This thesis is dedicated to the development of statistical and computational methods for the analysis of DNA sequences and gene expression time series.<br /><br />First we study a parsimonious Markov model called Mixture Transition Distribution (MTD) model which is a mixture of Markovian transitions. The overly high number of constraints on the parameters of this model hampers the formulation of an analytical expression of the Maximum Likelihood Estimate (MLE). We propose to approach the MLE thanks to an EM algorithm. After comparing the performance of this algorithm to results from the litterature, we use it to evaluate the relevance of MTD modeling for bacteria DNA coding sequences in comparison with standard Markovian modeling.<br /><br />Then we propose two different approaches for genetic regulation network recovering. We model those genetic networks with Dynamic Bayesian Networks (DBNs) whose edges describe the dependency relationships between time-delayed genes expression. The aim is to estimate the topology of this graph despite the overly low number of repeated measurements compared with the number of observed genes. <br /><br />To face this problem of dimension, we first assume that the dependency relationships are homogeneous, that is the graph topology is constant across time. Then we propose to approximate this graph by considering partial order dependencies. The concept of partial order dependence graphs, already introduced for static and non directed graphs, is adapted and characterized for DBNs using the theory of graphical models. From these results, we develop a deterministic procedure for DBNs inference. <br /><br />Finally, we relax the homogeneity assumption by considering the succession of several homogeneous phases. We consider a multiple changepoint<br />regression model. Each changepoint indicates a change in the regression model parameters, which corresponds to the way an expression level depends on the others. Using reversible jump MCMC methods, we develop a stochastic algorithm which allows to simultaneously infer the changepoints location and the structure of the network within the phases delimited by the changepoints. <br /><br />Validation of those two approaches is carried out on both simulated and real data analysis.

Les systèmes de dialogue homme-machine ont pour objectif de permettre un échange oral efficace et convivial entre un utilisateur humain et un ordinateur. Leurs domaines d'applications sont variés, depuis la gestion d'échanges commerciaux jusqu'au tutorat ou l'aide à la personne. Cependant, les capacités de communication de ces systèmes sont actuellement limités par leur aptitude à comprendre la parole spontanée. Nos travaux s'intéressent au module de compréhension de la parole et présentent une proposition entièrement basée sur des approches stochastiques, permettant l'élaboration d'une hypothèse sémantique complète. Notre démarche s'appuie sur une représentation hiérarchisée du sens d'une phrase à base de frames sémantiques. La première partie du travail a consisté en l'élaboration d'une base de connaissances sémantiques adaptée au domaine du corpus d'expérimentation MEDIA (information touristique et réservation d'hôtel). Nous avons eu recours au formalisme FrameNet pour assurer une généricité maximale à notre représentation sémantique. Le développement d'un système à base de règles et d'inférences logiques nous a ensuite permis d'annoter automatiquement le corpus. La seconde partie concerne l'étude du module de composition sémantique lui-même. En nous appuyant sur une première étape d'interprétation littérale produisant des unités conceptuelles de base (non reliées), nous proposons de générer des fragments sémantiques (sous-arbres) à l'aide de réseaux bayésiens dynamiques. Les fragments sémantiques générés fournissent une représentation sémantique partielle du message de l'utilisateur. Pour parvenir à la représentation sémantique globale complète, nous proposons et évaluons un algorithme de composition d'arbres décliné selon deux variantes. La première est basée sur une heuristique visant à construire un arbre de taille et de poids minimum. La seconde s'appuie sur une méthode de classification à base de séparateurs à vaste marge pour décider des opérations de composition à réaliser. Le module de compréhension construit au cours de ce travail peut être adapté au traitement de tout type de dialogue. Il repose sur une représentation sémantique riche et les modèles utilisés permettent de fournir des listes d'hypothèses sémantiques scorées. Les résultats obtenus sur les données expérimentales confirment la robustesse de l'approche proposée aux données incertaines et son aptitude à produire une représentation sémantique consistante / Spoken dialog systems enable users to interact with computer systems via natural dialogs, as they would with human beings. These systems are deployed into a wide range of application fields from commercial services to tutorial or information services. However, the communication skills of such systems are bounded by their spoken language understanding abilities. Our work focus on the spoken language understanding module which links the automatic speech recognition module and the dialog manager. From the user’s utterance analysis, the spoken language understanding module derives a representation of its semantic content upon which the dialog manager can decide the next best action to perform. The system we propose introduces a stochastic approach based on Dynamic Bayesian Networks (DBNs) for spoken language understanding. DBN-based models allow to infer and then to compose semantic frame-based tree structures from speech transcriptions. First, we developed a semantic knowledge source covering the domain of our experimental corpus (MEDIA, a French corpus for tourism information and hotel booking). The semantic frames were designed according to the FrameNet paradigm and a hand-craft rule-based approach was used to derive the seed annotated training data.Then, to derive automatically the frame meaning representations, we propose a system based on a two decoding step process using DBNs : first basic concepts are derived from the user’s utterance transcriptions, then inferences are made on sequential semantic frame structures, considering all the available previous annotation levels. The inference process extracts all possible sub-trees according to lower level information and composes the hypothesized branches into a single utterance-span tree. The composition step investigates two different algorithms : a heuristic minimizing the size and the weight of the tree ; a context-sensitive decision process based on support vector machines for detecting the relations between the hypothesized frames. This work investigates a stochastic process for generating and composing semantic frames using DBNs. The proposed approach offers a convenient way to automatically derive semantic annotations of speech utterances based on a complete frame hierarchical structure. Experimental results, obtained on the MEDIA dialog corpus, show that the system is able to supply the dialog manager with a rich and thorough representation of the user’s request semantics

