Reviewerly/exHarmony huggingface dataset:
PEMEROLEHAN BAHASA INDONESIA TATARAN KELAS KATA PADA ANAK USIA EMPAT TAHUN

The focus of the study of this research is the acquisition of the Indonesian language of the research subjects by paying attention to language production at the word class level of the subjects. The approach used in this study is descriptive qualitative because it intends to understand phenomena about the experiences of research subjects such as behavior, perceptions, motivations, actions, and others by way of descriptions in the form of words in a special or natural context with natural methods. The data collection technique used was the transcription of the recording results, and interviews with informants who were the subject's family. The form of data is in the form of transcription results and additional notes. The analysis technique in this study was carried out by recording transcripts and recording observations in the field, then coded to make it easier to track data, collect and classify data, then find patterns and relationships. The results of the study show that in the context of playing and carrying out daily activities at home, five types of word classes have been found that have been produced by the subject, namely verbs, nouns, adjectives, pronouns, and numerals. The production of word classes which are mostly produced are verbs and nouns, while the word classes of adjectives, pronouns, and numerals are still little produced. On the basis of the results of these observations, the subject can be classified as a child who has a speech delay, because he has not mastered the construction of sentences using particles and conjunctions as a complement.
Security and Error Recovery in a Banking Cloud using Blockchain

It is possible to store data in the cloud in a variety of different ways, including in a single database or across numerous servers; however, database clients are only able to save their information on a single server. No matter how straightforward the management of data in a single database may appear to be, there will invariably be complications regarding concerns of safety, consistency, availability, and bottlenecks. Distributed databases eliminate this issue by splintering the data among different servers, which in turn makes it possible for multiple clients to access the database at the same time. All of this information is readily available to the public and may be viewed whenever one chooses and from wherever one may be. Clients are able to quickly access replicas even in the event that the original is destroyed as a result of concurrent access. Users also have the option of protecting the data using either the Advanced Encryption Standard (AES) or the Triple Data Encryption Standard (3DES). Ensuring the privacy of sensitive data can be accomplished using any one of a number of distinct ciphering techniques, which can then be applied to any one of several distinct data formats. In the event that it is found out that the data was also altered, a procedure for recovering it will be carried out. In the case that a cloud instance experiences an error, the problem is fixed right away. This architecture protects sensitive data by removing potential points of failure from the design. Clients may not be aware of the encryption technology that is used to secure data or the particular cloud server instance that is responsible for delivering it if the system is configured in this manner. Hence, a unified strategy for the architecture of cloud-based databases offers higher levels of security, consistency, and accessibility.
Object Detection for Signboard using Deep Neural Network

The goal of this work is to help the visually impaired by making it possible for them to recognize hidden images on signs, which will hopefully lead to fewer accidents. Although traffic signs are essential for maintaining order, they can also increase the risk of accidents if they are obstructed. Individuals who are visually impaired, such as the blind, can benefit greatly from this technique. Furthermore, the proposed system is conducive to the operation of autonomous cars, as the billboards can be read automatically and the vehicles may be directed by the proposed system. When a potentially dangerous image is received, a warning is displayed to the driver. Throughout this piece, ”input image” will refer to the footage recorded by a camera situated at the front of the car. After converting the image to grayscale, a resizing method is applied, and a filter is used to get rid of any noticeable noise that remains. In the end, feature extraction and feature reduction methods are used to priorities certain features over others. Hence, the DNN procedure is an integral aspect of the system, as it is used for both speech recognition and image recognition. This project’s main goal is to recognize incoming photos by comparing them to data sets stored in a database, with successful matches sounding an alarm for the driver.
COVID 19 AND THE EDUCATIONAL SECTOR

Background: The covid-19 pandemic has negatively influenced the educational sector, causing changes in the teaching format and in the tools used to exercise the teaching profession. Objective: to present the experience of teacher training from the teaching internship under the Emergency Remote Teaching methodology in times of pandemic. Development: The author analyzed multiple dimensions of the formation of masters and doctors through correlations with the literature. The challenges of an emergency remote teaching in the context of the covid-19 pandemic were addressed because of the limitations faced by teachers and students of Social Work in the face of the educational model imposed by the situation. Conclusions: The rapid changes resulting from the pandemic caused a broad debate on the quality of education and training of professionals. The need to quickly adapt to the remote system, without adequate training for teachers and students, imposed a set of challenges and limits for educational practice when there was a need to review teaching programs, study texts, and a shrinking of remote meetings.
DATA AND WEB MINING

