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The need for higher-level reasoning capabilities beyond low-level sensor abilities has prompted researchers to use different types of sensor fusion techniques for better situational awareness in the intrusion detection environment. These techniques primarily vary in terms of their mission objectives. Some prioritize alerts for alert reduction, some cluster alerts to identify common attack patterns, and some correlate alerts to identify multi-staged attacks. Each of these tasks has its own merits. Unlike previous efforts in this area, this dissertation combines the primary tasks of sensor alert fusion, i.e., alert prioritization, alert clustering and alert correlation into a single framework such that individual results are used to quantify a confidence score as an overall assessment for global diagnosis of a system?s security health. Such a framework is especially useful in a multi-sensor environment where the sensors can collaborate with or complement each other to provide increased reliability, making it essential that the outputs of the sensors are fused in an effective manner in order to provide an improved understanding of the security status of the protected resources in the distributed environment. This dissertation uses a possibilistic approach in intelligent fusion of sensor alerts with Fuzzy Cognitive Modeling in order to accommodate the impreciseness and vagueness in knowledge-based reasoning. We show that our unified architecture for sensor fusion provides better insight into the security health of systems. A new multi-level alert clustering method is developed to accommodate inexact matching in alert features and is shown to provide relevance to more alerts than traditional exact clustering. Alert correlation with a new abstract incident modeling technique is shown to deal with scalability and uncertainty issues present in traditional alert correlation. New concepts of dynamic fusion are presented for overall situation assessment, which a) in case of misuse sensors, combines results of alert clustering and alert correlation, and b) in case of anomaly sensors, corroborates evidence from primary and secondary sensors for deriving the final conclusion on the systems? security health.

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"Much work has been done to develop intelligent tutoring systems in domains such as algebra, geometry, and computer programming. Our work is to develop an intelligent tutoring system to train US soldiers. One main difference in this domain is that one of the main skills to be learned is cooperation between teammates, so the tutor must emphasize collaboration as a skill. In addition, to help train this skill the system must be able to run in real-time, and provide both computer generated teammates, as well as intelligent opposing forces. This system is the first real-time, multi-user, model tracing tutor with simulated teammates. The goal of this thesis is to build a prototype system to validate that this is a valid approach for this domain."

<p>Battletank crew must perform many diverse tasks during a normal mission: Crewmembers have to navigate, communicate, control on-board systems, and engage with the enemy, to mention a few. As human processing capacity is limited, the crewmembers will find themselves in situations where task requirements, due to the number of tasks and task complexity, exceed their mental capacity. The stress that results from mental overload has documented quantitative and qualitative effects on performance; effects that could lead to mission failure. </p><p>This thesis describes a simulation of tankcrew during a mission where mental workload is a key factor to the outcome of mission performance. The thesis work has given rise to a number of results. First, conceptual models have been developed of the tank crewmembers. Mental workload is represented in these models as a behavior moderator, which can be manipulated to demonstrate and predict behavioral effects. Second, cognitive models of the tank crewmembers are implemented as Soar agents, which interact with tanks in a 3D simulated battlefield. The empirical data underlying these models was collected from experiments with tankcrew, and involved first hand observations and task analyses. Afterwards, the model’s behavior was verified against an a priori established behavioral pattern and successfully face validated with two subject matter experts.</p>

This thesis focuses on modeling Expectation-Violation Effect, which is the superior recall of weakly associated pairs of words over strongly associated pairs. The goal of this thesis is to provide an exploratory computational model. A virtual experiment is conducted based on the datasets used in the psychological experiment by Amster et al. (1992). The computational modeling of this phenomenon is carried in the medium of ACT-R cognitive architecture.

abstract: The present study explored the use of augmented reality (AR) technology to support cognitive modeling in an art-based learning environment. The AR application used in this study made visible the thought processes and observational techniques of art experts for the learning benefit of novices through digital annotations, overlays, and side-by-side comparisons that when viewed on mobile device appear directly on works of art. Using a 2 x 3 factorial design, this study compared learner outcomes and motivation across technologies (audio-only, video, AR) and groupings (individuals, dyads) with 182 undergraduate and graduate students who were self-identified art novices. Learner outcomes were measured by post-activity spoken responses to a painting reproduction with the pre-activity response as a moderating variable. Motivation was measured by the sum score of a reduced version of the Instructional Materials Motivational Survey (IMMS), accounting for attention, relevance, confidence, and satisfaction, with total time spent in learning activity as the moderating variable. Information on participant demographics, technology usage, and art experience was also collected. Participants were randomly assigned to one of six conditions that differed by technology and grouping before completing a learning activity where they viewed four high-resolution, printed-to-scale painting reproductions in a gallery-like setting while listening to audio-recorded conversations of two experts discussing the actual paintings. All participants listened to expert conversations but the video and AR conditions received visual supports via mobile device. Though no main effects were found for technology or groupings, findings did include statistically significant higher learner outcomes in the elements of design subscale (characteristics most represented by the visual supports of the AR application) than the audio-only conditions. When participants saw digital representations of line, shape, and color directly on the paintings, they were more likely to identify those same features in the post-activity painting. Seeing what the experts see, in a situated environment, resulted in evidence that participants began to view paintings in a manner similar to the experts. This is evidence of the value of the temporal and spatial contiguity afforded by AR in cognitive modeling learning environments. / Dissertation/Thesis / Doctoral Dissertation Educational Technology 2016

