Multiple Graphs and Low-Rank Embedding for Multi-Source Heterogeneous Domain Adaptation

Multi-source domain adaptation is a challenging topic in transfer learning, especially when the data of each domain are represented by different kinds of features, i.e., Multi-source Heterogeneous Domain Adaptation (MHDA). It is important to take advantage of the knowledge extracted from multiple sources as well as bridge the heterogeneous spaces for handling the MHDA paradigm. This article proposes a novel method named Multiple Graphs and Low-rank Embedding (MGLE), which models the local structure information of multiple domains using multiple graphs and learns the low-rank embedding of the target domain. Then, MGLE augments the learned embedding with the original target data. Specifically, we introduce the modules of both domain discrepancy and domain relevance into the multiple graphs and low-rank embedding learning procedure. Subsequently, we develop an iterative optimization algorithm to solve the resulting problem. We evaluate the effectiveness of the proposed method on several real-world datasets. Promising results show that the performance of MGLE is better than that of the baseline methods in terms of several metrics, such as AUC, MAE, accuracy, precision, F1 score, and MCC, demonstrating the effectiveness of the proposed method.

Construction of a Framework for Selecting an Effective Learning Procedure in the School-Level Sector of Online Teaching Informatics

Currently, there is no way soon to stop the coronavirus epidemic that has spread over the globe. People are alarmed by its quick and widespread expansion. COVID-19's transmission chain was then broken by everyone. There was a gradual decrease in social and physical closeness. Distancing yourself from others is a way to prevent the transmission of disease. The purpose of this research is to investigate how online learning can be implemented in Tamil Nadu, India, during the COVID-19 epidemic. This research works focuses to find efficient learning procedure in eLearning protocols. The findings indicated that Google Classroom, WhatsApp, and Zoom Clouds Meeting were consecutively the most commonly utilized programs to help in remote learning. Despite this, most instructors continue to use the learning paradigm while teaching in virtual environments. Online learning and remote education are the most common methods of learning. The instructor claims that the learning model used is beneficial to their work in creating a virtual classroom since it adheres to the model's structured grammar. The experimental test has been conducted with 125 students who anonymously filled out a questionnaire and voted for more visual based eLearning. The findings show that students in distance education believed that there were more tasks than in face-to-face education. At the same time, students indicated that they spent more time studying at home than in school.

Ensemble learning from ensemble docking: revisiting the optimum ensemble size problem

Despite considerable advances obtained by applying machine learning approaches in protein–ligand affinity predictions, the incorporation of receptor flexibility has remained an important bottleneck. While ensemble docking has been used widely as a solution to this problem, the optimum choice of receptor conformations is still an open question considering the issues related to the computational cost and false positive pose predictions. Here, a combination of ensemble learning and ensemble docking is suggested to rank different conformations of the target protein in light of their importance for the final accuracy of the model. Available X-ray structures of cyclin-dependent kinase 2 (CDK2) in complex with different ligands are used as an initial receptor ensemble, and its redundancy is removed through a graph-based redundancy removal, which is shown to be more efficient and less subjective than clustering-based representative selection methods. A set of ligands with available experimental affinity are docked to this nonredundant receptor ensemble, and the energetic features of the best scored poses are used in an ensemble learning procedure based on the random forest method. The importance of receptors is obtained through feature selection measures, and it is shown that a few of the most important conformations are sufficient to reach 1 kcal/mol accuracy in affinity prediction with considerable improvement of the early enrichment power of the models compared to the different ensemble docking without learning strategies. A clear strategy has been provided in which machine learning selects the most important experimental conformers of the receptor among a large set of protein–ligand complexes while simultaneously maintaining the final accuracy of affinity predictions at the highest level possible for available data. Our results could be informative for future attempts to design receptor-specific docking-rescoring strategies.

A comparison of spectral reconstruction methods applied to non-zero temperature NRQCD meson correlation functions

We present results from the fastsum collaboration’s programme to determine the spectrum of the bottomonium system as a function of temperature. Three different methods of extracting spectral information are discussed: a Maximum Likelihood approach using a Gaussian spectral function for the ground state, the Backus Gilbert method, and the Kernel Ridge Regression machine learning procedure. We employ the fastsum anisotropic lattices with 2+1 dynamical quark flavours, with temperatures ranging from 47 to 375 MeV.

