Reviewerly/exHarmony huggingface dataset:
A study analyzing the digital writing perception of university students

Objectives The purpose of this study was to analyze university students' perception of digital writing by gender and major, and also to analyze the factors that influence these differences on digital writing performance.&#x0D; Methods A digital writing recognition survey (9 sub-components, 20 questions) was conducted for 379 first-year students at a university in Busan. Independent sample t-test, one-way ANOVA, correlation analysis, simple linear regression, and multiple regression analysis using SPSS 25.0 version was performed.&#x0D; Results First, in descriptive statistics, the average score of digital writing value and writing anxiety was the highest. Second, as for the gender difference in digital writing recognition, only digital writing anxiety was statistically significant, and the average score of female students was higher than male students. Third, when analyzing the difference in the series of digital writing recognition by item, digital writing performance and digital writing recognition showed statistically significant results, and the average score of humanities and social sciences was higher than that of natural sciences and engineering. Finally, as a result of analyzing the factors affecting digital writing performance, it was found in the order of digital writing efficacy, reader consideration, digital writing value, and digital writing ethics.&#x0D; Conclusions In this study, it was emphasized that it should be considered that there is a difference from traditional writing as the literacy environment changes due to the influence of digital devices and technology. The implications of digital writing education were presented through the analysis of characteristics and perceptions of digital writing. Through the results of this study, it was confirmed that digital writing ethics education for university students is necessary and that it is necessary to strengthen writing performance and writing awareness among science and engineering college students. The biggest influence on digital writing performance was digital writing efficacy, which was consistent with the results of previous studies.
Relating statistical methods to machine learning predictive models

The paper reviewed the probabilistic feature of binomial distribution in the operation of machine learning (ML) classifications. It also examined a normal distribution and the concepts for approximating the binomial distribution to a normal distribution in estimating generalization error and its role in machine learning model selection. Again, it studied the confident interval and hypothesis testing and their estimations in the evaluation and comparison of the Performance metrics (Accuracy) of the learning algorithms. The paper highlighted their statistical significance to the ML models and classifiers as well as the differences in their utilization in statistics and machine learning.
Tsp-Graph for Agglomerative Clustering

Agglomerative clustering, and Ward's method in particular, provides good clustering accuracy for most applications. However, its use has been limited by quadratic time complexity which makes it slow for large datasets. It also has a quadratic memory consumption in the case of non-vectorial data. In this work, we propose a fast variant of Ward's method which reduces the number of distance calculations and only needs O(N) memory. It works by constraining the merges to neighbor nodes on a graph. The graph used in the method is a novel fully connected graph which consists of multiple approximate travelling salesman problem (TSP) solutions. This avoids the problems caused by disconnected graph components which may happen with a kNN graph. The method is general and works for all types of data where a distance function can be defined. For a dataset of size 1.28 million, the proposed method has a speedup factor of 25:1 for identical results with the best exact implementation of Ward's method. The algorithm allows a flexible compromise between clustering result quality and running time. For a moderate degradation in quality (NMI 0.90 to 0.80), the speedup factor improves further to 625:1. The proposed TSP-graph can also be used in other applications where a connected neighborhood graph is needed.
Application of Monte Carlo methods for solving the regular and degenerate problem of two-phase filtration

One of popular mathematical models of filtration is the classical elastic regime model describing the nonstationary equilibrium filtration. It is also called the Muskat-Leverett model. Solving filtration problems by Monte Carlo methods makes it possible to find the solution of the problem at an individual point of the domain and to estimate derivatives of the solution. This paper is devoted to applying algorithms of the Monte Carlo method to problems of filtration. The Monte Carlo algorithms of random walk by spheres and on boundaries are used for solving the stationary problem of filtration of two immiscible inhomogeneous incompressible fluids in a porous medium and for estimating the solution and the derivatives of the solution of this problem.
AMIM: An Adaptive Weighted Multimodal Integration Model for Alzheimer’s Disease Classification

Alzheimer's disease (AD) is an irreversible neurological disorder, so early medical diagnosis is extremely important.Magnetic resonance imaging (MRI) is one of the main medical imaging methods used clinically to detect and diagnose AD.However, most existing computer-aided diagnostic methods only use MRI slices for model architecture design.They ignore informational differences between all slices.In addition, physicians often use multimodal data, such as medical images and clinical information, to diagnose patients.The approach helps physicians to make more accurate judgments.Therefore, we propose an adaptive weighted multimodal integration model (AMIM) for AD classification.The model uses global information images, maximum information slices and clinical information as data inputs for the first time.It adopts adaptive weights integration method for classification.Experimental results show that our model achieves an accuracy of 99.00% for AD versus normal controls (NC), and 82.86% for mild cognitive impairment (MCI) versus NC.The proposed model achieves best classification performance in terms of accuracy, compared with most state-ofthe-art methods.
Application of artificial intelligence technology in the cultivation of English core literacy

