Reviewerly/exHarmony huggingface dataset:
THE CONCEPT OF MORALITY IN MODERN EDUCATION

The article deals with problems of formation of highly moral person in the course of educational activity.the question is raised about the necessity and methods of forming a panoramic style of pedagogical thinking, which is one of the factors in the formation of the educational environment.It talks about the axiological meaning of education.
A SYSTEMATIC LITERATURE REVIEW: BENEFITS AND CHALLENGES OF BLOCKCHAIN-BASED DIGITAL TRANSFORMATION

Digital transformation has emerged as an important research area in strategic IS research.Digital transformation has made profound impacts on many industries with digital technologies.Blockchain could play a vital role in the digital transformation strategy for enterprises by providing security-rich data sharing, dynamic applications, and decentralized identity.Currently, the research on blockchain-based digital transformation is still in its infancy.The current literature provides limited knowledge and empirical evidence to explain how blockchain technologies empower organizational digital transformation.This study conducts a systematic literature review to study the current state-of-the-art on the digital transformation of blockchain and identifies the benefits and challenges of blockchain-based digital transformation.
Development of I-Graciass Adoption and Implementation Model at Telkom Education Foundation Lemdikti Knowledge Management Systems Based

I-Graciass is one of the digicampus programs under the Directorate of Digital Transformation, Synergy &amp;Performance (DTSP) of the Telkom Education Foundation (YPT) in the form of a smart app &amp; information system as an integrated academic and non-academic management information system. Based on the Vision-Mission, Strategy and Tagline of YPT as The Leader of ICT Education Provider in Indonesia as well as the spirit of carrying out digital transformation in the foundation, so that I-Graciass becomes mandatory to be adopted and implemented in all Higher Education Institutions (Lemdikti) under the guidance of YPT. However, the facts on the ground of the process of adopting and implementing I-Graciass in each Lemdikti are not easy things. For this reason, this research was carried out in order to facilitate the process of adopting and implementing I-Graciass through the method of climbing Knowledge Management Systems (KMS) in all YPT Lemdikti, especially for new universities such as Telkom Institute of Technology Surabaya and other new universities that will later be established by YPT. The results of this study show that the factors that influence the success of the I-Graciass Adoption process at ITTelkom Surabaya and other Lemdikti consist of tennis and non-technical aspects that can refer to the implementation of best practices in the previous lemdikti. In addition, to support the success rate of adoption and implementation, an effective knowledge transfer process is needed through the use and development of the right KMS for an organization and its end users.
Teachers Practices on the Implementation of Communicative Language Teaching (CLT) Through Synchronous Online Learning: A Systematic Literature Review

This paper demonstrates an analysis of a systematic review based on the various past studies related to teacher practices on the implementation of communicative language teaching (CLT) via synchronous online learning.All the past studies are reviewed from 2015 to 2021 with the total of fifteen studies.This paper aims to review and highlight on the implementation of communicative language teaching (CLT) through synchronous online learning by the teachers that focusing on three major objectives which are how CLT is implemented via synchronous online learning, the challenges faced by teachers in conducting CLT via synchronous online learning, and the effectiveness of CLT via synchronous online learning.The findings indicate that various online platforms can be utilized in assisting teachers to implement CLT during synchronous online learning.The most used apps based on the past studies is Zoom Meeting and followed by Discord, Google Meet, Google Classroom, Webex, Youtube, WhatsApp and Email.Although there are various technologies and online platforms that have been advocated for the application of CLT via synchronous online learning, the problems and constraints still arose during the lesson for both teachers and students.For the teachers they had difficulties affiliated to the readiness in adapting elearning, the accessibility to e-learning devices and classroom management issues.However, for the students the problems are related to their attention spans, difficulty to apprehend the task, limited access to direct interactive communication and limitation to online learning access.Apart from that, most of the teachers agree that implementing CLT through synchronous online learning brings more drawbacks and hinder them to achieve the teaching and learning objectives to the fullest.
Design and Implementation of an Online Enrolment System for Higher Education Institution amidst covid-19 Pandemic in Zamboanga Peninsula, Philippines

Abstract: The purpose of the study is to design and implement an enrolment system through web based application intended for higher education institution in Zamboanga Peninsula amidst covid-19 pandemic. The functionalities of the system are guided using Use Cases identified during requirement phase. The existing system encountered several constraints on the process of the enrolment, especially in detecting conflict of course schedules and the availability of slots of the courses offered, handling large number of data, and in cases where modifications or errors in the program that need to be fixed. The methodology used was prototyping to take advantage of the limited experience of users in using computerized systems. It was implemented using Hypertext Pre-processor (PHP) programming language and MYSQL database along with JavaScript, Cascading Style Sheets (CSS), JQuery and Macromedia Dreamweaver as integrated development environment (IDE). The functionalities of the system get the approval of the school administrators allowing the program to be use in an actual enrolment setting.
Formation of Human Capital through Quality Education and Lifelong Learning: A Systematic Literature Review

