Reviewerly/exHarmony huggingface dataset:
English-only Versus Mother Tongue: An Analysis on Students’ Fluency and Self-confidence

This study evaluates the impact of English-only policy and mother tongue instruction on the fluency and self-confidence of one hundred-two second-year education students taking a Bachelor of Secondary Education degree major in English at Local Community Colleges in Misamis Oriental. The researcher used a mixed-method approach, combining Focus Group Discussions, Survey Questionnaires, and Classroom Observations to triangulate the data and establish reliable findings. The results show that the main factors that affect students’ self-confidence in speaking English in the classroom are their fear of making errors, such as mispronouncing words, peer pressure, ridicule from peers, and inadequate preparation. Furthermore, the findings suggest that students feel more nervous in a strictly English-only policy classroom setting compared to when they use their first language, as they tend to produce more words and express their thoughts more effectively in their mother tongue with less filled pauses and interruptions. To enhance their English oral fluency, the study recommends providing students with more opportunities to practice speaking English in communicative activities and oral discourse.
Introduction

Congratulations on choosing to learn the C++ programming language, and thank you for picking up this book. I will try to introduce you to a beautiful world of C++ to the best of my abilities.
Pengaruh Kualitas Informasi Akun Instagram @Kotajakartapusat Terhadap Citra Pemerintah

This research was conducted to determine the effect of the information quality of the @kotajakartapusat Instagram account on the image of the Central Jakarta Administrative City Government. The research method uses quantitative survey methods and this type of research is causality. Sampling used a non-probability sampling method with purposive sampling of 99 respondents. The results showed that from the results of partial hypothesis testing (T test) there was a significant effect of Information Quality (X) Instagram account @kotajakartapusat on the City Administration City Government's Image (Y) significantly by tcount 9.382 ≥ 1.985 ttable value and significance value 0.000 ≤ 0.05 , this means that Ho is rejected and Ha is accepted. The conclusion of the study is that the influence of the information quality of the Instagram @kotajakartapusat account is very good on the image of the central Jakarta administrative city government..&#x0D; &#x0D; Keywords: Central Jakarta Administration City Government, Image, Information Quality, Instagram
High School Students' Motivation and Engagement in Online Learning

This study aims to determine the effect of motivation toward student engagement of high school students in Padang city in online learning. The quantitative research method of simple linear regression analysis was used in this study. This study included 331 high school students who participated in online learning using random sampling techniques. The Motivated Strategies and Learning Questionnaire (MSLQ) and the Student Engagement Scale were applied to this study. Reliability on the motivation scale was .922 and the student engagement scale was .926. The results of this study indicate that motivation has a significant effect on student engagement in high school students in Padang City in online learning. This can be seen from the significance value of .000 (p less than .05) and the R-square coefficient (R2) value of .545. It can be concluded that motivation influences student engagement by 55%, while the remaining (45%) is influenced by other factors not investigated in this study.
Clustering of Data Represented by Pairwise Comparisons

Abstract In this paper, experimental data, given in the form of pairwise comparisons, such as distances or similarities, are considered. Clustering algorithms for processing such data are developed based on the well-known k-means procedure. Relations to factor analysis are shown. The problems of improving clustering quality and of finding the proper number of clusters in the case of pairwise comparisons are considered. Illustrative examples are provided.
Metallo Nanocomposites in the Matrix of Copolymer of 1-Vinyl-1,2,4, -Triazole with Methacrylic Acid

Polymer Science: Peer Review Journal Metallo Nanocomposites in the Matrix of Copolymer of 1-Vinyl-1,2,4, -Triazole with Methacrylic Acid Sargsyan SH1*, Sargsyan AS2, Khizantsyan KM1, Sargsyan TS2, Margaryan KS2 and Aghajanyan IG 1National Polytechnic University of Armenia, Armenia 2Yerevan State Medical University after Mkhitar Heratsi, Armenia *Corresponding author: Serzhik Sargsyan, National Polytechnic University of Armenia, Armenia Submission: January 05, 2022;Published: January 12, 2022 DOI: 10.31031/PSPRJ.2022.02.000547 ISSN: 2770-6613 Volume2 Issue5
Theoretical and methodological basis of the use of digital technologies in the formation of environmental competence of applicants for education

