Context-aware pervasive computing has attracted a significant research interest from both academy and industry worldwide. It covers a broad range of applications that support many manufacturing and daily life activities. For instance, industrial robots detect the changes of the working environment in the factory to adapt their operations to the requirements. Automotive control systems may observe other vehicles, detect obstacles, and monitor the essence level or the air quality in order to warn the drivers in case of emergency. Another example is power-aware embedded systems that need to work based on current power/energy availability since power consumption is an important issue. Those kinds of systems can also be considered as smart applications. In practice, successful implementation and deployment of context-aware systems depend on the mechanism to recognize and react to variabilities happening in the environment. In other words, we need a well-defined and efficient adaptation approach so that the systems' behavior can be dynamically customized at runtime. Moreover, concurrency should be exploited to improve the performance and responsiveness of the systems. All those requirements, along with the need for safety, dependability, and reliability pose a big challenge for developers.In this thesis, we propose a novel programming language called INI, which supports both event-based and rule-based programming paradigms and is suitable for building concurrent and context-aware reactive applications. In our language, both events and rules can be defined explicitly, in a stand-alone way or in combination. Events in INI run in parallel (synchronously or asynchronously) in order to handle multiple tasks concurrently and may trigger the actions defined in rules. Besides, events can interact with the execution environment to adjust their behavior if necessary and respond to unpredictable changes. We apply INI in both academic and industrial case studies, namely an object tracking program running on the humanoid robot Nao and a M2M gateway. This demonstrates the soundness of our approach as well as INI's capabilities for constructing context-aware systems. Additionally, since context-aware programs are wide applicable and more complex than regular ones, this poses a higher demand for quality assurance with those kinds of applications. Therefore, we formalize several aspects of INI, including its type system and operational semantics. Furthermore, we develop a tool called INICheck, which can convert a significant subset of INI to Promela, the input modeling language of the model checker SPIN. Hence, SPIN can be applied to verify properties or constraints that need to be satisfied by INI programs. Our tool allows the programmers to have insurance on their code and its behavior.

The wireless sensor network is a key technology in the 21st century because it has multitude applications and it becomes the new way of interaction between physical environment and computer system. Moreover, the wireless sensor network is a high resource constraint system. Consequently, the techniques used for the development of traditional embedded systems cannot be directly applied. Today wireless sensor nodes were implemented by using only one single processor architecture. This approach does not achieve a robust and efficient energy wireless sensor network for applications such as precision agriculture (outdoor) and telemedicine. The aim of this thesis is to develop a new approach for the realization of a wireless sensor network node using multicore architecture to enable to increase both its robustness and lifetime (reduce energy consumption).

近年來由於電腦軟硬體及人機介面介面技術的發展，互動數位敘事(Interactive Digital Storytelling, IDS)的應用也逐漸被重視，特別是在新型態電腦遊戲的設計，而這個趨勢也為即時虛擬攝影機的規劃帶來新的機會與挑戰。本研究旨在透過互動數位敘事腳本內容的分析，建置客製化攝影機運鏡實驗平台，即時自動產生符合情境情節、人物情緒的拍攝方式，並參考電影拍攝手法，結合攝影學的專業知識加入不同拍攝風格，讓同一段影片可以有不同的風格效果。我們希望能夠讓現有的互動敘事系統The Theater [1]中的運鏡技術有跡可循，不再只是以人工的方式憑藉直覺來設定攝影機的位置，而能使得虛擬攝影機的操控變得簡易，修正拍攝效果時將更加簡便，成功快速掌握運鏡的每一個細節。我們在The Theater的實驗平台之上，讓敘事者可以根據故事情境客製化虛擬攝影機的拍攝手法，並由電腦自動產生合宜的攝影機拍攝位置，快速完成攝影機規劃。我們以實例透過實驗的方式驗證此系統的有效性。 / The recent advances in computing technologies and human-computer interactions have attracted much attention in the development of interactive digital storytelling (IDS), especially in the application of novel computer game design. This trend does not only bring new opportunities but also new technological challenges to virtual camera planning. Our research in this work aims at building an experimental platform for customized virtual camera planning through the analysis of screen play in an in-teractive story. By adopting the domain knowledge of camera controls in existing films, we hope to design a computer-assisted system that allows an author to easily experiment with different styles of virtual cameras in a same story. We proposed to design an experimental platform based on “The Theater” IDS, which currently uses a pre-authored way to specify the camera position. In the proposed system, we allow an author to quickly customize virtual camera taking according to the context of a story fragment and let the computer generate appropriate camera configurations automati-cally. We use an example story to verify the effectiveness of the system through ex-periments.

