugueses, foi o objeto de estudo desta investigação. Nesta perspetiva, pretendeu-se identificar de que forma a formação de professores se articula com as dinâmicas da formação centrada nas escolas, no âmbito do PNEP, no desenvolvimento de competências de leitura dos alunos do 1.º CEB. Embora a aprendizagem do português padrão passe pelo ensino e aprendizagem de vários domínios, focalizou-se o estudo na relação entre a atitude reflexiva do professor e o desenvolvimento das capacidades leitoras dos alunos, através do ensino explícito de estratégias de compreensão de textos. O estudo empírico sustentou-se em autores como Alarcão (2002), Colomer & Camps (2008), García (1992), Lomas (2003), Pawlas, & Oliva, (2007), Sim-Sim (2007), entre outros. No estudo optou-se por uma metodologia mista, qualitativa e quantitativa. O caso em estudo, sobre o PNEP e a sua influência na formação docente, concretizou-se em contexto escolar e circunscreveu-se ao cenário profissional da investigadora. Durante o ano letivo 2009/10 sete professoras/formandas, sob a supervisão da investigadora/formadora, desenvolveram a sua prática pedagógica, num Agrupamento de Escolas na área de Gondomar. Assim, foram analisadas as reflexões contidas nos portefólios formativos das docentes. Em extensão, foi aplicado um inquérito por questionário, a cento e vinte e um professores dos Núcleos Regionais do Porto e Minho. Segundo os resultados obtidos, concluiu-se que a frequência do PNEP contribuiu para formar professores reflexivos que, ao mudarem práticas, potenciaram o desenvolvimento da competência de leitura dos alunos do 1.º CEB, o que se refletiu na melhoria dos resultados escolares, em todas as áreas do saber.

Inverse problems in dynamic cognitive modeling

Inverse problems for dynamical system models of cognitive processes comprise the determination of synaptic weight matrices or kernel functions for neural networks or neural/dynamic field models, respectively. We introduce dynamic cognitive modeling as a three tier top-down approach where cognitive processes are first described as algorithms that operate on complex symbolic data structures. Second, symbolic expressions and operations are represented by states and transformations in abstract vector spaces. Third, prescribed trajectories through representation space are implemented in neurodynamical systems. We discuss the Amari equation for a neural/dynamic field theory as a special case and show that the kernel construction problem is particularly ill-posed. We suggest a Tikhonov-Hebbian learning method as regularization technique and demonstrate its validity and robustness for basic examples of cognitive computations.

Road and Traffic Signs Recognition using Vector Machines

Intelligent Transportation System (ITS) is a system that builds a safe, effective and integrated transportation environment based on advanced technologies. Road signs detection and recognition is an important part of ITS, which offer ways to collect the real time traffic data for processing at a central facility.This project is to implement a road sign recognition model based on AI and image analysis technologies, which applies a machine learning method, Support Vector Machines, to recognize road signs. We focus on recognizing seven categories of road sign shapes and five categories of speed limit signs. Two kinds of features, binary image and Zernike moments, are used for representing the data to the SVM for training and test. We compared and analyzed the performances of SVM recognition model using different features and different kernels. Moreover, the performances using different recognition models, SVM and Fuzzy ARTMAP, are observed.

Fuzzy community structure detection by particle competition and cooperation

Identification and classification of overlapping nodes in networks are important topics in data mining. In this paper, a network-based (graph-based) semi-supervised learning method is proposed. It is based on competition and cooperation among walking particles in a network to uncover overlapping nodes by generating continuous-valued outputs (soft labels), corresponding to the levels of membership from the nodes to each of the communities. Moreover, the proposed method can be applied to detect overlapping data items in a data set of general form, such as a vector-based data set, once it is transformed to a network. Usually, label propagation involves risks of error amplification. In order to avoid this problem, the proposed method offers a mechanism to identify outliers among the labeled data items, and consequently prevents error propagation from such outliers. Computer simulations carried out for synthetic and real-world data sets provide a numeric quantification of the performance of the method. © 2012 Springer-Verlag.

Expressando-se: uma interface do ensino de artes visuais com a musicalidade

Pós-graduação em Artes - IA