In order to facilitate software interaction and increase user satisfaction, various research efforts have tackled the problem of software customization by modeling the user’s goals, skills, and preferences. In this thesis, we focus on run-time solutions for adapting various interface and interaction aspects of software. From an intelligent agent’s perspective, the system views this customization problem as a decision-theoretic planning problem under uncertainty about the user. We propose a methodological framework for developing intelligent software interaction and assistance. This framework has been instantiated in various case studies which are reviewed in the thesis. Through efforts of data collection experiments to learn model parameters, simulation experiments to assess system feasibility and adaptivity, and usability testing to assess user receptiveness, our case studies show that our approach can effectively carry out customizations according to different user preferences and adapt to changing preferences over time.

In order to facilitate software interaction and increase user satisfaction, various research efforts have tackled the problem of software customization by modeling the user’s goals, skills, and preferences. In this thesis, we focus on run-time solutions for adapting various interface and interaction aspects of software. From an intelligent agent’s perspective, the system views this customization problem as a decision-theoretic planning problem under uncertainty about the user. We propose a methodological framework for developing intelligent software interaction and assistance. This framework has been instantiated in various case studies which are reviewed in the thesis. Through efforts of data collection experiments to learn model parameters, simulation experiments to assess system feasibility and adaptivity, and usability testing to assess user receptiveness, our case studies show that our approach can effectively carry out customizations according to different user preferences and adapt to changing preferences over time.

La thèse s'inscrit dans le domaine de l'ingénierie de l'interaction Homme-Machine. Elle porte sur la plasticité des Interfaces Homme-Machine (IHM), c'est-à-dire leur capacité à s'adapter à leur contexte d'usage dans le respect des bonnes propriétés pour l'humain. Plus précisément, l'objet d'étude est la transformation de l'IHM pour accélérer l'interaction. / Research contributes to the engineering of Human Computer Interaction. It deals with the plasticity property, i.e. the ability of User Interfaces to withstand variations of the context of use while preserving user centered properties. More specifically, the object under study is the UI transformation for speeding up interaction.

Campus universitários são ambientes compostos de recursos e pessoas que utilizam os tais. Um dos principais recursos utilizados pela comunidade de um campus são os objetos de aprendizagem. Tais objetos existem de maneira abundante, espalhados no ambiente ou concentrados em um único local. Entretanto, a abundancia desses objetos faz com que uma pessoa sinta-se cognitivamente cansada ao ter que analisar vários objetos e selecionar apenas alguns. Esse cansaço cognitivo acaba levando a pessoa a escolher um conjunto de objetos de aprendizagem que não satisfarão suas necessidades e interesses da melhor maneira possível. A computação evoluiu de grandes mainframes a pequenos computadores espalhados em um ambiente. Hoje é possível a existência de ambientes pervasivos, onde os recursos computacionais estão sempre presentes e agindo de forma invisível ao usuário. Tais ambientes tornam possível o acompanhamento das atividades do usuário, provendo informações contextuais que podem ser utilizadas para ajudar a seleção dos melhores recursos (ex. objetos de aprendizagem, restaurantes, salas de aula) à determinada pessoa. A localização é uma informação contextual de grande importância na seleção de tais recursos. Tal informação pode ser facilmente obtida através do sinal de GPS do dispositivo móvel de um usuário e utilizada em conjunto com os interesses do usuário para recomendar os recursos próximos que melhor atenderão ao mesmo. Neste contexto este trabalho descreve uma abordagem para recomendar objetos de aprendizagem físicos ou virtuais que estejam relacionados aos prédios próximos a atual localização do usuário. Para executar tal tarefa é descrito um sistema de recomendação que utiliza a informação de localização, obtida através do dispositivo móvel do usuário, combinada à informações do perfil do usuário, dos objetos de aprendizagem relacionados aos prédios e informações tecnológicas do dispositivo para instanciar um modelo ontológico de contexto. Após instanciado o modelo são utilizadas regras semânticas, escritas em forma de antecedente e consequente, que fazem uma correspondência entre os interesses do usuário e o domínio de conhecimento do objeto de aprendizagem para filtrar os objetos próximos ao usuário. De posse desses objetos recomendados o sistema os apresenta em uma interface adaptativa que mostra a localização tanto dos objetos quanto do usuário. Para validar a abordagem apresentada é desenvolvido um estudo de caso onde as regras semânticas de recomendação são executadas sobre o modelo ontológico desenvolvido. O resultado gerado por tais regras é um conjunto de pares (usuário, objeto de aprendizagem recomendado) e prova a validade da abordagem. / University campus are environments composed of resources and people who use them. One of the main resources used by a campus community are learning objects. Such objects are abundantly even scattered in the environment or concentrated in one location. However the abundance of such objects makes a person feel cognitively tired when having to analyze various objects and select just a few of them. This cognitive fatigue eventually leads the person to choose a set of learning objects that do not meet their needs and interests in the best possible way. Computing has evolved from large mainframe to