Applying Platform Design to Improve Product-Service Systems Collaborative Development

Part 3: Product-Service Systems

Achieving Coherence between Strategies and Value Systems in Collaborative Networks

Part 8: Business Strategies Alignment

Collaborative Systems of Systems Need Collaborative Design

Part 1: Introduction

Collaborative e-learning and Learning Objects

The Learning Object (OA) is any digital resource that can be reused to support learning with specific functions and objectives. The OA specifications are commonly offered in SCORM model without considering activities in groups. This deficiency was overcome by the solution presented in this paper. This work specified OA for e-learning activities in groups based on SCORM model. This solution allows the creation of dynamic objects which include content and software resources for the collaborative learning processes. That results in a generalization of the OA definition, and in a contribution with e-learning specifications.

Sistema de recomendação e personalização de televisão

Com a expansão da Televisão Digital e a convergência entre os meios de difusão convencionais e a televisão sobre IP, o número de canais disponíveis tem aumentado de forma gradual colocando o espectador numa situação de difícil escolha quanto ao programa a visionar. Sobrecarregados com uma grande quantidade de programas e informação associada, muitos espectadores desistem sistematicamente de ver um programa e tendem a efectuar zapping entre diversos canais ou a assistir sempre aos mesmos programas ou canais. Diante deste problema de sobrecarga de informação, os sistemas de recomendação apresentam-se como uma solução. Nesta tese pretende estudar-se algumas das soluções existentes dos sistemas de recomendação de televisão e desenvolver uma aplicação que permita a recomendação de um conjunto de programas que representem potencial interesse ao espectador. São abordados os principais conceitos da área dos algoritmos de recomendação e apresentados alguns dos sistemas de recomendação de programas de televisão desenvolvidos até à data. Para realizar as recomendações foram desenvolvidos dois algoritmos baseados respectivamente em técnicas de filtragem colaborativa e de filtragem de conteúdo. Estes algoritmos permitem através do cálculo da similaridade entre itens ou utilizadores realizar a predição da classificação que um utilizador atribuiria a um determinado item (programa de televisão, filme, etc.). Desta forma é possível avaliar o nível de potencial interesse que o utilizador terá em relação ao respectivo item. Os conjuntos de dados que descrevem as características dos programas (título, género, actores, etc.) são armazenados de acordo com a norma TV-Anytime. Esta norma de descrição de conteúdo multimédia apresenta a vantagem de ser especificamente vocacionada para conteúdo audiovisual e está disponível livremente. O conjunto de recomendações obtidas é apresentado ao utilizador através da interacção com uma aplicação Web que permite a integração de todos os componentes do sistema. Para validação do trabalho foi considerado um dataset de teste designado de htrec2011-movielens-2k e cujo conteúdo corresponde a um conjunto de filmes classificados por diversos utilizadores num ambiente real. Este conjunto de filmes possui, para além da classificações atribuídas pelos utilizadores, um conjunto de dados que descrevem o género, directores, realizadores e país de origem. Para validação final do trabalho foram realizados diversos testes dos quais o mais relevante correspondeu à avaliação da distância entre predições e valores reais e cujo objectivo é classificar a capacidade dos algoritmos desenvolvidos preverem com precisão as classificações que os utilizadores atribuiriam aos itens analisados.

Collaborative environment to support a professional community

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para obtenção do grau de Mestre em Engenharia Electrotécnica e de Computadores

Recommending media content based on machine learning methods

Dissertação para obtenção do Grau de Mestre em Engenharia Informática

Improving Searching and Browsing Capabilities of Learning Object Repositories

Learning object repositories are a basic piece of virtual learning environments used for content management. Nevertheless, learning objects have special characteristics that make traditional solutions for content management ine ective. In particular, browsing and searching for learning objects cannot be based on the typical authoritative meta-data used for describing content, such as author, title or publicationdate, among others. We propose to build a social layer on top of a learning object repository, providing nal users with additional services fordescribing, rating and curating learning objects from a teaching perspective. All these interactions among users, services and resources can be captured and further analyzed, so both browsing and searching can be personalized according to user pro le and the educational context, helping users to nd the most valuable resources for their learning process. In this paper we propose to use reputation schemes and collaborative filtering techniques for improving the user interface of a DSpace based learning object repository.

