Reviewerly/exHarmony huggingface dataset:
A Performance Analysis of the Impact of PriorKnowledge on Computational Thinking

Previously acquired knowledge plays a significant role to learn new knowledge and skills.Previously acquired knowledge consists of Short-term memory and Long-term memory.Though it is a well-accepted learning phenomenon, it is challenging to empirically analyse the impact of prior knowledge on learning.In this paper, we use two systems models for human thinking proposed by Nobel Laureate Prof. Daniel Kahneman.This is a model for human cognition which uses two systems of thinking-the first being quick and intuitively known as fast thinking and the second being slow and tedious known as slow thinking.While slow thinking uses long-term memory, fast thinking uses short-term memory.The impact of prior knowledge of programming language is analyzed to learn a new programming language.We assigned a learning task to two different groups with one having learnt a programming language i.e. senior students and the second group without any prior knowledge of programming language i.e. freshers.The impact of prior knowledge is measured and compared against the time taken to answer quizzes.
Predicting Fraudulant Job Ads with Machine Learning

Online Recruitment frauds are becoming an important issue in cyber-crime region. Companies find it easier to hire people with the help of the internet rather than the old traditional way. But it has greatly attracted scammers. In this project, we have proposed a solution on how to detect ORF. We have presented our results based on the previous model and the methodologies, to create the ORF detection model where we have used Jobs_Frauds.csv . We have selected this dataset from Kaggle . Furthermore, Dummy Classifier, Random Forest Classifier, Support Vector Machine, Gradient Boosting, Naïve Bayes Classifiers, XG Boost, SGD classifier, Passive Aggressive and KNN are the algorithms that have been used. We have found the accuracy of different prediction models, where Passive Aggressive (98.12%) and Gaussian Naïve Bayes (96.72%) give the highest accuracy. Through this project, we tried to create a precise way for detecting fraudulent hiring posts.
EFEKTIVITAS TRANSFER LEARNING DALAM PENDETEKSIAN PENYAKIT PNEUMONIA MELALUI CITRA X-RAY PARU MANUSIA

Pneumonia is a disease that attacks the human lung system. This disease causes serious problems not only in Indonesia but is a serious problem for people around the world. By doing early detection of pneumonia can reduce mortality. X-ray imaging of the human chest is one of the most widely used to diagnose pneumonia. The X-ray method is a fast and easy method of detecting a disease. In this study, the Transfer Learning method was used to classify chest X-ray images labeled as non-pneumonia and pneumonia lungs. To classify this image recognition, the Google Collaboratory application uses the ResNet50V2 Architecture model. The dataset used for this study was 5863 training data by testing 30 times, the results obtained were an accuracy of 97% and a loss value of 0.4.
Optimization of Random Forest Prediction for Industrial Energy Consumption Using Genetic Algorithms

Abstract&#x0D; &#x0D; Saving electrical energy consumption in industries is crucial; hence, the prediction of industrial energy consumption needs to be performed. The random forest method can be applied to steel industry data to predict energy consumption. The purpose of this prediction is to increase energy savings in industries and optimize the performance of the random forest method. The results of the random forest show that the algorithm can predict energy consumption in industries effectively; however, it needs further optimization to achieve better predictions. Therefore, the genetic algorithm method will be used to optimize the previous method. The optimization results indicate that it is successfully conducted in terms of accuracy and kappa level. This optimization is beneficial to society, especially industrial companies.
CODE-SWITCHING IN ENGLISH CLASSROOM

Code-switching is one of sociolinguitics phenomenon when a a speaker of bilingual or multilingual switch from a language to another one. The research aims to figure out types of teacher’ code-switching in the teaching and learning process in English class. The study applied a case study of qualitative descriptive research. The participant of the research was an English teacher at a state Islamic school in Sumedang. Observation and interview were used to collect the research data, and the data were analyzed based on code-switching theory proposed by Poplack (1980). The study revealed that (1) teacher applied three types of code switching, namely: intrasentential code switching, inter-sentential code switching, and tag switching; (2) the most frequently used types of code switching used was intrasentential code switching followed by inter sentential code switching and tag switching. Code switching is used by the teacher to reduce either students' misunderstanding of the lesson or their difficulty understanding the English lesson offered by the English teachers in the classroom
Management of Online-Based Learning Media Through The Google Classroom Application in Increasing Student Learning Interest

