Une architecture sensible au contexte pour le développement des systèmes d'administration pour l'enseignement

The increasing use of mobile devices and wireless communication technologies has improved the access to web information systems. However, the development of these systems imposes new challenges mainly due to the heterogeneity of mobile devices, the management of context information, and the complexity of adaptation process. Especially in this case, these systems should be runable on a great number of mobile devices models. In this article, we describe a context-aware architecture that provides solutions to the challenges presented above for the development of education administration systems. Copyright 2009 ACM.

Pervasive multimedia guidance system for special needs passengers on public transport

[EN] This article presents a practical case of a pervasive multimedia guidance system for public transport passengers. In order to provide useful information to passengers, the system is capable of operating and adapting spontaneously to the different parts of a public transport network, using local data communication technologies. The multimedia data provided by the system are highly accessible, and adapt to the passengers' preferences, and are consequently suitable for special needs passengers. To this end, a paradigm of pervasive computing has been applied.

Self-organising coordination systems

Many research fields are pushing the engineering of large-scale, mobile, and open systems towards the adoption of techniques inspired by self-organisation: pervasive computing, but also distributed artificial intelligence, multi-agent systems, social networks, peer-topeer and grid architectures exploit adaptive techniques to make global system properties emerge in spite of the unpredictability of interactions and behaviour. Such a trend is visible also in coordination models and languages, whenever a coordination infrastructure needs to cope with managing interactions in highly dynamic and unpredictable environments. As a consequence, self-organisation can be regarded as a feasible metaphor to define a radically new conceptual coordination framework. The resulting framework defines a novel coordination paradigm, called self-organising coordination, based on the idea of spreading coordination media over the network, and charge them with services to manage interactions based on local criteria, resulting in the emergence of desired and fruitful global coordination properties of the system. Features like topology, locality, time-reactiveness, and stochastic behaviour play a key role in both the definition of such a conceptual framework and the consequent development of self-organising coordination services. According to this framework, the thesis presents several self-organising coordination techniques developed during the PhD course, mainly concerning data distribution in tuplespace-based coordination systems. Some of these techniques have been also implemented in ReSpecT, a coordination language for tuple spaces, based on logic tuples and reactions to events occurring in a tuple space. In addition, the key role played by simulation and formal verification has been investigated, leading to analysing how automatic verification techniques like probabilistic model checking can be exploited in order to formally prove the emergence of desired behaviours when dealing with coordination approaches based on self-organisation. To this end, a concrete case study is presented and discussed.

