In the thesis, we propose a dual-mode message delivery system with mechanisms of time constrained paging and multi-class message. The pairing decision depends on the effective pairing time defined by the system when a bluetooth device comes into service range. Within the constrained pairing time, central server can deliver a message to the bluetooth device directly without re-pairing. Otherwise, the bluetooth device has to be paired with an intermediate node before it can receive a message. In addition, we store the number of times that bluetooth devices can move into the service range into a data base in order to send multi-class messages to these bluetooth devices. To demonstrate the proposed schemes, we implement a central server on Linux system and intermediate nodes on Window Mobile platform. We also design control packets associated with their message formats. Control messages can be exchanged between the central server and the intermediate node by the control packets, and data messages can be transmitted in a heterogeneous network, consisting of bluetooth and Wi-Fi. Finally, we measured the time saved without using pairing procedure and also verified that the system can dynamically adjust the classes of messages according to the number of times that bluetooth devices enter to the service range.

[pt] Com a crescente quantidade de dados sendo gerados e coletados, torna-se mais comum cenários em que se dispõe de dados rotulados em larga escala, mas com recursos computacionais limitados, de modo que não seja possível treinar modelos preditivos utilizando todas as amostras disponíveis. Diante dessa realidade, adotamos o paradigma de Machine Teaching como uma alternativa para obter modelos eficazes utilizando um subconjunto representativo dos dados disponíveis. Inicialmente, consideramos um problema central da área de Machine Teaching que consiste em encontrar o menor conjunto de amostras necessário para obter uma dada hipótese alvo h(asterisco). Adotamos o modelo de ensino black-box learner introduzido em (DASGUPTA et al., 2019), em que o ensino é feito interativamente sem qualquer conhecimento sobre o algoritmo do learner e sua classe de hipóteses, exceto que ela contém a hipótese alvo h(asterisco). Refinamos alguns resultados existentes para esse modelo e estudamos variantes dele. Em particular, estendemos um resultado de (DASGUPTA et al., 2019) para o cenário mais realista em que h(asterisco) pode não estar contido na classe de hipóteses do learner e, portanto, o objetivo do teacher é fazer o learner convergir para a melhor aproximação disponível de h(asterisco). Também consideramos o cenário com black-box learners não adversários e mostramos que podemos obter melhores resultados para o tipo de learner que se move para a próxima hipótese de maneira suave, preferindo hipóteses que são mais próximas da hipótese atual. Em seguida, definimos e abordamos o problema de Aprendizado com Restrição de Tempo considerando um cenário em que temos um enorme conjunto de dados e um limite de tempo para treinar um dado learner usando esse conjunto. Propomos o método TCT, um algoritmo para essa tarefa, desenvolvido com base nos princípios de Machine Teaching. Apresentamos um estudo experimental envolvendo 5 diferentes learners e 20 datasets no qual mostramos que TCT supera métodos alternativos considerados. Finalmente, provamos garantias de aproximação para uma versão simplificada do TCT. / [en] With the growing amount of data being generated and collected, it becomes increasingly common to have scenarios where there are large-scale labeled data but limited computational resources, making it impossible to train predictive models using all available samples. Faced with this reality, we adopt the Machine Teaching paradigm as an alternative to obtain effective models using a representative subset of available data. Initially, we consider a central problem of the Machine Teaching area which consists of finding the smallest set of samples necessary to obtain a given target hypothesis h(asterisk). We adopt the black-box learner teaching model introduced in (DASGUPTA et al., 2019), where teaching is done interactively without any knowledge about the learner s algorithm and its hypothesis class, except that it contains the target hypothesis h(asterisk). We refine some existing results for this model and study its variants. In particular, we extend a result from (DASGUPTA et al., 2019) to the more realistic scenario where h(asterisk) may not be contained in the learner s hypothesis class, and therefore, the teacher s objective is to make the learner converge to the best available approximation of h(asterisk). We also consider the scenario with non-adversarial black-box learners and show that we can obtain better results for the type of learner that moves to the next hypothesis smoothly, preferring hypotheses that are closer to the current hypothesis. Next, we address the Time-Constrained Learning problem, considering a scenario where we have a huge dataset and a time limit to train a given learner using this dataset. We propose the TCT method, an algorithm for this task, developed based on Machine Teaching principles. We present an experimental study involving 5 different learners and 20 datasets in which we show that TCT outperforms alternative methods considered. Finally, we prove approximation guarantees for a simplified version of TCT.

