A strategy for promoting the use of computers across the curriculum at primary school level: a case study

A growing number of primary schools are acquiring computers, mainly through parent funding. The study concerns the promotion of computer use across the curriculum in primary schools. Teachers need to be trained in the use of computers as a teaching aid in different subjects. A study comparing two periods of training was undertaken. Two model C primary schools, administered by the Department of Education and Culture, with similar profiles of educational computer use, were selected for the purpose. A training course consisting of five sections, where the use of the word processor, spreadsheet and database, both as personal tools and as teaching aids were introduced, was offered. Care was taken to select topics from current syllabi and to demonstrate how these topics could be presented and enhanced by using the computer. The training was presented at school A over a period of 8 months and at school B over a period of 5 weeks. The supporting material and contents of the course were the same for both groups. A comparison between the effectiveness of the two training regimes was made. The researcher's initial perceptions of the teachers' initial low level of computer literacy were confirmed by a questionnaire, responded to by each participant. It would seem that the shorter raining period was more effective in raising the level of computer literacy and that if the training period was longer, the contact time should be increased to maintain support. Neither training period resulted in a significant increase in computer use, either as a personal tool or as a teaching aid. The failure to do so may be ascribed to a number of influences, one of which is the teaching style of individual teachers. Changing the teaching style of an experienced teacher takes time and more effort than was available for either training period

Recognition of Human Emotion using Radial Basis Function Neural Networks with Inverse Fisher Transformed Physiological Signals

Emotion is a complex state of human mind influenced by body physiological changes and interdependent external events thus making an automatic recognition of emotional state a challenging task. A number of recognition methods have been applied in recent years to recognize human emotion. The motivation for this study is therefore to discover a combination of emotion features and recognition method that will produce the best result in building an efficient emotion recognizer in an affective system. We introduced a shifted tanh normalization scheme to realize the inverse Fisher transformation applied to the DEAP physiological dataset and consequently performed series of experiments using the Radial Basis Function Artificial Neural Networks (RBFANN). In our experiments, we have compared the performances of digital image based feature extraction techniques such as the Histogram of Oriented Gradient (HOG), Local Binary Pattern (LBP) and the Histogram of Images (HIM). These feature extraction techniques were utilized to extract discriminatory features from the multimodal DEAP dataset of physiological signals. Experimental results obtained indicate that the best recognition accuracy was achieved with the EEG modality data using the HIM features extraction technique and classification done along the dominance emotion dimension. The result is very remarkable when compared with existing results in the literature including deep learning studies that have utilized the DEAP corpus and also applicable to diverse fields of engineering studies

Segmentation of Melanoma Skin Lesion Using Perceptual Color Difference Saliency with Morphological Analysis

The prevalence of melanoma skin cancer disease is rapidly increasing as recorded death cases of its patients continue to annually escalate. Reliable segmentation of skin lesion is one essential requirement of an efficient noninvasive computer aided diagnosis tool for accelerating the identification process of melanoma. This paper presents a new algorithm based on perceptual color difference saliency along with binary morphological analysis for segmentation of melanoma skin lesion in dermoscopic images. The new algorithm is compared with existing image segmentation algorithms on benchmark dermoscopic images acquired from public corpora. Results of both qualitative and quantitative evaluations of the new algorithm are encouraging as the algorithm performs excellently in comparison with the existing image segmentation algorithms

Training Pattern Classifiers with Physiological Cepstral Features to Recognise Human Emotion

The choice of a suitable set of features based on physiological signals to be utilised in enhancing the recognition of human emotion remains a burning issue in affective computing research. In this study, using the MAHNOB-HCI corpus, we extracted cepstral features from the physiological signals of galvanic skin response, electrocardiogram, electroencephalogram, skin temperature and respiration amplitude to train two state of the art pattern classifiers to recognise seven classes of human emotions. The important task of emotion recognition is largely considered a classification problem and on this basis, we carried out experiments in which the extracted physiological cepstral features were transmitted to Gaussian Radial Basis Function (RBF) neural network and Support Vector Machines (SVM) pattern classifiers for human emotion recognition. The RBF neural network pattern classifier gave the recognition accuracy of 99.5 %, while the SVM pattern classifier posted 75.0 % recognition accuracy. These results indicate the suitability of using cepstral features extracted from fused modality physiological signals with the Gaussian RBF neural network pattern classifier for efficient recognition of human emotion in affective computing system