A systematic literature review on the formation of human capital through quality education and lifelong learning is presented in this paper.This article explores the common themes that run through the 26 different papers that can be found in the databases of Sage Journal and Web of Science.The Preferred Reporting Items for Systematic Reviews (PRISMA) technique, which is a comprehensive guideline for both the systematic review and the gathering of data, was utilized in the execution and execution of the research project.Research studies that were published between 2017 and 2022 were considered for the review.The topics were organized under these three headings: quality education, aspects of life-long learning, and obstacles faced by LPH implementations.This article contains in-depth discussions on the aforementioned subjects, which are also covered in the accompanying literature review.An approach was presented that focuses on an early valuation of formation of human capital through quality education and lifelong learning in order to educate graduates with the appropriate information and skills.This was done rather than reiterating the literature research and recognizing the issue.According to the findings of this study, the quality of education, the elements that contribute to life-long learning, and the difficulties associated with the implementation of LPH are all affected by a variety of different circumstances.In addition, the significance of this study cannot be overstated because it contributes to the improvement of academics' comprehension of scholarly literature reviews conducted by their peers.As they continue their studies, it is helpful for them to get a deeper understanding of the building of human capital through the pursuit of quality education and learning that continues throughout one's life, in addition to other things.
On (Unknowingly) Using Near-Square RSA Primes

The invention in 1978 of the first practical asymmetric cryptosystem known as RSA was a breakthrough within the long history of secret communications. Since its inception, the RSA cryptosystem has become embedded in millions of digital applications with the objectives of ensuring confidentiality, integrity, authenticity, and disallowing repudiation. However, the generation of the RSA modulus, N=pq which requires p and q to be random primes, may accidentally entail the choice of a special type of prime called a near-square prime. This structure of N may be used unknowingly en masse in real-world applications since no current cryptographic implementation prevents its generation. In this study, we show that use of this type of prime will potentially lead to total destruction of RSA. We present three cases of near-square primes used as RSA primes, set in the form of (i) N=pq=(am−ra)(bm−rb); (ii) N=pq=(am+ra)(bm−rb); and (iii) N=pq=(am−ra)(bm+rb). Although (ii) and (iii) are quite similar, p and q must be within the same size range of n-bits, which results in different conditions for both cases. We formulate attacks using three different algorithms to better understand their feasibility. We also provide an efficient countermeasure that it is recommended is adopted by current cryptographic libraries with RSA implementation.
Studying cognitive services for websites search engine optimization

The subject of research in the article is machine learning models for classifying web-pages by quality and compliance with SEO rules. The goal of the article is improving the efficiency of search engines by establishing and using factors that have the greatest impact on the degree of SEO optimization of web pages. The article solves the following tasks: study of the effectiveness of using machine learning methods to build a classification model that automatically classifies web pages according to the degree of adaptation to SEO optimization recommendations; assessment of the influence of relevant page factors (text on a web page, text in meta tags, links, image, HTML code) on the degree of SEO optimization using the developed classification models. The following methods are used: machine learning methods, classification methods and statistical methods. The following results were obtained: analysis of the effectiveness of the application of machine learning methods to determine the degree of adaptation of a web page to SEO recommendations was carried out; classifiers were trained on a data set of web pages randomly selected from the DMOZ catalog and rated by three independent SEO experts in the categories: “low SEO”, “medium SEO” and “high SEO”; five main classifiers were tested (decision trees, naive Bayes, logistic regression, KNN and SVM), on the basis of which it was revealed that all the studied models received greater accuracy (from 54.69% to 69.67%) than the accuracy of the baseline (48.83%); the results of the experiments confirm the hypothesis about the effectiveness of adapting web pages to SEO recommendations using classification algorithms based on machine learning. Conclusions. It was confirmed that with the help of classification algorithms built on the basis of machine learning and the knowledge of experts, it is possible to effectively adjust web pages to SEO recommendations. The considered methods can be adapted for various search engines and applicable to different languages, provided that a stamping or lemmatization algorithm has been developed for them. The results of the study can be used in the development of automated software to support the work of SEO in audit technologies to identify web pages in need of optimization and in spam detection processes.
Retraction: Psychological Characteristics and Influencing Factors of Students with Learning Difficulties based on the Analysis of Big Data (J. Phys.: Conf. Ser. 1744 032245)

