A identificação de características que sejam importantes sobre um dado estudante durante o processo de ensino-aprendizagem tem sido alvo de muitos estudos nos últimos tempos. Docentes, coordenadores e pedagogos têm defendido que cada indivíduo possui características particulares e importantes dentro do processo de desenvolvimento do conhecimento. A necessidade de adaptar as estratégias de ensino de acordo com as preferências do aprendiz é uma realidade dentro das salas de aula, sejam estas presenciais ou virtuais. Porém, isto não significa que numa sala de aula deva haver criação de um método para cada aluno, mas sim que se aponte qual a melhor forma de interação para cada um deles construindo grupos de aprendizes com características comuns. Trabalhando desta forma se torna possível identificar perfis de aprendizes dentro de um conjunto de estudantes, possibilitando que se possa trabalhar com conteúdos e ambientes de aprendizagem mais adequados aos aprendizes.O objetivo deste trabalho é apresentar a arquitetura de um sistema que possibilita a criação de cenários de aprendizagem baseados no perfil do aluno. O docente deve indicar as características que devem ser observadas durante as interações do aprendiz em um dado ambiente de aprendizagem eletrônica. Estas características serão modeladas como informações de contexto, permitindo que os pontos definidos como observáveis sejam organizados e modelados de forma a facilitar a monitoração das interações. A classificação do aluno em um determinando tipo de perfil de aprendizagem, previamente definido pelo docente, é realizado considerando-se as informações sobre o contexto da interação e os tipos de perfis de aprendizagem. Para que seja possível construir os cenários de aprendizagem considerando o perfil do aluno este trabalho propõe criar um relacionamento entre os tipos de perfis de aprendizagem e as estratégias de ensino através das categorias descritoras dos objetos de aprendizagem. / The identification of characteristics of a given student that are important during the teachinglearning process has been the focus of numerous studies in recent years. Teachers, coordinators and pedagogues have defended the notion that each individual presents particular and important characteristics in the developing knowledge process. The need to adapt teaching strategies to the student\'s preferences is a reality in classrooms, being physical or virtual classrooms. However, this does not mean that a method should be created for each student in a classroom, but that the best form of interaction for each of them be identified, building groups of learners with common characteristics. Working in this way makes it possible to identify learner profiles within a group of students, allowing one to work with learning contents and environments more suited to the learners. The objective of this work is to present the architecture of a system that allows for the creation of learning scenarios based on the studen profile. The teacher should point out the features which may be observed during the student interaction in a given e-elearning environment. These features are designed as context information, allowing defined observation points to be organized and modeled for facilitating the monitoring of interactions. The student classification in a given learning profile type, defined previously by the teacher, is realized considering information about the context of interaction and the learning profile types. For the learning environment to build the learning scenarios according to the student profile, this work proposes to create a relationship between the learning profile types and the teaching strategies through the description of learning objects categories.