This study describes the application of managing online learning media through the Google Classroom application to increase student interest in MI Miftahul Islam. Media in daring learning is especially very influential on students' learning interest; we need a suitable press. This research uses a qualitative approach with a case study type. Data collection was carried out through direct observation and interviews. The informants in this study consisted of four people: the principal, the teacher, and two students. Data analysis was carried out through data collection, reduction, display, and drawing of conclusions. The study results found that management is carried out using; planning, implementing google classroom and evaluation. By implementing the direction of online learning media through the Google Classroom application, it can increase the learning interest of MI Miftahul Islam students supported by several factors, namely, (1) Ease of User Access, (2) Clear Exposure, (3) User Convenience, and (4) Interactive Communication. The implications obtained by managing google classroom learning media at MI Miftahul Islam can increase student learning interest marked by students' enthusiasm to participate in teaching and learning activities boldly.
Identification of Website-Based Product Sales Frequency Patterns using Apriori Algorithms and Eclat Algorithms at Rio Food in Bekasi

Sales reports that are not managed automatically may hinder businesses from accurately determining their progress in the short or long term. With increasing community needs for a product, business owners have an opportunity to market their products to a larger audience. The abundance of data highlights the need for information to produce patterns that can be used as a reference for making decisions in buying products on the website. Data mining algorithms can provide support for analysis, which can help avoid inaccurate business progress reports. In this study, the Apriori and Eclat algorithms were applied to analyze frequent itemsets in association rule mining. The dataset used in this study consists of 20 transaction data from frozen food sales. The results showed that the combination of Nugget and Chicken Sausage itemsets were the most frequent, with higher support, confidence, and lift ratio values than the others. These results can be used as product recommendations that are most in demand by customers.
Storyboard Design of Android-Based Learning Multimedia Integration Application Using Standard Process Tutorial Model

Bina Pribadi Islam is a subject taught in Islamic elementary schools with the aim of improving students' spiritual well-being through guidance from teachers or mentors. Islamic elementary schools have an educational system designed by the Ministry of National Education and adapted to Islamic law. In the process of learning Bina Pribadi Islam, there is a need for a strategy to increase students' motivation and interest by using a multimedia application that includes elements such as text, audio, graphics, video, and animation tailored to the standard process tutorial model. To create an interactive multimedia application, multimedia design should be prepared in accordance with the tutorial model. Therefore, to produce a good application, a storyboard design needs to be created first that is tailored to the tutorial model to facilitate the application's development. Based on the storyboard design created according to the tutorial model, three main menus are produced as the characteristic features of the tutorial model, namely introduction, tutorial material, and exercises. Each menu produces several sub-menus tailored to the material in the Bina Pribadi Islam subject.
COMPARISON OF SIFT AND ORB METHODS IN IDENTIFYING THE FACE OF BUDDHA STATUE

The statue is part of the heritage facial recognition process which is immobile and artistically stylized. Identifying the similarities between the statues can help provide an important reference for tourism in recognizing the faces of the statues which are different and have almost the same characteristics in every country, especially in Indonesia, among the facial recognition of the statues based on the condition, color, and shape of the face. The purpose of this study is to apply the original images that have characteristics, partially done manually to various types of transformations and calculate matching evaluation parameters such as the number of key points in the image, the level of matching, and the required execution time for each algorithm. To confirm the efficiency of the proposed method, experiments were carried out on private data sets obtained from statues under low light conditions and in different poses. The data was taken based on the image of the Buddha's face and matched with the facial image of the Buddha statue available in the database using comparisons resulting from data processing using the Sift and ORB methods with various types of transformations. The result will be seen in the image that is matched with the best algorithm for each type of distortion. The faces tested are images of the faces of the Buddha statues that are recognized, and photos of some of the original statues that were not saved due to unclear lighting and camera distance factors. The results show that the number of key points generated is the number of key points, the ORB method gives fewer results compared to the SIFT method and the average SIFT recognition and processing time shows better performance for an average of 100% at a SIFT matching rate of 2% with time 0.400285 and the ORB method is 1% for the time 0.400961
Cluster Analysis Using Principal Component Analysis Method and K-Means to Find Out the Compliance Group of Property Tax

Abstract&#x0D; &#x0D; The village of Kendal has experienced a decline in local income due to the high rate of property tax arrears, with 226 taxpayers (19% of residents) known to have outstanding payments. Additionally, with 1,159 separate residents residing in 10 block areas with varying tax amounts, it has become increasingly difficult for the Village Apparatus to profile taxpayers based on their characteristics. To overcome these problems, a data analysis model based on Machine Learning technology will be developed using the Principal Component Analysis (PCA) Method combined with the K-Means method. The objective of this study is to create a cluster analysis model that can accurately map the characteristics of taxpayers, making it easier for the Village Apparatus to identify and assist residents who need to pay their property tax. This proposed solution will also simplify the reporting process to the central government regarding the estimated regional revenue sourced from property tax.
View these Featured Solutions

