Recently, sport video analysis has attracted lots of researcher¡¦s attention because of its entertainment applications and potential commercial benefits. Sport video analysis aims to identify what trigged the excitement of audiences. Previous methods rely mainly on video decomposition using domain specific knowledge. The study and development of suitable and efficient techniques for sport video analysis have been conducted extensively over the last decade. However, several longstanding challenges, such as semantic gap and commercial detection are still waiting to be solved. In this work, we consider using semantic analysis to adjacent pitch scenes which we called ¡§gap length.¡¨ Difference kinds of baseball games show its specific distribution for gap length, which depicts the potential significance of each baseball game.

Learners often have difficulty finding and retrieving relevant learning materials to support their learning goals because of two main challenges. The vocabulary learners use to describe their goals is different from that used by domain experts in teaching materials. This challenge causes a semantic gap. Learners lack sufficient knowledge about the domain they are trying to learn about, so are unable to assemble effective keywords that identify what they wish to learn. This problem presents an intent gap. The work presented in this thesis focuses on addressing the semantic and intent gaps that learners face during an e-Learning recommendation task. The semantic gap is addressed by introducing a method that automatically creates background knowledge in the form of a set of rich learning-focused concepts related to the selected learning domain. The knowledge of teaching experts contained in e-Books is used as a guide to identify important domain concepts. The concepts represent important topics that learners should be interested in. An approach is developed which leverages the concept vocabulary for representing learning materials and this influences retrieval during the recommendation of new learning materials. The effectiveness of our approach is evaluated on a dataset of Machine Learning and Data Mining papers, and our approach outperforms benchmark methods. The results confirm that incorporating background knowledge into the representation of learning materials provides a shared vocabulary for experts and learners, and this enables the recommendation of relevant materials. We address the intent gap by developing an approach which leverages the background knowledge to identify important learning concepts that are employed for refining learners' queries. This approach enables us to automatically identify concepts that are similar to queries, and take advantage of distinctive concept terms for refining learners' queries. Using the refined query allows the search to focus on documents that contain topics which are relevant to the learner. An e-Learning recommender system is developed to evaluate the success of our approach using a collection of learner queries and a dataset of Machine Learning and Data Mining learning materials. Users with different levels of expertise are employed for the evaluation. Results from experts, competent users and beginners all showed that using our method produced documents that were consistently more relevant to learners than when the standard method was used. The results show the benefits in using our knowledge driven approaches to help learners find relevant learning materials.

The task of tagging, annotating or labelling image content automatically with semantic keywords is a challenging problem. To automatically tag images semantically based on the objects that they contain is essential for image retrieval. In addressing these problems, we explore the techniques developed to combine textual description of images with visual features, automatic region tagging and region-based ontology image retrieval. To evaluate the techniques, we use three corpora comprising: Lonely Planet travel guide articles with images, Wikipedia articles with images and Goats comic strips. In searching for similar images or textual information specified in a query, we explore the unification of textual descriptions and visual features (such as colour and texture) of the images. We compare the effectiveness of using different retrieval similarity measures for the textual component. We also analyse the effectiveness of different visual features extracted from the images. We then investigate the best weight combination of using textual and visual features. Using the queries from the Multimedia Track of INEX 2005 and 2006, we found that the best weight combination significantly improves the effectiveness of the retrieval system. Our findings suggest that image regions are better in capturing the semantics, since we can identify specific regions of interest in an image. In this context, we develop a technique to tag image regions with high-level semantics. This is done by combining several shape feature descriptors and colour, using an equal-weight linear combination. We experimentally compare this technique with more complex machine-learning algorithms, and show that the equal-weight linear combination of shape features is simpler and at least as effective as using a machine learning algorithm. We focus on the synergy between ontology and image annotations with the aim of reducing the gap between image features and high-level semantics. Ontologies ease information retrieval. They are used to mine, interpret, and organise knowledge. An ontology may be seen as a knowledge base that can be used to improve the image retrieval process, and conversely keywords obtained from automatic tagging of image regions may be useful for creating an ontology. We engineer an ontology that surrogates concepts derived from image feature descriptors. We test the usability of the constructed ontology by querying the ontology via the Visual Ontology Query Interface, which has a formally specified grammar known as the Visual Ontology Query Language. We show that synergy between ontology and image annotations is possible and this method can reduce the gap between image features and high-level semantics by providing the relationships between objects in the image. In this thesis, we conclude that suitable techniques for image retrieval include fusing text accompanying the images with visual features, automatic region tagging and using an ontology to enrich the semantic meaning of the tagged image regions.

