Reviewerly/exHarmony huggingface dataset:
THE DISTRIBUTION PATTERN OF NEW STUDENTS ADMISSIONS USING THE K-MEANS CLUSTERING ALGORITHM

The characteristics of prospective new students who re-register can produce a distribution pattern of new student admissions according to the data obtained. The results of the distribution pattern can be used as material for decision making in determining the socialization and promotion of study programs at universities. To produce a pattern, it requires grouping based on data that has the same character. The k-means algorithm, which is an algorithm in data mining processing methods for clustering, is successful in classifying new student admissions by maximizing the similarity of characteristics between data in one cluster which is different from data in other clusters. This is evidenced by direct application based on data mining procedures to data on new student admissions for Engineering Faculty, Manado State University for 2018, 2019, and 2020 with 8 selected variables. After clustering using the RStudio application, Data 2018 generates 6 clusters, 2019 data generates 3 clusters, and 2020 data generates 4 clusters. There are some re-registration characteristics that are common to all three years, which can be read easily after clustering.
ИЗВЛЕЧЕНИЕ ОТНОШЕНИЙ КЛИНИЧЕСКИХ ТЕКСТОВ

Digital modernization of the healthcare system is a global trend in the development of the industry; it is associated with the possibility of digitizing patient healthcare data. The accumulated data may be processed and analyzed to improve patient care and diagnosis. This paper proposes an approach to structuring medical text notes. A framework for relation extraction is proposed for notes by clinicians, as well as numerical experiments to construct a model of the semantic parser.
Research Guides: Introduction to eBooks: EBook Central Accessibility

This guide includes links to the Carl B.'s eBook collections and answers to eBook frequently asked questions.
A Rising Tide Lifts All Boats? Quality Correlation between Human Translation and Machine Assisted Translation

Does the human who produces the best translation without Machine Translation (MT) also produce the best translation with the assistance of MT? Our empirical study has found a strong correlation between the quality of pure human translation (HT) and that of machine-assisted translation (MAT) produced by the same translator (Pearson correlation coefficient 0.85, p=0.007). Data from the study also indicates a more concentrated distribution of the MAT quality scores than that of the HT scores. Additional insights will also be discussed during the presentation. This study has two prominent features: the participation of professional translators (mostly ATA members, English-into-Chinese) as subjects, and the rigorous quality evaluation by multiple professional translators (all ATA certified) using ATA’s time-tested certification exam grading metrics. Despite a major limitation in sample size, our findings provide a strong indication of correlation between HT and MAT quality, adding to the body of evidence in support of further studies on larger scales.
Explainability and the fourth AI revolution

This chapter discusses AI from the prism of an automated process for the organization of data, and exemplifies the role that explainability has to play in moving from the current generation of AI systems to the next one, where the role of humans is lifted from that of data annotators working for the AI systems to that of collaborators working with the AI systems.
EFFECT OF VOLATILITY OF THE UNDERLYING SPOT MARKET ON DEPTH OF THE NASCENT KENYAN DERIVATIVES MARKET

Derivatives market in Kenya is at its nascent stage which raises doubt on its depth in boththe short term and long term. NSE kicked off futures trading on 4th July 2019 which ischaracterized by very low volumes, a clear indication of shallow market which is at itsnascent stage. The study identified single stock futures and equity index futures as theinstruments traded in derivatives market in Kenya. The study explored the effect ofvolatility of the underlying spot market on the depth of the nascent derivative market inKenya. The study used Nairobi Security Exchange price list and contracts data ranging from4th July 2018 to 31st January 2020. The study employed descriptive statistics and inferentialstatistics namely granger causality tests, Johansen Cointegration and Autoregressivedistributed lags using Stata version 16. The results revealed that volatility of the underlyingspot market has a negative but statistically insignificant effect on single stock futures marketdepth. However, volatility of the underlying spot market has a positive but statisticallyinsignificant effect on equity index futures market depth. The study concluded that atnascent stage, volatility does not affect the derivatives market depth. Therefore, CapitalMarkets Authority and Nairobi Security Exchange should focus on other factors that canaccelerate the derivatives market depth such as introduction of new derivatives products.
Connotative Meaning In The Proverbs Of The Batak Angkola Language

This research was aimed to describe the connotative meaning in the proverbs of the Batak Language which used by people in Batak tribe who most live in North Sumatera. The writer formulates the problem of this research is how the connotative meaning in the proverbs of Batak language? The data of this research was the proverbs of Batak language which was taken from Hatobangon who used proverbs in cultural ceremonies in Tandihat Baru village region of Tapanuli Selatan, North Sumatera. The method of this research was descriptive qualitative in order to analyze the semantic connotative meaning of proverbs in Batak language. The results of this research showed that that the connotative meaning in the proverbs of Batak Language is having differences to denotative ones and its meaning depends on personal experience of Batak people experience and mostly relate the meaning of its proverbs to human life such as responsibility, hard work, social concern, and patience.
HUBUNGAN SELF EFFICACY TERHADAP PENINGKATAN MOTIVASI BELAJAR DAN HASIL BELAJAR MATEMATIKA SISWA DI SEKOLAH

