Reviewerly/exHarmony huggingface dataset:
관광 관련 전공자들의 메타인지가 전공학습몰입과 진로결정자기효능감에 미치는 영향 연구

The purpose of this study was to analyze the effect of meta-cognition on the relationship between tourism-related majors students on major learning commitment and career decision-making self-efficacy, to verify the mediating effect of major learning commitment, and to provide basic academic materials for enhancing competency in education by analyzing the difference between the two groups of males and females through a t-test. The study was conducted from January 1st to January 31st, 2021 using SPSS V. 21.0 on 272 tourism related major students in Korea. The results of the study showed that meta-cognition had a significant positive (+) effect on major learning commitment and career decision-making self-efficacy, and major learning commitment had a mediating effect, and as a result of analyzing the differences between the groups of males and females, there was a difference. According to these results, it can be determined that effective learning strategies are required to increase career opportunities and choices. This can be done by increasing the levels of academic achievement through the necessity of pre-learning and motivation for tourism related major students.
Data Augmentation with Unsupervised Machine Translation Improves the Structural Similarity of Cross-lingual Word Embeddings

Unsupervised cross-lingual word embedding (CLWE) methods learn a linear transformation matrix that maps two monolingual embedding spaces that are separately trained with monolingual corpora. This method relies on the assumption that the two embedding spaces are structurally similar, which does not necessarily hold true in general. In this paper, we argue that using a pseudo-parallel corpus generated by an unsupervised machine translation model facilitates the structural similarity of the two embedding spaces and improves the quality of CLWEs in the unsupervised mapping method. We show that our approach outperforms other alternative approaches given the same amount of data, and, through detailed analysis, we show that data augmentation with the pseudo data from unsupervised machine translation is especially effective for mapping-based CLWEs because (1) the pseudo data makes the source and target corpora (partially) parallel; (2) the pseudo data contains information on the original language that helps to learn similar embedding spaces between the source and target languages.
Combinatorial necessary conditions for regular graphs to induce periodic quantum walks

We derive combinatorial necessary conditions for discrete-time quantum walks defined by regular mixed graphs to be periodic. If the quantum walk is periodic, all the eigenvalues of the time evolution matrices must be algebraic integers. Focusing on this, we explore which ring the coefficients of the characteristic polynomials should belong to. On the other hand, the coefficients of the characteristic polynomials of $η$-Hermitian adjacency matrices have combinatorial implications. From these, we can find combinatorial implications in the coefficients of the characteristic polynomials of the time evolution matrices, and thus derive combinatorial necessary conditions for mixed graphs to be periodic. For example, if a $k$-regular mixed graph with $n$ vertices is periodic, then $2n/k$ must be an integer. As an application of this work, we determine periodicity of mixed complete graphs and mixed graphs with a prime number of vertices.
Sentiment Analysis for E-Commerce Products Using Natural Language Processing

Sentiment analysis is one of the ways to evaluate the attitude of consumers towards products and services. E-commerce businesses have grown to a larger level in recent years. Customers' opinions and preferences are collected to analyze them further to boost online businesses. Collecting real-time structured and unstructured data and performing sentiment analysis on them are challenging and need to be addressed. We have used PySpark, and resilient distributed dataset (RDD) based sentiment analysis using Spark NLP to address scalability and availability issues in sentiment analysis on the e-commerce platform. We have also used FLASK-based Restful APIs and Scrapy for web scrapping to collect useful data from an e-commerce site. Our findings indicate that the proposed method of Natural Language Processing (NLP) for e-commerce products in real-time has enhanced efficiency in terms of scalability, availability, and faster data collection
PEDAGOGICAL TECHNOLOGIES - AS A FACTOR TO INCREASE STUDENT KNOWLEDGE IN SCHOOL TECHNOLOGY CLASSES

The article highlights the importance of shaping students' creative abilities in school technology classes through the use of pedagogical technologies.
Research Guides: Education: Early Childhood & Elementary Education

Guide to research in education, including early childhood, elementary, secondary, and higher education.
Portable health clinic: concept, design, implementation and challenges

Portable health clinic (PHC) is a remote healthcare system, jointly developed by Grameen Communications in Bangladesh and Kyushu University in Japan with the aim to providing affordable primary healthcare services to people who do not have regular access to healthcare facilities including the low-income population living in rural unreached communities. This chapter introduces the basic concept and architecture of PHC; status of different pilot projects, findings, and observations; the technical and social challenges the projects faced during the implementation.
The Communicative Behavior Of Generation Z In The Educational Environment