Contemporary philosopher, Joseph Levine, has argued that human phenomenological experience cannot be explained solely through the resources of neuroscience, and that a significant ‘explanatory gap’ exists between the rich features of human experience and scientific explanations of the mind. This thesis examines Guiseppina D’Oro’s novel suggestion that the gap exists, but that it is a semantic rather than an empirical problem. D’Oro argues that the ‘gap’ is a persistent philosophical problem because of its semantic nature, and that advances in neuroscience will fail to resolve the gap because its source is a conceptual distinction that is not marked by empirical difference. In the thesis I will discuss some virtues and difficulties with D’Oro’s thesis, and the implications her claim has more broadly for philosophers of mind. / Graduate / 0422

Étant donnée l'évolution technologique des capteurs embarqués à bord des satellites, le potentiel d'images satellitaires accessible s'accroît de telle manière que se pose maintenant la question de son exploitation la plus efficace possible. C'est l'objectif du projet CARTAM-SAT que de fluidifier la chaîne de traitement depuis les satellites jusqu'aux utilisateurs des images. La thèse s'inscrit dans ce cadre. Les traitements relatifs aux images ont évolué au cours des années. Les images basse résolution étaient traitées par une approche dite pixel alors que la haute résolution a permis le développement d'une approche dite objet. Cette dernière s'attache à analyser non plus des pixels isolés, mais des groupes de pixels représentatifs d'objets concrets sur le terrain. Ainsi, en principe, ces groupes de pixels sont dotés d'une sémantique propre au domaine de la télédétection. La représentation des connaissances a évolué parallèlement aux images satellitaires. Les standards de représentation ont profité de l'expansion du web pour donner naissance à des standards comme OWL. Celui-ci repose en grande partie sur les logiques de description qui permettent l'utilisation de raisonneurs automatiques capables d'inférer une connaissance implicite.Cette thèse se place à la jonction de ces deux sciences et propose une approche ontologique d'analyse des images satellitaires. Il s'agit de formaliser différents types de connaissances et de conceptualisations implicitement utilisés par les logiciels de traitement d'image et par les experts en télédétection, puis de raisonner automatiquement sur la description d'une image pour en obtenir une interprétation sémantique.Ce principe général est susceptible de nombreuses déclinaisons techniques. La mise en œuvre a consisté en la réalisation d'un prototype alliant une bibliothèque d'analyse d'images satellitaires et un raisonneur basé sur les ontologies. L'implémentation proposée dans la thèse permet d'explorer quatre déclinaisons techniques du principe qui mènent à des discussions sur la complémentarité des paradigmes d'analyse pixel et objet, la représentation de certaines relations spatiales et la place de la connaissance par rapport aux traitements. / Given the technological development of embedded satellite sensors, the potential of available satellite images increases so that the question now arises of their most efficient exploitation possible. This is the purpose of the CARTAM-SAT project to fluidize the processing workflow from satellite images to users. The thesis is part of this framework.Processing operations relating to images have evolved over the years. Low-resolution images were processed by a so-called pixel approach while the high-resolution has allowed the development of a so-called object approach. The latter focuses on analysing not about the isolated pixels, but about groups of pixels representative of concrete objects on the ground. Thus, in principle, these are groups of pixels with a domain-specific remote sensing semantics.Along with satellite imagery, knowledge representation has evolved. The standards of representation have benefited from the expansion of the web to give rise to standards like OWL. This one is widely based on description logics that allow the use of automated reasoners able to infer implicit knowledge.This thesis is at the junction of these two sciences and provides an ontological approach for analysing satellite images. The aim is to formalize different types of knowledges and conceptualizations implicitly used by image processing programs and by remote sensing experts, and then reasoning automatically on an image description to obtain one semantic interpretation.This general principle may have numerous technical variations. The implementation consisted in a prototype combining a satellite image analysis library and an ontology-based reasoner. The implementation proposed in the thesis allows to explore four technical variations of the principle that lead to discussions on the complementarity of pixel and object analysis paradigms, the representation of some of the spatial relations and the role of knowledge in relation to processing.

