Reviewerly/exHarmony huggingface dataset:
Online Exam Web Portal

Abstract: This online examination should concentrate on developing effective assessment questions and providing students with exam feedback. The strategies that are relevant to the aspects of the assessment process: response submission, computerised grading, and post-submission feedback in this study. Because modern companies are automated and computers follow instructions, it is necessary for humans, commodities, and computers to operate together in a modern organisation. Through this paper, administrators, instructors, and students who are taking an online examination can communicate with the system, allowing for more effective implementation and monitoring of various activities related to online examinations, such as conducting exams on a scheduled basis and delivering results to that specific user or student. Additionally, the information of students who took the Online Examination is kept on file. Keywords: Online Exam, Web Portal, Database, PHP, Application, Server.
Currency Detector System for Visually Impaired

Abstract: In this modern era, many technologies have boom but still there are many problems to be deal with and one of the problems we are trying to solve is to help blind people to recognize the currency notes so as to help them avoid any fraudulent and scam by any shopkeeper or by any other means. To overcome this problem, we will build a system which will tell them the amount of the notes and a total amount that they have shown. In our system, we’ve applied Artificial Neural Network (ANNs). The classifier has Convolutional Neural Network (CNN) layers to extract features from the input image. With those features we will classify the images into 7 classes i.e., currency notes. The output will be provided with the help of a speakers that will tell the user the amount as well as the total amount they have shown to the system. Keywords: Currency, Notes, detector, ML, Machine learning, DL. Deep Learning, RaspberryPi
Data Preparation and Quality Challenges for the Personality Recognition in Indian Languages using Machine Learning and Deep Learning Approaches

Information about the user and their feelings, thoughts, and emotions are expressed through the status, comments, and updates on social media or other platforms. These user-generated contents are an important source for recognizing a user’s personality. Due to the increase in the amount of various Indian language contents on social media, there is a necessity to recognize personality from Indian languages. The challenges have increased in the collection and generation of datasets due to the lack of resources for Indian languages. In the field of personality recognition, the researchers have utilized machine learning and deep learning techniques to infer users’ personalities. The machine learning and deep learning models require enough labeled data for the training. Unlike traditional machine learning, deep learning techniques automatically generate features and require a significant amount of labeled data. For the personality recognition task from the Indian language, no sufficient annotated dataset is available and data preparation for the personality recognition task in the language has become a critical issue. This paper represents the existing gold standard dataset for personality recognition in English and also focuses on the challenges of a large amount of labeled data preparation in the Indian language.
Critical Appraisal of the Content, Quality, and Readability of Online Health Information for Adolescents with Dysmenorrhea

Dysmenorrhea is common in adolescents and has a significant impact on their quality of life. This population uses the Internet as their primary source of health information. We aimed to critically appraise the content, quality, and readability of online health information on adolescent dysmenorrhea.
Comparison of Models for Classification of Learning Achievement of Middle School Students in Indonesia in 2019 using the Support Vector Machine Algorithm, Conditional Inference Trees, and Random Forest

Indonesian JHS students' learning achievement is still low. During 2015-2019, the average national exam score for Indonesian JHS has always decreased. In the last national examination, the average national exam score was 52.82 and was included in the bad category. This certainly needs to be a concern for local governments and the education office. Therefore, it is necessary to form a classification model that can be used to identify cities/districts in Indonesia which are categorized as bad or enough. This study discusses the comparison of models for the classification of learning achievement categories as seen from the average 2019 JHS results in 514 districts/cities in Indonesia using the Support Vector Machine (SVM), Conditional Inference Trees (Ctree), and Random Forest (RF) algorithms. The three algorithms were chosen because of their respective advantages, namely the SVM algorithm is known to be very powerful, Ctree as an improvement from the usual decision tree, and RF to represent ensemble learning. The independent variables used are education budget, classroom conditions, school accreditation, and teacher qualifications. From the results of this study, it has been found that the SVM algorithm produces the highest accuracy (0,80), recall (0,97), kappa statistics (0,38), and F1-score (0,87) compared to the Ctree and RF algorithms, while only precision (0,80) has the same value as the Ctree algorithm. So, the SVM algorithm produces the best model for the classification of district/city learning achievement categories in Indonesia based on education budget, classroom conditions, school accreditation, and teacher qualifications.
Withdrawal: Research on Rural Community Environment Model Based on Climate Multi‐Characteristics Cognitive Computing

