Reviewerly/exHarmony huggingface dataset:
RESEARCH ON THE DIAGNOSIS OF DISTRIBUTION NETWORK FAULT DATA USING A FAULT PREDICTION MODEL

To ensure the stable operation of the distribution network, it is necessary to timely predict and diagnose its faults.In this paper, distribution network faults were briefly analyzed.To address the problem of redundant and incomplete fault data, they were reduced by the rough set (RS), and then the reduced data were predicted based on the back-propagation neural network (BPNN) model.To further improve the prediction performance, an improved particle swarm optimization (IPSO) algorithm was designed to optimize the parameters of the BPNN model and establish the IPSO-BPNN model.Through example analysis, it was found that RS reduction retained the discriminative ability of the data and improved the prediction accuracy.The accuracy of the IPSO-BPNN algorithm was 79.36% when using non-reduced data; the accuracy of the BPNN algorithm and the IPSO-BPNN algorithm was 87.64% and 95.77%, respectively, when using the reduced data.The experimental results demonstrate the reliability of the IPSO-BPNN algorithm for fault data prediction and its applicability in the actual distribution network.
FAIRness wizard: FAIR Micromet Portal FMP2.

The current state of weather-induced agricultural losses, water use for irrigation, the appearance of new invasive species and disease vectors (strongly depending on micrometeorological conditions), new environmental zoning of plant diseases and pests, deforestation, increased urbanization, rural-to-urban migration and increased urban energy consumption for cooling and heating impose scientific and societal demands for FAIR micrometeorological data. &amp;#160; It is important to remember the FAIR acronym for: Findability, Accessibility, Interoperability, and Reusability. This means that data and metadata should be easily discoverable by humans or machines, accessible under specific conditions or restrictions, conform to recognized formats and standards to be combined and exchanged, and licensed according to community norms, allowing users to know what kinds of reuse are permitted. While open data is the ultimate goal, it is important to have in mind that the FAIR concept implies open metadata only. Measurement results should be stored on a repository chosen by the data owner with a DOI and prefered licence, from closed to fully open with numerous options. More information can be found, for example at Open Data Commons (https://opendatacommons.org/licenses/). &amp;#160; Why? The lack of FAIR data costs Europe a minimum of &amp;#8364;10.2bn per year - approximately 78% of the Horizon 2020 budget per year (PwC EU Services, 2018). If data met the FAIR principle, it would improve data discovery and access, enable re-use, enhance understanding, especially across domains, reach as many people as possible, be cited more often, and open new routes to build cooperation. How? To support owners of micrometeorological data to make their data FAIR, the FAIR Micromet Portal (FMP2.0) was developed within the CA20108 COST Action. FMP2.0 is designed and built to guide owners through FAIR principles, in a step-by-step manner, to make large volumes of data FAIR compliant. More details about FMP2.0 and its functionalities can be found in Roantree et al. (2023). Literature PwC EU Services, 2018: The cost of not having FAIR research data. DOI 10.2777/02999 Roantree, M., Lalic, B., Savic, S., Milosevic, D., and Scriney, M., 2023: Constructing a Searchable Knowledge Repository for FAIR Climate Data, EGU General Assembly2023, Vienna, Austria, 24&amp;#8211;28 Apr 2023, EGU23-7786. https://doi.org/10.5194/egusphere-egu23-7786, 2023.
Research on the Cultivation of Students' Innovative Ability in Physical Education Teaching in Colleges and Universities Under the Environment of Information Technology

In college physical education teaching, how to cultivate students' innovative ability has become a new topic of higher education.Review the current situation of physical education teaching in colleges and universities, examine its existing problems in cultivating students' innovative ability, and on this basis, research and cultivate students' innovative ability.The method of cultivating students' innovative ability is explored in terms of college physical education teaching objectives, classroom structure, teaching content, teaching means, teaching methods, competitions inside and outside the class, extracurricular activities, and teaching staff.
SISTEM INFORMASI PELAYANAN ADMINISTRASI BERBASIS WEBSITE DENGAN METODE RUP (RATIONAL UNIFIED PROCESS) (STUDI KASUS KANTOR URUSAN AGAMA KECAMATAN PANJALU)