Mathematics as one of the most important subjects in life, knowledge of mathematics is very supportive of every activity that occurs in everyday life, so mathematics is one of the most important subjects to be mastered by students at school, but in fact mathematics is a subject which students dislike so much that it has a negative impact on the quality of student learning outcomes in schools. low mathematics learning outcomes in schools can be caused by low desire, students' self-confidence in mastering mathematics lessons, low motivation in students to be one of the triggers for low learning outcomes achieved. Students' self-efficacy (self-confidence) and student learning motivation are believed to have a role in improving the quality of students' mathematics learning outcomes at school. Therefore, in this study, researchers examined the relationship between self-efficacy and increased learning motivation and student learning outcomes in mathematics at school. The research was conducted on students at Madrasah Tsanawiyah Negeri 13, South Jakarta, the research sample was 65 students, the data were collected using an instrument questionnaire and analyzed by correlation and regression analysis. From the results of the study, it was found that self-efficacy had a positive relationship to students' learning motivation and mathematics learning outcomes at school, as well as learning motivation to have a positive relationship to students' mathematics learning outcomes at school. Thus, in improving students' mathematics learning outcomes, it is necessary to emphasize supervision, attention and appropriate strategies to improve students' self-efficacy and student motivation.
THE ROLE OF TEACHERS IN STIMULATING EARLY LITERACY DEVELOPMENT IN CHILDREN AGED 5-6 YEARS

Reading and writing skills are an important part of early childhood education. Early childhood educators must play a role in stimulating these skills, because the challenge for educators is currently faced with the problem of calistung in Kindergarten. Demands are currently emerging from the elementary school level that Kindergarten children after graduating from kindergarten must have these skills. Therefore, there are many studies in developed countries regarding the importance of early literacy in early childhood. Even so, teachers play an important role in stimulating the development of early literacy in children, but in fact the knowledge of teachers about early literacy is very low in many studies. Therefore, this study aims to determine the role that teachers have applied in the development of early literacy for children aged 5-6 years. The results of the questionnaire show that the teacher's role in the development of early literacy for children aged 5-6 years shows a percentage of 63.32%, which is included in the fairly involved category .
I often think about Leaving my organization

Hiring the best talent in the industry comes with a cost, nurturing them, developing them and keeping them motivated and happy doesn’t guarantee that an employee would stay in the organization for years to come. The competition is never ending putting pressure on the organizations to match the global standard, which puts undue pressure and expectation to perform above and beyond their KRAs. Which leads to job dissatisfaction because of work stress and performance pressure. Also, when the employee is skilled and talented, His expectation from the job increases and He starts looking for better salary, better designation, location, higher responsibility, bigger team, benefits and perks and the list go on. Other reasons can be work environment, organization culture, organizational conflict etc. It becomes imperative for an organization to investigate the causes and design strategies to reduce Turnover Intention. The purpose of the study is an attempt to summarize factors causing intention turnover and suggesting strategies to handle turnover and to determine and suggest various possible strategies that can help to minimize the employee turnover. Hence, the paper has proposed a conceptual framework that shows the major variables that contributes to employee turnover and strategies that can help to retain an employee.
Braille Detection Application Using Gabor Wavelet and Support Vector Machine

Difference any exchanging of several kinds information such visual, printed or written form between impairments vision or blind with normal will cause problem especially in written form. For instance in the assistance of a blind child by family with normal vision. One of family role is education to help their children to study and understand about their learning development from home, particularly for blind children. In this study, braille letters can be identified through images obtained using a scanner to help parents and families of a blind child in learning assistance by implementing the Gabor Wavelet feature extraction method. The features used are standard deviation, mean, variance, and median with theta angles of 00,300,450,600,1200,1350,1800 and wavelengths 3,6,13,28, and 58. These features will be combined and used as input as test data and training data. at the Support Vector Machine (SVM) classification stage and generates words in the alphabet. The braille letters detected in this study were small braille letters, capital braille letters, punctuation marks, and numbers. The test is carried out using a multi-class confusion matrix scenario to determine the level of accuracy, precision, and recall. Based on the results of tests carried out using 758 braille data, the accuracy value is 98.15%, the precision value is 97.66% and the recall value is 98.28%. From these results it can be concluded that the Gabor Wavelet feature extraction method and the Support Vector Machine (SVM) can be used to identify braille letters.
MENINGKATKAN HASIL BELAJAR MATEMATIKA PADA MATERI PECAHAN MELALUI MODEL PEMBELAJARAN KOOPERATIF TIPE TALKING STICKDIKELAS III B SD NEGERI 066652 BAKTI LUHUR MEDAN

