Learning relevant eye movement feature spaces across users

In this paper we predict the relevance of images based on a lowdimensional feature space found using several users’ eye movements. Each user is given an image-based search task, during which their eye movements are extracted using a Tobii eye tracker. The users also provide us with explicit feedback regarding the relevance of images. We demonstrate that by using a greedy Nystrom algorithm on the eye movement features of different users, we can find a suitable low-dimensional feature space for learning. We validate the suitability of this feature space by projecting the eye movement features of a new user into this space, training an online learning algorithm using these features, and showing that the number of mistakes (regret over time) made in predicting relevant images is lower than when using the original eye movement features. We also plot Recall-Precision and ROC curves, and use a sign test to verify the statistical significance of our results

Employing industrial quality management systems for quality assurance in outcome-based engineering education: a review

With the world becoming flat with fluid boundaries, engineers have to be global in their outlook and their pedigree. Due to the need for international acceptance of engineering qualification, the incorporation of Outcome-Based Education (OBE) has become common and global accreditation treaties such as the Washington Accord have been ratified. Further, it becomes important, especially for an engineering university with a global outlook preparing its students for global markets, to ensure that its graduates attain the planned outcomes. Additionally, the higher education institutions need to make sure that all the stakeholders, including students, parents, employers, and community at large, are getting a quality educational service, where quality is categorized as (1) product-based ensuring that the graduate attained the planned outcomes and skills, and (2) process-based keeping an eye on whether the process is simple, integrated, and efficient. The development of quality movements, such as Total Quality Movement (TQM), Six Sigma, etc., along with quality standards such as ISO 9001 has been instrumental in improving the quality and efficiency in the fields of management and services. Critical to the successful deployment of a quality culture is the institutionalization of an integrated Quality Management System (QMS) in which formally documented processes work according to the Vision and Mission of an institute. At the same time, commitment to Continuous Quality Improvement (CQI) to close the loop through effective feedback, would ensure that the planned outcomes are attained to the satisfaction of all the stakeholders, and that the process overall is improving consistently and continuously. The successful adoption of quality culture requires buy-in from all the stakeholders (and in particular, the senior leadership) and a rigorous training program. In this paper, we provide a review of how a QMS may work for the provision of quality higher education in a 21st-century university

Basic metric learning

This report presents a a novel Multiple Kernel Learning (MKL) algorithm for the 1-class support vector machine. The emphasis is placed on viewing the CBIR task with relevance feedback as a metric learning problem, where each image has 11 different feature extraction methods applied to it. Our method attempts at finding the most compact ball amongst the 11 different feature representations using a novel 1- and 2-norm regularisation technique for the 1-class SVM under the MKL framework. We also devise a simple way of including the set of negative examples whilst still utilising the 1-class SVM implementation

A Novel Metaheuristic Approach to Optimization of Neuro-Fuzzy System for Students' Performance Prediction

Data mining is being increasingly leveraged in educational settings for achieving various different outcomes including students' learning patterns, course and teaching outcome assessment, and students' expected achievement prediction. Utilizing data collected from daily curricular and non-curricular activities, machine learning techniques have benefited administrators in making efficient decisions. Based on students' behavioral information, this research proposes student performance prediction model using fuzzy-based neural network (FNN) trained by a novel metaheuristic approach. Because original gradient-based learning method associated with FNN limits its performance, this research employs Henry Gas Solubility Optimization (HGSO) algorithm for tuning FNN parameters. The empirical analysis suggests superiority of results produced by the proposed approach as compared with the FNN trained by the competitive methods