Reviewerly/exHarmony huggingface dataset:
National digital currency issuance under Vietnamese law

The article analyzes the current situation and debates about the digital currencies issued by central bank. The results show that only a few countries have issued the central bank digital currencies, the rest of the countries have begun to have institutional orientations for research on issuing and using the central bank digital currency. The strengths that lead to the central bank's choice of research and issuance of digital currency are a high level of security, convenience for money control in circulation, and the stable value guaranteed by the state, thereby contributing to the promotion of non-cash payments. In the context of accelerating the economy digitization, including digital banking, and promoting the cash payments, Vietnam has had an orientation to research mechanisms, policies and laws for the issuance of national digital currency. In order to do this, in addition to reviewing, amending and supplementing the Law on the State Bank of Vietnam related to currency issuance, it is also necessary to prepare fully the technical infrastructure system.
An analysis of errors in Korean-Chinese public sector translation - A case study on COVID-19 information materials -

This study aims to analyze the translation errors in Chinese COVID-19 information materials published by Korean public institutions and to explore ways to improve the translation quality of disaster information materials. First, this study discusses the factors to be considered for the quality assessment of public sector translation, as well as the criteria of translation evaluation in previous studies. Based on this, a framework for analyzing disaster information materials is identified. Then, this study analyzes the translation errors in Chinese COVID-19 information materials from January 2020 to February 2022. The analysis finds eight error types in the materials, with the most significant problem being low acceptability, as well as several mistranslations and grammatical errors. These findings suggest the need to select qualified translators and revisers, provide standard terminologies, and strengthen translation policies to convey disaster information to foreigners more effectively.
Synergistic effects between data corpora properties and machine learning performance in data pipelines

To analyse data, a computationally feasible pipeline must be developed for data modelling. Corpora properties affect performance variability of machine learning (ML) techniques in pipelines; however, this has not been thoroughly investigated using simulation methodologies. A Monte Carlo study is used to compare differences in the area under the curve (AUC) metric for large-n-small-p-corpora examining: 1) the choice of ML algorithm; 2) size of the training database; 3) measurement error; 4) class imbalance magnitude; 5) missing data pattern. Our simulations are consistent with established results under which these algorithms and corpora properties perform best, while providing insights into their synergistic effects. Measurement error negatively impacted pipeline performance across all corpora factors and ML algorithms. A larger training corpus ameliorated the decrease in predictive efficacy resulting from measurement error, class imbalance magnitudes, and missing data patterns. We discuss the implications of these findings for designing pipelines to enhance prediction performance.
When and How to Update Online Analytical Models for Predicting Students Performance?

One of the main concerns in online learning environments is the identification of students with learning difficulties. Conventionally, analytical models trained offline on pre-prepared datasets are used to predict student performance. However, as learning data become progressively available over time, this learning method is no longer sufficient in real-world applications. Nowadays, incremental learning strategies are increasingly applied to update online analytical models by re-training them on newly received data. Various online incremental learning approaches have been proposed to overcome different issues such as catastrophic forgetting and concept drift. However, no approach addresses the question of when to update the model and how to determine whether the new data provide important information that the model should learn. In this paper, we propose a method for determining when an online classifier that predicts student performance and receives a real-time data stream, should be updated. In addition, we use a typical approach that maintains balanced old and new data examples to re-train the model when necessary. As a proof of concept, we applied our method on real data of k-12 learners enrolled in an online physics-chemistry module.
AN ANALYSIS OF SPEECH FUNCTION IN ENGLISH CLASSROOM INTERACTION OF ENGLISH COURSE AT BALLE VILLAGE KAHU DISTRICT BONE REGENCY

