Reviewerly/exHarmony huggingface dataset:
EFL students' perspectives on the use of Wordwall.net as vocabulary learning media

Using ICT tools to teach vocabulary is a commonly researched topic in language teaching. Many popular web tools, such as Kahoot and Quizizz, are used as experimental tools with learners to investigate how effectively they enhance learners' vocabulary. This study analyzed the students' perceptions of Wordwall.net, a less known and researched game-based web tool, in their vocabulary learning. This study was conducted since there is still limited research conducted in Indonesia that analyses the students' perception of using Wordwall.net in English vocabulary learning. This research design was survey research with a sample of 29 8th-grade students at one of the Junior High Schools in Bengkulu Tengah. The data were collected using a close-ended questionnaire. It was analyzed quantitatively by implementing the Likert scale. The result of this research found that most students' perception of the use of Wodwall.net in their vocabulary learning was positive. Their feeling about Wordwall.net was great. They enjoyed the teamwork and competition; they perceived Wordwall.net as an exciting, interesting, motivating &amp; fun web tool to learn vocabulary. From the students' perceived effects of using Wordwall.net, Wordwall.net was a kind of awaited learning media for the students. In addition, Wordwall.net also influenced the students' engagement with the web tool and the classroom. It indicated that the students paid more attention to the learning process. The researchers strongly recommended that teachers start using Wordwall.net to learn vocabulary based on the results.
IDE-assisted visualization of indebted OO variability implementations

Object-Oriented (OO) variability-rich software systems often implement their variability in a single codebase, using the mechanisms provided by the host language (i.e., inheritance, overloading, design patterns). This variability is not documented and buried deep down in the code, thus impeding its identification and making it especially prone to variability debt at the code level. While this kind of variability implementation can now be detected, visualization support such as VariCity helps architects and developers understand the implemented variability using a city metaphor. In this paper, we demonstrate VariMetrics-IDE, an extension of VariCity that allows to visualize multiple quality metrics (e.g., code complexity, test coverage) together with the variability implementations, while supporting navigation between the source code and the visualization in an IDE. This extension thus facilitates the identification of zones of variability implementations with variability debt.
Metalinguistic Awareness is an Important Factor for Developing EFL Students’ Performance in Learning English Language

The development of metalinguistic awareness is an essential component of teacher preparation.Enhancing students' metalinguistic understanding of their different skill successes is essential.As a result, teachers should use a range of techniques for increasing metalinguistic awareness, such as word recognition, syntactic understanding, and vocabulary recognition exercises.However, these strategies are exclusively geared toward school-aged children.Because metalinguistic awareness is a component of all levels of education.Numerous studies have demonstrated that effective learners self-regulate their learning processes using metacognitive information and strategies.Effective learners demonstrate a high level of language control and analysis in a language learning context.It is therefore critical to foster learners' metalinguistic awareness, as metalinguistic awareness enables learners to exert greater control over their oral and written communication abilities.Additionally, it enables children to understand the significance of the messages they read or hear.Additionally, to the fact that they may greatly favour career prospects, these two benefits have an effect on learners' social status.
CLASSIFICATION OF THE POOR IN INDONESIA USING NAIVE BAYES ALGORITHM AND NAIVE BAYES ALGORITHM BASED ON PSO

The poverty rate in Indonesia is still quite high, due to the large population and uneven development and economic centre. With a large population and an archipelagic country that stretches from west to east, it is not easy for the government to level the economy in order to reduce poverty in Indonesia. This study was conducted to classify the poverty rate in districts on the island of Sumatra and Java using Nave Bayes and Nave Bayes based on Particle Swarm Optimization. Thus, it is hoped that the central government and local governments can monitor the implementation of programs in order to reduce poverty rates, especially in districts with high poverty rates. Based on research conducted on the classification of the poor in districts on the island of Sumatra and Java with confusion matrix testing and validation techniques using the Naïve Bayes algorithm, the accuracy rate is 59.75% and AUC 0.768 is included in the good classification. While the results of the classification using the Naïve Bayes algorithm based on Particle Swarm Optimization produces an accuracy rate of 82.93% and an AUC of 0.849 is included in the good classification. From the results of this study, it can be said that Al-Qur'an Naïve Bayes is a good technique for classification in data mining, and for maximum results using Particle Swarm Optimization.
Analysis Sentiment Cyberbullying In Instagram Comments with XGBoost Method