Withdrawal: Zhou, C., Tong, H. and Kong, Y. (2022), Research on Rural Community Environment Model Based on climate Multi‐Characteristics Cognitive Computing. Environ. Prog. Sustainable Energy. 2022, https://doi.org/10.1002/ep.13903 . The above article from Environmental Progress &amp; Sustainable Energy, published online on 18 May 2022 in Wiley Online Library (wileyonlinelibrary.com), has been withdrawn by agreement between the authors, the Editor‐in‐Chief Martin Abraham, Wiley Periodicals LLC, and the American Institute of Chemical Engineers. The Withdrawal has been agreed because the article was not approved for publication by all authors.
Penerapan Keluarga Model Spline Truncated Polinomial pada Regresi Nonparametrik

One approach that is often used by researchers to determine the form of the relationship pattern between the response variables and predictor variables in regression analysis, namely the nonparametric approach, where the approach is used when the shape of the regression curve is assumed to be unknown. The truncated spline is a polynomial model in nonparametric regression that has segmented properties, where these properties provide better flexibility than ordinary polynomial models and are able to handle data whose behavior changes in certain sub-intervals due to the knot points in it. This study aims to apply a family of spline truncated polynomial models to nonparametric regression in the case of automotive data. The estimation method used is Ordinary Least Square (OLS). The number of knot points tested is 1 to 4-knot points with a degree of p =1,2,3. Based on the results of the analysis, the best model that produces the smallest GCV value is the nonparametric spline truncated quadratic regression model with 4 knots, which produces a GCV value of 522.27 and a coefficient of determination of 79.77%.
ACTIVITIES OF THE INSTITUTION OF HIGHER EDUCATION IN THE CONDITIONS OF QUARANTINE RESTRICTIONS (BY THE CASE OF PAVLO TYCHYNA UMAN STATE PEDAGOGICAL UNIVERSITY)

The global pandemic has brought radical changes in life and communication. Doctors and educators had special trials and tasks. For Pavlo Tychyna Uman State Pedagogical University, 2020 has become a special year, as for all educational institutions, due to changes in the organization of the educational process.The article presents the peculiarities of the organization of the educational process and life of Pavlo Tychyna Uman State Pedagogical University in the pandemic of coronavirus disease COVID-19.The relevance of this study is determined by the need to improve the system of educational services in terms of distance and blended learning and increase the level of satisfaction of participants in the educational process with its organization and quality. New approaches to the organization of the educational process and life of the university are revealed.Effective forms, methods of its implementation and interaction of all subjects of the educational process are defined, in particular such as: guest lectures, online training, virtual excursions, film lectures, etc.Examples of distance learning, art and sports events are given: Art marathon; virtual tours, master classes; online competitions; online survey. Changes in living conditions in dormitories due to quarantine restrictions are presented.Features of explanatory work, vaccination of employees and students are revealed; creation of appropriate socio-psychological conditions for all subjects of the educational process (training technologies, conversations, classes).At the time of the global pandemic, it is strategic to introduce a new model of the financial policy of the university and social protection of participants in the educational process.&#x0D; Keywords: quarantine; pandemic; university; educational process; quality; distance learning; self-organization; vaccination.
Decisions in Continuous Integration and Delivery: An Exploratory Study

In recent years, Continuous Integration (CI) and Continuous Delivery (CD) has been heatedly discussed and widely used in part or all of the software development life cycle as the practices and pipeline to deliver software products in an efficient way.There are many tools, such as Travis CI, that offer various features to support the CI/CD pipeline, but there is a lack of understanding about what decisions are frequently made in CI/CD.In this work, we explored one popular open-source project on GitHub, Budibase, to provide insights on the types of decisions made in CI/CD from a practitioners' perspective.We first explored the GitHub Trending page, conducted a pilot repository extraction, and identified the Budibase repository as the case for our study.We then crawled all the closed issues from the repository and got 1,168 closed issues.Irrelevant issues were filtered out based on certain criteria, and 370 candidate issues that contain decisions were obtained for data extraction.We analyzed the issues using a hybrid approach combining predefined types and the Constant Comparison method to get the categories of decisions.The results show that the major type of decisions in the Budibase closed issues is Functional Requirement Decision (67.6%), followed by Architecture Decision (11.1%).Our findings encourage developers to put more effort on the issues and making decisions related to CI/CD, and provide researchers with a reference of decision classification made in CI/CD.
Improving object detection quality with structural constraints

