Reviewerly/exHarmony huggingface dataset:
Extension of simultaneous Diophantine approximation algorithm for partial approximate common divisor variants

A simultaneous Diophantine approximation (SDA) algorithm takes instances of the partial approximate common divisor (PACD) problem as input and outputs a solution. While several encryption schemes have been published and their securities depend on the presumed hardness of variant of the PACD problem, fewer studies have attempted to extend the SDA algorithm to be applicable to these variants. In this study, the SDA algorithm is extended to solve the general PACD problem. In order to proceed, first the variants of the PACD problem are classified and how to extend the SDA algorithm for each is suggested. Technically, the authors show that a short vector of some lattice used in the SDA algorithm gives an algebraic relation between secret parameters. Then, all the secret parameters can be recovered by finding this short vector. It is also confirmed experimentally that this algorithm works well.
Application of optical and radar data for lineaments mapping in Kerdous inlier of the Anti Atlas belt, Morocco

&amp;lt;p&amp;gt;Lineament mapping is an important step for lithological and hydrothermal alterations mapping. It is considered as an efficient research task which can be a part of structural investigation and mineral ore deposits identification. The availability of optical as well as radar remote sensing data, such as Landsat 8 OLI, Terra ASTER and ALOS PALSAR data, allows lineaments mapping at regional and national scale. The accuracy of the obtained results depends strongly on the spatial and spectral resolution of the data. The aim of this study was to compare Landsat 8 OLI, Terra ASTER, and radar ALOS PALSAR satellite data for automatic and manual lineaments extraction. The module Line of PCI Geomatica software was applied on PC1 OLI, PC3 ASTER and HH and HV polarization images to automatically extract geological lineaments. However, the manual extraction was achieved using the RGB color composite of the directional filtered images N - S (0&amp;amp;#176;), NE - SW (45&amp;amp;#176;) and E - W (90&amp;amp;#176;) of the OLI panchromatic band 8. The obtained lineaments from automatic and manual extraction were compared against the faults and photo-geological lineaments digitized from the existing geological map of the study area. The extracted lineaments from PC1 OLI and ALOS PALSAR polarizations images showed the best correlation with faults and photo-geological lineaments. The results indicate that the lineaments extracted from HH and HV polarizations of ALOS PALSAR radar data used in this study, with 1499 and 1507 extracted lineaments, were more efficient for structural lineament mapping, as well as the PC1 OLI image with 1057 lineaments.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;&amp;lt;strong&amp;gt;Keywords&amp;lt;/strong&amp;gt; Remote Sensing . OLI. ALOS PALSAR . ASTER . Kerdous Inlier . Anti Atlas&amp;lt;/p&amp;gt;
Role Of The University Socio-Cultural Environment In Formation Of Students’ Emotional Intelligence

The paper considers the relationship of such concepts as socio-cultural space, education, upbringing, emotional intelligence and profiling.The socio-cultural space of the university is presented as one of the factors that determine, on the one hand, the professional becoming of the future employee pool of the country, and on the other hand, the university development potential.Attention is also focused on the educational milieu as a subsystem of the socio-cultural environment whose specificity is determined by the local characteristics of the university.The paper describes the activities of Don State Technical University.It is the largest technical university in the Southern Federal District which implements a regional model of continuing comprehensive education.The analysis of socio-cultural environment of the university which contributes to the development of emotional intelligence of both intending students and the students of DSTU (Don State Technical University) is presented.Based on the analysis of normative documents, local normative acts and the socio-cultural infrastructure of a particular educational institution, the structural components of the socio-cultural environment of the university are described, its influence on the formation of students' emotional intelligence is determined.The role of the educational process subjects in the formation and transformation of the socio-cultural environment is considered.The ways of formation of universal cultural values, group and personal interests of students under the conditions of humanization of engineering education and the regionalization of vocational education as a whole are described.
Trajectory tracking method based on the circulation of feasible path planning

The control method is the central point of the unmanned vehicles. As the core system to guarantee the properties of self-decision and trajectory tracking of the unmanned vehicles, a new kind of trajectory tracking method based on the circulation of feasible path planning for the unmanned vehicles are proposed in this article which considered the dynamics and kinematics characteristics of vehicles. The multi-trace-points cooperative trajectory tracking control strategy on the basis of the circulation of feasible path generation method is proposed and the lateral controller is designed for trajectory tracking. The process of feasible path generation is conducted once the tracking error exceeded. A simulation platform of the trajectory tracking simulation of unmanned vehicles is built considering the mechanical properties of system elements and the mechanical characteristics. Finally, the proposed trajectory tracking method is verified. The tracking error would be reduced to make sure the vehicles move along the pre-set virtual track.
PENGGUNAAN MEDIA PEMBELAJARAN BERBASIS KONVENSIONAL DAN TEKNOLOGI INFORMASI OLEH GURU DALAM PROSES BELAJAR MENGAJAR DI SEKOLAH