The services provided by the government to the people continue to experience updates both in terms of paradigm and service format along with increasing public demands and changes within the government itself. At this time, the process of submitting letters provided by the religious affairs office of Panjalu sub-district to the community is still carried out manually, namely by visiting the office directly. This is due to the splitting or revitalization of the religious affairs office of Panjalu sub-district. Thus the information process obtained is ineffective and requires a long time in processing all administrative matters. Related to this, an administrative service information system is needed that can provide convenience in processing information effectively and can speed up the administrative process. In this study, a website-based administrative service information system was developed with the Rational Unified. This method is known as the Integrated Process Model because it has two axes that show dynamic and static aspects, and also has an iterative approach. The results of this research are in the form of an administrative service information system that can make it easier for the public to get information and speed up the process of submitting letters.
Robustness of privacy-preserving collaborative recommenders against popularity bias problem

Recommender systems have become increasingly important in today’s digital age, but they are not without their challenges. One of the most significant challenges is that users are not always willing to share their preferences due to privacy concerns, yet they still require decent recommendations. Privacy-preserving collaborative recommenders remedy such concerns by letting users set their privacy preferences before submitting to the recommendation provider. Another recently discussed challenge is the problem of popularity bias, where the system tends to recommend popular items more often than less popular ones, limiting the diversity of recommendations and preventing users from discovering new and interesting items. In this article, we comprehensively analyze the randomized perturbation-based data disguising procedure of privacy-preserving collaborative recommender algorithms against the popularity bias problem. For this purpose, we construct user personas of varying privacy protection levels and scrutinize the performance of ten recommendation algorithms on these user personas regarding the accuracy and beyond-accuracy perspectives. We also investigate how well-known popularity-debiasing strategies combat the issue in privacy-preserving environments. In experiments, we employ three well-known real-world datasets. The key findings of our analysis reveal that privacy-sensitive users receive unbiased and fairer recommendations that are qualified in diversity, novelty, and catalogue coverage perspectives in exchange for tolerable sacrifice from accuracy. Also, prominent popularity-debiasing strategies fall considerably short as provided privacy level improves.
Classification of Public Opinion on Social Media Twitter concerning the Education in Indonesia Using the K-Nearest Neighbors (K-NN) Algorithm and K-Fold Cross Validation

Developing country is a country that has perspective and idea which reflect its awareness of the importance of advancing the education sector. Assessment of the quality of education in Indonesia from the perspective of the community gets different responses. Therefore, it makes people respond differently. The community response is often found on social media, one of which is Twitter. Twitter is one of the application service that is popular due to its uses to interact and communicate with people in daily life. The sentiment analysis on Twitter can be a choice to see the community’s responses to the condition of education in Indonesia. The responses are classified into positive sentiments and negative sentiments using the K-Nearest Neighbors (K-NN) algorithm with a 10-fold cross validation model evaluation. K-NN has several advantages, they are fast training, simple, easy to learn, resistance toward training data which has noise, and effective if the training data is large. In this study, the sentiment classification uses Cosine Similarity distance measurement and four k value parameters which are 3, 5, 7, and 9. Data labelling is done manually and done by scoring sentiment. Visualization of positive and negative sentiments use Word Cloud. The test results show that public sentiment about education tends to be positive on Twitter and the parameter k = 7 obtained the highest accuracy value in data labelling that was done manually and done by scoring sentiment. In labelling data manually, it obtained an accuracy of 76.93% whereas, in labelling the data with scoring sentiment, it obtained an accuracy of 77.87%. Sentiment analysis is made using the RStudio programming language as the support software.
ACTORS OF DETECTION OF CRIMINAL OFFENSES RELATED TO ILLEGAL CONTENT ON THE INTERNET AND THE SCOPE OF THEIR SEARCH ACTIVITIES

The purpose of the article is to identify the actors of detection of criminal offenses related to illegal content on the Internet and the scope of their search activities.Results.The structure of the multilevel system of actors responsible for preventing criminal offenses related to illegal content on the Internet is determined, which is a totality of state bodies whose activities are fully or partially related to the prevention of using the Internet for unlawful purposes.The actors of the detection system are identified depending on the functions performed by them in the detection process: operational units, functions thereof include response to criminal offenses in the field of computer technology; operational units involved in the implementation of priority measures in the commission of criminal offenses related to illegal content on the Internet; confidants who perform "blocking" of objects where criminal intentions can be realised; employees of other law enforcement units who can receive primary information about the commission of criminal offenses related to illegal content on the Internet; state control bodies, functions thereof include ensuring cybersecurity of the state, and counteracting criminal offenses related to illegal content on the Internet (State Centre for Cyber Defence and Counteraction to Cyber Threats of the State Service of Special Communications and Information Protection of Ukraine).Conclusions.The scope of search activities during the detection of signs of placement and/or dissemination of illegal content is distinguished enabling to conclude that they have significant differences from the "classical" scope of search, due to the fact that the electronic environment in which the search for factual data is carried out is formed by a totality of information carriers, software and hardware for automated information processing and telecommunication networks (material media, electric fields and signals, means of their processing, communication channels, etc.)
A CASE STUDY ON VIRTUAL MACHINE LOAD BALANCING ALGORITHMS IN CLOUD DATA CENTER

