Reviewerly/exHarmony huggingface dataset:
Pengaruh Penyempitan Penampang Krueng Pase terhadap Kedalaman Air Normal dan Kritis

The Krueng Pase River is one of sources of raw water used for community needs.At this time The Krueng Pase River has undergone changes incross-sectional conditions, namely The occurrence of narrowing in several places. This cross-sectional change affects the flow pattern it passes through. Narrowing of the cross-sectional flow can cause changes in the river roughness coefficient Manning and also affect normal and critical flow conditions due to changes in flow velocity and cross-sectional area.Measurement using currentmeter and buoys for determining flow rate.the result is that there is a change in the slope to become more gentle whice causes a narrowing of the cross section due to reduced speed which affects the Manning value in the area (So&gt;Sc), steepflow.
Literature Review of Picture Book Teaching in Kindergarten

As the first reading for children, picture books are the main carrier of early parent-child co-reading as well as an important medium for kindergarten teaching.Based on the importance of picture books to children's development, picture books are rapidly becoming popular around the world, and researches on picture book teaching at home and abroad are gradually enriched.More and more education researchers begin to pay attention to picture books and conduct related exploratory researches.The number of published articles in related researches shows a developmental trend, and the research perspective and scope are constantly expanding.The content of the research is constantly refined and deepened.Through the study and reference of kindergarten picture books and related publications of picture book teaching in recent years.This paper mainly reviews and summarizes the existing researches from the aspects of the literature situation of kindergarten picture book teaching, the concept and development of picture books and picture book teaching, the focus of research on kindergarten picture book teaching activities (the value of picture book teaching, the existing problems of picture book teaching, the teaching strategies and methods of picture books), and the shortcomings of research.It will provide some reference for future research on picture book teaching in kindergartens.
Case Study of Digital Transformation Based on ChatGPT Teaching Application

With the wide application of ChatGPT in teaching, digital teaching is ready to emerge, and new requirements and expectations are put forward for the digital transformation of teaching system and its whole elements.As a natural language model with highly intelligent knowledge search and automatic text editing, ChatGPT has been widely used in teaching with its powerful Adaptive learning ability and convenient and practical human-computer dialogue system, and has gradually formed Digital transformation cases based on language learning, subject knowledge assistance, creative writing, learning guidance and many other teaching scenarios in the exploration of teaching reform practice.Based on ChatGPT application of teaching reform and exploration, this paper collects and sorts out teaching cases related to digital transformation.Through the investigation and analysis of the existing Digital transformation cases, this paper discusses the general characteristics and rules of digital transformation of teaching elements, and discusses the structural changes of the teaching system and teaching elements under the ChatGPT application scenario from the two aspects of application scenario creation and application effect evaluation, and puts forward countermeasures and suggestions for building a digital teaching system, which has important theoretical and practical significance for deepening curriculum and professional reform and accelerating independent training of innovative talents.
Effect of Gender Differences in Reading Habits of University Students in Sri Lanka

Reading is considered as an important learning tool which comprised of multiple skills.Efficient reading by university students plays a vital role in shaping our society's future.Reading habits of University students is determined by several factors including age, gender, field of study and life style.Hence, this study was conducted to explore the effect of gender differences in reading habits of university undergraduates in Sri Lanka by obtaining data using questionnaires from 192 male and 474 female students of six universities.The results revealed that enjoying reading, reading preferences, how often they are reading and reading frequency significantly varied between the two genders (p<0.05).Females enjoyed reading and spent more time than their male counterparts.Most female students preferred novels (28.5%), while male respondents (7.5%) selected books to read.The majority of the female students responded that preparation for exams (39.6%) was the main barrier against reading, while male students selected social networks (16.5%).In terms of frequency, the majority of the girls (19.5%) read once a week, while 9.3% of boys read less frequently, whereas 0.9% of them never read.Based on this study, female are better in reading and showed positive attitude towards it than male students in Sri Lankan universities.Thereby, it is recommended to the universities in Sri Lanka to provide necessary guidance and motivation in developing reading skills, especially among male students.
A Multi-view Breast-wise Abnormality Detection for Breast Cancer Screening using Mammography

