Reviewerly/exHarmony huggingface dataset:
Economic analysis of the world export of educational services

The research looks at the export of educational services as one of the promising spheres for the development of international economic relations. The study focuses on the experience of developed supplier and recipient countries. The theoretical basis of the study is formed by the typology of ways to export services proposed in the General Agreement on Tariffs and Trade. The importance of the issue under consideration is due, among other things, to the fact that, according to forecasts, the number of foreign students wishing to obtain higher education will increase to 150 million in 2025, and the volume of exports of higher education will reach $30 billion, which is 3 % of the total volume of trade in OECD services. Most prior research focuses on methodological aspects of education; however, the experience of foreign countries is presented in a limited scope. Using the example of the USA in the context of the management training program, it is shown that a fairly broad methodological system is used abroad, allowing specialists from all over the world to acquire the necessary skills and develop competencies. It is obvious that the pandemic has led to a sharp reduction in the number of foreign students in Germany, as in other exporting countries of educational services. Of particular interest is the EdTech market, which is attractive in terms of its size and growth rates. The key barriers to the global market of educational services are, among other things, increased competition, the presence of a larger number of exporters-providers of educational services, more active classes at mass open online courses, active use of mobile devices; the use of learning models that include personalized and blended learning methods; increasing the degree of confidentiality of data; active development of the institute of continuing education in conjunction with the active use of advanced training courses and vocational training. The research is expected to provide valuable information about the balance of powers on the global market of educational services and will serve as an information base for further scientific research in the field.
Knowledge management publication in two South African journals

This scholarly book focuses on the relationship between information knowledge and technology with regard to the Fourth Industrial Revolution.The latter is understood to be a set of highly disruptive technologies blurring the lines between the physical, digital and biological spheres, collectively referred to as cyber-physical systems.Technologies such as cognitive computing, cloud computing, the Internet of Things, big data, augmented or virtual reality, 3D systems, artificial intelligence and power supply are transforming social, economic and political systems.Ultimately, we need to transform, be more creative and innovative to develop and sustain existing and emerging capabilities in the 'new normal', which has been accidentally accelerated by COVID-19 requirements.Technologies for digital transformation create the opportunity for Africa to bypass traditional phases of industrial development as the continent has done successfully in the past.Challenges addressed in the book are inequality, social justice, ethics, access and success, policy, infrastructure and cybersecurity.The book contributes to the ongoing discourse among scholars in Africa.It consists of 10 chapters where conceptual and field research are combined.The first chapter, by Trywell Kalusopa from the University of Namibia, focuses on e-government, with an emphasis on digital government in the digital economy.The second chapter, by Kelvin J. Bwalya from the University of Johannesburg, focuses on e-government from an artificial intelligence perspective.The third chapter, by Tom Kwanya from the Technical University of Kenya, focuses on the perception of robots for information services.In the fourth chapter, Johannes Britz, from the University of Wisconsin, discusses information ethics in the digital economy.
Responsiveness of library and information science research and education to the Fourth Industrial Revolution

This scholarly book focuses on the relationship between information knowledge and technology with regard to the Fourth Industrial Revolution.The latter is understood to be a set of highly disruptive technologies blurring the lines between the physical, digital and biological spheres, collectively referred to as cyber-physical systems.Technologies such as cognitive computing, cloud computing, the Internet of Things, big data, augmented or virtual reality, 3D systems, artificial intelligence and power supply are transforming social, economic and political systems.Ultimately, we need to transform, be more creative and innovative to develop and sustain existing and emerging capabilities in the 'new normal', which has been accidentally accelerated by COVID-19 requirements.Technologies for digital transformation create the opportunity for Africa to bypass traditional phases of industrial development as the continent has done successfully in the past.Challenges addressed in the book are inequality, social justice, ethics, access and success, policy, infrastructure and cybersecurity.The book contributes to the ongoing discourse among scholars in Africa.It consists of 10 chapters where conceptual and field research are combined.The first chapter, by Trywell Kalusopa from the University of Namibia, focuses on e-government, with an emphasis on digital government in the digital economy.The second chapter, by Kelvin J. Bwalya from the University of Johannesburg, focuses on e-government from an artificial intelligence perspective.The third chapter, by Tom Kwanya from the Technical University of Kenya, focuses on the perception of robots for information services.In the fourth chapter, Johannes Britz, from the University of Wisconsin, discusses information ethics in the digital economy.
WALT PLATFORM FOR WEB APPLICATION DEVELOPMENT