The mission of the research presented in this thesis is to give computers the power to sense and react to human activities. Without the ability to sense the surroundings and understand what humans are doing, computers will not be able to provide active, timely, appropriate, and considerate services to the humans. To accomplish this mission, the work stands on the shoulders of two giants: Machine learning and ubiquitous computing. Because of the ubiquity of sensor-enabled mobile and wearable devices, there has been an emerging opportunity to sense, learn, and infer human activities from the sensor data by leveraging state-of-the-art machine learning algorithms. While having shown promising results in human activity recognition, most existing approaches using supervised or semi-supervised learning have two fundamental problems. Firstly, most existing approaches require a large set of labeled sensor data for every target class, which requires a costly effort from human annotators. Secondly, an unseen new activity cannot be recognized if no training samples of that activity are available in the dataset. In light of these problems, a new approach in this area is proposed in our research. This thesis presents our novel approach to address the problem of human activity recognition when few or no training samples of the target activities are available. The main hypothesis is that the problem can be solved by the proposed NuActiv activity recognition framework, which consists of modeling the hierarchical and sequential structure of human activities, as well as bringing humans in the loop of model training. By injecting human knowledge about the hierarchical nature of human activities, a semantic attribute representation and a two-layer attribute-based learning approach are designed. To model the sequential structure, a probabilistic graphical model is further proposed to take into account the temporal dependency of activities and attributes. Finally, an active learning algorithm is developed to reinforce the recognition accuracy using minimal user feedback. The hypothesis and approaches presented in this thesis are validated by two case studies and real-world experiments on exercise activities and daily life activities. Experimental results show that the NuActiv framework can effectively recognize unseen new activities even without any training data, with up to 70-80% precision and recall rate. It also outperforms supervised learning with limited labeled data for the new classes. The results significantly advance the state of the art in human activity recognition, and represent a promising step towards bridging the gap between computers and humans.

This thesis explores the field of context-aware information systems (CAIS). We present an approach called Compose, Learn, and Discover (CLD) to incorporate CAIS into the user daily workflow. The CLD approach is self-adjusting. It enables users to personalise the information views for different situations. The CAIS learns about the usage of the information views and recalls the right view in the right situation. We illustrate the CLD approach through an application in the health care field using the Clinical Document Architecture (CDA). In order to realise the CLD approach, we introduce Semantic Composition as a new paradigm to personalise information views. Semantic Composition leverages the type information in the domain model to simplify the user-interface composition process. We also introduce a pattern discovery mechanism that leverages data-mining algorithms to discover correlations between user information needs and different situations.

This thesis represents Bayesian joint audio-visual tracking for the 3D locations of multiple people and a current speaker in a real conference environment. To achieve this objective, it focuses on several different research interests, such as acoustic-feature detection, visual-feature detection, a non-linear Bayesian framework, data association, and sensor fusion. As acoustic-feature detection, time-delay-of-arrival~(TDOA) estimation is used for multiple source detection. Localization performance using TDOAs is also analyzed according to different configurations of microphones. As a visual-feature detection, Viola-Jones face detection is used to initialize the locations of unknown multiple objects. Then, a corner feature, based on the results from the Viola-Jones face detection, is used for motion detection for robust objects. Simple point-to-line correspondences between multiple cameras using fundamental matrices are used to determine which features are more robust. As a method for data association and sensor fusion, Monte-Carlo JPDAF and a data association with IPPF~(DA-IPPF) are implemented in the framework of particle filtering. Three different tracking scenarios of acoustic source tracking, visual source tracking, and joint acoustic-visual source tracking are represented using the proposed algorithms. Finally the real-time implementation of this joint acoustic-visual tracking system using a PC, four cameras, and six microphones is addressed with two parts of system implementation and real-time processing.

