Reviewerly/exHarmony huggingface dataset:
The Russian schoolchildren's digital reading: Factors affecting medium preferences and self-evaluation of digital reading practice

Introduction. While the importance of digital reading in modern education is constantly increasing, there are some knowledge gaps in investigating reading patterns (reading digital and print texts). There is still little scientific understanding of how Russian schoolchildren choose the medium of reading and how children assess their reading behavior in the digital environment. The purpose of the article is to reveal the factors that influence Russian schoolchildren’s choice of a reading medium, as well as the features of their self-evaluation of digital reading practices. Materials and Methods. To achieve the goal of the study we used the following methods: a questionnaire, statistical data processing, including t-test, correlation analysis using Spearman's rank correlation coefficient (rs) for ordinal variables and Pearson's coefficient (rp) for quantitative variables, comparative analysis based on the Mann-Whitney rank test for nonparametric data and ordinal scales, ANOVA. Results. During the research a questionnaire consisting of three blocks of questions was developed, tested and distributed among the target audience. 568 students of grades 5-11 from 54 regions of the Russian Federation took part in the survey. Correlation analysis of the data made it possible to reveal some factors which influence the Russian schoolchildren’s choice of the reading medium - digital or printed. Furthermore, the authors identified the features of schoolchildren’s subjective perception of digital reading and the factors determining this perception. Conclusions. The study identified factors which significantly affect the Russian children’s preference to read digitally: the type of text, the purpose of reading, the respondents’ age, their attitude to reading and the level of reading proficiency (according to self-report data). Children’s self-evaluation of digital reading showed that they identify both negative (increased fatigue, decreased interest in reading, etc.) and positive aspects of reading digital texts (efficiency of search reading). Keywords Reading; Digital reading; Reading medium; Preferences of reading medium; Reading self-evaluation; Reading practice; Russian schoolchildren
Manifold Learning-based Word Representation Refinement Incorporating Global and Local Information

Recent studies show that word embedding models often underestimate similarities between similar words and overestimate similarities between distant words.This results in word similarity results obtained from embedding models inconsistent with human judgment.Manifold learningbased methods are widely utilized to refine word representations by re-embedding word vectors from the original embedding space to a new refined semantic space.These methods mainly focus on preserving local geometry information through performing weighted locally linear combination between words and their neighbors twice.However, these reconstruction weights are easily influenced by different selections of neighboring words and the whole combination process is time-consuming.In this paper, we propose two novel word representation refinement methods leveraging isometry feature mapping and local tangent space respectively.Unlike previous methods, our first method corrects pre-trained word embeddings by preserving global geometry information of all words instead of local geometry information between words and their neighbors.Our second method refines word representations by aligning original and refined embedding spaces based on local tangent space instead of performing weighted locally linear combination twice.Experimental results obtained from standard semantic relatedness and semantic similarity tasks show that our methods outperform various state-of-the-art baselines for word representation refinement.
A General Framework for Adaptation of Neural Machine Translation to Simultaneous Translation.

Despite the success of neural machine translation (NMT), simultaneous neural machine translation (SNMT), the task of translating in real time before a full sentence has been observed, remains challenging due to the syntactic structure difference and simultaneity requirements. In this paper, we propose a general framework for adapting neural machine translation to translate simultaneously. Our framework contains two parts: prefix translation that utilizes a consecutive NMT model to translate source prefixes and a stopping criterion that determines when to stop the prefix translation. Experiments on three translation corpora and two language pairs show the efficacy of the proposed framework on balancing the quality and latency in adapting NMT to perform simultaneous translation.
Transparent Error Correcting in a Computationally Bounded World

We construct uniquely decodable codes against channels which are computationally bounded. Our construction requires only a public-coin (transparent) setup. All prior work for such channels either required a setup with secret keys and states, could not achieve unique decoding, or got worse rates (for a given bound on codeword corruptions). On the other hand, our construction relies on a strong cryptographic hash function with security properties that we only instantiate in the random oracle model.
Towards Privacy by Design in Learner Corpora Research: A Case of On-the-fly Pseudonymization of Swedish Learner Essays

This article reports on an ongoing project aiming at automatization of pseudonymization of learner essays.The process includes three steps: identification of personal information in an unstructured text, labeling for a category, and pseudonymization.We experiment with rule-based methods for detection of 15 categories out of the suggested 19 (Megyesi et al., 2018) that we deem important and/or doable with automatic approaches.For the detection and labeling steps, we use resources covering personal names, geographic names, company and university names and others.For the pseudonymization step, we replace the item using another item of the same type from the above-mentioned resources.Evaluation of the detection and labeling steps are made on a set of manually anonymized essays.The results are promising and show that 89% of the personal information can be successfully identified in learner data, and annotated correctly with an inter-annotator agreement of 86% measured as Fleiss kappa and Krippendorff's alpha.
The Scientometrics of Journal “Bibliosphere”: analysis of tendencies and future development

