Reviewerly/exHarmony huggingface dataset:
Random Forest and Extreme Learning Machine Algorithms for High Accuracy Credit Card Fraud Detection

Abstract: The banking sector is facing a huge issue with credit card fraud, and research has shown that machine learning algorithms are a useful tool for identifying fraudulent actions of this kind. In this investigation, we offer a method for detecting fraudulent use of credit cards that makes use of a hybrid of two machine learning algorithms known as Random Forest (RF) and Extreme Learning Machine (ELM). We compiled a dataset using information obtained from a wide variety of sources, and then we preprocessed it to eliminate any inconsistencies and errors. Following this, the RF and ELM algorithms were put into action and trained on the dataset in order to provide forecasts on the occurrence of fraudulent acts. Measures of performance such as determining how accurate the algorithms are are examples. According to the findings of our research, the ELM algorithm is more accurate than the RF algorithm when it comes to the detection of fraudulent credit card activity.
Titles

Chapter 11 discusses the importance of giving a detailed title; with key words towards the beginning, and more common and abstract words at the end. It covers the grammatical issues involved, and all the different types of titles found in the literature. It also suggests how a chatbot can be used to help you generate a title.
Structuring Paragraphs

Chapter 3 covers how to structure a paragraph by linking sentences together in the most logical order possible. It also suggests ways to break up long paragraph. You will learn that good writing means always thinking from a reader’s perspective:
fulltext: Behind the Scenes

fulltext is a package I maintain for text-mining the scholarly literature (package docs). You can search for articles, fetch article metadata and abstracts, and fetch full text of some articles. Text-mining the scholarly literature is a research tool used across disciplines. Full text of articles (entire article, not just the abstract) is the gold standard in text-mining in most cases.
Street Object Detection from Synthesized and Processed Semantic Image: A Deep Learning Based Study

Abstract Semantic image synthesis plays an important role in the development of Advanced Driver Assistance System (ADAS). Street objects detection might be erroneous during raining or when images from vehicle’s camera are blurred, which can cause serious accidents. Therefore, automatic and accurate street object detection is a demanding research scope. In this paper, a deep learning based framework is proposed and investigated for street object detection from synthesized and processed semantic image. Firstly, a Conditional Generative Adversarial Network (CGAN) has been used to create the realistic image. The brightness of the CGAN generated image has been increased using neural style transfer method. Furthermore, Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN) based image enhancement concept has been used to improve the resolution of style-transferred images. These processed images exhibit better clarity and high fidelity which is impactful in the performance improvement of object detector. Finally, the synthesized and processed images were used as input in a Region-based Convolutional Neural Network (Faster R-CNN) and a MobileNet Single Shot Detector (MobileNetSSDv2) model separately for object detection. The widely used Cityscape dataset is used to investigate the performance of the proposed framework. The results analysis shows that the used synthesized and processed input improves the performance of the detectors than the unprocessed counterpart. A comparison of the proposed detection framework with related state of the art techniques is also found satisfactory with a mean average precision (mAP) around 32.6%, whereas most of the cases, mAPs are reported in the range of 20–28% for this particular dataset.
Some Issues of Algorithmization and Organization of Investigation of Contactless Sale of Narcotic Drugs Carried Out Using Cryptocurrencies

The study of the investigation algorithm in forensic science is limited due to the lack of uniformity of under-standing by specialists of the essence of this phenomenon, a small number of studies and problems of its ap-plication in the presence of established forensic methodology. Algorithmization of investigation implies its split-ting into a set of simple actions, the order of implementation of which is subject to the actual investigative situa-tion and the importance of possible to obtain evidence. At the same time, the theory of the investigation algo-rithm itself does not conflict with existing forensic methods, but only provides an alternative to the established approach to investigative practice. In this study, the existing approaches to the concept of an investigation algo-rithm are analyzed, and the author’s definition is proposed. The article analyzes various interpretations of the concept of investigative algorithm and proposes the author’s definition. The elements of the algorithm of inves-tigation of illegal trafficking of narcotic drugs, psychotropic substances and their analogs, committed with the use of cryptocurrencies by means of contactless sales are considered. The peculiarities of this type of crime that distinguish it from other ways of distribution of narcotic drugs are highlighted. The considered organiza-tional features of the investigation of the contactless sale of narcotic drugs make it possible to determine the sources of orienting information about an impending or committed crime.
PERSONNEL BALLAST OR WHY THE HIGHER EDUCATION SYSTEM DOES NOT KEEP UP WITH LABOR MARKET TRANSFORMATIONS

