Reviewerly/exHarmony huggingface dataset:
PEMANFAATAN APLIKASI CANVA SEBAGAI MEDIA VIDEO PEMBELAJARAN KREATIF, INOVATIF, DAN KOLABORATIF

The use of technology in learning is very necessary in terms of designing, analyzing, evaluating, developing, and implementing material in the learning process. The important thing in the learning design process is the media used in learning. These learning media can be in the form of PowerPoint, digital modules, video tutorials, and other presentation materials. An educator should have learning media that is new and interesting in delivering material to his students, one of which is by presenting material through animated and fun learning videos. Canva is an application that can be used in designing learning media in the form of videos. How to use Canva Design is quite easy with many choices of design variations. This application provides a variety of design templates to use, ranging from free (free) to paid (pro). Canva can help users create and design a variety of creative designs, from designing brochures, advertisements, presentations, videos to infographics. This study aims to analyze the effectiveness of using the Canva application as a creative, innovative and collaborative learning video medium. The research method used is literature review. The method for searching for article data sources is carried out through the Google Scholar database resources (2020-2022) by taking relevant articles published in Indonesian. Key terms and phrases related to the Canva app, media, and learning videos were used in related subject searches. The results of the study show that the Canva application is effectively utilized as a medium for making creative, innovative and collaborative learning videos, so that it can assist educators in delivering material and make it easier for students to understand learning material. In addition, using Canva is also more efficient because it provides a variety of design templates for free and is available in several versions, namely web, Android and iPhone, so you can make learning videos simply by using your cellphone (gadget).
Correlation of Manual Peripheral Blood Smear Examination with RBC Indices and Histograms Obtained from an Automated Hematological Analyzer in Anemic Patients at Jimma Medical Center, Southwest Ethiopia, 2022

The manual peripheral blood smear examination is a comprehensive examination of the blood film to detect clinically significant abnormalities in leukocyte, erythrocyte, and platelet morphology. It is a time-consuming technique, but it is a more cost-effective and sensitive technique than an automated hematological analyzer. On the other hand, the automated hematology analyzer is faster, more objective, and reduces labor cost but cannot reveal the variety of abnormal cells.
A New Hybrid Diffie-Hellman and Caesar Cipher Algorithm for Cryptography

Caesar cipher algorithm is one of the ancient algorithms. However, due to advances in technology the algorithm is now much simple to breach. This is because; each letter in the message is replaced by the same letter as specified. To raise the safety, some changes can be made. So, here we are going to use another arithmetic algorithm Diffie - Hellman's mode of changing the key to find the secret key and use simple calculations to verify data encryption. After obtaining a private shared key using the Diffie-Hellman method, depending on the operating mode with 26 keys to get the value of 26 or less, then the current character is taken with the key when the additional value of the new character. In any letter in the position of an 'x', the key first increases with an 'x' and is adjusted to get the encrypted letter. Therefore, 2 messages are repeated 2 times and the third letter has 3. This increases safety. This technology can be
The impact of total quality management on the development of human resources in the public sector by application to the locality of East Al-Jazeera in sudan

The study aimed to demonstrate the impact of total quality management on the development of human resources in the public sector by applying it to the locality of East Al-Jazeera. The study hypothesized that there is a relationship between total quality management and human resources development. To achieve the objectives of the study, the analytical descriptive approach was used, and the data of the study were collected by designing a questionnaire. A simple random sample of (50) workers representing (100%) of the study population was chosen. To analyze the obtained information, the statistical packages for social sciences SPSS V23.0 program was used, and AMOS V23.0 path analysis program was used. The study found that there is a significant positive relationship with total quality management in its dimensions (teamwork, research and development, senior management support) and human resources development. The study recommended the need for senior management in the locality to adopt proposals that raise the level of quality of employees, which contributes to the development of human resources and the development of administrative work
Research on Bayesian Based Network Precision Computation Model

Due to the booming development of e-commerce information, the number of website users has significantly increased. How to use user access traces on e-commerce platforms to conduct big data analysis of customer behavior and achieve precise product promotion has attracted much research interest in e-commerce. However, in the current research methods and technical means, there are many technical challenges, especially in the effectiveness of data analysis, information, image modeling, and data processing. Thus, it is necessary to analyze website customer attributes, behavioral characteristics, and browsing records using big data technology. In this study, Bayesian network models were used to explore customer behavioral characteristics and estimate the posterior probability of potential product generation or consumption behavior and usage behavior. The Bayesian network-based recommendation algorithm proposed in this study showed an accuracy of about 20% higher than traditional product recommendation models.
Globalization and Africa’s Future Sustainable Development