small computers scattered in an environment. Today it is possible the existence of pervasive environments where computational resources are always present and acting in a manner invisible to the user. Such environments make it possible to monitor user activities, providing contextual information that can be used to help select the best resources (e.g. learning objects, restaurants, classrooms) to a particular person. The location is a contextual information of great importance in the selection of such resources. Such information can be easily obtained through the GPS signal from a mobile device and used with the user’s interests to recommend the nearby resources that best attend his needs and interests. In this context, this work describes an approach to recommend physical or virtual learning objects that are related to buildings near the user’s current location. To accomplish such a task we described a recommender system that uses the location information, obtained through the user's mobile device, combined with information from the user’s profile, learning objects related to buildings and technological information from the device to instantiate an ontological context model. Once the model is instantiated we used semantic rules, written in the form of antecedent and consequent, to make a match between the user’s interests and the knowledge domain of the learning object in order filter the user’s nearby objects. With such recommended objects, the system presents them in an adaptive interface that shows both the object and the user location. To validate the presented approach we developed a case study where the recommendation semantic rules are executed on the developed ontological model. The income generated by such rules is a set of pairs (user, recommended learning object) and proves the validity of the approach.

Campus universitários são ambientes compostos de recursos e pessoas que utilizam os tais. Um dos principais recursos utilizados pela comunidade de um campus são os objetos de aprendizagem. Tais objetos existem de maneira abundante, espalhados no ambiente ou concentrados em um único local. Entretanto, a abundancia desses objetos faz com que uma pessoa sinta-se cognitivamente cansada ao ter que analisar vários objetos e selecionar apenas alguns. Esse cansaço cognitivo acaba levando a pessoa a escolher um conjunto de objetos de aprendizagem que não satisfarão suas necessidades e interesses da melhor maneira possível. A computação evoluiu de grandes mainframes a pequenos computadores espalhados em um ambiente. Hoje é possível a existência de ambientes pervasivos, onde os recursos computacionais estão sempre presentes e agindo de forma invisível ao usuário. Tais ambientes tornam possível o acompanhamento das atividades do usuário, provendo informações contextuais que podem ser utilizadas para ajudar a seleção dos melhores recursos (ex. objetos de aprendizagem, restaurantes, salas de aula) à determinada pessoa. A localização é uma informação contextual de grande importância na seleção de tais recursos. Tal informação pode ser facilmente obtida através do sinal de GPS do dispositivo móvel de um usuário e utilizada em conjunto com os interesses do usuário para recomendar os recursos próximos que melhor atenderão ao mesmo. Neste contexto este trabalho descreve uma abordagem para recomendar objetos de aprendizagem físicos ou virtuais que estejam relacionados aos prédios próximos a atual localização do usuário. Para executar tal tarefa é descrito um sistema de recomendação que utiliza a informação de localização, obtida através do dispositivo móvel do usuário, combinada à informações do perfil do usuário, dos objetos de aprendizagem relacionados aos prédios e informações tecnológicas do dispositivo para instanciar um modelo ontológico de contexto. Após instanciado o modelo são utilizadas regras semânticas, escritas em forma de antecedente e consequente, que fazem uma correspondência entre os interesses do usuário e o domínio de conhecimento do objeto de aprendizagem para filtrar os objetos próximos ao usuário. De posse desses objetos recomendados o sistema os apresenta em uma interface adaptativa que mostra a localização tanto dos objetos quanto do usuário. Para validar a abordagem apresentada é desenvolvido um estudo de caso onde as regras semânticas de recomendação são executadas sobre o modelo ontológico desenvolvido. O resultado gerado por tais regras é um conjunto de pares (usuário, objeto de aprendizagem recomendado) e prova a validade da abordagem. / University campus are environments composed of resources and people who use them. One of the main resources used by a campus community are learning objects. Such objects are abundantly even scattered in the environment or concentrated in one location. However the abundance of such objects makes a person feel cognitively tired when having to analyze various objects and select just a few of them. This cognitive fatigue eventually leads the person to choose a set of learning objects that do not meet their needs and interests in the best possible way. Computing has evolved from large mainframe to small computers scattered in an environment. Today it is possible the existence of pervasive environments where computational resources are always present and acting in a manner invisible to the user. Such environments make it possible to monitor user activities, providing contextual information that can be used to help select the best resources (e.g. learning objects, restaurants, classrooms) to a particular person. The location is a contextual information of great importance in the selection of such resources. Such information can be easily obtained through the GPS signal from a mobile device and used with the user’s interests to recommend the nearby resources that best attend his needs and interests. In this context, this work describes an approach to recommend physical or virtual learning objects that are related to buildings near the user’s current location. To accomplish such a task we described a