Home > Uncategorized > Weekly QuEST Discussion Topics and News, 23 Oct

Weekly QuEST Discussion Topics and News, 23 Oct

QuEST 23 Oct 2015

We want to start this week talking about ISR challenges with respect to big Data (the discussions we’ve been having over the last several weeks on big-data applied to the issues we face in the ISR enterprise – we want to map where big data and QuEST will impact C4ISR ) our colleague Prof O has an opportunity on Monday to speak to some potential international collaborators so we will take the opportunity to refine our message on the QuEST perspectives on big data specifically for C4ISR

Next we also want to return to the discussion on pain – we didn’t make it to some points I still want to discuss – for example how do we truth human states that are below the level of consciousness – but the results are posted consciously – like pain and emotion – we will post a couple of articles on estimating emotion

Neural Network-Based Improvement in Class

Separation of Physiological Signals for Emotion

Classification

E. Leon, G. Clarke, F. Sepulveda, V. Callaghan

Department of Computer Science, University of Essex

Colchester, Essex, UK.

eeleon@essex.ac.uk, graham@essex.ac.uk, fsepulv@essex.ac.uk, vic@essex.ac.uk

Abstract—Computer scientists have been slow to become

aware of the importance of emotion on human decisions and

actions. Recently, however, a considerable amount of research

has focused on the utilisation of affective information with the

intention of improving both human-machine interaction and

artificial human-like inference models. It has been argued that

valuable information could be obtained by analysing the way

affective states and environment interact and affect human

behaviour. A method to improve pattern recognition among four

bodily parameters employed for emotion recognition is

presented. The utilisation of Autoassociative Neural Networks

has proved to be a valuable mechanism to increase inter-cluster

separation related to emotional polarity (positive or negative). It

is suggested that the proposed methodology could improve

performance in pattern recognition tasks involving physiological

signals. Also, by way of grounding the immediate aims of our

research, and providing an insight into the direction of our work,

we provide a brief overview of an intelligent-dormitory test bed

in which affective computing methods will be applied and

compared to non-affective agents.

Optimised Attribute Selection for Emotion classification using Physiological Signals

Leon, Clarke, Sepulveda, Callaghan

news summary (30)

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