Along with the times in the teaching and learning process in which there is a process of interaction between teachers and students, it is also not free from the influence of the times. In the past, teachers in teaching were sufficient to appear in front of the teaching class using simple methods and media, but it is time now that the description of teaching styles and styles has evolved to be more concise in adapting to the conditions of the development of science and technology that continues to develop. Therefore, teachers must have the knowledge and ability to use both conventional and information technology-based learning media in a collaborative manner so that it makes it easier for students to understand learning material through creative and varied presentations so that it will contribute to student achievement.&#x0D; Keywords: instructional media, conventional, information technology.
About creation of a unified center of competences

A constructive solution is proposed for the interaction of educational institutions with industrial enterprises based on the combination of scientific, educational and material and technical capabilities into a single center of competence. The creation of a single center of competence will allow integrating the efforts of education, science and industry for research, prompt solution of scientific and technical problems, creation of new solutions in the field of automation, electronic instrumentation, telecommunications and information technologies. The project for the creation and development of a single center of competence will become a multi-level intellectual system for solving urgent production problems, for training highly qualified specialists who meet the modern requirements of the world market, will become a modern research platform for young scientists, specialists in various fields of production in Kazakhstan and the world, graduates of educational institutions, university teachers. The results of the work of the ECC will make it possible to increase the efficiency of production, improve the qualifications of specialists in all fields, develop a scientific and technical base, and also make a huge contribution to the development of Kazakhstan.
Por una priorización de las personas con trastornos adictivos en la vacunación frente a la COVID-19

Despite the risks involved in relying solely on COVID-19 vaccines to eradicate the pandemic, given uneven vaccine rollout and acceptance, the limited duration of immunity and the emergence of new SARS-CoV-2 variants, mass vaccination against COVID-19 is key to returning to what was normal life before the appearance of SARS-CoV-2 (Oliu-Barton et al., 2021).
Hierarchical Bayesian Based Reliability Analysis of a Sharing Storage Processor

Hierarchical Bayesian
Malicious Activity Detection In Safe City Environment

Safe cities initiative promises the eradication of crimes specially the volume crimes which constitute fighting, shooting, and vandalism. This paper focuses on the detection of real-world volume crimes in a video using spatio-temporal convolutional neural network. Firstly, a benchmark dataset has been annotated on the fine-grained level to ensure reliable training. Secondly, a comparison is made between two state of the art networks recently used for spatio temporal analysis, over the developed dataset. Also, detailed analysis has been performed for detection and classification over the two networks. From the comparison it is concluded that the performance of C3D model is better than ResNet3D for binary classification. Whereas, ResNet3D outperforms C3d incase of multi-class classification.
Classification of Tweets Self-reporting Adverse Pregnancy Outcomes and Potential COVID-19 Cases Using RoBERTa Transformers

This study describes our proposed model design for SMM4H 2021 shared tasks. We fine-tune the language model of RoBERTa transformers and their connecting classifier to complete the classification tasks of tweets for adverse pregnancy outcomes (Task 4) and potential COVID-19 cases (Task 5). The evaluation metric is F1-score of the positive class for both tasks. For Task 4, our best score of 0.93 exceeded the mean score of 0.925. For Task 5, our best of 0.75 exceeded the mean score of 0.745.
The Ultimate Expectation of Distance Support Education: Improving the Quality of Rural Education

At present, there are still some problems in many remote rural primary schools, such as the shortage of high-quality teacher resources, single curriculum structure and content, and lagging educational information.Under the background of "Internet Plus Education", the development of information technology has greatly promoted the vigorous rise of informal education, and education has gradually entered the information age.At the same time, the gradual popularization of network technology also provided a brand-new opportunity for the development of rural education, and distance support education came into being and developed rapidly.In this study, the Distance Support Education Project of A University was taken as a case, and the data are collected through in-depth interviews with principals and teachers from remote rural primary schools and volunteers from distance support education projects.Based on the analysis of the existing curriculum structure and content of rural primary schools and the interview data, this study attempts to summarize the three types of curriculum that currently exist in distance support education: Assisted courses, Enhanced courses, and Extended courses, and explore a relatively ideal teaching curriculum model to achieve the ultimate expectation of distance support education.Namely, the fundamental purpose of distance support education is not to provide direct assistance to rural schools or rural students, but to find the direction for the independent development of rural schools, to realize the "symbiotic" development of rural schools, rural students, and volunteers from college, thus hoping to truly improve the quality of rural education.
The Construction of Scalable Decision Tree based on Fast Splitting and J-Max Pre-Pruning on Large Datasets