Redes Bayesianas Dinâmicas (DBNs) são modelos capazes de representar um sistema dinâmico por meio de uma rede complexa que codifica as independências estatísticas condicionais entre os seus estados internos. Entre seus métodos de aprendizagem estrutural a partir de dados, o uso daqueles baseados em teoria de informação têm ganhado bastante espaço nos últimos anos, devido às suas vantages de serem livres de modelo e permitirem uma aprendizagem offline a partir de medidas em múltiplas repetições do experimento. No entanto, resta uma exploração dos paralelos entre a área de aprendizagem de DBNs e aquela interessada em realizar medidas de transferência de informação entre elementos de um sistema neural, principalmente por meio de transferência de entropia (TE). O presente trabalho busca, assim, aproximar estes dois focos de pesquisa, identificando suas equivalências e tratando de alguns dos desafios relacionados à sua implementação em identificação de sistemas neurais. Nota-se que uma das maiores dificuldades relacionadas ao uso de teoria de informação em sistemas multivariados concerne a alta dimensionalidade das funções de distribuição de probabilidade, exigindo grandes quantidades de dados observados simultaneamente. Não obstante, a aplicação de DBNs e transferência de entropia em sistemas de tempo contínuo também envolve considerações sobre a discretização dos sistemas no tempo, o que implica na necessidade de relaxamento da suposição da propriedade de Markov de primeira ordem (presente na definição de DBNs), e leva, assim, à proposta de redes Bayesianas dinâmicas de altas ordens (HO-DBNs). Além de realizar uma revisão das principais propostas para a solução destas dificuldades, o trabalho primeiramente propõe que, sob a suposição de um sistema com elementos se comportando de forma igual, os valores das medidas baseadas em teoria de informação com baixa dimensionalidade podem ser utilizados para a aprendizagem de estruturas de rede. Isso é mostrado a partir do uso de informação mútua par a par para a aprendizagem de redes Bayesianas simuladas com distribuições de probabilidade condicional fixas. No que concerne o uso de HO-DBNs, também se propõe um algoritmo baseado em otimização por enxame de partículas (PSO) para percorrer o espaço de busca de estruturas de HO-DBNs de forma mais eficiente. Em seguida, duas aplicações de modelagem de DBNs com uso de teoria de informação são exploradas na área de sistemas neurais, tendo em vista a obtenção de conhecimento acerca de conectividade funcional e até uma aplicação futura em engenharia bioinspirada. Os desafios apresentados anteriormente são, assim, exemplificados, junto com algumas propostas de solução. A primeira área diz respeito à elicitação de conectividade funcional entre as sub-áreas do hipocampo, no cérebro humano, a partir de dados de ressonância magnética funcional (fMRI) de alta resolução. A partir de uma análise seed-to-voxel em grupo, regiões de interesse (ROIs) são identificadas e um modelo inicial de DBN é proposto, que é coerente com alguns estudos já feitos na literatura. A segunda área de aplicação concerne a conectividade neural do sistema neuromotor do gafanhoto, a partir de gravações intracelulares de potencial sináptico em neurônios sensores, motores e interneurônios, sob estimulação com um fórceps no órgão femoral cordotonal (FeCO). Embora um modelo completo de DBN ainda não seja possível devido à ausência de gravações simultâneas suficientes, os atrasos de transferência de entropia entre o estímulo e a resposta nos neurônios motores são obtidos e integrados a partir de uma análise Bayesiana, dado também um pré-processamento com análise de espectro singular (SSA) que, ao remover a não-estacionariedade do sinal (que se deve a fatores extrínsecos ao sistema), aumentou consideravelmente a quantidade de amostras disponíveis. Tais resultados, ao ajudar a reduzir o espaço de busca de DBNs, também servem para direcionar futuros experimentos e pesquisas na área. / Dynamic Bayesian Networks (DBNs) are models capable of representing a dynamical system by means of a complex network which codifies statistical conditional independencies between their internal states. Among their strucutural learning methods based on data, the use of ones based on information theory are gaining ground in recent years, due to their advantages of being model-free and permitting offline learning from multiple repetitions of an experiment. However, there still remains an exploration of the parallels between the areas of DBN structure learning and those interested in obtaining measures of information transfer between elements of neural systems, mainly through transfer entropy (TE). Thus, the current work seeks to approximate these two foci of research by identifying some of their equivalences and challenges related to their usage in neural systems identification. It is noted that one of the main difficulties related to the use of information theory in multivariate neural systems concerns the high dimensionality of the probability distribution functions, requiring thus great quantities of data observed simultaneously. Furthermore, the application of DBNs and transfer entropy on continuous time systems also involves considerations about their discretization on time, which implies the necessity of relaxing the first order Markov property (instrinsinc to the definition of DBNs), and thus leads to the proposal of high-order dynamic Bayesian networks (HO-DBNs). Besides performing a review on the main proposals for solving these difficulties, this work first proposes that, under the supposition of a system with elements behaving in a similar way, the values of information theory based measures with low dimensions can be employed for learning network structures. This is shown with the use of pairwise mutual information for learning simulated Bayesian networks with fixed conditional probability distributions. And concerning the use of HO-DBNs, an algorithm based on PSO is proposed in order to pass through their search space more efficiently. Next, two applications of DBN modeling with information theory are explored in the field of neural systems, in view of obtaining knowledge about functional connectivity and even of a future application of bioinspired engineering. The challenged presented earlier are then exemplified along with some proposals of solutions. The first field regards the elicitation of functional connectivity between hippocampal subfields on the human brain based of high resolution fMRI data. Starting from a seed-to-voxel group analysis, regions of interest (ROIs) are identified and an initial DBN model is proposed, which is coherent with some studies already conducted in the literature. The second field of application concerns the neural connectivity between the neuromotor system of the locust, based on intracellular synaptic potential recordings on sensory neurons, interneurons and motor neurons under stimulation by a forceps in the femoral chordotonal organ (FeCO). Although a complete DBN model is still not possible due to the absence of sufficient and simultaneous recordings, the transfer entropy delays between stimulus and responses on the motor neuros are obtained and integrated by a Bayesian analysis, given also a pre-processing based on Singular Spectrum Analysis (SSA) which, by removing the nonstationarity characteristics of the signal (which are due to extrinsic factors on the system), considerably increased the number of available samples for learning. Such results, by helping to reduce the search space of DBNs, also direct further experiments and studies on this field.