The subject of the study is the training of personnel in the system of higher education in terms of meeting the needs of the national labor market. The purpose of the study is to determine whether the professional structure and content of higher education have time to adapt to the new needs of the Russian economy and to establish what steps are needed to improve the quality and structure of higher education. The scientific novelty of the research lies in substantiating the need to change the organization and improve the efficiency of the higher education system in the Russian Federation. The relevance of studies of long-term trends in the structure and dynamics of demand for labor and its supply, the timeliness of the creation of institutions, and the development of methods for science-based forecasting of the medium-term and long-term needs of the economy in personnel are argued. The results of the study allowed us to draw the following conclusions. There is a need for a systemic restructuring of the entire education system, from school to higher education. In this process, the role of the state should be significantly increased. It is necessary to change the approach to the organization of career guidance counseling: a transition from informing young people and parents about the situation in the labor market to determining personal inclinations and opportunities in relation to different groups of specialties, organizing a system for shaping young people's value and professional orientations in accordance with the new needs of the home economy. Career guidance counseling at school should be based on applicants' attitudes adequate to the realities of the labor market in terms of the further education process. At the start of higher school studies, “entrance testing” should be introduced, which will allow identifying groups (segments) among first-year students based on their internal motives and educational attitudes, which will become the basis for introducing variable approaches to learning. The aspects of the organization and working conditions of a teacher that negatively affect the quality of higher education identified in the course of the study and analyzed in this article form an information base for determining the main measures to improve the situation.
The Effectiveness of Discovery Learning-Based E-Modules to Improve Student Understanding

This research aims to produce thematic e-module products based on discovery learning that are feasible and effective in improving the comprehension ability of learners. This research is a type of Research and Development (R&amp;D) research. The research was conducted referring to the theory of Borg &amp; Gall (2003). The population of this study was class V students of the Ki Hajar Dewantara Cluster. The sample of this study was determined by Cluster random sample and saturated sampling taken at two different schools and obtained as many as 50 students. Data collection tools use valid and reliable test instruments. The effectiveness data analysis technique uses an effect size test with results of 0.937 in sample A and 0.932 in sample B. Based on data analysis from the results of the study, it is concluded that thematic e-modules based on discovery learning are effective for improving the ability of elementary school students to understand.
Assessment of teacher readiness for mentoring in a general education organisation

The aim of the paper is to shed light on the approaches to determining the level of teacher readiness for mentoring and to identify possible conditions for its improvement using the method of comparative analysis. The paper substantiates the main factors that actualise the need for mentoring in the pedagogical environment. The basic approaches to the definition of the term “mentoring” are highlighted, the innovative essence of teacher readiness for mentoring is considered, an analysis of the methodological materials of teacher mentors and the results of their performances at a professional skills competition is presented, which allows determining the levels of teacher readiness for mentoring, typical shortcomings in its development are described. Special attention is paid to the importance of teamwork in the organisation of mentoring. The scientific novelty lies in expanding pedagogical knowledge about the organisation of mentoring in an educational institution, the development of teacher readiness for mentoring, systematising the reasons affecting the level of teacher readiness for mentoring. As a result of using the methods of theoretical and empirical research, innovative components of teachers’ mentoring activities have been substantiated, shortcomings in teacher readiness for mentoring have been identified, conditions for their elimination have been determined.
PENGARUH MODEL PEMBELAJARAN CONCEPT SENTENCES UNTUK MENINGKATKAN KETERAMPILAN MENULISTEKS DESKRIPSI PADA SISWA KELAS X SMA NURUL IMAN TANJUNG MORAWA TAHUN PEMBELAJARAN 2021-2022

This study aims to determine the effect of the Contextual Teaching and Learning Learning Model on the Ability to Write News Texts for Grade VIII students of MTs Darul Arifin in the 2021-2022 Academic Year. The method used in this research is quasi-experimental. Sampling was carried out using a purposive sampling technique. The research sample consisted of 20 experimental and 20 control class students. Measurement of student learning outcomes based on research instruments in the form of subjective tests in the form of essays. Based on the results of data analysis, it can be concluded that there is a significant influence on learning outcomes in Writing News Texts using the Contextual Teaching and Learning Learning Model. This is shown from the results of the calculation of the posttest hypothesis test through the t-test at a significant level of 0.05, where tcount&gt;ttable (2.864154 &gt; 1.721296296). The achievement of the average student score in the Contextual Teaching and Learning learning class with a value of 84.25 reinforces this result. In contrast, conventional learning obtains a value of 79.5. These results indicate that student activities in the Contextual Teaching and Learning learning model are better.
Password Management System Using Blockchain