This paper is mainly based on the in-depth exploration of English core literacy under artificial intelligence technology, and finds the existing problems and practical application effects from the application of artificial intelligence technology in English core literacy. Artificial intelligence technology uses intelligent processing system to improve the processing speed of English vocabulary and create English teaching scenes. At the same time, in the application of artificial intelligence technology in English, an English task positioning system with artificial intelligence system as the core is formed, and English teaching is carried out by integrating big data dynamic analysis model. Artificial intelligence technology can not only realize the intellectualization of teaching, but also provide students with personalized teaching guidance, quantitatively analyze English professional knowledge, and form an integrated thinking model map to help students master relevant contents faster.
A Survey on the Operation Case and the Course-Type Satisfactory of Curriculum of the Vocational High School Beauty Department about the Introduction of the 2022 Revised Curriculum and the High School Credit system

Objectives This thesis aims at investigating cases of the course-type curriculum in the vocational high school’s beauty department, which is a course-type model with in the department precisely selected from Vocational High school credit system models, followed by the implementation of the 2022 revised curriculum and the high school credit system, and satisfaction surveys.&#x0D; Methods From October to November 2022, a questionnaire was conducted by a total of 160 third-year students in the vocational high school beauty department in Seoul regarding selection of the course-type specific majors, their satisfaction level towards its curriculum and environment, and their preferred occupations. It was further undertaken by 20 teachers who performed the course-type curriculum. The survey results were then used to perform the frequency analysis, descriptive statistics, One-way ANOVA, t-statistics, Chi-square test and One Sample Kolmogorov-Smirnov test.&#x0D; Results The dominant factor upon course-type major selection was personal reason, followed by hair, makeup and skincare reasons, and the hair department students tend to select their majors most precisely. Their satisfaction with the educational environment appeared not to differ by each department, whereas respondents revealed varied level of satisfaction with the course-type curriculum in an order descending of Hair, Makeup and Skincare group. The hair group showed higher interest than others on desire to work in the industry. Teachers’ satisfaction were overall high except for the financial support on curriculum operation.&#x0D; Conclusions Prior to the complete implementation of the high school credit system, the development and coordination of a structured education curriculum have to take place through surveys. We hope that the findings of this study would be applied and assist in implementing and sustaining the course-type curriculum upon full implementation of the credit system. This study suggests follow-up research on comparative cases by different regions and different high school units for further investigating its limitations and management plans.
A Method of Extracting Discipline Inspection Cases Based on Deep Learning

Event extraction is one of the key tasks in information extraction tasks, and it has been widely used in various fields in recent years. In the field of disciplinary inspection and supervision, disciplinary inspection case text has the characteristics of multiple types, multiple professional terms, and strong correlation between event types and event arguments. In the face of huge data, relying on manual analysis by manpower has seriously affected the efficiency of disciplinary inspection. However, there is no corpus in the field of discipline inspection currently available. This article uses BIO annotation to construct a corpus of discipline inspection to lay the foundation for subsequent work. Proposed BERT-BiGRU-CRF event joint extraction model. Use the BERT model to train the discipline inspection corpus, and Combine BiGRU network and CRF network to realize event type recognition and argument extraction. The experimental results show that the model can effectively extract event information in the discipline inspection field. At the same time, in order to better facilitate the work of disciplinary inspection personnel, a disciplinary inspection and supervision event extraction system is constructed to systematically extract all event information contained in the case.
Banking sector intermediation and economic growth: new evidence from CEMAC countries

An abundant body of literature explores the impact of finance on economic growth. However, only a few studies examine this link for developing economies. This paper investigates the banking intermediation-growth nexus with respect to the Central African Economic and Monetary Community (CEMAC). Making use of two panel estimation techniques, we exert the positive influence of banking sector intermediation on long-run growth over the studied period (1990-2016). Despite finding a negative association between financial development proxies and growth, our results suggest that the banking system in CEMAC still performs its main function of pooling and allocating financial resources. The latter is evidenced by the positive association between credit proxies (domestic credit to the private sector and bank credit to bank deposits) and growth/fixed capital formation.
Latent Utility and Permutation Invariance: A Revealed Preference Approach

This paper provides partial identification results for latent utility models that satisfy an invariance property on unobservables such as exchangeability. We employ a simple revealed preference argument to "difference out'' unobservables and show that this gives identifying inequalities for utility indices. We show the differencing argument is also useful for counterfactual analysis. The framework generalizes existing work in discrete choice by allowing latent feasibility sets and by allowing individuals to purchase multiple (possibly continuous) goods. In a panel setting, we innovate by allowing preference for variety. We also present a new framework leveraging nesting structures that generalizes nested logit.
Fast recovery of sparse fringes in unknown freeform surface interferometry

