Despite the n-back task’s apparent effectiveness as a working memory (WM) training task, its status as a WM assessment is questionable. We analyzed the accuracy and reaction time data of participants performing of an adaptive n-back training task and developed a computational model to describe this performance. Application of our model to n-back training data suggests that performance is consis...

Infrared Data Association has developed the Advanced Infrared (AIr) protocol standard for infrared wireless LANs. AIr employs a Go-Back-N automatic repeat request (ARQ) scheme at the Link Control layer to deal with transmission errors. Alternatively, an optional Stop-and-Wait ARQ scheme at the Medium Access Control (MAC) layer may be employed. The effectiveness of employing the optional Stopand...

In this paper we study the application of Bayesian network models to classify multispectral and hyperspectral remote sensing images. Different models of Bayesian networks as: Naive Bayes (NB), Tree Augmented Naive Bayes (TAN) and General Bayesian Networks (GBN), are applied to the classification of hyperspectral data. In addition, several Bayesian multi-net models: TAN multi-net, GBN multi-net ...

Context prediction, highlighted by accurate location prediction, has been at the heart of ubiquitous decision support systems. To improve the prediction accuracy of such systems, various methods have been proposed and tested; these include Bayesian networks, decision classifiers, and SVMs. Still, greater accuracy may be achieved when individual classifiers are integrated into an ensemble system...

This paper introduces a new probabilistic graphical model called gated Bayesian network (GBN). This model evolved from the need to represent real world processes that include several distinct phases. In essence a GBN is a model that combines several Bayesian networks (BN) in such a manner that they may be active or inactive during queries to the model. We use objects called gates to combine BNs...

Bayesian networks (BNs) have become an essential tool for reasoning under uncertainty in complex models. In particular, the subclass of Gaussian Bayesian networks (GBNs) can be used to model continuous variables with Gaussian distributions. Here we focus on the task of learning GBNs from data. Factorization of the multivariate Gaussian joint density according to a directed acyclic graph (DAG) p...

A quasi-independence result holds for the go-back-n automatic repeat request (ARQ) protocol and the sliding window ow control protocol if packet errors are independent. The result is independent of the magnitude of the packet error probability or the cost of an error. A parallel result for the selective repeat ARQ protocol, however, does not appear to hold. Parts of this paper were presented as...

OBJECTIVE To describe the anthropometric and physical performance characteristics of a sample of senior A club rugby players and to highlight differences between the positional categories of the players within the forwards and backs. METHODS 94 senior A male rugby players were assessed on a number of anthropometric and physical performance assessments. The forwards were categorised into props...

We propose a new type of ubiquitous decision support system that is powered by a General Bayesian Network (GBN). Because complicated decision support problems are plagued by complexities when interpreting causal relationships among decision variables, GBNs have shown excellent decision support competence because of their flexible structure, which allows them to extract appropriate and robust ca...