Reviewerly/exHarmony huggingface dataset:
INFORMATION TECHNOLOGIES IN THE WORK OF A CONTEMPORARY TEACHER: CHALLENGES OF NUSH

Introduction.The article substantiates the importance of transformation of semantic content and modernization of educational tools, taking into account the current trendsin the development of the information socie-ty and the active use of information technologies based on the principles of the new Ukrainian school. The article substantiates the need to introduce innovations and change the emphasis in the priorities of the national edu-cation system, focusing on the disclosure of personal potential, the implementation of an individual educational trajectory and the applied nature of knowledge.Purpose. The purpose of the article: to identify the main directions of using information technologies in pri-mary school education, to analyze the possibilities of modern information technologies available for use in school and University settings, in particular, Google soft-ware and applications, in the context of the formation of key competencies and the concept of new Ukrainian school. Methods. To achieve this goal, we used a set of theo-retical (analysis of scientific literature, pedagogical expe-rience) and empirical (questionnaires, surveys, conversa-tions) research methods. Results. And the capabilities of modern information technologies available for use in school and degree set-tings, in particular Google software and applications. Originality. The author outlines the main directions of using information technologies in teaching younger stu-dents in the context of the formation of key competencies and the concept of a new Ukrainian school. Conclusion. The completed research does not exhaust all aspects of further scientific research and does not claim to be complete and complete solutions to the prob-lem. The most promising areas of research include improv-ing educational and methodological support (creating video lectures, electronic textbooks, taking into account modern requirements for the level of teacher training in the context of the NUS concept), improving forms and methods of teaching.
On 1/n neural representation and robustness

Understanding the nature of representation in neural networks is a goal shared by neuroscience and machine learning. It is therefore exciting that both fields converge not only on shared questions but also on similar approaches. A pressing question in these areas is understanding how the structure of the representation used by neural networks affects both their generalization, and robustness to perturbations. In this work, we investigate the latter by juxtaposing experimental results regarding the covariance spectrum of neural representations in the mouse V1 (Stringer et al) with artificial neural networks. We use adversarial robustness to probe Stringer et al's theory regarding the causal role of a 1/n covariance spectrum. We empirically investigate the benefits such a neural code confers in neural networks, and illuminate its role in multi-layer architectures. Our results show that imposing the experimentally observed structure on artificial neural networks makes them more robust to adversarial attacks. Moreover, our findings complement the existing theory relating wide neural networks to kernel methods, by showing the role of intermediate representations.
Phase transitions in lattice gauge theories: From the numerical sign problem to machine learning

Lattice simulations of Quantum chromodynamics (QCD) are an important tool of modern quantum field theory. They provide high precision results from first principle computations and as such allow for comparisonbetween experiment and theory. At finite baryon density, such simulations are no longer possible due to thenumerical sign problem which occurs when the action of the theory becomes complex, leading to integrals over highly oscillatory functions. We investigate two approaches to solve this problem. We employ complex Langevin method, which is a complexified stochastic process and investigate its properties. We apply it to QCD in a region where other methods are unreliable, we go up to $\mu/T_c\approx 5$. We finally investigate its applicability for SU(2) real-time simulations.We also investigate the Lefschetz Thimble method, which solves the sign problem by deforming the manifold of integration, such that there is no more oscillatory behavior.We discuss aspects of the method in simple models and develop algorithms for higher dimensions.\\Finally, we apply neural networks to lattice simulation data and use them to extract the order parameter forthe phase transition in the Ising model and SU(2) gauge theory. Thus, we uncover what the neural network learns.
Artificial Bee Colony Optimization Algorithm Based on Adaptive Evolution Strategy

An artificial bee colony optimization algorithm based on adaptive evolution strategy is proposed to improve the performance of the artificial bee colony algorithm. Each leader individual has four evolutionary strategies in the algorithm. In the iteration process, the evolutionary behavior of the leader individual is determined by calculating the immediate value, the future value and the comprehensive reward of each evolutionary strategy. And then a multi-strategy evolutionary probability mutation method is proposed to improve the individual search speed or to avoid falling into the local optimal solution. Typical high-dimensional complex function tests show that the algorithm has good convergence accuracy and computational speed.
The Influence of Think-Pair-Share in Enhancing Students’ Speaking Ability

