Reviewerly/exHarmony huggingface dataset:
Application of the Function in Basic Mathematics

Calculus is one of the most important branches of mathematics that deals with continuous changes.The two main concepts on which calculus is based are derivatives and integrals.The derivative of a function is a measure of the rate of change of the function, while the integral is a measure of the area under the curve of the function.The derivative gives an explanation of the function at a particular point, while the integral accumulates the discrete values of the function over a range of values.Elements of mathematical analysis occupy an important place in the school mathematics course.Students are introduced to the mathematical apparatus that can be used effectively in solving many problems in mathematics, physics and technology.With the help of differential calculus, the properties of functions are studied, their graphs are constructed and problems for the largest and smallest value are solved.In other words, the introduction of a new mathematical apparatus makes it possible to consider a number of problems that could not previously be solved by elementary methods.At the same time, the derivative can be used to solve elementary mathematical problems.This use of derivatives is very useful for two reasons.On the one hand, many traditional problems of elementary mathematics (proof of inequalities, identities, investigation and solution of equations and their systems) are efficiently solved using derivatives.On the other hand, the non-standard use of elements of mathematical analysis allows for a better understanding of the basic terms of the theory being studied, because one should choose a method for solving the task, check the conditions of its applicability and analyze the obtained results.In addition, the methods of mathematical analysis can be used not only to solve problems, but also be a source for obtaining new facts of elementary mathematics [1][2][3][4][5][6].
Utilizing an Entity-level Semantic Analysis Approach Towards Enhanced Policy Making

The tremendous growth and usage of social media in modern societies have led to the production of an enormous real-time volume of social texts and posts, including tweets, that are being produced by users. These collections of social data can be potentially useful, but the extent of meaningful data in these collections is still of high research and business interest. One of the main elements in several application domains, such as policy making, addresses the scope of public opinion analysis. The latter is recently realized through sentiment analysis and Natural Language Processing (NLP), for identifying and extracting subjective information from raw texts. An additional challenge refers to the exploitation and correlation of the sentiment that can be derived for different entities into the same text or even a sentence to analyze the different sentiments that can be expressed for specific products, services, and topics by considering all available information that can be depicted within a text in a holistic way. Hence, this paper evaluates the utilization of an Entity-Level Sentiment Analysis (ELSA) approach on Twitter Data. The latter seeks to enhance the knowledge derived from tweets with the ultimate objective the overall enhancement of the policy making procedures of modern organizations and businesses.
The Use of Tablet PCs in Previously Disadvantaged Secondary Schools in South Africa During the Covid-19 Pandemic

The South African Department of Basic Education (DBE) has considered the introduction of Tablet PCs an important aspect of improving the quality of education in South Africa.The Covid-19 pandemic has hastened the introduction of Tablet PCs due to the increased need for distance and online learning.Educators are often unwilling to accept and use Tablet PCs for teaching and learning because they do not have the necessary skills to integrate Tablet PCs into their pedagogical practices.This paper discusses the practical implications of the introduction of Tablet PCs into previously disadvantaged schools in South Africa specifically during the Covid-19 pandemic.To provide guidance to the DBE, principals, and educators to promote the effective use and acceptance of Tablet PCs by educators for classroom and distance learning.
Development Of Infographic Media Based On Qr Code Situs Duplang In History Learning With ASSURE Model

Learning media that serves as a communication tool used in learning, but learning media ccan be beneficial as a means in education which helps the teaching process so that learning does not seem static, so learning can be effective. 05 Jember, SMAN 02 Tanggul and SMAN Arjasa. (1) 66% of educators have difficulties in teaching history in the application of media learning, 33% in teaching methods; (2) The learning method used by educators has used the 100% Lecture method, 66% Problem Solving, 66% Discussion; (3) 66% educators add teaching media in the form of visual Power Point and 66% Video; (4) Educators have obstacles in the application of media in 66% Difficulty in designing learning media, 33% Difficulty in developing learning media. The facilities and infrastructure in the three schools are adequate, from Wi-Fi to projectors, but they are still lacking in utilization in the learning process Based on the questionnaire analytical of learning styles, it must be said that visual teaching styles are 48.3%, audio learning styles are 20.9% and kinesthetic learning styles are 40.7%. improvement of student teaching outcomes after use Infographics teaching media based on Qr Code. The increase in learning outcomes that increased significantly between the pre-test and post-test scores increased the average learning outcomes of students by 87.47%. This Qr Code-based Infographic Media Duplang Site has been developed by adding ideas and concepts to the media.&#x0D; Keywords: Infografis, Qr Code, Situs Duplang&#x0D;
An error anlysis on english descriptive text at the first grade of junior high school