recommender system that uses the location information, obtained through the user's mobile device, combined with information from the user’s profile, learning objects related to buildings and technological information from the device to instantiate an ontological context model. Once the model is instantiated we used semantic rules, written in the form of antecedent and consequent, to make a match between the user’s interests and the knowledge domain of the learning object in order filter the user’s nearby objects. With such recommended objects, the system presents them in an adaptive interface that shows both the object and the user location. To validate the presented approach we developed a case study where the recommendation semantic rules are executed on the developed ontological model. The income generated by such rules is a set of pairs (user, recommended learning object) and proves the validity of the approach.

Campus universitários são ambientes compostos de recursos e pessoas que utilizam os tais. Um dos principais recursos utilizados pela comunidade de um campus são os objetos de aprendizagem. Tais objetos existem de maneira abundante, espalhados no ambiente ou concentrados em um único local. Entretanto, a abundancia desses objetos faz com que uma pessoa sinta-se cognitivamente cansada ao ter que analisar vários objetos e selecionar apenas alguns. Esse cansaço cognitivo acaba levando a pessoa a escolher um conjunto de objetos de aprendizagem que não satisfarão suas necessidades e interesses da melhor maneira possível. A computação evoluiu de grandes mainframes a pequenos computadores espalhados em um ambiente. Hoje é possível a existência de ambientes pervasivos, onde os recursos computacionais estão sempre presentes e agindo de forma invisível ao usuário. Tais ambientes tornam possível o acompanhamento das atividades do usuário, provendo informações contextuais que podem ser utilizadas para ajudar a seleção dos melhores recursos (ex. objetos de aprendizagem, restaurantes, salas de aula) à determinada pessoa. A localização é uma informação contextual de grande importância na seleção de tais recursos. Tal informação pode ser facilmente obtida através do sinal de GPS do dispositivo móvel de um usuário e utilizada em conjunto com os interesses do usuário para recomendar os recursos próximos que melhor atenderão ao mesmo. Neste contexto este trabalho descreve uma abordagem para recomendar objetos de aprendizagem físicos ou virtuais que estejam relacionados aos prédios próximos a atual localização do usuário. Para executar tal tarefa é descrito um sistema de recomendação que utiliza a informação de localização, obtida através do dispositivo móvel do usuário, combinada à informações do perfil do usuário, dos objetos de aprendizagem relacionados aos prédios e informações tecnológicas do dispositivo para instanciar um modelo ontológico de contexto. Após instanciado o modelo são utilizadas regras semânticas, escritas em forma de antecedente e consequente, que fazem uma correspondência entre os interesses do usuário e o domínio de conhecimento do objeto de aprendizagem para filtrar os objetos próximos ao usuário. De posse desses objetos recomendados o sistema os apresenta em uma interface adaptativa que mostra a localização tanto dos objetos quanto do usuário. Para validar a abordagem apresentada é desenvolvido um estudo de caso onde as regras semânticas de recomendação são executadas sobre o modelo ontológico desenvolvido. O resultado gerado por tais regras é um conjunto de pares (usuário, objeto de aprendizagem recomendado) e prova a validade da abordagem. / University campus are environments composed of resources and people who use them. One of the main resources used by a campus community are learning objects. Such objects are abundantly even scattered in the environment or concentrated in one location. However the abundance of such objects makes a person feel cognitively tired when having to analyze various objects and select just a few of them. This cognitive fatigue eventually leads the person to choose a set of learning objects that do not meet their needs and interests in the best possible way. Computing has evolved from large mainframe to small computers scattered in an environment. Today it is possible the existence of pervasive environments where computational resources are always present and acting in a manner invisible to the user. Such environments make it possible to monitor user activities, providing contextual information that can be used to help select the best resources (e.g. learning objects, restaurants, classrooms) to a particular person. The location is a contextual information of great importance in the selection of such resources. Such information can be easily obtained through the GPS signal from a mobile device and used with the user’s interests to recommend the nearby resources that best attend his needs and interests. In this context, this work describes an approach to recommend physical or virtual learning objects that are related to buildings near the user’s current location. To accomplish such a task we described a recommender system that uses the location information, obtained through the user's mobile device, combined with information from the user’s profile, learning objects related to buildings and technological information from the device to instantiate an ontological context model. Once the model is instantiated we used semantic rules, written in the form of antecedent and consequent, to make a match between the user’s interests and the knowledge domain of the learning object in order filter the user’s nearby objects. With such recommended objects, the system presents them in an adaptive interface that shows both the object and the user location. To validate the presented approach we developed a case study where the recommendation semantic rules are executed on the developed ontological model. The income generated by such rules is a set of pairs (user, recommended learning object) and proves the validity of the approach.