This article has been retracted by IOP Publishing following an allegation that raises concerns this article may have been created, manipulated, and/or sold by a commercial entity. In addition, IOP Publishing has seen no evidence that reliable peer review was conducted on this article, despite the clear standards expected of and communicated to conference organisers. The authors of the article have been given opportunity to present evidence that they were the original and genuine creators of the work, however at the time of publication of this notice, IOP Publishing has not received any response. IOP Publishing has analysed the article and agrees there are enough indicators to cause serious doubts over the legitimacy of the work and agree this article should be retracted. The authors are encouraged to contact IOP Publishing Limited if they have any comments on this retraction. Retraction published: 16 September 2022
Exploring representation learning for small-footprint keyword spotting

In this paper, we investigate representation learning for low-resource keyword spotting (KWS). The main challenges of KWS are limited labeled data and limited available device resources. To address those challenges, we explore representation learning for KWS by self-supervised contrastive learning and self-training with pretrained model. First, local-global contrastive siamese networks (LGCSiam) are designed to learn similar utterance-level representations for similar audio samplers by proposed local-global contrastive loss without requiring ground-truth. Second, a self-supervised pretrained Wav2Vec 2.0 model is applied as a constraint module (WVC) to force the KWS model to learn frame-level acoustic representations. By the LGCSiam and WVC modules, the proposed small-footprint KWS model can be pretrained with unlabeled data. Experiments on speech commands dataset show that the self-training WVC module and the self-supervised LGCSiam module significantly improve accuracy, especially in the case of training on a small labeled dataset.
EPIC TTS Models: Empirical Pruning Investigations Characterizing Text-To-Speech Models

Neural models are known to be over-parameterized, and recent work has shown that sparse text-to-speech (TTS) models can outperform dense models. Although a plethora of sparse methods has been proposed for other domains, such methods have rarely been applied in TTS. In this work, we seek to answer the question: what are the characteristics of selected sparse techniques on the performance and model complexity? We compare a Tacotron2 baseline and the results of applying five techniques. We then evaluate the performance via the factors of naturalness, intelligibility and prosody, while reporting model size and training time. Complementary to prior research, we find that pruning before or during training can achieve similar performance to pruning after training and can be trained much faster, while removing entire neurons degrades performance much more than removing parameters. To our best knowledge, this is the first work that compares sparsity paradigms in text-to-speech synthesis.
Comparison of COVID-19 contact-tracing apps in the context of public policy: the cases of Turkey and South Korea

The healthcare systems of almost every country have been facing an unprecedented crisis since the beginning of the COVID-19 pandemic in March 2020. In response, numerous countries have designed and launched their unique applications to inform their citizens about COVID-19 and to follow their health situation. While South Korea has been showing an effective fight against COVID-19, Turkey is one of the most affected ones by the virus. This paper aims to identify key differences and similarities between the contact tracing apps that are used by Turkey and South Korea during COVID-19. In this way, it addresses how two members of the Organization for Economic Co-operation and Development (OECD), one developing (Turkey) and other developed (South Korea) countries, integrated their public policies and crisis management responses with the mobile phone applications and at what scale they are successful. Also, this paper investigates how these differences/similarities between their applications have affected their paths in the pandemic. Comparing their applications, it discusses policy-making processes, technological differences, and contact tracing strategies with policy recommendations for other countries.
The Internet and Its Role in the Acquisition of the Scientific Knowledge

Internet is one of the most important sources of information, knowledge and culture, through which the student, professor, researcher, technician, media, political, economic, social reformer, artist, and all official and civil institutions interact. It is an important tool for written, visual and audio communication, and the different spectrums of human society have gathered in different locations to issue their ideas and culture, which proves the sincerity of the scientifically known name of the World Wide Web. The contemplator on the nature of the work of (the spider) realizes that this small animal weaves its fine threads in a geometric shape that extends from the center to the rest of the walls to prey on insects, flies and mosquitoes so that it picks up prey from any location it wants according to the way God Almighty deposited his secrets in it and has affairs in his creation. The nature of the study necessitated that we divide it into three sections preceded by an introduction and followed by a conclusion and recommendations. While the second topic dealt with: the human relationship with the various uses of the Internet. The third and final topic was devoted to talking about the advantages and disadvantages of the Internet. Then we strengthened the end of the research with the most prominent results and recommendations. By referring to the references and articles related to the subject of the study.
Prediction of School Uniform Sales Using the K-Nearest Neighbor Method

