Reviewerly/exHarmony huggingface dataset:
Dampak Pembelajaran Online Dimasa Pandemi Terhadap Kualitas Tidur Mahasiswa Fakultas Kedokteran Universitas Yarsi Angkatan 2020

The online learning method is a learning method that utilizes internet network technology that can conduct connectivity and learning interactions through long distances directly without having to meet face to face. Online learning method is a solution used by almost all educational institutions in conducting learning activities during the COVID-19 pandemic. Many studies have been conducted explaining that online learning can disrupt a person's sleep quality. So this study is to find out how the effect of online learning during the pandemic on the quality of sleep of YARSI University Faculty of Medicine students class of 2020. From a population of 207 students of the Faculty of Medicine, YARSI University, Class of 2020, using the slovin formula method, a minimum sample of 137 students was obtained. Data collection is done online using a google form in which there is an inform concern dika indeed the data obtained will be processed into research data. From filling out the questionnaire, data from students was obtained as many as 142 respondents. Then the collected data is processed to find the frequency distribution and find the variable relationship with the spearmans correlation test. Based on the results of the study, the majority of respondents were female (81%) and the average age of respondents was 20 years (74.3%). A total of 119 (83.8%) respondents had poor sleep quality, and the average online learning time carried out by respondents was 5-10 hours per day (77.4%). The results of the spearman rank statistical test obtained a significant number or probability value (0.000) much lower than the significant standard of 0.510 or (p &lt;a). This means that there is a significant relationship between online learning and student sleep quality during the COVID-19 Pandemic.
IMPLEMENTASI SISTEM INFORMASI STOCK OPNAME WAREHOUSE PADA PD. SINAR MAS (HAKASIMA) JAMBI

Warehouse is one of the supporting elements that have a vital role in a company. Therefore, a small error in data storage or stock counting, can result in paralyzing a company and sometimes even creating a company. According to the author's assessment, the data storage system and stock counting in PD. Sinar Mas (Hakasima) Jambi, is still far from being effective. This is because there are still many manual records found by the author from the PD warehouse staff. Sinar Mas (Hakashima).
Pengaruh Model Discovery Learning Berbantuan Komik Digital Flip Book dalam Upaya Meningkatkan Hasil Belajar IPS di Sekolah Dasar

This research is motivated by the learning process which demands active participation from the learner, but in reality the learning process still tends to reduce the role and participation of students. This study intends to see how much influence the digital comic-assisted discovery learning model flip book has in efforts to improve social studies learning outcomes for fifth grade students in the 2022/2023 academic year to find out whether the digital comic-assisted discovery learning model is more effective than the digital flip book learning model. conventional. This study used a quasi-experimental method with a nonequivalent control group pre-test and post-test design. This research was conducted at SDN 1 Cibening with 25 students in class VA and 24 students in class VB as research samples. The instrument used in this study is a test. Based on the research findings, it shows that the discovery learning model assisted by digital flip book comics has an effect on student learning outcomes with an effect of 79.4%. The discovery learning model assisted by digital flip book comics is more effective than the conventional learning model with the difference in the pretest and posttest average scores, in the experimental class being greater than the control class. Overall, the increase in student learning outcomes in the experimental class belongs to the quite effective category, while the control class belongs to the less effective category. From the results of this study, it can be concluded that the comic digital flip book-assisted discovery learning model has an effect on efforts to improve social studies learning outcomes for fifth grade students in elementary schools and the digital flip book-assisted discovery learning model is more effective.
Genre-Based Learning Model With Literacy Activities and Character Strengthening: Effectiveness in Language Learning

During the implementation of online learning, some policies in the education sector cannot be implemented such as the National Literacy Campaign and Strengthening Character Education. These two programs can be integrated into the learning process including in language learning, especially the learning models and teaching materials. This study aims to develop a genre-based learning model by integrating literacy and character-strengthening activities. This Research &amp; Development (R &amp; D) used the Plomp Development model (Preliminary Research, Prototyping Phase &amp; Assessment Phase). In the Assessment Phase, the researcher tested the developed product to check its practicality and effectiveness. The instruments used were questionnaires, observation sheets, and tests. The subjects were students of State Senior High School 8 Padang. The results showed that the genre-based learning model which integrates literacy activities and character-strengthening effectively increases students’ literacy (reading and writing) and builds their character. This model does not significantly influence students' reading skills but significantly influences students’ writing skills.
Internet Chat as ‘Jargon’