The article presents the scientometric analysis of the journal Bibliosphere (founder – SPSTL SB RAS) for 15 years of its existence – from the first issue publication in 2005 to the end of 2019. The purpose of the article is to track changes in the quantitative bibliometric indicators of the journal in conjunction with both of the editorial policy and in the context of the journal’s mission – to increase the level of scientific research in the field of library and information science, book science, and bibliography and their integration into the world scientific space, to identify problematic issues and outline ways to solve them. The results of the analysis show that the recognition of the results of the research presented in the journal is gradually growing, however, this growth and the degree of integration of research into the international context is uneven. The most competitive are studies in the fields of library and information science, while the results in the field of book science often turn out to be unnoticed by colleagues not only abroad, but also in Russia.
SH-CNN: Shearlet Convolutional Neural Network for Gender Classification

Gender detection and age estimation become an active research area and a very important field today, wish has been widely used in various applications including them: biometrics, social network, Targeted advertising, access control, human-computer interaction, electronic customer, etc.The need to further improve the recognition or classification rate keeps increasing day after day.In this paper, we explore how deep learning techniques can help in the classification of gender from human face images and moreover raise the recognition rate.We propose in this contribution an approach called SH-CNN based on Discrete Shearlet Transform (DST) as a first step of feature extraction layer, and Deep Convolutional Neural Network (DCNN) as a second automatic feature extraction layer and also a classification step.The idea behind our contribution is to generate trough DST several features (in different decomposition and orientation) of an image.These features of each image will be the input of the DCNN, to enrich the training step, and so, improve the recognition rate.The obtained results have shown that the proposed approach import a significant enhancement.
A Bayesian Approach to Real-Time Dynamic Parameter Estimation Using PMU Measurement

In this work, we develop a polynomial-chaos-expansion (PCE)-based approach for decentralized dynamic parameter estimation through Bayesian inference. Using this approach, the non-Gaussian distribution of the inverted parameters is obtained. More specifically, we first represent the decentralized generator model with the PCE-based surrogate. This surrogate allows us to efficiently evaluate the time-consuming dynamic solver at parameter values through Metropolis-Hastings (M-H)-based Markov chain Monte Carlo (MCMC). Then, we propose a two-stage hybrid Markov chain Monte Carlo (MCMC) to recover a posteriori distribution of the decentralized generator model parameters. In the first stage, we use the gradient-enhanced Langevin MCMC algorithm to characterize an intermediate posterior parameter distribution. This algorithm is computationally scalable to the high-dimensional parameter space. Based on the intermediate posterior distribution, during the second stage, we use the adaptive MCMC algorithm to fine-tune the strong correlations between the parameters. Finally, the fully recovered a posterior distribution is obtained in the end. The simulation results show that the proposed PCE-based hybrid MCMC algorithm can accurately and efficiently estimate the high-dimensional generator dynamic model parameters with full probabilistic distribution provided.
Configuration of Public Sports Venues in China's Counties Under the Background of Urbanization

To promote the effective allocation of public sports venues in the county and promote the construction of new urbanization, the allocation of public stadiums in the county was studied under the background of new urbanization. Interviews and literature research methods were adopted. The differences between the new urbanization and the traditional urbanization of the county's public stadiums were discussed. Under the background of new urbanization, the status quo of the allocation of public stadiums in China's counties was studied. Under the background of new urbanization, the problems and influencing factors of the allocation of public stadiums in China's counties were analyzed. The corresponding proposal was put forward. The results showed that the number of county venues in China was greatly improved, but the total amount was still insufficient. The configuration structure was improved, but the structural imbalance was still significant. The way of configuration was innovative, but the government dominated the main position. The configuration policy was constantly improving. The legalization was not perfect. Therefore, the balance between supply and demand, the balance of urban and rural settings, the optimization of public sports venues, and the establishment of public stadiums under new urbanization are important. This will help to promote the study of the allocation of public sports venues in China's counties under the background of new urbanization.
Practical Video Authentication Scheme to Analyze Software Characteristics