Humans live in physical world and perform activities that are physical, natural and biological. But humans are forced to shift explicitly from physical world to virtual world and vice-versa in performing computer aided physical activities. The research reported here is investigating: How implicit human-computer interaction can be used as a means to bridge the gap between physical world and virtual world. An agent-centric approach is introduced to extend ubiquitous computing to unlimited geographical space and a framework for implicit human-computer interaction is also discussed. The benefits of standardized ontologies are used as a base upon which this framework is built. This semantic approach together with agent-centric approach is discussed to visualize the visions of implicit Human-Computer Interaction (i-HCI). / PHYVIR project

Pour fournir aux utilisateurs des services personnalisés d'adaptation en utilisant uniquement les ressources informatiques accessibles dans un environnement de cloud computing, les applications contexte, conscients besoin d'assimiler à la fois le contexte accessible et dérivés, c'est à dire une combinaison de plus d'un senti données et d'informations dans l'environnement. Contexte des données de dépendance, la dépendance qui se pose entre le contexte des données du producteur et du consommateur, peut se présenter dans un système en raison de nombreuses raisons. Mais comme le nombre de dépendances de contexte pour une augmentation des services, la plus complexe, le système devient à gérer. La thèse aborde les questions de la façon d'identifier les dépendances de contexte, représentent des dépendances de contexte tels, puis les réduire dans un système. Dans la première partie de la thèse, nous présentons deux approches efficaces pour déterminer les relations de dépendance entre les différents services du contexte dans l'environnement informatique ubiquitaire pour aider à mieux analyser les services omniprésents. Une approche est basée sur la théorie des graphes, et nous avons utilisé le tri topologique pour déterminer les dépendances de contexte. La deuxième approche est basée sur la résolution des réseaux de contraintes qui détermine si une entité est affectée lorsque l'état d'une certaine entité autre a changé son état, c.-à-détermination de la nature dynamique de la dépendance contexte. Dans la deuxième partie de la thèse, nous présentons un mode de représentation des dépendances de contexte au sein d'un système. Notre modèle qui représente les dépendances de contexte est basé sur la théorie des ensembles et la logique des prédicats du premier ordre. Le modèle de représentation contexte de dépendance représente également d'autres sources pour l'acquisition de contexte qui peuvent être utilisés dans une affaire dans laquelle les producteurs contexte privilégiées ne sont pas disponibles pour desservir le contexte souhaité pour le consommateur un contexte pertinent, pas plus. En outre, nous essayons de réduire les dépendances de contexte en présentant l'idée du contexte de profil, qui est basé sur la proposition d'un cadre ouvert pour l'acquisition de contexte, la gestion et la distribution. Cette approche heuristique est basée sur l'idée d'utiliser les nœuds mobiles dans un réseau ad hoc avec superposition de plus de ressources que le producteur lui-même contexte pour stocker diverses informations contextuelles sous la bannière du contexte profil, et en outre, fournir le contexte profil au lieu de chaque contexte individuellement sur la base sur les requêtes des nœuds reçoivent des consommateurs contexte. Réunissant les informations de contexte et de mises à jour de contexte à partir de diverses sources, le soutien aux décisions contexte, conscients peut être mis en œuvre efficacement dans un environnement mobile en s'attaquant aux problèmes de dépendance en utilisant le contexte contexte profil / To provide users with personalized adaptive services only using the accessible computing resources in a cloud environment, context aware applications need to assimilate both the accessed and derived context, i.e. a combination of more than one sensed data and information in the environment. Context data dependency, dependency that arises between the context data producer and consumer, may get introduced in a system due to numerous reasons. But as the number of context dependencies for a service increases, the more complex the system becomes to manage. The thesis addresses issues of how to identify context dependencies, represent such context dependencies and then reduce them in a system. In the first part of the thesis, we present two efficient approaches to determine context dependency relations among various services in ubiquitous computing environment to help better analyse the pervasive services. One approach is based on graph theory, and we have used the topological sort to determine the context dependencies. The second approach is based on solving constraint networks which determines whether an entity is affected when the state of a certain other entity has its state changed, i.e. determining the dynamic nature of context dependency. In