Reviewerly/exHarmony huggingface dataset:
Pengaruh Motivasi Eksternal oleh Guru Pendidikan Agama Kristen Terhadap Sikap Belajar Siswa Kelas VII SMP N 1 Harian Tahun Pembelajaran 2022/2023

The aim of this research is to determine the influence of external motivation by Christian religious education teachers on the learning attitudes of Class VII students at SMP N 1 Harian for the 2022/2023 academic year. The research method used is a quantitative method. The population is all 40 students of Class VII of SMP N 1 Harian. Data was collected using a positive closed questionnaire with 40 items, namely 20 items for variable 1) Test the analysis requirements: a) positive relationship test obtained rxy = 0.608 &gt; rtable(=0.05,n=40) = 0.312. b) Testing a significant relationship obtained tcount= 4.719 &gt; ttable(=0.05, dk=n-2=38)= 2.021. 2) Influence test: a) Regression equation test, obtained the regression equation Y ̂="13.29"+0.78X. b) Regression coefficient of determination test (r2) = 36.9%. 3) Test the hypothesis using the F test to obtain Fcount &gt; Ftable=(=0.05, dk numerator k=20, dk denominator=n-2=40-2=38) namely 22.26 &gt; 1.51. Thus Ha is accepted and H0 is rejected.
Canva Application as a Learning Media for Human Anatomy Physiology

Nursing education is one of the educations whose learning process through E-Learning during the covid-19 pandemic. The Canva application is an Android-based website that can be used as an online interactive learning medium. The purpose of this study was to analyze the effectiveness of the Canva application as an interactive learning medium for knowledge and practice of the anatomy and physiology of the respiratory and cardiovascular systems. This study is quasi-experimental with a pre-posttest approach with a control group. The sample in this study was 88 first-semester students of the Department of Nursing who were divided into two groups by simple random sampling. The research instruments used were questionnaires and observation sheets, and the results of this study were analyzed by the Mann-Whitney test, independent t-test, and N-Gain test. This study showed an increase in the average knowledge (p = 0.04) and practice (p = 0.012) in the group that was given the Canva Application media. However, in this study, the Canva application was ineffective as an interactive medium for learning anatomy and physiology (N-Gain = 30%). It is because learning with Canva can be affected by internet access, the ability of smartphone users, network availability, and internet quota.
SAE-PPL: Self-guided attention encoder with prior knowledge-guided pseudo labels for weakly supervised video anomaly detection

Recently, many weakly supervised video anomaly detection (WS-VAD) methods focus on generating reasonable pseudo-labels for frames by using video-level annotations. To achieve this goal, some existing label-noise cleaning techniques and pseudo-label generators are used by them, but their performance is limited. The main reason is that the useful prior knowledge, i.e., the graduality of anomaly events, is ignored. Here, we propose a Self-Training Framework, which assumes flexible soft boundaries between abnormal and normal clips. The framework consists of: (1) A prior knowledge guided pseudo-label generator that incorporates prior knowledge of video segment distributions into the MIL framework to generate high-confidence pseudo labels; (2) An improved self-guided attention encoder is developed to capture multiscale long-term spatiotemporal features, where dependencies among anomaly frames are preserved. Moreover, a pseudo-label based self-training scheme is adopted to supervise the encoder. Experimental results verify the superiority of our method over baseline approaches.
Information Security Awareness Raising Strategy Using Fuzzy AHP Method with HAIS-Q and ISO/IEC 27001:2013: A Case Study of XYZ Financial Institution

XYZ financial institution is a government institution that receives and processes transaction reports from banks and remittances, so its data classification is very confidential. However, during the Work from Home (WFH) policy in the Covid-19 pandemic, XYZ financial institution has received many spam/phishing attacks. Hence, this incident shows that some employees need an awareness of information security. The research offers a different Information Security Awareness (ISA) questionnaire using the Human Aspects of the Information Security Questionnaire (HAIS-Q) and ISO/IEC 27001:2013 as focus areas. The research uses the theory of Knowledge, Attitude, and Behavior (KAB) to determine the dimensions that need improvement and priority ranking using Fuzzy Analytical Hierarchy Process (FAHP). Furthermore, the research conducts a Focus Group Discussion (FGD) to explore the root causes of employee behavior. The FGD results show that there are still employees who do not know about information security, such as password combinations and length, so limited knowledge affects employees’ attitudes and behaviors. The research results from 34 respondents show that the employees’ information security awareness level is in the moderate category (78.8%). They still need to increase their awareness of information security, especially in managing passwords, using email and the Internet, and reporting incidents. Recommendations have been prepared to improve the dimensions and areas that have yet to be categorized as good. In the future, the ISA questionnaire is expected to be used in other organizations.
The Implementation of Spatial Model with K-Means Clustering Method to Cluster Flood Affected Areas in Bone Regency