Africa is a continent with vast human and material resources, but its progress towards sustainable development has been slow and sometimes discouraging. Some of its challenges have stemmed from its insertion into the global economy and accompanying power structures from at least the fifteenth century. As the continent’s economic progression has not reflected the full value of its assets, Africa houses alarming numbers of impoverished people and nations today. Even as these groups battle political and economic instability, the COVID-19 pandemic has inflicted additional burdens. As a developing continent, Africa must leverage its strengths and match the trajectory of a globalized economy that promises genuine and sustainable development. However, several challenges have created bottlenecks that hinder this agenda, which this work seeks to explore further. This chapter considers the interactions between globalization and sustainable development in Africa, including its state before globalization, the promises of sustainable development, and the benefits globalization and sustainable development may bring to Africa.
Using AR and YOLOv8-Based Object Detection to Support Real-World Visual Search in Industrial Workshop: Lessons Learned from a Pilot Study

Visual search requires focused attention to distinguish the target object from its surroundings. This task is increasingly more difficult when searching for a given object within a messy industrial workshop. Thus, we explored how we can support visual search with the help of an augmented reality (AR) head-mounted display (HMD) running YOLOv8 object detection model. We report on the results from a pilot user study with eleven participants tasked with searching for a series of objects in a real-world workshop. Consequently, we reflect on lessons learned concerning the experimental setup with AR interface enhanced with Segment Anything Model (SAM) for YOLOv8 training.
Survey Paper on :"Organ Donation Using Blockchain"

Organ donation is a critical issue that requires a secure and efficient system to ensure the timely and effective transfer of organs from donors to recipients. The traditional organ donation system is centralized, which leads to several challenges such as lack of transparency, fraud, and mismanagement. To address these challenges, blockchain technology can be used to create a decentralized and secure organ donation system. In this paper, we propose a blockchain-based organ donation system that enables real-time tracking of organs from donors to recipients, ensures the privacy and confidentiality of donor and recipient information, and provides a transparent and auditable system for all stakeholders involved in the process. Our system also includes smart contracts that automate the organ matching process, reduce waiting times for recipients, and minimize the risk of errors or miscommunications. Overall, our proposed blockchain-based organ donation system offers a promising solution to the challenges faced by traditional organ donation systems and has the potential to significantly improve the efficiency and effectiveness of organ donation processes
A regression approach for seismic first-break picking

The picking efficiency of seismic first breaks (FBs) has been greatly accelerated by deep learning (DL) technology. However, the picking accuracy and efficiency of DL methods still face huge challenges in low signal-to-noise ratio (SNR) situations. To address this issue, we propose a regression approach to pick FBs based on bidirectional long short-term memory (BiLSTM) neural network by learning the implicit Eikonal equation of 3D inhomogeneous media with rugged topography in the target region. We employ a regressive model that represents the relationships among the elevation of shots, offset and the elevation of receivers to their seismic traveltime to predict the unknown FBs, from common-shot gathers with sparsely distributed traces. Different from image segmentation methods which automatically extract image features and classify FBs from seismic data, the proposed method can learn the inner relationship between field geometry and FBs. In addition, the predicted results by the regressive model are continuous values of FBs rather than the discrete ones of the binary distribution. The picking results of synthetic data shows that the proposed method has low dependence on label data, and can obtain reliable and similar predicted results using two types of label data with large differences. The picking results of 9380 shots for 3D seismic data generated by vibroseis indicate that the proposed method can still accurately predict FBs in low SNR data. The subsequent stacked profiles further illustrate the reliability and effectiveness of the proposed method. The results of model data and field seismic data demonstrate that the proposed regression method is a robust first-break picker with high potential for field application.
Stablecoins: Past, Present, and Future

Since 2018, stablecoins have become the major digital currencies facilitating payment flows across various decentralized financial platforms. While in the simplest terms, stablecoins operate as low-volatility digital cash, they are not created equal. Broadly speaking, stablecoins are dichotomized into fiat-backed or algorithmic; algorithmic refers to a diverse set of stablecoins. By reviewing a brief history and analyzing the current state of affairs for stablecoins, we discuss critical facts about stablecoins and propose strategic pathways for the future. Fiat-backed stablecoins are similar to money market funds in their function and structure. As such, the regulatory framework for money market funds should be a guiding light. From this viewpoint, the necessary conditions for the proper functioning of fiat-backed stablecoins are: transparency, full disclosure, quality auditing, and oversight. Algorithmic stablecoins are in principle synthetic cash, or a derivative instrument. As such, they are perfectly positioned to play pivotal roles in digital financial derivative (contingent claims) markets. Due to the heterogeneity of their use-cases, the appropriate regulatory framework depends heavily on the underpinning economics. Lending-related algorithmic frameworks (i.e., credit contingent claims) can benefit from economics and regulatory environments of collateralized repo markets and the like. Others can be considered through the lens of derivatives instruments—futures, options, swaps—for finding best practices and optimal strategies.
Influence of the Price of Gold, the Price of Crude Oil, and Exchange Rate to Bitcoin Prices During the Covid-19 Pandemic