This study refers to the use of speech functions in English classroom interaction. The writer considers what types of speech function in English classroom interaction are important to be analyzed to know how the speech function is used in mentor-student classroom interaction and to show the most dominant types of speech function used in classroom interaction. The type of research used in this study used a qualitative descriptive approach. The research instruments were observation (non-participant observers) and interviews. The data analysis method used in this study refers to the theory of Miles and Huberman. Based on the results of the research, this study is that by analyzing the speech function used by English mentors during teaching, the researcher found the fact that James Holmes' theory of speech function has 6 namely expressive, directive, referential, metalinguistic, Phatic, and poetic. However, after this research was conducted, not all elements of the speech function were used by English mentors. The researchers could only figure out five of six kinds of speech functions. They are expressive, directive, referential, metalinguistic, and Phatic. So, the researchers found that English mentors were more dominant in using Expressive utterances, which was 36%.
Illuminating earthquakes

Earthquakes occur almost continuously, but most are too small to notice. For decades, geoscientists have used computers to analyse digital seismic signals, but many of the smallest earthquakes are undetected. While these tiny seismic events themselves pose little threat of damage, their waveforms hold information that can aid monitoring of larger, more destructive earthquakes. By […]
Research on financial information disclosure risk management method based on internet of things

In order to overcome the problem that the traditional risk management method has difficulty achieving the expected effect due to the careless classification of risk levels, this paper designs the financial information disclosure risk management method based on the internet of things. Based on the network financial information disclosure traceability model, the risk assessment index system is built, and the risk events are classified by ranking the risk events. The evaluation result is divided into five categories and the risk warning level is set. Then, the wireless sensor technology in the internet of things is used to design data fusion algorithm and distributed routing algorithm to achieve risk management. Simulation results show that the recall rate of the risk assessment index is above 94%, and the time to issue risk warning is always less than 6 min, which fully proves the effectiveness of the method.
A Study on the Reciprocal Defense Procurement Memorandum of Understanding(RDP MOU) between The Republic of Korea and the U.S.

With the recent emergence of emerging security threats around the world, there is a remarkable trend of domestic priority in international issues linked to the interests of each country. Since the Trump administration, the U.S. has advocated so-called "American First," and has implemented foreign and economic policies that put U.S. interests first in the international order, and President Biden has also advocated "Buy America." In particular, the U.S. government is opening up the defense procurement market by envisioning a reorganization of global supply chains with 28 allies through the Reciprocal Defense Procurement Memorandum of Understanding(RDP MOU) system. On May 21, the leaders of the two countries shared the importance and necessity of signing a Reciprocal Defense Procurement Memorandum of Understanding(RDP MOU) to continue the future-oriented developmental The Republic of Korea-U.S. alliance. In this study, we would like to present the future direction of The Republic of Korea-U.S. Reciprocal Defense Procurement Memorandum of Understanding(RDP MOU), focusing on the Biden administration's promotion of Buy America among the current U.S. Preferred Purchase Act(BAA).
An Exploratory Study on the Case of Flipped Class Application to Geometry and Analysis Subjects of Mathematics Education Department

Objectives This study is the result of observing and studying the changes of students and instructors in the case of applying Flipped Lessons method to mathematics lessons of the College of Education.&#x0D; Methods The mathematics classes of K-University located in Gyeongsang-do was operated by flipped learning method for seven years. Through this, student’s academic achievement and lecture evaluation results were investigated, and changes in class activities and evaluation were observed.&#x0D; Results The results of lecture evaluation and academic achievement of students who took mathematics classes in the Department of Mathematics Education were analyzed. Changes in the evaluation method for classes and changes in class activities over the past 7 years were also investigated. As a result, student’s academic achievement improved and there were significant changes in the lecture evaluation results. Flipped class also changed the instructor from a knowledge communicator to a learning assistant.&#x0D; Conclusions The role of the College of Education as a teacher training institution is to train preparatory teachers who can teach ‘good classes’. In particular, in order to nurture teachers who can teach ‘good math classes’, it is important to experience ‘good math classes’ through flipped classes at the college.
Malicious URL Detection with Distributed Representation and Deep Learning

