Reviewerly/exHarmony huggingface dataset:
Semantics of Japanese Benefactive Constructions: A Corpus-based Research

As benefactives in other languages of the world, Japanese benefactive constructions can be used in various meanings apart from the core benefactive meaning. In previous research their semantics is normally described separately for each of the auxiliary verbs, and the research itself is based either on introspection or working with sporadic texts and native-speakers. In this article we present a qualitative and quantitative analysis of corpora dataset with three groups of Japanese benefactive auxiliaries: yaru and ageru , kureru and kudasaru , morau and itadaku . For this purpose we propose a set of three semantic features: speaker’s attitude, volitivity of a logical subgect and presence of a sentient beneficiary. Combinations of these features underlie the definitions of six main meanings we mark out: core benefactive, etiquettical, positive emotivity, monstrative, intensification and malefactive. In constructions with yaru auxiliary all of the above mentioned meanings are possible except for the positive emotivity. With ageru we encounter core benefactive and monstrative and intensification meanings. With kureru auxiliary core benefactive, malefactive and positive emotivity meanings turn out to be possible. With kudasaru we encounter only core benefactive usage. For morau and itadaku auxiliaries we register core benefactive and etiquettical usage. Proposed features, meanings and analysed data are applicable for comparative studies of benefactives in other languages as well as for further development of semantic description of Japanese benefactives.
Corpus Study of The Choice of Personal Pronouns in Social Media Chats Among Tertiary Students

Purpose: Sociolinguistics variables such as age, gender and social class, among others, are said to bring the difference in the ways humans communicate. In this essence, this study investigated how age brings out the difference in the use of pronouns between three generational groups&#x0D; Design/Methodology/Approach: Group WhatsApp chats were collected from three generational groups of students based on their willingness to give it out for analysis. These three generational groups are postgraduate students and undergraduates (Level 100, Level 400). These groups were randomly collected from over four hundred (400 students of the University of Ghana and the University of Cape Coast in Ghana based on convenience. A quantitative research design was adopted for this study with the help of a corpus tool (AntConc) to analyse the huge data gathered based on percentages.&#x0D; Findings: This study found out that postgraduates and undergraduates (Level 100) use the first-person singular pronoun but postgraduates, especially those who represent the older generation tend to use the first-person pronoun more often while the second undergraduate group members (Level 400) use the third person pronouns. The study, per the findings, concludes that people tend to affiliate with others when they are young and lose group affiliation as they grow.&#x0D; Research Limitation: The study was limited to WhatsApp group chats of university students within the undergraduate and postgraduate academic levels who permitted their chats to be used. This resulted in a narrow scope for the groups used.&#x0D; Practical implication: The study reveals the preference or choice of pronoun usage among people based on their ages and the groups they affiliate more with. People may thus become more conscious of the choice they make of pronouns for usage as they become older or affiliate with older people. &#x0D; Social Implications: the study re-examines existing literature on the use of adverbs, especially personal pronouns. Since many studies have been done on the use of adverbs, this adds a new strand of knowledge to existing ones on the subject.&#x0D; Originality/Value: It provided empirical data on the demographic, precisely age characteristics of the subjects used for the research that affects their choice of personal pronouns among themselves and others.
A Study on the Construction of Deep Learning Field in College English Reading Courses

Based on Deep Learning Theory, this paper analyzes the construction of reading strategies, environments, tasks, dialogues, contents, feedback and other fields in the deep learning field of college English reading courses, so as to summarize the effective path of deep learning of college English reading courses.
The Metacognitive Strategy to Achieve Listening Comprehension in Kulni Modern Islamic Boarding School

Metacognitive is the process of thinking about our minds, which is how a process, and control that information to find new understanding/knowledge and seek relationships with other knowledge. The purpose of this study is to discover how students in class XI of Kulni Modern Islamic Boarding School at Cikande, Serang Banten in use English listening metacognitive strategies, as well as the differences in improving English listening comprehension among students before and after receiving metacognitive learning. The population of this study included 61 students from class XI of Kulni Modern Islamic Boarding School at Cikande, Serang Banten. The sample consisted of 29 students from A class XI. The researcher used quantitative data analysis in this study. Data is gathered by administering tests to the class. There are two kinds of tests: pre-test and post-test. According to the study's findings, the average pre-test score at researcher Modern Islamic Boarding School at Cikande, Serang Banten was 40.41, and the average post-test score in the experimental class was 57.21. Based on the data presented above, it is possible to conclude that there is a 41.5 percent change in learning outcomes. Based on the result of the study, applying metacognitive strategies for learning can help students become interested in listening activities and can be an alternative innovation in learning listening.
EFL Students' Perceptions toward the Integration of Bahasa Indonesia (L1) In English Language Teaching

