showing 6 related works from this author
Cognitive Reasoning and Inferences through Psychologically based Personalised Modelling of Emotions Using Associative Classifiers
2014
The development of Microsoft Kinect opened up the research field of computational emotions to a wide range of applications, such as learning environments, which are excellent candidates to trial computational emotions based algorithms but were never feasible for given consumer technologies. Whilst Kinect is accessible and affordable technology it comes with its' own additional challenges such as the limited number of extracted Action Units (AUs). This paper presents a new approach that attempts at finding patterns of interaction between AUs and each other on one hand and patterns that link the related AUs to a given emotion. In doing so, this paper presents the ground work necessary to reac…
Domain Specific Knowledge Representation for an Intelligent Tutoring System to Teach Algebraic Reasoning
2012
Translation of word problems into symbolic notation is one of the most challenging steps in learning the algebraic method. This paper describes a domain-specific knowledge representation mechanism to support Intelligent Tutoring Systems (ITS) which focus on this stage of the problem solving process. The description language proposed is based on the concept of a hypergraph and makes it possible to simultaneously a) represent all potential algebraic solutions to a given word problem; b) keep track of the student's actions; c) provide automatic remediation; and d) unequivocally determine the current state of the resolution process. An experimental evaluation with students at a public school su…
SOM-Based Class Discovery for Emotion Detection Based on DEAP Dataset
2018
This paper investigates the possibility of identifying classes by clustering. This study includes employing Self-Organizing Maps (SOM) in identifying clusters from EEG signals that could then be mapped to emotional classes. Beginning by training varying sizes of SOM with the EEG data provided from the public dataset: DEAP. The produced graphs showing Neighbor Distance, Sample Hits, and Weight Position are examined. Following that, the ground-truth label provided in DEAP is tested, in order to identify correlations between the label and the clusters produced by the SOM. The results show that there is a potential of class discovery using SOM-based clustering. It is then concluded that by eval…
Combining Supervised and Unsupervised Learning to Discover Emotional Classes
2017
Most previous work in emotion recognition has fixed the available classes in advance, and attempted to classify samples into one of these classes using a supervised learning approach. In this paper, we present preliminary work on combining supervised and unsupervised learning to discover potential latent classes which were not initially considered. To illustrate the potential of this hybrid approach, we have used a Self-Organizing Map (SOM) to organize a large number of Electroencephalogram (EEG) signals from subjects watching videos, according to their internal structure. Results suggest that a more useful labelling scheme could be produced by analysing the resulting topology in relation t…
Towards Psychologically based Personalised Modelling of Emotions Using Associative Classifiers
2016
Learning environments, among other user-centred applications, are excellent candidates to trial Computational Emotions and their algorithms to enhance user experience and to expand the system usability. However, this was not feasible because of the paucity in affordable consumer technologies that support the requirements of systems with advanced cognitive capabilities. Microsoft Kinect provides an accessible and affordable technology that can enable cognitive features such as facial expressions extraction and emotions detection. However, it comes with its own additional challenges, such as the limited number of extracted Animation Units (AUs). This paper presents a new approach that attempt…
Class discovery from semi-structured EEG data for affective computing and personalisation
2017
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link. Many approaches to recognising emotions from metrical data such as EEG signals rely on identifying a very small number of classes and to train a classifier. The interpretation of these classes varies from a single emotion such as stress [24] to features of emotional model such as valence-arousal [4]. There are two major issues here. First classification approach limits the analysis of the data within the selected classes and is also highly dependent on training data/cycles, all of which limits generalisation. Second issue is that it does not exp…