This thesis conceptualizes a generic monitoring framework for online markets, which has also been implemented in a prototypic fashion. Thereby identifying a set of arising challenges for which solutions have been developed. An introductory section gives a short overview of the field of research, states identified scientfic challenges and derives research questions thereof. The following articles describe (i) the general approach of an online market monitor, (ii) how to encapsulate domain-dependent configurations & functionalities from monitoring core modules to facilitate a generic approach, (iii) change frequency estimation for observational units in a dynamic and fuzzy population setting, (iv) the development of an adaptive harvest heuristic scheduling new observations by utilizing the change frequency estimator, (v) how to make use of collected market information in form of business intelligence reports and finally (vi) an exemplary meta-analysis showing how to draw further conclusions about market mechanisms.

<p>This research is concerned with theoretical and methodological aspects of geographic information transformation between different user contexts. In this dissertation I present theories and methodological approaches that enable a context sensititve use and reuse of geographic data in geographic information systems.</p><p>A primary motive for the reported research is that the patrons interested in answering environmental questions have increased in number and been diversified during the last 10-15 years. The interest from international, national and regional authorities together with multinational and national corporations embrace a range of spatial and temporal scales from global to local, and from many-year/-decade perspectives to real time applications. These differences in spatial and temporal detail will be expressed as rather different questions towards existing data. It is expected that geographic information systems will be able to integrate a large number of diverse data to answer current and future geographic questions and support spatial decision processes. However, there are still important deficiencies in contemporary theories and methods for geographic information integration</p><p>Literature studies and preliminary experiments suggested that any transformation between different users’ contexts would change either the thematic, spatial or temporal detail, and the result would include some amount of semantic uncertainty. Consequently, the reported experiments are separated into studies of change in either spatial or thematic detail. The scope concerned with thematic detatil searched for approaches to represent indiscernibility between categories, and the scope concerned with spatial detail studied semantic effects caused by changing spatial granularity.</p><p>The findings make several contributions to the current knowledge about transforming geographic information between users’ contexts. When changing the categorical resolution of a geographic dataset, it is possible to represent cases of indiscernibility using novel methods of rough classification described in the thesis. The use of rough classification methods together with manual landscape interpretations made it possible to evaluate semantic uncertainty in geographic data. Such evaluations of spatially aggregated geographic data sets show both predictable and non-predictable effects. and these effects may vary for different environmental variables.</p><p>Development of methods that integrate crisp, fuzzy and rough data enables spatial decision support systems to consider various aspects of semantic uncertainty. By explicitly representing crisp, fuzzy and rough relations between datasets, a deeper semantic meaning is given to geographic databasses. The explicit representation of semantic relations is called a Geographic Concept Topology and is held as a viable tool for context transformation and full integration of geographic datasets.</p>

This research is concerned with theoretical and methodological aspects of geographic information transformation between different user contexts. In this dissertation I present theories and methodological approaches that enable a context sensititve use and reuse of geographic data in geographic information systems. A primary motive for the reported research is that the patrons interested in answering environmental questions have increased in number and been diversified during the last 10-15 years. The interest from international, national and regional authorities together with multinational and national corporations embrace a range of spatial and temporal scales from global to local, and from many-year/-decade perspectives to real time applications. These differences in spatial and temporal detail will be expressed as rather different questions towards existing data. It is expected that geographic information systems will be able to integrate a large number of diverse data to answer current and future geographic questions and support spatial decision processes. However, there are still important deficiencies in contemporary theories and methods for geographic information integration Literature studies and preliminary experiments suggested that any transformation between different users’ contexts would change either the thematic, spatial or temporal detail, and the result would include some amount of semantic uncertainty. Consequently, the reported experiments are separated into studies of change in either spatial or thematic detail. The scope concerned with thematic detatil searched for approaches to represent indiscernibility between categories, and the scope concerned with spatial detail studied semantic effects caused by changing spatial granularity. The findings make several contributions to the current knowledge about transforming geographic information between users’ contexts. When changing the categorical resolution of a geographic dataset, it is possible to represent cases of indiscernibility using novel methods of rough classification described in the thesis. The use of rough classification methods together with manual landscape interpretations made it possible to evaluate semantic uncertainty in geographic data. Such evaluations of spatially aggregated geographic data sets show both predictable and non-predictable effects. and these effects may vary for different environmental variables. Development of methods that integrate crisp, fuzzy and rough data enables spatial decision support systems to consider various aspects of semantic uncertainty. By explicitly representing crisp, fuzzy and rough relations between datasets, a deeper semantic meaning is given to geographic databasses. The explicit representation of semantic relations is called a Geographic Concept Topology and is held as a viable tool for context transformation and full integration of geographic datasets.