Technological developments make social media widely used by the general public, which causes negative impacts, one of which is cyberbullying. Cyberbullying is an act of insulting, humiliating another person on social media. A system that can detect cyberbullying because of the large amount of information circulating on social media is impossible for humans to visit. One suitable method to solve this problem is Extereme Gradient Boosting (XGBoost). XGBoost was chosen because it can run 10 times faster than other Gradient Boosting methods. The process of changing sentences into vectors uses the TF-IDF method. The TF/IDF method is known as a simple but relevant algorithm in doing words on a document. XGBoost accepts input in the form of vectors obtained from the TF-IDF process. In this research, there are 1452 comments which will be broken down into training data and testing data. By using XGBoost and TF-IDF methods, the accuracy is 75.20%, precision is 71%, recall is 87%, and F1-score is 78%.
Strategi Kesantunan Berbahasa Mahasiswa UPS Tegal dalam Percakapan WhatsApp

This research aims to describe the politeness strategies of UPS Tegal students' language proficiency in communicating with lecturers through WhatsApp media. This type of research is qualitative descriptive. Data used in research this is what UPS Tegal students said to the lecturer when communicating through WhatsApp media. The method of providing data in this study was the method of listening with tapping techniques as a basic technique, followed by a skillful and involved listening and note taking technique. Data analysis method used is the pragmatic matching method. Presentation of data analysis is the informal method. The results showed that there were four politeness strategies used, which were straightforward strategies, indirect strategies, positive politeness strategies, and negative politeness strategies. (1) A straightforward strategy in the form of very short, firm, and straightforward speeches with concise, concise, direct targets. Speech with this strategy is a polite speech. (2) Indirect strategies of declarative speech intended to govern (imperative). (3) Positive politeness strategies in the form of sympathy, involving speakers and interlocutors in one activity, giving reasons, and giving gifts. (4) Negative politeness strategies in the form of speech containing pessimism, humbling, apologizing, and asking questions
SISTEM INFORMASI MANAJEMEN DATA KLIEN PADA PT. HULU BALANG MANDIRI MENGGUNAKAN FRAMEWORK LARAVEL

The development of technology in modern times is very fast which has an impact on the business outsourcing. Information systems are the most important thing in a company to ensure the quality of information that is fast, precise, and accurate which can support the performance of the company's business processes. Information technology makes access to information very easy and fast. This makes the company able to compete with other companies and have competitive advantages in various things such as company performance and service. Therefore, the existence of an information system has become an absolute necessity for companies in carrying out their business processes. The information system needed in company outsourcing is a client data information system that can manage all information related to partners or potential partners in order to make it easier to obtain information related to clients. With the development of the Client Data Management Information System, it is hoped that it will facilitate PT. Hulu Balang Mandiri in managing client data and making decisions so that the company grows quickly.
Decision-Making Employee Performance Evaluation at XYZ University Using the Mamdani Fuzzy Logic Method

Human resources (HR) of high quality will provide excellent job outcomes. Through the execution of quality and quality education, human quality is developed. As a tool for assessing the quality of education in Indonesia, the evaluation of a lecturer's performance plays a crucial role in education. This research intends to aid in assessing the performance of Lecturers using Fuzzy Logic. Mamdani is the fuzzy logic approach used in this study. The assessment input factors consist of Lecturer Presence in Teaching, Discipline Level, Student Assessment Results, loyalty, and Communication Level or engagement with students to provide two judgments based on a Lecturer's poor and excellent performance. This system may assist and give options for assessing the performance of Lecturers, allowing Lecturers to make optimum adjustments to recruit the most outstanding and competent lecturers. Based on the study findings, it can be inferred that an intelligent system based on fuzzy logic may be utilized to identify Lecturer performance judgments with a 90 percent degree of accuracy
Blockchain-Based System to Ensure the Integrity of Used Vehicle Purchase Transactions: Under Researchers’ Perspective

Through the increase in vehicle sales transactions globally, new ways are being created to change the current model of vehicle ownership transfer systems. The integrity of the data in this type of operation must be the highest priority to avoid different types of fraud. These range from the manipulation of vehicle data, to the falsification of the identity of the seller as well as the buyer. To address this problem, we propose the implementation of a system based on blockchain, which is characterized by decentralization, security, and transparency of information to let users know when a fraud may occur in the purchase transaction process. To accomplish this, a systematic review of the literature is carried out, through which the most appropriate elements for the implementation of the proposed system's infrastructure will be determined. In this review, the problem will be presented, then various applications of this technology in the automotive sector will be addressed, and finally, the most important concepts to be considered will be developed. Why this technology is the most appropriate to solve this problem will conclude the review.
Detection and Handling of Threats in Pre-Established Networks Through a Junior Perspective in Internship Scenario