the second part of the thesis, we present a mode for representation of context dependencies within a system. Our model that represents context dependencies is based on set theory and first-order predicate logic. The context dependency representation model also represents alternative sources for context acquisition that can be utilized in a case in which the preferred context producers are not available to service the desired context to the relevant context consumer any more. Further, we try to reduce the context dependencies by presenting the idea of profile context, which is based on the proposal of an open framework for context acquisition, management and distribution. This heuristic approach is based on the idea of utilizing mobile nodes in an ad hoc overlay network with more resources than the context producer itself to store various contextual information under the banner of profile context, and further, provide profile context instead of each context individually based on the queries the nodes receive from the context consumers. Bringing together the context information and context updates from various sources, support for context aware decisions can be implemented efficiently in a mobile environment by addressing the issues of context dependency using profile context

Reservoir Computing (RC) is an emerging machine learning paradigm where a fixed kernel, built from a randomly connected "reservoir" with sufficiently rich dynamics, is capable of expanding the problem space in a non-linear fashion to a higher dimensional feature space. These features can then be interpreted by a linear readout layer that is trained by a gradient descent method. In comparison to traditional neural networks, only the output layer needs to be trained, which leads to a significant computational advantage. In addition, the short term memory of the reservoir dynamics has the ability to transform a complex temporal input state space to a simple non-temporal representation. Adaptive real-time systems are multi-stage decision problems that can be used to train an agent to achieve a preset goal by performing an optimal action at each timestep. In such problems, the agent learns through continuous interactions with its environment. Conventional techniques to solving such problems become computationally expensive or may not converge if the state-space being considered is large, partially observable, or if short term memory is required in optimal decision making. The objective of this thesis is to use reservoir computers to solve such goal-driven tasks, where no error signal can be readily calculated to apply gradient descent methodologies. To address this challenge, we propose a novel reinforcement learning approach in combination with reservoir computers built from simple Boolean components. Such reservoirs are of interest because they have the potential to be fabricated by self-assembly techniques. We evaluate the performance of our approach in both Markovian and non-Markovian environments. We compare the performance of an agent trained through traditional Q-Learning. We find that the reservoir-based agent performs successfully in these problem contexts and even performs marginally better than Q-Learning agents in certain cases. Our proposed approach allows to retain the advantage of traditional parameterized dynamic systems in successfully modeling embedded state-space representations while eliminating the complexity involved in training traditional neural networks. To the best of our knowledge, our method of training a reservoir readout layer through an on-policy boot-strapping approach is unique in the field of random Boolean network reservoirs.

Computer Architecture is entering an era where hundreds of Processing Elements (PE) can be integrated onto single chips even as decades-long, steady advances in instruction, thread level parallelism are coming to an end. And yet, conventional methods of parallelism fail to scale beyond 4-5 PE's, well short of the levels of parallelism found in the human brain. The human brain is able to maintain constant real time performance as cognitive complexity grows virtually unbounded through our lifetime. Our underlying thesis is that contextual categorization leading to simplified algorithmic processing is crucial to the brains performance efficiency. But, since the overheads of such reorganization are unaffordable in real time, we also observe the critical role of sleep and dreaming in the lives of all intelligent beings. Based on the importance of dream sleep in memory consolidation, we propose that it is also responsible for contextual reorganization. We target mobile device applications that can be personalized to the user, including speech, image and gesture recognition, as well as other kinds of personalized classification, which are arguably the foundation of intelligence. These algorithms rely on a knowledge database of symbols, where the database size determines the level of intelligence. Essential to achieving intelligence and a seamless user interface however is that real time performance be maintained. Observing this, we define our chief