Denna kunskapsöversikt undersöker det forskningsfält som rör musikintegrerad matematikundervisning. Syftet med översikten är att få en inblick i hur musiken påverkar elevernas matematikprestationer samt hur forskningen ser ut inom denna kombination. Därför är vår frågeställning: Vad kännetecknar forskningen om integrationen mellan matematik och musik? För att besvara denna fråga har vi utfört litteratursökningar för att finna studier och artiklar som tillsammans bildar en överblick. Med hjälp av den metod som Claes Nilholm beskriver i SMART (2016) har vi skapat en struktur för hur vi arbetat. Ur det material som vi fann under sökningarna har vi funnit mönster som talar för musikens positiva inverkan på matematikundervisning. Förmågan att uttrycka sina känslor i form av ord eller beröra andra med dem har alltid varit enbeundransvärd och sällsynt egenskap. Det här projektet handlar om att skapa en text generatorkapabel av att skriva text i stil med enastående män och kvinnor med den här egenskapen. Arbetet har genomförts genom att träna ett neuronnät med citat skrivna av märkvärdigamänniskor såsom Oscar Wilde, Mark Twain, Charles Dickens, etc. Nätverket samarbetar med två olika semantiska modeller: Word2Vec och One-Hot och alla tre är delarna som vår textgenerator består av. Med dessa genererade texterna gjordes en enkätudersökning för att samlaåsikter från studenter om kvaliteten på de genererade texterna för att på så vis utvärderalämpligheten hos de olika semantiska modellerna. Efter analysen av resultatet lärde vi oss att de flesta respondenter tyckte att texterna de läste var sammanhängande och roliga. Vi lärde oss också att Word2Vec, presterade signifikant bättre än One-hot. / The ability of expressing feelings in words or moving others with them has always been admired and rare feature. This project involves creating a text generator able to write text in the style of remarkable men and women with this ability, this gift. This has been done by training a neural network with quotes written by outstanding people such as Oscar Wilde, Mark Twain, Charles Dickens, et alt. This neural network cooperate with two different semantic models: Word2Vec and One-Hot and the three of them compound our text generator. With the text generated we carried out a survey in order to collect the opinion of students about the quality of the text generated by our generator. Upon examination of the result, we proudly learned that most of the respondents thought the texts were coherent and fun to read, we also learned that the former semantic model performed, not by a factor of magnitude, better than the latter.