We propose a video authentication scheme to verify whether a given video file is recorded by a camera device or touched by a video editing tool. The proposed scheme prepares software characteristics of camera devices and video editing tools in advance, and compares them with the metadata of the given video file. Through practical implementation, we show that the proposed scheme has benefits of fast analysis time, high accuracy and full automation.
An Accent Marking Algorithm of English Conversion System Based on Morphological Rules

Facing the English conversion system, the existing accent marking algorithms cannot acquire the morphological rules of English, making the accent marking inaccurate, inefficient, and time-consuming. To solve these problems, this paper puts forward an accent marking algorithm of English conversion system based on morphological rules. Specifically, the English audios in a self-developed English corpus were classified by the speaker classification software based on hidden Markov model, as well as audio classification technology, producing the morphological rules of English. After that, the English accents were marked by the maximum entropy model in the English conversion system. The proposed method was proved accurate and efficient in accent marking through experiments. The research results provide a good reference for marking the accents in English conversion system.
Asynchronous Password-Based Authentication and Service_Provider_ID Module for Secured Cloud Environment

Clouds are now considered as the dawn of Computing Technology.As the people of Information Technology, Business, and other Organization are living in Cloud Generation and Cloud environment offers umpteen of services over the Internet, security concepts like confidentiality and authentication mechanisms require being more robustness to foil the attempt of unauthorized persons.A very crucial security function of the cloud environment is user authentication.Previously, many authentication mechanisms such as simple username and password with OTP, Graphical password, Smart Card based mechanism, and Biometrics were devised, but those were suffered from various attacks by the hackers, computational cost, and expensive to implement.Moreover, the Cloud environment is now requiring innovative and sturdy authentication mechanismto make an authentication process more complicated; this paper proposes a multi-level -Asynchronous Password-based authentication process and Service_Provider_ID module.This mechanism provides a different and distinctive password for every session, which ensures a well-built authentication process and harmonizing of Service Provider ID and User Session ID validates the User session with the Cloud environment.This uniqueness leads to the highly secured authentication process in the Cloud environment.
Comparative Analysis of Online Real-time Lecture and On-demand Contents Lecture under the COVID-19 Situation in Korea

Colleges have been forced to take non-face-to-face classes this year due to the COVID-19, and the situation is expected to continue unless the development of treatments and vaccines is carried out as soon as possible. In the situation where non-face-to-face classes are required under compulsion, two methodologies have been suggested as most representative alternatives to face-to-face classes: online real-time classes and on-demand contents classes. The purpose of this study is to compare the perceived convenience, self-fidelity, and preference of students between online real-time and on-demand contents classes by gender, school year grade, mostly using class media, and number of courses taken. Comparative results between online real-time and on-demand contents classes were statistically analyzed by surveying students at a university.
What do primary school students think about mobile programming education? ‘Developing my own mobile game’

The purpose of this study is to reveal the views of fourth-grade primary school students about mobile programming education. Data were collected by using interviews, metaphor and student drawings under the qualitative research method. Amongst the 135 students who participated in the mobile programming education, 24 fourth-grade primary school students, who were selected via the purposeful sampling method, took part in the study. Content analysis was used to analyse the data collected through the semi-structured interview form. The drawings of the students which reflected what the mobile programming education meant for them were evaluated through document analysis. As a result, it can be said that mobile programming education contributes positively to students' mobile programming, scientific and creative thinking skills and social skills. It is recommended to use MIT App Inventor in programming education for primary school students.&#x0D; &#x0D; Keywords: Mobile programming; mobile programming education; MIT app inventor; information technology; primary school students.
Discrete mixture representations of parametric distribution families: Geometry and statistics

We investigate existence and properties of discrete mixture representations P θ = i∈E w θ (i) Q i for a given family P θ , θ ∈ Θ, of probability measures.The noncentral chi-squared distributions provide a classical example.We obtain existence results and results about geometric and statistical aspects of the problem, the latter including loss of Fisher information, Rao-Blackwellization, asymptotic efficiency and nonparametric maximum likelihood estimation of the mixing probabilities.
Automatic Voter Recommendation Method for Closing Questions in Stack Overflow

Stack Overflow is the most popular programming question and answer community that continuously receives a large number of questions every day. To ensure the quality of questions, the community grants privileges for the moderators and a group of experienced users to review the quality of questions and close the low-quality ones (e.g. duplicate or irrelevant questions). The review process is a typical crowdsourcing job that relies on users’ volunteer participation, and the current practices of closing questions in Stack Overflow face two aspects of challenges: (1) an obvious increase in both the absolute number and the percentage of “closed” questions; (2) a considerable decrease in participation willingness of experienced users to close questions. In order to solve the problem, we present a novel model of user willingness for reviewing and voting questions by incorporating four types of user activity history, including questions, answers, comments and votes of closing questions. Then we propose an automatic recommendation method based on the model to assign experienced users proper questions, to utilize the forces of them to close questions. The evaluation shows that the successful recommendation probability in the top 5, top 10, top 20, top 30, top 40, top 50 users are 48.23%, 58.93%, 68.83%, 74.27%, 78.13% and 81%, respectively.
Accreditation of Engineering Degree Programs: What is New?