Body ares networks for human motor assessment

Healthcare, Human Computer Interfaces (HCI), Security and Biometry are the most promising application scenario directly involved in the Body Area Networks (BANs) evolution. Both wearable devices and sensors directly integrated in garments envision a word in which each of us is supervised by an invisible assistant monitoring our health and daily-life activities. New opportunities are enabled because improvements in sensors miniaturization and transmission efficiency of the wireless protocols, that achieved the integration of high computational power aboard independent, energy-autonomous, small form factor devices. Application’s purposes are various: (I) data collection to achieve off-line knowledge discovery; (II) user notification of his/her activities or in case a danger occurs; (III) biofeedback rehabilitation; (IV) remote alarm activation in case the subject need assistance; (V) introduction of a more natural interaction with the surrounding computerized environment; (VI) users identification by physiological or behavioral characteristics. Telemedicine and mHealth [1] are two of the leading concepts directly related to healthcare. The capability to borne unobtrusiveness objects supports users’ autonomy. A new sense of freedom is shown to the user, not only supported by a psychological help but a real safety improvement. Furthermore, medical community aims the introduction of new devices to innovate patient treatments. In particular, the extension of the ambulatory analysis in the real life scenario by proving continuous acquisition. The wide diffusion of emerging wellness portable equipment extended the usability of wearable devices also for fitness and training by monitoring user performance on the working task. The learning of the right execution techniques related to work, sport, music can be supported by an electronic trainer furnishing the adequate aid. HCIs made real the concept of Ubiquitous, Pervasive Computing and Calm Technology introduced in the 1988 by Marc Weiser and John Seeley Brown. They promotes the creation of pervasive environments, enhancing the human experience. Context aware, adaptive and proactive environments serve and help people by becoming sensitive and reactive to their presence, since electronics is ubiquitous and deployed everywhere. In this thesis we pay attention to the integration of all the aspects involved in a BAN development. Starting from the choice of sensors we design the node, configure the radio network, implement real-time data analysis and provide a feedback to the user. We present algorithms to be implemented in wearable assistant for posture and gait analysis and to provide assistance on different walking conditions, preventing falls. Our aim, expressed by the idea to contribute at the development of a non proprietary solutions, driven us to integrate commercial and standard solutions in our devices. We use sensors available on the market and avoided to design specialized sensors in ASIC technologies. We employ standard radio protocol and open source projects when it was achieved. The specific contributions of the PhD research activities are presented and discussed in the following. • We have designed and build several wireless sensor node providing both sensing and actuator capability making the focus on the flexibility, small form factor and low power consumption. The key idea was to develop a simple and general purpose architecture for rapid analysis, prototyping and deployment of BAN solutions. Two different sensing units are integrated: kinematic (3D accelerometer and 3D gyroscopes) and kinetic (foot-floor contact pressure forces). Two kind of feedbacks were implemented: audio and vibrotactile. • Since the system built is a suitable platform for testing and measuring the features and the constraints of a sensor network (radio communication, network protocols, power consumption and autonomy), we made a comparison between Bluetooth and ZigBee performance in terms of throughput and energy efficiency. Test in the field evaluate the usability in the fall detection scenario. • To prove the flexibility of the architecture designed, we have implemented a wearable system for human posture rehabilitation. The application was developed in conjunction with biomedical engineers who provided the audio-algorithms to furnish a biofeedback to the user about his/her stability. • We explored off-line gait analysis of collected data, developing an algorithm to detect foot inclination in the sagittal plane, during walk. • In collaboration with the Wearable Lab – ETH, Zurich, we developed an algorithm to monitor the user during several walking condition where the user carry a load. The remainder of the thesis is organized as follows. Chapter I gives an overview about Body Area Networks (BANs), illustrating the relevant features of this technology and the key challenges still open. It concludes with a short list of the real solutions and prototypes proposed by academic research and manufacturers. The domain of the posture and gait analysis, the methodologies, and the technologies used to provide real-time feedback on detected events, are illustrated in Chapter II. The Chapter III and IV, respectively, shown BANs developed with the purpose to detect fall and monitor the gait taking advantage by two inertial measurement unit and baropodometric insoles. Chapter V reports an audio-biofeedback system to improve balance on the information provided by the use centre of mass. A walking assistant based on the KNN classifier to detect walking alteration on load carriage, is described in Chapter VI.

Introduction to Reservoir Computing Methods

Il documento tratta la famiglia di metodologie di allenamento e sfruttamento delle reti neurali ricorrenti nota sotto il nome di Reservoir Computing. Viene affrontata un'introduzione sul Machine Learning in generale per fornire tutti gli strumenti necessari a comprendere l'argomento. Successivamente, vengono dati dettagli implementativi ed analisi dei vantaggi e punti deboli dei vari approcci, il tutto con supporto di codice ed immagini esplicative. Nel finale vengono tratte conclusioni sugli approcci, su quanto migliorabile e sulle applicazioni pratiche.

Integration of Internet of Things and Cloud computing. A case study on vital signs monitoring.