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Currently, we seek to make the systems more oriented to end-user, reducing the distance between how the user carries out its activities and modeling these in Information Systems in Health, the doctor providing adaptive and personalized forms of use, configuration and control system, based on historical usage, or a profile. To meet these requirements, the project ClinicSpace, prototyped a software architecture that is developed from the point of view of the user (doctor), led to clinical activities, context-aware, based on mobile technologies and pervasive, and uses the techniques of programming end-user. Identify the context that affects an activity within a hospital clinic, and identify ways to express your doctor interest in a particular context, linking context to their daily activities, are the goals of this dissertation. From the identification of some basic elements of context, including the patient, device and environmental resources, was created to describe this ontological domain to be associated with clinical tasks modeled by the doctor. For introduce context associated with the tasks in the architecture ClinicSpace, changed the editing interface tasks of architecture. Moreover, various departments of the subsystems involved have been re-implemented in architecture, highlighting the Access Service Task, which was re-implemented to support the loading of the ontology that describes the tasks and context, and thus provide structures computational (objects Java) to use the Interface Editing Tasks and Context used by the doctor. Load tests were conducted to identify the impact that the solution used in the architecture introduces ClinicSpace allowing refine it and refine it for use by the system. / Atualmente, busca-se deixar os sistemas mais orientados ao usuário-final, diminuindo a distância entre a forma como o usuário realiza suas atividades e a modelagem destas nos Sistemas de Informação em Saúde, disponibilizando ao médico formas adaptativas e personalizadas de utilização, configuração e controle do sistema, baseadas em um histórico de uso ou em um perfil. Para atender tais requisitos, o projeto ClinicSpace prototipa uma arquitetura de software que é desenvolvida sob o ponto de vista do usuário (médico), orientada às atividades clínicas, consciente do contexto, baseada em tecnologias móveis e pervasivas, e utiliza técnicas da programação do usuário-final. Identificar o contexto que afeta uma atividade clinica dentro de um hospital, bem como identificar formas de o médico expressar seu interesse em um determinado contexto, associando contexto às suas atividades diárias, são os objetivos desta dissertação. A partir da identificação de alguns elementos básicos de contexto, entre eles paciente, dispositivo e recursos ambientais, foi criado a descrição ontológica deste domínio para ser associado às tarefas clínicas modeladas pelo médico. Para introduzir contexto associado às tarefas na arquitetura ClinicSpace, foi alterada a interface de edição de tarefas da arquitetura. Além disso, diversos serviços dos subsistemas envolvidos foram re-implementados na arquitetura, destacando-se o Serviço de Acesso a Tarefas, o qual foi re-implementado para suportar o carregamento da ontologia que descreve as tarefas e contexto, e, assim, disponibilizar estruturas computacionais (objetos Java) para utilização pela Interface de Edição de Tarefas e Contexto utilizada pelo médico. Testes de carga foram realizados para identificar o impacto que a solução utilizada introduz na arquitetura ClinicSpace, permitindo refiná-la e melhorá-la para uso pelo sistema.