The article contains the results of scientific research on the theoretical and methodological foundations of the use of digital technologies in the formation of environmental competence of applicants for education. The purpose of the study is to conduct an analysis of theoretical and methodological foundations of the use of digital technologies in the formation of environmental competence of applicants for education in order to create methodological recommendations for teachers to carry out distance and blended learning. Peculiarities of the use of digital technologies in the formation of environmental competence of applicants for education are analyzed (intensification of learning due to the use of attractive and rapidly changing forms of information presentation, promotion of learning individualization; interrelation of portions of information and tests; strict logical sequence, structural and logical schemes; doing tests and solving tasks; having several attempts, learning the material anew or reperforming tasks). Characteristics of online platforms for use in the process of forming environmental competence of applicants for education according to their features, advantages and disadvantages, including Google Apps Education Edition, Google Classroom, Google Meets, Teams, Moodle, Zoom, Classtools, LearningApps, Edmodo, etc., were studied. A method of using mobile applications with ecological content in the formation of environmental competence of applicants for education has been developed through the determination of ways of their integration into educational and cognitive activities, locations (autonomous work in the educational building or remotely); frequency of their application (a prepared educational course, a project with usage of mobile technologies or a one-time integration into an educational session in the process of exploring a certain topic); creation and use of existing mobile applications; use of personal mobile devices or devices of educational institutions.
Visualization of distance learning

The article is devoted to highlighting the current problems of creating visual support for distance learning of pedagogical workers in the conditions of postgraduate education. The current experience in training teachers to create a visualization of distance learning of teachers in postgraduate education is considered. Attention is paid to the modern problems of distance learning, the experience of organizing advanced training courses, implementation of thematic, author's courses for teachers of general secondary education institutions and vocational pre-higher education institutions is described. A description of the content of the training of teachers on the author's remote professional development courses is provided. A description of the preparation of a visual series for remote training of teachers in the postgraduate education system is given. Features of the use of modern digital tools and technology for creating electronic materials for the organization of distance learning are considered. A description of the use of video conferencing systems in distance learning is provided, examples of creating video materials for conducting distance classes are described. The experience of organizing distance classes is described and a description of the types of classes in distance learning is provided.&#x0D; The list of problems of the pedagogical community in the process of organizing distance learning in crisis conditions is presented. The problems and needs of professional improvement of teachers, which can be solved during the period of professional development in the system of postgraduate education, are singled out.&#x0D; The results of research in the post-graduate education system are described, which demonstrate the solution to the problems of distance learning visualization based on the author's professional development courses. The meaningful content of distance, network, electronic training of teachers, the selection of effective technologies for the preparation of visual support for conducting classes in a distance format are recognized
Powerful Prose: How Textual Features Impact Readers

edited by R.L. Victoria Pöhls &amp; Mariane Utudji. transcript, 2021. 264 pp. $55.
Adaptive Negative Evidential Deep Learning for Open-set Semi-supervised Learning

Semi-supervised learning (SSL) methods assume that labeled data, unlabeled data and test data are from the same distribution. Open-set semi-supervised learning (Open-set SSL) considers a more practical scenario, where unlabeled data and test data contain new categories (outliers) not observed in labeled data (inliers). Most previous works focused on outlier detection via binary classifiers, which suffer from insufficient scalability and inability to distinguish different types of uncertainty. In this paper, we propose a novel framework, Adaptive Negative Evidential Deep Learning (ANEDL) to tackle these limitations. Concretely, we first introduce evidential deep learning (EDL) as an outlier detector to quantify different types of uncertainty, and design different uncertainty metrics for self-training and inference. Furthermore, we propose a novel adaptive negative optimization strategy, making EDL more tailored to the unlabeled dataset containing both inliers and outliers. As demonstrated empirically, our proposed method outperforms existing state-of-the-art methods across four datasets.
ChatGPT for Programming Numerical Methods

ChatGPT is a large language model recently released by the OpenAI company. In this technical report, we explore for the first time the capability of ChatGPT for programming numerical algorithms. Specifically, we examine the capability of GhatGPT for generating codes for numerical algorithms in different programming languages, for debugging and improving written codes by users, for completing missed parts of numerical codes, rewriting available codes in other programming languages, and for parallelizing serial codes. Additionally, we assess if ChatGPT can recognize if given codes are written by humans or machines. To reach this goal, we consider a variety of mathematical problems such as the Poisson equation, the diffusion equation, the incompressible Navier-Stokes equations, compressible inviscid flow, eigenvalue problems, solving linear systems of equations, storing sparse matrices, etc. Furthermore, we exemplify scientific machine learning such as physics-informed neural networks and convolutional neural networks with applications to computational physics. Through these examples, we investigate the successes, failures, and challenges of ChatGPT. Examples of failures are producing singular matrices, operations on arrays with incompatible sizes, programming interruption for relatively long codes, etc. Our outcomes suggest that ChatGPT can successfully program numerical algorithms in different programming languages, but certain limitations and challenges exist that require further improvement of this machine learning model.
MV-MR: multi-views and multi-representations for self-supervised learning and knowledge distillation