In the adaptive freeform surface interferometer, the adaptive algorithms were equipped to find the required aberration compensation, making interferogram with dark areas (incomplete interferogram) sparse. However, traditional blind search-based algorithms are limited by convergence rate, time consumption, and convenience. As an alternative, we propose an intelligent approach composed of deep learning and ray tracing technology, which can recover sparse fringes from the incomplete interferogram without iterations. Simulations show that the proposed method has only a few seconds time cost with the failure rate less than 4‰. At the same time, the proposed method is easy to perform because it does not require the manual intervention of internal parameters before execution as in traditional algorithms. Finally, the feasibility of the proposed method was validated in the experiment. We believe that this approach is much more promising in the future.
The Role of Edge Computing in Pandemic and Epidemic Situations with Its Solutions

Managing sensory data captured in leveraging is a challenge, especially during a pandemic when trying to capture the psychological, emotional, and physiology standards. The advanced technology of edge computing and IIoMT together help to reach promising outcome results from the home environment using psychological feelings and somatic health equivalent data. The basic application of Deep Learning leads to the asset-constraint of edge computing, which provides a way to move the data that is collected from IIoMT devices to various locations. All kinds of data related to health can exist in a particular place of user edge while assuring the security, privacy, and low latency of the inference system. In this article, an Internet of Medical system is developed that uses Deep Learning to detect risky types of health-related symptoms and generates reports and alerts for pandemic and epidemic situations, which helps in decision-making support. In these pandemic and epidemic situations, a lot of applications have been identified and implemented with their descriptions for the upcoming support for the real-time trials. We have developed smart applications in edge computing manuals. The overall output clearly allows us to view the fixed smart systems during the pandemic with the Smart Health Management system (SHMs).
Analysis of the reported speech patterns for exploring grammar education contents - Focusing on the sentence final ending “-다고” in Internet articles -

이 연구에서는 언어 사용 주체에 의해 이루어지고 있는 인용의 다양한 사용 양상을 ‘오류’가 아닌 ‘변이형’으로 수용함으로써 ‘인용 방식의 변화’ 과정을 탐색하는 일이 인용 표현 교육을 위한 문법 교육의 내용으로 수용되어야 함을 주장하였다. 이에 따라, 이 연구에서는 새로운 인용 표현의 교육 내용을 탐색하기 위한 목적으로 인터넷 포털 기사문의 인용 방식에 주목하여 인용구문에 나타나는 종결형 ‘-다고’ 구문의 사용 양상을 분석하였다. 먼저, 인용이 이루어지는 발화 상황을 고려하여 ‘원청자와 현실 화자가 동일한 경우’와 ‘현실 화자가 원청자로 가정되는 경우’로 유형을 나누고, 각각의 유형에서 확인되는 인용의 종결형 ‘-다고’의 특징적 사용 양상을 인용구문 내포절의 행위 주체가 전면화되는 경우, 피인용문을 통해 핵심 정보가 전면화되는 경우, 인용구문 내포절의 발화 주체가 전면화되는 경우, 피인용문이 담화 내용 전환에 활용되는 경우로 하위 유형화하여 분석하였다. 마지막으로 이 연구에서는 이러한 분석 결과를 바탕으로, 앞으로의 인용 표현 교육에서 다루어야 하는 활동으로 주어진 인터넷 포털 기사문에 나타난 인용의 일차적 발화 상황을 추론해 보는 활동, 인용의 종결형 ‘-다고’ 구문에 주목하여 주어진 인터넷 포털 기사문을 읽어 보는 활동, 인용의 종결형 ‘-다고’ 구문의 실효성을 따져 보고 이 구문이 일반적인 인용 표현으로 사용될 가능성이 있는지를 타진해 보는 활동을 제안하였다. This study argued that exploring the process of “change in reported speech patterns” should be accepted as the new content in grammar education. Furthermore, this study analyzed the use of a sentence final ending “-다고” in the reported speech phrase. Internet newspapers are used for exploring the grammar educational content of the new citation expression. The analysis embodied in this study is summarized as follows: First, where quotative clauses are made, the sentence types were divided into two categories, “the original listener and the real speaker are the same” and “the real speaker is assumed to be the original listener”. Second, the characteristic use of the sentence final ending “-다고” identified in each of these types was subtyped into i) when the actor of the quotative clause was revealed, ii) when the speaker was revealed in the quotative clause, and iii) when the quotative clause helped convert the discourse. Finally, based on these analysis results, this study proposed activities to be dealt with in future citation expression education.
"Fantastic" prospects for educational strategies and social expectations of the future in the context of the development of schoolchildren's research activities

