Reviewerly/exHarmony huggingface dataset:
Physics Textbook Enriched with Videos Based on Augmented Reality Technology: Practice in Problem Solving Skill in Dynamics of Rotation Concept for Senior High School Students

Problem-solving skills are one of the skills needed in the 21st century. This article describes the results of research on the development of physics textbooks added to videos based on augmented reality technology on rotational dynamics material. This research result textbook is designed for practicing the problem-solving skills of high school students. The research method used is Research and Development with the Dick &amp; Carey model. Physics textbooks have been created which are presented in various views with stages to practice problem-solving skills. The presentation is presented in video-based augmented reality technology, table data, graphics, images, mathematical analysis, and verbal analysis for each sub-topic. The stages of problem-solving in the developed physics textbook present problems at the beginning of the chapter, present several questions to focus on the problem, formulate hypotheses about the problem, current material in various views, and explain phenomena based on physics concepts. To learn. The results of this study have been validated by physical material experts, learning experts, and textbook media experts. Experts provide an extensive opinion on all aspects assessed.
The TPACK Implementation in Physics Textbook with Augmented Reality: Enhance The 4C Skills at Mechanics Wave Concept

The 21st-century skills require a minimum of 4C skills. Learning media also need to apply TPACK (Technological, Pedagogical, and Content Knowledge) framework. This article describes the research and development result of the TPACK implementation in a high school physics textbook to enhance 4C skills for students’ 21st-century skills. The obtained physics textbook uses dick and Carey’s research and development model. Technology in this textbook using augmented reality media. The TPACK physics textbook assisted by AR (Augmented Reality) had produced for the 11th grade even semester at mechanics wave concept to enhance students’ 4C skills. The book feasibility received an assessment from the content expert, media expert, and pedagogical expert using the continuum scale 4 in the questionnaire instrument. The final result shows the implementation of TPACK in a resulting high school physics textbook product is proper to enhance 4C skills as a provision for 21st-century skills.
Quantum Dynamics of Two Nitrogen-Vacancy Center Ensembles Coupled to a Driven Superconducting Quantum Circuit

We theoretically model and simulate the dynamics of a hybrid quantum system consisting of two non-local ensembles of nitrogen-vacancy center and a superconducting transmon qubit mediated by two transmission line resonators. We apply a time-dependent external field to enhance this system’s speed and fidelity to function as a controlled-phase gate. Our simulation result shows that a high-fidelity entangled state of two non-local NV spins is 92%. It is achievable under realistic parameter regimes within a timescale of 1.1 nanoseconds. Our result paves the way to improving potential quantum computing and sensing applications.
Implementation of Augmented Reality (AR) using Assembler in High School Applied Physics Education with the ADDIE Model Approach

Since early March 2020, Indonesia has been shocked by the emergence of the SARS-COV-2 or Covid-19 virus from Wuhan, China. The Pandemic crisis has stopped the education system through face-to-face school learning. One adaptive learning model with the current situation through the network. The technology can be applied in the form of the use of Augmented Reality (AR). In addition, using the ADDIE model approach consists of Analysis, Design, Development, Implementation, and Evaluation. This model can be used in developing learning models, learning strategies, learning methods, learning media, and teaching materials. In general, some previous studies/writing only focused on one object of discussion. Where this study offers several objects that will be displayed, some of these objects are in the form of quantities and units, fluids, elasticity, vectors, energy and efforts, momentum and impulses, dynamics, and thermodynamics.
Review of Trends in Learning Media of Augmented Reality Integrated with STEM Approach to Improve Students’ Creative Thinking Skill

This research is about Augmented Reality (AR) Technology learning media by integrating the STEM approach to improve students’ creative thinking skills. This study needs the studies on previous research. The purpose is to identify trends in the Learning Media of Augmented Reality based on the STEM approach to improving students’ creative thinking skills. The method is a bibliometric analysis of 60 scientific journal articles on the Publish or Perish (PoP) application machine. The articles were analyzed with the VOS viewer application machine and synthesized. It has been found that five years ago, the studies of Augmented Reality (AR) Technology learning media in physics learning were widely, but without the STEM approach, and also not measure students’ creative thinking skills. Based on the results of this synthesis, we need to develop Learning Media Augmented Reality that integrates STEM approaches to improve students’ creative thinking skills.
Estimation of structural parameters in balanced Bühlmann credibility model with correlation risk