Recent researches revealed object detection networks using the simple “classification loss + localization loss” training objective are not effectively optimized in many cases, while providing additional constraints on network features could effectively improve object detection quality. Specifically, some works used constraints on training sample relations to successfully learn discriminative network features. Based on these observations, we propose Structural Constraint for improving object detection quality. Structural constraint supervises feature learning in classification and localization network branches with Fisher Loss and Equi-proportion Loss respectively, by requiring feature similarities of training sample pairs to be consistent with corresponding ground truth label similarities. Structural constraint could be applied to all object detection network architectures with the assist of our Proxy Feature design. Our experiment results showed that structural constraint mechanism is able to optimize object class instances’ distribution in network feature space, and consequently detection results. Evaluations on MSCOCO2017 and KITTI datasets showed that our structural constraint mechanism is able to assist baseline networks to outperform modern counterpart detectors in terms of object detection quality.
Cryptographic Analysis of P2DPI

This paper represents an analysis of the recently proposed P2DPI encryption scheme for deep packet inspection.Constructs of the algorithm are challenged with a complete confidentiality and integrity evaluation.Later in the article, there is proof that, in the current form, usage of P2DPI can compromise user privacy.
Bibliometric Analysis of Carbon Accounting Research

The issue of the environment continues to receive special attention from researchers. This is related to business operations' importance in paying attention to the profit aspect and environmental impacts to maintain the surrounding environment. Therefore, activities that affect carbon emissions must be reported annually. This is called carbon accounting. This study aims to map carbon accounting articles for 2012 to 2021 on the dimensions.ai database. Using the keyword 'carbon accounting', the collected data was 500 articles. The study results found that the journal most relevant to carbon accounting is ‘The Science of the Total Environment’. Meanwhile, the journals that have the most significant impact are ‘Global Change Biology’, ‘Scientific Reports’, and ‘The Science of the Total Environment’. Meanwhile, the most productive journal is ‘The Science of the Total Environment’, followed by ‘Environmental Science and Pollution Research’. The most relevant and prolific authors were Wang Y, followed by Zhang Y and Zhang X. In addition, Wang Y and Zhang X were the authors who had the greatest impact. The distribution of words in articles with the theme of carbon accounting is dominated by 'carbon' and 'accounting'. This research shows that the theme is undergoing good development. Research data shows that the word 'carbon' is rapidly from 2012 to 2021. This study predicts that articles on carbon accounting will continue to increase in the years to come, especially the themes of 'emissions' and 'study' which have high development although their relevance is quite low. The development of research on carbon accounting is closely related to environmental issues which are currently being studied by many researchers, especially to achieve sustainable development goals.
Implementation of the RAD Method to Build Catering Application Android-based

With the rise of culinary businesses that sell food and beverages at this time, business actors must be ready to compete to win and retain their customers so that their business can survive. Ara Catering is one of the culinary business actors in Rantauprapat City which focuses on the food and beverage ordering business. The ordering process applied by Ara Catering is still conventional, where customers come directly to the business location or place an order by telephone. The conventional model turned out to cause problems, including errors in ordering and ordering notes that had piled up. Therefore, a solution must be found to overcome this problem. Based on these problems, this study aims to build an android-based catering ordering application. The method applied in this research is the Rapid Application Development (RAD) method in which the process stages include: requirements planning, user design, and implementation. After the design and implementation have been carried out, the results show that the RAD method can be applied in building an Android-based catering application quickly and effectively. The conclusion drawn from this research is that to build an android-based catering application using the RAD method, the phases of the method must be carried out thoroughly. Hopefully the results of this research can make a positive contribution to Ara Catering in developing its business.
The Text Structure of The Simalungunese Turiturian Art, Culture, Media, and Education