This research is a classroom action research (Classroom Action Reseach), using the talking stick cooperative learning model can improve student learning outcomes in Mathematics subject matter in class IIIB Elementary School 066652 Bakti Luhur T.A 2018/2019.Every cycle there are two meetings. Before applying the research action, pre-cycle activities were carried out with the value of student learning outcomes completeness level is 0% with an average value of 40.5%. Student learning outcomes in the first cycle, the level of disability becomes 55% with an average value of 60.5%. Then the results of student learning cycle II, the level of completeness to 100% with an average value of 86.5%. Percentage of student cycle I participation was 77.50%, Cycle II was 86.25%. In the percentage of activities of teachers of Siklius I was 76.92% and Cycle II was 82.69%.
Contribution of Collaborative Filtering Approach on Environmental Big Data Analytics Context

In recent years and following the spread of internet using, the way of life in our society as well as the management of the main production sectors have changed towards digitalization (E-Commerce, E-Surgery, road traffic management, etc.) to minimize the. This change challenged researchers and organizations to come up with solutions to manage, maintain, classify and make the best decision from the gigantic flows of data created daily while reducing environmental Damage (Consumption of Energical resources….), From which was born the Collaborative filtering approach which facilitate decision making based on the experience feedback of Internet users. This approach is used generally the big Data Analytics Algorithm to predict and classify data
Exploring Non-Autoregressive Text Style Transfer.

In this paper, we explore Non-AutoRegressive (NAR) decoding for unsupervised text style transfer. We first propose a base NAR model by directly adapting the common training scheme from its AutoRegressive (AR) counterpart. Despite the faster inference speed over the AR model, this NAR model sacrifices its transfer performance due to the lack of conditional dependence between output tokens. To this end, we investigate three techniques, i.e., knowledge distillation, contrastive learning, and iterative decoding, for performance enhancement. Experimental results on two benchmark datasets suggest that, although the base NAR model is generally inferior to AR decoding, their performance gap can be clearly narrowed when empowering NAR decoding with knowledge distillation, contrastive learning, and iterative decoding.
EFFORTS TO GROW LITERATURE DURING THE COVID-19 PANDEMIC THROUGH LEARNING ASSISTANCE AND READING CORNER FOR CHILDREN IN SOKA HAMLET

Condition of literacy in Indonesia is less. The existence of a pandemic that has been running for almost two years has a fatal impact, especially in terms of education. One of the effects is that school literacy programs become less than optimal. This is felt by the children in Dusun Soka. Therefore, KKN 105 UIN Sunan Kalijaga group 5 provides learning tutoring program facilities and reading corners for the children of Dusun Soka. This program is an effort to foster literacy in children during online school period. This research was conducted using qualitative methods. Data retrieval is done with observations, interviews, and documentation to strengthen the data generated during the study. The efforts made in this study are to provide learning tutoring and reading corners for children in Dusun Soka. The results of this study showed that the interest in learning and reading of Soka children increased by marked changes in reading habits in their daily lives and high enthusiasm for children in participating in learning mentoring programs. These programs include Tuesday Storytelling, computer classes, and others.
A Study on improvement of curriculum for music education in elementary, middle and high schools: Focusing on statistical comparative analysis and meta-analysis

The purpose of this study is to analyze the contents of the current 2015 revised music curriculum in Korea and to present the contents of the music to be improved. Research methods were documentary research, Delphi investigations, and FGI. The comparative criteria for documentary research were the music curriculum of 2009 that was previously revised in Korea and the content of music that was recently discussed in the OECD. The Delphi survey and FGI also included a total of 20 music education professors and teachers. As a result of analysis, first, in the future, Korean music curriculum should include various music cultures from around the world and their contributions. Second, ethical content related to music should be added, and third, music content that develops creative skills and thinking skills in the music curriculum must be additionally included.
Neural Network-Based Generation of Sport Summaries: a Preliminary Study

This paper presents a global summarization method for live sport commentaries for which we have a human-written summary available.This method is based on a neural generative summarizer.The amount of data available for training is limited compared to corpora commonly used by neural summarizers.We propose to help the summarizer to learn from a limited amount of data by limiting the entropy of the input texts.This step is performed by a classification into categories derived by a detailed analysis of the human-written summaries.We show that the filtering helps the summarization system to overcome the lack of resources.However, several improving points have emerged from this preliminary study, that we discuss and plan to implement in future work.
Moral Stories: Situated Reasoning about Norms, Intents, Actions, and their Consequences