In this paper, the longitudinal data analysis is used to interpret the balanced Bühlmann credibility model with correlation risk, and the homogeneous credibility estimator is derived. We obtain the restricted maximum likelihood estimators (RMLE) for the structural parameters involved in the credibility factor and show that they are unbiased. In addition, the linear Bayes method is employed to estimate the structural parameters, and the proposed linear Bayes estimators (LBE) appear to outperform RMLE in terms of the mean squared error matrix (MSEM) criterion. Simulation studies show that the proposed LBE performs well.
Predicting visual difference maps for computer‐generated images by integrating human visual system model and deep learning

The quality of images generated by computer graphics rendering algorithms is mainly affected by visible distortion at some pixel locations. Image quality assessment (IQA) metrics are commonly utilized to assess the quality of rendered images, but their results are a global difference value, which does not provide pixel-wise differences to optimize the renderings. In contrast, visibility difference models including visual perception models and deep learning models can calculate pixel-wise visibility difference between distorted images and reference images. However, they either are only applied to a single type of visible distortion or are seriously dependent on datasets. To this end, the authors propose a novel model, dubbed Human Visual Perception and Deep Learning Image Difference Metric (HPDL-IDM), which combines the Human Visual System (HVS) model and deep learning. HPDL-IDM primarily consists of two modules: (i) the visual perception feature calculation module, which calculates difference maps between various kinds of features extracted from the reference image and the distorted image according to the visual characteristics of human eyes and concatenates them, and (ii) the deep learning module, which utilizes a neural network of encoder–decoder structure to train on the LocvisVC and VisTexRes datasets whose input and output are these concatenated feature difference maps and the final image distortion visibility difference map respectively. Additionally, the authors pool the final difference map into a global difference value between 0 and 1 to apply their model to many image processing tasks related to Image quality metrics (IQMs). Experimental results show that HPDL-IDM's generalization capacity and accuracy are improved by a large margin compared to other models.
Assessing word commonness

Abstract The article investigates the two main corpus indicators of word commonness, frequency and dispersion, through a cross-validation analysis of frequency and four dispersion measures (‘Range’, ‘Chi-squared’, ‘Deviation of Proportions’ and ‘Juilland’s D’). The approach provides an estimation of the capacity of the named measures to predict the distribution of corpus items in an extracted language sample. Based on a dataset of 273 Norwegian compounds, the results show that especially Deviation of Proportions is a robust measure of dispersion that can be used in conjunction with frequency to substantiate assertions of word commonness based on corpus data. In addition, dispersion measures do not only reflect what sort of distribution the frequency statistic is generated from, but also how reliable the frequency estimation in the corpus sample is in terms of giving an accurate representation of frequency in the language variety that the corpus is sampled from.
Impact of atmospheric extinction coefficient on the solar spectrum at selective wavelengths at Helwan in Egypt

Solar irradiance alterations due to the various atmospheric conditions have received great scientific concern. These alternations can be investigated by both monochromatic extinction coefficient and atmospheric transparency. In this work, continuous measurements of solar spectral irradiation using the Spectroradiometer for six selective wavelengths (350, 368, 500, 615, 780, and 870 nm) are carried out at the Helwan station, (NRIAG) in Egypt. Twelve clear-sky days of continuous observations have been monitored under different atmospheric conditions. The results have shown that using Langley method under cloudless sky for the total atmospheric extinction at the selective wavelengths was correlated. The extinction coefficient at the selected wavelengths shows obvious diurnal variations in each season. The perceptible effect of local climate has been obtained from the comparison of seasonal extinctions. The highest value of extinction coefficient was 0.59 km −1 at 615 nm in summer while the lowest one at 350 nm was 0.07 km −1 in winter. Moreover, it has been found that the afternoon values of transparency have a shift toward the longer wavelengths.
Newton polygon of exponential sums in two variables with triangular base

Let f(x1,x2) be a two-variable polynomial over a finite field of characteristic p, and assume that its convex hull is a triangle Δ. The goal of this paper is to study the Newton polygon NP(f,χ) of the L-function associated to f and a finite character χ of Zp. It is a partial generalization of [DWX], in which the authors studied polynomials of one variable. In this paper, we prove an improved lower bound IHP(Δ) for NP(f,χ), which, when f is non-ordinary, is strictly higher than the classical Hodge polygon. Moreover, we prove that if NP(f,χ) and IHP(Δ) coincide at one certain point, then they coincide at infinitely many points. This recovers the key property of the proof from [DWX].
SGORNN: Combining scalar gates and orthogonal constraints in recurrent networks