Data and web mining involves the use of data mining techniques to extract information from the web in order to gain insights into online behavior, customer preferences, and trends. The process requires data preprocessing to clean and prepare the data for analysis, and pattern recognition to identify relationships and patterns within the data. The benefits of data and web mining include improved decision-making and business growth, while challenges include the large volume of data and ensuring data quality. Web mining can be divided into three main categories: web content mining, web structure mining, and web usage mining. Web content mining involves extracting information from the content of web pages, such as text, images, and videos. Web structure mining involves analyzing the links between web pages to identify patterns and relationships. Web usage mining involves analyzing user behavior on the web, such as clickstream data and session logs, to extract useful information. KEYWORDS: data mining, web mining, information extraction, data preprocessing, pattern recognition, online behavior, customer preferences,
QUANTUM COMPUTING

Quantum computing is a revolutionary technology that has been the focus of much research due to its potential to revolutionize computing power. This paper will discuss the potential of quantum computing, its applications, and the challenges that remain. The paper will also discuss the current state of quantum computing, and potential future developments. Finally, this paper will explore the implications of quantum computing for the world of computing. KEYWORDS: Quantum computing, applications, challenges, current state.
COMPUTER NETWORKS

Computer networks are essential components of modern computing systems. They allow computers to communicate with each other and access shared resources, such as databases and file servers. This paper examines the different types of computer networks, their components, and how they interact. It also discusses the various protocols used for communication and how they are used to establish reliable network connections. Finally, the paper concludes with a discussion of the importance of network security and the potential risks associated with computer networks. Computer networking refers to the interconnection of multiple devices and computers with the help of communication channels such as cables, routers, switches, and other network devices. It enables devices to communicate and share information with each other, which in turn facilitates data exchange and collaboration between individuals and organizations.The purpose of computer networking is to establish a communication infrastructure that can transmit data between devices and users in a secure, efficient, and reliable manner. This involves the use of protocols, algorithms, and network technologies that ensure seamless communication between devices regardless of their location. KEYWORDS: Computer Networks, Network Protocols, Network Security, Potential Risks
Analysis of Consumer Big Data Issues for Savings Banks" Entry into the Chinese Financial Market

중국의 금융시장 개방으로 인해 한국의 금융권이 중국 진출을 위한 전략적인 분석이 필요한 실정이다. 이를 위해서 한국 금융산업의 강점인 핀테크를 활용하여 진출하는 전략이 필요하다. 현재 금융 산업은 핀테크 기술을 도입하면서 기술의 진보를 이루고 있으나 아직 도입단계에 있는 실정이다. 핀테크 산업이 발전하기 위해서는 금융업무를 이용하는 소비자들의 니즈를 분석해야 한다. 본 연구에서는 핀테크에 대한 중국 소비자들의 니즈를 파악하고자 빅데이터를 활용하였다. 분석 도구는 텍스톰 차이나 프로그램(Textom China)을 통하여 중국 소셜네트워크 상의 존재하는 빅데이터를 수집하고 유씨넷6(Ucinet6) 분석 도구 프로그램을 이용하여 데이터를 분석하였다. 분석 결과 ‘발전(发展)’, ‘근무(服务)’, ‘새로운(新)’, 인터넷(互联网)’의 단어가 높은 빈도를 가졌다. 네트워크 분석 결과 ‘발전(发展)’, ‘근무(服务)’, ‘체계(体系)’, ‘마케팅(营销)’, ‘관리(管理)’, ‘기업(企业)’ 등이 중심에 위치했으며, B2B 관리에 집중이 필요함을 나타낸다. 중심성 분석과 CONCOR 분석에서는 고객들의 금융자산 데이터에 대한 보안과 애플리케이션의 관리, 인공지능의 도입과 금융상품의 다각화의 필요성이 나타났다. 따라서 저축은행은 중국 진출 시 해당 부분에 전략 필요하다. Due to China"s opening of the financial market, a strategic analysis is needed for the Korean financial sector to enter China. To this end, it is necessary to enter the fintech sector, which is the strength of the Korean financial industry. Currently, the financial industry is making technological progress by introducing fintech technology, but it is still in the introduction stage. Therefore, in order for the fintech industry to develop, it is necessary to analyze the needs of consumers using financial affairs. In this study, big data was used to understand the needs of Chinese consumers for fintech. The analysis tool collected big data existing on Chinese social networks through Textom China and analyzed the data using the Ucinet6 analysis tool program. As a result of the analysis, the words "year-on-year(同比)", "development(发展)", "work(服务)", "new(新)", and "internet(互联网)" had a high frequency. As a result of network analysis, "development(发展)", "work(服务)", "system(体系)", "marketing(营销)", "management(管理)", and "company(企业)" were located at the center, indicating that focus is needed on b2b management. Centrality analysis and CONCOR analysis showed the need for security and application management of customers" financial asset data, the introduction of artificial intelligence, and diversification of financial products. Therefore, savings banks need strategies in these areas when entering China.
INVESTIGATION OF METHODS OF AUTOMATIC STITCHING OF PANORAMIC IMAGES