In this paper, there are two questions that we seek to answer, what are the tools used in the industry nowadays regarding its cybersecurity? How is cybersecurity being approached and dealt with by the companies and their greatest difficulties? Along the way it’s observed how the entire interaction with clients is made as also how the investigation on possible threats is conducted and handled, passing through how to detect, analyze and interact with the client team in the mitigation of it, this taking into consideration their infrastructure and capabilities. This paper is taken from the point of view of a junior utilizing free tools to analyze threats and dealing with attempts of infiltrating the network utilizing social engineering tactics as well as more technical skills.
Animation in React Native

Animation does not seem to belong to the core user experience of any apps. Not true. Conversely, it is vital to do the animation correctly. Why? Read on.
Application of The Multiple Intelligent Level Determination for Interest and Talent Development

This research focused on making applications to determine the dominant multiple intelligences. Everyone has eight different intelligences that reflect different ways of interacting with the world. The dominant multiple intelligence needs to be known so that the child's growth and development is in accordance with his or her talents and interests. Therefore, it is necessary to make an application with tools that have been tested by experts, which can determine the level of intelligence so that the potential of children can be developed. This application was created using the extreme Programming software development method. The reasons for the choice of this method were because the resources to create the application were readily available, the programming was conducted by a small team, and the application development time was short. This application used the PHP and MySQL by displaying a list of 24 questions that had to be filled out by the users. The application testing method used black box testing. The accuracy of the application was 100% from 150 respondents who used this application. The output of this application is a sequence of multiple intelligences owned by users ranging from linguistic, logical-mathematical, musical, spatial, kinaesthetic, intrapersonal, interpersonal abilities, and naturalist.
A Corpus for Evaluation of Cross Language Text Re-use Detection Systems

In recent years, the availability of documents through the Internet along with automatic translation systems have increased plagiarism, especially across languages.Cross-lingual plagiarism occurs when the source or original text is in one language and the plagiarized or re-used text is in another language.Various methods for automatic text re-use detection across languages have been developed whose objective is to assist human experts in analyzing documents for plagiarism cases.For evaluating the performance of these systems and algorithms, standard evaluation resources are needed.To construct cross lingual plagiarism detection corpora, the majority of earlier studies have paid attention to English and other European language pairs, and have less focused on low resource languages.In this paper, we investigate a method for constructing an English-Persian cross-language plagiarism detection corpus based on parallel bilingual sentences that artificially generate passages with various degrees of paraphrasing.The plagiarized passages are inserted into topically related English and Persian Wikipedia articles in order to have more realistic text documents.The proposed approach can be applied to other less-resourced languages.In order to evaluate the compiled corpus, both intrinsic and extrinsic evaluation methods were employed.So, the compiled corpus can be suitably included into an evaluation framework for assessing cross-language plagiarism detection systems.Our proposed corpus is free and publicly available for research purposes.
Federated Learning Versus Classical Machine Learning: A Convergence Comparison

In the past few decades, machine learning has revolutionized data processing for large scale applications. Simultaneously, increasing privacy threats in trending applications led to the redesign of classical data training models. In particular, classical machine learning involves centralized data training, where the data is gathered, and the entire training process executes at the central server. Despite significant convergence, this training involves several privacy threats on participants' data when shared with the central cloud server. To this end, federated learning has achieved significant importance over distributed data training. In particular, the federated learning allows participants to collaboratively train the local models on local data without revealing their sensitive information to the central cloud server. In this paper, we perform a convergence comparison between classical machine learning and federated learning on two publicly available datasets, namely, logistic-regression-MNIST dataset and image-classification-CIFAR-10 dataset. The simulation results demonstrate that federated learning achieves higher convergence within limited communication rounds while maintaining participants' anonymity. We hope that this research will show the benefits and help federated learning to be implemented widely.
The effect of the chemistry practicals on the academic performance of Ward Secondary School students in Momba District in Tanzania

