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In this paper, we analyse the emotion of children's stories in sentence level by considering the context information. We demonstrate that the emotion of a sentence is not only dependent on its content, but also affected by its neighbours in a story. A Hidden Markov Model (HMM) based method is proposed to model the emotion sequence and to detect whether a sentence is neutral or not. We show the important...
This paper presents various trading models for the stock market and test whether they are able to consistently generate excess returns from the Singapore Exchange (SGX). Instead of conventional ways of modeling stock prices, we construct models which relate the market indicators to a trading decision directly. Furthermore, unlike a reversal trading system or a binary system of buy and sell, we allow...
The nonstationary nature of the brain signals provides a rather unstable input resulting in uncertainty and complexity in the control. Intelligent processing algorithms adapted to the task are a prerequisite for reliable BCI applications. This work presents a novel intelligent processing strategy for the realization of an effective BCI which has the capability to improved classification accuracy and...
EEG recordings provide an important means of brain-computer communication, but their classification accuracy is limited by unforeseeable signal variations due to artifacts or recognizer-subject feedback. A number of techniques recently have been developed to address the related problem of recognizer robustness to uncontrollable signal variation. In this paper, we propose a classification method entailing...
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