ABSTRACT Today there are lots of different health care computer systems in use. However, according to recent studies many of them lack necessary usability. Within Nordic pediatric cancer care, analogue treatment protocols on paper are currently used, as a complement to the digital medical records and prescription systems. In these protocols, doctors and nurses note information regarding the patient’s treatment. Comments and changes are noted in the margin, which sometimes leads to making the protocol messy and difficult to grasp. Since several people are involved in the handling of the treatment protocols it occasionally happens that the protocol disappears for periods of time. We had two aims with this project. The first was to examine and map requirements for a usable interactive treatment plan for acute lymphoblastic leukemia, ALL. The second was to investigate if our suggested combination of methods would be sufficient to acquire these requirements in a setting where the users, i.e. physicians, were time-­‐constrained.   Based on large variety of theories and methods from educational science and research in human computer interaction, we have conducted a qualitative study, iterating a combination of user-­‐centered design methods, with a revision of the requirements as well as the design following each iteration. The requirements analysis was performed in close collaboration with the doctors at the Astrid Lindgren Children's Hospital, Karolinska University Hospital, Stockholm, Sweden.   Our results indicate that by using a combination of methods from usability engineering and participatory design, a well-­‐defined list of requirements from the doctors could be identified which might be sufficient to develop an interactive prototype for a digital treatment protocol. In addition we found that our method enabled an exchange of knowledge between the designers and the users. In conclusion, these combined methods were suitable for enhancing the software designer’s understanding of the user needs in this time-­‐constrained environment.

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Le contexte d’application de cette recherche a été le projet CRIBA. CRIBA vise à industrialiser une solution intégrée de rénovation et d’isolation de grands bâtiments. De ce fait, une part importante de la valeur ajoutée est transférée des chantiers de rénovation vers des usines de fabrications devant être synchronisées avec les chantiers. La planification est l'une des étapes importantes de la gestion de projets. S’adaptant à une organisation, elle vise une réalisation optimale en considérant les facteurs de temps, coût, qualité ainsi que l’affectation efficace des ressources. Cette affectation est d’autant plus complexe lorsqu’un ensemble de projets se partagent les ressources, renouvelables ou non renouvelables. L'objectif global de notre étude est de développer un outil d’aide à la décision pour un décideur visant à planifier plusieurs projets en intégrant l'allocation des ressources renouvelables, et la planification des flux de ressources non-renouvelables vers ces projets. Dans ce cadre, les ressources non renouvelables telles que les machines et la main-d'œuvre ont une disponibilité initiale limitée sur les chantiers. Cependant, nous supposons que des quantités limitées supplémentaires peuvent être achetées. En outre, nous prenons en compte la volonté des coordinateurs des projets pour l’approvisionnement des chantiers en juste à temps (just in time), en particulier pour les ressources peu demandées, encombrantes et à forte valeur. Ceci oblige à étendre le cadre du modèle de la planification des projets en incluant la planification de la chaîne logistique qui approvisionne les ressources non renouvelables des chantiers. Enfin, pour répondre au besoin d’outils décisionnels responsables sur le plan environnemental, le modèle prévoit le transport et le recyclage des déchets des chantiers dans les centres appropriés. Un modèle linéaire mixte du problème est ainsi posé. Puisqu’il rentre dans la classe des modèles d'optimisation NP-durs, une double résolution est proposée. D’abord à l’aide d’un solveur puis une méta-heuristique basée sur un algorithme génétique. De plus, pour faciliter l'utilisation du modèle par des utilisateurs peu familiers avec la recherche opérationnelle, un système d'aide à la décision basé sur une application web a été développé. L’ensemble de ces contributions ont été évaluées sur des jeux de test issus du projet CRIBA. / The application context of the current study is on a CRIBA project. The CRIBA aims to industrialize an integrated solution for the insulation and thermal renovation of building complexes in France. As a result, a significant part of the added value is transferred from the renovation sites to the manufacturing centers, making both synchronized. Planning is one of the important steps in project management. Depending on the different viewpoints of organizations, successful planning for projects can be achieved by performing to optimality within the time, cost, quality factors as well as the efficient assignment of resources. Planning for the allocation of resources becomes more complex when a set of projects is sharing renewable and non-renewable resources. The global objective of the study is to develop a decision-making tool for decision-makers to plan multiple projects by integrating the allocation of the renewable resources and planning the flow of non-renewable resources to the project worksites. In this context, non-renewable resources such as equipment and labor have a limited initial availability at the construction sites. Nevertheless, we assume that additional limited amounts can be added to the projects. In addition, we take into account the interest of the project coordinators in supplying the non-renewable resources in a just-in-time manner to the projects, especially for low-demand resources with a high price. This requires extending the framework of the project planning by including the planning of the supply chain which is responsible. Finally, in order to meet the requirements for environmentally responsible decision-making, the model envisages the transportation and recycling of waste from project sites to appropriate centers. A mixed integer linear model of the problem is proposed. Since it falls within the class of NP-hard optimization models, a double resolution is targeted: first, using a solver and then a metaheuristic based on the genetic algorithm. In addition, in order to facilitate the use of the model by users unfamiliar with operational research, a web-based decision-making support system has been developed. All the contributions are evaluated in a set of case studies from the CRIBA project.