Cloud computing is understood as a virtual pool of resources that are made available to customers online. It offers customers practically limitless computing resources that are to be paid for only based on usage, with no consideration given to maintenance for the preexisting infrastructure.Utilizing resources effectively and efficiently is the primary goal of cloud computing service providers, and this stresses scheduling to a cloud domain.It is accountable for ensuring that allocation takes place efficiently while utilising a variety of services to implement an acceptable framework. Thus, an effort is made to understand the various scheduling techniques and service broker algorithms that are covered. The study clarifies a hazy definition of cloud computing's scheduling and service broker algorithms. We have described many Round Robin, Throttled, and Equally Spread Current Execution Load balancing techniques in this work.
A CASE STUDY ON MULTI-DIMENSIONAL SCHEDULING ALGORITHM FOR CLOUD DATA CENTERS

In distributed environments load balancing is essential for efficient operations. The reliability and efficiency of resources in data centers are greatly enhanced by load balancing solutions. Allocating and moving reconfigurable virtual machines (VMs) while taking into account integrated characteristics of Physical machine (PM) hosting is one of the difficult resource scheduling issues in cloud data centres. As a result, load balancing has grown to be a significant research subject due to its difficulties and significance for the cloud. Numerous algorithms were put out as effective ways to manage the client's requests to the available Cloud nodes. In this research, we study the various multi-dimensional resource scheduling techniques employed in cloud computing to schedule various tasks and balance load.
HANDWRITTEN CHARACTER RECOGNITION USING MACHINE LEARNING AND DEEP LEARNING

The process of detecting and converting characters from pictures, documents, and other sources into machine-readable structures for subsequent processing is known as handwritten character recognition (HCR). Recognizing intricately constructed compound handwritten characters properly remains a significant challenge. Convolutional neural networks (CNN) have made significant advances in HCR by learning discriminating properties from massive amounts of raw data. In this study, CNN is used to recognise characters in a test dataset. The main goal of this research is to look into CNN's ability to recognise characters from image collections, as well as the recognition accuracy during training and testing. We tested our CNN implementation against the A-Z Handwritten Data to measure the correctness of handwritten characters. dataset in csv. On 372450 images of alphabets in 28*2, the test result shows accuracy of 97.75%.
A SURVEY ON RECENT OPTICAL CHARACTER RECOGNITION ALGORITHMS

Identifying and Recognition of characters in the document, images etc. is a tedious and complicated task. Many procedures already have been implemented to identify the characters in these documents. In this research paper, we are reviewing some of the important methods used to identify handwritten and printed characters of various languages and different types of documents such as text documents, number plates, captcha, camera-based images, banners etc… The documents considered for recognition may contain clear backgrounds or may contain unclear backgrounds with ink bleed-through, noisy background, variable contrast etc, which makes character recognition a difficult task.
OPTICAL CHARACTER RECOGNITION METHODS TO RECOGNISE HANDWRITTEN DIGITS

Handwritten Digit Recognition (HDR) is one of the most difficult issues in the world of Optical Character Recognition (OCR). There are some challenges with HDR regardless of language, most of which result from differences in writing styles between people, writing media, and environments, inability to retain the same strokes while repeatedly writing any digit, etc. OCR (Optical Character Recognition), which recognises characters and digitises printed texts, is a common component of real-time applications. In the past, it was difficult to convert handwritten numbers to digital characters. Without transforming the physical papers to digital versions, which takes a large amount of time and effort, the physical documents cannot be processed effectively. A number of methods and techniques have been proposed throughout the years to solve the challenge of handwriting classification. The goal of this study is to accurately estimate the identity of a digit by giving the computer a handwritten representation of the digit.
The Analysis of Indonesian Regional Websites with SEO Methods

Regional websites are a crucial element in the systematic implementation of e-government development through realistic and measurable stages, which leverage information and communication technology (ICT) to enhance interconnectivity between the government and stakeholders.In Indonesia, the websites for the regions of West Java, DKI Jakarta, and Central Java rank highly on Google when searching for "regional website."Therefore, to identify the advantages of websites in these regions, a search engine optimization (SEO) analysis was conducted using White Hat techniques to test six variables related to Meta Tags.Additionally, an analysis using Black Hat SEO such as Similarweb.com,Website.grader.com, and SmallSeoTools.com was performed to identify any illegal techniques.The result showed that the jakarta.go.id website outperformed the other two, ranking 3,305th globally and 72nd in Indonesia.However, it received a less-than-maximum score of 25/30 in the White Hat SEO category due to the absence of a Meta Description, with no illegal method detected in the Black Hat SEO analysis.
Effect of Awareness about Emerging Infectious Disease and Self-efficacy on Preventive Health Behavior of Nursing Students

