Reviewerly/exHarmony huggingface dataset:
Application of Artificial Intelligence in the Music Industry of Ukraine: an Analytical Approach

The purpose of the research is to analyze the application of artificial intelligence in Ukraine in the music field and to outline its technologies’ impact on the domestic music industry. The research methodology is based on the use of empirical, experimental, theoretical and theoretical research methods, which made it possible to study the phenomenon of artificial intelligence application in the modern domestic music industry, systematize the available knowledge and facts on the issue, draw conclusions and theoretically summarize the impact of artificial intelligence on the development of music in Ukraine. The scientific novelty of the research lies in the fact that for the first time in the cultural and musicological scientific space, the points of the application of artificial intelligence technologies in the music industry of Ukraine are analyzed, aspects of the popularity of individual resources, practical application and their impact on product popularization are highlighted. Conclusions. The use of artificial intelligence technologies in the Ukrainian music industry has not yet gained mass recognition, which is officially recorded in several facts, including the creation of a music product and the production of a video for a music video. The issue of legal regulation of the involvement of artificial intelligence in the development of a music product and the definition of intellectual property rights remains open. The author has outlined the main areas of artificial intelligence technologies’ usage in applications related to the music industry. The role of artificial intelligence in the popularity of a music product is analyzed.
ANALISIS SENTIMEN ARTIKEL BERITA PEMILU BERBASIS METODE KLASIFIKASI

The distribution of information in the form of online news is so massive in the wider community, that it is difficult to distinguish between haox news and positive news. So that a classification is needed regarding public sentiment about the implementation of elections using mainstream media news article data using 1064 dataset test data. The methods used in this study are the naive Bayes algorithm, the random forest algorithm, and the support vector machine algorithm. The test model uses smote where the performance results are carried out by the algorithm used using smote and not using smote, where random forest produces an accuracy of 91.88%, while without using a smote support vector machine it produces an accuracy of 92.05%.
Implementasi Sistem Informasi Pencatatan Laporan Tahfiz pada Yayasan Majelis Hifzhu Syifaa Cileungsi

This article discusses the implementation of the website based Tahfiz Report Recording Information System application at the Assembly Hifzhu Syifaa Cileungsi Foundation because of community service activities. The main purpose of implementing this application is to make it easier for the Foundation to manage and record the students' memorization reports. This website-based application allows teachers to easily enter grades and notes related to students' memorization through the application, thereby speeding up the recording and assessment process. This application can be accessed via a web browser, providing access flexibility for users. In application development, special attention is paid to a simple user interface to make it easy for novice users to use. The research method used is descriptive qualitative with data collection through questionnaires and research. The development technique applied is Prototype, where application functions are tested with relevant input. The results of this implementation present a website based Tahfiz Report Recording Information System application as a practical solution for the Assembly Hifzhu Syifaa Cileungsi Foundation in managing rote reports. It is hoped that this application will provide significant benefits and can be adopted by foundations or similar institutions that require an effective and efficient tahfiz report recording system.
ORALSCREEN-ORAL CANCER DETECTION USING DEEP LEARNING

Oral cancer is a severe public health problem with a high fatality rate, and early identification is critical for increasing survival rates.We propose a deep learning-based technique for detecting oral cancer utilizing two types of oral cavity pictures: normal oral images and histopathologic images in this work.We trained our models on a huge dataset of oral cavity pictures using two cutting-edge convolutional neural network (CNN) models, ResNet50 and VGG16, using transfer learning.According to our findings, ResNet50 had an accuracy of 95% and VGG16 had an accuracy of 94% in identifying oral cavity pictures as cancerous or non-cancerous.We have integrated our model into OralScreen, an online tool that can be utilized by both patients and medical experts such as physicians and histopathologists.Our findings show that deep learning-based techniques have the potential to increase the accuracy and efficiency of oral cancer diagnosis dramatically.
ALICE – Applying BERT to Italian Emails

ALICE is an Artificial Intelligence solution that allows the automatic classification of email-type documents based on their information content, analyzed almost in real-time. The emails are written in Italian language. The classification classes are equal to 591, and in the use of the service, they will be able to grow further. The aim of this paper is to explores the implementation of the BERT model in the ALICE email classification system for multiclass classification in the Italian language. The main objective of the ALICE solution is the automation of classification processes performed manually by dedicated operators. To improve the performance of the BERT model and to allow the addition of further classification classes, a transfer learning process has been envisaged. The ALICE service, which uses the BERT model trained on the provided dataset, presently has a 76% accuracy.
Research and Practice of a Blended Teaching Mode based on Small Private Online Courses Under an Informatization Background