Cuando las personas interactúan con los ordenadores, hay mucha información que no se proporciona a propósito. Mediante el estudio de estas interacciones implícitas es posible entender qué características de la interfaz de usuario son beneficiosas (o no), derivando así en implicaciones para el diseño de futuros sistemas interactivos. La principal ventaja de aprovechar datos implícitos del usuario en aplicaciones informáticas es que cualquier interacción con el sistema puede contribuir a mejorar su utilidad. Además, dichos datos eliminan el coste de tener que interrumpir al usuario para que envíe información explícitamente sobre un tema que en principio no tiene por qué guardar relación con la intención de utilizar el sistema. Por el contrario, en ocasiones las interacciones implícitas no proporcionan datos claros y concretos. Por ello, hay que prestar especial atención a la manera de gestionar esta fuente de información. El propósito de esta investigación es doble: 1) aplicar una nueva visión tanto al diseño como al desarrollo de aplicaciones que puedan reaccionar consecuentemente a las interacciones implícitas del usuario, y 2) proporcionar una serie de metodologías para la evaluación de dichos sistemas interactivos. Cinco escenarios sirven para ilustrar la viabilidad y la adecuación del marco de trabajo de la tesis. Resultados empíricos con usuarios reales demuestran que aprovechar la interacción implícita es un medio tanto adecuado como conveniente para mejorar de múltiples maneras los sistemas interactivos. / Leiva Torres, LA. (2012). Diverse Contributions to Implicit Human-Computer Interaction [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/17803

Οι τεχνολογικές εξελίξεις των τελευταίων ετών, έχουν αναβαθμίσει τις κινητές συσκευές, από απλά κινητά τηλέφωνα σε συσκευές κινητού υπολογισμού. Οι σύγχρονες κινητές συσκευές είναι πλέον εξοπλισμένες με φωτογραφικούς φακούς υψηλής ανάλυσης, δέκτη GPS, δέκτη WiFi (802.11 b/g/n), κυκλώματα NFC, δυνατότητες σύνδεσης στο διαδίκτυο μέσω δικτύων κινητής τηλεφωνίας (3G/4G), επιταχυνσιόμετρα, αισθητήρες καταγραφής καρδιακών παλμών κ.α. Οι αισθητήρες αυτοί, είναι σε θέση να συλλέγουν πληροφορίες για το πλαίσιο του χρήστη (user’s context), το οποίο συμπεριλαμβάνει πληροφορίες όπως η τοποθεσία του, η τρέχουσα μέρα και ώρα, ο προσανατολισμός της συσκευής, το αν ο χρήστης κινείται και με πόση ταχύτητα, την τρέχουσα δραστηριότητα του χρήστη (π.χ. ομιλία, πληκτρολόγηση κειμένου) κ.α. Επίσης, οι ίδιοι οι χρήστες αποθηκεύουν στις συσκευές όλο και περισσότερες προσωπικές πληροφορίες, όπως το ιστορικό της επικοινωνίας τους, σύντομα μηνύματα, φωτογραφίες, emails, μουσική και βίντεο, υπενθυμίσεις και γεγονότα στο ημερολόγιο κ.α. Ο συνδυασμός του πλαισίου του χρήστη με την κοινωνική πληροφορία του χρήστη, δημιουργούν ένα δυναμικά οριζόμενο πλαίσιο, το κοινωνικό κινητό πλαίσιο του χρήστη (user’s mobile social context). Σκοπός της διδακτορικής διατριβής, είναι η υποστήριξη του χρήστη για την ανάκτηση προσωπικών πληροφοριών από συσκευές κινητού υπολογισμού, με αξιοποίηση του κινητού κοινωνικού πλαισίου. Στα πλαίσια της διδακτορικής διατριβής, ως περίπτωση χρήσης, μελετήθηκε η ανάκτηση επαφών από το χρήστη με σκοπό την επικοινωνία (κλήση, μήνυμα κ.λπ.). Παρόλα αυτά, η μεθοδολογία που ακολουθήθηκε μπορεί να εφαρμοστεί για την ανάκτηση οποιουδήποτε είδους προσωπικής πληροφορίας, όπως φωτογραφίες, βίντεο, μουσική κ.