Password management is the process of safeguarding and maintaining passwords from creation to expiration by adhering to a set of sustainable standards. As the IT landscape evolves, passwords serve as the first line of security for sensitive data, but they are also a prominent target for hackers and may cause significant issues when mishandled or hacked. Due to a rise in businesses falling victim to cyber-attacks because of inadequate password security, secure administration of privileged account credentials has gained importance in recent years. However, consumers have no real assurance that their passwords will remain secure, as loopholes and defective coding make it possible for even security solutions to be compromised. However, blockchain technology, with its immutability, resistance to hacking, and entirely decentralized nature, promises far greater security. Combining security solutions like password managers with blockchain considerably increases the likelihood that stored data is secure. This article aims to demonstrate how, by utilizing blockchain, the security of password management applications may be improved since user credentials are no longer kept on centralized servers.
Development of augmented reality applications using iOS for aerospace training

Augmented reality (AR), often used for creating games and apps that blend the real and digital world, also offers significant advantages for the teaching and learning process. It allows students to interact with concepts in a way that would not be possible just through books, contributing to better academic results and increasing student engagement in the classroom. Therefore, the use of AR in aerospace training becomes essential to offer a more interactive and immersive learning experience. With this in mind, this work sought to develop an AR app for the iOS and iPadOS operating systems, with the goal of assisting the practice of future professionals in assembling and maintaining the NanosatC-BR1 in the aerospace industry. The development of the app is directed towards Apple devices such as iPhones and iPads due to more robust support for technologies such as advanced cameras, LiDAR, and other sensors important for AR, while hardware specifications vary widely among different manufacturers in the case of Android devices. As a result, it was possible to realize that AR, combined with teaching and learning, is an exceptional tool, especially in the aerospace industry.
Publishing and browsing articles on R-universe

&lt;strong&gt;Browsing articles on R-universe&lt;/strong&gt; In the past weeks we have extended the R-universe build system and front-ends with some new features for publishing &lt;em&gt;articles&lt;/em&gt;. Articles can be used for documentation of R packages, but also for other purposes, such as reports, tutorials, papers, or research compendia.Publishing of articles in R-universe is based on the R vignette system, and we may add support for other types of articles la
INVARIANT CRITERIA FOR THE CLASSIFICATION OF MODERN MASS MEDIA GENRES

International scientific conference «The European development trends in journalism, PR, media and communication» : conference proceedings (September 6–7, 2023, Wloclawek, the Republic of Poland). Riga, Latvia : «Baltija Publishing», 2023. 64 pages.
A Study on Effect of Compensation Management in a Organization towards Shri Amman Steels &amp; Allied Industries Pvt Ltd., with Reference to Trichy

The research purpose is to determine the study of the performance appraisal of employees. Performance appraisal refers to the regular review of an employee’s job performance and overall contribution to a company. The objective is to know the effect of performance appraisal on employee motivation. The reveals that performance appraisal leads to improved employees’ performance. Which results from increased productivity in the organization? The research design used is descriptive in nature. The data collection instrument is primary data that is collected through questionnaires. In the research of 100 respondents were taken. Employees are satisfied with the current performance appraisal of the company. The majorities of the employees are motivated of the organization through feedback and increased productivity. Furthermore, the general environment of the organization is very friendly, and as a result, the corporation has not imposed any specific measure to address employee performance assessment issues.
ACMKC: A Compact Associative Classification Model Using K-Modes Clustering with Rule Representations by Coverage

The generation and analysis of vast amounts of data have become increasingly prevalent in diverse applications. In this study, we propose a novel approach to address the challenge of rule explosion in association rule mining by utilizing the coverage-based representations of clusters determined by K-modes. We utilize the FP-Growth algorithm to generate class association rules (CARs). To further enhance the interpretability and compactness of the rule set, we employ the K-modes clustering algorithm with a distance metric that binarizes the rules. The optimal number of clusters is determined using the silhouette score. Representative rules are then selected based on their coverage within each cluster. To evaluate the effectiveness of our approach, we conducted experimental evaluations on both UCI and Kaggle datasets. The results demonstrate a significant reduction in the rule space (71 rules on average, which is the best result among all state-of-the-art rule-learning algorithms), aligning with our goal of producing compact classifiers. Our approach offers a promising solution for managing rule complexity in association rule mining, thereby facilitating improved rule interpretation and analysis, while maintaining a significantly similar classification accuracy (ACMKC: 80.0% on average) to other rule learners on most of the datasets.
Extreme Rainfall Analysis Of The West Papua Province Using Schlather’s Model Of Max Stable Process