This research is an applied research that aims to determine the clusters of areas affected by floods in bone regency. This study user the K-means method in clustering flood data in 2020 and 2021. The data is grouped based on the number of families affected and the resulting damage. In determining the number of clusters to be used, this study validated 3, 4 and 5 clusters using the davis bouldin index (DBI) validation in determining the best cluster. The results of this validation resulted in the best number of clusters, namely k=4 for 2020 data with a minimum DBI of 0,73 and k=4 for 2021 data with a minimum DBI of 0,44. After clustering, the cluster number for data for 2020 from the firs to fourth cluster member are 34, 7, 2 and 3 sequentially, while the data for years 2021 sequentially are 3, 22, 3 and 1. Then the cluster results are displayed in a spatial form that is created using ArcGIS.
Implementasi Data Mining Untuk Memprediksi Status Gizi Anak Balita Pada Puskesmas Gedung Sari Menggunakan Polynomial Regression

Predictions of children's nutritional Status can provide important input to. efforts to monitor and intervene in child nutrition, as well as support better decision-making in the field of child health. This study took data from Puskesmas data. Gedung Sari Health Center is a community health center located in Anak Ratu Aji District, Central Lampung Regency. Based on the results and discussion of the data analysis on the data above, related to the prediction of the nutritional status of toddlers at the Gedung Sari Health Center using polynomial regression, a conclusion is obtained. The conclusion of predicting nutritional status using polynomials depends on specific data analysis and modeling and the methods used. However, in general, polynomials can be used to model the relationship between input and output variables in the case of predicting nutritional status. predicted nutritional status of children and total predicted nutritional status obtained Predictions based on age in 2019 = 290,806; Predictions based on nutritional status in 2020 = 68,176; Predictions based on nutritional status in 2021 = 122,239. More research needs to be done to validate and increase the reliability of a prediction and integrate the results into health practice to improve child nutrition monitoring and interventions in the community health center.
Real Time E-learning Students Monitoring for Optimization Facial Landmark Recognition Based on Hybrid Deep Learning Techniques

The onset of digital education, propelled by the global COVID-19 crisis, has revolutionized the education sector, presenting unique difficulties, including the crucial task of preserving academic honesty. This study explores the possibilities of computer vision technologies, specifically face recognition and detection systems, to deter dishonest practices in online learning contexts. In this article we aim to construct efficacious strategies that leverage these technologies to track student actions in real-time and alert educators about possible cheating instances. This study presents two innovative models addressing cheating in online learning settings using cutting-edge computer vision techniques. Our initial model is an ensemble learning based face recognition system that blends the functionalities of three different deep learning (DL) structures: VGG, MobileNet, and DenseNet. This ensemble learning approach aims to offset the shortcomings of individual models while amplifying the overall effectiveness. The model’s efficiency will be gauged by juxtaposing it with other models and testing its performance against renowned benchmark datasets. Following this, we propose a second model designed for real-time face and cheating detection. This model integrates the FaceMesh model, facial landmarks analysis, and head pose estimation to identify possible cheating behaviors, such as significant shifts from a neutral or forward-facing head position. This model’s efficiency will be assessed through testing in simulated cheating scenarios and using authentic data from online learning contexts. Upon testing and validation, our proposed models have shown encouraging outcomes. The ensemble learning model outstripped individual models by attaining a remarkable accuracy rate of 91% through soft voting. Furthermore, the face detection system showcased sturdy abilities in recognizing faces under diverse conditions and accurately pinpointed potential cheating behaviors based on head pose estimation.
PRODUCT CLUSTERING USING K-MEANS METHOD IN CV. JAYA ABADI

CV. Jaya Abadi is a company engaged in the distribution of goods, or, to be more precise, a distributor of raw materials for making bread. The fluctuating number of requests from consumers results in stock that must be prepared to be unstable. In addition, many types of products make stock management inaccurate. Sometimes we do not want to have a shortage of stock, goods, or certain products when consumer demand is high. The purpose of this research is to analyse sales data mining through the clustering method with the k-means algorithm. The method used for this analysis uses clustering with the k-means algorithm. So that this research is not subjective, the authors also use research methods in the form of observation, interviews, and documentation. The results of this study were carried out by analysing sales by applying data mining through the clustering method with the k-means algorithm. Data mining that has been going on for a long time can be used as a reference in the management of CV Jaya Abadi. Apart from being used for administrative purposes each period, it can also be used as a decision-making solution in order to maximize existing products and sales at CV Jaya Abadi.
Politeness Strategies in Classroom Interaction between Teacher and Students and among Students at Senior High School

