Ami-deu : un cadre sémantique pour des applications adaptables dans des environnements intelligents

Cette thèse vise à étendre l’utilisation de l'Internet des objets (IdO) en facilitant le développement d’applications par des personnes non experts en développement logiciel. La thèse propose une nouvelle approche pour augmenter la sémantique des applications d’IdO et l’implication des experts du domaine dans le développement d’applications sensibles au contexte. Notre approche permet de gérer le contexte changeant de l’environnement et de générer des applications qui s’exécutent dans plusieurs environnements intelligents pour fournir des actions requises dans divers contextes. Notre approche est mise en œuvre dans un cadriciel (AmI-DEU) qui inclut les composants pour le développement d’applications IdO. AmI-DEU intègre les services d’environnement, favorise l’interaction de l’utilisateur et fournit les moyens de représenter le domaine d’application, le profil de l’utilisateur et les intentions de l’utilisateur. Le cadriciel permet la définition d’applications IoT avec une intention d’activité autodécrite qui contient les connaissances requises pour réaliser l’activité. Ensuite, le cadriciel génère Intention as a Context (IaaC), qui comprend une intention d’activité autodécrite avec des connaissances colligées à évaluer pour une meilleure adaptation dans des environnements intelligents. La sémantique de l’AmI-DEU est basée sur celle du ContextAA (Context-Aware Agents) – une plateforme pour fournir une connaissance du contexte dans plusieurs environnements. Le cadriciel effectue une compilation des connaissances par des règles et l'appariement sémantique pour produire des applications IdO autonomes capables de s’exécuter en ContextAA. AmI- DEU inclut également un outil de développement visuel pour le développement et le déploiement rapide d'applications sur ContextAA. L'interface graphique d’AmI-DEU adopte la métaphore du flux avec des aides visuelles pour simplifier le développement d'applications en permettant des définitions de règles étape par étape. Dans le cadre de l’expérimentation, AmI-DEU comprend un banc d’essai pour le développement d’applications IdO. Les résultats expérimentaux montrent une optimisation sémantique potentielle des ressources pour les applications IoT dynamiques dans les maisons intelligentes et les villes intelligentes. Notre approche favorise l'adoption de la technologie pour améliorer le bienêtre et la qualité de vie des personnes. Cette thèse se termine par des orientations de recherche que le cadriciel AmI-DEU dévoile pour réaliser des environnements intelligents omniprésents fournissant des adaptations appropriées pour soutenir les intentions des personnes.Abstract: This thesis aims at expanding the use of the Internet of Things (IoT) by facilitating the development of applications by people who are not experts in software development. The thesis proposes a new approach to augment IoT applications’ semantics and domain expert involvement in context-aware application development. Our approach enables us to manage the changing environment context and generate applications that run in multiple smart environments to provide required actions in diverse settings. Our approach is implemented in a framework (AmI-DEU) that includes the components for IoT application development. AmI- DEU integrates environment services, promotes end-user interaction, and provides the means to represent the application domain, end-user profile, and end-user intentions. The framework enables the definition of IoT applications with a self-described activity intention that contains the required knowledge to achieve the activity. Then, the framework generates Intention as a Context (IaaC), which includes a self-described activity intention with compiled knowledge to be assessed for augmented adaptations in smart environments. AmI-DEU framework semantics adopts ContextAA (Context-Aware Agents) – a platform to provide context-awareness in multiple environments. The framework performs a knowledge compilation by rules and semantic matching to produce autonomic IoT applications to run in ContextAA. AmI-DEU also includes a visual tool for quick application development and deployment to ContextAA. The AmI-DEU GUI adopts the flow metaphor with visual aids to simplify developing applications by allowing step-by-step rule definitions. As part of the experimentation, AmI-DEU includes a testbed for IoT application development. Experimental results show a potential semantic optimization for dynamic IoT applications in smart homes and smart cities. Our approach promotes technology adoption to improve people’s well-being and quality of life. This thesis concludes with research directions that the AmI-DEU framework uncovers to achieve pervasive smart environments providing suitable adaptations to support people’s intentions