Inside Precision MedicineVol. 10, No. 2 MarketplaceView these Featured SolutionsPublished Online:12 Apr 2023https://doi.org/10.1089/ipm.10.02.18AboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail View articleFiguresReferencesRelatedDetails Volume 10Issue 2Apr 2023 InformationCopyright © GEN PublishingTo cite this article:View these Featured Solutions.Inside Precision Medicine.Apr 2023.65-65.http://doi.org/10.1089/ipm.10.02.18Published in Volume: 10 Issue 2: April 12, 2023PDF download
Fusing Artificial Intelligence with Scrum Framework

Abstract In today’s era, AI has become a huge part of people’s lives. AI technology’s ability to think, evaluate, and respond like a human brain has reduced human effort by automating a large number of tasks. However, the development of such applications is difficult and requires mass data evaluation and manipulation. With innovations, changing customer needs, and increasing complexity, AI project development require a renovated approach to improve the development procedure. Employing Scrum Framework prescribed under the Agile Software Development Methodology can prove to be an effective way to improve product quality and revenue and reduce production complicacy. Scrum Framework aims to develop small pieces of the product in increments and continuously test and evaluate it to minimize errors and maximize responsiveness. The repetitive, inspective, and adoptive approach followed by Scrum Framework can make AI-based project development and management easier and more effective.
Transfer Learning for Algorithm Recommendation

Meta-Learning is a subarea of Machine Learning that aims to take advantage of prior knowledge to learn faster and with fewer data [1]. There are different scenarios where meta-learning can be applied, and one of the most common is algorithm recommendation, where previous experience on applying machine learning algorithms for several datasets can be used to learn which algorithm, from a set of options, would be more suitable for a new dataset [2]. Perhaps the most popular form of meta-learning is transfer learning, which consists of transferring knowledge acquired by a machine learning algorithm in a previous learning task to increase its performance faster in another and similar task [3]. Transfer Learning has been widely applied in a variety of complex tasks such as image classification, machine translation and, speech recognition, achieving remarkable results [4,5,6,7,8]. Although transfer learning is very used in traditional or base-learning, it is still unknown if it is useful in a meta-learning setup. For that purpose, in this paper, we investigate the effects of transferring knowledge in the meta-level instead of base-level. Thus, we train a neural network on meta-datasets related to algorithm recommendation, and then using transfer learning, we reuse the knowledge learned by the neural network in other similar datasets from the same domain, to verify how transferable is the acquired meta-knowledge.
A Comparison of Results among Waste Management Producers: A Case Study for the Process of Waste Management

A mathematical model was applied to analyze cooperation among producers in waste management.• The Shapely value was used to show the potential cost to the waste producers in the case of cooperation.• A case study for the process of waste management was investigated.In this paper, we apply a mathematical model based on cooperative game theory to model cooperation among producers in waste management.Then, we use the Shapely value as a solution concept in a cooperative game to evaluate the cooperation existing among producers, in order to show the potential cost to the waste producers in the case of cooperation and reduce the overall costs of processing non-recyclable waste during the cooperation.Thus, this study concludes that all producers divide sanitary landfills or incineration of nonrecyclable waste, meaning that for each producer who earns more, there will be a greater contribution to the disposal or treatment of non-recyclable waste.
Research on Lightweight Few-Shot Learning Algorithm Based on Convolutional Block Attention Mechanism

Few-shot learning can solve new learning tasks in the condition of fewer samples. However, currently, the few-shot learning algorithms mostly use the ResNet as a backbone, which leads to a large number of model parameters. To deal with the problem, a lightweight backbone named DenseAttentionNet which is based on the Convolutional Block Attention Mechanism is proposed by comparing the parameter amount and the accuracy of few-shot classification with ResNet-12. Then, based on the DenseAttentionNet, a few-shot learning algorithm called Meta-DenseAttention is presented to balance the model parameters and the classification effect. The dense connection and attention mechanism are combined to meet the requirements of fewer parameters and to achieve a good classification effect for the first time. The experimental results show that the DenseAttentionNet, not only reduces the number of parameters by 55% but also outperforms other classic backbones in the classification effect compared with the ResNet-12 benchmark. In addition, Meta-DenseAttention has an accuracy of 56.57% (5way-1shot) and 72.73% (5way-5shot) on the miniImageNet, although the number of parameters is only 3.6[Formula: see text]M. The experimental results also show that the few-shot learning algorithm proposed in this paper not only guarantees classification accuracy but also has the characteristics of lightweight.
ANALISIS KWALITAS WEBSITE LAYANAN ASPIRASI &amp; PENGADUAN MASYARAKAT TANGKAR (TANGGAP KARAWANG) DI DINAS KOMUNIKASI DAN INFORMATIKA KABUPATEN KARAWANG MENGGUNAKAN METODE WEBQUAL 4.0

