Reviewerly/exHarmony huggingface dataset:
DEVELOPMENT OF BOOK CREATOR-BASED ELECTRONIC MODULES ON LEARNING THE HUMAN DIGESTIVE SYSTEM IN MADRASAH IBTIDAIYAH

This development research aims to describe the effectiveness of book creator-based electronic modules on learning the human digestive system in madrasah ibtidaiyah. Electronic modules or e-modules are developments in information technology that develop learning resources from printed modules into modules packaged in digital form. The research method used is the Research and Development (R&amp;D) method with the ADDIE development model which consists of 5 stages, namely analysis, design, development, iplementations, and evaluations. The subjects of the electronic module product assessment for validity were 3 media experts and 3 material experts. The target of the small group trial was 1 teacher and 12 grade V students of MI Darussalam Wonodadi. The target of the large group trial was 2 teachers and 27 students of grade V MIN 14 Blitar. The first steps taken are data collection, planning, product draft development, small group trials and product validation, revision of small group trials, large group trials and product improvement. The results of product validation from 3 media experts obtained a percentage of 90.33%. The validation results of 3 material experts obtained a percentage of 90.22%. Small group trials obtained 86% and large groups 93.88%. The results of the electronic module effectiveness test are effectively used in the learning process. Keywords: Development, Electronic module, Book Creator
APLIKASI MEDIA PEMBELAJARAN ALAT MUSIK TRADISIONAL KHAS SUMATERA UTARA BERBASIS AUGMENTED REALITY

&lt;p&gt;Augmented Reality (AR), is a technology with the idea of fusing the real world's dimensions with the virtual world's dimensions that are shown in real time. A three-dimensional object in augmented reality is depicted on a markerless surface using a distinctive pattern that the application can identify. Among the many applications for augmented reality is as a form of educational media. The lack of information conveyed on traditional North Sumatran musical instruments is one of the obstacles for students in recognizing traditional North Sumatran musical instruments. In this final project, an Augmented Reality application was developed for learning media for traditional North Sumatran musical instruments by displaying 2-dimensional objects into 3- dimensional objects in a more attractive way, thereby creating a sense of love for the culture and history of musical instruments in North Sumatra. participate in maintaining and preserving traditional North Sumatran musical instruments which have begun to be forgotten with the emergence of modern musical instruments. This application will be able to read markerless traditional musical instruments which will display three-dimensional musical instruments, sounds, and their history. This application is also equipped with materials and quizzes that serve to evaluate the user's understanding of the material.&lt;/p&gt;
PENGEMBANGAN WEB GOOGLE SITES PADA MATERI KALOR DAN PERPINDAHAN UNTUK MENINGKATAN KETERAMPILAN BERARGUMENTASI SISWA PADA PEMBELAJARAN IPA

This study aims to determine the validity, practicality, and effectiveness of Google Sites Web development on heat and displacement materials to improve junior high school students' argumentation skills. The data analysis technique used in this research has 3 tests. namely validity test, practicality test, and effectiveness test. The results of the validity test for Google Sites Web development with a validity percentage of 84% and were included the very valid category. The results of the practicality test reached an average of 93% overall and were included in the very high category. The results of the effectiveness test obtained an average N-gain score of 0.70 in the high category. Overall, the results of the research in the form of Web Google Sites conclude that they are feasible and valid to use as teaching materials in science learning to improve students' argumentation skills and suggestions can be given for future researchers to be used as guidelines in developing better and more perfect Google Web sites.AbstrakPenelitian yang telah dilakukan dengan tujuan untuk mengetahui nilai validitas, kepraktisan, dan keefektifan pada produk yang dikembangkan. Penelitian ini berfokus pada pengembangan Web Google Sites pada materi kalor dan perpindahannya untuk meningkatan keterampilan berargumentasi siswa SMP. Teknik analisis data dalam pengembangan produk Web Google Sites dilakukan dalam 3 uji yaitu uji kevalidan, uji kepraktisan, dan uji efektivitas. Hasil uji validitas pengembangan Web Google Sites dengan presentase validitas 84% masuk kedalam kategori sangat valid. Hasil uji kepraktisan mencapai rata-rata 93% secara keseluruhan dan masuk kedalam kategori sangat tinggi Hasil uji efektivitas memperoleh nilai skor rata-rata N-gain sebesar 0,70 dengan kategori tinggi. Secara keseluruhan, hasil penelitian yang berupa Web Google Sites dapat disimpulkan layak dan valid digunakan sebagai bahan ajar dalam pembelajaran IPA untuk meningkatkan keterampilan berargumentasi siswa.
Chronotope problem in gothic works