Secondary schools obliged the students to master English skills. In fact, students have many shortcomings in mastering those English skills, especially speaking skills faced by the eighth-grade students of MTs Khazanah Kebajikan in the academic year 2017/2018. They had difficulties in speaking English directly. It was found by our observation in which the students were lack of vocabulary, self-confidence, and motivation. Additionally, they were difficult to elaborate on their ideas through the spoken form. Hence, the students are needed the new strategy to construct their shortcomings by the implementation of Think-Pair-Share. The objective of the study was to know the influence of Think-Pair-Share on students’ speaking ability of short monologue. This study used a quantitative method with a quasi-experimental design. The population was 102 students with 50 students as the sample of this study. The technique of data collection in this study consisted of pre-test and post-test. The data analysis techniques were used t-test, also included the test of normality and homogeneity. In addition, this study was also supported by measuring the effect size. The result of the study showed that students’ speaking score in the experimental class was higher than the controlled class. It found that H&lt;sub&gt;0 &lt;/sub&gt;(Null Hypothesis) is rejected and H&lt;sub&gt;a&lt;/sub&gt; (Alternative Hypothesis) is accepted. Also, the result of the effect size was 2.91 at a large level (significant). It can be concluded that Think-Pair-Share is an effective way to improve students’ speaking ability, especially in short monologue.
Preview of: A Primer on Motion Capture with Deep Learning: Principles, Pitfalls, and Perspectives

Recent advances in deep learning have greatly simplified the measurement of animal behavior and advanced our understanding of how animals and humans behave. The article previewed here provides readers with an excellent overview of the topic of motion capture with deep learning and will be of interest to the wider data science community.
An Intelligent Fault Diagnosis Model Based on FastDTW for Railway Turnout

The turnout handles the direction of the train which is one of the key equipment in the railway transportation system.In this paper, by using real action current data obtained from switch machine model No.ZD7, a turnout fault diagnosis model based on the FastDTW pattern recognition algorithm was proposed.Firstly, the original current curve was segmented relate to the features of them.Then the warping path distance between the standard sample and the tested current curve was obtained according to FastDTW algorithm.Finally a dynamic optimized threshold was used to confirm whether there is a fault happened in the turnout.According to the experiment results, the proposed diagnose model without the prior knowledge of fault samples can works well both with single and double acting type turnout machines, owning to the following elements: the diagnose accuracy can be more than 96%, the time-cost can be improved more than 5 times compared with traditional DTW based algorithms.
Wavelet Channel Coding: An Algebraic Approach

In this paper, the wavelet channel coding (WCC) is revisited in a more general analysis embracing flat real wavelet matrices derived from a Haar matrix and complex input symbols. WCC encoding and decoding are algebraically described and a probability distribution of wavelet symbols is formulated. Signal constellations for transmission of wavelet symbols are proposed and the constellation average energy is deduced from probability generating functions of the wavelet symbols. System performance over a flat Rayleigh channel is analyzed and compared with symbol-by-symbol detecting systems and diversity two space-time block coding (STBC) systems. Simulation results show that WCC presents better performance than ordinary symbol-by-symbol detecting systems, particularly at higher signal-to-noise ratios for higher spectral efficiencies, and STBC systems for spectral efficiency of 1 bit/s/Hz.
Diagnosis of Preeklamsia In Pregnant Women Based On K-Nearest Neighbor Algorithm: Diagnosis Preeklamsia Pada Ibu Hamil Berdasarkan Algoritme K- Nearest Neighbour

Maternal deaths are divided into two namely direct and indirect deaths. Globally 80% of direct maternal deaths, preeclampsia are included in direct maternal deaths. Preeclampsia conditions of pregnancy with hypertension occur after the 20th week in women who previously had normal blood pressure. Preeclampsia can also be characterized by hypertension (systolic blood pressure â‰¥ 140 mmHg or diastolic blood pressure â‰¥ 90 mmHg) accompanied by proteinuria (â‰¥ 300 mg / dl in tamping urine 24 hours). In this study, an analysis of medical records in the Purbalingga and Banyumas areas using 8 attributes, namely age, body weight, blood pressure, edema, multiple pregnancy, history of hypertension, how many children, urine protein, and preeclampsia class. From calculations using the K-NN (K-Nearest Neighbour) algorithm, the Sensitivity performance value of 98.19%, Specificity 100%, and Accuracy 98.33%.
Building Data Mining Classification Model For Pixilated Digit Recognition System

Building data mining classification model for pixilated digit recognition system - Recognition is one of the major areas that
Reasoning About Foreign Function Interfaces Without Modelling the Foreign Language