The research formulates and explains the structure, the value, and the norms of disclosing local wisdom in the structure text of TS (Simalungunese 'turiturian' folklore). The narrative text identifies words, paragraphs, phrases, based on the structural text as Orientation ^ Sequence of event (crisis and climax) ^ Resolution ^closure. It is analyzed by using LSF (Functional Systemic Linguistics) text structure which is realized into narrative text structure by using FI (Ideational Function). Oral text sees the establishment of text, co-text, and context in the study of local wisdom. The research design uses interactive modal with descriptive qualitative, using documentary study paradigm in the content analysis in four TS Text. The type of narrative text finds the form of irregular structure texts is identified by the symbol of brackets ([]) which can occur in every stage in the TS text: (Abstract) ^ Orientation ^ ([Evaluation]) ^ Complication ^ Resolution ^ (Code). Ideational function indicates more dominant data in each stage of the proportion of material process of 85.7%, circumstance location of 80% so that para-tactical multiple logic us 1x2 60%. This proportional data indicates that there are 16 verbal words in material data, circumstance data indicate data location which modify 12 location data, logic meaning data indicate that the data taken from verbal data are identified with conjunctions such as that, then, (and) after that, for that, etc. Value and norms contained in the TS text which dominate every text are the values of hard work, togetherness, and discipline. The local wisdom is found in the value and norms of the people's custom described as the forms of behavior and custom in the TS Text. Therefore, this discussion is a unit which also discusses the structure and values of the local cultural wisdom.
Review on product derivation approaches in the software product line

: Product derivation is one of the most important challenges in the software product line. Obtaining the individual product from the shared software is a costly and time-consuming job. Various approaches have been proposed for product derivation in the software product line in previous times. This paper will review approaches concerning the product derivation in the software product line. This paper will provide a state-of-the-art literature review on product derivation in software line approaches. Moreover, this will be more useful in order to obtain a novel valid feather combination approach for product derivation in software product line.
Upaya Peningkatan Bahasa Inggris di Kampung Wowara, Waimangura

This Community Service aims to provide English assistance for children domiciled in Wowara village which is one of the villages in Southwest Sumba regency. This community service is made based on the concerns of the Implementer to children who are quite difficult in getting English learning at the elementary level. In addition, the progress of the tourism industry on the island of Sumba demands that it has ready human resources. In this community service process, an English learning group was formed that was divided into small groups and is expected to continue to be sustainable. The results of this process show that children who are in elementary school have an interest and motivation to learn English. In addition, they have also been equipped with basic English knowledge that will support their academics when they continue to the Junior High School stage
Job Satisfaction as Mediator between Job Characteristics, Personality Traits, Work Engagement and Voluntary Turnover Intention: A Conceptual Paper

In today's workplace, the rate of turnover has risen.Employee turnover is divided into two categories, which defined as voluntary turnover and involuntary turnover.Due to the rising voluntary turnover rate, organizations are confronted with difficulties, since the issue of voluntary turnover has become a major concern for businesses all over the world.Thus, this study focus on investigating voluntary turnover and predicting factors which influence voluntary turnover intentions with job satisfaction as the mediator.Besides, this paper introduces a new conceptual framework to better understanding about voluntary turnover intentions.Most studies on turnover intention (dependent variable) focus on establishing the direct relationship between the independent variables (predictors) and the dependent variable.To date, there is not been sufficient research that examine the "indirect" relationship between the predictors and the dependent variable.Hence, the current study attempts to fill the gap by using job satisfaction as a mediator to determine whether an indirect relationship exist, which can have enhanced our understanding between the predictors and the dependent variable.The present study also fulfilled the gaps in adopting different factor models in predicting job satisfaction and voluntary turnover other than personality traits, which are job characteristics model and work engagement, as recommended by previous researcher that follow up analysis could investigate whether different factor models have greater goodness of fit in different groups.The present study adopted Partial Least Squares-Structural Equation Modeling (PLS-SEM) as the research method for data analysing in this paper, the expected output in this study is to determine whether the hypotheses tested are accepted or rejected, and the significance of output in this study is to contribute to literature on factors that can reduce voluntary turnover intentions.
Extraction and segmentation process for the Iraqi paper currency

Different application like image segmentation, moving objects detection and objects tracking, required an object detection and segmentation techniques. Recently, these techniques become so important especially when only a specific part of image is important. This research paper presents an efficient algorithm that employed for objects detection and extraction process. This algorithm consists of a several steps and the validity of this algorithm is measured based on different denomination of Iraqi currency. These steps arrange as following: image conversion, deleting small and unnecessary objects from images, extraction of interest objects boundary, finally calculating the rotation angle automatically, and rotate image based on the calculated angle. The validity of algorithm measured on the seven denominations of Iraqi currency (250, 500, 1000, 5000, 10000, 25000, and 50000). This image saved in a database that contain 40 images for each denomination and the total images for all denominations are 280 images. After testing the validity of designed algorithm on all captured image, the algorithm shows high accuracy which equally to 99.6%.