It is known that Gothic literature had previously become the object of study of many writers. The history of the formation and stages of the development of Gothic literature and plot specifics, chronotope, the way of transmitting characters and the system of motives, etc. were considered as an urgent problem of the study. Although the manifestation of Gothic elements was considered in English, European, Russian literature, the picture of the Gothic genre in Kazakh literature has not yet been considered to the fullest extent. In the works of writers of the period of independence, D. Kostolin, K. Mubarak, M. Omarova, M. Malik, A. Adilbek, national codes that reproduce our national knowledge, the suffix of folklore forgotten today, shaking consciousness are set forth. We analyze the works of the authors based on a small plot, a national worldview based on a new form, a formal search. The method of transmitting the Gothic style was analyzed, including the authors' consideration of the chronotopic situation. New practices of writers in providing time and space are considered.
A COMPARATIVE ANALYSIS OF TECHNIQUES FOR CROWD BEHAVIOUR DETECTION IN DENSE SCENES

Behaviour analysis is considered a critical area of research in the computer vision research community. Recently, visual monitoring systems of human gathering have found application in different areas such as safety, security, entertainment, and personal archives. Although many approaches have been proposed, certain limitations exist and many unresolved issues remain open. The objective of the paper is to present recent advances on abnormal human behaviour analysis and hierarchical crowd behaviour classification based on the level of complexity. The paper also provides a clear perspective with a broad and in-depth review of the research conducted in this area. In addition, it points out unresolved problems that demand improvement. In general, researchers can use this paper as a starting point for further advancement of behavioural analytic methods to propose novel approaches as well as an exploration of approaches that have received meager attention. This study also investigates the performance comparison of state-of-the-art techniques on anomaly detection: hand-crafted feature approach, and deep learning approach. Finally, limitations of the current methods and promising future research directions are presented.Keywords: Human detection, crowd anomaly, deep learning, crowd analysis, artificial intelligence
Improving malaria prediction with ensemble learning and robust scaler: An integrated approach for enhanced accuracy

Mosquito bites are the primary transmission method for malaria, a prevalent and significant health concern worldwide. In the context of malaria incidence, Indonesia is the second most affected country after India. According to the Ministry of Health's report, Papua Province reported 216,380 malaria cases in 2019. Additionally, East Nusa Tenggara and West Papua said 12,909 and 7,029 points, respectively, reflecting the substantial national burden of this disease. Predicting malaria occurrence based on symptomatic presentation is a crucial preventive strategy. Machine learning models offer a promising approach to malaria prediction. This study focused on malaria detection by using patient data from Nigeria. This research proposes a detection system utilizing the Ensemble method, such as Decision Tree, Random Forest, and Bagging. This study also employing Robust Scaler for effective normalization and integrating K-fold cross-validation to enhance model robustness. Various experiments were conducted by systematically varying K values and the number of decision trees to ascertain the most effective hyperparameters yielding the highest accuracy. The findings indicate that the optimal accuracy 82% is achieved at a K value of 20, showing comparable accuracies across different decision tree quantities, underlining the robustness of the employed method. This research significantly advances malaria detection strategies, offering valuable insights into the effective deployment of machine learning in healthcare decision-making.
Do social media and Other Platforms Require Greater Regulation?

Social media and other platforms play a crucial role in modern society, influencing information dissemination, shaping public opinion, driving economic activities, and affecting political elections. However, with the rise and development of these platforms, a series of issues have emerged, including privacy concerns regarding users' personal information, platform data storage, platform monopolistic behavior, and abuse of media regulation by both the public and the platforms themselves. This paper aims to discuss the role of social media in the development of media from traditional to digital forms, the existing means of regulating social media platforms, and analyze the monopolistic behavior of leading companies in the platform capitalism context and explore the necessary forms of regulation for social media and other platforms.
A Fixed-Parameter Tractable Algorithm for Counting Markov Equivalence Classes with the same Skeleton

Causal DAGs (also known as Bayesian networks) are a popular tool for encoding conditional dependencies between random variables. In a causal DAG, the random variables are modeled as vertices in the DAG, and it is stipulated that every random variable is independent of its ancestors conditioned on its parents. It is possible, however, for two different causal DAGs on the same set of random variables to encode exactly the same set of conditional dependencies. Such causal DAGs are said to be Markov equivalent, and equivalence classes of Markov equivalent DAGs are known as Markov Equivalent Classes (MECs). Beautiful combinatorial characterizations of MECs have been developed in the past few decades, and it is known, in particular that all DAGs in the same MEC must have the same "skeleton" (underlying undirected graph) and v-structures (induced subgraph of the form $a\rightarrow b \leftarrow c$). These combinatorial characterizations also suggest several natural algorithmic questions. One of these is: given an undirected graph $G$ as input, how many distinct Markov equivalence classes have the skeleton $G$? Much work has been devoted in the last few years to this and other closely related problems. However, to the best of our knowledge, a polynomial time algorithm for the problem remains unknown. In this paper, we make progress towards this goal by giving a fixed parameter tractable algorithm for the above problem, with the parameters being the treewidth and the maximum degree of the input graph $G$. The main technical ingredient in our work is a construction we refer to as shadow, which lets us create a "local description" of long-range constraints imposed by the combinatorial characterizations of MECs.
Remote Sensing Image Compression Method Based on Implicit Neural Representation

