Situational awareness can be a valuable indicator of the performance of flight crews and the way pilots manage navigation information can be relevant to its estimation. In this research, dynamic Bayesian networks are applied to a dataset of variables both collected in real time during simulated flights and added with expert knowledge. This paper compares different approaches to the discretizati...

of the Dissertation Acoustically-Driven Talking Face Animations Using Dynamic Bayesian Networks

It is challenging to recognize facial action unit (AU) from spontaneous facial displays, especially when they are accompanied by speech. The major reason is that the information is extracted from a single source, i.e., the visual channel, in the current practice. However, facial activity is highly correlated with voice in natural human communications. Instead of solely improving visual observat...

In recent years, the growing interest toward complex critical infrastructures and their interdependencies have solicited new efforts in the area of modeling and analysis of large interdependent systems. Cascading effects are a typical phenomenon of dependencies of components inside a system or among systems. The present paper deals with the modeling of cascading effects in a power grid. In part...

Bayesian networks have been successfully applied to the assessment of user properties which remain unchanged during a session. However, many properties of a person vary over time, thus raising new questions of network modeling. In this paperwe characterize different types of dependencies that occur in networks that deal with the modeling of temporally variable user properties. We show how exist...

We present a speech modeling methodology where no a priori assumption is made on the dependencies between the observed and the hidden speech processes. Rather, dependencies are learned form data. This methodology guarantees improvement in modeling fidelity compared to HMMs. In addition, it gives the user a control on the trade-off between modeling accuracy and model complexity. Furthermore, the...

The effect of inaccuracies in the parameters of a dynamic Bayesian network can be investigated by subjecting the network to a sensitivity analysis. Having detailed the sensitivity functions involved in our previous work, we now study the effect of parameter inaccuracies on a recommended decision in view of a threshold decisionmaking model. We describe the effect of varying one or more parameter...

Evidence of the strong relationship between learning and emotion has fueled recent work in modeling affective states in intelligent tutoring systems. Many of these models are based on general models of affect without a specific focus on learner emotions. This paper presents work that investigates the benefits of using theoretical models of learner emotions to guide the development of Bayesian n...