Cette thèse en informatique présente un travail de modélisation cognitive computationnelle, à partir de données de mouvements oculaires lors de tâches de recherche d'information dans des textes. Nous nous intéressons à cette situation quotidienne de recherche d'informations dans un journal ou une page web, dans laquelle il faut juger si un texte est sémantiquement relié ou non à un but, exprimé par quelques mots. Parce que le temps est souvent une contrainte, les textes ne sont souvent pas entièrement lus avant qu'intervienne la décision. Plus précisément, nous avons analysé les mouvements des yeux dans deux tâches de recherche d'information consistant à lire un paragraphe et à décider rapidement i) s'il est associé à un but donné et ii) s'il est plus associé à un but donné qu'un autre paragraphe traité auparavant. Un modèle est proposé pour chacune de ces situations. Nos simulations sont réalisées au niveau des fixations et des saccades oculaires. En particulier, nous prédisons le moment auquel les participants décident d'abandonner la lecture du paragraphe parce qu'ils ont suffisamment d'information pour prendre leur décision. Les modèles font ces prédictions par rapport aux mots qui sont susceptibles d'être traités avant que le paragraphe soit abandonné. Les jugements d'association sémantiques humains sont reproduits par le calcul des similarités sémantiques entre mots produits par l'analyse de la sémantique latente (LSA, Landauer et al., 2007). Nous avons suivi une approche statistique paramétrique dans la construction de nos modèles. Ils sont basés sur un classifieur bayésien. Nous proposons un seuil linéaire bi-dimensionnel pour rendre compte de la décision d'arrêter de lire un paragraphe, utilisant le Rang de la fixation et i) la similarité sémantique (Cos) entre le paragraphe et le but ainsi que ii) la différence de similarité sémantique (Gap) entre chaque paragraphe et le but. Pour chacun des modèles, les performances montrent que nous sommes capables de reproduire en moyenne le comportement des participants face aux tâches de recherche d'information étudiées durant cette thèse. Cette thèse comprend deux parties principales : 1) la conception et la passation d'expériences psychophysiques pour acquérir des données de mouvements oculaires et 2) le développement et le test de modèles cognitifs computationnels. / This computer science thesis presents a computational cognitive modeling work using eye movements of people faced to different information search tasks on textual material. We studied situations of everyday life when people are seeking information on a newspaper or a web page. People should judge whether a piece of text is semantically related or not to a goal expressed by a few words. Because quite often time is a constraint, texts may not be entirely processed before the decision occurs. More specifically, we analyzed eye movements during two information search tasks: reading a paragraph with the task of quickly deciding i) if it is related or not to a given goal and ii) whether it is better related to a given goal than another paragraph processed previously. One model is proposed for each of these situations. Our simulations are done at the level of eye fixations and saccades. In particular, we predicted the time at which participants would decide to stop reading a paragraph because they have enough information to make their decision. The models make predictions at the level of words that are likely to be fixated before a paragraph is abandoned. Human semantic judgments are mimicked by computing the semantic similarities between sets of words using Latent Semantic Analysis (LSA) (Landauer et al., 2007). We followed a statistical parametric approach in the construction of our models. The models are based on a Bayesian classifier. We proposed a two-variable linear threshold to account for the decision to stop reading a paragraph, based on the Rank of the fixation and i) the semantic similarity (Cos) between the paragraph and the goal and ii) the difference of semantic similarities (Gap) between each paragraph and the goal. For both models, the performance results showed that we are able to replicate in average people's behavior faced to the information search tasks studied along the thesis. The thesis includes two main parts: 1) designing and carrying out psychophysical experiments in order to acquire eye movement data and 2) developing and testing the computational cognitive models.

Battletank crew must perform many diverse tasks during a normal mission: Crewmembers have to navigate, communicate, control on-board systems, and engage with the enemy, to mention a few. As human processing capacity is limited, the crewmembers will find themselves in situations where task requirements, due to the number of tasks and task complexity, exceed their mental capacity. The stress that results from mental overload has documented quantitative and qualitative effects on performance; effects that could lead to mission failure. This thesis describes a simulation of tankcrew during a mission where mental workload is a key factor to the outcome of mission performance. The thesis work has given rise to a number of results. First, conceptual models have been developed of the tank crewmembers. Mental workload is represented in these models as a behavior moderator, which can be manipulated to demonstrate and predict behavioral effects. Second, cognitive models of the tank crewmembers are implemented as Soar agents, which interact with tanks in a 3D simulated battlefield. The empirical data underlying these models was collected from experiments with tankcrew, and involved first hand observations and task analyses. Afterwards, the model’s behavior was verified against an a priori established behavioral pattern and successfully face validated with two subject matter experts.