This article is devoted to the main socio-psychological characteristics of generation Z, which determine the features of their communicative behavior in the educational environment, including the higher education system.Obviously, education acts as an instrument for the translation of knowledge and values, formation of identity; when starting the educational process, an individual not only learns a certain set of knowledge and skills, but also masters the rules of the cognitive process, determined by the values of the particular society.At the same time, nowadays people, who were born after 2000 and have obviously different requests for the organization and content of the educational process, enter schools and universities.The research showed that the main features of the communication practices, common for generation Z, are mobility, accessibility, efficiency, performance, excitement and easiness.The article presents the analysis of methods for overcoming the contradictions between the "classical" educational technologies that have proved their effectiveness at the previous stage, used by the present generation of teachers, and the features of cognitive and research activities of generation Z.There comes the conclusion, that the focus on technological innovations (equipment modernization) only and transition to "online" are unable to solve this problem properly.It is necessary to use educational methods and technologies that combine the achievements of older generations and the needs of future ones.
OVERVIEW OF THE MODERN EDUCATION SYSTEM AND INTELLIGENT TECHNOLOGIES USED FOR TRAINING GRADUATES

The authors conduct a brief overview of Russian education system from a perspective of the ongoing changes that take place due to the spread of intelligent technologies. The main idea of the study is analyzing the present state of Russian education system and revealing the degree of its relevance to the changes taking place on labor market. The research aim is study the possible consequences of the penetration of artificial intelligence (AI) into educational activity, and to study the response of the education system to the introduction of AI. In the study, the authors base on using quantitative and qualitative methods, retrospective, structural-functional and systemic approaches. The work has a high degree of novelty reflected in the evaluation of the influence of intelligent technologies on educational process and possible consequences of their application when preparing the personnel for modern economy. The article also discusses the possibilities of achieving harmonization of the information technologies used, both in the educational process and in the design of training programs. The research concludes that it is necessary to develop and test methods of decision making support in the educational process based on the results of data analysis on the implementation of intelligent technologies. Attention is also drawn to the fact that the next change in the education system is associated either with the arrival of new technologies or with a change in political views. Along with it, each subsequent period is shorter than the preceding one. Nowadays, in the era of “digitalization”, a radical change in the education system is possible according to the model of creating a single digital educational portal. The authors believe that this period will last no more than 5–7 years, taking into account a cyclic nature of the system's change. Such a short period of time will not allow to adjust a new system, moreover, it will ruin the old one. Therefore, the authors emphasize that "digitalization" should not be aimed at fundamental changes of the present system. The existing education system already has all the necessary tools for introducing various technologies into the educational process. It is proved by the fact that during the 2020 pandemic, existing intelligent technologies have became irreplaceable assistants in personnel training. Based on the foregoing, the authors conclude that the current model of the Russian education system is quite functional, but requires some adjustment due to the growing importance of intelligent technologies.
Penerapan Metode Question Student Have Untuk Meningkatkan Aktivitas Belajar Peserta Didik

The background this research is the low learning activity of students, especially in questioning skills in the learning process. This study aims to determine whether there is an increase in the learning activities of students after applying the question student have method. This research is a Classroom Action Research (CAR). The sample of this research was the fourth grade students of the 2018/2019 academic year with 20 students. This research was completed in 2 cycles consisting of four stages, namely planning, acting, observing, and reflecting. The data collection instruments used were observation and documentation. The results showed that using the question student have method can improve the learning activities of students in mathematics in fourth grade of Elementary School Pahlawan. The activity level of students before the question student have method was applied was 54.28% in the low category. After applying the question student have method in Cycle I, there was an increase of 61.07% in the middle category, and in Cycle II there was an increase of 80.72% in the high category. Based on the results of this study, it can be concluded that the application of the question student have method can improve the learning activities of students in mathematics.
Gold mineralization occurrences of Saqez–Baneh zone (north Sanandaj-Sirjan, Iran): structural settings in a microscopic scale