Theoretical Interpretations and Applications of Radial Basis Function Networks

Medical applications usually used Radial Basis Function Networks just as Artificial Neural Networks. However, RBFNs are Knowledge-Based Networks that can be interpreted in several way: Artificial Neural Networks, Regularization Networks, Support Vector Machines, Wavelet Networks, Fuzzy Controllers, Kernel Estimators, Instanced-Based Learners. A survey of their interpretations and of their corresponding learning algorithms is provided as well as a brief survey on dynamic learning algorithms. RBFNs' interpretations can suggest applications that are particularly interesting in medical domains

Towards high-level fuzzy control specifications for building automation systems

UID/CEC/50021/2019. Grant: FCT/MCTES TUBITAK/0008/2014 FCT/DAAD - 2018/2019 (Poc. DAAD 441.00). UID/EMS/50022/2019. project TIN2015-73566-JIN and by the Consellería de Cultura, Educación e Ordenación Universitaria (accreditation 2016–2019, ED431G/08 and reference competitive group 2019–2021, ED431C 2018/29) and the European Regional Development Fund (ERDF).The control logic underlying building automation systems has consisted, traditionally, of embedded discrete programs created using either low-level or proprietary scripting languages, or using general purpose fourth-generation visual languages like Simulink. It is also well known that programs developed in this way are hard to evolve, test, and maintain. These difficulties are intensified when continuous control problems have to be tackled or when the actuation must vary continually subject to the sensor inputs. Such is the case in day-lighting or occupancy-based control applications. In this paper, we propose a declarative high-level Domain-Specific Language that aims to reduce the effort required to specify the control logic of building automation systems. Our language combines fuzzy logic and temporal logic, enabling to define the behaviour in terms of domain abstractions. Finally, the approach has been validated in two ways: (i) in a case study that simulates the control system of an automated office room and (ii) by means of an empirical study to confirm usability (with a System Usability Scale questionnaire) and effectiveness, here regarded from the perspective of correctness, of the proposed language with respect to a well-known language like Simulink.authorsversionpublishe

Logic-based Technologies for Intelligent Systems: State of the Art and Perspectives

Together with the disruptive development of modern sub-symbolic approaches to artificial intelligence (AI), symbolic approaches to classical AI are re-gaining momentum, as more and more researchers exploit their potential to make AI more comprehensible, explainable, and therefore trustworthy. Since logic-based approaches lay at the core of symbolic AI, summarizing their state of the art is of paramount importance now more than ever, in order to identify trends, benefits, key features, gaps, and limitations of the techniques proposed so far, as well as to identify promising research perspectives. Along this line, this paper provides an overview of logic-based approaches and technologies by sketching their evolution and pointing out their main application areas. Future perspectives for exploitation of logic-based technologies are discussed as well, in order to identify those research fields that deserve more attention, considering the areas that already exploit logic-based approaches as well as those that are more likely to adopt logic-based approaches in the future

Handling Real-World Context Awareness, Uncertainty and Vagueness in Real-Time Human Activity Tracking and Recognition with a Fuzzy Ontology-Based Hybrid Method

Human activity recognition is a key task in ambient intelligence applications to achieve proper ambient assisted living. There has been remarkable progress in this domain, but some challenges still remain to obtain robust methods. Our goal in this work is to provide a system that allows the modeling and recognition of a set of complex activities in real life scenarios involving interaction with the environment. The proposed framework is a hybrid model that comprises two main modules: a low level sub-activity recognizer, based on data-driven methods, and a high-level activity recognizer, implemented with a fuzzy ontology to include the semantic interpretation of actions performed by users. The fuzzy ontology is fed by the sub-activities recognized by the low level data-driven component and provides fuzzy ontological reasoning to recognize both the activities and their influence in the environment with semantics. An additional benefit of the approach is the ability to handle vagueness and uncertainty in the knowledge-based module, which substantially outperforms the treatment of incomplete and/or imprecise data with respect to classic crisp ontologies. We validate these advantages with the public CAD-120 dataset (Cornell Activity Dataset), achieving an accuracy of 90.1% and 91.07% for low-level and high-level activities, respectively. This entails an improvement over fully data-driven or ontology-based approaches.This work was funded by TUCS (Turku Centre for Computer Science), Finnish Cultural Foundation, Nokia Foundation, Google Anita Borg Scholarship, CEI BioTIC Project CEI2013-P-3, Contrato-Programa of Faculty of Education, Economy and Technology of Ceuta and Project TIN2012-30939 from National I+D Research Program (Spain). We also thank Fernando Bobillo for his support with FuzzyOWL and FuzzyDL tools