Redes Bayesianas Dinâmicas (DBNs) são modelos capazes de representar um sistema dinâmico por meio de uma rede complexa que codifica as independências estatísticas condicionais entre os seus estados internos. Entre seus métodos de aprendizagem estrutural a partir de dados, o uso daqueles baseados em teoria de informação têm ganhado bastante espaço nos últimos anos, devido às suas vantages de serem livres de modelo e permitirem uma aprendizagem offline a partir de medidas em múltiplas repetições do experimento. No entanto, resta uma exploração dos paralelos entre a área de aprendizagem de DBNs e aquela interessada em realizar medidas de transferência de informação entre elementos de um sistema neural, principalmente por meio de transferência de entropia (TE). O presente trabalho busca, assim, aproximar estes dois focos de pesquisa, identificando suas equivalências e tratando de alguns dos desafios relacionados à sua implementação em identificação de sistemas neurais. Nota-se que uma das maiores dificuldades relacionadas ao uso de teoria de informação em sistemas multivariados concerne a alta dimensionalidade das funções de distribuição de probabilidade, exigindo grandes quantidades de dados observados simultaneamente. Não obstante, a aplicação de DBNs e transferência de entropia em sistemas de tempo contínuo também envolve considerações sobre a discretização dos sistemas no tempo, o que implica na necessidade de relaxamento da suposição da propriedade de Markov de primeira ordem (presente na definição de DBNs), e leva, assim, à proposta de redes Bayesianas dinâmicas de altas ordens (HO-DBNs). Além de realizar uma revisão das principais propostas para a solução destas dificuldades, o trabalho primeiramente propõe que, sob a suposição de um sistema com elementos se comportando de forma igual, os valores das medidas baseadas em teoria de informação com baixa dimensionalidade podem ser utilizados para a aprendizagem de estruturas de rede. Isso é mostrado a partir do uso de informação mútua par a par para a aprendizagem de redes Bayesianas simuladas com distribuições de probabilidade condicional fixas. No que concerne o uso de HO-DBNs, também se propõe um algoritmo baseado em otimização por enxame de partículas (PSO) para percorrer o espaço de busca de estruturas de HO-DBNs de forma mais eficiente. Em seguida, duas aplicações de modelagem de DBNs com uso de teoria de informação são exploradas na área de sistemas neurais, tendo em vista a obtenção de conhecimento acerca de conectividade funcional e até uma aplicação futura em engenharia bioinspirada. Os desafios apresentados anteriormente são, assim, exemplificados, junto com algumas propostas de solução. A primeira área diz respeito à elicitação de conectividade funcional entre as sub-áreas do hipocampo, no cérebro humano, a partir de dados de ressonância magnética funcional (fMRI) de alta resolução. A partir de uma análise seed-to-voxel em grupo, regiões de interesse (ROIs) são identificadas e um modelo inicial de DBN é proposto, que é coerente com alguns estudos já feitos na literatura. A segunda área de aplicação concerne a conectividade neural do sistema neuromotor do gafanhoto, a partir de gravações intracelulares de potencial sináptico em neurônios sensores, motores e interneurônios, sob estimulação com um fórceps no órgão femoral cordotonal (FeCO). Embora um modelo completo de DBN ainda não seja possível devido à ausência de gravações simultâneas suficientes, os atrasos de transferência de entropia entre o estímulo e a resposta nos neurônios motores são obtidos e integrados a partir de uma análise Bayesiana, dado também um pré-processamento com análise de espectro singular (SSA) que, ao remover a não-estacionariedade do sinal (que se deve a fatores extrínsecos ao sistema), aumentou consideravelmente a quantidade de amostras disponíveis. Tais resultados, ao ajudar a reduzir o espaço de busca de DBNs, também servem para direcionar futuros experimentos e pesquisas na área. / Dynamic Bayesian Networks (DBNs) are models capable of representing a dynamical system by means of a complex network which codifies statistical conditional independencies between their internal states. Among their strucutural learning methods based on data, the use of ones based on information theory are gaining ground in recent years, due to their advantages of being model-free and permitting offline learning from multiple repetitions of an experiment. However, there still remains an exploration of the parallels between the areas of DBN structure learning and those interested in obtaining measures of information transfer between elements of neural systems, mainly through transfer entropy (TE). Thus, the current work seeks to approximate these two foci of research by identifying some of their equivalences and challenges related to their usage in neural systems identification. It is noted that one of the main difficulties related to