Nos travaux portent sur la scénarisation dans les environnements virtuels pour la formation. Nous nous intéressons particulièrement à la formation dans des environnements sociotechniques complexes comme par exemple la gestion des risques. Dans ces environnements, la variabilité des situations que les opérateurs peuvent rencontrer rend difficile la mise en place d'une formation exhaustive. Il est pourtant crucial d'offrir les moyens permettant l'entrainement à ces situations et les environnements virtuels peuvent apporter des solutions efficaces. En effet, ils peuvent offrir une grande liberté d'action et permettre un apprentissage de type essai-erreur. Le contrôle pédagogique de ces environnements peut alors permettre de personnaliser et d’adapter les contenus à chaque apprenant. Cependant, il est difficile pour les concepteurs d'environnements virtuels d'imaginer, de concevoir et décrire toutes les séquences d'actions et d'événements menant aux situations d'intérêt tout en autorisant une grande liberté d'action pour les apprenants. L'approche de description exhaustive se révèle trop coûteuse, voire vouée à l'échec. Pour palier au goulet d'étranglement de l'écriture et du codage des contenus, nous proposons de générer dynamiquement l'enchainement des situations d'apprentissage au sein d'une simulation. L'architecture TAILOR que nous proposons permet la scénarisation dynamique de chaque session d'apprentissage, en accord avec un modèle du parcours d'apprentissage, en utilisant des modèles à base de connaissances. Pour cela, nous avons tout d'abord proposé le langage \textsc{World-DL} permettant de produire du contenu scénaristique reconfigurable, adaptable et générique pour des environnements virtuels pour la formation. Ce langage permet à la fois de décrire le modèle du monde, les objectifs scénaristiques ainsi que de maintenir la base de connaissances liée à la simulation.Afin de ne pas s'appuyer sur une élicitation du domaine d'apprentissage, nous avons proposé un modèle de l'apprenant opérationnalisant la théorie de la Zone Proximale de Développement. Celui-ci repose sur un espace vectoriel de classes de situation auxquelles sont associées des valeurs de croyance sur la capacité de l'apprenant à gérer les situations qu'elles décrivent. La scénarisation que nous proposons est essentiellement intra-diégétique : elle s'intègre au monde simulé par l'environnement virtuel. Pour cela, nous proposons une méthode de génération dynamique et adaptative de situations d'apprentissage s'appuyant sur des modèles de l'activité et de la causalité inspirés d'analyses ergonomiques. Par ailleurs, les situations d'apprentissage générées sont articulées sous la forme d'une fiction grâce au processus de diégétisation inspiré du courant structuraliste de la sémiologie. Les travaux sur l'architecture TAILOR ont donné naissance au moteur du même nom au sein de la plateforme logicielle HUMANS. L'approche a été appliquée dans un environnement virtuel pour la formation des assembleurs en aéronautique. / This work addresses the issues of the specification of the scenario in virtual environment for training. We especially address adult lifelong training in complex domains where technical systems are difficult to apprehend and human factors are critical. Workers have to be trained to react to a wide range of situations. Virtual environment can provide this kind of training by offering them the possibility to experiment different behavior in a situation. Yet to foster learning, such environment should provide a wide range of appealing scenarios adapted to learners’ need. The design and the production of all possible scenarios and of all their adaptations is a tedious task. It requires designers to imagine and describe every possible sequence of events which leads to interesting learning situation. Such a descriptive approach conflicts with the need for a smoother production process.To tackle the authoring bottleneck, we propose the TAILOR architecture to dynamically generate sequences of learning situations in a simulation. It takes into account a learner profile and expert knowledge informed in semantic models. We used a space of classes of situations coupled with a belief model to represent the Zone of Proximal Development of a learner. Each point of the space images the ability of the learner to handle a kind of situation. As we are essentially dealing with intra-diegetic orchestration, i.e. what is happening in the world depicted by the simulation, we propose to use expert model of the domain. We distinguished three kind of knowledge: world knowledge, activity knowledge and causality knowledge. They are used at runtime to procedurally generate a learning situation which will enlarge the Zone of Proximal Development of the learner.To this end, we design the WORLD-DL language to author scenario content for virtual environment for training in a reconfigurable, adaptable and generic way through an ontological representation. This language is used both to describe scenario objectives and to maintain a knowledge-based world state. Moreover, we operationalize structuralist view of narrative to build a story upon generated learning situation through an automated diegetization process. This process relies on abstract story model describe in the ontological metamodel DIEGETIC.This work have been implemented in the TAILOR engine used in the HUMANS platform. It was used both for aeronautic assembly virtual training and for baby sitter virtual training.

CoNLL 2015 featured a shared task on shallow discourse parsing. In 2016, the efforts continued with an increasing focus on sense classification. In the case of implicit sense classification, there was an interesting mix of traditional and modern machine learning classifiers using word representation models. In this thesis, we explore the performance of a number of these models, and investigate how they perform using a variety of word representation models. We show that there are large performance differences between word representation models for certain machine learning classifiers, while others are more robust to the choice of word representation model. We also show that with the right choice of word representation model, simple and traditional machine learning classifiers can reach competitive scores even when compared with modern neural network approaches.