USING PRACTICAL CONTENT EXERCISES IN TEACHING 'MOMENTUM' - PHYSICS 10 TO DEVELOP STUDENTS' ABILITY TO APPLY KNOWLEDGE AND SKILLS

This study investigates the impact of integrating practical exercises into the teaching of "Momentum" in Physics 10, aiming to enhance students' ability to apply theoretical knowledge and skills. Recognizing the gap between theoretical physics education and its application, this research employs a comprehensive methodology, combining theoretical research, expert surveys, pedagogical experimentation, and statistical analysis to explore the efficacy of practical exercises. The pedagogical experiments, conducted in a controlled classroom setting, involved practical tasks that required students to apply concepts of momentum to solve real-world problems. The findings reveal a significant improvement in students' understanding and application of physics principles, particularly momentum, highlighting the value of experiential learning in physics education. Students demonstrated enhanced problem-solving abilities, deeper conceptual understanding, and increased engagement and interest in physics. Moreover, the study underscores the importance of practical exercises in bridging the gap between theoretical knowledge and real-world application, suggesting that such an approach not only facilitates a better grasp of scientific principles but also prepares students to tackle practical challenges effectively. The research advocates for the broader implementation of practical exercises in the physics curriculum, emphasizing their potential to transform traditional educational methodologies into more engaging and impactful learning experiences. Overall, this study contributes to the pedagogical discourse by affirming the critical role of practical exercises in developing competent and versatile learners capable of applying their knowledge and skills in diverse contexts, thus enhancing the quality of physics education and fostering a generation of problem-solvers equipped to navigate the complexities of the modern world

Formation, Concept Development in Pedagogical Environment and Educational Solutions to Improve Conceptualization for Vietnamese Students

Concepts are both the result of the reflection of the human objective world, and the reasoning means for people to continue to perceive and improve the world. Concept is the first stage in the process of perceiving human reason. Comprehending the concept, in order to be effective, requires a lot of effort and effort of the subject of awareness, needs to have a certain understanding of the law of perception, the law of psychophysiology ... and the help of people. ahead. Keywords: Concept formation, Concept development, pedagogical environment, Comprehension improvement solution, Concept. DOI: 10.7176/JEP/12-10-06 Publication date: April 30th 202

BILINGUAL EDUCATION PROGRAM PROPOSED IN VIETNAM

Given that EFL is playing an important role in the national education system of Vietnam for its development and global integration, this paper proposes a bilingual education program with both Vietnamese and English subjects for primary schools. Descriptions and justifications for the proposed program are presented in details. Also, teaching methods and assessments are analyzed. As a pilot, this program is hoped to be widely implemented.   Article visualizations

Improving GAN with neighbors embedding and gradient matching

We propose two new techniques for training Generative Adversarial Networks (GANs). Our objectives are to alleviate mode collapse in GAN and improve the quality of the generated samples. First, we propose neighbor embedding, a manifold learning-based regularization to explicitly retain local structures of latent samples in the generated samples. This prevents generator from producing nearly identical data samples from different latent samples, and reduces mode collapse. We propose an inverse t-SNE regularizer to achieve this. Second, we propose a new technique, gradient matching, to align the distributions of the generated samples and the real samples. As it is challenging to work with high-dimensional sample distributions, we propose to align these distributions through the scalar discriminator scores. We constrain the difference between the discriminator scores of the real samples and generated ones. We further constrain the difference between the gradients of these discriminator scores. We derive these constraints from Taylor approximations of the discriminator function. We perform experiments to demonstrate that our proposed techniques are computationally simple and easy to be incorporated in existing systems. When Gradient matching and Neighbour embedding are applied together, our GN-GAN achieves outstanding results on 1D/2D synthetic, CIFAR-10 and STL-10 datasets, e.g. FID score of $30.80$ for the STL-10 dataset. Our code is available at: https://github.com/tntrung/ganComment: Published as a conference paper at AAAI 201

Building Footprint Extraction in Dense Areas using Super Resolution and Frame Field Learning

Despite notable results on standard aerial datasets, current state-of-the-arts fail to produce accurate building footprints in dense areas due to challenging properties posed by these areas and limited data availability. In this paper, we propose a framework to address such issues in polygonal building extraction. First, super resolution is employed to enhance the spatial resolution of aerial image, allowing for finer details to be captured. This enhanced imagery serves as input to a multitask learning module, which consists of a segmentation head and a frame field learning head to effectively handle the irregular building structures. Our model is supervised by adaptive loss weighting, enabling extraction of sharp edges and fine-grained polygons which is difficult due to overlapping buildings and low data quality. Extensive experiments on a slum area in India that mimics a dense area demonstrate that our proposed approach significantly outperforms the current state-of-the-art methods by a large margin.Comment: Accepted at The 12th International Conference on Awareness Science and Technolog

Improving Pareto Front Learning via Multi-Sample Hypernetworks

Pareto Front Learning (PFL) was recently introduced as an effective approach to obtain a mapping function from a given trade-off vector to a solution on the Pareto front, which solves the multi-objective optimization (MOO) problem. Due to the inherent trade-off between conflicting objectives, PFL offers a flexible approach in many scenarios in which the decision makers can not specify the preference of one Pareto solution over another, and must switch between them depending on the situation. However, existing PFL methods ignore the relationship between the solutions during the optimization process, which hinders the quality of the obtained front. To overcome this issue, we propose a novel PFL framework namely PHN-HVI, which employs a hypernetwork to generate multiple solutions from a set of diverse trade-off preferences and enhance the quality of the Pareto front by maximizing the Hypervolume indicator defined by these solutions. The experimental results on several MOO machine learning tasks show that the proposed framework significantly outperforms the baselines in producing the trade-off Pareto front.Comment: Accepted to AAAI-2