Abstract Breast cancer has the highest incidence rate among women in Ethiopia compared to other types of cancer. Unfortunately, many cases are detected at a stage where a cure is delayed or not possible. To address this issue, mammography-based screening is widely accepted as an effective technique for early detection. However, the interpretation of mammography images requires experienced radiologists in breast imaging, a resource that is limited in Ethiopia. In this research, we have developed a model to assist radiologists in mass screening for breast abnormalities and prioritizing patients. Our approach combines an ensemble of efficient-based classifiers with YOLOv5, a suspicious mass detection method, to identify abnormalities. The inclusion of YOLOv5 detection is crucial in providing explanations for classifier predictions and improving sensitivity, particularly when the classifier fails to detect abnormalities. To further enhance the screening process, we have also incorporated an abnormality detection model. The classifier model achieves an F1-score of 0.87 and a sensitivity of 0.82. With the addition of suspicious mass detection, sensitivity increases to 0.89, albeit at the expense of a slightly lower F1-score of 0.79.
Advancements in Machine Learning: From Theory to Practice

Advancements in Machine Learning: From Theory to Practice
Machine Learning Algorithms in Big Data Analytics for social media data based sentimental analysis

Due to the extensive usage of the Internet, social media has grown to play a significant role in our daily lives. Twitter is one of the most popular social media platforms in use today. People express their ideas through tweets on a variety of topics, including politics, sports, the economy, and more. The massive dataset produced by the daily millions of tweets caught the interest of data scientists, who decided to concentrate on it for sentiment analysis. This research propose novel technique in machine learning algorithm in big data for social media based sentimental analysis. Here the input data has been collected as social media based sentimental data and processed for noise removal. Then this data has been clustered using Fuzzy-C means clustering and feature extracted using differential multi-layer whale optimization. The experimental analysis has been carried out in terms of accuracy, precision, recall, AUC and RMSE. The proposed technique attained accuracy of 95%, precision of 72%, recall of 62%, AUC of 44%, RMSE of 52%.
MNASreID: Grasshopper Optimization based Neural Architecture Search for Motorcycle re-IDentification

Abstract Object re-IDentification (reID) is the process of matching the same object across multiple cameras. An approach for object reID is crucial for identifying objects like people, cars, motorcycles etc., in video surveillance systems. This is an active area of research in both industry and academia due to the ever-growing population and need for smart surveillance, public safety and traffic management. This work proposes an approach for automatically designing a deep convolutional neural network, named MNASreID, for motorcycle re-identification task. Most current reID methods use deep convolutional neural networks as the backbone that are manually designed, which does not have the optimum settings as the network complexity increases. MNASreID uses Neural Architecture Search (NAS) to search for the backbones of the Deep Neural Network (DNN) model. In addition, the designed search space includes parameters of the architecture and its hyperparameters to find the optimum architecture for the reID task. This work uses Grasshopper Optimization Algorithm (GOA) based NAS to find the optimal DNN model. The outcomes of experiments on two motorcycle datasets demonstrate that our method can automatically find the efficient DNN model for motorcycle reID.
THE EFFECT OF LEARNING MODELS (TPS, TPSq, CONVENSIONAL) AND STUDENTS' TAHSIN ABILITY ON PAI LEARNING OUTCOMES

The cooperative learning model can be supported in the PAI learning process. This study analyzed the effect of learning models (TPS, TPSq, and Conventional) and students' abilities on Islamic education learning outcomes. This research method uses a quantitative type, with the Nonequivalent Posttest_Only Control Group design, with statistical test analysis using Two Way Anova. The results of the analysis of this study indicate that there are differences in learning models (TPS, TPSq, and Conventional) and students' Tahsin abilities in PAI learning outcomes; it can be seen from the output rank that the TPS learning model is 17.65 less than the mean rank of students in the class model. Learning TPSq is 31.35 (17.65 31.35). The mean rank value of students in the TPSq learning model class is 32.35, more significant than that of students in the conventional learning model class, which is 16.65 (32.35 16.65). The mean rank value of students in the TPS learning model class is 26.96, more significant than that of students in the Conv learning model class, which is 22.04 (26.96 22.04). There are differences in Islamic education learning outcomes for class VII A and B students based on Tahsin’s ability; based on the Statistical Test results, the Sig.2-tailed is 0.000, which is more minor () than 0.05. There are differences in the learning outcomes of students in classes VII B and C based on Tahsin’s abilities; based on the results of the Test Statistics, the value of Sig.2-tailed is 0.006, this value is more minor ( ) than 0.05, and there are differences in the learning outcomes of students in class VII A and C. based on Tahsin ability, based on the results of the Statistical Test the value of Sig.2-tailed is 0.000 this value is more minor () than 0.05.
Maximally informative feature selection using Information Imbalance: Application to COVID-19 severity prediction