The process of net communication, which is internet dependent, takes place among millions of individual (private) or group of participants sharing the same room in order to comment, discuss, or pass time .While the communicators are of different countries, cultures, backgrounds, and languages they exchange their thoughts and linguistic messages throughout this experience which is termed as “Internet Chatting”.The researcher presents the definition of “Jargon” and “Chatting” in addition to the related terms .He submits the structure of morphemes / words, phrases , and sentences in chatting in addition to how the chatters manipulate parts of words, abbreviations, numerals, emoticons…etc. in order to carry out this experience. The researcher, after analyzing the presented examples in the obtained data, concludes that the chatting process can be regarded as a sort of ‘Jargon’. Thus the hypothesis of the study is affirmed.
Development of Android-Based Sikair Interactive Learning Media to Improve Learning Outcomes of Class V Elementary School Students on the Material of the Water Cycle

This study aims to develop a product in the form of interactive learning media based on Android to improve student learning outcomes in elementary schools. This study uses the Research and Development method. This research was conducted at SDN 2 Courts. The results of the validation of the Android-based interactive interactive media were carried out by experts such as material experts, media experts and linguists. Each expert is as follows: the material expert gives a final score of 1 with the eligibility criteria of 100% or can be said to be "Very Eligible", the media expert gives a score of 0.9 with the eligibility criteria of 90% or can be said to be "Very Eligible", and the linguist gives score 1 with the eligibility criteria of 100% or you could say "Very Eligible". The results of the small group trials obtained an average score of 88.5% with the eligibility criteria "Very Eligible", and the results of the large group trials obtained an average score of 90.5% with the eligibility criteria "Very Eligible". This shows that the Android-based interactive sikair learning media is very suitable to be used as a natural science learning media, especially in water cycle material, the field research stage of the Android-based interactive sikair media obtained an average N-gain score of 0.8 in the "High" category and "interpretation category" Effective". Based on the results obtained, the Android-based interactive interactive media was declared feasible and effective for use in elementary schools.
Electronic sex between criminalization and legalization according to Palestinian legislation

This study aimed to identify the legal adaptation of electronic sex according to the Palestinian legislation, especially with regard to whether electronic sex is a criminal or permissible behavior, and whether it constitutes a crime of adultery, indecent assault, sexual harassment, an act of immodest foreplay, or a crime of seduction in both forms, Hymen with the promise of marriage, or spoiling the marital bond, and The researcher used the analytical approach based on the analysis of the Jordanian Penal Code No. 16 of 1960 and applied in the West Bank. And The researcher reached a set of results, perhaps the most prominent of which is that electronic sex is considered permissible and non-criminal behavior except in certain circumstances and certain conditions, where electronic sex can be considered a crime of defloration of the virginity of the promise of marriage, or spoiling the marital bond, And the study recommended the necessity of criminalizing sex Electronic crime as an independent crime due to its danger to practitioners, their families and society, And adding a legislative text in the Cybercrime Law to punish this behavior, which reads as follows: (Anyone who uses an information technology medium to communicate with another person for a sexual purpose shall be punished with imprisonment or a fine, or both penalties
AAR:Attention Remodulation for Weakly Supervised Semantic Segmentation

Abstract Weakly Supervised Semantic Segmentation (WSSS) is a crucial task in computer vision. However, existing methods that utilize Class Activation Maps (CAMs) with classification tasks can only identify a small part of the region. To address this limitation, we propose a novel Attention Activation Remodulation (AAR) scheme that leverages traditional CAMs and the remodulation branch to obtain weighted CAMs for recalibrated supervision. The AAR scheme re-arranges important features' distribution from the channel and space perspectives, which regulates segmentation-oriented activation responses. In addition, we propose a Feature Pixel Extraction Module (FPEM) that utilizes contextual information to improve pixel prediction. Furthermore, the proposed scheme can be combined with other methods to improve overall performance. Extensive experiments on the PASCAL VOC 2012 dataset demonstrate the effectiveness of the AAR mechanism and FPEM module.
Upaya Meningkatkan Hasil Belajar Siswa dan Retensinya Menggunakan Metode Mnemonics

The mnemonic method is a powerful technique for memorizing the subject matter we study. This study aims to determine the effect of using the mnemonic method on male reproductive system material on student learning outcomes and retention. This classroom action research involved 30 class XI students of SMAN 10 Pontianak for the 2022/2023 academic year. It uses two learning maintenance cycles. Two male reproductive system lesson plans, two Likert scales for observing teacher performance, and two achievement tests were administered in this study. The research results show that the average score of student learning achievement increased from 56.0 to 81.0. The percentage of learning completeness is 83.3%. In addition, some students' notes use mnemonic techniques. It was concluded that the mnemonic technique succeeded in increasing student learning outcomes and their retention (in the high category) in the male reproductive system material. Some recommendations are that this technique should be used by teachers in schools
Enhancement of Sentiment Analysis Classification Performance of Spotify Reviews through NLP-based Approaches