Abstract This research consists of problems, objectives, methods, findings, and conclusions of Error Analysis in English Descriptive Text at the First Grade of Junior High School. This study aimed to determine the types of grammatical errors, the most dominant errors and to analyze the causes of grammatical errors made by students in writing descriptive text essays. This research was conducted on grade I students of SMP Assalam Pontianak in the academic year 2021/2022. the total population is 94 students from three classes. The sample was grade one C which consists of 35 students. This type of research uses descriptive analysis with a qualitative approach. Data collection techniques used were direct communication techniques and documentation. Data collection tools were interview guides and student worksheets. This study found that from 35 students who had made descriptive texts, there were 402 grammatical errors. The data collected were from 61 English essays written by 35 students. Data were analyzed based on student worksheets and interview transcripts to find the types of errors and dominant errors and the causes of errors. Based on data analysis, the results of this study showed that the errors of 35 students in producing English writing were (1) misformation 47%, (2) omission 40%, (3) Addition 10%, and (4) misordering 3%. Based on the results of the interview, it can be classified that translation was the main cause of grammatical errors.
Study of Student’s Anxiety in Speaking Skill A Case Study in Islamic Criminal Law Student

AbstractThis study aims to describe about the elements cause students’anxiety in speaking skill and the students strategies to cope the anxiety in speaking skill at Syaichona Cholil Bangkalan Islamic in Islamic Criminal Law Major Student of first semester in academic year 2021-2022 as participants. There are 22 students as participants researcher take by purposive sampling. Researcher also take 3 students in class of Medical Record. Descriptive Qualitative was used in this study. The data were through structured Interviews and classroom observations. The data were describe by interview transcripts. The result of this study describe three elementss that cause students’ anxiety in speaking English. The first is Self Perception of students that always feel English is difficulty to Learn and speak also there a lot of referencees in English Language related to Islamic Criminal law Students’s. The second elements is Class Condition of Performance make students got anxiety when they speak in front of their friends and lecture. The third is students’ Anxiety in speaking English is Fear of Making Mistakes. The result of this study also found there are three strategies that students used to cope the anxiety in speaking English. The first is Preparation, eight students did preparation always felt better before speaking English. The second is Positive thinking, seven participants did positive thinking can cope the anxiety in speaking English. The last is Relaxation, there five students take this strategie if students feel anxiety and failed speaking English.
AN ANALYSIS OF ENGLISH FOREIGN LANGUAGE (EFL) STUDENTS’ ANXIETY IN SPEAKING CLASS

This research aimed find out the most common cause of English Foreign Language (EFL) students’ anxiety in speaking class to the Third Semester Students of English Education Study Program of IKIP PGRI Pontianak. The subject of this research was only one class, the number of subject was 31 students from B Morning class. This research was a descriptive research. The data collected from the questionnaire and interview. The data which have been collected are analyzed by using google form as a tool to collect the data. The findings showed the percentages of English Foreign Language (EFL) students’ anxiety in cognitive, behavioural, and physiological factors. The mean score of English Foreign Language (EFL) students’ anxiety in cognitive factor was 74.2% Then, the mean score in behavioural factor was 74%. And then the mean score in physiological factor was 72%. The students in speaking class they are lack of preparation, fear of making mistakes, afraid of the classmates that they could not do their best in speaking class. With these findings, therefore the researcher concluded that the most common cause of English Foreign Language (EFL) students’ anxiety in speaking class was cognitive factors. Keyword: English Foreign Language (EFL) students’ anxiety, speaking anxiety, descriptive research.
College Enquiry Chat Bot Using Machine Learning

Abstract: To develop a college enquiry Chatbot that answers any queries post by students like college details, course-related questions, location of the college, fee structure etc. The College Enquiry Chatbot project is built using Deeplearning, Neural Network with Natural Language Processing that analyze user’s queries and understand the user's message. This System is a web application that provides answers to the query. Any individual just has to query through the bot. The answers are appropriate to what the user queries.
University Strategy Management According to the Vision 2030: Requirements as Perceived by Faculty Members College of Administrative Sciences - University of Najran – model

This study aims to develop a framework (model) to the strategic management of Najran University, which reflects its ability to fulfill its role with the vision (2030), through the opinions of the faculty members of the College of Administration Sciences at the Najran University as a model. The Exploratory and Confirmatory factor analysis was used in dealing with the subject. The study concluded a three-dimensional model: strategy formulation, strategy implementation, and strategy evaluation. The study recommended the adoption of the model to management of the strategy of other university colleges.
A flexible symbolic regression method for constructing interpretable clinical prediction models