performance goal as: Maintaining constant real time performance against ever increasing algorithmic and architectural complexities. Our solution is a method for Malleable Contextual Partitioning (MCP) that enables closer personalization to user behavior. We conceptualize a novel architectural framework, the Dream Architecture for Lateral Intelligence (DALI) that demonstrates the MCP approach. The DALI implements a dream phase to execute MCP in ideal MISD parallelism and reorganize its architecture to enable contextually simplified real time operation. With speech recognition as an example application, we show that the DALI is successful in achieving the performance goal, as it maintains constant real time recognition, scaling almost ideally, with PE numbers up to 16 and vocabulary size up to 220 words. / Master of Science

O processo de digitalização da TV em diversos países do mundo tem contribuído para o aumento do volume de programas de TV, o que gera uma sobrecarga de informação. Consequentemente, o usuário está enfrentando dificuldade para encontrar os programas de TV favoritos dentre as várias opções disponíveis. Diante deste cenário, os sistemas de recomendação destacam-se como uma possível solução. Tais sistemas são capazes de filtrar itens relevantes de acordo com as preferências do usuário ou de um grupo de usuários que possuem perfis similares. Entretanto, em diversas recomendações o interesse do usuário pode depender do seu contexto. Assim, torna-se importante estender as abordagens tradicionais de recomendação personalizada por meio da exploração do contexto do usuário, o que poderá melhorar a qualidade das recomendações. Para isso, este trabalho descreve uma infraestrutura de software de suporte ao desenvolvimento e execução de sistemas de recomendação sensíveis ao contexto para TV Digital Interativa - intitulada de PersonalTVware. A solução proposta fornece componentes que implementam técnicas avançadas para recomendação de conteúdo e processamento de contexto. Com isso, os desenvolvedores de sistemas de recomendação concentram esforços na lógica de apresentação de seus sistemas, deixando questões de baixo nível para o PersonalTVware gerenciar. As modelagens de usuário, e do contexto, essenciais para o desenvolvimento do PersonalTVware, são representadas por padrões de metadados flexíveis usados na TV Digital Interativa (MPEG-7 e TV-Anytime), e suas devidas extensões. A arquitetura do PersonalTVware é composta por dois subsistemas: dispositivo do usuário e provedor de serviços. A tarefa de predição de preferências contextuais é baseada em métodos de aprendizagem de máquina, e a filtragem de informação sensível ao contexto tem como base a técnica de filtragem baseada em conteúdo. O conceito de perfil contextual também é apresentado e discutido. Para demonstrar e validar as funcionalidades do PersonalTVware em um cenário de uso, foi desenvolvido um sistema de recomendação sensível ao contexto como estudo de caso. / The process of digitalization of TV in several countries around the world has, contributed to increase the volume of TV programs offered and it leads, to information overload problem. Consequently, the user facing the difficulty to find their favorite TV programs in view of various available options. Within this scenario, the recommender systems stand out as a possible solution. These systems are capable of filtering relevant items according to the user preferences or the group of users who have similar profiles. However, the most of the recommender systems for Interactive Digital TV has rarely take into consideration the users contextual information in carrying out the recommendation. However, in many recommendations the user interest may depend on the context. Thus, it becomes important to extend the traditional approaches to personalized recommendation of TV programs by exploiting the context of user, which may improve the quality of the recommendations. Therefore, this work presents a software infrastructure in an Interactive Digital TV environment to support context-aware personalized recommendation of TV programs entitled PersonalTVware. The proposed solution provides components which implement advanced techniques to recommendation of content and context management. Thus, developers of recommender systems can concentrate efforts on the presentation logic of their systems, leaving low-level questions for the PersonalTVware managing. The modeling of user and context, essential for the development of PersonalTVware, are represented by granular metadata standards used in the Interactive Digital TV field (MPEG-7 and TV-Anytime), and its extensions required. The PersonalTVware architecture is composed by two subsystems: the users device and the service provider. The task of inferring contextual preferences is based on machine learning methods, and context-aware information filtering is based on content-based filtering technique. The concept of contextual user profile is presented and discussed. To demonstrate the functionalities in a usage scenario a context-aware recommender system was developed as a case study applying the PersonalTVware.