The relevance of panoramic stitching is explained by the fact that powerful computers and image processing algorithms are currently available, which allow you to automatically stitch many images into a panorama with a high degree of accuracy and quality. This makes panoramic stitching an important tool for both professional photographers and amateur photographers, as well as in many other areas related to image processing and computer vision. The leading trend in the development of panoramic stitching is to improve the accuracy and speed of algorithms, as well as to expand the possibilities for working with large amounts of data. One of the directions of its development is the development of tools for creating interactive panoramic images and virtual tours. The paper proposes a method of absolutely automatic stitching of panoramic images using methods of invariant local functions for finding key points and their descriptors, projective transformation using the RANSAC algorithm, image alignment based on the calculation of homographic parameters of the camera, multi-band image mixing. To test the proposed method, a software prototype was implemented, photographs from the Huns exhibition at the M.N. Khangalov Museum of the History of the Republic of Buryatia were taken as experimental data. The results of the software prototype are panoramic images obtained based on the processing of these photos. The conducted computational experiments allow us to conclude that the results obtained show high accuracy when compared with the real world.
Pengembangan Media Pembelajaran Museum Virtual pada Mata Pelajaran Sejarah Kelas X SMA

The problem with this development for history subjects are not fully understood by students. This is characterized by the materials and media used not contributing to maximum learning. Only textbooks are used as teaching materials, and the media used by the teacher is in the form of his PPT, but the PPT media does not achieve the learning objectives. The use of PPT media has disadvantages in the form of lack of attractiveness of the materials, students are easily bored and do not study seriously. Also, lack of interest in the material leads to lack of enthusiasm in these students. A decline in student learning outcomes. This development study aims to create a valid, practical and effective history class X SMA virtual media learning museum. Media verification sheets, material verification sheets, student response questionnaires, and effectiveness questions serve as tools. The results of the needs analysis, i.e. the learning materials taught are not fully understood by the students and the materials and media used are given the greatest possible consideration when studying in the test subjects of high school student Adabiah 2 Padang in class X. It wasn't. The results of this study show a media validation result of 4.9 (98%) on a very valid basis. The material validation result is 4.9 which is a very valid criterion. Student Evaluation Results 4.75. The validity result obtained is t-count is 18.28 and t-table is 2.060, so the result is t-count &gt; t-table. From this, we can conclude that there is a significant difference in learning outcomes before and after the use of virtual museum media for history class X subjects.
Penggunaan Metode Inkuiri dalam Peningkatan Hasil Belajar Matematika di Kelas V Sekolah Dasar

The Use of the Inquiry Method in Improving Mathematics Learning Outcomes in Grade V Elementary School. This study aims to explain the steps of the inquiry method in improving student achievement in mathematics in grade V elementary school. The research was conducted at SDN 2 Jagong in two cycles which in the end the increase in learning achievement reached 88.5% as expected by the teacher in this lesson. These results indicate that the application of the inquiry method is very appropriate in increasing learning achievement on the theme of building space. In learning mathematics that uses the inquiry method, the teacher uses stages, including forming groups so that students can and are able to work together to formulate problems in groups, solve problems in groups given by the teacher with responsibility. Furthermore, the teacher monitors or observes student activities in the implementation of learning. The application of this inquiry method emphasizes the activeness, attention, and ability of students in answering questions given. by the teacher. Keywords: Inquiry method, learning outcomes, mathematics
Sparse estimation in semi‐parametric finite mixture of varying coefficient regression models

Finite mixture of regressions (FMR) are commonly used to model heterogeneous effects of covariates on a response variable in settings where there are unknown underlying subpopulations. FMRs, however, cannot accommodate situations where covariates' effects also vary according to an "index" variable-known as finite mixture of varying coefficient regression (FM-VCR). Although complex, this situation occurs in real data applications: the osteocalcin (OCN) data analyzed in this manuscript presents a heterogeneous relationship where the effect of a genetic variant on OCN in each hidden subpopulation varies over time. Oftentimes, the number of covariates with varying coefficients also presents a challenge: in the OCN study, genetic variants on the same chromosome are considered jointly. The relative proportions of hidden subpopulations may also change over time. Nevertheless, existing methods cannot provide suitable solutions for accommodating all these features in real data applications. To fill this gap, we develop statistical methodologies based on regularized local-kernel likelihood for simultaneous parameter estimation and variable selection in sparse FM-VCR models. We study large-sample properties of the proposed methods. We then carry out a simulation study to evaluate the performance of various penalties adopted for our regularized approach and ascertain the ability of a BIC-type criterion for estimating the number of subpopulations. Finally, we applied the FM-VCR model to analyze the OCN data and identified several covariates, including genetic variants, that have age-dependent effects on OCN.
Sentiment Analysis on User Reviews Mutual Fund Ajaib App with K-Nearest Neighbor (KNN) Method