There exist numerous solutions to detect malicious URLs based on Natural Language Processing and machine learning technologies. However, there is a lack of comparative analysis among approaches using distributed representation and deep learning. To solve this problem, this paper performs a comparative study on phishing URL detection based on text embedding and deep learning algorithms. Specifically, character-level and word-level embedding were combined to learn the feature representations from the webpage URLs. In addition, three deep learning models, including Convolutional Neural Network (CNN), Bidirectional Gated Recurrent Unit (BiGRU), and Bidirectional Long Short-Term Memory (BiLSTM), were constructed for effective classification of phishing websites. Several experiments were conducted and various evaluation metrics were used to assess the performance of these deep learning models. The findings obtained from the experiments indicated that the combination of the character-level and word-level embedding approach produced better results than the individual text representation methods. Also, the CNN-based model outperformed the other two deep learning algorithms in terms of both detection accuracy and execution time.
Influencing Factors of COVID-19 Health Behavior for Middle and High School Teachers

Purpose: This study was conducted to investigate the influencing factors of COVID-19-related health behavior based on health belief models and supplementing health literacy for middle and high school teachers. Methods: Data was collected with structured self-report online questionnaires from 93 middle and high school teachers in Seoul and the metropolitan area and analyzed with descriptive statistics and multiple regression analysis using SPSS/PC 23.0. Results: From the study results, it was shown that COVID-19 health behavior was significantly influenced by the subjects’ distinguishing factors of education level, and exercise. However, the significant influences of those characteristics were changed to be insignificant when the psychosocial variables were included in the regression model. Among psychosocial factors, perceived benefit, self-efficacy, health social support, and literacy significantly influenced COVID-19 health behavior. Conclusion: The results of this study might be used as a base to develop a health education program for other infectious or pandemic diseases for middle and high school teachers. It is necessary to conduct further studies for various groups at various points in the outbreak of contagious diseases.
Liferay Customization

Portlets are the main component when it comes to creating any portal. Liferay is no different. Themes are developed to give these portlets headers and footers so they have a project-specific look and feel. But what if you have an out-of-box Liferay DXP portlet that matches your requirements, and a minor tweak can make it serve your project-specific business needs? What should you do in that case? To address this scenario, Liferay DXP provides an approach to customize behavior. These customizations range from simple JSP changes and extensions to a level where you can modify the overall behavior of the Liferay DXP portal. This chapter explains how it’s done.
Predicting Integer Overflow Errors via Supervised Learning

An integer overflow error occurs when an integer operation in computer software evaluates a value out of the integer range. It can lead to a fatal system failure. The existing approaches to detecting integer overflow errors rely on data/control-flow analysis of the code or execution of the code with test cases. This paper presents a supervised learning approach to predicting whether each method in a given Java program has an integer overflow error by treating the source code as text. Built upon real-world programs, our Java dataset covers all integer data types and operations in Java, the methods for preventing integer overflow errors, and adversarial samples. We have evaluated six classification models, BERT, DistilBERT, codeBERT, Code2Vec, fastText, and NBSVM. They represent different text embedding techniques for dealing with source code. The experiment results show that BERT and its variants have outperformed other models. We have applied the resultant BERT model to eleven real-world projects, including JDK13.0 and ten top-ranked GitHub projects, and revealed 181 integer overflow errors. In addition, we have evaluated the classification models with a C/C++ dataset. The result is similar to that of the Java dataset.
The Effect of Tutoring Center Instructors' Job Factors on Employment Insecurity: Focusing on the Dual Labor Market Theory

The purpose of this study was to analyze the effect of tutoring center instructors' job factors on employment insecurity. To this end, an empirical analysis was performed on the employment insecurity of tutoring center instructors based on the dual labor market theory by conducting a survey. In order to achieve the research objectives, a survey was performed with 215 tutoring center instructors with more than one year of teaching experience.&#x0D; The results of analyzing are as follows: first, there was no statistically significant difference in the research subjects' employment insecurity according to their personal factors, while a significant difference was found depending on their professional experience and lecture subjects. Second, the job factors of the tutoring center instructors were found to affect their employment insecurity: employment insecurity was higher in instructors with a lower wage, those working at a smaller institution, and those without employment insurance. In other words, tutoring center instructors who work at a small institute for a low wage without social benefits such as employment insurance show higher employment insecurity. This indicates that the dual structure of the job market is also applied to the occupational group of tutoring center instructors. Therefore, it is necessary to pay more attention to tutoring center instructors working at smaller institutions for a lower wage and to provide expanded employment insurance programs for tutoring centers.
The analysis on the Satisfaction of the Career Education in University: Focusing on Students in College of Humanities and Social Sciences