Informed recommender: basing recommendations on consumer product reviews

Recommender systems attempt to predict items in which a user might be interested, given some information about the user's and items' profiles. Most existing recommender systems use content-based or collaborative filtering methods or hybrid methods that combine both techniques (see the sidebar for more details). We created Informed Recommender to address the problem of using consumer opinion about products, expressed online in free-form text, to generate product recommendations. Informed recommender uses prioritized consumer product reviews to make recommendations. Using text-mining techniques, it maps each piece of each review comment automatically into an ontology

Active du/dt Filtering for Variable-Speed AC Drives

An oscillating overvoltage has become a common phenomenon at the motor terminal in inverter-fed variable-speed drives. The problem has emerged since modern insulated gate bipolar transistors have become the standard choice as the power switch component in lowvoltage frequency converter drives. Theovervoltage phenomenon is a consequence of the pulse shape of inverter output voltage and impedance mismatches between the inverter, motor cable, and motor. The overvoltages are harmful to the electric motor, and may cause, for instance, insulation failure in the motor. Several methods have been developed to mitigate the problem. However, most of them are based on filtering with lossy passive components, the drawbacks of which are typically their cost and size. In this doctoral dissertation, application of a new active du/dt filtering method based on a low-loss LC circuit and active control to eliminate the motor overvoltages is discussed. The main benefits of the method are the controllability of the output voltage du/dt within certain limits, considerably smaller inductances in the filter circuit resulting in a smaller physical component size, and excellent filtering performance when compared with typical traditional du/dt filtering solutions. Moreover, no additional components are required, since the active control of the filter circuit takes place in the process of the upper-level PWM modulation using the same power switches as the inverter output stage. Further, the active du/dt method will benefit from the development of semiconductor power switch modules, as new technologies and materials emerge, because the method requires additional switching in the output stage of the inverter and generation of narrow voltage pulses. Since additional switching is required in the output stage, additional losses are generated in the inverter as a result of the application of the method. Considerations on the application of the active du/dt filtering method in electric drives are presented together with experimental data in order to verify the potential of the method.

Papyres : un système de gestion et de recommandation d’articles de recherche

Les étudiants gradués et les professeurs (les chercheurs, en général), accèdent, passent en revue et utilisent régulièrement un grand nombre d’articles, cependant aucun des outils et solutions existants ne fournit la vaste gamme de fonctionnalités exigées pour gérer correctement ces ressources. En effet, les systèmes de gestion de bibliographie gèrent les références et les citations, mais ne parviennent pas à aider les chercheurs à manipuler et à localiser des ressources. D'autre part, les systèmes de recommandation d’articles de recherche et les moteurs de recherche spécialisés aident les chercheurs à localiser de nouvelles ressources, mais là encore échouent dans l’aide à les gérer. Finalement, les systèmes de gestion de contenu d'entreprise offrent les fonctionnalités de gestion de documents et des connaissances, mais ne sont pas conçus pour les articles de recherche. Dans ce mémoire, nous présentons une nouvelle classe de systèmes de gestion : système de gestion et de recommandation d’articles de recherche. Papyres (Naak, Hage, & Aïmeur, 2008, 2009) est un prototype qui l’illustre. Il combine des fonctionnalités de bibliographie avec des techniques de recommandation d’articles et des outils de gestion de contenu, afin de fournir un ensemble de fonctionnalités pour localiser les articles de recherche, manipuler et maintenir les bibliographies. De plus, il permet de gérer et partager les connaissances relatives à la littérature. La technique de recommandation utilisée dans Papyres est originale. Sa particularité réside dans l'aspect multicritère introduit dans le processus de filtrage collaboratif, permettant ainsi aux chercheurs d'indiquer leur intérêt pour des parties spécifiques des articles. De plus, nous proposons de tester et de comparer plusieurs approches afin de déterminer le voisinage dans le processus de Filtrage Collaboratif Multicritère, de telle sorte à accroître la précision de la recommandation. Enfin, nous ferons un rapport global sur la mise en œuvre et la validation de Papyres.

