Reviewerly/exHarmony huggingface dataset:
Translation of scientific and technical litreature and need for popularisation of Hindi terminology

Terminology plays a very important role in the process of technical translation. But in Hindi, many types of practical difficulties and problems are talked about regarding the terminology. It is a fact that despite the constitutional status, English is more prevalent in our country in technical education and research. The number of users of the terminology of Hindi is limited. As a result, Hindi vocabulary created by tireless hard work is not gaining much popularity. Hindi writers and translators of science find it difficult to preserve the quality of intelligibility in their original writings and translation works. Considering English as a lingua franca and not considering Hindi as a competent medium for expression of scientific and technical subjects, a bias arises in the minds of many scholars towards Hindi terminology.This bias needs to be removed because presently it should be our priority to present various branches of science in Hindi and for this, science topics need to be explained in simple language so that we can strive for popularization of technical terminology of Hindi later on. Creative translation can be used for this. For a difficult concept or latest scientific discovery, if there is no appropriate word in the target language (Hindi) or if there is a problem related to communication, then expressing it in a simple way is more effective because the main purpose of translation is to communicate the information given in the source language. By keeping English/foreign words for complex terminology and using simple expressions of Hindi, it will be more effective if the technical terminology of Hindi is kept in the parentheses for the purpose of popularisation.
Free Speech on Public Platforms

Abstract Suppose the US government establishes and operates a social media platform to create a forum for citizen expression. Under the First Amendment and international human rights law, what rules could the government establish on the platform? Much commentary has recognized that private platform moderation is distinct insofar as it is private. Commentators have focused less on the ways private platform moderation is distinct because it involves platforms. The legality of content moderation on a hypothetical government-run platform should depend not simply on whether regulation involves content discrimination or even viewpoint discrimination, but on the nature and degree of the burden imposed on speakers, the government’s purposes in imposing those burdens, the risk of arbitrary or corrupt enforcement, and—relatedly—the clarity and consistency of the rules the government has put in place.
Analysis of Covid-19 Risk Perception Survey Result Using Generalized Structured Component Analysis

The capital city of Indonesia, Jakarta, became the province with the highest number of Covid-19. Response this situation, LaporCovid-19 collaborate with the Social Resilience Lab, Nanyang Technological University conducted a survey to measure how Jakarta residents perceive the risk of Covid-19 from May 29 to June 20 2020. Factors of risk perception are variables that cannot be measured directly, so they are analyzed used a Structural Equation Modeling (SEM) approach, namely Generalized Structured Component Analysis (GSCA). The Likert scale used can be considered as interval or ordinal depending on the point of view of the theory built. Therefore, this study will compare the GSCA method with the nonlinear GSCA and evaluate six variables, namely risk perception, knowledge, information, health behavior , social capital, and economy. Evaluation of the overall model showed that the nonlinear GSCA model can explain the diversity of qualitative data better than the GSCA model with FIT &gt; 0.9. Based on GSCA nonlinear model, information has significantly influence of knowledge, economy and social capital have a real reciprocal relationship, along knowledge and risk perception have significantly influence of health behavior.
Assessment of the Effect of Personal and Familial Characteristics on Students’ Attitudes Towards Writing on Paper and Digital Writing

The aim of this study is to investigate whether the high school students' attitudes towards writing on paper and digital writing were affected by their personal characteristics and familial characteristics. Survey method was used to determine high school students' attitudes towards writing on paper and digital writing. The sample of the study was determined using convenience sampling. Accordingly, a total of 301 students studying at Kahramanmaraş Anatolian High School during the 2019-2020 academic year were included in the study. “Inventory of Attitudes Towards Writing on Paper and Digital Writing” was used to collect the research data. Mann-Whitney U, Kruskal Wallis and Wilcoxon Signed-Rank tests were used to analyse the data. Based on the findings of the research, it was concluded that high school students’ attitudes towards writing on paper and digital writing did not differ according to their personal characteristics or familial characteristics, except for one independent variable, that is the father's educational attainment.
INDUSTRIAL WORK PLACEMENT OF PEDAGOGICAL UNIVERSITY STUDENTS: INSIDE AND OUTSIDE PERSPECTIVES

The article analyzes the organization and content of the industrial work placement carried out within the framework of the specialist programmes, as well as within the framework of the bachelor's degree in 3+ and 3++ standards. The authors present an empirical study of undergraduate students and employers’ subjective views on the content and organization of the industrial work placement. The interpretation of the obtained results is carried out and a psychological explanation of the revealed phenomena is given. The authors conclude that transformations in the social and professional sphere determine new requirements for the organization of industrial work placement.
Mathematical Modeling in Digital Environment and its Implications on Teaching and Learning Extreme Values and Functions’ Monotonicity