Recurrent Neural Network (RNN) models have been applied in different domains, producing high accuracies on time-dependent data. However, RNNs have long suffered from exploding gradients during training, mainly due to their recurrent process. In this context, we propose a variant of the scalar gated FastRNN architecture, called Scalar Gated Orthogonal Recurrent Neural Networks (SGORNN). SGORNN utilizes orthogonal matrices at the recurrent step. Our experiments evaluate SGORNN using two recently proposed orthogonal parametrizations for the recurrent weights of an RNN. We present a constraint on the scalar gates of SGORNN, which is easily enforced at training time to provide a probabilistic generalization gap which grows linearly with the length of sequences processed. Next, we provide bounds on the gradients of SGORNN to show the impossibility of exponentially exploding gradients through time. Our experimental results on the addition problem confirm that our combination of orthogonal and scalar gated RNNs are able to outperform other orthogonal RNNs and LSTM on long sequences. We further evaluate SGORNN on the HAR-2 classification task, where it improves upon the accuracy of several models using far fewer parameters than standard RNNs. Finally, we evaluate SGORNN on the Penn Treebank word-level language modeling task, where it again outperforms its related architectures and shows comparable performance to LSTM using far less parameters. Overall, SGORNN shows higher representation capacity than the other orthogonal RNNs tested, suffers from less overfitting than other models in our experiments, benefits from a decrease in parameter count, and alleviates exploding gradients during backpropagation through time.
Characterizing data patterns with core–periphery network modeling

Traditional classification techniques usually classify data samples according to the physical organization, such as similarity, distance, and distribution, of the data features, which lack a general and explicit mechanism to represent data classes with semantic data patterns. Therefore, the incorporation of data pattern formation in classification is still a challenge problem. Meanwhile, data classification techniques can only work well when data features present high level of similarity in the feature space within each class. Such a hypothesis is not always satisfied, since, in real-world applications, we frequently encounter the following situation: On one hand, the data samples of some classes (usually representing the normal cases) present well defined patterns; on the other hand, the data features of other classes (usually representing abnormal classes) present large variance, i.e., low similarity within each class. Such a situation makes data classification a difficult task. In this paper, we present a novel solution to deal with the above mentioned problems based on the mesostructure of a complex network, built from the original data set. Specifically, we construct a core–periphery network from the training data set in such way that the normal class is represented by the core sub-network and the abnormal class is characterized by the peripheral sub-network. The testing data sample is classified to the core class if it gets a high coreness value; otherwise, it is classified to the periphery class. The proposed method is tested on an artificial data set and then applied to classify x-ray images for COVID-19 diagnosis, which presents high classification precision. In this way, we introduce a novel method to describe data pattern of the data “without pattern” through a network approach, contributing to the general solution of classification.
An Appraisal-based Comparative Study on English and Chinese Movie Review

With appraisal theory as its theoretical foundation and basing on a self-built small-scaled Chinese and English movie review corpus, the research is conducted from the perspective of types of attitude. Combining qualitative method and quantitative method together, the author tries to make in-depth analyses about the differences of distributions of attitudinal resources in English and Chinese movie reviews. The research concludes that the movie reviewer's culture background will influence their way of using attitudinal resources.
Polya tree Monte Carlo method

Markov Chain Monte Carlo (MCMC) methods have been widely used in Statistics and machine learning research. However, such methods have several limitations, including slow convergence and the inefficiency in handling multi-modal distributions. To overcome these limitations of MCMC methods, a new, efficient sampling method has been proposed and it applies to general distributions including multi-modal ones or those having complex structure. The proposed approach, called the Polya tree Monte Carlo (PTMC) method, roots in constructing a Polya tree distribution using the idea of Monte Carlo method, and then using this distribution to approximate and facilitate sampling from a target distribution that may be complex or have multiple modes. The associated convergence property of the PTMC method is established and computationally efficient sampling algorithms are developed based on the PTMC. Extensive numerical studies demonstrate the satisfactory performance of the proposed method under various settings including its superiority to the usual MCMC algorithms. The evaluation and comparison are carried out in terms of sampling efficiency, computational speed and the capacity of identifying distribution modes. Additional details about the method, proofs and simulation results are provided in the Supplementary Web Appendices online.
Management and Impact of COVID-19 on Intelligent Transportation System