The paper highlights changes and recommendations to the procedures of engineering degree programs accreditation to be accounted by accreditation agencies and Higher Education Institutions due to covid-19 pandemia and restrictions at universities caused by it.
A Web Application-Based Secured Image Retrieval System With an IoT-Cloud Network

Many encryption and searching techniques have been used, but they did not prove effective to support smart devices in order to provide input image. Therefore, based on these facts, an effective and novel system has been developed in this paper which is based on content-based search concentrated on encrypted images. Four type of features, namely color moment (CM), Gray level co-occurrence matrix (GLCM), hybrid of CM and GLCM, and lastly, a deep belief network (DBN) has been used here. This deep neural network is based on clustering in combination with indexing and the developed model is called as cluster-based deep belief network (CBDBN) in the present work. A web based application has also been developed using Apache Tomcat server and MATLAB engine. Analysis of many parameters like precision, recall, entropy, correlation coefficient, and time has been done here on benchmark datasets, namely WANG and COIL.
PSYCHOLOGICAL AND PEDAGOGICAL ASPECTS OF THE PHYSICAL EDUCATION TEACHING QUALITY AT THE UNIVERSITY

Purpose. The article reveals the patterns of successful transfer of socio-pedagogical experience from a teacher to students, the pedagogical influence on personal development and instructional importance in the educational process. Being a way of organizing educational activities, learning is the most reliable way to obtain a systematic education. It contributes to the assimilation of knowledge, skills and abilities and development of personal qualities that help a person to adapt to external conditions and express one’s personality. The quality of administration of educational process has a big impact on the development of creative and independent mentality of students, acquisition of professional competencies for achieving a high level of proficiency to withstand competitiveness in the labor market and develop the potential for self-development and self-education. The teacher of physical culture is fully responsible for ensuring the modern administration of the educational process and social activities of the high quality to train qualified specialists with theoretical and applicable knowledge, skills and competencies. Research methods and organization. The authors use the methods of questionnaire survey, pedagogical observation and comparative assessment to reveal the psychological and instructional aspects of the quality of physical education at the university, as well as students' interest in Physical education as university subject. Research results. The research resulted in the identification of students’ attitude to the quality of the educational process, their satisfaction with the quality of provided education, and the quality of the administration of physical and sports activities. Conclusion. We have substantiated the significance of psychological and pedagogical approach used by educators at physical education classes. It is especially relevant in modern conditions for improvement of the quality of educational process through the attitude of students to the Physical education and sports subject.
Optimal approximate sampling from discrete probability distributions

This paper addresses a fundamental problem in random variate generation: given access to a random source that emits a stream of independent fair bits, what is the most accurate and entropy-efficient algorithm for sampling from a discrete probability distribution ( p 1 , …, p n ), where the probabilities of the output distribution ( p̂ 1 , …, p̂ n ) of the sampling algorithm must be specified using at most k bits of precision? We present a theoretical framework for formulating this problem and provide new techniques for finding sampling algorithms that are optimal both statistically (in the sense of sampling accuracy) and information-theoretically (in the sense of entropy consumption). We leverage these results to build a system that, for a broad family of measures of statistical accuracy, delivers a sampling algorithm whose expected entropy usage is minimal among those that induce the same distribution (i.e., is “entropy-optimal”) and whose output distribution ( p̂ 1 , …, p̂ n ) is a closest approximation to the target distribution ( p 1 , …, p n ) among all entropy-optimal sampling algorithms that operate within the specified k -bit precision. This optimal approximate sampler is also a closer approximation than any (possibly entropy-suboptimal) sampler that consumes a bounded amount of entropy with the specified precision, a class which includes floating-point implementations of inversion sampling and related methods found in many software libraries. We evaluate the accuracy, entropy consumption, precision requirements, and wall-clock runtime of our optimal approximate sampling algorithms on a broad set of distributions, demonstrating the ways that they are superior to existing approximate samplers and establishing that they often consume significantly fewer resources than are needed by exact samplers.