Reviewerly/exHarmony huggingface dataset:
Examining the Effectiveness of Teaching Strategies for Alleviating EFL Students’ Writing Anxiety: A Mixed-Method Study

At higher education, students at the end of the study are required to write a final project. Writing English papers is challenging and might create writing anxiety. Therefore, it is necessary to look at what strategies are used by the teachers and their effect on anxiety levels and performance. Furthermore, it is also crucial to accommodate students’ perceptions of the strategies so that students’ voices provide valid information on how the classroom should be. This research uses a mixed-method methodology to analyze lecturers' anxiety-reduction strategies and students' perceptions of them. Three instruments were used to collect data, namely a Second Language Writing Anxiety Inventory (SLWAI), open-ended questions about the perception, and students’ writing scores. A paired sample t-test was used to compare students' writing anxiety and performance before and after treatment. Content analysis was used to assess student opinions of the strategies. The statistics showed that students' writing anxiety fell from high (71.35) to moderate (66.47). Meanwhile, students' writing grade rises from 72.00 to 79.03. SPSS analysis showed that anxiety-reducing strategies significantly affect writing anxiety and student performance (p = 0.000). Consequently, this study's findings should be beneficial in writing classes.
A COMPARATIVE ANALYSIS OF INFORMATION SECURITY INCIDENTS IN PUBLIC ADMINISTRATION IN SELECTED EUROPEAN UNION COUNTRIES

The article presents the issue of information security incident management in public administration. The goal of security incident management is to minimize the negative impact of incidents and ensure the continuity of the organization's operations. It is critical to know what the threats are, including the number and types of incidents reported. The article outlines key incident management and legal issues related to the topic of security incidents and the work of computer incident response teams. Examples of incidents that took place recently in public administration units in Poland were also presented. This was followed by an analysis of statistical data on reported incidents in Poland in the years 2020-2022, and the results were compared with the number of incidents reported in selected European Union countries. The results of the study show that the dominant type of incidents is fraud (mainly phishing), and public administration is one of the main targets of cybercriminals' attacks. Difficulties in conducting such comparative analysis have also been demonstrated.
Study of methods for searching and localizing objects in images from aircraft using convolutional neural networks

The use of unmanned and manned aerial vehicles for remote object localization and classification is very common. These methods are used in various systems, ranging from territory surveys to law enforcement. Methods of object localization and classification using neural networks require a detailed study and research of the quality of their work on data that has certain specifics, such as vehicle detection. The use of neural networks to detect certain types of objects using images obtained from aircraft can also help in the study of hard-to-reach locations. Therefore, the main subject of this paper is the localization and classification of objects in images obtained using digital cameras mounted on aircraft. The main focus is on determining the accuracy of object localization and detection using selected types of neural networks, which are the most important indicators of neural network efficiency. The speed of a neural network is also an equally important characteristic as it directly affects its ability to be used in tasks that require fast object localization, such as video surveillance or automated car control systems. The main goal of this study is to study the accuracy of object localization and classification in images obtained with the help of cameras mounted on aircraft, as well as to study the speed of neural networks and determine the effectiveness of their application in real-world conditions. The objectives of this study are to train YOLO v5, SSD, and Faster RCNNs on the VisDrone dataset and to further study them on the vehicle localization dataset. The main goal of this work is to obtain statistics on the performance of neural networks trained on the VisDrone dataset. On the basis of the obtained statistics, conclusions are drawn about the effectiveness of the considered neural networks. The conclusions are drawn by considering the speed of the model, localization (IoU), and classification (Precision, Recall) metrics. Possible directions for further development of the topic under study are presented as conclusions.
A novel anomaly detection model for the industrial Internet of Things using machine learning techniques

In recent decades, the pervasive integration of the Internet of Things (IoT) technologies has revolutionized various sectors, including industry 4.0, telecommunications, cloud computing, and healthcare systems. Industry 4.0 applications, characterized by real-time data exchange, increased reliance on automation, and limited computational resources at the edge, have reshaped global business dynamics, aiming to innovate business models through enhanced automation technologies. However, ensuring security in these environments remains a critical challenge, with real-time data streams introducing vulnerabilities to zero-day attacks and limited resources at the edge demanding efficient intrusion detection solutions. This study addresses this pressing need by proposing a novel intrusion detection model (IDS) specifically designed for Industry 4.0 environments. The proposed IDS leverages a Random Forest classifier with Principal Component Analysis (PCA) for feature selection. This approach addresses the challenges of real-time data processing and resource limitations while offering high accuracy. Based on the Bot-IoT dataset, the model achieves a competitive accuracy of 98.9% and a detection rate of 97.8%, outperforming conventional methods. This study demonstrates the effectiveness of the proposed IDS for securing Industry 4.0 ecosystems, offering valuable contributions to the field of cybersecurity.
An Evaluation of the Quality Educational Services in D3 Computerized Accounting at Politeknik Negeri Banjarmasin with the Matrix Method Importance Performance Analysis