In this study, sentiment analysis of Spotify reviews was conducted utilizing Natural Language Processing (NLP) techniques. Specific preprocessing steps were implemented to enhance the performance of sentiment classification, aiming to achieve superior outcomes compared to NLP-based solutions. These preprocessing procedures hold significant importance in effectively categorizing emotions. The resultant sentiment analysis categorized sentiments into three classes: negative, neutral, and positive. These derived classes were subjected to a comparative analysis employing machine learning methodologies. The comparative assessment revealed that the Xgboost method exhibited a more successful classification performance compared to other approaches. To address data imbalance within the dataset, various handling imbalance methods were employed and juxtaposed against one another. Through this investigation, the study contributes to a more comprehensive understanding of sentiment analysis of Spotify reviews using NLP, shedding light on optimal preprocessing strategies and effective machine learning techniques for sentiment classification in this context.
OPTIMIZATION ISO 25010 WITH THE VORD METHOD AND C4.5 ALGORITHM IN SAVING LOAN COOPERATIVE

Advances in technology have made many companies and agencies, especially cooperatives, use information technology for their operational activities. Not infrequently in the operation of its business cooperatives have several obstacles caused by data processing and loan decision making still using conventional models. Cooperatives need a computerized information system in achieving organizational goals. To overcome this problem, a system is created that is integrated in one database which will be implemented by the Cooperative to facilitate its business operations. This study uses the Viewpoint Oriented Requirement Definition (VORD) method to analyze system requirements based on the user's point of view. In addition, the C4.5 Algorithm and the Naïve Bayes Algorithm as decision making for loan approval which will be compared in recommending and classifying loans using the confusion matrix, the results of testing the two algorithms with an accuracy value of the C4.5 Algorithm of 88.00% and an accuracy rate of the Naïve Bayes Algorithm of 76.00%. It can be concluded that the accuracy value of the C4.5 Algorithm is feasible to be implemented into the Koperasi Jasa Pratama system. The application that is made is then tested using the ISO 25010 standard to produce an optimal application. After conducting a needs analysis using the VORD method for ISO optimization, an application test is carried out using the ISO 25010 standard. From the test results, the characteristics of the Functional Suitability test are: Appropriate; Usability : 90.08; Performance Efficiency : Grade "B"; Portability : no errors. The test results show that the system is appropriate and feasible to use.
ANALISIS KESULITAN PEMBELAJARAN MATEMATIKA MENGGUNAKAN METODE DARING DI SMP NEGERI 11 LUBUKLINGGAU

This study aims to determine the difficulties of learning mathematics using online methods atSMP Negeri 11 Lubuklinggau for the 2020/2021 academic year. This research is a quantitative descriptive type of research. The data collection technique in this study used a questionnaire distributed to students in the form of a link and interviews with a class VIII mathematics teacher at SMP Negeri 11 Lubuklinggau. The results in this study are: 1) Implementation of mathematics learning using the online method using the WhatsApp application, 2) analysis of technical difficulties with a total average of 83.87%; analysis of the implementation of learning a total average of 83.11%; and analysis of the difficulty of external factors with a total average of 85.35%.
An Analysis of Students' Grammatical Errors in Writing Recount Text at Eleventh Grade of SMA Negeri 5 Prabumulih

This thesis is entitled "An Analysis of Students' Grammatical Errors in Writing Recount Text at Eleventh Grade of SMA Negeri 5 Prabumulih". Formulation of the problems: What is the most common Grammatical Error made by the Eleventh Grade Students of SMA Negeri 5 Prabumulih? The research objectives were to find out the most common grammatical errors made by the eleventh-grade students of SMA Negeri 5 Prabumulih. The population of this research was all the eleventh grade of SMA Negeri 5 Prabumulih in the academic year of 2020/2021, which consisted of five classes with a total population of 133 students. The sample of this study was 30 students taken using the purposive sampling technique. The researchers used a descriptive qualitative method. The data was collected through a writing test for students to make a recount text. The analysis finding showed that the total of student errors was 76. There were 31 omission errors (42, 46%), 22 addition errors (30, 13%), 15 misformation errors (20, 54%), and 8 misordering errors (10, 95%). The researchers conclude that the highest percentage of errors was omission errors, which were the dominant errors.
Knowing Your Annotator: Rapidly Testing the Reliability of Affect Annotation

The laborious and costly nature of affect annotation is a key detrimental factor for obtaining large scale corpora with valid and reliable affect labels. Motivated by the lack of tools that can effectively determine an annotator's reliability, this paper proposes general quality assurance (QA) tests for real-time continuous annotation tasks. Assuming that the annotation tasks rely on stimuli with audiovisual components, such as videos, we propose and evaluate two QA tests: a visual and an auditory QA test. We validate the QA tool across 20 annotators that are asked to go through the test followed by a lengthy task of annotating the engagement of gameplay videos. Our findings suggest that the proposed QA tool reveals, unsurprisingly, that trained annotators are more reliable than the best of untrained crowdworkers we could employ. Importantly, the QA tool introduced can predict effectively the reliability of an affect annotator with 80% accuracy, thereby, saving on resources, effort and cost, and maximizing the reliability of labels solicited in affective corpora. The introduced QA tool is available and accessible through the PAGAN annotation platform.
E3CM: Epipolar-Constrained Cascade Correspondence Matching