Mures : Un système de recommandation de musique

Pendant la dernière décennie nous avons vu une transformation incroyable du monde de la musique qui est passé des cassettes et disques compacts à la musique numérique en ligne. Avec l'explosion de la musique numérique, nous avons besoin de systèmes de recommandation de musique pour choisir les chansons susceptibles d’être appréciés à partir de ces énormes bases de données en ligne ou personnelles. Actuellement, la plupart des systèmes de recommandation de musique utilisent l’algorithme de filtrage collaboratif ou celui du filtrage à base de contenu. Dans ce mémoire, nous proposons un algorithme hybride et original qui combine le filtrage collaboratif avec le filtrage basé sur étiquetage, amélioré par la technique de filtrage basée sur le contexte d’utilisation afin de produire de meilleures recommandations. Notre approche suppose que les préférences de l'utilisateur changent selon le contexte d'utilisation. Par exemple, un utilisateur écoute un genre de musique en conduisant vers son travail, un autre type en voyageant avec la famille en vacances, un autre pendant une soirée romantique ou aux fêtes. De plus, si la sélection a été générée pour plus d'un utilisateur (voyage en famille, fête) le système proposera des chansons en fonction des préférences de tous ces utilisateurs. L'objectif principal de notre système est de recommander à l'utilisateur de la musique à partir de sa collection personnelle ou à partir de la collection du système, les nouveautés et les prochains concerts. Un autre objectif de notre système sera de collecter des données provenant de sources extérieures, en s'appuyant sur des techniques de crawling et sur les flux RSS pour offrir des informations reliées à la musique tels que: les nouveautés, les prochains concerts, les paroles et les artistes similaires. Nous essayerons d’unifier des ensembles de données disponibles gratuitement sur le Web tels que les habitudes d’écoute de Last.fm, la base de données de la musique de MusicBrainz et les étiquettes des MusicStrands afin d'obtenir des identificateurs uniques pour les chansons, les albums et les artistes.

Informed recommender: basing recommendations on consumer product reviews

Recommender systems attempt to predict items in which a user might be interested, given some information about the user's and items' profiles. Most existing recommender systems use content-based or collaborative filtering methods or hybrid methods that combine both techniques (see the sidebar for more details). We created Informed Recommender to address the problem of using consumer opinion about products, expressed online in free-form text, to generate product recommendations. Informed recommender uses prioritized consumer product reviews to make recommendations. Using text-mining techniques, it maps each piece of each review comment automatically into an ontology

Design for design: support for creative practice in computer-supported collaborative work (CSCW) in design

This paper describes a novel methodology for observing and analysing collaborative design by using the concepts of cognitive dimensions related to concept-based misfit analysis. The study aims at gaining an insight into support for creative practice of graphical communication in collaborative design processes of designers while sketching within a shared white board and audio conferencing environment. Empirical data on design processes have been obtained from observation of groups of student designers solving an interior space-planning problem of a lounge-diner in a shared virtual environment. The results of the study provide recommendations for the design and development of interactive systems to support such collaborative design activities.

Adaptive Rückmeldungen im intelligenten Tutorensystem LARGO

Das intelligente Tutorensystem LARGO für die Rechtswissenschaften soll Jurastudenten helfen, Argumentationsstrategien zu lernen. Im verwendeten Ansatz werden Gerichtsprotokolle als Lernmaterialien verwendet: Studenten annotieren diese und erstellen graphische Repräsentationen des Argumentationsverlaufs. Das System kann dabei zur Reflexion über die von Anwälten vorgebrachten Argumente anregen und Lernende auf mögliche Schwächen in ihrer Analyse des Disputs hinweisen. Zur Erkennung von Schwächen verwendet das System Graphgrammatiken und kollaborative Filtermechanismen. Dieser Artikel stellt dar, wie in LARGO auf Basis der Bestimmung eines „Benutzungskontextes“ die Rückmeldungen im System benutzungsadaptiv gestaltet werden. Weiterhin diskutieren wir auf Basis der Ergebnisse einer kontrollierten Studie mit dem System, welche mit Jurastudierenden an der University of Pittsburgh stattfand, in wie weit der automatisch bestimmte Benutzungskontext zur Vorhersage von Lernerfolgen bei Studenten verwendbar ist.