Reviewerly/exHarmony huggingface dataset:
The impact of the COVID-19 pandemic on vocational education in Russia and foreign countries

The article is devoted to the study of the impact of the COVID-19 pandemic on the sphere of higher education in conditions of limited attendance of educational institutions, the transition to a new format of education and the search for solutions in response to the challenges of the education system in a critical situation. In the context of the outbreak of a new coronavirus infection, the pandemic has had the greatest impact on the field of education (including vocational) on a global scale. However, this area, despite a number of certain problems, has become one one of the few industries that turned out to be prepared to transfer most of their processes online. Currently, national governments, international organizations and research centers are conducting research on the problems of the education system common to most States that arise on the way to the transition to the digital format of education. Study of the impact of the pandemic COVID-19 in the field of higher education was conducted based on the results of a report by the Vladimir Potanin Charitable Foundation, the National Training Foundation and a VTSIOM survey in June 2020.
ВОЗМОЖНОСТИ ПО ИСПОЛЬЗОВАНИЮ ВЫСОКОТОЧНОЙ МАГНИТОРАЗВЕДКИ, ГРАВИРАЗВЕДКИ И ЭЛЕКТРОРАЗВЕДКИ ПРИ ПОИСКАХ УГЛЕВОДОРОДНОГО СЫРЬЯ

The geophysical methods for detecting hydrocarbon deposits using new equipment and processing the data are analyzed in detail. A review of problematic issues during hydrocarbon exploration is given. Particular attention is paid to the method of conducting high-precision magnetic surveys, which allows direct searches for hydrocarbon deposits by signs of the confinement of oil and gas prospective structures to areas of calm, low-amplitude, large­ sized negative magnetic anomalies. Based on the analysis of materials of modern gravity exploration, it was found that hydrocarbon deposits in the gravitational field correspond to local negative anomalies, which are an indirect sign. They outline these deposits in plan. It is important that these anomalies above the deposits in the gravitational field do not depend on the type of traps and collectors. By the magnitude of the amplitude of local anomalies, the predicted hydrocarbon resources can be estimated. The effectiveness of modern electrical exploration during prospecting for hydrocarbons is quite high. The rationale for the possibility of applying the electric prospecting method in the search for hydrocarbon deposits is associated with two points. Firstly, with a sharp increase in the total electrical resistance of the oil and gas bearing reservoir relative to the host aquifers (oil and gas deposits have increased total electrical resistance) is a direct sign. Secondly, an indirect sign, the presence of areas with dispersed ore minerals near the surfaceabovethereservoir.
Analysis of the dominant motivation factors of reading Arabic text for students at the State Islamic University of Maulana Malik Ibrahim

This study’s justification is to analyze the dominant elements of the motivation for students learning to read Arabic text. This study uses a quantitative approach with a procedure using factor analysis. All data were tested for homogeneity, validity and reliability. First, the data were collected through a validated questionnaire with a sample size of 97 respondents. The motivation to read Arabic text as the dependent variable (y) is influenced by ten independent variables (xi). From the factor analysis, it was obtained four groupings of independent variables which were labeled: Free – Bound - Interpretation Factor, Free - Permanent-Interpretation Factor, Socio - Cultural factor, and Permanent - Free Factor. The most dominant group of independent variables is the Free-Bound-Interpretation Factors group, namely reading Arabic-language printed media, reading al-Qur'an verses, reading classical religious books in Arabic.
Scrum Team Competence Based on Knowledge, Skills, Attitude in Global Software Development

Global Software Development (GSD) and Scrum are two pivotal tools in software engineering, which meet the needs of global economics in dealing with various disruptions, such as physical distancing due to the COVID-19 pandemic.This research focuses on Scrum with its three roles: Product Owner, Scrum Master and Development Team.They require a distinct set of competencies, including Knowledge, Skills, and Attitude (KSA).Therefore, this study aims to develop a list of Scrum team competence based on KSA in GSD.The authors conducted a comprehensive literature review to create the list.Then, it was followed by in-depth interviews with nine experts involving practitioners and academics having experience in Scrum, GSD, and educational psychology for validation.This study proposes a compilation of KSA-based competencies for all roles of the Scrum team.Each competency has been grouped and given a level based on Bloom's taxonomy.The research findings can be the basis for successful competency development for the Scrum team.
Implementation of an oral dialogue based on a neural network of artificial intelligence in the online platform in secondary school

