With the growth on the concern about context-aware applications, it becomes important to recognize and share user context. Even though there are some applications, it is still limited in managing simple contexts. In this paper, we propose a context-aware messenger application that exploits dynamic Bayesian networks to automatically infer a user’s context and shares contextual information to enr...

This paper explores the use of multisensory information fusion technique with Dynamic Bayesian networks (DBNs) for modeling and understanding the temporal behaviors of facial expressions in image sequences. Our approach to the facial expression understanding lies in a probabilistic framework by integrating the DBNs with the facial action units (AUs) from psychological view. The DBNs provide a c...

Most practical uses of Dynamic Bayesian Networks (DBNs) involve temporal influences of the first order, i.e., influences between neighboring time steps. This choice is a convenient approximation influenced by the existence of efficient algorithms for first order models and limitations of available tools. We focus on the question whether constructing higher time-order models is worth the effort ...

We report on ongoing work on a pronunciation model based on explicit representation of the evolution of multiple linguistic feature streams. In this type of model, most pronunciation variation is viewed as the result of asynchrony between features and changes in feature values. We have implemented such a model using dynamic Bayesian networks. In this paper, we extend our previous work with a me...

A model for probabilistic assessment of excavation performance of tunnel projects is presented. The model is based on Dynamic Bayesian Networks (DBN) and enables to consider the quality of the design and construction process. It is applied on a case study, the excavation of a road tunnel by means of the New Austrian Tunnelling Method. The influence of main model parameters and assumptions (e.g....

Recently, mobile devices became essential mediums in order to implement ambient intelligence. Since people can always keep these mobile devices, it is easy for them to collect diverse user information. Therefore, many research groups have attempted to provide useful services based on this ubiquitous information. This paper proposes a method to predict user activity in the sequence of mobile con...

Modern society relies heavily on complex software systems for everyday activities. Dependability of these systems thus has become a critical feature that determines which products are going to be successfully and widely adopted. In this paper, we present an approach to modeling reliability of software systems at the architectural level. Dynamic Bayesian Networks are used to build a stochastic r...

The application of the Bayesian Structural EM algorithm to learn Bayesian networks for clustering implies a search over the space of Bayesian network structures alternating between two steps: an optimization of the Bayesian network parameters (usually by means of the EM algorithm) and a structural search for model selection. In this paper, we propose to perform the optimization of the Bayesian ...

We present an algorithm for inducing Bayesian networks using feature selection. The algorithm selects a subset of attributes that maximizes predictive accuracy prior to the network learning phase, thereby incorporating a bias for small networks that retain high predictive accuracy. We compare the behavior of this selective Bayesian network classiier with that of (a) Bayesian network classiiers ...

This paper describes a structure of a standalone Intrusion Detection System (IDS) based on a large Bayesian network. To implement the IDS we develop the design methodology of large Bayesian networks. A small number of natural templates (idioms) are defined which make the design of Bayesian network easier. They are related to specific fragments of Bayesian networks representing the basic element...