Il progresso scientifico e le innovazioni tecnologiche nei campi dell'elettronica, informatica e telecomunicazioni, stanno aprendo la strada a nuove visioni e concetti. L'obiettivo della tesi è quello d'introdurre il modello del Cloud computing per rendere possibile l'attuale visione di Internet of Thing. Nel primo capitolo si introduce Ubiquitous computing come un nuovo modo di vedere i computer, cercando di fare chiarezza sulla sua definizione, la sua nascita e fornendo un breve quadro storico. Nel secondo capitolo viene presentata la visione di Internet of Thing (Internet delle “cose”) che si avvale di concetti e di problematiche in parte già considerate con Ubiquitous computing. Internet of Thing è una visione in cui la rete Internet viene estesa agli oggetti di tutti i giorni. Tracciare la posizione degli oggetti, monitorare pazienti da remoto, rilevare dati ambientali sono solo alcuni esempi. Per realizzare questo tipo di applicazioni le tecnologie wireless sono da considerare necessarie, sebbene questa visione non assuma nessuna specifica tecnologia di comunicazione. Inoltre, anche schede di sviluppo possono agevolare la prototipazione di tali applicazioni. Nel terzo capitolo si presenta Cloud computing come modello di business per utilizzare su richiesta risorse computazionali. Nel capitolo, vengono inizialmente descritte le caratteristiche principali e i vari tipi di modelli di servizio, poi viene argomentato il ruolo che i servizi di Cloud hanno per Internet of Thing. Questo modello permette di accelerare lo sviluppo e la distribuzione di applicazioni di Internet of Thing, mettendo a disposizione capacità di storage e di calcolo per l'elaborazione distribuita dell'enorme quantità di dati prodotta da sensori e dispositivi vari. Infine, nell'ultimo capitolo viene considerato, come esempio pratico, l'integrazione di tecnologie di Cloud computing in una applicazione IoT. Il caso di studio riguarda il monitoraggio remoto dei parametri vitali, considerando Raspberry Pi e la piattaforma e-Health sviluppata da Cooking Hacks per lo sviluppo di un sistema embedded, e utilizzando PubNub come servizio di Cloud per distribuire i dati ottenuti dai sensori. Il caso di studio metterà in evidenza sia i vantaggi sia le eventuali problematiche che possono scaturire utilizzando servizi di Cloud in applicazioni IoT.

Towards Self-Learning Radio-Based Localization Systems

Location-awareness indoors will be an inseparable feature of mobile services/applications in future wireless networks. Its current ubiquitous availability is still obstructed by technological challenges and privacy issues. We propose an innovative approach towards the concept of indoor positioning with main goal to develop a system that is self-learning and able to adapt to various radio propagation environments. The approach combines estimation of propagation conditions, subsequent appropriate channel modelling and optimisation feedback to the used positioning algorithm. Main advantages of the proposal are decreased system set-up effort, automatic re-calibration and increased precision.

Das Internet der Dinge unter dem Aspekt der Selbststeuerung-Ein Überblick

In den letzten Jahren wurde die Vision einer Welt smarter Alltagsgegenstände unter den Begriffen wie Ubiquitous Computing, Pervasive Computing und Ambient Intelligence in der Öffentlichkeit wahrgenommen. Die smarten Gegenstände sollen mit digitaler Logik, Sensorik und der Möglichkeit zur Vernetzung ausgestattet werden. Somit bilden sie ein „Internet der Dinge“, in dem der Computer als eigenständiges Gerät verschwindet und in den Objekten der physischen Welt aufgeht. Während auf der einen Seite die Vision des „Internet der Dinge“ durch die weiter anhaltenden Fortschritte in der Informatik, Mikroelektronik, Kommunikationstechnik und Materialwissenschaft zumindest aus technischer Sicht wahrscheinlich mittelfristig realisiert werden kann, müssen auf der anderen Seite die damit zusammenhängenden ökonomischen, rechtlichen und sozialen Fragen geklärt werden. Zur Weiterentwicklung und Realisierung der Vision des „Internet der Dinge“ wurde erstmals vom AutoID-Center das EPC-Konzept entwickelt, welches auf globale netzbasierte Informationsstandards setzt und heute von EPCglobal weiterentwickelt und umgesetzt wird. Der EPC erlaubt es, umfassende Produktinformationen über das Internet zur Verfügung zu stellen. Die RFID-Technologie stellt dabei die wichtigste Grundlage des „Internet der Dinge“ dar, da sie die Brücke zwischen der physischen Welt der Produkte und der virtuellen Welt der digitalen Daten schlägt. Die Objekte, die mit RFID-Transpondern ausgestattet sind, können miteinander kommunizieren und beispielsweise ihren Weg durch die Prozesskette finden. So können sie dann mit Hilfe der auf den RFID-Transpondern gespeicherten Informationen Förderanlagen oder sonstige Maschinen ohne menschliches Eingreifen selbstständig steuern.