Foreign function interfaces (FFIs) allow programs written in one language (called the host language) to call functions written in another language (called the guest language), and are widespread throughout modern programming languages, with C FFIs being the most prevalent. Unfortunately, reasoning about C FFIs can be very challenging, particularly when using traditional methods which necessitate a full model of the guest language in order to guarantee anything about the whole language. To address this, we propose a framework for defining whole language semantics of FFIs without needing to model the guest language, which makes reasoning about C FFIs feasible. We show that with such a semantics, one can guarantee some form of soundness of the overall language, as well as attribute errors in well-typed host language programs to the guest language. We also present an implementation of this scheme, Poseidon Lua, which shows a speedup over a traditional Lua C FFI.
Combining Linear Classifiers Using Probability-Based Potential Functions

The score function can be used as a measure for evaluating predicted probabilities of the classification models. In multiple classifiers systems, one of the problems is the diversity of the way of determining the scoring function of individual base classifiers. To alleviate this limitation, in this article, we propose a novel concept of calculating a scoring function defined by the probability-based potential function. The proposed potential functions take into account the distance of the recognized object from the decision boundary as well as a prior probability of the class labels. The proposed score function has the same nature for all linear base classifiers, which defined the multiple classifiers model. Additionally, the proposed method is compared with other ensemble algorithms based on homogeneous linear base classifiers. The experiments on seventy databases demonstrate the effectiveness of our method. To discuss the results of our experiments, we use multiple classification performance measures dedicated to standard and imbalanced datasets. The statistical analysis of the experiments is also performed.
Competencias digitales en estudiantes del séptimo ciclo de educación secundaria a través de la red social educativa Edmodo

The purpose of this study was to establish the level of digital&nbsp;literacy in students from 3rd, 4th and 5th grade of a public high&nbsp;school in Lima, by a group of activities in the educational social&nbsp;network Edmodo. In order to collect information about the digital&nbsp;literacy of students, before and after the intervention, it has developed&nbsp;a questionnaire based on the ICT Competency Inventory.&nbsp;The instrument was applied to a sample of 162 students divided&nbsp;into two groups: one control group (80) and one experimental&nbsp;group (82). The results showed that the second group achieved&nbsp;a medium level in the variable “digital literacy”, in which the&nbsp;“knowledge of digital tools” outperformed.
Developing a cloud EBC system with 2P-Cloud architecture

ABSTRACTIt does not need to be mentioned that Business cards are essential for businesses and consumers alike across all industries irrespective of size of the business. Today, these cards not only help in giving contact details but building a brand. In digital era the business card is also going through the journey of digital transformation. While some of the expectation from such electronic business card can be articulated as – Easy to share, cost-effective, Eco-friendly, Easy to customize, store the information conveniently and contact management. While taking a closer look at these expectation the authors were convinced that such system should be based on a cloud architecture .The authors with their experience on the 2P-cloud Architecture realized that the concept can be extended for cloud services 4-tier architectures and an Electronic Business card system can be built on it. In this study, we proposal an instance of the cloud electronic business card (EBC) generation framework which is according to the Key point of view of Business models, Operational processes and User experiences of Digital transformation studied by Abhijit et al. and the five skills and competencies of CDO studied by Anna et al. and the 2P-Cloud architecture studied by Chuang et al as main. In this study, we focused on the system availability of cloud EBC system in the development processes, which is to attend the smooth process of Human–Computer Interaction when we design an IT system.
Challenges of Sarcasm Detection for Social Network : A Literature Review

Nowadays, sarcasm recognition and detection simplified with various domains knowledge, among others, computer science, social science, psychology, mathematics, and many more. This article aims to explain trends in sentiment analysis especially sarcasm detection in the last ten years and its direction in the future. We review journals with the title’s keyword “sarcasm” and published from the year 2008 until 2018. The articles were classified based on the most frequently discussed topics among others: the dataset, pre-processing, annotations, approaches, features, context, and methods used. The significant increase in the number of articles on “sarcasm” in recent years indicates that research in this area still has enormous opportunities. The research about “sarcasm” also became very interesting because only a few researchers offer solutions for unstructured language. Some hybrid approaches using classification and feature extraction are used to identify the sarcasm sentence using deep learning models. This article will provide a further explanation of the most widely used algorithms for sarcasm detection with object social media. At the end of this article also shown that the critical aspect of research on sarcasm sentence that could be done in the future is dataset usage with various languages that cover unstructured data problem with contextual information will effectively detect sarcasm sentence and will improve the existing performance.
Verbal Projection of Comparable Translations of a Detective Story