In this paper, we present a way of introducing teaching and learning method based on the principles of mathematical modeling empowered by digital technologies. The role of mathematical modeling in educational practice is discussed as also the implications of the realization of mathematical modeling in a computer environment. In the example of teaching Extreme values and functions’ monotonicity, step by step procedure of making a mathematical model is explained – from starting preparation, to implementing GeoGebra as the software tool. The experiences of students and teachers during and after learning and teaching using mathematical modeling in the GeoGebra environment were supportive concerning further implications of this method.
Multifeedback Behavior-Based Interest Modeling Network for Adaptive Click-Through Rate Prediction

With the rapid development of the Internet, the recommendation system is becoming more and more important in people’s life. Click-through rate prediction is a crucial task in the recommendation system, which directly determines the effect of the recommendation system. Recently, researchers have found that considering the user behavior sequence can greatly improve the accuracy of the click-through rate prediction model. However, the existing prediction models usually use the user click behavior sequence as the input of the model, which will make it difficult for the model to obtain a comprehensive user interest representation. In this paper, a unified multitype user behavior sequence modeling framework named as MBIN, a.k.a. multifeedback behavior-based Interest modeling network, is proposed to cope with uncertainties in the noisy data. The proposed adaptive model uses deep learning technology, obtains user interest representation through multihead attention, denoises user interest representation using the vector projection method, and fuses the user interests using adaptive dropout technology. First, an interest denoising layer is proposed in the MBIN, which can effectively mitigate the noise problem in user behavior sequences to obtain more accurate user interests. Second, an interest fusion layer is introduced so as to effectively model and fuse various types of interest representations of users to achieve personalized interest fusion. Then, we used auxiliary losses based on behavior sequences to enhance the effect of behavior sequence modeling and improve the effectiveness of user interest characterization. Finally, we conduct extensive experiments based on real-world and large-scale dataset to validate the effectiveness of our approach in CTR prediction tasks.
Arabic-English Code-Switching in the Saudi Video Gaming Community: A Sociolinguistic Perspective

In this sociolinguistics paper, I discuss code-switching behaviour while playing an online video game. The purpose of the study is to bridge the knowledge gap in the literature regarding code-switching within the Saudi Arabian gaming community. Although a significant amount of research has been carried out on the topic of code-switching, the phenomenon of code-switching among online gamers has received little attention. The focus of this research is Saudi online gamers playing online video games, specifically Overwatch (a team-based online multiplayer game). The research questions investigated how the game format (casual or ranked) and the age of the players influence the occurrence of code-switching. Data collection was based on a quantitative approach and participating in Overwatch matches. Observing the presence of players and their frequency of code-switching allowed for the creation of objective data. The findings indicate that both the format of Overwatch matches and the age of the players had an impact on code-switching. Matches that took place in an intense setting (ranked matches) had more instances of code-switching than those in a casual setting. The results show that the age of the players affected code-switching because younger players were less likely to code-switch than older players were. The research illuminates the ways in which individuals who are part of Saudi Arabia&amp;rsquo;s gaming community interact with one another and sheds light on the online settings in which code-switching is most prevalent. Future studies should investigate other video game genres to broaden the understanding of the phenomenon of code-switching in online video games.
Predict stock prices using the Generative Adversarial Networks

Predicting the price of a stock is very difficult. Due to very volatile prices. Many traders incorrectly predict stock prices, forex or trading commodities. It takes an analysis of each price movement. The purpose of the analysis is to predict price movements. One of them is the use of indicators that seek to help predict prices. Currently the development of Artificial Intelligence (AI) has grown very rapidly. Machine learning which is part of AI is also used to predict prices. Stocks are data that are related to time. Just like the weather. If the stock is analyzed then the suitable method is the time series method. The method used is Deep Learning, namely Recurrent Neural Network (RNN). A recurrent Neural Network is the same as Artificial Neural Network (ANN). ANN performs the processing of sequential data. RNN does not discard past problem data information, but will also enter past information as input. This is what distinguishes RNN and ANN. In the Recurrent Neural Network, there is a Long Short Term Memory Algorithm, Gated Recurrent Unit (GRU). One of the algorithms that can be used to predict stock prices is the Generative Adversarial Network. This algorithm was modified before being used. In the GAN algorithm, there are Generators and Discriminators. Because stock is a process that is carried out in the presence of time or time series, the Generative is modified with Long Short Term Memory and Discriminator uses Long Short Term Memory
A HYBRID FUZZY C-MEDOIDS CLUSTERING USING THE WHALE OPTIMIZATION ALGORITHM