The article describes the Web Application Lego Toolkit (WALT), which is a template-orientedplatform designed for the development of web applications of various degrees of complexity. Unlikesome other platforms, which represent a "magic black box", the main idea of WALT is to providetransparent, extensible, and modifiable tools for solving some specific problems that arise whendeveloping web applications. WALT is primarily intended for use by individual full-stack developersor small groups of developers. The article depicts the WALT architecture and provides examples of itsuse for the development of JINR corporate web applications.
Impact of Swear and Negative texts on Social Media Users

Nowadays, the use of social media has increased exponentially. People show different behavior on social media depending on the kind of responses and behavior of people around them. It is important now to analyze the behavior of social media users and the way how they affect their friends. In this paper, behavioral analysis of people is done based on Twitter data. An algorithm is proposed which helps in finding the impact of text written by someone on social media and its effect on others. The impact of written text is calculated with the help of the number of retweets done for the same tweet. The severity of the used word is calculated based on AFINN dictionary. According to the proposed algorithm, the score of the dictionary is recalculated when a negative word is forwarded multiple times. This is done with the understanding that if a less severe negative word is used many times, it may affect the person in a highly negative manner. With this, Severity of words is recalculated and its impact on people is found with the help of the proposed algorithm. The impact of using negative words on social media affect 32 % of the total users (in their friend-list). Behavior change is demonstrated with the help of graphs week-wise, month-wise and year-wise analyses. The research helps in finding the impact of swear words on social media users depending on the frequency and severity score of the words.
Instructional Design in the Face of COVID-19: Learned Lessons and Pending Tasks

Faced with confinement due to COVID-19, educational institutions with face-to-face models had to continue their activities under conditions and with resources not used up to that moment. For this, the institutions formulated and put into operation continuity plans, which involved everything from remote education to hybrids between the latter and online education. Institutions that already had online or hybrid education programs were able to apply that experience to their face-to-face programs, allowing them to respond more quickly than those that did not. The stages of the teaching-learning process that were "adjusted" during this emergency in order to give continuity to educational activities were the last two, namely: the development of instructional material and teaching. In this work, an intervention is proposed in a previous stage of the process, that is, in the instructional design (ID), using the ASSURE model derived from the ADDIE model or approach. This intervention is based on the lessons learned during the pandemic, for the preparation or reformulation of study plans that consider information and communication technologies as a platform to enhance the effectiveness of learning, selecting them and establishing their use strategy from the stage in which the materials are designed, which may be useful considering that even if the students return to the classrooms, a virtual part will be preserved, that is, a hybrid model, in which the face-to-face-virtual ratio will be determined by the educational strategy of the institution.
Convolutional Feature-Interacted Factorization Machines for Sparse Contextual Prediction

Factorization Machines (FMs) are extensively used for sparse contextual prediction tasks by modeling feature interactions. Despite successful application of FM and its abundant deep learning variants, these improved FMs mainly focus on capturing feature interaction at the vector-wise level while ignoring the more sophisticated bit-wise information. In this paper, we propose a novel Convolutional Feature-interacted Factorization Machine (CFFM), which learns crucial interactive patterns from enhanced feature-interacted maps with non-linearity. Specifically, in the high-order feature interactions part of CFFM, we propose a special Convolutional Max Pooling (Conv-MP) block to adequately learn interaction patterns from both vector-wise and bit-wise perspectives. Besides, we improve linear regression in FMs by incorporating a linear attention mechanism. Extensive experiments on two public datasets demonstrate that CFFM outperforms several state-of-the-art approaches.
REPERCUSSÕES DA PANDEMIA DA COVID-19 NOS PROFISSIONAIS DE SAÚDE: COMO ESTÁ QUEM ESTÁ NA LINHA DE FRENTE?