Accurate and robust correspondence matching is of utmost importance for various 3D computer vision tasks. However, traditional explicit programming-based methods often struggle to handle challenging scenarios, and deep learning-based methods require large well-labeled datasets for network training. In this article, we introduce Epipolar-Constrained Cascade Correspondence (E3CM), a novel approach that addresses these limitations. Unlike traditional methods, E3CM leverages pre-trained convolutional neural networks to match correspondence, without requiring annotated data for any network training or fine-tuning. Our method utilizes epipolar constraints to guide the matching process and incorporates a cascade structure for progressive refinement of matches. We extensively evaluate the performance of E3CM through comprehensive experiments and demonstrate its superiority over existing methods. To promote further research and facilitate reproducibility, we make our source code publicly available at https://mias.group/E3CM.
VisAhoi: Towards a Library to Generate and Integrate Visualization Onboarding Using High-level Visualization Grammars

Visualization onboarding supports users in reading, interpreting, and extracting information from visual data representations. General-purpose onboarding tools and libraries are applicable for explaining a wide range of graphical user interfaces but cannot handle specific visualization requirements. This paper describes a first step towards developing an onboarding library called VisAhoi, which is easy to integrate, extend, semi-automate, reuse, and customize. VisAhoi supports the creation of onboarding elements for different visualization types and datasets. We demonstrate how to extract and describe onboarding instructions using three well-known high-level descriptive visualization grammars - Vega-Lite, Plotly.js, and ECharts. We show the applicability of our library by performing two usage scenarios that describe the integration of VisAhoi into a VA tool for the analysis of high-throughput screening (HTS) data and, second, into a Flourish template to provide an authoring tool for data journalists for a treemap visualization. We provide a supplementary website that demonstrates the applicability of VisAhoi to various visualizations, including a bar chart, a horizon graph, a change matrix or heatmap, a scatterplot, and a treemap visualization.
MoMA: Momentum Contrastive Learning with Multi-head Attention-based Knowledge Distillation for Histopathology Image Analysis

There is no doubt that advanced artificial intelligence models and high quality data are the keys to success in developing computational pathology tools. Although the overall volume of pathology data keeps increasing, a lack of quality data is a common issue when it comes to a specific task due to several reasons including privacy and ethical issues with patient data. In this work, we propose to exploit knowledge distillation, i.e., utilize the existing model to learn a new, target model, to overcome such issues in computational pathology. Specifically, we employ a student-teacher framework to learn a target model from a pre-trained, teacher model without direct access to source data and distill relevant knowledge via momentum contrastive learning with multi-head attention mechanism, which provides consistent and context-aware feature representations. This enables the target model to assimilate informative representations of the teacher model while seamlessly adapting to the unique nuances of the target data. The proposed method is rigorously evaluated across different scenarios where the teacher model was trained on the same, relevant, and irrelevant classification tasks with the target model. Experimental results demonstrate the accuracy and robustness of our approach in transferring knowledge to different domains and tasks, outperforming other related methods. Moreover, the results provide a guideline on the learning strategy for different types of tasks and scenarios in computational pathology. Code is available at: \url{https://github.com/trinhvg/MoMA}.
CL-MAE: Curriculum-Learned Masked Autoencoders

Masked image modeling has been demonstrated as a powerful pretext task for generating robust representations that can be effectively generalized across multiple downstream tasks. Typically, this approach involves randomly masking patches (tokens) in input images, with the masking strategy remaining unchanged during training. In this paper, we propose a curriculum learning approach that updates the masking strategy to continually increase the complexity of the self-supervised reconstruction task. We conjecture that, by gradually increasing the task complexity, the model can learn more sophisticated and transferable representations. To facilitate this, we introduce a novel learnable masking module that possesses the capability to generate masks of different complexities, and integrate the proposed module into masked autoencoders (MAE). Our module is jointly trained with the MAE, while adjusting its behavior during training, transitioning from a partner to the MAE (optimizing the same reconstruction loss) to an adversary (optimizing the opposite loss), while passing through a neutral state. The transition between these behaviors is smooth, being regulated by a factor that is multiplied with the reconstruction loss of the masking module. The resulting training procedure generates an easy-to-hard curriculum. We train our Curriculum-Learned Masked Autoencoder (CL-MAE) on ImageNet and show that it exhibits superior representation learning capabilities compared to MAE. The empirical results on five downstream tasks confirm our conjecture, demonstrating that curriculum learning can be successfully used to self-supervise masked autoencoders. We release our code at https://github.com/ristea/cl-mae.