Evaluation of the quality of educational services is an important process to assess the extent to which an educational institution is able to meet the needs of students and provide quality services. Therefore, there is a need for consistent evaluation of continuous improvements to the quality of educational services to achieve customer needs and satisfaction, especially for the D3 Computerized Accounting Study Program, Accounting Department, Politeknik Negeri Banjarmasin (POLIBAN). By improving the satisfaction of all stakeholders involved in academic services, it is hoped that it can increase the number of prospective new students and the competitiveness of graduates so that they can be accepted into the D3 Computerized Accounting Study Program, Accounting Department, POLIBAN. This research aims to evaluate the quality of educational services in the D3 Computerized Accounting Study Program, Accounting Department, POLIBAN using the Importance Performance Analysis Matrix method. The research results found that there was a significant difference between expectations and perceptions regarding educational services in the D3 Computerized Accounting Study Program, Accounting Department, POLIBAN. This research provides recommendations to the D3 Computerized Accounting Study Program, Accounting Department, POLIBAN, to make continuous improvements so as to increase the number of students entering the D3 Computerized Accounting Study Program, Accounting Department, POLIBAN.
Komparasi dan Implementasi Algoritma Regresi Machine Learning untuk Prediksi Indeks Harga Saham Gabungan

Indeks Harga Saham Gabungan (IHSG) or Indonesia Composite Index (ICI) is part of the macro indicators of a country that describes the economic condition of a country. ICI is an interesting study to research since its existence will be able to show market sentiment regarding an event that occurred in a country. This research tries to predict the ICI in the future based on historical data. The dataset used in this research is publicly available in Yahoo Finance. The experiment is conducted by implementing some regression machine learning algorithms, such as Decision Tree, Random Forest, k-Nearest Neighbor (kNN), and Linear Regression. As a result, Decision Tree has the lowest MSE value compared to other methods: 1268.242. In this research, a website-based application prototype was also developed that can be used to view IHSG graphs and make future predictions, using the 4 (four) tested algorithms.
Rancang Bangun Aplikasi Keuangan Untuk Mengatur Jumlah Pengeluaran Pribadi Berbasis Android

Not everyone is able to manage their finances well. Consumptive lifestyles and lack of awareness about the importance of managing finances make a person unable to escape financial problems. In an effort to reduce the impact of existing problems, everyone should have the awareness and effort to manage their finances by recording their financial expenses regularly. In modern times and the rapid development of technology like today, almost everyone has a mobile device at least one Android device. By utilizing existing technology, various things can be done using only their Android devices such as shopping, communicating with someone even though it is quite far away or even to record financial activities. By identifying and collecting data and interviews from related problems, this research aims to be a solution for someone in managing their finances by recording every financial activity in an existing application and making the use of stationery and books to record and recap every expense can be abandoned. With this application, a person can easily record finances anytime and anywhere without fear of losing the notes that have been made. As a result, it is hoped that eating will become wiser and know how much and where the money is spent so that the record can be used as a picture of a person in controlling their money.
Pengumpulan Informasi pada Situs Web Dengan Menyusun Kerangka Kerja Keamanan Siber NIST

In the current era, the rapid development of websites has made them one of the most significant modern information media. Website creation is not only focused on the design and information presented, but also focuses on security aspects. The presence of security on a website is very important, considering the need to protect the data and information contained therein. Information Gathering is one method used to test a website's security. This information gathering is the earliest stage to obtain ownership and other sensitive information. This research aims to conduct security testing of the oase.poltektegal.ac.id website using tools in the form of penetration testing software; then, the testing results are entered into the cybersecurity framework issued by N.I.S.T. The test results obtained and adjusted to N.I.S.T. Cybersecurity are that the oase.poltektegal.ac.id website has vulnerabilities in the form of CVE-2003-1418 (apache webserver vulnerability), CVE-2005-3299 (PHP vulnerability), CVE-2010-4344 ( Buffer Overflow Vulnerability), CVE-2007-6750 (XSS). The solution to this vulnerability is updating the software and closing unused ports. These results will be used as a benchmark in creating or improving similar websites to increase awareness and vigilance in achieving cyber resilience
Evaluation of Different Stemming Techniques on Arabic Customer Reviews