This research is part of the practical activity of Interconsultation carried out in the years 2015 and 2016 at Hospital Universitário Pedro Ernesto, teaching hospital of the Rio de Janeiro State University -UERJ.Interconsultation consists of an activity that, following the request for a technical opinion by the team, after apprehending some subjective question from the patient or family, the psychology professional is required to be monitored.Thus, we received the demand for GJ, 75 years old, diagnosed with Cutaneous T-Cell Lymphoma.In the service, we noticed a strong anguish in the health team, which signalized that the listening request was not restricted to the patient who, at that moment, was itself out of therapeutic care.The psychologist inside the general hospital is responsible for listening carefully to each case, creating space and opening possibilities for the speeches of the patient, family and team to emerge.In this study, a cut was made in this triad, emphasizing in the team.The research approach is characterized by highlighting the listening of the mental health professional in a multidisciplinary team as a differential in the treatment.A qualitative research was carried out, choosing the observation and analytical listening of the health team.The medical Psychology treatment offer and the unique look of Psychoanalysis operate as a contribution to the hospital context and to the health field.
Detection of Fake Currency using Image Processing

In its definition, the term 'currency' defines an agreed-upon exchange item, the national currency being the legal entity used by the selected controlling entity. Throughout history, issuers have faced 1 common threat: counterfeit. In recent years fake money note has been printed that has resulted in significant losses and damage to society. Therefore, it becomes necessary to build a tool for earning money. This research project proposes a way to look at the note of counterfeit money distributed in our country through their image. After selecting an image use pre-processing. In pre-processing, the acquired image is cropped, smooth, and adjust. Change the image to grey-scale. After conversion use image separation. Features are extracted and reduce. Finally, compare the picture to be real or fake. Duplicate money has been a major problem in the market. There are currency counting machines available in banks and other trading venues to check financial authenticity. Most people do not have access to such programs which is why there is a need for fake money laundering software, which can be used by ordinary people. This proposed framework uses Image Processing to determine whether the money is real or counterfeit. The research project program is built entirely using Python's programming language. It has the methods such as grayscale conversion, edge detection, segmentation, etc.
Analyzing Commercial Collaboration Attractiveness in YouTube Based on Micro-Analytical Content Labeling and Audience Retention

For creators of online video content, it is essential to understand how the audience reacts to different details within the content and how those reactions affect their viewing behavior, such as rewatching or skipping a moment or closing the video altogether.Understanding detailed viewing in terms of audience retention statistics is crucial for commercial collaborative content, as online video hosting such as YouTube freely allows commercial collaboration of different kinds, unlike many TV channels, for example.This paper proposes a research agenda for micro labeling commercial collaborative content to understand how to make it increasenot ruinaudience retention.We employ sport entertainment media as a case example but derive implications for not only media channel owners but also to marketers, influencer personalities and platform developers.
Upcoming Meetings

Artificial OrgansVolume 45, Issue 6 p. 641-641 UPCOMING MEETINGS Upcoming Meetings First published: 04 June 2021 https://doi.org/10.1111/aor.13965Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume45, Issue6June 2021Pages 641-641 RelatedInformation
Design of University Graduation Project Management System Based on Micro Service Architecture

Based on the concept of microservice, the study integrates MyBatis+Spring Boot framework, and uses Ajax technology and MySql database technology to develop the graduation design (thesis) management system.It has realized the information management of 14 core links, such as project declaration, topic selection, assignment, opening report, mid-term inspection, paper submission, paper review, defense and material filing.Through application demonstration, the result is nice, and the system has high scalability and wide applicability.
Bernstein and Bernstein-Like Inequalities for Modulation Spaces

It is shown that the modulation spaces Mwp can be characterized by the approximation behavior of their elements using Local Fourier bases. In analogy to the Local Fourier bases, we show that the modulation spaces can also be characterized by the approximation behavior of their elements using Gabor frames. We derive direct and inverse approximation theorems that describe the best approximation by linear combinations of N terms of a given function using its modulates and translates.
SISTEM INFORMASI PEMENUHAN NILAI GIZI MAKANAN IBU HAMIL DENGAN METODE COOPER BERBASIS ANDROID STUDI KASUS: PUSKESMAS MRANGGEN II