The decision tree is one of the most important algorithms in the classification which offers a comprehensible model of data. In building a tree we may encounter a memory limitation. The present study aimed to implement an incremental scalable approach based on fast splitting and present pruning to construct the decision tree on a large dataset as the complexity of the tree decreases. The proposed algorithm constructs the decision tree without storing the entire dataset in the primary memory by using a minimum number of parameters. Furthermore, the J-max Pre pruning method was used to reduce the complexity with acceptable results. Experimental results show that this approach can create a balance between the accuracy and complexity of the tree and overcome the difficulties of the complexity of the tree. In spite of the appropriate accuracy and time, the proposed algorithm could produce a decision tree with less complexity on the large dataset.
Underwater image reconstruction using convolutional auto-encoder

One of the main tasks of AUVs is to capture deep-sea images like fishes, crabs, other living organisms and resources for information leading to research on deep-sea ecosystems.Acoustic transmission are used to establish wireless underwater communications between the AUV and the ship.However, there are some limitations in the communication channels due to limited bandwidth, multi-path, temperature distribution and change in the direction of transmitting source and receiving sensor which results in losses in data being transmitted.Initially, the captured images are enhanced to reduce the effect of light attenuation and then compressed for transmission through acoustic modems.Only an important part of image is being transmitted through set of data packets.The received data packets in the ship will be reconstructed to predict the presence of living organisms.The loss in data during transmission creates a difficulty for the operators to predict the exact information.In this research, to compensate this transmission loss, an efficient compression and reconstruction technique using convolutional autoencoder with minimal distortion is proposed.Finally, for evaluation of the proposed image compression technique, the quality of reconstruction of images with and without data loss will be compared using the quality metrics signal to noise ratio (PSNR), structural similarity index(SSIM) and perceptual quality of image.
Algorithmic Thinking and New Digital Literacy

The article discusses the concept of algorithmic thinking in the context of the history of the formation of school informatics, in the competencies of new digital literacy and in the system of developmental education. The structure of digital literacy based on algorithmic supports is shown and an example of an educational template of algorithmic tasks for younger students is given.
Fuzzy Logic Based Perceptual Image Hashing Algorithm in Malaysian Banknotes Detection System for the Visually Impaired

Visually impaired persons have difficulty in business that dealing with banknote. This paper proposed a Malaysian banknotes detection system using image processing technology and fuzzy logic algorithm for the visually impaired. The Malaysian banknote reader will first capture the inserted banknote image, sending it to the cloud server for image processing via Wi-Fi medium. The cloud server is established to receive the banknote image sending from the banknote reader, processing them using perceptual hashing based image searching and fuzzy logic algorithm, then return the detected banknote’s value results back to the banknote reader. The banknote reader will display the results in terms of voice message played on the mini speaker attached on it, to allow visually impaired persons knowing the banknote’s value. This hardware mechanism reduces the size and costs for the banknote reader carried by the visually impaired persons. Experimental results showed that this Malaysian banknotes detection system reached an accuracy beyond 95% by running test on 600 different worn, torn and new Malaysian banknotes. After the banknote image being taken by the banknote reader’s camera, the system able to detect the banknote value in about 480 mili-seconds to 560 mili-seconds for a single sided banknote recognition. The banknotes detection speed was also comparable with human observers reading banknotes, with the response of 1.0908 second per banknote slight difference reading time. The IoT and image processing concepts were successfully blended and it provides an alternative to aid the visually impaired person their daily business transaction activities in a better way.
Detection of Important Scenes in Baseball Videos via Bidirectional Time Lag Aware Deep Multiset Canonical Correlation Analysis

A novel method for detection of important scenes in baseball videos based on correlation maximization between heterogeneous modalities via bidirectional time lag aware deep multiset canonical correlation analysis (BiTl-dMCCA) is presented in this paper. The proposed method enables detection of important scenes by collaboratively using baseball videos and their corresponding tweets. The technical contributions of this paper are twofold. First, since there are time lags between not only “tweets and corresponding multiple previous events” but also “events and corresponding multiple following posted tweets”, the proposed method considers these bidirectional time lags. Specifically, the representation of such bidirectional time lags into the derivation of their covariance matrices is newly introduced. Second, the proposed method adopts textual, visual and audio features calculated from tweets and videos as multi-modal time series features. Important scenes are detected as abnormal scenes via anomaly detection based on a generative adversarial network using multi-modal features projected by BiTl-dMCCA. The proposed method does not need any training data with annotation. Experimental results obtained by applying the proposed method to actual baseball matches show the effectiveness of the proposed method.
PROFESSIONAL TRAINING OF FOREIGN LANGUAGE TEACHERS: TODAY’S GLOBAL REALITIES