In classroom, politeness is needed to be applied to build a good relationship, good social interaction, and comfortable communication between teacher and students and avoid conflict which possibly happens. The purpose of this research is to know the types of politeness strategies used by students and teachers in classroom interaction and to describe the politeness strategies are realized in the teacher – student’s classroom interaction. This research are expected to be valuable for the teacher as the model in English teaching, the teacher can teach moral education in the teaching and learning process. Students will aware of the importance of politeness in English teaching and learning. The people who use polite language will be more honored and accepted in community. Polite in communication make conversation get closer between sepaker and hearer. There are four types of polieness strategies, there are Blad On Record, Positive Politeness Strategies, Negative Politeness Strategies and Off Recordthe researcher found the dominant strategy in teacher-students communication was Bald On Record Strategy which has 13 frequency (44,82%), and followed by Positive which has 10 fruquency (34,48%)In communication among students, the researcher found the most dominant politeness strategy used among students is positive politeness which has 21 frequency (46,66%) and followed by bald on record 17 frequency (37,77%).
Federated and Transfer Learning Applications

The classic example of machine learning is based on isolated learning—a single model for each task using a single dataset [...]
Telextiles: End-to-end Remote Transmission of Fabric Tactile Sensation

The tactile sensation of textiles is critical in determining the comfort of clothing. In remote scenarios such as online shopping, sensors need to distinguish different garments even with hand-held sensors, and current actuation devices can only present a limited number of known patterns and cannot transmit unknown tactile sensations. We propose Telextiles, an interface for remotely transmitting textile tactile sensations, which uses contrastive self-supervised learning to create a latent space that reflects the relative proximity of textiles. We convert the latent features into a scalar for the one-dimensional structure of the roller. We then select 16 equidistant samples from this line to represent different regions of the latent space. The roller rotates to select the textile with the closest feature. We also show visually the relationship between a textile touched by a remote user and a set of textiles previously registered in our system.
Quantum Computing for Quantum Chemistry: Short-Term Pessimism

Quantum computing gets a lot of attention these days.
Gradient Boost Tree Network based on Extensive Feature Analysis for Popularity Prediction of Social Posts

Social media popularity (SMP) prediction is a complex task, affected by various features such as text, images, and spatial-temporal information. One major challenge in SMP is integrating features from multiple modalities without overemphasizing user-specific details while efficiently capturing relevant user information. This study introduces a robust multi-modality feature mining framework for predicting SMP scores by incorporating additional identity-related features sourced from the official SMP dataset when a user's path alias is accessible. Our preliminary analyses suggest these supplemental features significantly enrich the user-related context, contributing to a substantial improvement in performance and proving that non-identity features are relatively unimportant. This implies that we should focus more on discovering the identity-related features than other meta-data. To further validate our findings, we perform comprehensive experiments investigating the relationship between those identity-related features and scores. Finally, the LightGBM and TabNet are employed within our framework to effectively capture intricate semantic relationships among different modality features and user-specific data. Our experimental results confirm that these identity-related features, especially external ones, significantly improve the prediction performance of SMP tasks.
The Effect of Board Game on Students’ Vocabulary and Grammar Mastery in Senior High School

This research was conducted based on some problems in learning English especially in vocabulary and grammar. It was also aimed to find out students’ vocabulary and grammar who are taught by using Board GameTechnique and students’ vocabulary and grammar who are taught without using Board Games and find out whether there was any significant effect of using Board Game on the tenth-grade students’ ability in vocabulary and grammar at SMA Kristen Immanuel. In this quasi-experimental design research, the researcher took two of 2 classes as the sample, the experimental class with 20 students in it, and the control class with 20 students as well. The research method was pre-test and post-test of the experimental research. The data collection technique was pre-test and post-test score result. The data were analyzed by using compare means paired sample T-test by comparing the result of pre-test and post-test design, and t-test to know the significant effect in students’ English vocabulary mastery after using Board Game. The result showed there was a significant different between pretest and post test mean. Ho was rejected because significance level was &lt; 0.05. Therefore, there was a significant effect of using board game towards students’ vocabulary mastery of the research. The research of data analysis showed that there was a positive effect of using Board Games on students’ vocabulary and grammar. In which in vocabulary, the t-obtained had a higher value than the t-table either at 5% or 1% significance levels (1,68595 &lt; 4,174&gt; 2,42857). Therefore, Ho is rejected and Ha is accepted; or there is a significant difference in using Board Games on student’s vocabulary and grammar ability of tenth grade students. The result of grammar, the condition of the experimental class was getting better. The students were more active in English learning process. In summing up it may be stated that using Board Game can effect the students‟ grammar mastery and students learning activities at the tenth grader of SMA Kristen Immanuel Medan.
Advanced product review summarization in e-commerce marketplaces: elevating beyond tf-idf and lexrank method