Abstract Clinical databases typically include, for each patient, many heterogeneous features, for example blood exams, the clinical history before the onset of the disease, the evolution of the symptoms, the results of imaging exams, and many others. We here propose to exploit a recently developed statistical approach, the Information Imbalance, to compare different subsets of patient features and automatically select the set of features which is maximally informative for a given clinical purpose, especially in minority classes. We adapt the Information Imbalance approach to work in a clinical framework, where patient features are often categorical, and are generally available only for a fraction of the patients. We apply this algorithm to a data set of ~ 1,300 patients treated for COVID-19 in Udine hospital before October 2021. Using this approach, we find combinations of features which, if used in combination, are maximally informative of the clinical fate and of the severity of the disease. The optimal number of features, which is determined automatically, turns out to be between 10 and 15. These features can be measured at admission. The approach can be used also if the features are available only for a fraction of the patients, does not require imputation and, importantly, is able to automatically select features with small inter-feature correlation. Clinical insights deriving from this study are also discussed.
Sistem Informasi Pengaduan Perbaikan Jalan Desa (Program SIG) Berbasis Web

Several damaged roads in the Kebasen area during the rainy season are continuous work for the government. As an effort to assist this public service, it provides an opportunity for the public to submit complaints or complaints when public facilities have not met expectations. The following is an alternative to the procedure for submitting complaints or complaints for road repairs online which can help better communication. This study aims to build a GIS web-based road repair complaint information system. The system prototype was designed in stages with the development of the waterfall model system and the PHP programming language. In making this system the author uses the framework CodeIgniter (Ci) and Object Oriented Analysis and Design (OOAD). The interface for connecting several applications uses the Google Application Programming Interface (API) and MySQL database. The results of this study are expected to be the system of choice for conveying the aspirations of the people up to date in complaints about road repairs, environmental conditions, traffic conditions, public road management conditions, security conditions, street lighting conditions and presenting information optimally
Pendekatan Algoritma Klasifikasi Machine Learning untuk Deteksi Penyakit Demensia

Early detection of dementia through the use of machine learning classification algorithms is important for providing appropriate interventions to patients. In this context, this study aims to compare the performance of several machine learning classification algorithms in detecting dementia using the attribute selection method. In the early stages, patient data including medical history, cognitive test results, and other attributes were collected as input, an attribute selection algorithm was used to select the most informative attribute subset in detecting dementia. The subset of attributes used as input for training machine learning classification models, several classification algorithms such as Extra Trees (ET), Linear Discriminant Analysis (LDA), Random Forest (RF) and Ridge. In this study, accuracy is used as the main metric to compare algorithm performance. The evaluation results show that the Random Forest (RF) algorithm produces the best performance with an accuracy of 91.56%. The Extra Trees (ET) algorithm has an almost comparable accuracy of 91.44%, while Ridge and Linear Discriminant Analysis (LDA) have an accuracy of 90.44% respectively. In the context of dementia detection, the performance of the Random Forest algorithm with the attribute selection method proved to be the best with an accuracy of 91.56%. These results indicate that the developed model is capable of recognizing complex patterns and relationships between features that are relevant to dementia status. The use of the attribute selection method also contributes to increasing the accuracy and efficiency of the classification algorithm.
Aplikasi Akuntansi Penerimaan Dan Pengeluaran Kas Pada PT Kinarya Gemilang Adhitama Menggunakan Model Waterfall

PT Kinarya Gemilang Adhitama manages cash receipts and disbursements using Microsoft Excel by recapitulating or calculating one by one transactions in the form of notes into financial reports. The cash management process tends to take a long time, and allows for a considerable risk of data redundancy and the quality of data and information to become inaccurate. Utilizing technology by building a cash receipts and disbursement application with the aim of developing and improving existing systems. By building a financial management application, it is hoped that it can help with the process of managing cash receipts and disbursements at PT Kinarya Gemilang Adhitama to become more efficient and effective. The software development method used in building the application is the SDLC method with the Waterfall model. The application is built with an automatic and unique cash number system so that it can reduce the risk of data redundancy. Furthermore, the application will be tested with Black Box which focuses on software in terms of logic and functionality which is carried out on the 4 main features of the application. The test results show valid results, which means that the application function is in accordance with the design.
Prediksi Keberhasilan Pemasaran Layanan Jasa Perbankan Mengunnakan Algoritma Logistic Regreesion