The Sanandaj-Sirjan Zone (SSZ) is the metamorphic-magmatic core of Zagros Orogen in Iran. The SSZ has produced several styles of gold deposits during the Phanerozoic eon. The Saqez–Sardasht zone includes a NE-trending goldfield belt, located at the northern SSZ. This area, embodying the main orogenic gold deposits (e. g. Qolqoleh, Kervian, Qabaqhlujeh, Hamzeh Gharanain, Mirgeh Naghshineh) was chosen for this research in order to study the relationship between deformation phases and gold-accompanying minerals in a microscopic scale. It is demonstrated that the study area has experienced 2 ductile phases, 1 semi-ductile phase and 1 brittle phase of deformation, each associated with the shear zone progression. Gold mineralization via each deformation phase is relevant with the precipitation of new pyrites and shearing/rotating of old pyrites.
Bh\=a$\unicode{x1E63}$\=acitra: Visualising the dialect geography of South Asia

We present Bhā$\unicode{x1E63}$ācitra, a dialect mapping system for South Asia built on a database of linguistic studies of languages of the region annotated for topic and location data. We analyse language coverage and look towards applications to typology by visualising example datasets. The application is not only meant to be useful for feature mapping, but also serves as a new kind of interactive bibliography for linguists of South Asian languages.
On the Language Coverage Bias for Neural Machine Translation

Language coverage bias, which indicates the content-dependent differences between sentence pairs originating from the source and target languages, is important for neural machine translation (NMT) because the target-original training data is not well exploited in current practice. By carefully designing experiments, we provide comprehensive analyses of the language coverage bias in the training data, and find that using only the source-original data achieves comparable performance with using full training data. Based on these observations, we further propose two simple and effective approaches to alleviate the language coverage bias problem through explicitly distinguishing between the source- and target-original training data, which consistently improve the performance over strong baselines on six WMT20 translation tasks. Complementary to the translationese effect, language coverage bias provides another explanation for the performance drop caused by back-translation. We also apply our approach to both back- and forward-translation and find that mitigating the language coverage bias can improve the performance of both the two representative data augmentation methods and their tagged variants.
Identification Of Drug Addiction By Implementation Of An Expert System Using Machine Learning Algorithm

The key enabler of all the diseases is drug addiction. The individual who uses drugs is dependent upon different ailments. Only few people refuse to take drugs, while many of the teens are curious about using drugs.   The reason is to build up a specialist framework (Expert system) that can anticipate whether the individual is dependent on drugs or not. In Artificial intelligence, an expert system is a specialist in logic, development and management, capable of advising, instructing man in the prize of the decision, of the demonstration, of the explanation, of the results and of the conclusion. It uses reasoning procedures to solve difficult problems by using a set of complex mathematical equations. Expert systems are understandable and highly sensitive.The Expert system is utilized in numerous fields, for example, finance, agriculture, education, computer design, and so on. But medicine is the most mainstream field that assists specialists with forestalling and fix of powerless sicknesses early, for example, heart diseases, skin cancer, lung disease, diabetes and some more. Here an Expert system is created utilizing the decision tree algorithm ID3, which is a psychological test that contains in excess of 65,000 guidelines, which separates the low, moderate and extreme condition of the client dependent on the test in which to react.
Manifold-constrained Gaussian process inference for time-varying parameters in dynamic systems

Abstract Identification of parameters in ordinary differential equations (ODEs) is an important and challenging task when modeling dynamic systems in biomedical research and other scientific areas, especially with the presence of time-varying parameters. This article proposes a fast and accurate method, TVMAGI (Time-Varying MAnifold-constrained Gaussian process Inference), to estimate both time-constant and time-varying parameters in the ODE using noisy and sparse observation data. TVMAGI imposes a Gaussian process model over the time series of system components as well as time-varying parameters, and restricts the derivative process to satisfy ODE conditions. Consequently, TVMAGI completely bypasses numerical integration and achieves substantial savings in computation time. By incorporating the ODE structures through manifold constraints, TVMAGI enjoys a principled statistical construct under the Bayesian paradigm, which further enables it to handle systems with missing data or unobserved components. The Gaussian process prior also alleviates the identifiability issue often associated with the time-varying parameters in ODE. Unlike existing approaches, TVMAGI can be applied to general nonlinear systems without specific structural assumptions. Three simulation examples, including an infectious disease compartmental model, are provided to illustrate the robustness and efficiency of our method compared with numerical integration and Bayesian filtering methods.
Community policing in Nigeria: A mere rhetoric than practical

This paper is designed to investigate into the various reasons to incorporate members of the community in Nigeria in the business of crime control thereby making them to be part and parcel of security outfit in their environs. This is premised on the grounds that with the involvement of the indigenes of the community in crime control and social disorder it will go a long way in checkmating crime. The paper will also dissect the reasons for introducing community policing and its benefits in creating an environment where crime cannot flourish. It is also expected to x-ray lapses in the current policing structure in Nigeria and why community policing will be like pouring water on a rocky ground. Finally, it will expose some salient areas to be addressed by the police in order to create a fertile ground for community policing to thrive.
Multilingual Neural Semantic Parsing for Low-Resourced Languages