The application of the first language (L1) in English language teaching (ELT) has long been practiced, and its practice has been controversial in foreign language teaching. This research aims to investigate students’ attitudes toward the application of Bahasa Indonesia (Indonesian students’ L1) in ELT. This research is framed through a quantitative survey in which the 32 participants were invited to take online self-administered questionnaires. Result suggests that the integration of Bahasa Indonesia in English language teaching as a pedagogical tool is considered beneficial to improve students’ English proficiency, enhance English vocabulary, and improve comprehension. In addition, the participants also believed that the numerous use of L1 or Bahasa Indonesia may provide less opportunity for students to develop their speaking skills. When it comes to preferences of language use in ELT, most students agreed that their English teachers should integrate both Bahasa Indonesia and English, in which the use of Bahasa Indonesia to be minimized while English to be predominated.
Support Vector Classification with Hyperparameters for Analysis of Public Sentiment on Data Security in Indonesia

The development of Information Technology makes increasing use of the internet. This raises the vulnerability of data security. Cyber attacks in Indonesia caused many tweets on social media Twitter. Some are positive, and some are negative. The problem of this study is to determine the public sentiment towards data security in Indonesia, while the purpose of this study is how the response or evaluation of the government of Indonesia to the many perceptions of people who lack confidence in data security in Indonesia. Data obtained from twitter with as much as 706 data was processed using python with a percentage of 10% test data and 90% training data. Weighting is done using TF-IDF, and then the Data is processed using the Support Vector Machine algorithm using the SVC (Support Vector Classification) library. Support Vector Classification with RBF kernel classifies Text well to obtain AUC value with good classification category. Utilizing one of the hyperparameter techniques, which is a grid search technique that can compare the accuracy of test results. The test results using SVC with RBF kernel obtained an accuracy value of 0.87, Precision of 0.82, recall of 0.94, and F1_Score of 0.87. This study is expected to be used by decision-makers related to public confidence in data security in Indonesia
Online greedy identification of linear dynamical systems

This work addresses the problem of exploration in an unknown environment. For multi-input multi-output, linear time-invariant dynamical systems, we use an experimental design framework and introduce an online greedy policy where the control maximizes the information of the next step. We evaluate our approach experimentally and compare it with more elaborate gradient-based methods. In a setting with a limited number of observations, our algorithm has low complexity and shows competitive performances. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
CRITICAL THINKING DEVELOPMENT IN THE 10TH GRADE STUDENTS DURING THE STUDY OF THE THEME «BIODIVERSITY»

The article deals with the study of the critical thinking development in the tenth grade students and methods of its implementation in biology lessons during the study of the theme «Biodiversity». A pedagogical experiment was conducted and characterized on the basis of Klevan Lyceum №1. It was proven that the application of various elements of critical thinking technology in biology lessons contributes to the effective development of students’ critical thinking, forms the ability to analyze and objectively evaluate, distinguish facts from opinions, solve problems, be able to find contradictions and find mistakes. A learning strategy with an implementation of a three-phase model «challenge – understanding – reflection» was used for studying the effectiveness of the methodology for the critical thinking development during the study of the theme «Biodiversity». The main stages of the model are characterized and examples of practical application of critical thinking development techniques in biology lessons in the10th grade are given, the effectiveness of the implementation of the methodology is analyzed. It was found that the average values of the main criteria of critical thinking after the experiment increased from 43.0% (input control) to 65.9%. The levels of indicators formation of students’ critical thinking significantly improved according to all evaluation criteria. Thus, in the experimental class, the high level of indicators of critical thinking formation according to value-need and regulatory-evaluative criteria is 20.8% higher than in the control class. The use of methods and techniques of technology for the critical thinking development, which were used in the study, showed their effectiveness during the knowledge assimilation by students, the formation of key competencies and the development of personal qualities. Thus, they are recommended for implementation in the educational process.
Implementasi Algoritma Freeman Chain Code dan Algoritma K-Nearest Neighbor Dalam Pengenalan Huruf Mandarin