Widespread adoption of virtualization has resulted in an increased interest in Virtual Machine (VM) introspection. To perform useful analysis of the introspected VMs, hypervisors must deal with the semantic gap between the low-level information available to them and the high-level OS abstractions they need. To bridge this gap, systems have proposed making assumptions derived from the operating system source code or symbol information. As a consequence, the resulting systems create a tight coupling between the hypervisor and the operating systems run by the introspected VMs. This coupling is undesirable because any change to the internals of the operating system can render the output of the introspection system meaningless. In particular, malicious software can evade detection by making modifications to the introspected OS that break these assumptions. Instead, in this thesis, we introduce Architectural Introspection, a new introspection approach that does not require information about the internals of the introspected VMs. Our approach restricts itself to leveraging constraints placed on the VM by the hardware and the external environment. To interact with both of these, the VM must use externally specified interfaces that are both stable and not linked with a specific version of an operating system. Therefore, systems that rely on architectural introspection are more versatile and more robust than previous approaches to VM introspection. To illustrate the increased versatility and robustness of architectural introspection, we describe two systems, Patagonix and P2, that can be used to detect rootkits and unpatched software, respectively. We also detail Attestation Contracts, a new approach to attestation that relies on architectural introspection to improve on existing attestation approaches. We show that because these systems do not make assumptions about the operating systems used by the introspected VMs, they can be used to monitor both Windows and Linux based VMs. We emphasize that this ability to decouple the hypervisor from the introspected VMs is particularly useful in the emerging cloud computing paradigm, where the virtualization infrastructure and the VMs are managed by different entities. Finally, we show that these approaches can be implemented with low overhead, making them practical for real world deployment.

Widespread adoption of virtualization has resulted in an increased interest in Virtual Machine (VM) introspection. To perform useful analysis of the introspected VMs, hypervisors must deal with the semantic gap between the low-level information available to them and the high-level OS abstractions they need. To bridge this gap, systems have proposed making assumptions derived from the operating system source code or symbol information. As a consequence, the resulting systems create a tight coupling between the hypervisor and the operating systems run by the introspected VMs. This coupling is undesirable because any change to the internals of the operating system can render the output of the introspection system meaningless. In particular, malicious software can evade detection by making modifications to the introspected OS that break these assumptions. Instead, in this thesis, we introduce Architectural Introspection, a new introspection approach that does not require information about the internals of the introspected VMs. Our approach restricts itself to leveraging constraints placed on the VM by the hardware and the external environment. To interact with both of these, the VM must use externally specified interfaces that are both stable and not linked with a specific version of an operating system. Therefore, systems that rely on architectural introspection are more versatile and more robust than previous approaches to VM introspection. To illustrate the increased versatility and robustness of architectural introspection, we describe two systems, Patagonix and P2, that can be used to detect rootkits and unpatched software, respectively. We also detail Attestation Contracts, a new approach to attestation that relies on architectural introspection to improve on existing attestation approaches. We show that because these systems do not make assumptions about the operating systems used by the introspected VMs, they can be used to monitor both Windows and Linux based VMs. We emphasize that this ability to decouple the hypervisor from the introspected VMs is particularly useful in the emerging cloud computing paradigm, where the virtualization infrastructure and the VMs are managed by different entities. Finally, we show that these approaches can be implemented with low overhead, making them practical for real world deployment.