Nutrition and nutrition of pregnant women is an important thing that must be met during pregnancy. The risk to the health of the fetus being conceived and the mother who is pregnant will be reduced if pregnant women get balanced nutrition and nutrition. Pregnant women who are malnourished can lead to premature birth, fetal death, abnormalities of the baby's central nervous system, as well as abnormal development, congenital defects, anemia in infants, low birth weight, and newborns with low health status.The problem that often arises today is the lack of knowledge of pregnant women about the importance of fulfilling nutrition / nutrition for pregnant women. Due to the lack of information and education of pregnant women. This study, designed a system for fulfilling the nutritional value of food for pregnant women using the Android-based COOPER method. Aims to make it easier for pregnant women to find information about nutritional intake through the selection of food menus.The author uses the Cooper method, because this method is very suitable for calculating the number of calories needed by pregnant women. The Cooper method calculates the number of calories by processing the ideal weight of pregnant women, the activities of pregnant women, and the number of hours of sleep pregnant women according to Poedysmoro (2005).To solve the problems above, it is necessary to design an Android-based information system application for the fulfillment of the nutritional value of pregnant women's food which is expected to help the role of the community, making it easier for pregnant women to find information about nutritional intake through the selection of food menus. This information can later be accessed by pregnant women to find out the fulfillment of nutrition through the food menu that must be consumed. Through the Android display that offers solutions so that it can be done anytime and anywhere.
Recommended System

In this study searched for the survey paper on recommendation system. Recommendation system sorts through massive amounts of data to identify interest of users and makes the information search easier. For that purpose many methods have been used. Collaborative Filtering (CF) is a method of making automatic predictions about the interests of customers by collecting information from number of other customers, for that purpose many collaborative base algorithms are used. Web Recommendation(WR) help the website visitors for easy navigation of web pages, quickly reaching their destination and to obtain relevant information. Content based filtering method(CBF) filtering is done based on customer’s interested items. In content-based filtering technique, the web pages are recommended for a user very quickly from ancient database. In that database different content of items are added that the user has used in the ancient times and/or user’s personal information and preferences.
Business strategy and blockchain adoption

Although blockchain has drawn significant attention since its introduction in 2008, determinants of its adoption remain largely unknown.Relying on the Resource-Based View (hereafter, RBV) of the firm as a theoretical guide, we investigate whether a firm's business strategy affects its decision on blockchain adoption.We split firms into prospectors (risk takers) and defenders (interested in cost stability) consistent with the business strategy framework to determine if the former group is more likely to adopt blockchain.Using a sample of 208 firms from 2015 to 2019, we find that prospectors are more likely to adopt blockchain than defenders.Results suggest blockchain brings more net benefits to prospectors than to defenders.The results support RBV and business strategy theories and are robust to the consistency test, factor analysis, and placebo test.The findings imply that the alignment between business strategy and technology characteristics motivates firms to adopt specific technology.
DOES RUSSIA NEED INDEPENDENT UNIVERSITIES? TRANSFORMATION OF THE EDUCATIONAL SPACE OF RUSSIA: ON THE WAY TO A FAIR COMPETITIVE ENVIRONMENT (PART 2)

Part 2 of the article is devoted to the consideration of the system of measures for the transformation of the educational space of Russia related to the development of non-state education. Based on the analysis of the genesis of non-state segment of education in Russia, carried out in Part 1 of the article, proposals for its modernization and further integration into the general educational space of Russia are formulated. Proposals to support non-state education during the period of restrictive measures related to countering the spread of COVID-19 coronavirus infection are highlighted. The article presents the position of the Association of Private Educational Organizations of Russia — ANVUZ of Russia.
Deep collaborative filtering based on user's long and short intention for Recommendation

The user-based collaborative filtering (UCF) algorithms have been widely used in recommender systems. Existing UCF algorithms recommend based on the user's interaction history, but they rarely consider the time trajectory information of user behavior. Actually, the user's intention will change with time. Generally, the user's long-term intentions change slowly over time, while short-term intentions change drastically. How to dynamic recommendation introducing nonlinearity and assigning different weights to user's long-term and short-term intentions is currently a challenge. Therefore, in this paper, we propose a deep collaborative filtering recommendation algorithm based on user's long-term and short-term intentions, short for DeepLS. Firstly, the time-based interaction sequence utilizes a self-attention mechanism to mine the user's short-term intention. Secondly, representation learning-based methods map users and items into a common representation space and model the user's long-term intention. Then, the two intentions are fused to jointly learn the representation of users and items using concatenation or empirical scheme (ES). Finally, we send the joint representation into a fully connected layer to calculate the matching score for prediction. Experiments on three real datasets show that our proposed DeepLS framework better than the state-of-the-art methods.