the use of information theory in multivariate neural systems concerns the high dimensionality of the probability distribution functions, requiring thus great quantities of data observed simultaneously. Furthermore, the application of DBNs and transfer entropy on continuous time systems also involves considerations about their discretization on time, which implies the necessity of relaxing the first order Markov property (instrinsinc to the definition of DBNs), and thus leads to the proposal of high-order dynamic Bayesian networks (HO-DBNs). Besides performing a review on the main proposals for solving these difficulties, this work first proposes that, under the supposition of a system with elements behaving in a similar way, the values of information theory based measures with low dimensions can be employed for learning network structures. This is shown with the use of pairwise mutual information for learning simulated Bayesian networks with fixed conditional probability distributions. And concerning the use of HO-DBNs, an algorithm based on PSO is proposed in order to pass through their search space more efficiently. Next, two applications of DBN modeling with information theory are explored in the field of neural systems, in view of obtaining knowledge about functional connectivity and even of a future application of bioinspired engineering. The challenged presented earlier are then exemplified along with some proposals of solutions. The first field regards the elicitation of functional connectivity between hippocampal subfields on the human brain based of high resolution fMRI data. Starting from a seed-to-voxel group analysis, regions of interest (ROIs) are identified and an initial DBN model is proposed, which is coherent with some studies already conducted in the literature. The second field of application concerns the neural connectivity between the neuromotor system of the locust, based on intracellular synaptic potential recordings on sensory neurons, interneurons and motor neurons under stimulation by a forceps in the femoral chordotonal organ (FeCO). Although a complete DBN model is still not possible due to the absence of sufficient and simultaneous recordings, the transfer entropy delays between stimulus and responses on the motor neuros are obtained and integrated by a Bayesian analysis, given also a pre-processing based on Singular Spectrum Analysis (SSA) which, by removing the nonstationarity characteristics of the signal (which are due to extrinsic factors on the system), considerably increased the number of available samples for learning. Such results, by helping to reduce the search space of DBNs, also direct further experiments and studies on this field.

Ces dernières années, les attaques de pirates contre des navires ou des champs pétroliers n'ont cessé de se multiplier et de s'aggraver. Pour exemple, l'attaque contre la plateforme Exxon Mobil en 2010 au large du Nigeria s'est soldée par l'enlèvement de dix-neuf membres d'équipage et la réduction de 45.000 barils de sa production pétrolière quotidienne ce qui a engendré une montée des prix à l'échelle internationale.Cet exemple est une parfaite illustration de l'ampleur des dommages sur la sécurité des infrastructures pétrolières offshore.Dans le cadre de notre recherche, nous proposons une démarche de pilotage et de management du risque de piraterie en se basant sur le concept des réseaux bayésiens qui permettent la représentation des connaissances et le calcul des probabilités conditionnelles.Une dimension temporelle a été ajoutée par le recours aux réseaux bayésiens qualifiés de « dynamiques ». Ces réseaux, fondés sur les chaines de Markov cachées ou filtres de Kalman, se révèlent très performants dans le domaine de l'analyse des risques.L'application de ces réseaux au domaine de la piraterie a été envisagée, les apports et les limites seront évalués dans le cadre de cette thèse. / In recent years, pirate attacks against ships or oil fields have continued to multiply and worsen. For example, the attack against the Exxon Mobil platform in 2010 in the coast of Nigeria has resulted in the removal of nineteen crew members and the reduction of 45,000 barrels of daily oil production which resulted in a rise prices internationally.This example is a perfect illustration of the extent of damage on the safety of offshore oil infrastructure.As part of our research, we propose an approach to control and management of the risk of piracy based on the concept of Bayesian networks that enable knowledge representation and calculation of conditional probabilities.A temporal dimension was added by the use of Bayesian networks called "dynamic". These networks, based on Markov chains hidden or Kalman filters, are proving very effective in the field of risk analysis.The application of these networks on piracy was considered, the contributions and limitations will be evaluated as part of this thesis.