α. Συγκεκριμένα, αφού μελετήθηκαν πραγματικά δεδομένα χρήσης από δύο σύνολα δεδομένων, σχεδιάστηκε και υλοποιήθηκε ένας νέος αλγόριθμος για την πρόβλεψη της μελλοντικής επικοινωνίας του χρήστη συσκευών κινητού υπολογισμού. Στη συνέχεια, έγινε σύγκριση του συγκεκριμένου αλγορίθμου με υπάρχουσες προσεγγίσεις, οι οποίες αναφέρονται στη βιβλιογραφία. Κατόπιν, σχεδιάστηκε και υλοποιήθηκε σύστημα για συσκευές κινητού υπολογισμού το οποίο ενσωματώνει τον προτεινόμενο αλγόριθμο. Παράλληλα, σχεδιάστηκαν, υλοποιήθηκαν και αξιολογήθηκαν, τόσο στο εργαστήριο όσο και με πειράματα πεδίου, υβριδικές προσαρμοστικές διεπαφές για την παρουσίαση των προτάσεων του αλγορίθμου και, γενικότερα, την υποστήριξη ανάκτησης προσωπικής πληροφορίας, όπως οι επαφές, σε συσκευές κινητού υπολογισμού. Αναλυτικότερα, στο πρώτο κεφάλαιο, παρουσιάζεται η τρέχουσα κατάσταση στο πεδίο των συσκευών κινητού υπολογισμού, σε επίπεδο υλικού, σε επίπεδο δικτύων και σε επίπεδο λειτουργικών συστημάτων των συσκευών κινητού υπολογισμού. Επίσης, παρουσιάζεται συνοπτικά η έννοια του context και των context aware συστημάτων. Τέλος, γίνεται μια πρώτη καταγραφή των ερευνητικών θεμάτων της διδακτορικής διατριβής και παρουσιάζεται συνοπτικά η συνεισφορά της. Στο δεύτερο κεφάλαιο, γίνεται μια εκτενής παρουσίαση της τρέχουσας ερευνητικής δραστηριότητας στον τομέα της διδακτορικής διατριβής. Συγκεκριμένα, αναπτύσσονται οι έννοιες του context και των context-aware συστημάτων και στη συνέχεια γίνεται αναφορά στις έννοιες του mobile και social context. Κατόπιν, παρουσιάζονται μεθοδολογίες ανάκτησης και μοντελοποίησης του context, με έμφαση στο context των συσκευών κινητού και διάχυτου υπολογισμού. Στη συνέχεια, παρουσιάζονται χαρακτηριστικά παραδείγματα κινητών context aware συστημάτων, με έμφαση στα context aware συστήματα για τη διαχείριση προσωπικής πληροφορίας σε συσκευές κινητού υπολογισμού και ειδικότερα στη διαχείριση των επαφών και της επικοινωνίας του χρήστη. Τέλος, παρουσιάζονται νέα είδη διεπαφών (adaptive interfaces), οι οποίες προσαρμόζονται στο τρέχον context της συσκευής κινητού υπολογισμού. Στο τρίτο κεφάλαιο, παρουσιάζεται το πρώτο μέρος του ερευνητικού έργου της παρούσας διδακτορική διατριβής. Συγκεκριμένα, παρουσιάζεται η μελέτη και υλοποίηση ενός αλγορίθμου για την ανάκτηση προσωπικής πληροφορίας από συσκευές κινητού υπολογισμού. Ως περίπτωση χρήσης, εστιάσαμε στην ανάκτηση των επαφών. Αρχικά, παρουσιάστηκε ο τρόπος με τον οποίο επιλέξαμε να αναπαραστήσουμε το context του χρήστη. Στη συνέχεια, για να ερευνηθεί ποιες διαστάσεις του context είναι οι σημαντικότερες για την ανάκτηση επαφών, πραγματοποιήθηκε η ανάλυση δύο συνόλων δεδομένων, ενός που συλλέχθηκε στα πλαίσια της διδακτορικής διατριβής και ενός που παραχωρήθηκε από την εταιρία ΝΟΚΙΑ. Κατόπιν, παρουσιάζεται ο σχεδιασμός και η πειραματική αξιολόγηση του αλγορίθμου. Τέλος, παρουσιάζεται η σύγκριση του προτεινόμενου αλγορίθμου με αντίστοιχους αλγορίθμους και προσεγγίσεις της βιβλιογραφίας. Στο τέταρτο κεφάλαιο, παρουσιάζεται η υλοποίηση ενός συστήματος ανάκτησης επαφών σε συσκευές κινητού υπολογισμού. Συγκεκριμένα, υλοποιήθηκε ο αλγόριθμος που παρουσιάστηκε στο προηγούμενο κεφάλαιο και στη συνέχεια σχεδιάστηκαν και υλοποιήθηκαν context aware προσαρμοστικές διεπαφές, για την ανάκτηση επαφών σε συσκευές κινητού υπολογισμού. Στη συνέχεια, παρουσιάζεται η αξιολόγηση του συστήματος που υλοποιήθηκε, με την πραγματοποίηση πειράματος πεδίου, διάρκειας ενός μήνα. Κατά τη διάρκεια του πειράματος, συλλέγονταν ανώνυμα στατιστικά χρήσης του συστήματος, ενώ, στο τέλος του πειράματος, έγινε και συλλογή ποιοτικών δεδομένων, με χρήση ερωτηματολογίων. Στο τελευταίο, πέμπτο, κεφάλαιο, παρουσιάζεται μία σύνοψη της διδακτορικής διατριβής, καταγράφονται τα συμπεράσματα της και γίνεται αναφορά στις μελλοντικές επεκτάσεις του ερευνητικού έργου. / Recent technological advances have transformed the mobile phones from simple communication devices to pervasive computing devices. Today’s mobiles are equipped with a great variety of hardware, such as high definition cameras, GPS receivers, WiFi capabilities, NFC chips, 3G/4G mobile network connections, compass, accelerometers etc. Those sensors collect a huge amount of information about mobile device’s user and her environment (user’s context), for