With the continuous development of information technologies, the pace of education informatizationis accelerating increasingly. The traditional classroom-teaching mode cannot fully meet students’learningdemands. Due to the emergence of new teaching forms in the post-pandemic era, rapid development of ITapplications, national education-informatization policies and deployment, and the plight of the traditional teaching mode, there is a need to explore new ideas and patterns of integrating information technologies and teaching approaches. With the course of Heat Transfer as an example, this study expounds a blendedteaching mode based on small private online courses (SPOC) on course aims, teaching contents, teachingmethods, and evaluation approaches under the background of informatization. The results showthat this teaching model can enhance students' learning ef ect and comprehensive ability ef ectively. The rules of blended teaching were explored to guide teaching improvement.
Clustering an African Hairstyle Dataset using PCA and K-Means

The adoption of digital transformation was not expressed in building an African face shape classifier. In this paper, an approach is presented that uses k-means to classify African women images. African women rely on beauty standards recommendations, personal preference, or the newest trends in hairstyles to decide on the appropriate hairstyle for them. In this paper, an approach is presented that uses K-means clustering to classify African women's images. In order to identify potential facial clusters, Haarcascade is used for feature-based training, and K-means clustering is applied for image classification.
Approaches to Improving English Teaching Quality in Rural Areas under the Background of Rural Revitalization Strategy

With the implementation of the rural revitalization strategy proposed, more and more studies are focusing on English education in rural elementary and secondary schools.According to the present study, three main aspects of the current problems have been found, namely, society, schools, and teachers.The social level is mainly due to the macroscopic policy and the uneven distribution of resources, leading to the uneven education quality in a large area.At the school level, due to the limited conditions in rural areas, teachers are treated relatively poorly in the same occupation level.The schools themselves have inadequate hardware facilities and outdated multimedia equipment, leading to a decline in the quality of education.The third aspect is the teachers themselves, and the choice of teaching methods plays a crucial factor in the quality of education.Therefore, combining with the previous literature on education research in Sichuan and Chongqing, the author proposes supplementary comments to make it more available.This paper aims to make valuable suggestions for the standard of teaching in rural schools, promoting the balance of national education systems, and narrowing the gap in education quality.
Classification of Arabic Alphabets Using a Combination of a Convolutional Neural Network and the Morphological Gradient Method

The field of Optical Character Recognition (OCR) is the process of converting an image of text into a machine-readable text format. The classification of Arabic manuscripts in general is part of this field. In recent years, the processing of Arabian image databases by deep learning architectures has experienced a remarkable development. However, this remains insufficient to satisfy the enormous wealth of Arabic manuscripts. In this research, a deep learning architecture is used to address the issue of classifying Arabic letters written by hand. The method based on a convolutional neural network (CNN) architecture as a self-extractor and classifier. Considering the nature of the dataset images (binary images), the contours of the alphabets are detected using the mathematical algorithm of the morphological gradient. After that, the images are passed to the CNN architecture. The available database of Arabic handwritten alphabets on Kaggle is utilized for examining the model. This database consists of 16,800 images divided into two datasets: 13,440 images for training and 3,360 for validation. As a result, the model gives a remarkable accuracy equal to 99.02%.
The Number of Occurrences of Patterns in a Random Tree or Forest Permutation

The classes of tree permutations and forest permutations were defined by Acan and Hitczenko (2016). We study random permutations of a given length from these classes, and in particular the number of occurrences of a fixed pattern in one of these random permutations. The main results show that the distributions of these numbers are asymptotically normal.&#x0D; The proof uses representations of random tree and forest permutations that enable us to express the number of occurrences of a pattern by a type of U-statistics; we then use general limit theorems for the latter.
Computer Programming to Estimate the Global Daily and Hourly solar Radiation of any location around the Globe

Solar data collection and radiation analysis are basic to study several solar energy-related types of research. But, most researchers get difficulty while collecting such valuable data. In most developing countries and rural areas of the world, it is a bottleneck situation to get the ground-level solar radiation data. The intended purpose of this research is to develop a code that can estimate the daily and hourly variation of global solar radiation for any location around the globe. A python script is developed to make users flexible to use for any climatic region of the world. As an illustration, the developed code is implemented for three regions namely, Algeria, Pakistan, and Nigeria that researchers developed a correlation for each climatic region. The output shows that it is possible to use a computer program for any climatic region that researchers or any users want to find the estimated global daily and hourly solar radiation data.
Mathematics smart books based on QR Code as a digital learning media material limit function