A safe and sustainable intelligent transportation and human behavior system can reliably extract knowledge from traffic data in cities. The data is safeguarded utilizing blockchain and learning techniques, with a threaded Graphics Processing Unit (GPU) for scalability. Different improvements are also available to efficiently process information on the GPU. To combine local information found on every site into global knowledge, a reinforced profound trained model is also built. Internet of Things (IoTs) technologies play a critical role in global transportation. Communication between automobiles via IoT will usher in a new era of communication that will lead to intelligent transportation systems (ITS). ITS technology is emerging as a worldwide revolution in recent years, having a significant influence on the transport and automation industries. As a result, academics’ focus has shifted to the utility of ITS technology, motivating us to give a complete study. As a result, this study brings together current research as well as some potential uses of intelligent transportation system technology in transportation. It traces the growth of ITS studies, also known as transportation models, since their inception and tries to provide a comprehensive analysis of various ITS technologies, including their strengths and drawbacks. Energy management is viewed as a cost-effective, creative solution to high-efficiency power plants. It optimizes conventional sources of the IoT-based intelligent transport system while also assisting in the automation of railroads, highways, airways, and shipways, hence improving customer experience. ITS is being developed to provide informed judgment and cooperative sensing in order to address the growing need for better autonomous transportation. However, in the dynamic era of current apps and the inflexible architecture of legacy Internet, software-defined transportation infrastructure must be adaptable, inventive, adaptive, and programmable (SD-ITS). The logically centralized intelligence of SD-ITS might be a key target of current cyber-threats and assaults, causing the entire network to go down in flames.
Distributed Optimization in Machine Learning

Machine learning is revolutionizing data-driven decision-making in a myriad applications including search and recommendations, self-driving cars, robotics, and medical diagnosis.
Quantized and Sparsified Distributed SGD

The communication delay in sending and receiving gradients or model updates between the worker nodes and the parameter server can be significantly, especially in networks with limited bandwidth and high latency on the communication links.
Credit Scoring with Drift Adaptation Using Local Regions of Competence

Abstract Despite the advances in machine learning (ML) methods which have been extensively applied in credit scoring with positive results, there are still very important unresolved issues, pertaining not only to academia but to practitioners and the industry as well, such as model drift as an inevitable consequence of population drift and the strict regulatory obligations for transparency and interpretability of the automated profiling methods. We present a novel adaptive behavioral credit scoring scheme which uses online training for each incoming inquiry (a borrower) by identifying a specific region of competence to train a local model. We compare different classification algorithms, i.e., logistic regression with state-of-the-art ML methods (random forests and gradient boosting trees) that have shown promising results in the literature. Our data sample has been derived from a proprietary credit bureau database and spans a period of 11 years with a quarterly sampling frequency, consisting of 3,520,000 record-months observations. Rigorous performance measures used in credit scoring literature and practice (such as AUROC and the H-Measure) indicate that our approach deals effectively with population drift and that local models outperform their corresponding global ones in all cases. Furthermore, when using simple local classifiers such as logistic regression, we can achieve comparable results with the global ML ones which are considered “black box” methods.
New efficient Hadoop scheduler: Generalized particle swarm optimization and simulated annealing<scp>‐</scp>dominant resource fairness

Summary Digital growth during the Corona pandemic has generated massive data. The Hadoop in big data has to be more efficient in resource handling and job scheduling. This article proposes the improved job scheduler which is more efficient in fair job scheduling even with the heterogeneous resources. The faster job execution depends upon the localization too. The nearer the slots are, the faster is the execution. So, this article proposes a hybrid metaheuristic algorithm with fair scheduling and data locality as the two objectives in job scheduling. The dominant resource fairness policy in Hadoop YARN is updated by hybrid generalized particle swarm optimization and simulated annealing for minimum locality and maximum fairness in scheduling. The algorithm is tested on various workloads for heterogeneous resources.
Case Studies for Practice

This chapter is written exclusively for researchers and industry professionals, who can look or predict many useful benefits of intelligent transportation system (ITS) in many sectors.
Convergence of SGD and Variance-Reduced Variants

In this chapter we will analyze the convergence of gradient descent (GD) and stochastic gradient descent (SGD) to determine the number of iterations required to reach a target error.
Beyond Distributed Training in the Cloud

Let us summarize the concepts that we learned through this book, and discuss the future of distributed machine learning beyond cloud-based implementations that we studied in this book.