Made available in DSpace on 2014-12-17T15:47:49Z (GMT). No. of bitstreams: 1 IsanioLAS.pdf: 1324306 bytes, checksum: 06b1bb191919f02b1e6524146d71c0d3 (MD5) Previous issue date: 2008-11-10 / Ubiquitous computing systems operate in environments where the available resources significantly change during the system operation, thus requiring adaptive and context aware mechanisms to sense changes in the environment and adapt to new execution contexts. Motivated by this requirement, a framework for developing and executing adaptive context aware applications is proposed. The PACCA framework employs aspect-oriented techniques to modularize the adaptive behavior and to keep apart the application logic from this behavior. PACCA uses abstract aspect concept to provide flexibility by addition of new adaptive concerns that extend the abstract aspect. Furthermore, PACCA has a default aspect model that considers habitual adaptive concerns in ubiquitous applications. It exploits the synergy between aspect-orientation and dynamic composition to achieve context-aware adaptation, guided by predefined policies and aim to allow software modules on demand load making possible better use of mobile devices and yours limited resources. A Development Process for the ubiquitous applications conception is also proposed and presents a set of activities that guide adaptive context-aware developer. Finally, a quantitative study evaluates the approach based on aspects and dynamic composition for the construction of ubiquitous applications based in metrics / Aplica??es para a computa??o ub?qua operam em ambientes onde a disponibilidade de recursos muda significativamente durante a sua opera??o. Tal caracter?stica demanda que aplica??es sejam adaptativas e cientes do seu contexto de execu??o. Visando atender esses requisitos, ? proposto o PACCA (Projeto de Aplica??es Ciente ao Contexto e Adaptativas), um arcabou?o para desenvolvimento e execu??o de aplica??es adaptativas cientes de contexto. O paradigma de orienta??o a aspectos ? usado no PACCA para modularizar o comportamento adaptativo e dissoci?-lo da l?gica da aplica??o. Para prover maior flexibilidade o PACCA utiliza o conceito de aspecto abstrato para permitir a extens?o e adi??o de novos interesses adaptativos, al?m de um modelo de aspectos default que contempla interesses adaptativos comuns a grande parte das aplica??es ub?quas. A orienta??o a aspectos aliada ? composi??o din?mica de software oferece suporte para adapta??o ciente ao contexto, guiada por pol?ticas previamente definidas e tem por objetivo permitir a carga de m?dulos de software sob demanda possibilitando melhor utiliza??o dos recursos limitados de um dispositivo m?vel. Um Processo de Desenvolvimento para a constru??o de aplica??es ub?quas tamb?m ? proposto e visa demonstrar um conjunto de atividades a serem executadas para a concep??o de aplica??es ub?quas. Por fim, ? realizado um estudo quantitativo com o intuito de avaliar com base em m?tricas a abordagem baseada em aspectos e composi??o din?mica para a constru??o de aplica??es ub?quas

Le concept du « smart » envahit de plus en plus notre vie quotidienne. L’exemple type est sans doute le smartphone. Celui-ci est devenu au fil des ans un appareil incontournable. Bientôt, c’est la ville, la voiture, la maison qui seront « intelligentes ». L’intelligence se manifeste par une capacité d’interaction et de prise de décision entre l’environnement et l’utilisateur. Ceci nécessite des informations sur les changements d’états survenus des deux côtés. Les réseaux de capteurs permettent de collecter ces données, de leur appliquer des pré-traitements et de les transmettre aux applications. Ces réseaux de par certaines de leurs caractéristiques se rapprochent de l’intelligence collective, dans le sens, où des entités de faibles capacités se coordonnent automatiquement, sans intervention humaine, de façon décentralisée et distribuée pour accomplir des tâches complexes. Ces méthodes bio-inspirées ont servi à la résolution de plusieurs problèmes, surtout l’optimisation, ce qui nous a encouragé à étudier la possibilité de les utiliser pour les problèmes liés à l’Ambient Assisted Living ou AAL et à la classification automatique de données. L’AAL est un sous-domaine des services dits basés sur le contexte, et a pour objectifs de faciliter la vie des personnes âgées et handicapées dans leurs défis quotidiens. Pour ce faire, il détermine le contexte et, sur cette base, propose divers services. Deux éléments du contexte nous ont intéressé : le handicap et la position. Bien que la détermination de la position (localisation, positionnement) se fasse à l’extérieur des bâtiments avec des précisions très satisfaisantes, elle rencontre plusieurs difficultés à l’intérieur des bâtiments, liées à la propagation des ondes électromagnétiques dans les milieux difficiles, aux coûts des systèmes, à l’interopérabilité, etc. Nos travaux se sont intéressés au positionnement des personnes handicapées à l’intérieur de bâtiments en utilisant un réseau de capteurs afin de déterminer les caractéristiques de l’onde électromagnétique (puissance, temps, angle) pour estimer la position par méthodes géométriques (triangulation, latération), méthodes de fingerprinting (k plus proches voisins), par des filtres baysiens (filtre de Kalman). L’application est d’offrir des services types AAL tel que la navigation. Nous avons élargi la notion de réseau de capteurs pour prendre en compte tout appareil capable d’émettre et de recevoir une onde électromagnétique et se trouvant dans l’environnement. Nous avons aussi appliqué l’algorithme API sur la classification automatique de données. Enfin, nous avons proposé une architecture à middleware pour la localisation indoor. / The concept of « smart » invades more and more our daily life. A typical example is the smartphone, which becames by years an essential device. Soon, it’s the city, the car and the home which will become « smart ». The intelligence is manifested by the ability for the environment to interact and to take decisons in its relationships with users and other environments. This needs information on state changes occurred on both sides. Sensor networks allow to collect these data, to apply on them some pre-processings and to transmit them. Sensor network, towards some of their caracteristics are closed to Swarm Intelligence in the sense that small entities with reduced capababilities can cooperate automatically, in unattended, decentralised and distributed manner in order to accomplish complex tasks. These bio-inspired methods have served as basis for the resolution of many problems, mostly optimization and this insipired us to apply them on problems met in Ambient Assisted Living and on the data clustering problem. AAL is a sub-field of context-aware services, and its goals are to facilitate the everyday life of elderly and disable people. These systems determine the context and then propose different kind of services. We have used two important elements of the context : the position and the disabilty. Although positioning has very good precision outdoor, it faces many challenges in indoor environments due to the electromagnetic wave propagation in harsh conditions, the cost of systems, interoperabilty, etc. Our works have been involved in positioning disabled people in indoor environment by using wireless sensor network for determining the caracteristics of the electromagnetic wave (signal strenght, time, angle) for estimating the position by geometric methods (triangulation, lateration), fingerprinting methods (k-nearest neighbours), baysiens filters (Kalman filter). The application is to offer AAL services like navigation. Therefore we extend the definition of sensor node to take into account any device, in the environment, capable of emiting and receiving a signal. Also, we have studied the possibility of using Pachycondylla Apicalis for data clustering and for indoor localization by casting this last problem as data clustering problem. Finally we have proposed a system based on a middleware architecture.

La diversité des contenus recommandation et la variation des contextes des utilisateurs rendent la prédiction en temps réel des préférences des utilisateurs de plus en plus difficile mettre en place. Toutefois, la plupart des approches existantes n'utilisent que le temps et l'emplacement actuels séparément et ignorent d'autres informations contextuelles sur lesquelles dépendent incontestablement les préférences des utilisateurs (par exemple, la météo, l'occasion). En outre, ils ne parviennent pas considérer conjointement ces informations contextuelles avec les interactions sociales entre les utilisateurs. D'autre part, la résolution de problèmes classiques de recommandation (par exemple, aucun programme de télévision vu par un nouvel utilisateur connu sous le nom du problème de démarrage froid et pas assez d'items co-évalués par d'autres utilisateurs ayant des préférences similaires, connu sous le nom du problème de manque de donnes) est d'importance significative puisque sont attaqués par plusieurs travaux. Dans notre travail de thèse, nous proposons un modèle probabiliste qui permet exploiter conjointement les informations contextuelles actuelles et l'influence sociale afin d'améliorer la recommandation des items. En particulier, le modèle probabiliste vise prédire la pertinence de contenu pour un utilisateur en fonction de son contexte actuel et de son influence sociale. Nous avons considérer plusieurs éléments du contexte actuel des utilisateurs tels que l'occasion, le jour de la semaine, la localisation et la météo. Nous avons utilisé la technique de lissage Laplace afin d'éviter les fortes probabilités. D'autre part, nous supposons que l'information provenant des relations sociales a une influence potentielle sur les préférences des utilisateurs. Ainsi, nous supposons que l'influence sociale dépend non seulement des évaluations des amis mais aussi de la similarité sociale entre les utilisateurs. Les similarités sociales utilisateur-ami peuvent être établies en fonction des interactions sociales entre les utilisateurs et leurs amis (par exemple les recommandations, les tags, les