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A standard equation on teaching workload calculation in the previous academic setting only includes the contact hours with students through lecture, tutorial, laboratory and in-person consultation (i.e. one-to-one final year project consultation). This paper discusses teaching workload factors according to the current higher-education setting. Devising a teaching workload equation that includes all teaching and learning strategies in the 21^st century higher education learning setting is needed. This is indeed a challenging task for the academic administrators to scrutinize every single parameter that accounted for teaching and learning. In this work, we have discussed the parameters which are significant in teaching workload calculation. For instance, the conventional in-person contact with the students, type of delivery, type of assessment, the preparation of materials for flipped classroom as well as MOOC, to name a few. Teaching workload also affects quality teaching and from the academic perception, the higher workload means lower-quality teaching.

Research on detecting, recognising and interpreting cardiovascular magnetic resonance images (CMRIs) has started since the 1980s. Time consuming and the need of expert evaluation are the key problems in the manual tracing efforts of CMRIs in a routine investigation. CMRIs manual tracing is also dependent on image quality, and there is no one-size-fits-all MRI setting for an optimum image result. In this paper, we present an approach using 2-Standard Division (2-SD) correlation along with the Sum of Absolute Difference technique and Otsu Watershed to automatically detect the left ventricle (LV) wall and blood pool in the effort to automatically assist the assessment of cardiac function. We test the approach using the Sunnybrook Cardiac Data, a standard benchmark dataset. The results shown that the proposed method had improved the automatic detection of the epicardium and endocardium.

In this paper, we carried out a modular approach human 3D face recognition across neutral and six basic facial expressions experiments. Initially, a face model is decomposed into several modules before the 3D facial points for each of the modules are extracted. Three sizes of modules are used in our experiments: 2-Module, 6-Module and 10-Module. We apply Support Vector Machines as the classifier to each of the modules. A Majority Voting Scheme (MVS) and Weighted Voting Scheme (WVS) are constructed to infer the emotion underlying a collection of modules. From the analysis, we conclude that 10-Module outperforms 2-Module and 6-Module. In addition, the modules with low amount feature vectors and only contain boundary feature vectors perform worst.

-We present a novel method for measuring task performance using gaze regions, i.e., scene regions fixated by a subject as he or she performs a familiar manual task. The scene regions are learned as a bag of features representation, using library lookup based on the Histogram of Oriented Gradients feature descriptor [1]. By establishing a set of task-specific exemplar models, i.e., models sourced from Pareto optimal sequences, the approach recognizes the local optima within a set of task-specific unlabeled models by estimating the distance (of each unlabeled model) to the exemplar models. During testing, the method is evaluated against a dataset of egocentric sequences, each containing gaze data, belonging to three manual skill-based activities. The results show perfect classification's accuracy on several proposed schemes.