In social settings, much of human behavior is governed by unspoken rules of conduct rooted in societal norms. For artificial systems to be fully integrated into social environments, adherence to such norms is a central prerequisite. To investigate whether language generation models can serve as behavioral priors for systems deployed in social settings, we evaluate their ability to generate action descriptions that achieve predefined goals under normative constraints. Moreover, we examine if models can anticipate likely consequences of actions that either observe or violate known norms, or explain why certain actions are preferable by generating relevant norm hypotheses. For this purpose, we introduce Moral Stories, a crowd-sourced dataset of structured, branching narratives for the study of grounded, goal-oriented social reasoning. Finally, we propose decoding strategies that combine multiple expert models to significantly improve the quality of generated actions, consequences, and norms compared to strong baselines.
Features of the formation and development of mathematical representations in preschool children

The article deals with the actual problems of the formation of elementary mathematical representations in children. The paper shows that this approach in mathematics education based on action learning in conjunction with the natural motivation stemming from common sense is effective. Also, stimulating questions and classical famous problems are important motivating tools in mathematics, which are particularly beneficial in the framework of action learning. The analysis of psychological, pedagogical and methodological literature on the topic of research the author provides an experimental methodology for studying elementary mathematical representations, characterizes their features in young children. Key words: preschool children, mathematical representations, intellectual development of preschool children, mental education, features of development, elementary mathematical representations
PELATIHAN BAHASA INGGRIS PRAKTIS BAGI IBU-IBU PKK DALAM MENGANTISIPASI MELONJAKNYA KEDATANGAN TURIS DI KOTA TOMOHON

Today,English is very urgently useful for all countries, so this is a good time to increase in any parts of humans’, such as education, economy,social,culture,industry,etc,and all of those parts must be supported as soon as possible.It is because global market for all fields are really available now.It must be moved rapidly.One aspect which has the role in increasing English is tourism.Even now, tourism is shaken by pandemic of covid-19 happened at the end of 2019 and run quickly in the early of 2020 (especially in Indonesia), but after about one year later, the government tried to overcome the situation, so at the early of 2021, tourism sector run slowly and slowly wake up toward to any features related to tourism by still applied the health protocol. One effort to increase the government income, from the upper authority up to the lower authority,by preparation many tour guides who have very important role because there are many interesting ( tourism) places opened,both private and public sectors.These are really interesting things to gain many tourists to visit them.Day by day, there is a significant increasing in tourism sector and it needs to prepare so many tour guides to welcome the tourists.To overcome this situation, it must be available of local citizens as the human’s resources to master English. Short English course for the women covered as PKK group in Kolongan country, Tomohon District is a good way also to anticipate this one. The short practical English course used the methods such as, explanation, discuss, question and answer,and direct practice of English, so the members could be increased their capability both in theory and especially in practice (more focus on speaking skill).This course aimed to support learning Englih materials effectively.Also, this activity as an applied activity for society at Kolongan country, Tomohon District could run well whenever and wherever they are.
Reliable and Trustworthy Machine Learning for Health Using Dataset Shift Detection

Unpredictable ML model behavior on unseen data, especially in the health domain, raises serious concerns about its safety as repercussions for mistakes can be fatal. In this paper, we explore the feasibility of using state-of-the-art out-of-distribution detectors for reliable and trustworthy diagnostic predictions. We select publicly available deep learning models relating to various health conditions (e.g., skin cancer, lung sound, and Parkinson's disease) using various input data types (e.g., image, audio, and motion data). We demonstrate that these models show unreasonable predictions on out-of-distribution datasets. We show that Mahalanobis distance- and Gram matrices-based out-of-distribution detection methods are able to detect out-of-distribution data with high accuracy for the health models that operate on different modalities. We then translate the out-of-distribution score into a human interpretable CONFIDENCE SCORE to investigate its effect on the users' interaction with health ML applications. Our user study shows that the \textsc{confidence score} helped the participants only trust the results with a high score to make a medical decision and disregard results with a low score. Through this work, we demonstrate that dataset shift is a critical piece of information for high-stake ML applications, such as medical diagnosis and healthcare, to provide reliable and trustworthy predictions to the users.
Tax evasion through cryptocurrencies

Bitcoin is an innovative currency that is not regulated by governments like any other cryptocurrency. Cryptocurrencies can make payments very quickly and at almost no cost. One of the advantages of bitcoin is the fact that the virtual currency avoids the main problem inherent in conventional currencies, namely inflation. One of the big advantages is anonymity. Nobody knows how much bitcoin a person owns. It is also not known what transactions make and a new way to avoid paying taxes.