A Computação Ciente de Contexto permite a obtenção e utilização de informações de contexto adquiridas de dispositivos computacionais no ambiente, com o objetivo de prover serviços; esta dinâmica aliada à evolução das redes de computadores vem provocando profundas modificações nos aspectos sociais e comportamentais das pessoas, uma vez que gradativamente têm necessidade de viverem imersas na tecnologia e integradas ao ambiente, com transparência e mobilidade, e de tal forma que as aplicações de software se adaptam ao comportamento das pessoas e nas informações de contexto capturadas do ambiente. Um dos desafios desta interação ser humano - ambiente - tecnologia - ubiqüidade é garantir a segurança. Como principal inovação e contribuição, esta tese propõe um mecanismo de autenticação contínua de usuários que faz uso de informações de contexto do ambiente, da análise do comportamento do usuário, da biometria facial, das teorias comportamentais de Skinner e da Confiança Matemática da Teoria das Evidências de Dempster-Shafer, para compor uma política de segurança adaptativa e um Sistema de Autenticação Contínua de Usuários Conhecidos - KUCAS (Known User Continuous Authentication System), que estabelece níveis de confiança para autenticar o usuário através da análise do comportamento dele em um ambiente ou domínio específico nas redes de computadores, num determinado período de tempo. A dinâmica de gerenciamento incluso nesse sistema compara o comportamento atual com o histórico de comportamentos anteriores do usuário e com as restrições de atribuição de confiança; caso haja indícios de mudanças no comportamento do usuário, aciona por meio de sensores, a Tecnologia de Reconhecimento Facial Tridimensional (3D), que captura a imagem da face do usuário, validando-a e armazenando-a nos bancos de dados de imagens; havendo incertezas e divergências, mecanismos de segurança e sinais de alerta são acionados. O Sistema KUCAS proposto possui uma infra-estrutura de um framework F-KUCAS, um Módulo de Segurança S-KUCAS e um Algoritmo de Autenticação A-KUCAS. / Context-aware Computing allows to obtain and use context informations acquired through devices in the environment, with the goal to provide services. This dynamics, allied to the computer networks evolution, has been provoking deep modifications in peoples social and behavior aspects, seeing that they have the necessity to live immersed in technology and integrated with the environment, with transparency and mobility, anywhere, anytime, so that the software applications adapt themselves to the persons behavior, based on the context information captured through the environment. One of the challenges of this human ? environment - technology ? ubiquity interaction is to provide security. As main innovation and contribution, this thesis presents an authentication mechanism of users which makes use of environmental context information, users behavior analysis, the face recognition technology, the behavior theories of Skinner and the Mathematical Confidence of the Theory of the Evidences of Dempster-Shafer, to compose an adaptative security policy and the Known User Continuous Authentication System (KUCAS) that establishes trust levels to authenticate the user by his behavior analysis in a specific domain of the computer networks, in a period of time. The dynamics of enclosed management in this system compares the current behavior with the users previous behaviors description and with the trust restrictions. In case of indications of changes in the users behavior, the 3D Technology Face Recognition is set in motion by sensors, which capture the image of the users face, validating it and storing it in the data bases of images. If there are uncertainties and divergences, mechanisms of security and signals of alert are set in motion. The KUCAS System has an infrastructure of one framework F-KUCAS, a Security Module S-KUCAS and an Algorithm of Authentication A-KUCAS.

A Computação Ciente de Contexto permite a obtenção e utilização de informações de contexto adquiridas de dispositivos computacionais no ambiente, com o objetivo de prover serviços; esta dinâmica aliada à evolução das redes de computadores vem provocando profundas modificações nos aspectos sociais e comportamentais das pessoas, uma vez que gradativamente têm necessidade de viverem imersas na tecnologia e integradas ao ambiente, com transparência e mobilidade, e de tal forma que as aplicações de software se adaptam ao comportamento das pessoas e nas informações de contexto capturadas do ambiente. Um dos desafios desta interação ser humano - ambiente - tecnologia - ubiqüidade é garantir a segurança. Como principal inovação e contribuição, esta tese propõe um mecanismo de autenticação contínua de usuários que faz uso de informações de contexto do ambiente, da análise do comportamento do usuário, da biometria facial, das teorias comportamentais de Skinner e da Confiança Matemática da Teoria das Evidências de Dempster-Shafer, para compor uma política de segurança adaptativa e um