Mandarin is the second international language used by the world's population and is the language most studied by students in Indonesia. This research was conducted to determine the success rate of the Freeman Chain Code algorithm and the K-Nearest Neighbor algorithm in mandarin letter recognition. In recognizing Chinese characters, there are several stages that must be passed, namely pre-processing, feature extraction, and letter recognition. The pre-processing stage uses grayscale, Gaussian Blur, binaryization, and thinning. The feature extraction stage uses the Freeman Chain Code Algorithm and the Depth First Search (DFS) Algorithm. The classification stage uses the K-Nearest Neighbor Algorithm and the L1-Metric Algorithm (Manhattan Distance). In this study, there were 10 letter classes with each letter having 100 sample images. The distribution ratio of this research is 70% training data and 30% testing data. This research produces an application that is able to recognize Chinese characters. The success rate resulting from this study was 72% with 216 of the 300 images successfully recognized
Hate Speech Hashtag Classification on Twitter Using the Hybrid Classifier Method

Hate speech on social media, especially Twitter, often takes the form of racism, sexism, or political interests aimed at certain individuals or groups. These actions can trigger crime, riots, violence and even resistance to individuals or groups. Therefore, we need a process of classifying a tweet whether it is hate speech or not to reduce the abuse that occurs on Twitter. The technology used in the classification of hate speech that is most commonly used is neural networks that require user data and meta data. In previous studies, the Naïve Bayes (NB) method has been used using the bigram, unigram and feature selection features with an accuracy of 80-85%. The k-Nearest Neighbor (kNN) method has also been used which has an accuracy of 70-85% on the clarification of hate speech by political figures. Meanwhile, the most widely used method is the Support Vector Machine (SVM) method with an accuracy of 70 to the highest 95%. To get a higher accuracy in the classification of hate speech, this study will perform a Hybrid Classifier on the Hate Speech Hashtag Classification process using a combination method of MLP, kNN, NB. The data used in this study are Twitter Tweets from November 2021 to June 2022 regarding trending hashtags. The average accuracy performance results obtained using MLP, kNN, NB were 72%, 63%,73% respectively. To improve the accuracy of the classification results of the three methods, a combination of methods using the Hybrid Classifier is carried out. Experimental results show Hybrid Classifier with voting method can increase accuracy up to 74%. It was found that the use of a hybrid can provide a better system performance than the 3 classifiers in its composition, namely kNN, NB and MLP
Penerapan Metode Regresi Linear Pada Sistem Peringatan Dini Banjir Berbasis Internet of Things (IoT)

Flood is an event when water inundates an area that is usually not flooded for a certain period of time. Floods usually occur because of continuous rainfall and result in overflow of river. Floods can cause negative impact such as puddles of water that enter homes of those affected. Therefore, we need a system to monitor the weather and can provide flood early warning. In this study the weather monitoring system and flood early warning were made based on internet of things by applying linear regression methods. The system consists of a weather sensor node, a water level sensor node and software in the form of a website. The system measures rainfall, air temperature, humidity, and water level. The process of sending data from the sensor to the server uses ESP32 as a microcontroller which is connected to a wifi network and internet. The system will send a notification if the water level is above the normal level. Based on the test results obtained as many as 45 occurrences of rainfall. The percentage of success in predicting water levels using the linear regression method is 94,4% with an error value of 5,6%.
Penerapan Data Mining Untuk Menentukan Segmentasi Pelanggan Dengan Menggunakan Algoritma K-Means dan Model RFM Pada E-Commerce

The growth of technology has a big impact on life, one of which is the use of the internet. In Indonesia, internet usage has increased every year, this has an impact on changes in the buying and selling process carried out by the community. The basic reason caused by these changes is a wider market reach. So it can be said that the presence of the online market is a positive impact caused by the development of these technologies. E-Commerce is a process of buying and selling transactions and also leasing services that are carried out by utilizing internet technology that is already available on certain media. On this basis, business actors in E-Commerce should have their own characteristics that are easily recognized by consumers. It is necessary for business people to segment potential customers. However, the problem of customer segmentation has become a problem in itself for business actors, this is due to the lack of knowledge of how to segment the customers. Data mining is a process that can be done to solve problems. With data mining the process is carried out by processing the dataset and generating new information. In data mining itself, there are several completion processes, namely the K-Means algorithm for the clustering process and the RFM model for segmentation analysis. The results obtained are that there are 4 customer segments. This can be seen from the testing process carried out from K = 2 to K = 5, in the K = 4 test the most optimal BDI value is 0.6788
Studi Perbandingan Library Untuk Implementasi Network Automation Menggunakan Paramiko Dan Netmiko Pada Router Mikrotik