Customer opinion and reviews play a vital role in marketing expansion. Big companies all over the world assign a lot of their efforts to analyzing customers’ feedback to keep track of their needs. Natural Language Processing (NLP) is widely used to analyze such review texts. Arabic customer analysis and classification also began to gain researchers’ attention due to the wide range of Arabic language speakers. Working with Arabic Language is a very challenging task because of the orthographic nature of Arabic. Also, customers often write their reviews in their dialectical style, which often diverts from standard Arabic. This study presents a method to classify Arabic customer reviews using four classifiers (K-nearest Neighbor (KNN), Support Vector Machine (SVM), Logistic Regression (RL), and Naïve Bayes (NB)). The classification is implemented with three stemming techniques (Snowball, Khoja, and Tashaphyne). The HARD dataset is adopted to perform the experiments. The results stated that the stemming methods can enhance classification performance despite the complexity of Arabic scripts and dialects. This work sheds light on utilizing and investigating more machine learning (ML) techniques and evaluating the results.
Permainan bahasa dalam penggunaan bahasa Inggris oleh pedagang asongan di pantai Kuta Lombok

The playfulness of English is the result of language interference, which can be a source of creating new English variations. The purpose of this study is to describe the form of English playfulness by hawkers at Kuta Beach, Lombok, and the factors of English playfulness. The method is descriptive qualitative. The data collection by ‘simak libat cakap’ or involved conversation observation technique, recording, and taking notes technique. The data analysis method is reduction, presentation, and conclusion. Data analysis shows that the playfulness of English by hawkers comes from loan translation in the form of reduplication of Indonesian and Sasak words into English. There are two forms of loan translation: a change in the meaning of English in accordance with Sasak and Indonesian, and not experiencing a change in meaning but experiencing the addition of English meaning in accordance with Sasak and Indonesian. The factors causing the English playfulness are internal factors such as the difference in vocabulary structure from Sasak and Indonesian to English and the influence of Indonesian and Sasak as the dominant language. Factors external the language are influential in English playfulness, such as level of education, economic status, occupation, regional origin, and the situation of speech.
Storybook berbasis augmented reality (AR) meningkatkan hasil belajar bahasa Inggris siswa

This study focuses on the combination of digital media and students' literacy, utilizing an Augmented Reality (AR)-based storybook as a tool for English language subject. The aim of this research is to assess how the use of AR-based storybooks can influence and enhance students' learning outcomes in English language subject. Employing an experimental research design, two groups were exposed to different stimuli. The experimental group's test results showed a significant difference with a two-tailed significance value of 0.032, indicating an impact attributed to the utilization of AR-based storybooks. Additionally, the N-gain percentage results revealed a 60% improvement, categorizing the AR-based storybook as moderately effective in enhancing students' learning outcomes compared to conventional storybook media. The findings of this study indicate that the implementation of AR-based storybooks significantly and effectively enhances students' learning outcomes in English language subject.
Dampak strategi quick write berbantuan media animasi terhadap keterampilan menulis teks persuasi siswa

This research is motivated by the need for innovation in the learning process in the Industrial Revolution 4.0 era. This research aims to determine the impact of the quick write (QW) strategy assisted by animation media on the ability to write persuasive texts for class VIII students. This research implemented a pretest-posttest control group design involving students from SMP Negeri 5 Pontianak. Students were divided into two groups consisting of 64 students selected through cluster random sampling. The instrument consists of essay questions in the form of a pre-test and post-test. The data were analyzed using the t-test and the results showed that 1) students who were treated with the QW strategy assisted by animation media showed a significant increase in their ability to write persuasive texts, and 2) the QW strategy assisted by animation media contributed to student independence in learning to write, students were active and able to highlight the argument process quickly and responsively related to actual problems with the linguistic skills they have. These findings show that students who use the QW strategy assisted by animation media have a better improvement in their ability to write persuasive texts than students who use direct learning.
Library Book Recommendation System Using Content-Based Filtering