Based on research data conducted by Cambridge International through the Global Education Census, it is stated that 67% of Indonesian students use smart phones in class, while 90% of teachers in Indonesia still use blackboards when teaching. Through this research, produced a learning medium, namely Smart Book based on QR Code, which allows students to more easily access digital learning materials so that they can adapt to the habits of students in class. The type of research used is Research Development with a 3D model (Design, Develop, and Deliver) and two data analysis techniques, namely: validity data analysis and student response data analysis. The procedure for making QR Code-Based Math Smart Books relies on the functions of several technologies, namely: QR Code, QR Scanner, and Google Drive. After the manufacturing process, media validation and material validation were carried out. The results of media and material validation showed the validity level of 3.83 and 4 which were in the very feasible category. Based on the results of the trial with 6 respondents, the students responded well. Therefore, based on validation and trials, it can be concluded that the Smart Mathematics Book media can overcome student learning gaps where students use smartphones more often in class.
Human Activity Recognition in the Presence of Occlusion

The presence of occlusion in human activity recognition (HAR) tasks hinders the performance of recognition algorithms, as it is responsible for the loss of crucial motion data. Although it is intuitive that it may occur in almost any real-life environment, it is often underestimated in most research works, which tend to rely on datasets that have been collected under ideal conditions, i.e., without any occlusion. In this work, we present an approach that aimed to deal with occlusion in an HAR task. We relied on previous work on HAR and artificially created occluded data samples, assuming that occlusion may prevent the recognition of one or two body parts. The HAR approach we used is based on a Convolutional Neural Network (CNN) that has been trained using 2D representations of 3D skeletal motion. We considered cases in which the network was trained with and without occluded samples and evaluated our approach in single-view, cross-view, and cross-subject cases and using two large scale human motion datasets. Our experimental results indicate that the proposed training strategy is able to provide a significant boost of performance in the presence of occlusion.
Antimicrobial and Metal Tolerance of Bacteria Isolated from Underground Water Sample of Aged Crude Oil Contaminated Site

The study was aimed at evaluating metal tolerant and antibiotic resistant bacteria isolated from underground water around aged crude oil polluted site. Samples were collected from different locations around aged crude oil polluted site and control sample from an uncontaminated site of Bodo community, Gokana Local Government, Rivers state, Nigeria. The samples were cultured on nutrient agar, Bushnell Hass and MacConkay agar using standard microbial technique. Antibiogram of the isolated and identified bacteria were determined by Kirby-Bauer disc diffusion method. The bacterial tolerance of different concentrations of the heavy metals, Chromium, Vanadium, Arsenic, Cadmium and Lead was determined. The total heterotrophic bacterial count (THBC) of the samples ranged from 1.26×104 CFU/ml to 3.6×105 CFU/ml. Count of hydrocarbon utilizing bacteria (HUB) ranged from 1.02×103 to 3.2×103 CFU/ml and the coliform count of the sample ranged from 4.2×103 CFU/ml to 4.0×103ml. The predominant bacteria identified were Bacillus sp, Micrococcus sp, and Staphylococcus sp, Enterobacter sp and Proteus sp. All (100%) the Gram-positive bacteria were resistant to the antibiotic, Ceftazidime, Cefprozil and Cloxacin, 92% were resistant to Gentamycin and Erythromycin while 80% were resistant to Augmentin. All (100%) of the Gram-negative bacteria isolates were resistant to cefluroxime, 66% were resistant to Augmentin and Ceftazidim, and 33% were observed to be resistant to Nitrofurantin and Gentamicin. It was observed that all the isolates were tolerant to 50 µg/ml concentration, 70 to 100% of the isolates were tolerant to 100 µg/ml concentration, 17 to 100% were tolerant to 200 µg/ml concentration while 11 to 41% were tolerant to 300 µg/ml concentration of all the heavy metals studied. From this study, it was revealed that petroleum aged contamination could be a source of heavy-metal tolerance and antibiotic resistance in bacteria.
Pendampingan Penyusunan Soal Evaluasi Pembelajaran Berbasis Digital Bagi Para Guru Madrasah Aliyah Islamiah Malo Bojonegoro

Madrasah Aliyah Malo, Bojonegoro Regency, the media used in learning and evaluation are still relatively difficult for students to focus on learning. Included here are PAI subjects. This school has an adequate computer laboratory. Also, teachers and their students have smartphones that are no less sophisticated to be used as learning media. It's just that they have not been able to optimize these facilities. So the first problem can be formulated: how to understand teachers about optimizing ICT for learning in schools and secondly, how to grow teacher skills in optimizing the creation of digital-based learning evaluation questions. The method applied uses the Asset Based Community Development (ABCD) approach. The result of the implementation of this training and mentoring is that teachers have digital skills and knowledge and there is a significant change in the seriousness and understanding of students in participating in learning. As a suggestion of this training be used and practiced in learning to improve student understanding.
Dyslexia and Eye Tracking