L’étude de la fiabilité des systèmes mécatroniques est un axe de recherche relativement récent. Ces études doivent être menées au plus tôt au cours de la phase de conception, afin de prévoir, modéliser et concevoir des systèmes fiables, disponibles et sûrs et de réduire les coûts et le nombre de prototypes nécessaires à la validation d’un système. Après avoir défini les systèmes mécatroniques et les notions de sûreté de fonctionnement et de fiabilité, nous présentons un aperçu des approches existantes (quantitatives et qualitatives) pour la modélisation et l’évaluation de la fiabilité, et nous mettons en évidence les points d’amélioration et les pistes à développer par la suite.Les principales difficultés dans les études de fiabilité des systèmes mécatroniques sont la combinaison multi-domaines (mécanique, électronique,informatique) et les différents aspects fonctionnels et dysfonctionnels (hybride, dynamique, reconfigurable et interactif). Il devient nécessaire d’utiliser de nouvelles approches pour l’estimation de la fiabilité.Nous proposons une méthodologie d’évaluation de la fiabilité prévisionnelle en phase de conception d’un système mécatronique, en prenant en compte les interactions multi-domaines entre les composants, à l’aide de la modélisation par Réseaux de Pétri,Réseaux bayésiens et fonctions de croyance.L’évaluation de la fiabilité en phase de développement doit être robuste, avec une confiance suffisante et prendre en compte tant les incertitudes épistémiques concernant les variables aléatoires d’entrée du modèle utilisé que l’incertitude sur le modèle pris en hypothèse. L’approche proposée a été appliquée à l’«actionneur intelligent» de la société Pack’ Aero. / Reliability analysis of mechatronic systems is one of the most dynamic fields of research. This analysis must be conducted during the design phase, in order to model and to design safe and reliable systems. After presenting some concepts of mechatronic systems and of dependability and reliability, we present an overview of existing approaches (quantitatives and qualitatives) for the reliability assessment and we highlight the perspectives to develop. The criticality of mechatronic systems is due, on one hand, to multi-domain combination (mechanical, electronic, software), and, on the other hand, to their different functional and dysfunctional aspects (hybrid, dynamic, reconfigurable and interactive). Therefore, new approaches for dependability assessment should be developped. We propose a methodology for reliability assessment in the design phase of a mechatronic system, by taking into account multi-domain interactions and by using modeling tools such as Petri Nets and Dynamic Bayesian Networks. Our approach also takes into account epistemic uncertainties (uncertainties of model and of parameters) by using an evidential network adapted to our model. Our methodology was applied to the reliability assessment of an "intelligent actuator" from Pack’Aero

La modélisation de l’expérience de l’utilisateur dans les Interactions Homme-Machine est un enjeu important pour la conception et le développement des systèmes adaptatifs intelligents. Dans ce contexte, une attention particulière est portée sur les réactions émotionnelles de l’utilisateur, car elles ont une influence capitale sur ses aptitudes cognitives, comme la perception et la prise de décision. La modélisation des émotions est particulièrement pertinente pour les Systèmes Tutoriels Émotionnellement Intelligents (STEI). Ces systèmes cherchent à identifier les émotions de l’apprenant lors des sessions d’apprentissage, et à optimiser son expérience d’interaction en recourant à diverses stratégies d’interventions. Cette thèse vise à améliorer les méthodes de modélisation des émotions et les stratégies émotionnelles utilisées actuellement par les STEI pour agir sur les émotions de l’apprenant. Plus précisément, notre premier objectif a été de proposer une nouvelle méthode pour détecter l’état émotionnel de l’apprenant, en utilisant différentes sources d’informations qui permettent de mesurer les émotions de façon précise, tout en tenant compte des variables individuelles qui peuvent avoir un impact sur la manifestation des émotions. Pour ce faire, nous avons développé une approche multimodale combinant plusieurs mesures physiologiques (activité cérébrale, réactions galvaniques et rythme cardiaque) avec des variables individuelles, pour détecter une émotion très fréquemment observée lors des sessions d’apprentissage, à savoir l’incertitude. Dans un premier lieu, nous avons identifié les indicateurs physiologiques clés qui sont associés à cet état, ainsi que les caractéristiques individuelles qui contribuent à sa manifestation. Puis, nous avons développé des modèles prédictifs permettant de détecter automatiquement cet état à partir des différentes variables analysées, à travers l’entrainement d’algorithmes d’apprentissage machine. Notre deuxième objectif a été de proposer une approche unifiée pour reconnaître simultanément une combinaison de plusieurs émotions, et évaluer explicitement l’impact de ces émotions sur l’expérience d’interaction de l’apprenant. Pour cela, nous avons développé une plateforme hiérarchique, probabiliste et dynamique permettant de suivre les changements émotionnels de l'apprenant au fil du temps, et d’inférer automatiquement la tendance générale qui caractérise son expérience d’interaction à savoir : l’immersion, le blocage ou le décrochage. L’immersion correspond à une expérience optimale : un état dans lequel l'apprenant est complètement concentré et impliqué dans l’activité d’apprentissage. L’état de blocage correspond à une tendance d’interaction non optimale où l'apprenant a de la difficulté à se concentrer. Finalement, le décrochage correspond à un état extrêmement défavorable où l’apprenant n’est plus du tout impliqué dans l’activité d’apprentissage. La plateforme proposée intègre trois modalités de variables diagnostiques permettant d’évaluer l’expérience de l’apprenant à savoir : des variables physiologiques, des variables comportementales, et des mesures de performance, en combinaison avec des variables prédictives qui représentent le contexte courant de l’interaction et les caractéristiques personnelles de l'apprenant. Une étude a été réalisée pour valider notre approche à travers un protocole expérimental permettant de provoquer délibérément les trois tendances ciblées durant l’interaction des apprenants avec différents environnements d’apprentissage. Enfin, notre troisième objectif a été de proposer de nouvelles stratégies pour influencer positivement l’état émotionnel de l’apprenant, sans interrompre la dynamique de la session d’apprentissage. Nous avons à cette