Sistema de Autenticação Contínua de Usuários Conhecidos - KUCAS (Known User Continuous Authentication System), que estabelece níveis de confiança para autenticar o usuário através da análise do comportamento dele em um ambiente ou domínio específico nas redes de computadores, num determinado período de tempo. A dinâmica de gerenciamento incluso nesse sistema compara o comportamento atual com o histórico de comportamentos anteriores do usuário e com as restrições de atribuição de confiança; caso haja indícios de mudanças no comportamento do usuário, aciona por meio de sensores, a Tecnologia de Reconhecimento Facial Tridimensional (3D), que captura a imagem da face do usuário, validando-a e armazenando-a nos bancos de dados de imagens; havendo incertezas e divergências, mecanismos de segurança e sinais de alerta são acionados. O Sistema KUCAS proposto possui uma infra-estrutura de um framework F-KUCAS, um Módulo de Segurança S-KUCAS e um Algoritmo de Autenticação A-KUCAS. / Context-aware Computing allows to obtain and use context informations acquired through devices in the environment, with the goal to provide services. This dynamics, allied to the computer networks evolution, has been provoking deep modifications in peoples social and behavior aspects, seeing that they have the necessity to live immersed in technology and integrated with the environment, with transparency and mobility, anywhere, anytime, so that the software applications adapt themselves to the persons behavior, based on the context information captured through the environment. One of the challenges of this human ? environment - technology ? ubiquity interaction is to provide security. As main innovation and contribution, this thesis presents an authentication mechanism of users which makes use of environmental context information, users behavior analysis, the face recognition technology, the behavior theories of Skinner and the Mathematical Confidence of the Theory of the Evidences of Dempster-Shafer, to compose an adaptative security policy and the Known User Continuous Authentication System (KUCAS) that establishes trust levels to authenticate the user by his behavior analysis in a specific domain of the computer networks, in a period of time. The dynamics of enclosed management in this system compares the current behavior with the users previous behaviors description and with the trust restrictions. In case of indications of changes in the users behavior, the 3D Technology Face Recognition is set in motion by sensors, which capture the image of the users face, validating it and storing it in the data bases of images. If there are uncertainties and divergences, mechanisms of security and signals of alert are set in motion. The KUCAS System has an infrastructure of one framework F-KUCAS, a Security Module S-KUCAS and an Algorithm of Authentication A-KUCAS.

Submitted by Maria Aparecida (cidazen@gmail.com) on 2017-08-31T13:25:42Z No. of bitstreams: 1 Claudio Manoel.pdf: 2235875 bytes, checksum: 09f2ae854210974f87252bad3c5b1df9 (MD5) / Made available in DSpace on 2017-08-31T13:27:28Z (GMT). No. of bitstreams: 1 Claudio Manoel.pdf: 2235875 bytes, checksum: 09f2ae854210974f87252bad3c5b1df9 (MD5) Previous issue date: 2015-05-27 / The use and popularity of mobile devices has been growing vertiginously, as well an the cloud computing as instrument to store data and services, making feasible the combination of these two technologies. One of the biggest obstacles is the mobile device’s security and ergonomics to provide security for transporting its data without performance degradation. This work proposes a context-aware security mechanism for mobile devices in cloud computing. In addition, it is used the TLS technology to create a secure channel of data sent between mobile device’s client and the cloud server, authenticating them via public key cryptography. At the same time, it provides an appropriate level of security, and analyses the context of the mobile device from the Fuzzy logic. The impact of the mechanism on the mobile device’s performance were measured through experiments. / O uso e a popularidade dos dispositivos móveis vem crescendo vertiginosamente, bem como a computação em nuvem como instrumento para armazenar dados e serviços, tornando-se viável a união dessas duas tecnologias. Um dos maiores obstáculos é a questão da segurança e ergonomia do dispositivo móvel que necessita provê-lo ao trafegar dados sem haver degradação de seu desempenho. Este trabalho propõe então um mecanismo de segurança ciente do contexto para Computação em Nuvem Móvel. Em adição, utiliza-se a tecnologia Transport Layer Security (TLS) para criar um canal seguro de envio de dados entre o cliente do dispositivo móvel e o servidor da Nuvem, autenticando-o por meio de criptografia de chave pública. Paralelamente, para prover um nível de segurança apropriado, analisase o contexto do dispositivo móvel a partir da lógica Fuzzy. O impacto do mecanismo no desempenho do dispositivo móvel foi medido por meio de experimentos.