In today's digital era, libraries are developed to adapt to student needs. Students can easily search for books on digital library services. However, the large number of books sometimes makes it difficult for students to find the books they want. Overcoming this problem can be done by using a recommendation system. A recommendation system is a system used to provide suggestions to users. This research aims to develop a library book recommendation system at Darul Mustofa Bangkalan Vocational School using content-based filtering. The content-based filtering method offers recommendations based on user preferences according to the item description. The algorithms used are Term Frequency Inverse Document Frequency (TF-IDF) and Cosine similarity. The method used in this research is research and development with a waterfall model. The researcher's testing stage used black box testing. Black box testing results were obtained from validation by system experts, website experts, and user tests with the qualification "Very Eligible."
Conceding Sentiment Prognosis on Twitter Data

Twitter is the biggest micro-blogging website that gives people a platform to share their opinions about any new happenings around the world. The size of tweets is generally short which makes it very suitable for opinion mining. The key focus of the paper is to analyze the feelings and ideas. In this paper, analysis is done on the classification of tweets on a particular keyword. The tweets related to the given keyword are collected, analyzed, and the result is generated in the form of percentage of positive, neutral and negative sentiments, which gives us a sense of overall sentiment of the keyword. Further, Classification is done using supervised learning algorithms and the best among these will be found by calculating the accuracy of each.
1. THE MOST POPULAR ARTIFICIAL INTELLIGENCE APPLICATION USED BY AEROSPACE ENGINEERING STUDENTS’ TO LEARN MATHEMATICS

Artificial intelligence (AI) develops over time. Education is concurrently the AI evolution,sometimes in front, sometimes behind. Education underlies AI, and later on, AI is used in education. Therefore, the utilization of AI in education cannot be denied. There are many advantages, but also some limitations and ethical issues that arise from the use of AI in education. There are many applications of AI in mathematics scattered throughout society, but not all of them are useful enough. Thus, the purpose of this research is to gain the famous application AI in mathematics (app AI math) that is mostly used and recommended by the students’ experience in doing and learning mathematics. T-test independent samples were used to test the score of two groups, using the AI math app or not, in order to know whether it was affected or not. The result of this research is that the most popular app is Photomath, followed by ChatGPT and Mathway. But still, many students did not use this app. Relating to their score, the students who have used the AI math app to learn math before are not different from the students who do not use the AI math app.&#x0D;
Digital Literacy Activities

In an increasingly technological-focused environment, it is paramount that library instructors infuse digital literacy skills into their teaching. Librarians as information professionals are in a prime position to impart digital literacy skills into patrons. This not only creates a more exciting lesson plan but also has the potential to teach students necessary digital literacy skills that are transferable beyond their schoolwork. In this chapter, the author examines the position of digital literacy in instruction and as a way to build employable skills. Examples of digital literacy activities' that can be incorporated into information literacy instruction sessions no matter the length of the session are also offered. These activities include digital footprint tracking, games, and website building. The chapter also discusses the assessment of these activities which evaluated the students perceived value of the information and skills learned from the digital literacy activities.
Intersecting Minds and Machines

The integration of technology in the medical sector, particularly for data acquisition and analysis via the internet of health things (IoHT), offers significant potential for enhancing community health management by monitoring individual lifestyle patterns. A systematic literature review focusing on articles published between 2019 and 2021 explored the behavioral intention toward using IoHT-based healthcare systems. Analyzing 20 selected articles revealed a preference for theories such as the technology acceptance model (TAM), the unified theory of acceptance and use of technology (UTAUT), and the theory of planned behaviour (TPB) to facilitate behavioral change. Key factors influencing behavioral intention toward these systems include performance expectancy, perceived usefulness, effort expectancy, social influence, and resistance to change. The findings highlight the importance of user-centric design in developing IoHT-based healthcare systems to effectively meet community health needs, emphasizing that healthcare solutions should be tailored to individual health behaviors and preferences.
Narrative Threads and Cinematic Connections Using Intelligent Systems to Enhance Movie Recommendations with Market Basket Analysis and Advanced Algorithms

Movie streaming services are businesses driven by data and strategies to predict future viewing patterns based on historical data. Without unsupervised learning techniques, industries like movie streaming services might face laborious tasks and issues in anticipating customer preferences and forecasting changes in customer behavior. In this chapter, market basket analysis (MBA) and recommender systems were implemented on MovieLens Data. In MBA, movie watching patterns were identified using two types of rule-generating algorithms, namely the apriori algorithm and the FP-growth algorithm. Three visualization idioms were generated to understand the association rules extracted in the MBA. Secondly, five types of recommender systems, namely memory-based collaborative filtering, model-based collaborative filtering, content filtering, context filtering, and the hybrid method, were implemented to suggest relevant movies to customers. Each recommender system was experimented with three different TopN configurations, and the results were evaluated using information retrieval metrics.