fin introduit le concept de stratégies émotionnelles implicites : une nouvelle approche pour agir subtilement sur les émotions de l’apprenant, dans le but d’améliorer son expérience d’apprentissage. Ces stratégies utilisent la perception subliminale, et plus précisément une technique connue sous le nom d’amorçage affectif. Cette technique permet de solliciter inconsciemment les émotions de l’apprenant, à travers la projection d’amorces comportant certaines connotations affectives. Nous avons mis en œuvre une stratégie émotionnelle implicite utilisant une forme particulière d’amorçage affectif à savoir : le conditionnement évaluatif, qui est destiné à améliorer de façon inconsciente l’estime de soi. Une étude expérimentale a été réalisée afin d’évaluer l’impact de cette stratégie sur les réactions émotionnelles et les performances des apprenants. / Modeling the user’s experience within Human-Computer Interaction is an important challenge for the design and development of intelligent adaptive systems. In this context, a particular attention is given to the user’s emotional reactions, as they decisively influence his cognitive abilities, such as perception and decision-making. Emotion modeling is particularly relevant for Emotionally Intelligent Tutoring Systems (EITS). These systems seek to identify the learner’s emotions during tutoring sessions, and to optimize his interaction experience using a variety of intervention strategies. This thesis aims to improve current methods on emotion modeling, as well as the emotional strategies that are presently used within EITS to influence the learner’s emotions. More precisely, our first objective was to propose a new method to recognize the learner’s emotional state, using different sources of information that allow to measure emotions accurately, whilst taking account of individual characteristics that can have an impact on the manifestation of emotions. To that end, we have developed a multimodal approach combining several physiological measures (brain activity, galvanic responses and heart rate) with individual variables, to detect a specific emotion, which is frequently observed within computer tutoring, namely : uncertainty. First, we have identified the key physiological indicators that are associated to this state, and the individual characteristics that contribute to its manifestation. Then, we have developed predictive models to automatically detect this state from the analyzed variables, trough machine learning algorithm training. Our second objective was to propose a unified approach to simultaneously recognize a combination of several emotions, and to explicitly evaluate the impact of these emotions on the learner’s interaction experience. For this purpose, we have developed a hierarchical, probabilistic and dynamic framework, which allows one to track the learner’s emotional changes over time, and to automatically infer the trend that characterizes his interaction experience namely : flow, stuck or off-task. Flow is an optimal experience : a state in which the learner is completely focused and involved within the learning activity. The state of stuck is a non-optimal trend of the interaction where the learner has difficulty to maintain focused attention. Finally, the off-task behavior is an extremely unfavorable state where the learner is not involved anymore within the learning session. The proposed framework integrates three-modality diagnostic variables that sense the learner’s experience including : physiology, behavior and performance, in conjunction with predictive variables that represent the current context of the interaction and the learner’s personal characteristics. A human-subject study was conducted to validate our approach through an experimental protocol designed to deliberately elicit the three targeted trends during the learners’ interaction with different learning environments. Finally, our third objective was to propose new strategies to positively influence the learner’s emotional state, without interrupting the dynamics of the learning session. To this end, we have introduced the concept of implicit emotional strategies : a novel approach to subtly impact the learner’s emotions, in order to improve his learning experience. These strategies use the subliminal perception, and more precisely a technique known as affective priming. This technique aims to unconsciously solicit the learner’s emotions, through the projection of primes charged with specific affective connotations. We have implemented an implicit emotional strategy using a particular form of affective priming namely : the evaluative conditioning, which is designed to unconsciously enhance self-esteem. An experimental study was conducted in order to evaluate the impact of this strategy on the learners’ emotional reactions and performance.

La modélisation de l’expérience de l’utilisateur dans les Interactions Homme-Machine est un enjeu important pour la conception et le développement des systèmes adaptatifs intelligents. Dans ce contexte, une attention particulière est portée sur les réactions émotionnelles de l’utilisateur, car elles ont une influence capitale sur ses aptitudes cognitives, comme la perception et la prise de décision. La modélisation des émotions est particulièrement pertinente pour les Systèmes Tutoriels Émotionnellement Intelligents (STEI). Ces systèmes cherchent à identifier les émotions de l’apprenant lors des sessions d’apprentissage, et à optimiser son expérience d’interaction en recourant à diverses stratégies d’interventions. Cette thèse vise à améliorer les méthodes de modélisation des émotions et les stratégies émotionnelles utilisées actuellement par les STEI pour agir sur les émotions de l’apprenant. Plus précisément, notre premier objectif a été de proposer une nouvelle méthode pour détecter l’état émotionnel de l’apprenant, en utilisant différentes sources d’informations qui permettent de mesurer les émotions de façon précise, tout en tenant compte des variables individuelles qui peuvent avoir un impact sur la manifestation des émotions. Pour ce faire, nous avons développé une approche multimodale combinant plusieurs mesures physiologiques (activité cérébrale, réactions galvaniques et rythme cardiaque) avec des variables individuelles, pour détecter une émotion très fréquemment observée lors des sessions d’apprentissage, à savoir l’incertitude. Dans un premier lieu, nous avons identifié les indicateurs physiologiques clés qui sont associés à cet état, ainsi que les caractéristiques individuelles qui contribuent à sa manifestation. Puis, nous avons développé des modèles prédictifs permettant de détecter automatiquement cet état à partir des différentes