commentaires). Nous proposons alors de prendre en compte l'influence sociale en fonction de la mesure de similarité utilisateur-ami afin d'estimer les préférences des utilisateurs. Nous avons mené une série d'expérimentations en utilisant un ensemble de donnes réelles issues de la plateforme de TV sociale Pinhole. Cet ensemble de donnes inclut les historiques d'accès des utilisateurs-vidéos et les réseaux sociaux des téléspectateurs. En outre, nous collectons des informations contextuelles pour chaque historique d'accès utilisateur-vidéo saisi par le système de formulaire plat. Le système de la plateforme capture et enregistre les dernières informations contextuelles auxquelles le spectateur est confronté en regardant une telle vidéo.Dans notre évaluation, nous adoptons le filtrage collaboratif axé sur le temps, le profil dépendant du temps et la factorisation de la matrice axe sur le réseau social comme tant des modèles de référence. L'évaluation a port sur deux tâches de recommandation. La première consiste sélectionner une liste trie de vidéos. La seconde est la tâche de prédiction de la cote vidéo. Nous avons évalué l'impact de chaque élément du contexte de visualisation dans la performance de prédiction. Nous testons ainsi la capacité de notre modèle résoudre le problème de manque de données et le problème de recommandation de démarrage froid du téléspectateur. Les résultats expérimentaux démontrent que notre modèle surpasse les approches de l'état de l'art fondes sur le facteur temps et sur les réseaux sociaux. Dans les tests des problèmes de manque de donnes et de démarrage froid, notre modèle renvoie des prédictions cohérentes différentes valeurs de manque de données. / Due to the diversity of alternative contents to choose and the change of users' preferences, real-time prediction of users' preferences in certain users' circumstances becomes increasingly hard for recommender systems. However, most existing context-aware approaches use only current time and location separately, and ignore other contextual information on which users' preferences may undoubtedly depend (e.g. weather, occasion). Furthermore, they fail to jointly consider these contextual information with social interactions between users. On the other hand, solving classic recommender problems (e.g. no seen items by a new user known as cold start problem, and no enough co-rated items with other users with similar preference as sparsity problem) is of significance importance since it is drawn by several works. In our thesis work, we propose a context-based approach that leverages jointly current contextual information and social influence in order to improve items recommendation. In particular, we propose a probabilistic model that aims to predict the relevance of items in respect with the user's current context. We considered several current context elements such as time, location, occasion, week day, location and weather. In order to avoid strong probabilities which leads to sparsity problem, we used Laplace smoothing technique. On the other hand, we argue that information from social relationships has potential influence on users' preferences. Thus, we assume that social influence depends not only on friends' ratings but also on social similarity between users. We proposed a social-based model that estimates the relevance of an item in respect with the social influence around the user on the relevance of this item. The user-friend social similarity information may be established based on social interactions between users and their friends (e.g. recommendations, tags, comments). Therefore, we argue that social similarity could be integrated using a similarity measure. Social influence is then jointly integrated based on user-friend similarity measure in order to estimate users' preferences. We conducted a comprehensive effectiveness evaluation on real dataset crawled from Pinhole social TV platform. This dataset includes viewer-video accessing history and viewers' friendship networks. In addition, we collected contextual information for each viewer-video accessing history captured by the plat form system. The platform system captures and records the last contextual information to which the viewer is faced while watching such a video. In our evaluation, we adopt Time-aware Collaborative Filtering, Time-Dependent Profile and Social Network-aware Matrix Factorization as baseline models. The evaluation focused on two recommendation tasks. The first one is the video list recommendation task and the second one is video rating prediction task. We evaluated the impact of each viewing context element in prediction performance. We tested the ability of our model to solve data sparsity and viewer cold start recommendation problems. The experimental results highlighted the effectiveness of our model compared to the considered baselines. Experimental results demonstrate that our approach outperforms time-aware and social network-based approaches. In the sparsity and cold start tests, our approach returns consistently accurate predictions at different values of data sparsity.