Data from Badan Pusat Statistik (BPS) in 2021 notes the province of West Papua as the province with the 5th highest rainfall in Indonesia with a rainfall of 3,811 mm. The province also recorded 268 rainy days, the most amongst all provinces in the country. The excess amount of rain is one of the causes of disasters such as floods. This research uses rainfall data from the Regencies of Manokwari, Fakfak, and Kaimana. The method used is Spatial Extreme Value particularly Schlather's Model of the Max Stable Process. The data used is hourly rainfall for the period of 13 March 2022 to 17 October 2022 with the proportion of training and testing data respectively 85.84% and 14.16%. Extreme data collection was carried out using the Block Maxima method with a fitting to the Generalized Extreme Value (GEV) distribution before being transformed into the Frechet Z margin units. The calculation of the extreme coefficient resulted in a value between 1.4 to 1.85, indicating a relationship between the locations. Next, the best trend surface model was determined, which involves latitude coordinates for the calculation of the location parameter and both longitude and latitude coordinates for the calculation of the scale parameter. The spatial parameter estimation is carried using the powered exponential correlation function. Then, model validation was carried out using MAPE based on a comparison of return levels and testing data. The MAPE values obtained was 22.61% for the BFGS iteration method. The final step is to calculate return levels for periods of 2, 4, 6, 8, and 10 years ahead. All the results were categorized under very heavy rain. These results can be used by related parties to carry out disaster mitigation efforts.
Analysis Of Fire Risk For Forest And Land In West Kalimantan Using Logistic Regression Method With Generalized Extreme Value Approach

Forests in Indonesia have been reduced by half due to fires. Forest and land fires often occur during the long dry season in places such as the island of Borneo. West Kalimantan is an area passed by the equator which is directly above the Pontianak area. The main effect is to make West Kalimantan a tropical area with high air temperatures so that forest and land fires often occur. This study aims to obtain the results of the probability of land and forest fires in each district in West Kalimantan. The method used is binary logistic regression analysis with response variables in the form of data categories based on spatial data and analysis of extreme values with Generalized Extreme Value (GEV). Spatial analysis uses the help of ARCGIS software in processing raster data (grid cells). The data used is data on maximum temperature and maximum wind speed taken from October 7, 2021 to October 31, 2022 from the official NASA website. The spatial data used in this study is forest and land fire vulnerability data taken from the BNPB website in the form of raster data. The results of logistic regression analysis found that the maximum temperature variable has a negative relationship with the response variable, while the maximum speed of wind variable has a positive relationship with the response variable. The temporal probability of the resulting GEV is getting higher with a longer period of years ahead. The probability of forest and land fires is obtained by multiplying the log probability by the GEV temporal probability. In this study, it was found that the highest chance of forest and land fires occurring in Sanggau Regency was suspected to occur due to an increase in temperature every year.
THE USE OF NAÏVE BAYES ALGORITHM IN FORECASTING THE FURTHER STUDY OF HIGH SCHOOL STUDENT

Schools with a large number of students who continue their studies to college will be the main choice as secondary schools from the elementary level. Therefore, increasing the number of students continuing their studies is very important to meet the competition. To get around this, schools can predict the continuation of high school/vocational high school students' studies to college. The goal is that the percentage of prediction results can be used as a reference for improving the quality of education services in schools. In making this prediction, the Naïve Bayes method or algorithm is used. In this case, the Naïve Bayes algorithm is a classification method with a probability and statistical approach that is suitable for predicting the continuation of high school/vocational high school students' studies to college. The prediction result of continuing study to university using Naïve Bayes on test data has an accuracy of 0.740.
IS/IT STRATEGIC PLANNING USING WARD &amp; PEPPARD METHOD (CASE STUDY : STIKES BINA CIPTA HUSADA)

STIKes Bina Cipta Husada (STIKes BCH) in its business operations has used information systems and information technology. However, the existing information system is still not integrated between one unit and another, causing data redundancy and slow operational processes. Strategic planning of IS/IT is needed to support strategic activities in STIKes BCH, because the lack of planning in the use and development of IS/IT can also impede the organization in achieving its goals and objectives. Therefore, this research focuses on Strategic Planning of IS/IT, which aims to use the output of this research as a reference for the 5-year strategic plan of IS/IT and improve the competitiveness in the education institution competition. The Ward &amp; Peppard method was chosen in the process of IS/IT strategic planning because it has the advantage of relatively complete analysis tools, not only focusing on IS/IT conditions, but also considering internal and external business conditions and IS/IT management. The tools used to formulate the IS/IT strategy include SWOT, Value Chain, PEST, Porter's Five Force, McFarlan's Strategic Grid, and CSF. The result of this research is the IS Business Strategy, IT Strategy, and IS/IT Management Strategy that can be implemented in STIKes BCH.