Today in society there is a need for artificial intelligence that teaches schoolchildren through oral communication, questions and answers. In the first quarter of the 2020-2021 academic year, secondary school teachers in Kazakhstan spent more time checking students' written responses compared to traditional teaching in previous years. At this time, the teachers noted that there was not enough time to verbally explain the lessons and provide high-quality oral feedback to all students. To solve this problem, we propose to use an artificial intelligence neural network to verbally explain the topic of the lesson in an online learning platform. The proposed article analyses and investigates the methodology for the implementation of oral teaching of schoolchildren using artificial intelligence neural networks in online learning in general education schools. The neural network recognises a school textbook and processes it in a text and audio editor. oral explanation to the student of the theoretical material from the textbook alternates with the assessment of the student's voice response. An online demonstration of ways to solve programming problems is carried out in an online editor installed on an online platform. The proposed online platform can be effectively used in traditional face-to-face education.
Play to Grade: Testing Coding Games as Classifying Markov Decision Process

Contemporary coding education often presents students with the task of developing programs that have user interaction and complex dynamic systems, such as mouse based games. While pedagogically compelling, there are no contemporary autonomous methods for providing feedback. Notably, interactive programs are impossible to grade by traditional unit tests. In this paper we formalize the challenge of providing feedback to interactive programs as a task of classifying Markov Decision Processes (MDPs). Each student's program fully specifies an MDP where the agent needs to operate and decide, under reasonable generalization, if the dynamics and reward model of the input MDP should be categorized as correct or broken. We demonstrate that by designing a cooperative objective between an agent and an autoregressive model, we can use the agent to sample differential trajectories from the input MDP that allows a classifier to determine membership: Play to Grade. Our method enables an automatic feedback system for interactive code assignments. We release a dataset of 711,274 anonymized student submissions to a single assignment with hand-coded bug labels to support future research.
Time Series Denoising Using Nonlinear Schr&amp;ouml;dinger Equation

In order to remove noise in time series with quantities of noise, a denoising method based on dynamical stochastic resonance is presented.By analyzing the dynamical stochastic resonance in nonlinear optical systems, the nonlinear disturbance in nonlinear Schrödinger equation is derived, the propagation model is obtained and applied to time series denoising.Firstly, the time series signal is normalized as system input.Then the parameters of the system propagation equation are determined by the adaptive particle swarm optimization algorithm.The numerical solution of the propagation equation is obtained by the split step Fourier method as system output.Compared with the existing denoising methods, proposed method improves signal-to-noise ratio by 0.3-3.7 dB, reduces by 0.03-0.11on average for typical time series signals.Experimental results show the better denoising ability of proposed method.
Only Train Once: A One-Shot Neural Network Training And Pruning Framework

Structured pruning is a commonly used technique in deploying deep neural networks (DNNs) onto resource-constrained devices. However, the existing pruning methods are usually heuristic, task-specified, and require an extra fine-tuning procedure. To overcome these limitations, we propose a framework that compresses DNNs into slimmer architectures with competitive performances and significant FLOPs reductions by Only-Train-Once (OTO). OTO contains two keys: (i) we partition the parameters of DNNs into zero-invariant groups, enabling us to prune zero groups without affecting the output; and (ii) to promote zero groups, we then formulate a structured-sparsity optimization problem and propose a novel optimization algorithm, Half-Space Stochastic Projected Gradient (HSPG), to solve it, which outperforms the standard proximal methods on group sparsity exploration and maintains comparable convergence. To demonstrate the effectiveness of OTO, we train and compress full models simultaneously from scratch without fine-tuning for inference speedup and parameter reduction, and achieve state-of-the-art results on VGG16 for CIFAR10, ResNet50 for CIFAR10/ImageNet and Bert for SQuAD.
The Using of Google Form for Digital Evaluation for Senior High School English Teacher Association, Hulu Sungai Utara, South Kalimantan

The development of technology also gives positive effect for education. One of them is the using of google form in education. Google form is one of online application that is used to collect the data of questionnaires. Nowadays, we can use it in education sector. That is for digital evaluation at school. The using of google form for digital evaluation is so effective. That’s why this community service gives training of the google form using for digital evaluation for  Senior High School English Teacher  Association, Hulu Sungai Utara, South Kalimantan. So they can use this application for English evaluation in their class. Some teachers who as the volunteer for this community service were interested to learn how to use this application. They practiced a lot during the training and at last they understand how to apply it for digital evaluation in their class. So next their English evaluation becomes English digital evaluation by using google form.
ОСОБЕННОСТИ ВОСПИТАТЕЛЬНОЙ ДЕЯТЕЛЬНОСТИ В ОМСКОМ ФИЛИАЛЕ УНИВЕРСИТЕТА «СИНЕРГИЯ»