The knowledge of chemistry is fundamental in the development of industries of any country. Acquiring chemistry knowledge starts from lower schools to higher schools. In acquiring chemistry knowledge, practicals are very important in equipping students with the knowledge and skills of actual practices of the concepts they learn. Practicals are used as a catalyst in understanding chemistry concepts. The purpose of this study was to examine the effect of chemistry practicals on the academic performance of ward secondary students in the Momba District in Tanzania. The study examined the effects of chemistry practicals on the academic performance of students in ward secondary school. This study utilized a quantitative approach, with a quasi-experimental design. A quasi-experimental design was designed in form of pre- and post-test. The data were collected through students’ chemistry achievement tests, and it involved 92 students, 46 from the experimental and other 46 from the control group. Descriptive statistics such as mean and standard deviation were used, and inferential statistics involved the independent samples t-tests. The findings show that there was a significant difference in performance between students who studied chemistry through practicals and those who studied chemistry without practicals. Therefore, the study recommends the consideration of practicals works in the teaching and learning of chemistry.
Currency Denomination Identifier Application

Abstract: For visually challenged people, distinguishing between different denominations of cash is a difficult process. Even though unique symbols are engraved on various currencies in India, the work is still difficult for blind individuals. The inadequacy of identifying devices prompted the development of a portable gadget for denominational segregation. This project aims to create an Android application that will assist visually and hearing-impaired people in detecting Indian cash denominations by putting a banknote in front of the camera. The work uses machine learning and Android programming approaches and is based on real-world applications. The android application uses text to speech concept to read the value of note to the user and then it converts the text value into speech. To harness the power of them all, we are leveraging the Keras, TensorFlow, Fastai, and PyTorch libraries, as well as different machine learning techniques like ResNet and MobileNet. Various technologies like machine learning models, python and many more libraries are used for the backend part of application. And for front end, java concepts and android development techniques are employed. Altogether they are integrated into a single platform which is highly user-friendly and makes it easy to use and implement in our daily life. Keywords: Denomination, Hearing-impaired, Android, Keras, TensorFlow, Fastai, PyTorch, ResNet, MobileNet
Evaluation Methods for Breast Cancer Prediction in Machine Learning Field

Breast cancer is the most common malignant tumor found in women, and there is no cure for advanced breast cancer. Early detection and treatment can effectively improve patient survival. This paper uses five machine learning classification models, namely Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), and K-Nearest Neighbors Algorithm (KNN). The training data for the five models are provided by the Wisconsin Breast Cancer Dataset (WBCD). By evaluating and comparing the performance of the five models in accuracy, F1Score, ROC curve, and PR curve, the study finds that LR has the best performance.
AN EMPIRICAL STUDY OF TECHNOLOGY ACCEPTANCE IN HIGHER EDUCATION DURING COVID-19 PANDEMIC

Information technologies provide various advantages to compete in global markets.More businesses adopt these technologies to gain competitive advantages such as quick market response, fast and reliable supply chains, quick decision based on big data.It is a challenging process to acquire a new technology and there are different factors that affect the acceptance speed of a technology.The effects of some factors may vary due to various reasons such as natural disasters, economic crises, market structure.Since the beginning of 2020, Covid-19 pandemic caused many different businesses and supply chain to adapt new conditions.Higher education industry is one of the profoundly affected sectors from pandemic and it is forced to shift rapidly from traditional teaching to online teaching.This study aims to investigate the possible effects of self-efficacy, user experience, innovativeness, usefulness, ease of use and intention on acceptance of distance education systems under Covid-19 pandemic conditions.The study is conducted at Ardahan University, Turkey with 598 of students.The hypotheses were tested using PLS-SEM (Partial Least Squares Structural Equation Modelling).Findings reveal that self-efficacy and innovativeness have effects on perceived ease of use while self-efficacy and user experience do not have positive impact on perceived usefulness.Results also revealed that perceived ease of use has positive impacts on perceived usefulness, and perceived usefulness has positive impact on intention.
Regular Flow Field Sparse Processing and Blue Finance of Ocean City Research Based on GIS System

In order to explore the development path of blue finance in ocean cities, this paper combines GIS to conduct blue finance research in ocean cities, draws on the traditional direct visualization method of point icons, and uses arrow symbols to render vector flow field data. Moreover, this paper uses GIS means to perform statistical analysis on flow field data, studies the adaptive sparse algorithm of flow field data spatial structure based on GIS, and proposes a program implementation method for regular flow field sparse processing suitable for the general flow of the sparse algorithm. The experimental research shows that the GIS technology proposed in this paper can play an important role in the research of blue finance in ocean cities and has a certain role in promoting the development of ocean blue finance.