Improving Speech Interaction in Vehicles Using Context-Aware Information through A SCXML Framework

Speech Technologies can provide important benefits for the development of more usable and safe in-vehicle human-machine interactive systems (HMIs). However mainly due robustness issues, the use of spoken interaction can entail important distractions to the driver. In this challenging scenario, while speech technologies are evolving, further research is necessary to explore how they can be complemented with both other modalities (multimodality) and information from the increasing number of available sensors (context-awareness). The perceived quality of speech technologies can significantly be increased by implementing such policies, which simply try to make the best use of all the available resources; and the in vehicle scenario is an excellent test-bed for this kind of initiatives. In this contribution we propose an event-based HMI design framework which combines context modelling and multimodal interaction using a W3C XML language known as SCXML. SCXML provides a general process control mechanism that is being considered by W3C to improve both voice interaction (VoiceXML) and multimodal interaction (MMI). In our approach we try to anticipate and extend these initiatives presenting a flexible SCXML-based approach for the design of a wide range of multimodal context-aware HMI in-vehicle interfaces. The proposed framework for HMI design and specification has been implemented in an automotive OSGi service platform, and it is being used and tested in the Spanish research project MARTA for the development of several in-vehicle interactive applications.

Explicitly Context-Aware Publish/Subscribe with Context-Invariant Subscriptions

Although context could be exploited to improve the performance, elasticity and adaptation in most distributed systems that adopt the publish/subscribe (P/S) model of communication, only very few works have explored domains with highly dynamic context, whereas most adopted models are context agnostic. In this paper, we present the key design principles underlying a novel context-aware content-based P/S (CA-CBPS) model of communication, where the context is explicitly managed, focusing on the minimization of network overhead in domains with recurrent context changes thanks to contextual scoping. We highlight how we dealt with the main shortcomings of most of the current approaches. Our research is some of the first to study the problem of explicitly introducing context-awareness into the P/S model to capitalize on contextual information. The envisioned CA-CBPS middleware enables the cloud ecosystem of services to communicate very efficiently, in a decoupled, but contextually scoped fashion.

Generating context-aware recommendations using banking data in a mobile recommender system

The increasing adoption of smartphones by the society has created a new area of research in recommender systems. This new domain is based on using location and context-awareness to provide personalization. This paper describes a model to generate context-aware recommendations for mobile recommender systems using banking data in order to recommend places where the bank customers have previously spent their money. In this work we have used real data provided by a well know Spanish bank. The mobile prototype deployed in the bank Labs environment was evaluated in a survey among 100 users with good results regarding usefulness and effectiveness. The results also showed that test users had a high confidence in a recommender system based on real banking data.