Although the fuzzy c-means algorithm (FCM) has been widely used in many fields, they are sensitive to noise and outliers. Recently, the Fuzzy C-Medoids (FCMdd) algorithm has been shown to be more effective in dealing with noise data. The difference between FCM and FCMdd is the formation mechanism of clusters. At the same time, FCM builds clusters based on membership function and samples in the cluster. FCMdd selects some of the existing actual samples as cluster medoids. This results in FCMdd being able to handle noise better than FCM. This paper presents a hybrid approach of the whale optimization algorithm WOA with FCMdd (FWCMdd) to optimize the clustering process. This hybridization prevents FWCMdd from falling into the local trap and rapidly converging. This solution has been compared with the Fuzzy k-Medoids (FKM) algorithm and the primitive FCMdd. The results indicate that the proposed method is better than most of the evaluation indicators.
CIRCULARVIZ: AN ENHANCED RADVIZ VISUALIZING FOR MULTIDIMENSIONAL DATA

Visualizing for multi-dimensional data points in data space is a central topic with many supplications in many branches of science. RadViz visualizes multi-dimensional data points in the 2D visual space based on force-based methods. Radviz visualizes information about relationships of multi-dimensional data points of data space. The traditional Radviz method finds the best order of anchor points on the circumference of the circle with radius one to enhance the relationships of multi-dimensional data sets. In our paper, we modify the Radviz method named CircularViz method that improves the Radviz layout for the group’s preservation the relationships of multi-dimensional data sets. The idea of the CircularViz method presents data dimensions by circular segments on the circle with radius 1 and finds the most suitable to gaining insights into class structures of multi-dimensional data sets. Our approach make provision an augmentation in visualizing group structures of multi-dimensional data on the 2D visual space. The effectiveness of the CircularViz method evidences by quality visualization measurements and several supervised high-dimensional data sets.
Implementasi Service Oriented Architecture (SOA) pada Sistem Pencatatan Keuangan untuk UMKM

One of the indications of a UMKM's success is financial statements. UMKM can use SAK EMKM to prepare financial reports. Many UMKM, on the other hand, have yet to incorporate SAK EMKM. UMKM can use information technology to improve its financial records. With the quantity and scope of UMKM, as well as the demand for information technology, flexible systems must be established on a variety of platforms in order to be sustainable. As a result, SOA must be used in the financial recording system for UMKM. As a result of these issues, a SOA-based Financial Recording System was developed. SOA allows the system to be more adaptable and reusable. The system built consists of a service provider and a cloud consumer. Owners of UMKM can use the Android application to keep financial records and generate reports from service providers. According to the results of the Black Box approach, every service offered at the service provider, as well as the cloud consumer's function, has been performing as predicted. And, according to the proprietor of UMKM, the system has been performing admirably.
Komparasi Metode Backpropagation Neural Network dan Convolutional Neural Network Pada Pengenalan Pola Tulisan Tangan

Historical manuscripts are one of documents that important to be preserved because they contain a lot of information, one example of them is script as the historical documents . Historical document mostly still use handwriting in so many reserch. Currently, there are many research regarding the preservation of characters. One way of preservation that can be used is the digitization process. Digitizing’s process tanable by recognizing existing information using technology. The technology that can be used is machine learning. Handwriting is a complex case because of the many variations of these characters and the output of the author where variations of the author will produce different writings. The relevant fields for text and documents are Optical Chacarter Recognition (OCR) and handwriting recognition. There are several methods that can be used in the machine learning process, including Artificial Neural Network (ANN) and Convolutional Neural Network (CNN). Both of these methods are methods that can accept complex image input to be processed and recognized, therefore this method is highly recommended for processing handwriting.
S2NMF: Information Self-Enhancement Self-Supervised Nonnegative Matrix Factorization for Recommendation

Nonnegative matrix factorization (NMF), which is aimed at making all elements of the factorization nonnegative and achieving nonlinear dimensional reduction at the same time, is an effective method for solving recommendation system problems. However, in many real-world applications, most models learn recommendation models under the supervised learning paradigm. Since the recommendation performance of NMF models relies heavily on initialization, the user-item interaction information is often very sparse. In many cases, supervised information about the data is difficult to obtain, resulting in a large number of existing models for supervised learning being inapplicable. To address this problem, we propose an information self-supervised NMF model for recommendation. Specifically, this model is based on the matrix factorization idea and introduces a self-supervised learning mechanism based on the NMF model to enhance the sparse data information of sparse data, and an easily extensible self-supervised NMF model was proposed. Furthermore, a corresponding gradient descent optimization algorithm was proposed, and the complexity of the algorithm was analysed. A large number of experimental results show that the proposed S2NMF has better performance.
Effect of a Virtual Reality Simulation Program of Mental Health Nursing