Oceanography-oriented remote sensing image is characterized by being multi-source, heterogeneous, and massive. In recent years, the scale of remote sensing data is exploding, necessitating efficient compression methods for storage. Traditional algorithms have low compression ratio, poor flexibility, and are unsuitable for storing large-scale remote sensing image data. To address this, we propose a remote sensing image lossy compression and storage method based on implicit neural representation. We used an implicit neural network to learn the mapping relationship between longitude, latitude coordinates, and values. Then, we compressed the network weights using quantization. Our method can significantly reduce storage space by approximately 80% for multi-source and heterogeneous remote sensing image data. And the reconstructed quality is capable of meeting the requirements of downstream tasks related to marine AI and visualization.
Penyuluhan Kecemasan pada Remaja Siswa/I Kelas X di Sekolah Menengah Atas Kalam Kudus II, Jakarta

Anxiety among adolescents is a growing concern due to its significant impact on their mental and physical well-being. Providing anxiety education in schools can be a crucial step in imparting understanding and stress management strategies to teenagers. This activity aims to conduct an anxiety education program for 10th-grade students at Kalam Kudus II Senior High School, Jakarta. The program is implemented using the PDCA (Plan-Do-Check-Act) method, comprising four stages. The planning stage involves identifying objectives, analyzing needs, planning the content, and scheduling the sessions. Subsequently, in the execution stage, 10th-grade students are given education on anxiety and stress management through presentations and group discussions. Demonstrations and practice of stress management techniques are provided to enable students to apply the skills taught. Upon completing the program, an evaluation is conducted to assess students' understanding of the material and their application of stress management techniques in their daily lives. Feedback from students is collected to evaluate the effectiveness of the activity and identify areas for improvement in future sessions. This initiative is expected to benefit 10th-grade students at SMA Kalam Kudus II, Jakarta, in coping with anxiety and stress during their adolescent years. The education program aims to enhance their understanding of anxiety and equip them with valuable skills for stress management. Ultimately, it is hoped that these teenagers will be better prepared to face life's challenges and achieve improved mental well-being&#x0D;
Analisis Dampak Jual Beli Online Dengan Sistem Cash On Delivery (COD) Terhadap Costumer Dan Kurir Dalam Kajian Ekonomi Islam di Kelurahan Sidorame

This study aims to identify and analyze the implementation of cash on delivery in buying and selling online at the Shopee market place. To find out and analyze the impact of buying and selling online with the cash on delivery (COD) system on customers and couriers in the perspective of Islamic Economics. The research design used was qualitative descriptive research. Data collection techniques in this study used interviews and documentation techniques. Data analysis techniques in this study used data reduction, data presentation and conclusion. The results of the study show that the mechanism or method of buying and selling online with the Cash On Delivery system that is carried out by cash on delivery at the Shopee Community of Sidorame Village is to create a Shopee account first, then promote goods from suppliers or online shops that have been selected from the Shopee application by uploading photos. - interesting photos to social media, when there is an order then start searching/selecting and buying goods on the Shopee application, after finishing selecting then fill in the complete buyer's address, then select the payment method to be used, then check the words "send as cash on delivery. An Islamic economic study of buying and selling online with the Cash On Delivery system carried out by cash on delivery at the Shopee Community of the Sidorame Village shows that this buying and selling is not in line with the concept of bai' assalam, especially parallel greetings &amp; basic principles/values ​​of business ethics in the Islamic economy where there are elements the lack of clarity regarding the goods sold by Shopee cash on delivery, because cash on delivery does not own, know or see the original form of the goods in real terms. Then there is no element of honesty (openness) by the cash on delivery party to the buyer regarding the system used.
WITH OR WITHOUT THE CROWD? THE INFLUENCE OF CODER CHARACTERISTICS ON CODING DECISIONS COMPARING CROWDWORKERS AND TRADITIONAL CODERS.

