Relevancy of New Higher Education Approaches in ‘Second Republic Zimbabwe’

Through face-to-face interviews with lecturers, this research explored the relevancy of new higher education approaches in Zimbabwe particularly Education 5.0 and virtual learning environments (VLEs). The main finding suggests that the five missions of Education 5.0 are not new in higher education and training in the country. However, they being nigh on greased following the outbreak of the coronavirus disease (COVID 19) is exceptional and unplanned for up take. Education 5.0 charges that turned to be virtually oiled have already been cherished under the auspices of Education 3.0. Thus, lecturers do not perceive it as new. But, only the means and ways upon which it is delivered to learners in the COVID 19 era. The hype around it coming from the ‘Second Republic’ government is factory-made and politically calculated. Besides, it is difficult to underpin the development given the economic problems the country is currently facing. This research also finds out that VLEs are just more than a saga in Zimbabwe since she still lags behind the world order of internet of things. A few of the interviewees were of the view that teaching and learning though virtual means and ways is not different from the old face-to-face model.

CONSIDERING ONLINE TRAINING: RESPONSE AND EXPECTATION IN THE PANDEMIC COVID-19

E-learning in the training is becoming inevitability with condition changes due to pandemics. However, it urges the researcher to study some comparison on how the training actors give responses and expectation towards the conventional face-to-face model which once was prima donna. This study uses descriptive analytical studies and the data taken from questionnaires against 187 training participants as respondents, 7 committees, and 12 trainers or widyaiswaras. The data is processed and analyzed by using SPSS. The results yielded that the average scores of distance learning were more dominant (greater) than those in classical for all respondent’s categories (the committees, widyaiswaras, and participants). The study shows a tendency that distance training is more effective compared to classical training.

Detail 3D Face Reconstruction Based on 3DMM and Displacement Map

How to accurately reconstruct the 3D model human face is a challenge issue in the computer vision. Due to the complexity of face reconstruction and diversity of face features, most existing methods are aimed at reconstructing a smooth face model with ignoring face details. In this paper a novel deep learning-based face reconstruction method is proposed. It contains two modules: initial face reconstruction and face details synthesis. In the initial face reconstruction module, a neural network is used to detect the facial feature points and the angle of the pose face, and 3D Morphable Model (3DMM) is used to reconstruct the rough shape of the face model. In the face detail synthesis module, Conditional Generation Adversarial Network (CGAN) is used to synthesize the displacement map. The map provides texture features to render to the face surface reconstruction, so as to reflect the face details. Our proposal is evaluated by Facescape dataset in experiments and achieved better performance than other current methods.

Telehealth: rapid adoption in community palliative care due to COVID-19: patient and professional evaluation

Background/objectiveThe COVID-19 pandemic has brought the use of telehealth to the fore, as many people have been unable to interact directly with healthcare professionals (HCP). For community palliative care (CPC) services, this has meant a sudden change from a predominantly face-to-face model of care to one that incorporates telehealth. Understanding patient and HCP experiences with telehealth and how telehealth compares to ‘usual’ care will be crucial in planning future CPC services.MethodologyAll patients of the Barwon Health CPC service between 1 April and 31 May 2020 were invited to complete a questionnaire evaluating their interactions with the palliative care service and specifically their involvement with telehealth consultations. Palliative care HCP who provided clinical services during the same time period were also surveyed.Results/conclusionSeventy-four patients (response rate 36%) and 22 HCP returned surveys. Both groups felt comfortable using telehealth, however, also encountered a range of issues when undertaking telehealth consultations. Despite reporting issues, the preference of both groups was for a CPC service model, which combined face-to-face and telehealth consultations. This study is one of the first to directly ask this question and as such provides useful guidance for health services when planning future CPC service models.

Process-Based Model Prediction of Coastal Dune Erosion through Parametric Calibration

Coastal dunes are important morphological features for both ecosystems and coastal hazard mitigation. Because understanding and predicting dune erosion phenomena is very important, various numerical models have been developed to improve the accuracy. In the present study, a process-based model (XBeachX) was tested and calibrated to improve the accuracy of the simulation of dune erosion from a storm event by adjusting the coefficients in the model and comparing it with the large-scale experimental data. The breaker slope coefficient was calibrated to predict cross-shore wave transformation more accurately. To improve the prediction of the dune erosion profile, the coefficients related to skewness and asymmetry were adjusted. Moreover, the bermslope coefficient was calibrated to improve the simulation performance of the bermslope near the dune face. Model performance was assessed based on the model-data comparisons. The calibrated XBeachX successfully predicted wave transformation and dune erosion phenomena. In addition, the results obtained from other two similar experiments on dune erosion with the same calibrated set matched well with the observed wave and profile data. However, the prediction of underwater sand bar evolution remains a challenge.

Human emotional state recognition using 3D facial expression features

In recent years there has been a growing interest in improving all aspects of the interaction between human and computers. Emotion recognition is a new research direction in human-computer interaction (HCI) which is based on affective computing that is expected to significantly improve the quality of HCI system and communications. Most existing works address this problem using 2D features, but they are sensitive to head pose, clutter, and variations in lighting conditions. In light of such problems, two 3D visual feature based approaches are presented in this dissertation. First, we present a recognition method based on the Gabor library for real 3D visual features extraction and an improved kernel canonical correlation analysis (IKCCA) algorithm for emotion classification. Second, to reduce the computation cost and provide a more general approach, we propose using a fiducial points' controlled 3D face model to recognize human emotion from video sequences. An Elastic body spine (EBS) technique is applied for deformation feature extraction and a discriminative Isomap (D-Isomap) based classification is used for the final decision. The most significant contributions of this work are detecting and tracking fiducial points automatically from video sequences to construct a generic 3D face model, and the introduction of EBS deformation features for emotion recognition. The experimental results show the robustness and effectiveness of the proposed methods.

Human emotional state recognition using 3D facial expression features

In recent years there has been a growing interest in improving all aspects of the interaction between human and computers. Emotion recognition is a new research direction in human-computer interaction (HCI) which is based on affective computing that is expected to significantly improve the quality of HCI system and communications. Most existing works address this problem using 2D features, but they are sensitive to head pose, clutter, and variations in lighting conditions. In light of such problems, two 3D visual feature based approaches are presented in this dissertation. First, we present a recognition method based on the Gabor library for real 3D visual features extraction and an improved kernel canonical correlation analysis (IKCCA) algorithm for emotion classification. Second, to reduce the computation cost and provide a more general approach, we propose using a fiducial points' controlled 3D face model to recognize human emotion from video sequences. An Elastic body spine (EBS) technique is applied for deformation feature extraction and a discriminative Isomap (D-Isomap) based classification is used for the final decision. The most significant contributions of this work are detecting and tracking fiducial points automatically from video sequences to construct a generic 3D face model, and the introduction of EBS deformation features for emotion recognition. The experimental results show the robustness and effectiveness of the proposed methods.