This paper addresses to common problems of training a generation of students with clip thinking, fluent in IT‑technologies and modern gadgets. This generation grew up surrounded by constantly improving digital technologies, accompanied by the media, which constantly provides the viewer with information mainly of entertaining or intimidating nature (talk shows, sitcoms, TV shows, contests, games, constantly interrupted by commercials, news, etc.). As a result, the brain is rebuilt, adapting to reality, and a new type of thinking arises – the socalled ‘Clip thinking’, which is characterized by fragmentation, non-linearity, emotionality, quick switching from one topic to another, illogicality. In accordance with this, the features of students’ thinking, over the years of rapid technological progress and the development of the media, have acquired new characteristics. Unlike the previous generation with digital thinking, which is characterized by rationality, ability to think algorithmically and multitasking, students with clip thinking need new approaches in teaching and preparing them for future professional activities. Given the above, it is necessary to create a new teaching technology that will contribute to the quality training of a new generation of future foreign language teachers.
Optimised URL Shortener using Cloud Platform﻿

Short URLs have become commonplace. Short URLs, which are especially popular in social networking services, have witnessed a significant increase in their use in recent years, largely due to Twitter's restriction on message length to 140 characters. We рrоvidе a first сhаrасterizаtiоn in the usage of short URLs in this рарer. Our specific goal is to create a system architecture for short URLs and have an optimized solution of how to use them and as well as their potential impact on web performance. Our project is made while keeping in mind that the generated URL has to be one third the size of a given url. This allows the user the functionality to share the URL with multiple individuals. Our system also incorporates the idea of tracking each individual shortened URL by the usage of a short code. It gives the user the control over their generated URLs.
Terapi Controller pada Asma

Asthma, according to the National Heart Lung and Blood Institute (NHBLI) in 2002, is a chronic inflammatory disorder of the airways that involves the role of many cells and cellular components. The long-term goals of asthma management are to achieve good symptom control and maintain normal living activities and to minimize the risk of exacerbations, airway obstruction, and side effects. The drugs available for asthma therapy are divided into long-term control or controller medication and rapid-acting or reliever medication based on their pharmacodynamics and clinical effects. Controller is a therapy given to reduce chronic airway inflammation, control symptoms, and reduce the risk of exacerbations and decreased lung function. There are several asthma controller drug options that have their respective advantages and disadvantages. Selection of the right controller will provide good asthma control.&#x0D; Keyword: Ashtma, therapy, controller
Correction to: Chinese secondary school students’ reading engagement profiles: associations with reading comprehension

A correction to this paper has been published: https://doi.org/10.1007/s11145-021-10171-4
ESTIMATING THE SIZE OF OPEN-SOURCE PHP-BASED APPS BY NONLINEAR REGRESSION MODELS WITH VARIOUS FACTORS

The problem of estimating the software size in the early stage of a software project is important because a software size estimate is used for predicting the software development efforts, including open-source PHP-based apps.The purpose of the work is to increase the prediction accuracy of early software size estimation of open-source PHPbased apps.The object of study is the process of estimating the software size of open-source PHP-based apps.The subject of study is the three-factor nonlinear regression models with various factors to estimate the software size of open-source PHP-based apps.To build the three-factor nonlinear regression models we use the technique based on the multivariate normalizing transformations and prediction intervals.These models are constructed based on the Johnson four-variate normalizing transformation for S B family of the non-Gaussian data set from 44 apps hosted on GitHub.The data set was obtained using the PhpMetrics tool (https://phpmetrics.org/).The three-factor nonlinear regression models are built around the metrics of class diagrams: the number of classes, the average number of methods per class, the sum of average afferent coupling and average efferent coupling per class, DIT (depth of inheritance tree) mean per class.To compare the prediction accuracy of the three-factor nonlinear regression models we used the well-known prediction accuracy metrics such as a multiple coefficient of determination R 2 , a mean magnitude of relative error MMRE, and prediction percentage at the level of magnitude of relative error of 0.25, PRED(0.25).The nonlinear regression model constructed around the number of classes, the average number of methods per class, DIT mean per class has the larger PRED(0.25)value and about the same values of R 2 and MMRE that the model in which the third factor is the sum of average afferent coupling and average efferent coupling per class.The scientific novelty of obtained results is that the three-factor nonlinear regression model for estimating the software size of open-source PHP-based apps has been improved by introducing a new factorthe DIT mean per class.This allowed us to increase the PRED(0.25)value by 8%.The practical importance of obtained results is that the software realizing the constructed model is developed in the sci-language for Scilab.