example, the location, current day and time, device’s orientation, if she is on the move and how fast, the current task (e.g. talking, typing text) etc. Besides the automatically generated information, users keep a lot of personal information on their mobile devices like communication history, short messages, photos, emails, music and videos, reminder and calendar events etc. If we combine the user’s context with the stored social information, a dynamic context is created, which can be termed the user’s mobile social context. The aim of this PhD thesis is the exploitation of user’s mobile social context in order to support the extraction of personal information from mobile computing devices. As an example of personal information, this thesis studies the support of user’s communication by extracting the desired contacts. However, the same methodology applies to all kinds of personal information like photos, videos, music etc. First of all, real mobile usage statistics from two different datasets were analyzed in order to design and implement a new prediction algorithm of the future user’s communication on mobile devices. Secondly, a comparison between the proposed algorithm and relative algorithms was conducted. Then, a system for mobile devices that embeds the proposed algorithm was developed. At the same time, adaptive interfaces for the presentation of the proposals of the algorithm were designed and evaluated, conducting both lab and field experiments. Chapter 1 presents the current status in the field of mobile computing devices, in terms of hardware, mobile networks and mobile operating systems. The terms “context” and “context aware” systems are also briefly presented. Finally, Chapter 1 presents the research question of this thesis and its research contribution. Chapter 2 presents the related work in the field. First, the terms “context” and “context aware” are discussed in detail and then the terms of “mobile” and “social context” are introduced. The chapter discusses proposals for retrieving and modeling the user’s context, giving detailed information about the mobile user’s context. Also, in the same chapter, examples of context aware systems are presented, focusing mainly to context aware systems for communication management and contacts retrieval. Finally, novel, adaptive, context aware interfaces are also presented. Chapter 3 presents the first part of the thesis’ research. A new algorithm for the task of contacts retrieval is proposed. In order to choose the proper dimensions of the user’s context for the specific task, an analysis and examination of two different dataset is perfomed. The first dataset is preliminary and it is collected in the context of this thesis. The second one is provided by NOKIA and is the outcome of a large scale initiative that took place in Geneva area of Switzerland, the NOKIA Lausanne Data Collection Campaign. A comparative evaluation of the proposed algorithm against the two mentioned datasets is finally performed. Finally, a comparison of the proposed algorithm with similar approaches and algorithms is presented. In Chapter 4, the design and implementation of a Google Android system for contacts retrieval from mobile computing devices is presented. A set of context aware adaptive interfaces for contacts retrieval was designed and developed, to make use of the algorithm discussed in Chapter 3. These interfaces were tested using a large scale field experiment (1 month duration) in order to evaluate the system in real conditions and with real users. During the experiment, anonymous usage statistics from users were collected and a questionnaire for the qualitative evaluation of our system was also distributed. In the final chapter (chapter 5), the conclusions of the thesis and future work suggestions are presented.