Abstract Based on the systemic functional framework, this paper attempts to compare verbal projection in two comparable translated texts of a detective story entitled A Scandal in Bohemia , one from the early 20th century (henceforth TT 1) and the other from the early 21st century (henceforth TT 2). Approximately one hundred years apart, these two translations are strikingly different in their language use, with classical Chinese being used in TT 1 and plain (colloquial) Chinese being used in TT 2. By analysing and comparing the lexicogrammatical features of the verbal clauses in the two translated texts, this paper summarises the choices made by the translators in these two different historical moments: when translating the source text, TT 1 translators show more flexibility by incorporating more addition and omission into their translation than TT 2 translators.
Application of Weighted Sum Model (WSM) for Determining Development Priorities in Rural

Currently, the government has received special attention to advance the economy and human resources of the village community through the regulation of the village minister, development of disadvantaged areas and transmigration of the Republic of Indonesia number 19 of 2017 concerning the prioritization of using village funds, therefore attention to supervising development in villages is very necessary to ensure that the development in the village is right on target and takes into account the priority needs of the village, as stated in Permendes Number 19 of 2017 CHAPTER II Article 3 concerning the determination of priority for the use of funds. The purpose of this research is to carry out the government's appeal on increasing community participation in the implementation and development of the village in accordance with the government's appeal for supervision of village funds as stipulated in Permendes Number 19 of 2017 so that development in villages prioritizes more urgent community interests and justice for all villagers without distinction. -different, a decision support system is needed to support this activity to be better and more efficient.The stages of the method in the research are direct field visits, namely the village of Jati Baru, kecamatan merbau, Deli Serdang Regency, North Sumatra, conducting interviews with the village head, village officials and local villagers in order to obtain information related to this research then look for references and literature studies through research journals, laws, government regulations, and other matters relevant to this research, then apply the Weigthed Sum method. The model is a method of solving problems, designing systems, building systems, testing systems and implementing a decision support system for determining development priorities in Jati Baru Village, Pagar Merbau District, Deli Serdang Regency, North Sumatra. The results of this study are the decision support system for determining development priorities in the village and the system is proven to be able to solve the problem of determining development priorities in the village based on predetermined alternatives and criteria.
TEACHING ENGLISH AS A FOREIGN LANGUAGE(2nd Edition)

Having taught the methods course for decades and given teachertraining seminars, I am really interested in sharing my knowledge and experience with English teachers and prospective English teachers in Indonesia. I must thank my colleagues and students at my university. This book would not have been written in the ﬁ rst place if it were not for their inﬂ uence. I am very grateful to them. I am also indebted to Earl Stevick, Diane Larsen- Freeman, and Calleg Cattegno. Being a student of these outstanding experts at the School for International Training, Vermont- USA has inspired me to write this book. Finally, I must express my deep appreciation to my spouse, Ika Dewi, who has given me her support and shared her experience in teaching English at schools.
The Statistical Cost of Robust Kernel Hyperparameter Turning

This paper studies the statistical complexity of kernel hyperparameter tuning in the setting of active regression under adversarial noise. We consider the problem of finding the best interpolant from a class of kernels with unknown hyperparameters, assuming only that the noise is square-integrable. We provide finite-sample guarantees for the problem, characterizing how increasing the complexity of the kernel class increases the complexity of learning kernel hyperparameters. For common kernel classes (e.g. squared-exponential kernels with unknown lengthscale), our results show that hyperparameter optimization increases sample complexity by just a logarithmic factor, in comparison to the setting where optimal parameters are known in advance. Our result is based on a subsampling guarantee for linear regression under multiple design matrices, combined with an {\epsilon}-net argument for discretizing kernel parameterizations.
An Effective Manifold Learning Approach to Parametrize Data for Generative Modeling of Biosignals

Modeling data generated by physiological systems is a crucial step in many problems such as classification, signal reconstruction and data augmentation. However finding appropriate models from high-dimensional data sampled from biosignals is in general unpracticable due to the problem known as the “curse of dimensionality”. Dimensionality reduction, that is representing data in some lower-dimensional space, is the commonly adopted technique to handle these data. In this context manifold learning has drawn great interests as a promising nonlinear dimensionality reduction method. Neverthless the main drawback of methods based on manifold learning is that they learn data implicitly, that is with no explicit model of data belonging to the manifold. The aim of this article is to develop a manifold learning approach to parametrize data for generative modeling of biosignals, by deriving an explicit function that represents the local parametrization of the manifold. The approach involves two main stages, i) estimation of the intrinsic dimension of data, that is the dimension of the manifold, and ii) estimation of the function representing the local parametrization of the manifold. Experimental results both on synthetic and real-world data shown the effectiveness of the presented approach. The source code of the algorithm for unsupervised learning of data is available at https://codeocean.com/capsule/6692152/tree/v3.