In the fiercely competitive domain of online product sales, wherein engendering trust among prospective buyers assumes paramount significance, the role of product reviews cannot be understated. However, a prevailing issue in online marketplaces resides in the presence of product reviews that do not consistently align with the overall product rating. Furthermore, the sheer abundance of comments often leads potential consumers to confine their scrutiny to the initial comments, thus leaving a substantial volume of reviews unexplored. To rectify this challenge, this study introduces an automated text summarization system for product reviews, leveraging the LexRank methodology. This system underwent rigorous evaluation using the Rouge metric, with results manifesting substantial promise. At a threshold of 0.1, Rouge-1 exhibited an accuracy of 16.67%, while Rouge-2 scored 3.01%, and Rouge-L reached 16.50%. At a threshold of 0.2, Rouge-1 yielded a score of 16.08%, Rouge-2 registered 2.64%, and Rouge-L scored 16.57%. The second evaluation, performed with a distinct test dataset, notably excelled, emphasizing the system's competence. Specifically, at the 0.2 threshold, the system displayed superior performance, underscoring its efficacy in refining product review summarization within online marketplaces
#MassTestingNowPH tweets as acts of citizenship: The rhetorical functions of tweets in pandemic-stricken Philippines

Constrained physical mobility and oppositional action during the COVID-19 pandemic in the Philippines drove many Filipinos to turn to social media affordances, like Twitter’s hashtags, as sites of free speech, dissent, and collective action. One of which, #MassTestingNowPH, called for the implementation of mass testing for the vulnerable population and objected to Rodrigo Duterte’s militaristic pandemic response. This paper examined how #MassTestingNowPH tweets served as acts of citizenship and exerted their rhetorical functions in the digital space during this global medical crisis. Using rhetorical political analysis, this research found that #MassTestingNowPH tweets manifested acts of citizenship by asserting citizens’ rights and responsibilities, and exacting government’s accountability in newly-formed ad hoc publics. Users criticized the country’s COVID-19 response and the injustices of VIP testing for some of its officials. These criticisms enabled them to generate collective grievances for medical frontliners and the marginalized. With these sentiments, netizens called on their audiences to act on their judgment and assert their citizenship in online and offline platforms. These tweets, as acts of citizenship, performed three rhetorical functions: forensic, epideictic, and deliberative. This rhetorical process shaped Twitter’s hashtag as an ad hoc public and the meaning of citizenship in our highly-networked world.
JavaScript Reminders

Here's the final chapter, "JavaScript Reminders," of our book Master React in 5 Days. Before diving into the world of React, it's important to recall the basics of JavaScript. This chapter aims to help you remember and brush up on the key concepts of JavaScript that are used throughout the book.
Differences between Indicators of Self-Actualization Characteristics, Tolerance to Uncertainty, and Metacognitive Skills in Working Boys and Girls

The paper is concerned with the comparative study of indicators of self-actualization characteristics, tolerance to uncertainty and metacognitive skills in working young boys and girls. The sample comprised 116 young men and 84 young women aged 18 to 22 years. The overall number of people under study is 200 people working in the fields of law, creativity, engineering, architecture, management, IT, medicine, etc. Significant differences between the indicators of 17 personality attributes of working girls and boys were obtained. Working boys eval-uate their self-actualization and cognitive qualities higher than girls, while working girls are more tolerant of uncertain situations and novelty, but are inferior to boys in making decisions amid incomplete information. Working boys are more capable than girls of identifying the essentials, actively pursuing their own goals, mak-ing quick choices, concentrating well and managing their time. The results obtained from the study are of prac-tical relevance for employers to utilize the personal potential of employees in a more constructive way.
Human Activity Recognition Using Multi Features

Human Activity Recognition (HAR) is a multidisciplinary field that uses computational algorithms to understand and categorize human behaviors. Recently, the focus has been on utilizing multiple aspects to enhance the depth and accuracy of identification algorithms. This survey provides a comprehensive overview of the advances made in HAR, with a particular emphasis on the integration and synergistic use of various features. We explore a wide range of properties that are relevant to HAR, including spatial and temporal data, physiological indicators, and detailed environmental variables. By adopting a multi-feature approach, we can broaden the scope of recognizable activities while also improving the precision and adaptability of identification systems, addressing the dynamic and multifaceted nature of human activities. However, combining multiple attributes complicates data processing, model training, and real- time applications. This survey delves into these issues, providing insights into potential solutions and future prospects. As we enter a new era in HAR, the combination of various aspects promises a more comprehensive, context-aware, and nuanced understanding of human actions, with enormous potential for applications ranging from healthcare to smart home systems.