The article shows that the development of a gifted personality depends on a number of factors of cultural determination. The global dynamics of social transformations, high social mobility, and intensive communication force us to constantly rethink the changing reality, overcoming the situation of uncertainty and discomfort. Exploratory behavior is a necessary condition for the development of the potential of an innovative personality, since it is directly related to the semantic processes. The highest form of cognitive activity and exploratory behavior is scientific cognition. That is why the introduction of project-research technology into modern school education is effective for solving the problems of educating a gifted individual. This article is devoted to the issue of development and transformation of the space of personal meanings in research activities. Its purpose is to analyze the impact of the research activities of a teacher and a student on the processes of rethinking (meaning formation, meaning generation, meaning change, meaning creativity) both in discovering knowledge for oneself and in self-construction of the hierarchy of personal meanings. This process is connected with the issue of self-determination and constant creative renewal of the vision of one's own life.
How Reusing Trained Machine Learning Models Accelerates and Improves the Work of Operational Geoscientists

We show examples of trained machine learning models that are applied ‘AS IS’ to solve similar problems on unseen datasets. A huge benefit of this methodology is the speed of application. Operational geoscientists simply select a model from a library of trained models and apply this to their data. Within minutes they have a result that they can either use, or discard. In many cases, the alternative solution involves expensive and time-consuming reprocessing efforts. We show examples of detecting features, enhancing data, and predicting missing data.
New Core Competencies due to Digitize Business·Account·Affair Job and Retraining Programs Adaptive to Future

Objectives To predict job changes in management, accounting, and office jobs due to digital economy, and to select future core competencies to develop job digitalization retraining programs.&#x0D; Methods Following Gast &amp; Vaughn(2012)’s Strategic Job Analysis, consisted of 3 procedure and 6 small steps. It focuses on anticipating performance requirement from changes in technology, organizational structure, organizational functions. As the aim of this study was to predict occupation in transition and to make workers adaptive to new mission, this methods perfectly match to the study purpose. It is used in study scenes of a large insurance company and US Army Select 21 etc.&#x0D; Results NCS and 2018 HEF foundational digital skill was used as a survey indicating 5 likert scale, key results were that importance of management planning and fund maintained at 4 points or more, in contrast, management evaluation decreased under 4 point. Planning and decision making task was maintained especially marketing planning close to 4, and typical support duties like general affairs personnel was down, the overall importance was forecasted to decrease because of digitalization spread out most organizational member job. Experts emphasized employee should be try more professional and upgrade self management ability as much as working system flex. and offered 3 level educational program and opinions on some useful contents and digital tools worth to learn.&#x0D; Conclusions Digital data utilization ability, job crafting, value-oriented planning ability, and context-based communication ability were finally presented as future core competencies, and developed four job retraining programs. The concept of situational learning, organizational dynamic capability will be needed to include in educational course as background theoretic concept, tried to overcome the problem of main competence concept vague and setback of member’s resistance on job change.
2-D Deep Convolutional Neural Network for Predicting the Intensity of Seismic Events

Machine learning has advanced rapidly in the last decade, promising to significantly change and improve the function of big data analysis in a variety of fields.When compared to traditional methods, machine learning provides significant advantages in complex problem solving, computing performance, uncertainty propagation and handling, and decision support.In this paper, we present a novel end-to-end strategy for improving the overall accuracy of earthquake detection by simultaneously improving each step of the detection pipeline.In addition, we propose a Conv2D convolutional neural network (CNN) architecture for processing seismic waveforms collected across a geophysical system.The proposed Conv2D method for earthquake detection was compared to various machine-learning approaches and state-of-the-art methods.All of the methods used were trained and tested on real data collected in Kazakhstan over the last 97 years, from 1906 to 2022.The proposed model outperformed the other models with accuracy, precision, recall, and f-score scores of 63%, 82.4%, 62.7%, and 83%, respectively.Based on the results, it is possible to conclude that the proposed Conv2D model is useful for predicting realworld earthquakes in seismic zones.
Quantum density peak clustering

Abstract Clustering algorithms are of fundamental importance when dealing with large unstructured datasets and discovering new patterns and correlations therein, with applications ranging from scientific research to medical imaging and marketing analysis. In this work, we introduce a quantum version of the density peak clustering algorithm, built upon a quantum routine for minimum finding. We prove a quantum speedup for a decision version of density peak clustering depending on the structure of the dataset. Specifically, the speedup is dependent on the heights of the trees of the induced graph of nearest-highers, i.e. the graph of connections to the nearest elements with higher density. We discuss this condition, showing that our algorithm is particularly suitable for high-dimensional datasets. Finally, we benchmark our proposal with a toy problem on a real quantum device.