The article analyzes the experience of organizing and implementing educational activities in the Omsk branch of the Synergy University. The aim of the study was to identify the features of the educational activities of the Omsk branch of the University Synergy. The research was based on: documents on educational work (programs and plans for educational activities of the branch, reports on self-examination of the branch), interviews with employees of the branch. The chronological scope of the study was limited from 2005 to 2021. The selection of materials for the study was carried out by random sampling. The analysis of the data obtained made it possible to identify the goals and objectives of educational activities in different academic years, to determine the successive directions of educational activities with students in the branch, to see new trends in the development of programs and plans for educational activities. As a result of the study, it became clear that when carrying out educational activities with students, the pedagogical staff of the branch prioritizes such a goal as creating conditions for the formation of a professionally and socially competent student's personality. We also found that for 10 years the following areas remain unchanged for educational activities carried out with students within the walls of the branch: professional-labor (labor), organizational, patriotic (military-patriotic, civilpatriotic), spiritual and moral, aesthetic ( moral and aesthetic, cultural and aesthetic), sports and health. But in the context of the development of educational activities with the students of the branch at the present stage, the teaching staff of the branch proposed new directions of educational activities with students, namely: charitable activities and prevention of self-relieving behaviors and antisocial manifestations in the student environment. The analysis also showed that one of the striking features of educational activities in the branch is the preservation and continuous development of their own original educational activities.
Batch Active Learning at Scale

The ability to train complex and highly effective models often requires an abundance of training data, which can easily become a bottleneck in cost, time, and computational resources. Batch active learning, which adaptively issues batched queries to a labeling oracle, is a common approach for addressing this problem. The practical benefits of batch sampling come with the downside of less adaptivity and the risk of sampling redundant examples within a batch -- a risk that grows with the batch size. In this work, we analyze an efficient active learning algorithm, which focuses on the large batch setting. In particular, we show that our sampling method, which combines notions of uncertainty and diversity, easily scales to batch sizes (100K-1M) several orders of magnitude larger than used in previous studies and provides significant improvements in model training efficiency compared to recent baselines. Finally, we provide an initial theoretical analysis, proving label complexity guarantees for a related sampling method, which we show is approximately equivalent to our sampling method in specific settings.
A Comparative Study of Contemporary Programming Languages in Implementation of Classical Algorithms

Computer algorithm is the nucleus of computer science and vital prerequisite of computer science professionals. However, it is hard to comprehend. Issues in learning of algorithms are typically addressed through expounding the algorithms with their implementation in a programming language. As there are numerous programming languages, the choice of apposite programming language for plausible implementation of algorithms remains a challenging issue. In this article, standard computer algorithms of data structures are measured by analyzing their implementation in C, C++, Java and Python. During the study, 200 standard algorithms are chosen and their implementation in selected languages is analyzed. In total, 800 programs are examined with Halstead’s complexity metrics and further analyzed with the Kolmogorov-Smirnov test, Shapiro-Wilk test and KruskalWallis Test. The results of the study suggest that the implementation of basic-level algorithms in Python is less difficult and requires the smallest number of mental comparisons as compared to C++, Java and C. Its programs require minimal time to write and mental endeavors to understand and also have the minimal number of bugs. Following Python, is C++ less difficult; however, its program implementations need more time to write and understand as well as have a greater number of bugs than that in C. It is less difficult to implement the algorithms in Java as compare to that in C, but requires the most prominent number of mental efforts and time. More bugs are encountered in the implementation of Java programs as compare to the other modern languages. The study signifies that Python could be a basic language among the other languages within the study.
Digitization and Its Impact

The world has undergone massive changes since the ushering of the digital age. Digitizations have challenged the convention in many spheres. Cybersecurity has thus the necessary protection against possible cyber-warfare. Many countries are increasingly bolstering their assets in cyber space. Electronic services are the implementation of information and communication technologies to provide services to people or some particular organization. The Internet has become a key e-business innovation. Advancing from a military and instructive system to a business Internet in the mid-1990s, the premise of sites and web-based business was set with the improvement of the program Netscape in 1994, and the establishing of Amazon and eBay in 1995. The Internet, cellular gadgets and different agencies contributing to gather, preserve, examine, and covey data carefully are currently being embraced by different government segments. The most recent cybersecurity policy of the United States of America was introduced by President Trump in 2018.
PENINGKATAN KEMAMPUAN MENULIS CERPEN MELALUI MODEL RANCANG BANGUN PADA PESERTA DIDIK KELAS IX A SMPN I TANJUNG