Objectives This study was conducted to understand the effect of the virtual reality (VR) simulation education program of mental health nursing applied to the mental health nursing theory classes through online video classes for third-grade nursing students on mental health literacy, clinical performance, critical thinking, and educational satisfaction. Through this, it was conducted to use it as a basis for the application of nursing curriculum and VR programs. Methods The research design was a non-equivalent control group pretest-posttest quasi-experimental study. The participants were 103 nursing students. The experimental group (n=63) took a total of 13 mental health nursing theory classes applied with VR simulation, and the control group (n=40) only took mental health nursing theory classes without VR simulation. Descriptive statistics were used for the collected data using the SPSS 25 program. Homogeneity between the experimental and the control group was confirmed using x2-test, Fisher's exact test, and independent t-test. Paired t-test was performed for the difference between before and after the experimental group. Independent t-test were used as the difference between two groups. Results As a result of pretest and posttest the experimental group, there were statistically significant differences in mental health literacy, clinical performance, and critical thinking score. As a result of statistical analysis between the two groups in the experimental group and the control group, a significant difference was found in the score of mental health literacy and educational satisfaction. Conclusions Based on the results of this study, the VR simulation education program of mental health nursing was found to be effective in improving mental health literacy, clinical performance, critical thinking, and educational satisfaction of nursing students. Recommendations are made for use in nursing curriculum and classes incorporating VR simulation.&#x0D;
The Ethnographic Study on the Principles of Democratic Management of Innovative Elementary Schools

Objectives The purpose of this study was to investigate the principles of democratic management of innovative elementary schools. Methods For this purpose, qualitative data were collected and analyzed through participatory observation and Focus Group Interviews from Gwangju OO Elementary School and 14 teachers operating as an innovative school. and literature data (internal departments) of the school were also analyzed in parallel. Results The operating principles for creating a of democratic school culture in innovative elementary schools were found to be shared philosophy, autonomy and spontaneity, professional learning community, curriculum reorganization, and principal's leadership. Conclusions For the democratic management of innovative elementary schools, it is necessary for members to form a community by sharing the school philosophy, and to develop professionalism and reward through the formation of relationships between teachers. It is also necessary to ensure that a democratic school culture is formed through the professional leadership of the principal and the cooperation of teachers.&#x0D;
Replay (Far) Away: Exploiting and Fixing Google/Apple Exposure Notification Contact Tracing

Digital contact tracing offers significant promise to help reduce the spread of SARS-CoV-2 and other viruses. Google and Apple joined together in 2020 to create the Google/Apple Exposure Notification (GAEN) framework to determine encounters with anonymous users later diagnosed COVID-19 positive. However, as GAEN lacks geospatial awareness, it is susceptible to geographically distributed replay attacks. Anonymous, low-cost, crowd-sourced replay attack networks deployed by malicious actors (or far away nation-state attackers) who utilize malicious (or innocent) users’ smartphones to capture and replay GAEN advertisements can drastically increase false-positive rates even in areas that otherwise exhibit low positivity rates. In response to this powerful replay attack, we introduce GAEN+ , a solution that enhances GAEN with geospatial awareness while maintaining user privacy, and demonstrate its ability to effectively prevent geographically distributed replay attacks.
AGEING COLLABORATIONS: CO-DESIGNING FUTURE TECHNOLOGY OPPORTUNITIES

The Ageing experience and increased longevity presents innovation opportunity for emergent technology, e.g., mobile and wearable.In turn, these technologies can enhance quality of life and independence as we age; however, they can also increase social inequalities and exclusion.The Covid-19 pandemic affected how we interact, communicate engage and interact with people and also technologies.Peopleacross the generations were impacted and disrupted, motivating new behaviours in how they conducted everyday tasks and activities.Co-Design approaches have previously revealed successful collaborations between older adults, students, researchers, designers and other disciplines as a means to define unmet needs.Edinburgh Napier University provided funding to the PI (Principal Investigator) as a means to mobilise a 'Creative Cross-Education Team' (CCET) consisting of undergraduate students, research assistants (post