Human-in-the-loop simulators have long been used in the research community as well as in industry. The aviation field has been the pioneers in the use of simulators for design purposes. In contrast, corresponding activities in the automotive area have been less widespread. Published reports on experimental activities based on human-in-the-loop simulations have focused on methods used in the study, but nobody seems to have taken a step back and looked at the wider methodological picture of Simulator-Based Design. The purpose of this thesis is to fill this gap by drawing, in part, upon the author’s long experience in this field. In aircraft and lately also in ground vehicles there has been a technology shift from pure mechanics to computer-based systems. The physical interface has turned into screen-based solutions. This trend towards glass has just begun for ground vehicles. This development in vehicle technology has opened the door for new design approaches, not only for design itself, but also for the development process. Simulator-Based Design (SBD) is very compatible with this trend. The first part of this thesis proposes a structure for the process of SBD and links it to the corresponding methodology for software design. In the second part of the thesis the focus changes from methodology to application and specifically to the design of three-dimensional situation displays. Such displays are supposed to support the human operator with a view of a situation beyond the more or less limited visual range. In the aircraft application interest focuses on the surrounding air traffic in the light of the evolving free-flight concept, where responsibility for separation between aircraft will be (partly) transferred from ground-based flight controllers to air crews. This new responsibility must be supported by new technology and the situational view must be displayed from the perspective of the aircraft. Some basic design questions for such 3D displays were investigated resulting in an adaptive interface approach, where the current situation and task govern the details of information presentation. The thesis also discusses work on situation displays for ground vehicles. The most prominent example may be the Night Vision system, where the road situation ahead is depicted on a screen in the cab. The existing systems are based on continuous presentation, an approach that we have questioned, since there is strong evidence for negative behavioral adaptation. This means, for example, that the driver will drive faster, since vision has been enhanced, and thereby consume the safety margins that the system was supposed to deliver. Our investigation supports a situation-dependant approach and no continuous presentation. In conclusion, the results from our simulator-based studies showed advantages for adaptive interface solutions. Such design concepts are much more complicated than traditional static interfaces. This finding emphasizes the need for more dynamic design resources in order to have a complete understanding of the situation-related interface changes. The use of human-in-the-loop simulators and deployment of Simulator-Based Design will satisfy this need.

This work introduces a new UIMS (User Interface Management System), which aims to solve numerous problems in the field of user-interface development arising from hard-coded use of user interface toolkits. The presented solution is a concrete system architecture based on the abstract ARCH model consisting of an interface abstraction-layer, a dialog definition language called GIML (Generalized Interface Markup Language) and pluggable interface rendering modules. These components form an interface toolkit called GITK (Generalized Interface ToolKit). With the aid of GITK (Generalized Interface ToolKit) one can build an application, without explicitly creating a concrete end-user interface. At runtime GITK can create these interfaces as needed from the abstract specification and run them. Thereby GITK is equipping one application with many interfaces, even kinds of interfaces that did not exist when the application was written. It should be noted that this work will concentrate on providing the base infrastructure for adaptive/adaptable system, and does not aim to deliver a complete solution. This work shows that the proposed solution is a fundamental concept needed to create interfaces for everyone, which can be used everywhere and at any time. This text further discusses the impact of such technology for users and on the various aspects of software systems and their development. The targeted main audience of this work are software developers or people with strong interest in software development.