Leveraging machine learning to enhance climate models: a review

Recent achievements in machine learning (Ml) have had a significant impact on various fields, including climate science. Climate modeling is very important and plays a crucial role in shaping the decisions of governments and individuals in mitigating the impact of climate change. Climate change poses a serious threat to humanity, however, current climate models are limited by computational costs, uncertainties, and biases, affecting their prediction accuracy. The vast amount of climate data generated by satellites, radars, and earth system models (ESMS) poses a significant challenge. ML techniques can be effectively employed to analyze this data and extract valuable insights that aid in our understanding of the earth climate. This review paper focuses on how ml has been utilized in the last 5 years to boost the current state-of-the-art climate models. We invite the ml community to join in the global effort to accurately model the earth climate by collaborating with other fields to leverage ml as a powerful tool in this endeavor.

Measuring Student Behavioral Engagement using Histogram of Actions

In this paper, we propose a novel technique for measuring behavioral engagement through students' actions recognition. The proposed approach recognizes student actions then predicts the student behavioral engagement level. For student action recognition, we use human skeletons to model student postures and upper body movements. To learn the dynamics of student upper body, a 3D-CNN model is used. The trained 3D-CNN model is used to recognize actions within every 2minute video segment then these actions are used to build a histogram of actions which encodes the student actions and their frequencies. This histogram is utilized as an input to SVM classifier to classify whether the student is engaged or disengaged. To evaluate the proposed framework, we build a dataset consisting of 1414 2-minute video segments annotated with 13 actions and 112 video segments annotated with two engagement levels. Experimental results indicate that student actions can be recognized with top 1 accuracy 83.63% and the proposed framework can capture the average engagement of the class.

Occlusion Aware Student Emotion Recognition based on Facial Action Unit Detection

Given that approximately half of science, technology, engineering, and mathematics (STEM) undergraduate students in U.S. colleges and universities leave by the end of the first year [15], it is crucial to improve the quality of classroom environments. This study focuses on monitoring students' emotions in the classroom as an indicator of their engagement and proposes an approach to address this issue. The impact of different facial parts on the performance of an emotional recognition model is evaluated through experimentation. To test the proposed model under partial occlusion, an artificially occluded dataset is introduced. The novelty of this work lies in the proposal of an occlusion-aware architecture for facial action units (AUs) extraction, which employs attention mechanism and adaptive feature learning. The AUs can be used later to classify facial expressions in classroom settings. This research paper's findings provide valuable insights into handling occlusion in analyzing facial images for emotional engagement analysis. The proposed experiments demonstrate the significance of considering occlusion and enhancing the reliability of facial analysis models in classroom environments. These findings can also be extended to other settings where occlusions are prevalent.