Abstract Machine learning (ML) models trained for triggering clinical decision support (CDS) are typically either accurate or interpretable but not both. Scaling CDS to the panoply of clinical use cases while mitigating risks to patients will require many ML models be intuitively interpretable for clinicians. To this end, we adapted a symbolic regression method, coined the feature engineering automation tool (FEAT), to train concise and accurate models from high-dimensional electronic health record (EHR) data. We first present an in-depth application of FEAT to classify hypertension, hypertension with unexplained hypokalemia, and apparent treatment-resistant hypertension (aTRH) using EHR data for 1200 subjects receiving longitudinal care in a large healthcare system. FEAT models trained to predict phenotypes adjudicated by chart review had equivalent or higher discriminative performance ( p &lt; 0.001) and were at least three times smaller ( p &lt; 1 × 10 −6 ) than other potentially interpretable models. For aTRH, FEAT generated a six-feature, highly discriminative (positive predictive value = 0.70, sensitivity = 0.62), and clinically intuitive model. To assess the generalizability of the approach, we tested FEAT on 25 benchmark clinical phenotyping tasks using the MIMIC-III critical care database. Under comparable dimensionality constraints, FEAT’s models exhibited higher area under the receiver-operating curve scores than penalized linear models across tasks ( p &lt; 6 × 10 −6 ). In summary, FEAT can train EHR prediction models that are both intuitively interpretable and accurate, which should facilitate safe and effective scaling of ML-triggered CDS to the panoply of potential clinical use cases and healthcare practices.
Research on Library Face Book Return Model Based on Hybrid PCA and Kernel Function

With the improvement in the quality of university education in China, the behavior of college students and school teachers to borrow and return books in the library is becoming more and more frequent.In peak periods of returning books, managers cannot even assist in returning books in time.Therefore, this research uses kernel function, multi-dimensional principal component analysis method, and multi-dimensional linear discriminant analysis method to construct a new face recognition algorithm for the automatic return of books in the university library.The test results show that the XT_2D_PL algorithm designed in this study has a face recognition rate of 96.8%.When the number of face samples of each type in the test sample set is 11, and when the number of feature dimensions is 14, the recognition rate of 96.3% reaches the highest level.However, if the sample to be processed is 500 pictures, the calculation speed is 1.072ms/per photo, higher than most comparison algorithms.The proposed face recognition algorithm has high recognition accuracy on the library face data; the calculation speed meets the needs of practical applications, and has certain practical promotion potential.
Exposition of Karuna Rasa (Sentiment of Pathos) in William Shakespeare’s Macbeth

Rasa theory is an Eastern literary theory. Rasa realization in the works produces pleasure and conveys themes and ideas. This study has explored karuna rasa (pathetic sentiment) experience in Shakespeare’s Macbeth. This study used a textual analysis method to expose karuna rasa. The finding of the study is that karuna rasa is identified angi (major) rasa in Macbeth. The karuna rasa (pathetic emotional forces) help to heighten sentiment of tragic feelings in Macbeth. The emotion of sorrow produces the rasa of pathos in the literary texts. The depiction and realization of karuna rasa (pathetic sentiment) upraises the grandeur of the play. Understanding rasa formation and rasa realization in the play make lively existence of the play Macbeth. Karuna rasa has elevated the state of being liked and enjoyed of Macbeth. The play represents human emotions and messages to audience.
Perbandingan Metode Random Forest dan Naïve Bayes dalam Email Spam Filtering

Email is an important tool not only for communicating and transferring files but also it can be used for advertising media over the Internet. Since the increase in email user numbers, many users send viruses, fraud, and even pornography contained emails. Those kinds of emails were called spam, where unexpected emails sent in bulk. Many email users are annoyed by the amount of time spent deleting individual spam messages. This study provides a comparison between the Random Forest and Naïve Bayes classification methods for email spam predicting. It aims for searching the most accurate method. The data used in this study is an email dataset totaling 2607 data with two variables, namely the body variable (which shows the contents of the email) and the label variable (which shows labeling) where 1 indicates spam and 0 indicates not spam. From the test result using the confusion matrix, it is known that the random forest method has the highest accuracy value, namely 98%, and Naïve Bayes 73%.
The professional worldview of future teachers of natural sciences in the structure of their readiness for professional activity

International scientific conference «Research activities and achievements in pedagogy and psychology» : conference proceedings (April 5–6, 2023. Częstochowa, the Republic of Poland). Riga, Latvia : «Baltija Publishing», 2023. 188 pages.
LegalNERo: A linked corpus for named entity recognition in the Romanian legal domain