We present a new method of self-supervised learning and knowledge distillation based on the multi-views and multi-representations (MV-MR). The MV-MR is based on the maximization of dependence between learnable embeddings from augmented and non-augmented views, jointly with the maximization of dependence between learnable embeddings from augmented view and multiple non-learnable representations from non-augmented view. We show that the proposed method can be used for efficient self-supervised classification and model-agnostic knowledge distillation. Unlike other self-supervised techniques, our approach does not use any contrastive learning, clustering, or stop gradients. MV-MR is a generic framework allowing the incorporation of constraints on the learnable embeddings via the usage of image multi-representations as regularizers. Along this line, knowledge distillation is considered a particular case of such a regularization. MV-MR provides the state-of-the-art performance on the STL10 and ImageNet-1K datasets among non-contrastive and clustering-free methods. We show that a lower complexity ResNet50 model pretrained using proposed knowledge distillation based on the CLIP ViT model achieves state-of-the-art performance on STL10 linear evaluation. The code is available at: https://github.com/vkinakh/mv-mr
LiDARFormer: A Unified Transformer-based Multi-task Network for LiDAR Perception

There is a recent trend in the LiDAR perception field towards unifying multiple tasks in a single strong network with improved performance, as opposed to using separate networks for each task. In this paper, we introduce a new LiDAR multi-task learning paradigm based on the transformer. The proposed LiDARFormer utilizes cross-space global contextual feature information and exploits cross-task synergy to boost the performance of LiDAR perception tasks across multiple large-scale datasets and benchmarks. Our novel transformer-based framework includes a cross-space transformer module that learns attentive features between the 2D dense Bird's Eye View (BEV) and 3D sparse voxel feature maps. Additionally, we propose a transformer decoder for the segmentation task to dynamically adjust the learned features by leveraging the categorical feature representations. Furthermore, we combine the segmentation and detection features in a shared transformer decoder with cross-task attention layers to enhance and integrate the object-level and class-level features. LiDARFormer is evaluated on the large-scale nuScenes and the Waymo Open datasets for both 3D detection and semantic segmentation tasks, and it outperforms all previously published methods on both tasks. Notably, LiDARFormer achieves the state-of-the-art performance of 76.4% L2 mAPH and 74.3% NDS on the challenging Waymo and nuScenes detection benchmarks for a single model LiDAR-only method.
EZtune: A Package for Automated Hyperparameter Tuning in R

Statistical learning models have been growing in popularity in recent years. Many of these models have hyperparameters that must be tuned for models to perform well. Tuning these parameters is not trivial. EZtune is an R package with a simple user interface that can tune support vector machines, adaboost, gradient boosting machines, and elastic net. We first provide a brief summary of the the models that EZtune can tune, including a discussion of each of their hyperparameters. We then compare the ease of using EZtune, caret, and tidymodels. This is followed with a comparison of the accuracy and computation times for models tuned with EZtune and tidymodels. We conclude with a demonstration of how how EZtune can be used to help select a final model with optimal predictive power. Our comparison shows that EZtune can tune support vector machines and gradient boosting machines with EZtune also provides a user interface that is easy to use for a novice to statistical learning models or R.
Using Differential Evolution to avoid local minima in Variational Quantum Algorithms

Variational Quantum Algorithms (VQAs) are among the most promising NISQ-era algorithms for harnessing quantum computing in diverse fields. However, the underlying optimization processes within these algorithms usually deal with local minima and barren plateau problems, preventing them from scaling efficiently. Our goal in this paper is to study alternative optimization methods that can avoid or reduce the effect of these problems. To this end, we propose to apply the Differential Evolution (DE) algorithm to VQAs optimizations. Our hypothesis is that DE is resilient to vanishing gradients and local minima for two main reasons: (i) it does not depend on gradients, and (ii) its mutation and recombination schemes allow DE to continue evolving even in these cases. To demonstrate the performance of our approach, first, we use a robust local minima problem to compare state-of-the-art local optimizers (SLSQP, COBYLA, L-BFGS-B and SPSA) against DE using the Variational Quantum Eigensolver algorithm. Our results show that DE always outperforms local optimizers. In particular, in exact simulations of a 1D Ising chain with 14 qubits, DE achieves the ground state with a 100\% success rate, while local optimizers only exhibit around 40\%. We also show that combining DE with local optimizers increases the accuracy of the energy estimation once avoiding local minima. Finally, we demonstrate how our results can be extended to more complex problems by studying DE performance in a 1D Hubbard model.
Chunking Strategy to Improve Students' Reading Comprehension: An Action Research