Multilingual semantic parsing is a cost-effective method that allows a single model to understand different languages. However, researchers face a great imbalance of availability of training data, with English being resource rich, and other languages having much less data. To tackle the data limitation problem, we propose using machine translation to bootstrap multilingual training data from the more abundant English data. To compensate for the data quality of machine translated training data, we utilize transfer learning from pretrained multilingual encoders to further improve the model. To evaluate our multilingual models on human-written sentences as opposed to machine translated ones, we introduce a new multilingual semantic parsing dataset in English, Italian and Japanese based on the Facebook Task Oriented Parsing (TOP) dataset. We show that joint multilingual training with pretrained encoders substantially outperforms our baselines on the TOP dataset and outperforms the state-of-the-art model on the public NLMaps dataset. We also establish a new baseline for zero-shot learning on the TOP dataset. We find that a semantic parser trained only on English data achieves a zero-shot performance of 44.9% exact-match accuracy on Italian sentences.
Axiological approach to the psychological and pedagogical training of future teachers of the new generation

The role of axiological approach in the training of new generation teachers is theoretically substantiated in this article. The axiological approach makes it possible to identify a set of priority values in the education, upbringing and self-development of a person in modern society and to design their reflection in the norms and aims of future teachers in the process of psychological and pedagogical training. Possible ways of implementing an axiological approach in the educational process of higher education institutions are outlined, such as: the identification of value orientations of higher education applicants, with further adjustment regarding ethical positions in psychological and pedagogical activity; informing students of higher education about value orientations and identification of the most important ones in teaching the cycle of psychological and pedagogical disciplines; adaptation of higher education students to the system of professional value orientations through educational, practically-oriented, independent activity and interaction with teachers as carriers of moral principles and others. Thus, 45.83% of respondents found: having ethical knowledge about generally accepted norms and principles of pedagogical morality; mastering the basic requirements of pedagogical ethics; ability to act positively according to moral choice; the need to work independently to improve themselves and improve their moral qualities.
Correction to: Analyzing outliers activity from the time-series transaction pattern of bitcoin blockchain

A correction to this paper has been published: https://doi.org/10.1007/s40844-021-00215-3
A Journal Article Selection based on a Combination of Scanning and Skimming Techniques

Typically, an academic writing style of journals is complicated because those journals use complex vocabularies and complex sentences. It might be hard for students to read documents in detail in short period of time. Therefore, this research proposes a Journal Article Selection (JAS) based on a combination of scanning and skimming techniques to reduce number of documents sufficient to support unskilled readers. JAS is developed by python on Google Colaboratory (Bisong, 2019). The designed JAS is assessed in three aspects which are the optimal number of topics and subtopics by considering the coherence value and the relevant terms within topic, the correctness of model and the percentage of the document reduction. The experimental results revealed that there are three layers of topic model: Medical, Business Law and Computer are in layer 1, Social Network and Information System are in layer 2, and Process Model, General Problem and Artificial Intelligence are in layer 3. On other aspects, the proposed model is achieved in 77.36 average percentage of F-measure and 94.15 average percentage of unnecessary document reduction. In conclusion, it can be concluded that JAS can reduce documents sufficient for readers to read in short period of time.Keywords: Journal Article Selection, Topic Modeling, Scanning, Skimming
FMEA를 활용한 뒷문거래 개선모델 개발

This paper proposes the FMEA-based model to avoid backdoor transactions when purchasers select suppliers for products and services. In the model, backdoor transactions consist of two categories: backdoor selling and maverick buying. Both of which influence negative effects on cost savings due to not only uncompetitive advantage but also unusable purchasing leverage by unethical and misbehavior of purchase requestors. For the risk evaluation based on FMEA, three and five risk types of backdoor selling and maverick buying are identified respectively. Current risk priority numbers(RPN) based on those risk types are calculated by three categories: occurrence, detection and severity. Six risk mitigation strategies and fourteen mitigation tactics are identified to improve current RPN. In order to validate the model, questionnaires are collected from fifteen companies and statistically analyzed. The analysis result shows that the model reduces backdoor transaction risks and has no differences in reduction of backdoor transaction risks regardless of the type of purchasing organization units and existence of purchasing procedures in the organization.