Determining public interest in marketing banking services using data mining techniques. Prospect segmentation is one of the processes used in the marketing strategy of the banking industry. Data mining support plays an important role in classifying potential bank customers and evaluating the success of marketing their services. This is important to support the conclusion about the success rate of telemarketers in carrying out bank marketing tasks. a product whose way of working requires information about potential customers. This is a classification technique that is often used to classify prospects using logistic regression according to research maps supporting prospect data mining. Defining an accurate data mining classification algorithm to predict telemarketing success based on a 2010 experiment. In marketing banking service products, the results of the evaluation process of this algorithm are determined by cross-validation, Confusion Matrix, ROC curve and T-test. The logistic regression algorithm is more accurate with an accuracy of 92.32% and an AUC value of 0.962, so the algorithm used is included in the good classification group.
Recent seismicity in the Messina area, southern Italy, and comparison to the local geology and tectonics

We have investigated the seismicity occurred during 2000-2021 in the area of Messina, the town which suffered greatest loss of human lives over the territory devastated by the magnitude 7.1 earthquake of December 28, 1908. We have found that most of recent activity was located beneath the historical centre of the town, in and near the very peculiar sickle-shaped harbor zone which prompted the Greek colonizers in the VII Century B.C. to give Messina the old name of “Zancle”(“Sickle” in the ancient Greek language). Extracting from the whole dataset (consisting of hundred earthquakes of maximum magnitude 3.8) the data relative to a small sequence of 28 events concentrated in a few days at the end of 2013, and performing high-quality Bayesian hypocenter locations of these events, we have found very clear epi-hypocentral trends suitable for comparison with the local structural scenario. The joint analysis of seismic, geological and geomorphological data including morphobathymetric curves of the sea bottom in the study area, has brought us to propose that the small sequence in question (and probably most of activity recorded during the whole 22-years period) may have been generated by internal dynamics of a local horst/graben system, the position of which (i) appears to correspond to one of the minor horsts documented in the Messina Strait basin area and (ii) is very close to the upper edge of the 1908 earthquake blind source reported in the Database of Individual Seismogenic Sources of the Italian National Institute of Geophysics and Volcanology.
ESL Teachers’ and Students’ Experience of Online Learning via Microsoft Teams

This paper investigated how teachers and students of English as a Second Language view the effectiveness of Microsoft Teams for remote and online learning. This research employed descriptive survey and qualitative methods, which involved a questionnaire and an interview for data collection. Teachers and students of English as a Second Language were surveyed and interviewed about their perceptions of Microsoft Teams' usefulness. Frequencies and percentages were employed as statistical methods for the study. The results revealed that teachers and students have positive perceptions of Microsoft Teams. In an online class, Microsoft Teams optimally supports and favors the learning environments of students. In addition, the findings indicate that Microsoft Teams is an effective virtual learning platform due to its quality, user friendliness, and functionalities. Teacher participants perceived that the tool played an important role in promoting interactive learning and receiving and providing feedback.
Development of Learning Media Using the KineMaster Application for Learning English Class V SDN 065853 Tegal Sari Mandala II Kec. Denai Medan

The research aims to determine the feasibility, practicality, and effectiveness of learning media using the Kine Master application in learning English for class V SDN 065853 Tegal Sari Mandala II T.A. 2022/2023. This type of research is R&amp;D (Research and Development) using the 4D (Four-D) model which consists of Define, Design, Develop, and Disseminate. The subjects in the study were fifth grade students at SDN 065853 Tegal Sari Mandala II, Kec. Medan Denai, T.A 2022/2023. The object in this study is a learning medium using the Kine Master application. The results of this study indicate that learning media using the KineMaster application for learning English is very feasible. Validation results by media experts obtained a percentage score of 93.33% with very feasible criteria, then material validation by material experts obtained a score percentage of 93.84% with very feasible criteria, practicality validation was carried out by practical experts obtained a score percentage of 100% with very practical criteria , in field trials by carrying out learning outcomes tests obtained an average effectiveness of 88.6 with effective criteria. So it can be concluded that the learning media uses the KineMaster application in learning English for class V SDN 065853 Tegal Sari Mandala II, Kec. Medan Denai, very feasible, very practical, and effective for use in the learning process.
Educational retrospective and use of ICT before the confinement of COVID-19

The purpose of the work was to identify and analyze which were the elements that influenced the use of ICT in Latin America before COVID-19 by teachers in Latin American countries and independently, in Mexico.The chosen method was the Heckman two-stage selection method.The factors that affect the use of information and communication technologies in teachers to teach classes, the need for budget for technologies in schools, salary and job satisfaction of the teacher were identified.It is concluded that the importance of carrying out this analysis based on historical data allows us to know the situation of educational institutions before the confinement of COVID-19.