variables analysées, à travers l’entrainement d’algorithmes d’apprentissage machine. Notre deuxième objectif a été de proposer une approche unifiée pour reconnaître simultanément une combinaison de plusieurs émotions, et évaluer explicitement l’impact de ces émotions sur l’expérience d’interaction de l’apprenant. Pour cela, nous avons développé une plateforme hiérarchique, probabiliste et dynamique permettant de suivre les changements émotionnels de l'apprenant au fil du temps, et d’inférer automatiquement la tendance générale qui caractérise son expérience d’interaction à savoir : l’immersion, le blocage ou le décrochage. L’immersion correspond à une expérience optimale : un état dans lequel l'apprenant est complètement concentré et impliqué dans l’activité d’apprentissage. L’état de blocage correspond à une tendance d’interaction non optimale où l'apprenant a de la difficulté à se concentrer. Finalement, le décrochage correspond à un état extrêmement défavorable où l’apprenant n’est plus du tout impliqué dans l’activité d’apprentissage. La plateforme proposée intègre trois modalités de variables diagnostiques permettant d’évaluer l’expérience de l’apprenant à savoir : des variables physiologiques, des variables comportementales, et des mesures de performance, en combinaison avec des variables prédictives qui représentent le contexte courant de l’interaction et les caractéristiques personnelles de l'apprenant. Une étude a été réalisée pour valider notre approche à travers un protocole expérimental permettant de provoquer délibérément les trois tendances ciblées durant l’interaction des apprenants avec différents environnements d’apprentissage. Enfin, notre troisième objectif a été de proposer de nouvelles stratégies pour influencer positivement l’état émotionnel de l’apprenant, sans interrompre la dynamique de la session d’apprentissage. Nous avons à cette fin introduit le concept de stratégies émotionnelles implicites : une nouvelle approche pour agir subtilement sur les émotions de l’apprenant, dans le but d’améliorer son expérience d’apprentissage. Ces stratégies utilisent la perception subliminale, et plus précisément une technique connue sous le nom d’amorçage affectif. Cette technique permet de solliciter inconsciemment les émotions de l’apprenant, à travers la projection d’amorces comportant certaines connotations affectives. Nous avons mis en œuvre une stratégie émotionnelle implicite utilisant une forme particulière d’amorçage affectif à savoir : le conditionnement évaluatif, qui est destiné à améliorer de façon inconsciente l’estime de soi. Une étude expérimentale a été réalisée afin d’évaluer l’impact de cette stratégie sur les réactions émotionnelles et les performances des apprenants. / Modeling the user’s experience within Human-Computer Interaction is an important challenge for the design and development of intelligent adaptive systems. In this context, a particular attention is given to the user’s emotional reactions, as they decisively influence his cognitive abilities, such as perception and decision-making. Emotion modeling is particularly relevant for Emotionally Intelligent Tutoring Systems (EITS). These systems seek to identify the learner’s emotions during tutoring sessions, and to optimize his interaction experience using a variety of intervention strategies. This thesis aims to improve current methods on emotion modeling, as well as the emotional strategies that are presently used within EITS to influence the learner’s emotions. More precisely, our first objective was to propose a new method to recognize the learner’s emotional state, using different sources of information that allow to measure emotions accurately, whilst taking account of individual characteristics that can have an impact on the manifestation of emotions. To that end, we have developed a multimodal approach combining several physiological measures (brain activity, galvanic responses and heart rate) with individual variables, to detect a specific emotion, which is frequently observed within computer tutoring, namely : uncertainty. First, we have identified the key physiological indicators that are associated to this state, and the individual characteristics that contribute to its manifestation. Then, we have developed predictive models to automatically detect this state from the analyzed variables, trough machine learning algorithm training. Our second objective was to propose a unified approach to simultaneously recognize a combination of several emotions, and to explicitly evaluate the impact of these emotions on the learner’s interaction experience. For this purpose, we have developed a hierarchical, probabilistic and dynamic framework, which allows one to track the learner’s emotional changes over time, and to automatically infer the trend that characterizes his interaction experience namely : flow, stuck or off-task. Flow is an optimal experience : a state in which the learner is completely focused and involved within the learning activity. The state of stuck is a non-optimal trend of the interaction where the learner has difficulty to maintain focused attention. Finally, the off-task behavior is an extremely unfavorable state where the learner is not involved anymore within the learning session. The proposed framework integrates three-modality diagnostic variables that sense the learner’s experience including : physiology, behavior and performance, in conjunction with predictive variables that represent the current context of the interaction and the learner’s personal characteristics. A human-subject study was conducted to validate our approach through an experimental protocol designed to deliberately elicit the three targeted trends during the learners’ interaction with different learning environments. Finally, our third objective was to propose new strategies to positively influence the learner’s emotional state, without interrupting the dynamics of the learning session. To this end, we have introduced the concept of implicit emotional strategies : a novel approach to subtly impact the learner’s emotions, in order to improve his learning experience. These strategies use the subliminal perception, and more precisely a technique known as affective priming. This technique aims to unconsciously solicit the learner’s emotions, through the projection of primes charged with specific affective connotations. We have implemented an implicit emotional strategy using a particular form of affective priming namely : the evaluative conditioning, which is designed to unconsciously enhance self-esteem. An experimental study was conducted in order to evaluate the impact of this strategy on the learners’ emotional reactions and performance.