The covid 19 pandemic has a negative impact globally, such as financial crisis, political crisis, high unemployment rate, disruption supply chain. This study is examining the relationship between the price of gold, the price of crude oil, and exchange rate (USD/IDR) to Bitcoin during the Covid-19. A time series analysis by using ADCC-GARCH method and a simple linear regression are conducted in this research. The result shows that USD/IDR exchange rate doesn’t have a significant relationship to Bitcoin, while there’s a significant relationship between the price of gold and Bitcoin price. In addition, the price of crude oil has a significant relationship to Bitcoin price.
Innovative image interpolation based reversible data hiding for secure communication

Abstract In an increasingly digitized world, secure communication plays a pivotal role in safeguarding sensitive information and ensuring the confidentiality of data transmission. Conventional encryption techniques are robust but often result in a loss of data during transmission, making it challenging to achieve both security and image quality simultaneously. To address this issue, an innovative technique for interpolation-based reversible data hiding (RDH) within images is presented in this study. The interpolation scheme considers the Min function of the neighboring pixels and generates the cover image. After that, a data hiding method is applied to the cover image by considering the intensity range of the pixels using the least-significant bit (LSB) substitution method. The suggested embedding approach first encrypts the secret message, and then it encodes the encrypted secret message into interpolated pixels based on pixel intensity range groups. This technique enables the image to be consistently recreated once the data has been extracted. The percentage increment in the embedding capacity and peak signal-to-noise ratio (PSNR) is 40.00 and 8.64% for the proposed method as compared to the existing method. The experimental results indicate that the proposed technique enhanced the embedding capacity for all test pictures and produced higher PSNR values.
Fault-tolerant qubit encoding using a spin-7/2 qudit

The authors propose a quantum memory, implemented on a spin-7/2 nucleus coupled to an electron spin-1/2 qubit, which provides $X$, $Y$, and $Z$ error correction using fewer quantum resources than the equivalently effective qubit-based protocols.
MENINGKATKAN HASIL BELAJAR MATEMATIKA MELALUI MODEL PEMBELAJARAN MAKE A MATCH MATERI BILANGAN PECAHAN PADA SISWA KELAS IV DI SD SWASTA ABDI SUKMA KECAMATAN MEDAN JOHOR

This research aims to determine learning through: (1) student learning outcomes before using the make a match learning model in Mathematics subjects in class IV of Abdi Sukma Private Elementary School, Medan Johor District. (2) Application of the make a match learning model in mathematics subjects in class IV of Abdi Sukma Private Elementary School, Medan Johor District. (3) Student learning outcomes after using the make a match learning model in Mathematics subjects in class IV of the Abdi Sukma Private Elementary School, Medan Johor District. The type of research used is Classroom Action Research (PTK) with the subjects of this research being fourth grade students at Abdi Sukma Private Elementary School, Medan Johor District. Based on the research results, it can be concluded that the results before implementing the make a match learning model obtained an average teacher activity result of 83.32% and an average student activity result of 80.08% and an average student learning result of 70%. Based on the results above, it can be concluded that the use of the make a match learning model can improve student learning outcomes in mathematics subjects, the material of recognizing Roman number symbols in class IV D Swasta Abdi Sukma, Medan Johor District.
Interactive Databases for Recordkeeping and Analyzing R&amp;D Results of Educational Institutions Subordinate to the Ministry of Agriculture of Russia

The current state and main paths of the formation of digital information resources for recording the results of intellectual activity (RIA) of educational institutions subordinate to the Ministry of Agriculture of Russia are shown. Information is presented on the formation of an open branch-wise digital environment for monitoring, recording and analyzing R&amp;D results. The ways to increase the commercialization of RIA are considered.
Evaluation of SSD Architecture for Small Size Object Detection: A Case Study on UAV Oil Pipeline MonitoringEvaluation of SSD Architecture for Small Size Object Detection: A Case Study on UAV Oil Pipeline Monitoring