and usage from the community's perspective towards the website is the reason why this research is conducted. Community satisfaction is one of the factors or measures of success for any information system development in a government. Good service quality is not only based on the perspective or perception of the service provider, but also based on the perspective or perception of the community. The community that enjoys the service is the community that can determine service quality. The community's perception of service quality is a comprehensive assessment of the excellence of a service. WebQual 4.0 is a method for determining website quality based on community or user perception. The WebQual 4.0 method consists of three categories, namely usability, information quality, and service interaction. These three categories are used as a reference in creating a questionnaire to analyze the quality of the community aspiration and complaint service website. The analysis results are expected to provide recommendations for local governments in developing quality websites so that the development of community aspiration and complaint service websites in Indonesia can mature, stabilize and ultimately reach a level of utilization.
A Study on the Differences in Perceptions between High School Teachers and Admissions Officers on the Direction of Reform of the College Admission System Following the Introduction of the High School Credit System

본 연구는 2025년 전면 도입을 앞두고 있는 고교학점제와 관련하여 대입제도를 어떠한 방향으로 개편해야 하는지를 모색하기 위하여 고등학교 교사와 대학의 입학사정관을 대상으로 향후 대입제도 개편 방향에 대해 어떠한 인식을 가지고 있는지 살펴보았다. 주요 조사 분야는 1) 고교학점제에 부합하는 대입전형, 2) 고교-대학 연계의 대입제도 개선 방향, 3) 대학 자율성과 책무성 강화 방향, 4) 정부의 제도 개입 방향으로 나누어 설문하였다. 대입제도의 주요 이해관계자인 교사와 입학사정관의 인식과 태도의 공통점과 차이점을 살펴봄으로써, 향후 대입정책의 개편에 대한 시사점을 얻고자 하였다. 연구결과, 고등학교 교사와 대학 입학사정관들은 공통적으로 학생부 종합전형을 고교 학점제에 가장 부합하는 전형이라고 인식하고 있었으며, 대체로 고등학교-대학이 서로 연계되는 방향으로 대입제도가 개편되어야 한다고 생각하고 있었다. 또한 대학은 지원자의 능력 중심으로 선발하되, 공정성과 책무성을 담보해야 하며, 정부는 입시부담을 완화하는 방향으로 대입정책을 설계되어야 한다고 보았다. 그러나 개별 사안에 따라 교사와 입학사정관이 인식을 달리하는 지점도 있었으며, 소재지역과 학교 규모에 따른 차이도 발견되었다. 교사와 입학사정관의 인식에서의 공통점과 차이점을 토대로 향후 대입제도 개편에 대한 정책적 시사점을 제안하였다.This study examines the commonalities and differences in perceptions and attitudes between high school teachers and college admissions officers about how the college admissions system should be changed in relation to the high school credit system. The main survey areas were 1) which college admissions type is in line with the high school credit system, 2) how to link high school and college systems, 3) how to strengthen university autonomy and accountability, and 4) how government should involve in a systematic change. By examining the positions of the major stakeholders, we seek implications for the design of college entrance policies. We found that high school teachers and university admissions officers commonly support 1) the comprehensive student record screening, 2) connecting high school and college, 3) universities selecting applicants based on their academic ability as well as fairness, and 4) easing the burden of admission requirements. However, they also had different opinions depending on their location and school size. Based on these commonalities and differences, policy implications for college admission system reform were proposed.
EXAMINING THE EFFECTIVENESS OF ARTIFICIAL INTELLIGENCE-BASED CYBERBULLYING MODERATION ON ONLINE PLATFORMS: TRANSPARENCY IMPLICATIONS

Cyberbullying remains a significant problem for children that appears to have been exacerbated by Covid-19 related lockdowns, which moved a lot of children's offline activities online. Transparency reports shared by social network and gaming platform companies indicate increased take-downs of offensive and harmful comments, posts or content by artificially intelligent (AI) tools. Nonetheless, little is known about how such tools are designed and developed, what data they are trained on; and how this is done in practice. Many studies have discussed the opacity of such algorithmic _moderation, detection and prevention_ solutions, and called for their greater transparency to understand *how* and *what* user-interactive engagement features help in AI decision-making. This study examines a) the use of AI-solutions by social media and gaming companies to proactively address cyberbullying on their platforms, b) explore the current cyberbullying detection, prevention and proactive intervention strategies by such companies, and c) through comprehensive database search, review of existing computational literature on monitoring, detection and intervention strategies to address cyberbullying incidents amongst children. Our findings show that *very scarce* resources are available in the public domain to build AI algorithmic solutions to combat cyberbullying, and _little information_ is publicly available that would allow scrutiny of platforms' enforcement mechanisms.