Impact of COVID-19 on monthly water consumption on a tropical tourism island: case study of Phuket (Thailand)

Phuket is a tropical island in Thailand that is famous for tourism. The COVID-19 pandemic resulted in the number of tourists reducing to almost zero. Since tourism contributes around one-half of the gross provincial product of Phuket, the impact was so severe that even the numbers of people employed and registered as locals decreased. Analysing the data from January 2015 to March 2021, we found that the total, residential and non-residential monthly consumptions dropped significantly after Thailand's State of Emergency was declared in March 2020. Unlike other studies that reported residential consumption increasing when people are required to stay home for a prolonged period, Phuket's residential consumption decreased by more than 10% from the pre-COVID-19 level, possibly due to the drop in peer-to-peer accommodation bookings. To study the impact on consumption in detail, we modelled using cascade regression analysis by dividing the predictors into three groups, namely socioeconomics, weather and calendar period. The results showed that the number of guest arrivals was the most statistically significant in all types of consumption and should be used as a predictor for water demand forecasting models in tourism areas.

Cascade Regression-Based Face Frontalization for Dynamic Facial Expression Analysis

Facial expression recognition has seen rapid development in recent years due to its wide range of applications such as human–computer interaction, health care, and social robots. Although significant progress has been made in this field, it is still challenging to recognize facial expressions with occlusions and large head-poses. To address these issues, this paper presents a cascade regression-based face frontalization (CRFF) method, which aims to immediately reconstruct a clean, frontal and expression-aware face given an in-the-wild facial image. In the first stage, a frontal facial shape is predicted by developing a cascade regression model to learn the pairwise spatial relation between non-frontal face-shape and its frontal counterpart. Unlike most existing shape prediction methods that used single-step regression, the cascade model is a multi-step regressor that gradually aligns non-frontal shape to its frontal view. We employ several different regressors and make a ensemble decision to boost prediction performance. For facial texture reconstruction, active appearance model instantiation is employed to warp the input face to the predicted frontal shape and generate a clean face. To remove occlusions, we train this generative model on manually selected clean-face sets, which ensures generating a clean face as output regardless of whether the input face involves occlusions or not. Unlike the existing face reconstruction methods that are computational expensive, the proposed method works in real time, so it is suitable for dynamic analysis of facial expression. The experimental validation shows that the ensembling cascade model has improved frontal shape prediction accuracy for an average of 5% and the proposed method has achieved superior performance on both static and dynamic recognition of facial expressions over the state-of-the-art approaches. The experimental results demonstrate that the proposed method has achieved expression-preserving frontalization, de-occlusion and has improved performance of facial expression recognition.

Energy Efficient Pupil Tracking Based on Rule Distillation of Cascade Regression Forest

As the demand for human-friendly computing increases, research on pupil tracking to facilitate human–machine interactions (HCIs) is being actively conducted. Several successful pupil tracking approaches have been developed using images and a deep neural network (DNN). However, common DNN-based methods not only require tremendous computing power and energy consumption for learning and prediction; they also have a demerit in that an interpretation is impossible because a black-box model with an unknown prediction process is applied. In this study, we propose a lightweight pupil tracking algorithm for on-device machine learning (ML) using a fast and accurate cascade deep regression forest (RF) instead of a DNN. Pupil estimation is applied in a coarse-to-fine manner in a layer-by-layer RF structure, and each RF is simplified using the proposed rule distillation algorithm for removing unimportant rules constituting the RF. The goal of the proposed algorithm is to produce a more transparent and adoptable model for application to on-device ML systems, while maintaining a precise pupil tracking performance. Our proposed method experimentally achieves an outstanding speed, a reduction in the number of parameters, and a better pupil tracking performance compared to several other state-of-the-art methods using only a CPU.