Språkrådet besvarar språkfrågor på internet via sin webbaserade frågelåda, men den ger inte så många svar som den skulle kunna. Jag har undersökt hur frågelådan bjuder in besökarna till dialog och hur den upprätthåller dialogen i enlighet med inbjudan. Jag har också undersökt hur den tekniska plattform som frågelådan vilar på bidrar till kommunikationen. Det visade sig att en del av frågelådans erbjudande är nästan omöjligt att ta del av på grund av tekniska och språkliga begränsningar, men också att det bör vara möjligt att med relativt enkla språkteknologiska medel minska det glapp som finns mellan frågelådan och dess sökare.

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A l'heure actuelle, les images satellites constituent une source d'information incontournable face à de nombreux enjeux environnementaux (déforestation, caractérisation des paysages, aménagement du territoire, etc.). En raison de leur complexité, de leur volume important et des besoins propres à chaque communauté, l'analyse et l'interprétation des images satellites imposent de nouveaux défis aux méthodes de fouille de données. Le parti-pris de cette thèse est d'explorer de nouvelles approches, que nous situons à mi-chemin entre représentation des connaissances et apprentissage statistique, dans le but de faciliter et d'automatiser l'extraction d'informations pertinentes du contenu de ces images. Nous avons, pour cela, proposé deux nouvelles méthodes qui considèrent les images comme des données quantitatives massives dépourvues de labels sémantiques et qui les traitent en se basant sur les connaissances disponibles. Notre première contribution est une approche hybride, qui exploite conjointement le raisonnement à base d'ontologie et le clustering semi-supervisé. Le raisonnement permet l'étiquetage sémantique des pixels à partir de connaissances issues du domaine concerné. Les labels générés guident ensuite la tâche de clustering, qui permet de découvrir de nouvelles classes tout en enrichissant l'étiquetage initial. Notre deuxième contribution procède de manière inverse. Dans un premier temps, l'approche s'appuie sur un clustering topographique pour résumer les données en entrée et réduire de ce fait le nombre de futures instances à traiter par le raisonnement. Celui-ci n'est alors appliqué que sur les prototypes résultant du clustering, l'étiquetage est ensuite propagé automatiquement à l'ensemble des données de départ. Dans ce cas, l'importance est portée sur l'optimisation du temps de raisonnement et à son passage à l'échelle. Nos deux approches ont été testées et évaluées dans le cadre de la classification et de l'interprétation d'images satellites. Les résultats obtenus sont prometteurs et montrent d'une part, que la qualité de la classification peut être améliorée par une prise en compte automatique des connaissances et que l'implication des experts peut être allégée, et d'autre part, que le recours au clustering topographique en amont permet d'éviter le calcul des inférences sur la totalité des pixels de l'image. / Satellite images have become a valuable source of information for Earth observation. They are used to address and analyze multiple environmental issues such as landscapes characterization, urban planning or biodiversity conservation to cite a few.Despite of the large number of existing knowledge extraction techniques, the complexity of satellite images, their large volume, and the specific needs of each community of practice, give rise to new challenges and require the development of highly efficient approaches.In this thesis, we investigate the potential of intelligent combination of knowledge representation systems with statistical learning. Our goal is to develop novel methods which allow automatic analysis of remote sensing images. We elaborate, in this context, two new approaches that consider the images as unlabeled quantitative data and examine the possible use of the available domain knowledge.Our first contribution is a hybrid approach, that successfully combines ontology-based reasoning and semi-supervised clustering for semantic classification. An inference engine first reasons over the available domain knowledge in order to obtain semantically labeled instances. These instances are then used to generate constraints that will guide and enhance the clustering. In this way, our method allows the improvement of the labeling of existing classes while discovering new ones.Our second contribution focuses on scaling ontology reasoning over large datasets. We propose a two step approach where topological clustering is first applied in order to summarize the data, in term of a set of prototypes, and reduces by this way the number of future instances to be treated by the reasoner. The representative prototypes are then labeled using the ontology and the labels automatically propagated to all the input data.We applied our methods to the real-word problem of satellite images classification and interpretation and the obtained results are very promising. They showed, on the one hand, that the quality of the classification can be improved by automatic knowledge integration and that the involvement of experts can be reduced. On the other hand, the upstream exploitation of topographic clustering avoids the calculation of the inferences on all the pixels of the image.