The purpose of this study was to find out about increasing students' learning motivation in the application of the design model and to find out the improvement in learning outcomes of Indonesian language subjects, especially in short story writing material. In this study, two data collection techniques were used, namely the performance test technique and the observation technique. To analyze the data, the results of performance tests and observations were analyzed by comparative description, namely comparing the initial conditions, cycle I, cycle II. The procedure of classroom action research (CAR) which consists of 2 (two) cycles. In the first cycle, discussions with independent work consisting of 4-5 students are presented and responded to by other groups. In cycle II, the results of group work discussions were presented in front of the class and other groups responded. The results showed that there was an increase in the ability to write short stories from an average initial condition of 69.30, after the first cycle of actions was carried out an average of 74.30, to 88.4 at the end of cycle II. By applying the design model, the teacher in the learning process is more creative and applicable.&#x0D; ABSTRAKTujuan penelitian ini untuk mengetahui tentang peningkatan motivasi belajar peserta didik dalam penerapan model rancang bangun dan serta mengetahui peningkatkan hasil belajar mapel bahasa Indonesia terutama pada materi menulis cerpen. Dalam penelitian ini menggunakan dua teknik pengumpulan data yaitu dengan teknik tes unjuk kerja dan teknik observasi. Untuk menganalisis data, hasil tes unjuk kerja dan observasi dianalisis dengan deskripsi komperatif, yaitu membandingkan kondisi awal, siklus I, siklus II. Prosedur penelitian penelitian tindakan kelas (PTK) yang terdiri dari 2 (dua ) siklus.Pada siklus I diskusi dengan kerja mandiri yang terdiri dari 4-5peserta didik dipresentasikan dan ditanggapi oleh kelompok yang lain. Pada siklus II hasil diskusi kerja kelompok dipresentasikan di depan kelas dan ditanggapi oleh kelompok yang lain.Hasil penelitian dapat diketahui bahwa terjadi peningkatan kemampuan menulis cerpen dari kondisi awal rata-rata 69,30, setelah dilaksanakan tindakan siklus I rata-rata 74,30 ,menjadi 88,4 pada akhir siklus II.Penerapan model rancang bangun, guru dalam proses pembelajaran lebih kreatif dan aplikatif.
Cloud storage data integrity audit based on an index–stub table

With the development of cloud computing technology, more individuals and organizations have chosen cloud services to store and maintain their data and reduce the burden on local storage and corresponding maintenance costs. However, although the cloud computing infrastructure is more powerful and reliable than personal computing devices, the cloud storage server is not completely trusted due to various internal and external threats;therefore, users need to regularly check whether their data stored in the cloud server are intact. Therefore, in recent years, researchers have proposed a variety of schemes for data integrity auditing in cloud storage. Among them, in a part of public auditing schemes for cloud storage based on homomorphic authenticators, random sampling of data blocks, and random masking techniques, users need to store and maintain a two-dimensional(2 D) table related to the index information of data blocks in the file. When a user’s outsource data need to be frequently updated to avoid forgery attacks due to the similar index value of data block being reused, the design and maintenance of the 2 D table become cumbersome. In this study, to solve the abovementioned problem, an index–stub table structure was first proposed, which is simple and easy to maintain. On the basis of this structure, a thirdparty auditor auditing scheme with a privacy-preserving property was proposed for cloud storage. This scheme can effectively support various remote dynamic operations for outsource data at the block level. Then, a formal security proof for data integrity guarantee provided by the scheme was given under the random oracle model. A formal security analysis was also given for the privacy-preserving property of the audit protocol. Finally, the performance of the scheme was theoretically analyzed and compared with relevant experiments. Results indicate that the scheme has high efficiency.
On the Language-specificity of Multilingual BERT and the Impact of Fine-tuning

Recent work has shown evidence that the knowledge acquired by multilingual BERT (mBERT) has two components: a language-specific and a language-neutral one. This paper analyses the relationship between them, in the context of fine-tuning on two tasks – POS tagging and natural language inference – which require the model to bring to bear different degrees of language-specific knowledge. Visualisations reveal that mBERT loses the ability to cluster representations by language after fine-tuning, a result that is supported by evidence from language identification experiments. However, further experiments on ‘unlearning’ language-specific representations using gradient reversal and iterative adversarial learning are shown not to add further improvement to the language-independent component over and above the effect of fine-tuning. The results presented here suggest that the process of fine-tuning causes a reorganisation of the model’s limited representational capacity, enhancing language-independent representations at the expense of language-specific ones.
Sentiment Analysis of Hotel User Review using RNN Algorithm

Sentiment analysis in user review is a growing research area at the current time. Usually, the website becomes a source of data in knowing the quality of the hotel services, and the provider can utilize the review for monitoring and evaluation. However, determining the positive or negative sentiment of a user review in unstructured textual data takes a long time. As a result, we present a model to classify positive or negative sentiment in user reviews in this article. This study suggests the RNN method in building an effective model to classify user sentiment. Based on the experiment, our model can produce accurate results in organizing hotel reviews. Furthermore, the proposed method achieved a higher evaluation metrics score with an f1-score of 91.0%.