Our model, based on Support Vector Machine binary classifier, achieves 80.18\% accuracy using the most informative features. As far as we know, this is the first time eye tracking measures have been used to automatic prediction of dyslexic readers using machinelearning.
Machine learning in event detection

Any events that have already been detected manually or algorithmically can be used to train the classifier to perform a similar classification of other data without the user having to define parameters. In this study, we investigate the use of random machine learning techniques in the forest to detect fixations, jerks and post-saccadic oscillations (PSOs). To demonstrate the practical applicability of the proposed method for applications involving eye movement classification algorithms, we present an example of the method in an eye-movement-driven biometric application. We conclude that machine learning techniques lead to better recognition performance than current state-of-the-art event recognition algorithms and can match the performance of manual coding.
Implementasi Aplikasi Learning Management System and Student Attendance pada IDN Academy Berbasis Web

The purpose of this service activity is to assist IDN Academy in improving the learning process and student Attendance through the implementation of the Learning Management System (LMS). Application and web-based Attendance system for students. A waterfall research collaboration service methodology is used to perform the stages of system analysis, system design, implementation, testing, and analysis of results. During the service process, a system analysis was performed to determine the needs and requirements of the LMS application and web-based student Attendance system. In addition, we will create a use case diagram that explains the functions of the entire system and design the system. We then ran the implementation phase, distributing questionnaires on a Likert scale of 1 to 5, and testing the application with black box and system quality tests. Test results showed that the LMS application and web-based student Attendance system enabled IDN Academy to manage student learning and Attendance effectively and efficiently. Respondents also agreed on the quality of the system, achieving an average score of 4.2 on the distributed questionnaire. The results of this service are expected to enable IDN Academy to leverage its LMS application and web-based student Attendance system to improve the quality of student learning and Attendance.
The Development of Social Science Learning Media Based on Wordwall Digital Game in Elementary Schools

This study aims to (1) describe the need for the Development of social science learning media based on digital game word walls in elementary schools; (2) know the description of social science learning media design based on digital word wall games for elementary school students ; (3) know the description validity of digital game word wall- based social science learning media for elementary school students ; (4) knowing the practicability of social science learning media based on the digital game word wall for elementary school students ; (5) knowing the effectiveness of social science learning media based on the digital game word wall in improving student learning outcomes at Elementary Schools. This type of research is Research and Development (R &amp; D). The research and development procedure used is 4-D: define, design, develop and disseminate. The subjects of this study were fourth-grade students at SD Negeri Minasa Upa Makassar, consisting of 31 students and two teachers. Data was collected through test techniques, observation, and questionnaires. The data analysis technique used is quantitative analysis according to the research and development stage. The results of this study indicate that : (1) The need to develop social science learning media based on the digital word wall games is a fundamental demand in the digital era in learning where challenging and fun interactions are needed in the form of word wall games for elementary school students; (2) Social science learning media based on the digital word wall games are designed through the stages of selecting games, compiling quizzes, selecting formats, initial design, and limited trials; (3) digital word wall game- based social science learning media has an adequate level of validity in all components. The display aspect of word wall game digital media, stages, and tests obtained the maximum value from the validator. (4) Social science learning media based on digital game word walls fulfill the practicability requirements through the positive response of teachers and students, which is marked by increased student activity in learning. (5) Social science learning media based on digital game word walls is practical for social studies learning, marked by increased student learning outcomes through pretest and posttest.
PEMBANGUNAN COMPILER DOMAIN SPECIFIC LANGUAGE SEBAGAI GENERATOR FORM HTML MENGGUNAKAN PYTHON SLY

The rapid development of information technology has a significant impact on the way companies operate. Companies can utilize technology to simplify and accelerate their business processes, including recording and managing transactional data. PT XYZ is one of the largest retail companies, with a vast amount of data and requiring various types of different HTML forms. Therefore, standardization in program code writing is necessary to minimize errors and facilitate application maintenance. This research aims to build a Compiler Domain Specific Language (DSL) as a web-based HTML form generator for PT XYZ. The study uses the Python SLY library, passing the Lexer and Parser processes in DSL development. The result of this research is a web-based HTML form generator that facilitates developers in creating standardized and automatic HTML forms. The use of DSL can speed up the program code writing process, minimize errors, and create structured and easily understood code by developers.