Standardized manual content analysis is an important methodology to capture the messages in journalistic and social media. Specifically, for supervised machine learning aproaches, human-generated training data is needed. The process of coding as well as the selection of suitable coders is crucial for obtaining good data quality. However, little research has been done on how the coding process should be designed and how personal characteristics of the coders might influence data quality. This blind spot becomes even more crucial because coding is nowadays increasingly performed with the help of crowdworkers. When working with such anonymous coders, the process of coding can then be less controlled by the researchers, which can lead to loss of quality. In our comparative mixed-methods study we compare data from a content analysis on the topic of legalizing abortion (n = 300 tweets). We conducted this in two ways: Firstly, with a team of four student coders who also received training and secondly with 150 crowdworkers. All coders had to complete a short survey on their socio-demographics and personality traits. The results show that both validity and reliability are higher for the student coders, especially for tricky coding tasks. Further, multivariate (logistic) regression analysis reveals that personal characteristics such as formal education and emotional sensitivity also have an impact on coding quality. Hence, with a reflective selection of coders as well as a thoughtful design of the coding process and the codebook, the quality of data collection can be increased—even when relying on crowdworkers.
A Comparative Effectiveness of Training of Teachers Performance Conducted Under JICA, GTZ and PITE: A Case Study of Khyber Pakhtunkhwa (Pakistan)

The current research paper aimed to investigate the effectiveness of training of teachers' performance conducted under JICA, GTZ, and PITE, A case study of Khyber Pakhtunkhwa. The survey research design was applied as per the nature of the study. A sample of 114 JICA-trained teachers, 379 GTZ-trained teachers, and 760 PITE-trained teachers was chosen. The size was determined using Krejcie and Morgan's (1970) sample table. The researcher used the stratified sampling method. A questionnaire was developed on a five-point Likert scale including five dimensions of training, i.e., objectives, organization and easiness of training content, expertise of trainer, participation level of trainees, and facilities provided by three organizations. Content validation was completed through experts' judgment, whereas the reliability of research too was measured through Cronbach's Alpha, which was estimated at 0.793. The researcher personally administered the questionnaire to the respondents and considered the researcher while gathering data from the teachers. Analysis of Variance (ANOVA) was used. The key findings of the study reveal that teachers who received training are satisfied with the accompaniment of objectives, organization, and easiness of training content, the expertise of trainers, the participation level of trainees, and facilities provided by three organizations. The study recommended that the Government of KP developed a flow-up mechanism for such teachers to improve the professional skills of the teachers.
OA-900 A bilingual training platform in data management and sharing to support open science across the EDCTP networks of excellence

<h3></h3> The EDCTP Regional Networks of Excellence (NoE) have been increasing capacity for knowledge and training in data management and sharing as part of EDCTP2. The aim of this new work was to enhance and leverage the EDCTP Knowledge Hub to support secondary data analysis that could be utilised across all four NoEs. The hub would provide overarching content, as well as deeper-dive training materials allowing networks to develop tailored in-person sessions addressing local use cases. This training initiative is a joint project of the EDCTP NoEs, The Global Health Network and the Infectious Diseases Data Observatory (IDDO) and has several components: enhanced online modules in English and French covering an essential curriculum in data management and sharing; dissemination through a Data Management and Sharing Technical Working Group consisting of NoE members to ensure that content meets their training needs; additional resources for local face-to-face training developed from the online training course, with tools and case studies made available alongside the online training package for download and use. The content and delivery of this training have been tailored specifically for the needs of the EDCTP NoEs, enhancing knowledge on data management and sharing to support secondary analysis that responds directly to the clinical research context of sub-Saharan Africa. Leveraging the powerful web-based training platform, this initiative draws on experience from across EDCTP NoEs and partners to deliver a comprehensive curriculum in both English and French. As the EDCTP NoEs continue to strengthen their capacity and look towards more advanced data management and sharing solutions that fit their context, this training platform will provide sustainable knowledge and training foundations for supporting this growth in expertise, aligned with the EDCTP NoE aims of research collaboration to further raise the quality of clinical research and practice across sub-Saharan Africa.
Image Forgery Detection Using Comprint: A Comprehensive Study

This chapter presents Comprint, a method to detect and localize image forgeries or manipulations by utilizing a “compression fingerprint” or “comprint”. Detecting image forgeries is important because image manipulation tools are prevalent and make it easy to spread misinformation. Existing forgery detection methods are still challenged to accurately detect image manipulations, especially when the doctored image is of a low quality (e.g., due to recompression). To address this issue, Comprint distinguishes between tampered and pristine regions by extracting a compression fingerprint. This method is trained on pristine data only, and therefore generalizes well to a wide variety of manipulations. This chapter provides a comprehensive overview of Comprint’s architecture and summarizes our findings. We show that Comprint outperforms existing methods on five evaluation datasets. Additionally, we analyze the performance of Comprint when manipulated images are exposed to differences in JPEG compression, such as small quality factor differences, non-standard quantization tables, and DCT implementations, as well as when exposed to recompression. The results demonstrate that Comprint is robust and generalizes well to situations unseen during training. Additionally, increasing the performance for extremely subtle JPEG parameter changes is identified as a future work opportunity. In conclusion, Comprint offers a valuable tool for detecting manipulated images in the wild. The chapter provides a thorough examination of the method and highlights its strengths and limitations. As such, it a valuable resource for both multimedia-forensics researchers and fact checkers or forensic investigators.