Information regarding monitoring of operational expenditures of the Regional Public Service Agency at the Emergency Ambulance Service Unit of the DKI Jakarta Health Office. Because the finances issued or used must be accurately informed. In this case, monitoring the operational expenses of the Regional Public Service Agency at the Emergency Ambulance Service Unit of the DKI Jakarta Health Service still has many obstacles. Constraints faced are such as the technical executor. The activity of submitting an accountability letter still has a long stage, the Verifier is not there so that the process of checking the accountability letter stops, the input of reports is carried out one by one by the Spending Treasurer using Microsoft Excel. The process of monitoring the operational expenses of the Regional Public Service Agency at the Emergency Ambulance Service Unit of the DKI Jakarta Health Office takes a long time. And another obstacle is the occurrence of classifying the type of operational expenditure. In this data analysis method, using Balanced Scorecard (BSC) analysis, system design uses Visual Paradigm to create UML (Unified Modeling Language), PHP programming language, MySQL to create databases. The result of this research is that an information system for monitoring operational expenditures of the Regional Public Service Agency can create and submit a letter of accountability and verification letters are carried out computerized, the Expenditure Treasurer no longer enters the general cash books one by one, the Expenditure Treasurer no longer classifies the types of operational expenditure in recap operational expenditure.
Investigating Metal-Induced Vegetation Stress with Landsat ETM+ Satellite Images

Remote sensing technology is used in many areas today, facilitating spatial analysis operations under difficult conditions. This technology offers solutions in different fields such as forest degradation, land classification, change analysis and mine detection. In particular, mine detection is of great importance for the economies of countries. Detecting mines with terrestrial measurement techniques in vegetated areas is a challenging situation, but mine sites can be easily detected, by using satellite images in a shorter time. When plants are exposed to heavy metals, anomalies occur and they cause reduction in the amount of chlorophyll. This can be observed as decrease in the reflectance values. In this study, it is aimed to detect the stress of the plants due to heavy metal by measuring the amount of the change in the reflectance values of plants in mining area and non-mining area. In this context, VIGS index is exploited on Landsat ETM+ satellite images belong to Cukuralan/Izmir and Kisladag/Usak regions for both gold mine and non-gold mine areas. Other vegetation indices (NDVI, GNDVI, BNDVI) were also computed on these sites when they were covered with vegetation. According to the evaluation results, it was seen that the difference between the values of the VIGS and NDVI indexes in the mine and not mine areas were higher, and therefore it was suggested that they could be used to determine the amount of anomaly in the wooded areas.
Spatial Analysis of the Components of Agricultural Development in the Countryside of Karma District

Agricultural development is one of the most important means that can contribute to reducing the phenomenon of food deficit. Policy of agricultural development focuses on all capabilities and programs that are organized within a unified progressive program aims at developing the agricultural sector and establishing institutions to serve market-oriented production, regulate land tenure and improve the income level.&#x0D; Therefore, the main objective of this research is to study and analyze the most important natural and human constituents and the role that these components play in developing this sector after standing on the most prominent obstacles and determinants what impedes the development of agricultural lands. One of the reasons for choosing Karma district is related to its agricultural capabilities that qualify it for agricultural expansion in the production of agricultural crops، especially grain crops.
Sequential Multi-task Learning for Histopathology-Based Prediction of Genetic Mutations with Extremely Imbalanced Labels

H &E images can be utilized to predict genetic mutations as biomarkers to potentially substitute many molecular biomarker assays in order to aid patients. Having a single model built by conducting prediction tasks simultaneously can save computation resources and provide a more generalizable model for future usage. A basic technique for generating such a comprehensive and efficient model is to employ a multi-task learning approach. However, overfitting the model to the trivial answers can occur in training for multiple tasks with extremely imbalanced class labels where resampling and rebalancing for all minor classes simultaneously are prohibited. Herein we propose a sequential multi-task learning approach to train a single model capable of predicting multiple genetic mutations while avoiding overfitting to trivial answers for imbalanced classes. We compared our strategy to the baseline multi-task training, as well as two more advanced approaches: (1) using weighted loss and (2) using self-supervised pre-training. We also used a trimming method to deal with noisy labels. To assess our methods, we trained models to predict 10 genetic mutations on the H &E images of the TCGA-LUAD dataset. AUROC and F1 score are reported, while we demonstrate that F1 score may be a more suitable metric for multi-task learning with imbalanced labels. It is shown that our proposed trimming strategy combined with sequential learning could improve the predictions on all of the mutations compared with other multi-task learning approaches. Also, we investigated the application of continual learning.