Online investment is an investment activity when viewed from the age range, these investors are dominated by the millennial generation.There are many examples of mutual fund online investment applications that have been widely downloaded by the public, for example, Ajaib.Sentiment Analysis is a technique for extracting text data to obtain information about positive, or negative sentiment.This research aims to analyze sentiment on user reviews of online investment applications, namely Ajaib by utilizing review analysis on the Google Play Store.Ajaib is a mobile application-based online stock and mutual fund investment platform that was simultaneously released in 2019, where its users are millennials.The many differences in online investment applications make Ajaib one of the superior applications in investing.This study aims to determine whether or not there are differences in features in terms of applications and services, prices, and promotions on interest in using the Ajaib application.The K-Nearest Neighbor method can help overcome the classification of opinions that are positive, or negative.After testing with K of 12, the highest accuracy results were obtained when K = 11 with an accuracy value of 85.0% with a precision of 90.2%, recall 87.5%, f1 value 88.8% with a tendency to also obtain reviews of the Ajaib mutual fund application tend to be positive.
Collision Avoidance and Trajectory Tracking for Multiple Autonomous Mobile Robots in Robot Operating System

Increasing the demand for robot usage in different industries has considerably changed companies' operation and management complexity.Meanwhile, automation is a further trend to satisfy the shortage of human resources and enhance productivity.Because of the popularity of 5G technology and industry 4.0, the linkage of devices becomes more stable to connect with multiple robots simultaneously to track their location precisely.Therefore, it can boost the development of Robot Operating Systems (ROS) and the popularity of robot applications across different industries.In the current situation, the company's trend is to apply Automatic Guided Vehicle (AGV) in operation to increase the productivity and decrease human power involved.This technology is only suitable in a static environment to complete some assigned jobs with collision avoidance.AGV focuses much on repetitive work without any humans involved.The main weaknesses of AGV are not capable and flexible when applied in dynamic and unknown environments.However, Autonomous Mobile Robot (AMR) can work in these environments while it can help to achieve Human-Robot Interaction without collision avoidance.This research studies collision avoidance and trajectory tracking for multiple AMR.Adopting a path planning approach based on discrete optimization of path tracking is normally assumed in AMR in real-life scenarios.Dynamic path tracking should be investigated to solve the current issue of choosing the best path from a limited number of options while calculating vehicle acceleration and speed simultaneously.Meanwhile, the study provides a literature review to show the existing algorithm, navigation method, simulation method applied on AMR.Based on the above research, experimental results display more realistic data using a simulation modelling approach based on a real-life case study.The case study is set in different changes of the retail environment and different parameter changes of the robot to indicate how efficient the robot is, and which set of robots is suitable in an actual retail environment.Three case studies have been considered for multiple simulation scenarios, including a small convenience store, a medium-sized restaurant, and a supermarket.Gazebo software and Turtlebot 3 are used in the simulation.The service robot in a retail environment is evitable to conflict with humans while doing job tasks.The conflict resolution and human-machine cooperation are shown in simulation under complex task prioritization how the robot follows its own task-related goals while it has a conflict with humans.The results are shown with multiple parameters comparison, including the number of AMRs, different environment, types of algorithm and human involvement.The paper can conclude which method is suitably applied in multiple AMRs to achieve collision avoidance, trajectory tracking, and human-robot interaction.The findings and recommendations presented in this study can further assist with ROS effectively by implementing AMR into real-world scenarios.
CurSa: scripts to curate metadata and sample genomes from GISAID for analysis and display in nextstrain and microreact

The coronavirus SARS-CoV-2 is the most sequenced pathogen ever, with several million genome copies deposited in the GISAID database. This large amount of genomic information poses non-trivial bioinformatic challenges for those interested in studying the evolution of SARS-CoV-2. One common problem when studying the phylogeny of the coronavirus in its geographical context is to count with accurate information of the location of the samples. However, this information is filled by hand by research groups all over the world and sometimes typos and inconsistencies are introduced in the metadata when submitting the sequences to GISAID. Correcting these errors is laborious and time-consuming. Here, we provide a suite of Perl scripts designated to facilitate the curation of this vital information and perform a random sampling of genome sequences if necessary. The scripts provided here can be used to curate geographic information in the metadata and sample the sequences from any country of interest to ease the preparation of files for Nextstrain and Microreact, thus accelerating evolutionary studies of this important pathogen. CurSa scripts are accessible via: https://github.com/luisdelaye/CurSa/.