This study was to investigate the degree awareness, self-efficacy, and preventive health behaviors related to the emerging infectious disease in nursing students, and to provide basic data on ways to improve them by identifying factors affecting preventive health behaviors. 227 nursing students were analyzed using descriptive statistics, t-test, ANOVA, Scheffé test, Pearson’s correlation coefficient, and stepwise multiple regression analysis with SPSS/WIN 23.0 Program. As a result of the study, Awareness was found to be positively correlated with self-efficacy (r=.570, p<.001) and healthy preventive behavior (r=.489, p<.001). Self-efficacy showed a positive correlation with the healthy preventive behavior (r=.449, p<. 001). The predictive factor affecting the preventive health behavior was regular exercise (β=.18, p=.015), drinking (β=-.16, p=.009), quality meal (β=.13, p=.048), awareness (β=.29, p<.001), self-efficacy (β=.23, p<.001), explanatory power was 32.8%, and the regression model was statistically significant. Therefore, if you develop content that enhances awareness and self-efficacy against emerging infectious disease, and actively utilizes them when operating infection prevention programs, it will be an effective way to prevent the spread of infectious disease to the local community. And will contribute to the preventive health behavior enhance of nursing students.
Nexus Between Social Dimension and Financial Performance: a Bidirectional Study of the Nigerian Banking Industry

The study of CSR is gaining prominence because it is a critical component of business strategy that cannot be downsized due to its continued relevance to critical environmental, social, and governance impacts on our present world. The article mirrors the CSR implementation of Nigerian banks and its implication on their financial performance through the lens of legitimacy theory and vice versa. To know the financial indicator that determines the level of CSR performance. A panel data design was used to collect, arrange and analyse data. In other to regress the relationship between the social dimension of CSR and financial performance, we regress social dimension with PAT, EPS, and ROA using multivariate linear regression after taking into consideration all the relevant assumptions. The findings reveal that the social dimension has a positive relationship with PAT, EPS, and ROA. While social dimension shows a significant relationship between PAT and EPS but no significant relationship with ROA. PAT appeared to be a better predictor among the three financial predictors considered. To increase earnings, the findings suggest that firms can increase their social spending. As a single-case study, the findings may not be adequate for theoretical generalizations and therefore limited to the context of the study. This study adds to and expands the assumptions of legitimacy theory constructs by rethinking and exploring new assumptions in the context of the social dimension of CSR within the context of a developing country. It brings the theory to practice on strategic use of social dimension
Analysis of the Difference in Satisfaction with The Vocational High School Credit System Curriculum According to the Student's Career Decision Level

Analysis of the Difference in Satisfaction with The Vocational High School Credit System Curriculum According to the Student's Career Decision Level - Career Decision Level;Vocational High School;Vocational High School Credit System;Vocational High School Credit System
Performance comparison of machine learning algorithms for credit card fraud detection

Digital payment methods like credit cards have increased in popularity and usage dramatically in the past two decades along with the whole world transforming to digital and the emergence of E-commerce like Amazon, Shopify, and eBay that completely changed peoples spending habits. There is no doubt that online shopping and credit cards have brought huge conveniences to everyones daily lives, however, it also makes committing payment fraud easier than ever before. Payment fraud can be categorized into a variety of types and the research is focused on fraud where a credit card gets accessed by unknown individuals or groups with unauthorized transactions. The research consists of three main parts: 1. Determine a suitable metric to measure how well different machine learning algorithms work in terms of the ability to correctly identify fraudulent transactions and the practicability when being utilized in real-life scenarios. 2. Compares the performance of the algorithms including Gaussian Naive Bayes, Logistic Regression, SVM, Random Forest, and Sequential Neutral Network. 3. Explores techniques like random undersampling and smote oversampling that handle imbalanced data and evaluates their impacts on the performance of the algorithms. Throughout the research, it is shown that the recall and precision for fraudulent transactions should be the priority metric to measure how capable the machine learning algorithms are in detecting credit card fraud. Random forest performed the best overall compared to 4 other algorithms examined in the research. In addition, random undersampling made the overall performance of the algorithms worse, and smote oversampling had positive impacts on the recall for the Random Forest and Sequential Neural Network algorithms but with a slight drop in precision.