A comparison between probabilistic and Dempster-Shafer Theory approaches to Model Uncertainty Analysis in the Performance Assessment of Radioactive Waste Repositories

Model uncertainty is a primary source of uncertainty in the assessment of the performance of repositories for the disposal of nuclear wastes, due to the complexity of the system and the large spatial and temporal scales involved. This work considers multiple assumptions on the system behavior and corresponding alternative plausible modeling hypotheses. To characterize the uncertainty in the correctness of the different hypotheses, the opinions of different experts are treated probabilistically or, in alternative, by the belief and plausibility functions of the Dempster-Shafer theory. A comparison is made with reference to a flow model for the evaluation of the hydraulic head distributions present at a radioactive waste repository site. Three experts are assumed available for the evaluation of the uncertainties associated with the hydrogeological properties of the repository and the groundwater flow mechanisms

Detecting Riots with Uncertain Information on the Semantic Web

PhDThe ubiquitous nature of CCTV Surveillance cameras means substantial amounts of data being generated. In case of an investigation, this data must be manually browsed and analysed in search of relevant information for the case. As an example, it took more than 450 detectives to examine the hundreds of thousands of hours of videos in the investigation of the 2011 London Riots: one of the largest the London's MET police has ever seen. Anything that can help the security forces save resources in investigations such as this, is valuable. Consequently, automatic analysis of surveillance scenes is a growing research area. One of the research fronts tackling this issue, is the semantic understanding of the scene. In this, the output of computer vision algorithms is fed into Semantic Frameworks, which combine all the information from different sources and try to reach a better knowledge of the scene. However, representing and reasoning with imprecise and uncertain information remains an outstanding issue in current implementations. The Demspter-Sha er (DS) Theory of Evidence has been proposed as a way to deal with imprecise and uncertain information. In this thesis we use it for the main contributions. In our rst contribution, we propose the use of the DS theory and its Transferable Belief Model (TBM) realisation as a way to combine Bayesian priors, using the subjectivist view of the Bayes' Theorem, where the probabilities are beliefs. We rst compute the a priori probabilities of all the pair of events in the model. Then a global potential is created for each event using the TBM. This global potential will encode all the prior knowledge for that particular concept. This has the bene t that when this potential is included in a knowledge base because it has been learned, all the knowledge it entails comes with it. We also propose a semantic web reasoner based on the TBM. This reasoner consists of an ontology to model any domain knowledge using the TBM constructs of Potentials, Focal Elements, and Con gurations. The reasoner also consists of the implementations of the TBM operations in a semantic web framework. The goal is that after the model has been created, the TBM operations can be applied and the knowledge combined and queried. These operations are computationally complex, so we also propose parallel heuristics to the TBM operations. This allows us to apply this paradigm on problems of thousands of records. The nal contribution, is the use of the TBM semantic framework with the method to combine the prior knowledge to detect riots on CCTV footage from the 2011 London riots. We use around a million and a half manually annotated frames with 6 di erent concepts related to the riot detection task, train the system, and infer the presence of riots in the test dataset. Tests show that the system yields a high recall, but a low precision, meaning that there are a lot of false positives. We also show that the framework scales well as more compute power becomes available

Belief augmented frames

Ph.DDOCTOR OF PHILOSOPH

Reconnaissance de contexte stable pour l'habitat intelligent

L'habitat intelligent est l'objet de nombreux travaux de recherche. Il permet d'assister des personnes âgées ou handicapées, d'améliorer le confort, la sécurité ou encore d'économiser de l'énergie. Aujourd'hui, l'informatique ubiquitaire se développe et s'intègre dans l'habitat intelligent notamment en apportant la sensibilité au contexte. Malheureusement, comprendre ce qui se passe dans une maison n'est pas toujours facile. Dans cette thèse, nous explicitons comment le contexte peut permettre de déployer des services adaptés aux activités et aux besoins des habitants. La compréhension du contexte passe par l'installation de capteurs mais aussi par l'abstraction des données brutes en données intelligibles facilement exploitables par des humains et des services. Nous mettons en avant une architecture multi-couches de fusion de données permettant d'obtenir des données contextuelles de niveaux d'abstraction différents. La mise en place des couches basses y est présentée en détail avec l'application de la théorie des fonctions de croyance pour l'abstraction de données brutes issues de capteurs. Enfin, sont présentés le déploiement d'un prototype nous ayant permis de valider notre approche, ainsi que les services déployés.Smart home is a major subject of interest. It helps to assist elderly or disabled people, improve comfort, safety, and also save energy. Today, ubiquitous computing is developed and integrated into the smart home providing context-awareness. Unfortunately, understanding what happens in a home is not always easy. In this thesis, we explain how context can be used to deploy services tailored to the activities and needs of residents. Understanding context requires the installation of sensors but also the abstraction of raw data into easily understandable data usable by humans and services. We present a multi-layer architecture of data fusion used to obtain contextual information of different levels of abstraction. The implementation of the lower layers is presented in detail with the application of the theory of belief functions for the abstraction of raw sensor data. Finally, are presented the deployment of a prototype that allowed us to validate our approach and the deployed services.RENNES1-Bibl. électronique (352382106) / SudocSudocFranceF

Implementing Belief Function Computations

This papers discusses several implementation aspects for Dempster-Shafer belief functions. The main objective is to propose an appropriate representation of mass functions and efficient data structures and algorithms for the two basic operations of combination and marginalization.