Critical Thinking Formation in the Scope of Connectivism

Critical thinking (CT) formation is a complex and abstract process that hasn’t been studied comprehensively by any existing learning model today. Connectivism, a new learning theory of the information era, provides brand new perspectives to learning, thus has gained considerable attention. The purpose of this study is to examine CT formation in the scope of cognitivism by contrasting this theory to the previous learning theories. This study used the key concepts of chaos, network model, ecology, flow inhibitors, and flow accelerators in connectivism to illuminate some areas of the formation of critical thinking that have not been examined fully. In the scope of connectivism, this study also provides constructive suggestions to teachers to facilitate students’ critical thinking cultivation, i.e., introducing some learning materials that might trigger students’ critical analyzing; evaluating students’ learning procedure from a network perspective; paying more attention to students’ CT disposition development and establishing healthy CT ecology, etc.

Active Learning Using Quizizz in Procedure Text Class XI SMK Nusantara 02 Kesehatan

In this COVID-19 situation, it is a challenge for teachers to still be able to create fun, interesting and active learning. Although learning is carried out remotely or online, teachers must be able to increase student activity. Although learning is carried out remotely or online, teachers must be able to increase student activity. Student activity can be created through the application of interesting learning media. One of the interesting learning media in online learning is using the Quizizz educational game. This quizizz educational game usually uses a laptop or smartphone and can be carried out anywhere. The Quizizz educational learning is used for learning Procedure Text in class XI SMK Nusantara 02 Kesehatan. Through the descriptive method, this learning is supported by material observation, evaluation and interviews. The results obtained are that students enjoy this educational game and understand the material available in the Quiziz application faster.

Infer Thermal Information from Visual Information: A Cross Imaging Modality Edge Learning (CIMEL) Framework

The measurement accuracy and reliability of thermography is largely limited by a relatively low spatial-resolution of infrared (IR) cameras in comparison to digital cameras. Using a high-end IR camera to achieve high spatial-resolution can be costly or sometimes infeasible due to the high sample rate required. Therefore, there is a strong demand to improve the quality of IR images, particularly on edges, without upgrading the hardware in the context of surveillance and industrial inspection systems. This paper proposes a novel Conditional Generative Adversarial Networks (CGAN)-based framework to enhance IR edges by learning high-frequency features from corresponding visual images. A dual-discriminator, focusing on edge and content/background, is introduced to guide the cross imaging modality learning procedure of the U-Net generator in high and low frequencies respectively. Results demonstrate that the proposed framework can effectively enhance barely visible edges in IR images without introducing artefacts, meanwhile the content information is well preserved. Different from most similar studies, this method only requires IR images for testing, which will increase the applicability of some scenarios where only one imaging modality is available, such as active thermography.

Region-Enhancing Network for Semantic Segmentation of Remote-Sensing Imagery

Semantic segmentation for high-resolution remote-sensing imagery (HRRSI) has become increasingly popular in machine vision in recent years. Most of the state-of-the-art methods for semantic segmentation of HRRSI usually emphasize the strong learning ability of deep convolutional neural network to model the contextual relationship in the image, which takes too much consideration on every pixel in images and subsequently causes the problem of overlearning. Annotation errors and easily confused features can also lead to the confusion problem while using the pixel-based methods. Therefore, we propose a new semantic segmentation network—the region-enhancing network (RE-Net)—to emphasize the regional information instead of pixels to solve the above problems. RE-Net introduces the regional information into the base network, to enhance the regional integrity of images and thus reduce misclassification. Specifically, the regional context learning procedure (RCLP) can learn the context relationship from the perspective of regions. The region correcting procedure (RCP) uses the pixel aggregation feature to recalibrate the pixel features in each region. In addition, another simple intra-network multi-scale attention module is introduced to select features at different scales by the size of the region. A large number of comparative experiments on four different public datasets demonstrate that the proposed RE-Net performs better than most of the state-of-the-art ones.

A New Open-Source Web Application with Animations to Support Learning of Neuron-to-Neuron Signaling

Pesticides and their associated modes of action serve as real-world examples of chemical toxicity, stimulating student interest and supporting their understanding of nervous system function and cell signaling. An open-source web application called “Neuron-to-Neuron Normal and Toxic Actions” hosts narrated animations of pesticide toxic actions and exists as a resource for instructors of advanced secondary or undergraduate biology courses. This article describes the features of the web application, reports student feedback on the animations, and details a cooperative learning procedure for instructors to use the web application in online learning environments or in-person classroom settings with technology support.