O processo de digitalização da TV em diversos países do mundo tem contribuído para o aumento do volume de programas de TV, o que gera uma sobrecarga de informação. Consequentemente, o usuário está enfrentando dificuldade para encontrar os programas de TV favoritos dentre as várias opções disponíveis. Diante deste cenário, os sistemas de recomendação destacam-se como uma possível solução. Tais sistemas são capazes de filtrar itens relevantes de acordo com as preferências do usuário ou de um grupo de usuários que possuem perfis similares. Entretanto, em diversas recomendações o interesse do usuário pode depender do seu contexto. Assim, torna-se importante estender as abordagens tradicionais de recomendação personalizada por meio da exploração do contexto do usuário, o que poderá melhorar a qualidade das recomendações. Para isso, este trabalho descreve uma infraestrutura de software de suporte ao desenvolvimento e execução de sistemas de recomendação sensíveis ao contexto para TV Digital Interativa - intitulada de PersonalTVware. A solução proposta fornece componentes que implementam técnicas avançadas para recomendação de conteúdo e processamento de contexto. Com isso, os desenvolvedores de sistemas de recomendação concentram esforços na lógica de apresentação de seus sistemas, deixando questões de baixo nível para o PersonalTVware gerenciar. As modelagens de usuário, e do contexto, essenciais para o desenvolvimento do PersonalTVware, são representadas por padrões de metadados flexíveis usados na TV Digital Interativa (MPEG-7 e TV-Anytime), e suas devidas extensões. A arquitetura do PersonalTVware é composta por dois subsistemas: dispositivo do usuário e provedor de serviços. A tarefa de predição de preferências contextuais é baseada em métodos de aprendizagem de máquina, e a filtragem de informação sensível ao contexto tem como base a técnica de filtragem baseada em conteúdo. O conceito de perfil contextual também é apresentado e discutido. Para demonstrar e validar as funcionalidades do PersonalTVware em um cenário de uso, foi desenvolvido um sistema de recomendação sensível ao contexto como estudo de caso. / The process of digitalization of TV in several countries around the world has, contributed to increase the volume of TV programs offered and it leads, to information overload problem. Consequently, the user facing the difficulty to find their favorite TV programs in view of various available options. Within this scenario, the recommender systems stand out as a possible solution. These systems are capable of filtering relevant items according to the user preferences or the group of users who have similar profiles. However, the most of the recommender systems for Interactive Digital TV has rarely take into consideration the users contextual information in carrying out the recommendation. However, in many recommendations the user interest may depend on the context. Thus, it becomes important to extend the traditional approaches to personalized recommendation of TV programs by exploiting the context of user, which may improve the quality of the recommendations. Therefore, this work presents a software infrastructure in an Interactive Digital TV environment to support context-aware personalized recommendation of TV programs entitled PersonalTVware. The proposed solution provides components which implement advanced techniques to recommendation of content and context management. Thus, developers of recommender systems can concentrate efforts on the presentation logic of their systems, leaving low-level questions for the PersonalTVware managing. The modeling of user and context, essential for the development of PersonalTVware, are represented by granular metadata standards used in the Interactive Digital TV field (MPEG-7 and TV-Anytime), and its extensions required. The PersonalTVware architecture is composed by two subsystems: the users device and the service provider. The task of inferring contextual preferences is based on machine learning methods, and context-aware information filtering is based on content-based filtering technique. The concept of contextual user profile is presented and discussed. To demonstrate the functionalities in a usage scenario a context-aware recommender system was developed as a case study applying the PersonalTVware.