LegalNERo is a manually annotated corpus for named entity recognition in the Romanian legal domain. It provides gold annotations for organizations, locations, persons, time expressions and legal resources mentioned in legal documents. Furthermore, GeoNames identifiers are provided. The resource is available in multiple formats, including span-based, token-based and RDF. The Linked Open Data version is available for both download and querying using SPARQL.
Dampak Pemberlakuan Sistem Zonasi terhadap Daya Saing Pelajar di Indonesia

Zoning is a new system promoted by the government as a program that is expected to equalize education in Indonesia. However, in practice the zoning itself is felt to be immature and seems rushed because there are still high disparities between schools in Indonesia which can be seen from the differences in quality, facilities, facilities &amp; infrastructure so that this system is considered unfair. Zoning replaces the National Examination (UN) scores which used to serve as a reference for whether or not students are accepted to continue their education at public schools so that students whose homes are close to public schools don't have to work hard and study competitively because the zoning system allows them to to be accepted into public schools. This is seen as reducing the motivation and competitiveness of students in Indonesia because previously one of their motivations in studying was to achieve their dreams of being able to continue their education at their favorite state schools. The data collection in this article uses the Systematic Literature Review method where data is obtained from various books, articles, and journals accessed by the author online.
A new construction of an MDS convolutional code of rate 1/2

Maximum distance separable convolutional codes are characterized by the property that the free distance reaches the generalized Singleton bound, which makes them optimal for error correction. However, the existing constructions of such codes are available over fields of large size. In this paper, we present the unique construction of MDS convolutional codes of rate [Formula: see text] and degree [Formula: see text] over the field [Formula: see text].
Library Catalogue Projects

This paper looks at a number of library projects that, together, have improved the quality of the college's library catalogue. Through a necessary project involving searching for thousands of titles on a title-by-title basis, several smaller related library catalogue projects developed resulting in overall catalogue enhancement and improvement. Ultimately, this work has resulted in an increase in usage of our physical resources. The paper also discusses the benefits of such projects to both the library and patrons and considers future related projects that could be undertaken.
Deep Dive Text Analytics and Natural Language Understanding

Abstract In this chapter, we present a comprehensive overview of text analytics and Natural Language Understanding (NLU) from the perspective of digital language equality (DLE) in Europe. We focus on the research that is currently being undertaken in foundational methods and techniques related to these technologies as well as on the gaps that need to be addressed in order to offer improved text analytics and NLU support across languages. Our analysis includes eight recommendations that address central topics for text analytics and NLU, e. g., the role of language equality for social good, the balance between commercial interests and equal opportunities for society, and incentives to language equality, as well as key technologies like language models and the availability of cross-lingual, cross-modal, and cross-sector datasets and benchmarks.
Research on the Joint Learning Method of Intent Detection and Slot Filling by Fusing Tag Semantic Information

Intention recognition and slot filling are the main functions of natural language understanding. In view of the insufficient consideration of the semantic information of intention and slot label in most current models, this paper proposes a method to integrate the semantic information of intention and slot label into the joint model. First, in order to make full use of the semantic information of intention and slot, we transform intention, slot label and label "B, I, O" into natural language; Secondly, the pre-training model BERT is used to encode the natural language and interact with the coded representation of the sentence, so that the sentence fully contains the intention and semantic slot information, and the intention and semantic slot that best match the sentence and word are output. Finally, we conducted experiments on three data sets, ATIS, SNIPS and Facebook. The experimental results show that the method proposed in this paper can effectively use the semantic information of tags, and the accuracy of intention recognition and F1 value of slot filling are improved, which verifies the effectiveness of the method in this paper.
Building a Knowledge Graph from Historical Newspapers: A Study Case in Ecuador

History shows that different events occur every day in the world. In the past, knowledge of these events could only be orally transmitted from generation to generation due to a lack of appropriate technology. Currently, vast amounts of valuable historical information rests in deteriorated historical newspapers, which result very difficult to deal with. In this work, we use text digitization, text mining, and Semantic Web technologies to generate a knowledge graph comprising events occurred in Ecuador in the XIX-XX centuries.
Development of a Web-Based School Payment Administration Information System Using the Laravel Framework

A school’s payment administration management system is crucial for its well-being because all activities require payment administration to run successfully. The administration department is responsible for managing the payment, including recording payment of Education Development Contribution (SPP) and other contribution fees and generating reports. Unfortunately, these tasks are carried out manually and have not been digitized, resulting in the possibility of erroneous data and a lengthy recording procedure. Accordingly, it necessitates developing a web-based system for school payment administration management to decrease the risk of payment and recording errors. Laravel was utilized as the application’s framework, with PHP as the language. Database management system employed MySQL. Observation, interviews, and system analysis were performed to create a web application. This application could resolve issues, making payment data recapitulation more effective and efficient while reducing the risk of data inaccuracy