In recent years, network automation has emerged as a solution to the impracticality of manually configuring network devices by network administrators. By implementing network automation, network administrators can configure and manage network devices automatically using scripts or software that is run from one of the hosts or servers in the network. This paper aims to conduct a comparative study through the implementation of cases for network automation. Four scenarios are taken as examples for the implementation of network automation, among others, static routing, dynamic routing, network address translation (NAT), and simple network management protocol (SNMP). The automation scripts are realized using Python libraries, namely Paramiko and Netmiko. Tests and simulations were carried out using the GNS3 simulator software with a network topology consisting of two Mikrotik routers, one Ethernet switch, one Network Automation server, and two computers as clients. The experiment results show that automation using the Paramiko library is better than automation using the Netmiko library in terms of the time required for the execution of configuration scripts. The average execution time for the four implemented scenarios is 3.66 seconds.
Rancang Bangun Sistem Informasi Pendaftaran Pembuatan Surat Izin Mengemudi Berbasis Web

Police have a very important role in society. The main task and function of the police in protecting the community is the main thing to do in order to create order in society. The traffic police currently acts as a service that takes care of making a driving license (SIM). So far, SIM registration is still done manually by police officers. Therefore, the applicant must go directly to the Deli Serdang Police Traffic Unit to register and fill out the form, then the officer will enter the applicant's data individually. So it must require higher accuracy for officers when entering applicant data, then it can result in long queues for applicants and can take longer. To assist officers in managing applicant data and avoid long queues. So a website-based driving license registration information system was built. With the creation of this system, it is hoped that it can be the best alternative for the Deli Serdang Police Traffic Unit officers in improving performance, especially in terms of services for managing a driving license (SIM). So that there are no longer queues that are so long and applicant files that accumulate in the archive warehouse. In building this system the author uses MYSQL as a database, PHP and HTML as a programming language, and uses the waterfall method in designing and formulating problems
Analisis Sentimen Ulasan Pengguna Aplikasi Myim3 Pada Situs Google Play Menggunakan Support Vector Machine

Technological developments are increasingly rapid, this makes it easier to communicate information and shopping transactions, one of the innovations that are being adopted is digital services, such as self-service. One of the self-services is myim3 which is a product of PT Indosat Ooredoo Hutchison as an internet network service provider company, with the increasing number of users of the application, many opinions or public sentiments are shared in the comments or reviews column, therefore it is necessary to analyze this MyIM3 application review to find out public opinion about the application. The review data is obtained from the Google Play website which is retrieved using the scraping method with the help of 3rd party libraries in python. The amount of data obtained in this study was 3484 data. Experts assist in data labeling to determine positive and negative. In the preprocessing stage, the data is cleaned to reduce the less influential attributes. In the next stage, perform the transformation process with TF-IDF. The classification process is divided into several scenarios with the algorithm used as a support vector machine with 2 kernels, linear and RBF. The best results are in the scenario (70:30) for the linear kernel with 87% accuracy and the scenario (90:10) with 87% accuracy in the RBF kernel. The classification process produces the most frequently occurring words in each sentiment class which is visualized with a word cloud. The word "good" is the most dominant in the positive review data, while the word "network" is the most dominant in the harmful review data of the MyIM3 application
Deep RL-based Abnormal Behavior Detection and Prevention in Network Video Surveillance

An important source of information for ensuring public safety is control room video surveillance. A Decision-Support System (DSS) designed for the security task force is vital and necessary to take decisions rapidly using a sea of information. In case of mission-critical operation, Situational Awareness (SA) which consists of being aware of what is going on around you in real time plays a crucial role across a variety of industries and should be placed at the center of our system. In this paper, in order to satisfy the understanding and projection levels of SA in real time, we propose a method based on Reinforcement Learning (RL) using an Actor-Critic algorithm. This algorithm permits our DSS to first, keep SA knowledge up to date by performing online adaptive learning and second, develop a long-term vision and strategy by exploiting a non-myopic agent for real-time sequential decision-making. In contrast to other approaches, our results permit demonstrating the practical potential of our method in real-world scenarios by preventing abnormal situations in an ever-changing environment.