Contribution to proactivity in mobile context-aware recommender systems

Los sistemas de recomendación son potentes herramientas de filtrado de información que permiten a usuarios solicitar sugerencias sobre ítems que cubran sus necesidades. Tradicionalmente estas recomendaciones han estado basadas en opiniones de los mismos, así como en datos obtenidos de su consumo histórico o comportamiento en el propio sistema. Sin embargo, debido a la gran penetración y uso de los dispositivos móviles en nuestra sociedad, han surgido nuevas oportunidades en el campo de los sistemas de recomendación móviles gracias a la información contextual que se puede obtener sobre la localización o actividad de los usuarios. Debido a este estilo de vida en el que todo tiende a la movilidad y donde los usuarios están plenamente interconectados, la información contextual no sólo es física, sino que también adquiere una dimensión social. Todo esto ha dado lugar a una nueva área de investigación relacionada con los Sistemas de Recomendación Basados en Contexto (CARS) móviles donde se busca incrementar el nivel de personalización de las recomendaciones al usar dicha información. Por otro lado, este nuevo escenario en el que los usuarios llevan en todo momento un terminal móvil consigo abre la puerta a nuevas formas de recomendar. Sustituir el tradicional patrón de uso basado en petición-respuesta para evolucionar hacia un sistema proactivo es ahora posible. Estos sistemas deben identificar el momento más adecuado para generar una recomendación sin una petición explícita del usuario, siendo para ello necesario analizar su contexto. Esta tesis doctoral propone un conjunto de modelos, algoritmos y métodos orientados a incorporar proactividad en CARS móviles, a la vez que se estudia el impacto que este tipo de recomendaciones tienen en la experiencia de usuario con el fin de extraer importantes conclusiones sobre "qué", "cuándo" y "cómo" se debe notificar proactivamente. Con este propósito, se comienza planteando una arquitectura general para construir CARS móviles en escenarios sociales. Adicionalmente, se propone una nueva forma de representar el proceso de recomendación a través de una interfaz REST, lo que permite crear una arquitectura independiente de dispositivo y plataforma. Los detalles de su implementación tras su puesta en marcha en el entorno bancario español permiten asimismo validar el sistema construido. Tras esto se presenta un novedoso modelo para incorporar proactividad en CARS móviles. Éste muestra las ideas principales que permiten analizar una situación para decidir cuándo es apropiada una recomendación proactiva. Para ello se presentan algoritmos que establecen relaciones entre lo propicia que es una situación y cómo esto influye en los elementos a recomendar. Asimismo, para demostrar la viabilidad de este modelo se describe su aplicación a un escenario de recomendación para herramientas de creación de contenidos educativos. Siguiendo el modelo anterior, se presenta el diseño e implementación de nuevos interfaces móviles de usuario para recomendaciones proactivas, así como los resultados de su evaluación entre usuarios, lo que aportó importantes conclusiones para identificar cuáles son los factores más relevantes a considerar en el diseño de sistemas proactivos. A raíz de los resultados anteriores, el último punto de esta tesis presenta una metodología para calcular cuán apropiada es una situación de cara a recomendar de manera proactiva siguiendo el modelo propuesto. Como conclusión, se describe la validación llevada a cabo tras la aplicación de la arquitectura, modelo de recomendación y métodos descritos en este trabajo en una red social de aprendizaje europea. Finalmente, esta tesis discute las conclusiones obtenidas a lo largo de la extensa investigación llevada a cabo, y que ha propiciado la consecución de una buena base teórica y práctica para la creación de sistemas de recomendación móviles proactivos basados en información contextual. ABSTRACT Recommender systems are powerful information filtering tools which offer users personalized suggestions about items whose aim is to satisfy their needs. Traditionally the information used to make recommendations has been based on users’ ratings or data on the item’s consumption history and transactions carried out in the system. However, due to the remarkable growth in mobile devices in our society, new opportunities have arisen to improve these systems by implementing them in ubiquitous environments which provide rich context-awareness information on their location or current activity. Because of this current all-mobile lifestyle, users are socially connected permanently, which allows their context to be enhanced not only with physical information, but also with a social dimension. As a result of these novel contextual data sources, the advent of mobile Context-Aware Recommender Systems (CARS) as a research area has appeared to improve the level of personalization in recommendation. On the other hand, this new scenario in which users have their mobile devices with them all the time offers the possibility of looking into new ways of making recommendations. Evolving the traditional user request-response pattern to a proactive approach is now possible as a result of this rich contextual scenario. Thus, the key idea is that recommendations are made to the user when the current situation is appropriate, attending to the available contextual information without an explicit user request being necessary. This dissertation proposes a set of models, algorithms and methods to incorporate proactivity into mobile CARS, while the impact of proactivity is studied in terms of user experience to extract significant outcomes as to "what", "when" and "how" proactive recommendations have to be notified to users. To this end, the development of this dissertation starts from the proposal of a general architecture for building mobile CARS in scenarios with rich social data along with a new way of managing a recommendation process through a REST interface to make this architecture multi-device and cross-platform compatible. Details as regards its implementation and evaluation in a Spanish banking scenario are provided to validate its usefulness and user acceptance. After that, a novel model is presented for proactivity in mobile CARS which shows the key ideas related to decide when a situation warrants a proactive recommendation by establishing algorithms that represent the relationship between the appropriateness of a situation and the suitability of the candidate items to be recommended. A validation of these ideas in the area of e-learning authoring tools is also presented. Following the previous model, this dissertation presents the design and implementation of new mobile user interfaces for proactive notifications. The results of an evaluation among users testing these novel interfaces is also shown to study the impact of proactivity in the user experience of mobile CARS, while significant factors associated to proactivity are also identified. The last stage of this dissertation merges the previous outcomes to design a new methodology to calculate the appropriateness of a situation so as to incorporate proactivity into mobile CARS. Additionally, this work provides details about its validation in a European e-learning social network in which the whole architecture and proactive recommendation model together with its methods have been implemented. Finally, this dissertation opens up a discussion about the conclusions obtained throughout this research, resulting in useful information from the different design and implementation stages of proactive mobile CARS.