The study focused on Chunking Strategy to Improve Student’s Reading Comprehension in the Nonequivalent Control Group Design, a form of quasi-experimental group design. This research consisted of two groups (experimental and controlled groups). Both groups were given pre-test and post-test to determine whether the chunking strategy could improve the students' reading comprehension. This research was conducted on the Junior High School Students of Sibugay Integrated School. Moreover, The researchers used the Reading Comprehension Test Research Instrument by Wigfield and Guthrie (1995) which was developed to evaluate many facets of students' reading motivation. The findings revealed a significant difference in the post-test and pretest scores of the students who underwent the chunking strategy. The findings of this study affirmed that using a chunking method was useful for teaching reading comprehension in the educational process. The pre-test and post-test scores of the students showed significant improvement. By using this strategy, teachers may make it easier for students to comprehend and retain material, which will boost their performance.
Critical Discourse Analysis as a Literature Learning Model

Literature in the past became the main entertainment that was very popular with the community, literature was used for various purposes, such as education, writing history, or spreading teachings. Align with the development of technology; printed literature has been displaced by other entertainment through electronic media. However, literature remains a good medium for teaching as well as entertainment. Therefore, literature needs to be preserved and taught continuously to the next generations. This paper intends to examine the benefit to study literature with a literary model of critical discourse analysis. Through critical discourse analysis, students are ultimately expected to be accustomed for being critical and creative in responding to various phenomena and meanings contained in literary, as a product of the nation's culture. Students' understanding of the various meanings and values contained in literary discourse is the main priority of this critical discourse analysis model.
Learning in the Midst of Uncertainties: Stakeholders’ Lived Experiences in Delivering Instruction to Junior High School Students during Emergency Remote Teaching (ERT) in Selected Public Schools in Cebu City

This study aims to explore the experiences of learners, parents, teachers and school leaders in delivering instruction to Junior High School students during Emergency Remote Teaching (ERT) in Cebu City South Districts for the School Year 2021-2022.The study utilized the phenomenological design to explore the experiences of learners, teachers, parents, and school leaders in delivering instruction to junior high school students during Emergency Remote Teaching in Cebu City South Districts for the school year 2021-2022. The emergent themes generated from the data gathered in this study include (1) partnership &amp; linkages experiences, (2) resource management experiences, (3) instructional management experiences, (4) strategic engagement management experiences, and (5) human development management experiences.
Gaussian kernel quadrature Kalman filter

The solution to practical nonlinear filtering problems broadly relies on Gaussian filtering. The Gaussian filtering involves intractable integrals that are numerically approximated during the filtering. The literature witnesses various Gaussian filters with varying accuracy and computational demand, which are developed using different numerical approximation methods. Among them, the quadrature rule based Gaussian filters are known for offering the best accuracy. They apply the univariate Gauss-Hermite quadrature rule for approximating the intractable integrals. For the practical multivariate filtering problems, they additionally apply a univariate-to-multivariate conversion rule. This paper develops a new quadrature rule based Gaussian filter, named Gaussian kernel quadrature Kalman filter (GKQKF). The proposed GKQKF replaces the univariate Gauss-Hermite quadrature rule with the univariate Gaussian kernel quadrature rule and uses the product rule for extending the univariate quadrature rule in the multivariate domain. The Gaussian kernel quadrature rule improves the numerical approximation accuracy, which results in improved estimation accuracy of the proposed GKQKF over the existing quadrature rule based Gaussian filters. As the quadrature rule based Gaussian filters are the most accurate existing Gaussian filters, the proposed GKQKF outperforms the other existing Gaussian filters as well. The improved accuracy of the proposed GKQKF is validated for three different simulation problems.
Encoding Binary Concepts in the Latent Space of Generative Models for Enhancing Data Representation

Binary concepts are empirically used by humans to generalize efficiently. And they are based on Bernoulli distribution which is the building block of information. These concepts span both low-level and high-level features such as "large vs small" and "a neuron is active or inactive". Binary concepts are ubiquitous features and can be used to transfer knowledge to improve model generalization. We propose a novel binarized regularization to facilitate learning of binary concepts to improve the quality of data generation in autoencoders. We introduce a binarizing hyperparameter $r$ in data generation process to disentangle the latent space symmetrically. We demonstrate that this method can be applied easily to existing variational autoencoder (VAE) variants to encourage symmetric disentanglement, improve reconstruction quality, and prevent posterior collapse without computation overhead. We also demonstrate that this method can boost existing models to learn more transferable representations and generate more representative samples for the input distribution which can alleviate catastrophic forgetting using generative replay under continual learning settings.