Oil pipeline monitoring using Unmanned Airborne Vehicles (UAV) can be done by utilizing Deep Learning. Deep Learning can be used to automatically detect harmed or unauthorized objects near the pipeline for further action by the authority. Input video in the pipeline area taken from the UAV has unique characteristics. It has low resolution with dense composition object in the image. The detected object also has a small scale as the objects are far away from the UAV. Thus, the selection of the Deep Learning algorithm is important to get a desirable result with the following conditions. Single Shot Multi-Box (SSD) is one of the popular Deep Learning algorithms with fast calculation compared to others and suitable for real-time object detection. Previous works on this topic using low to medium altitude dataset (20–200 m). This paper provides an evaluation of SSD implementation to detect vehicles on high-altitude dataset (300 m). As much as 2482 dataset is fed into SSD architecture and trained to detect 3 class of vehicles. The result shows the mAP and mAR are 0.026360 and 0.067377, respectively. However, the low lost function value shows that the model is able to classify the object correctly. In conclusion, the SSD cannot process low density information to correctly locate the object.
Design Of Heavy Equipment Rental System At Pt. Tiger Perkasa Sumatra Based Website

PT. Tiger Perkasa Sumatra is a construction company located at Jalan Komplek Golden Land, Deli Serdang Regency. PT. Tiger Perkasa Sumatra, which is engaged in contractors and heavy equipment, currently still processes inventory data and makes inventory reports manually. Judging from the process of using manual processing of inventory data, there will often be problems, including: the stored data is often lost; it takes a long time to process and find the necessary inventory data; there is often duplication of data, making reports longer; the results are less accurate; and there are often errors in inputting data. The results of research on the existence of a heavy equipment rental system at PT. Tiger Perkasa Sumatra are based on a website that makes it easier for tenants of PT. Tiger Perkasa Sumatra to do digital rentals, and the system is built effectively and efficiently.
Type-2 Intuitionistic Interpolation Cubic Fuzzy Bézier Curve Modeling using Shoreline Data

The notion of fuzzy sets is fast becoming a key instrument in defining the uncertainty data and has increasingly been recognised by practitioners and researchers across different disciplines in recent decades. The uncertainty data cannot be modeled directly and this causes hindrance in obtaining accurate information for analysis or predictions. Hence, this paper contributes to another approach in which an application of type-2 intuitionistic fuzzy set (T-2IFS) in geometric modeling onto complex uncertainty data where the data are defined using the type-2 fuzzy concept. T-2IFS is the generalized forms of fuzzy sets, intuitionistic fuzzy sets, interval-valued fuzzy sets, and interval-valued intuitionistic fuzzy sets. Based on the concept of T2IFS, type-2 intuitionistic fuzzy point (T-2IFP) is defined in order to generate a type-2 intuitionistic fuzzy control point (T-2IFCP). Following, the T-2IFCP will be blended with the Bernstein blending function through the interpolation method, resulting to a type-2 intuitionistic interpolation cubic fuzzy Bézier curve. Shoreline data is used as the data and further verifies that the model can be conceivably accepted. In conclusion, the proposed methods are reliable and can be expanded to many other areas.
Perception and Attitudes towards Neural Machine Translation in French Language Education

Objectives The objective of this study was to examine the perceptions and attitudes of undergraduate French language majors about neural machine translation through a survey.&#x0D; Methods The study involved students enrolled in courses such as “French Literature and Translation” and “Advanced French Composition 2” at a university in Seoul. Over the course of a semester, students actively participated in translation and composition exercises aided by machine translation technology and draw up Integrated Problem and Decision Reporting (IPDR) detailing the use of machine translation for problem-solving in the translation process. Subsequently, a survey was conducted to assess the participants' perceptions and attitudes toward machine translation.&#x0D; Results The participants exhibited highly positive responses towards the effectiveness and convenience of using machine translation for learning, highlighting benefits such as time-saving and enhanced learning outcomes. However, the participants displayed a flexible attitude toward the machine-generated results and modified them instead of accepting unconditionally. While statistical analysis did not reveal significant differences in perception and attitude based on enrolled courses or grades, open-ended responses indicated that high-achieving students emphasized the learning benefits and effectiveness of machine translation, whereas lower-achieving students focused on its convenience, particularly in terms of time-saving.&#x0D; Conclusions With the increasing integration of machine translation into foreign language education, understanding how French language majors perceive machine translation can aid educators in devising teaching methods and learning strategies that optimize efficient and effective learning outcomes. This study contributes to the exploration of using machine translation as a tool to enhance language learning.