The purpose of this study is to investigate the satisfaction of career education with 231 college students who are studying in college of humanities and social sciences of University A. The main research methodology is as follows. Firstly, the educational satisfaction is composed of overall satisfaction curriculum, instructor, educational method, contents, facilities and environment, and educational service. Secondly, we tested comparative analysis in gender, two colleges, correlation analysis and factor analysis sub-satisfaction factors. Finally, we tested regression for main effects factor for satisfaction.&#x0D; As a result, the satisfaction of career education that was overall 3.62, the satisfaction of instructors was the highest scale at 4.25 and the satisfaction of facility and environment was the lowest scale at 3.67. In terms of gender, female students were satisfied more that male students. The main variables influencing educational satisfaction, in order, that is instructor, contents, and facilities and environment, as well as these factors have explanatory power of three variables was around 53%.
Study on drift identification of distributed beacon nodes in WSN

In order to solve the problems of large routing overhead, low identification accuracy and long identification time of traditional node drift identification methods, a drift identification method of distributed beacon nodes in WSN is proposed. In this paper, the multi-classification detection method is used to detect the distributed beacon nodes in WSN, and the behaviour analysis model of the nodes is constructed. According to the results of node behaviour analysis, the direct neighbour node and INFO are defined, and then a node identification list is constructed to identify the drift information of two hop info nodes, which is forwarded clearly. The distributed beacon node drift identification is realised by the node identification list. Experimental results show that the average routing overhead of this method is 31.8 metric, the recognition accuracy is always more than 90%, and the recognition time is always less than 1.2 s, the practical application effect is good.
Why IT Security Matters

Information technology (IT) security is one of the most ignored parts of IT. IT security should be considered a subset of physical security. When it is, the company is much less likely to ignore IT security processes. You wouldn’t leave the company’s front door unlocked, and IT security should have the same level of attention paid to it.
Scaling Agile Delivery

This chapter covers project management at an organizational level, referred to as Scaled Delivery. It compares project and portfolio management with scaled Agile approaches , including Scaled Agile Framework (SAFe), Large-Scale Scrum (LeSS) , and Scrum@Scale. We discuss the complexities of large-scale project delivery, including enterprise-level prioritization, project and product portfolio management, organizational alignment, and related organizational structures, as well as organizational culture and mindset. We also cover the concept of organizational Agile transformation and successful patterns (pilots, change management models, communities of practice), as well as teamwork and innovation at the enterprise level.
A simple but strong baseline for online continual learning: Repeated Augmented Rehearsal

Online continual learning (OCL) aims to train neural networks incrementally from a non-stationary data stream with a single pass through data. Rehearsal-based methods attempt to approximate the observed input distributions over time with a small memory and revisit them later to avoid forgetting. Despite its strong empirical performance, rehearsal methods still suffer from a poor approximation of the loss landscape of past data with memory samples. This paper revisits the rehearsal dynamics in online settings. We provide theoretical insights on the inherent memory overfitting risk from the viewpoint of biased and dynamic empirical risk minimization, and examine the merits and limits of repeated rehearsal. Inspired by our analysis, a simple and intuitive baseline, Repeated Augmented Rehearsal (RAR), is designed to address the underfitting-overfitting dilemma of online rehearsal. Surprisingly, across four rather different OCL benchmarks, this simple baseline outperforms vanilla rehearsal by 9%-17% and also significantly improves state-of-the-art rehearsal-based methods MIR, ASER, and SCR. We also demonstrate that RAR successfully achieves an accurate approximation of the loss landscape of past data and high-loss ridge aversion in its learning trajectory. Extensive ablation studies are conducted to study the interplay between repeated and augmented rehearsal and reinforcement learning (RL) is applied to dynamically adjust the hyperparameters of RAR to balance the stability-plasticity trade-off online. Code is available at https://github.com/YaqianZhang/RepeatedAugmentedRehearsal