Abstract Critical Infrastructures (CIs) are an integral part of our society and economy. Services like electricity supply or telecommunication services are expected to be available at all times and a service failure may have catastrophic consequences for society or economy. Current CI protection strategies are from a time when CIs or CI sectors could be operated more or less self-sufficient and interconnections among CIs or CI sectors, which may lead to cascading service failures to other CIs or CI sectors, where not as omnipresent as today. In this PhD thesis, a cross-sector CI model for on-line risk monitoring of CI services, called CI security model, is presented. The model allows to monitor a CI service risk and to notify services that depend on it of possible risks in order to reduce and mitigate possible cascading failures. The model estimates CI service risk by observing the CI service state as measured by base measurements (e.g. sensor or software states) within the CI service components and by observing the experienced service risk of CI services it depends on (CI service dependencies). CI service risk is estimated in a probabilistic way using a Bayesian network based approach. Furthermore, the model allows CI service risk prediction in the short-term, mid-term and long-term future, given a current CI service risk and it allows to model interdependencies (a CI service risk that loops back to the originating service via dependencies), a special case that is difficult to model using Bayesian networks. The representation of a CI as a CI security model requires analysis. In this PhD thesis, a CI analysis method based on the PROTOS-MATINE dependency analysis methodology is presented in order to analyse CIs and represent them as CI services, CI service dependencies and base measurements. Additional research presented in this PhD thesis is related to a study of assurance indicators able to perform an on-line evaluation of the correctness of risk estimates within a CI service, as well as for risk estimates received from dependencies. A tool that supports all steps of establishing a CI security model was implemented during this PhD research. The research on the CI security model and the assurance indicators was validated based on a case study and the initial results suggest its applicability to CI environments. / Tiivistelmä Tässä väitöskirjassa esitellään läpileikkausmalli kriittisten infrastruktuurien jatkuvaan käytön riskimallinnukseen. Tämän mallin avulla voidaan tiedottaa toisistaan riippuvaisia palveluita mahdollisista vaaroista, ja siten pysäyttää tai hidastaa toisiinsa vaikuttavat ja kumuloituvat vikaantumiset. Malli analysoi kriittisen infrastruktuurin palveluriskiä tutkimalla kriittisen infrastruktuuripalvelun tilan, joka on mitattu perusmittauksella (esimerkiksi anturi- tai ohjelmistotiloina) kriittisen infrastruktuurin palvelukomponenttien välillä ja tarkkailemalla koetun kriittisen infrastruktuurin palveluriskiä, joista palvelut riippuvat (kriittisen infrastruktuurin palveluriippuvuudet). Kriittisen infrastruktuurin palveluriski arvioidaan todennäköisyyden avulla käyttämällä Bayes-verkkoja. Lisäksi malli mahdollistaa tulevien riskien ennustamisen lyhyellä, keskipitkällä ja pitkällä aikavälillä, ja mahdollistaa niiden keskinäisten riippuvuuksien mallintamisen, joka on yleensä vaikea esittää Bayes-verkoissa. Kriittisen infrastruktuurin esittäminen kriittisen infrastruktuurin tietoturvamallina edellyttää analyysiä. Tässä väitöskirjassa esitellään kriittisen infrastruktuurin analyysimenetelmä, joka perustuu PROTOS-MATINE -riippuvuusanalyysimetodologiaan. Kriittiset infrastruktuurit esitetään kriittisen infrastruktuurin palveluina, palvelujen keskinäisinä riippuvuuksina ja perusmittauksina. Lisäksi tutkitaan varmuusindikaattoreita, joilla voidaan tutkia suoraan toiminnassa olevan kriittisen infrastruktuuripalvelun riskianalyysin oikeellisuutta, kuin myös riskiarvioita riippuvuuksista. Tutkimuksessa laadittiin työkalu, joka tukee kriittisen infrastruktuurin tietoturvamallin toteuttamisen kaikkia vaiheita. Kriittisen infrastruktuurin tietoturvamalli ja varmuusindikaattorien oikeellisuus vahvistettiin konseptitutkimuksella, ja alustavat tulokset osoittavat menetelmän toimivuuden. / Kurzfassung In dieser Doktorarbeit wird ein Sektorübergreifendes Modell für die kontinuierliche Risikoabschätzung von kritische Infrastrukturen im laufenden Betrieb vorgestellt. Das Modell erlaubt es, Dienstleistungen, die in Abhängigkeit einer anderen Dienstleistung stehen, über mögliche Gefahren zu informieren und damit die Gefahr des Übergriffs von Risiken in andere Teile zu stoppen oder zu minimieren. Mit dem Modell können Gefahren in einer Dienstleistung anhand der Überwachung von kontinuierlichen Messungen (zum Beispiel Sensoren oder Softwarestatus) sowie der Überwachung von Gefahren in Dienstleistungen, die eine Abhängigkeit darstellen, analysiert werden. Die Abschätzung von Gefahren erfolgt probabilistisch mittels eines Bayessches Netzwerks. Zusätzlich erlaubt dieses Modell die Voraussage von zukünftigen Risiken in der kurzfristigen, mittelfristigen und langfristigen Zukunft und es erlaubt die Modellierung von gegenseitigen Abhängigkeiten, die im Allgemeinen schwer mit Bayesschen Netzwerken darzustellen sind. Um eine kritische Infrastruktur als ein solches Modell darzustellen, muss eine Analyse der kritischen Infrastruktur durchgeführt werden. In dieser Doktorarbeit wird diese Analyse durch die PROTOS-MATINE Methode zur Analyse von Abhängigkeiten unterstützt. Zusätzlich zu dem vorgestellten Modell wird in dieser Doktorarbeit eine Studie über Indikatoren, die das Vertrauen in die Genauigkeit einer Risikoabschätzung evaluieren können, vorgestellt. Die Studie beschäftigt sich sowohl mit der Evaluierung von Risikoabschätzungen innerhalb von Dienstleistungen als auch mit der Evaluierung von Risikoabschätzungen, die von Dienstleistungen erhalten wurden, die eine Abhängigkeiten darstellen. Eine Software, die alle Aspekte der Erstellung des vorgestellten Modells unterstützt, wurde entwickelt. Sowohl das präsentierte Modell zur Abschätzung von Risiken in kritischen Infrastrukturen als auch die Indikatoren zur Uberprüfung der Risikoabschätzungen wurden anhand einer Machbarkeitsstudie validiert. Erste Ergebnisse suggerieren die Anwendbarkeit dieser Konzepte auf kritische Infrastrukturen.