Reviewerly/exHarmony huggingface dataset:
Sejarah Perkembangan Bahasa Melayu Menjadi Bahasa Indonesia

The Indonesian language that we currently speak has its roots in one of the regional tongues spoken throughout the archipelago. For four reasons, the Malay language has a chance of dominating the Archipelago: 1) The Sriwijaya people are naturally inclined to trade. 2) Sriwijaya, the epicenter of the Buddhist movement, and 3). In educational institutions, Malay is the medium of instruction. 4) The official language of conferences between regional organizations is Malay. The data cannot have come from field research because the library research method makes this study more current. Ten congresses for the Indonesian language were convened in order to preserve its existence. The first congress took place in Solo from June 25–28, 1938. From October 28 to November 1, 1954, the city of Medan hosted the second conference. Jakarta hosted the Third Congress from October 28 to November 2, 1978. Between November 21 and 26 of 1988, Jakarta hosted the Fourth Congress. Jakarta hosted the V Language Congress from October 28 until November 3, 1988. Jakarta hosted the VI Congress from October 28 to November 2, 1993. On October 26–30, 1998, Jakarta hosted the VII Congress. VIII Congress, 14–17 October 2003, Jakarta Jakarta hosted the IX Congress from October 28 to November 1, 2008, and the X Indonesian Language Congress from October 28 to October 31, 2013.
Current Status and Tasks of Character Education

Purpose: The purpose of this study is to explore the concept of character by reviewing the current status and problems of character education, and to propose characteristics and methods of character education and tasks for character education.&#x0D; Methods: The researcher reviewed data on character education at home and abroad, analyzed the difficulties of character education in school education, explored the concept of ‘character’ as the main concept, identified the characteristics of character education, and proposed methods and tasks for character education.&#x0D; Results: First, the difficulties of character education were largely analyzed into five categories, and the concept of ‘character’ was divided into good nature, personality, and character. In addition, since character education works meaningfully in the field of practice rather than in the theoretical field, it was reviewed that it has a value-integrated and curriculum-integrated characteristic with various values and various subjects.&#x0D; Conclusion: The above research results suggest that character education can be understood as a work that provides the basis for a good mind before teaching knowledge or skills, and above all, practice in life plays an important role.
The Governance of School Operational Assistance (SOA) Funds for Elementary Schools in Medan

This study aimed to test the measurement and structural models of SOA fund governance constructed by participation, transparency, and accountability. A total of 348 respondents were involved in this study consisting, including the headmaster (n=20), teachers (n=76), and parents (n=252). Data were collected using a questionnaire distributed to respondents using Google Forms for four months. Data analysis used confirmatory factor analysis through Partial Least Squares-Path Modeling (PLS-PM), which assisted the Smart PLS. Testing the measurement model using construct validity and internal consistency reliability showed two items (P4-participation and A3-accountability) having loading factors that do not satisfy the criteria. Likewise, both items were excluded from the model, which impacted the acceptance of the model fit. Furthermore, testing the quality of the structural model using the goodness of fit (GoF=0.524) was more significant than 0.36, which meant the quality of the structural model was very strong and significant. The transparency aspect had the most robust coefficient, which signals that everyone regarding the implementation of SOA funds in schools freely obtained the disclosure of financial and academic information. Other scholars can explore the role of the principal, teachers, and parents in making learning successful in schools by optimizing the use of SOA funds.
A Systematic Survey on Personalized Learning Framework based Recommendation System

The purpose of this study will be to conduct a literature review on the topic of a personalized learning framework-based educational resource recommendation system. The goal of this framework is to improve the learning process of students, emphasizing the significance of providing relevant and valuable knowledge. First, for a reliable study of recommendation models for recommendation systems, more than 100 prestigious publications and presentations posted in Google Scholar between 2019 and 2021 were collected and examined, and research on recommendations for educational content in environments that support personalized and adaptive learning. This study, which offers a thorough overview of recommendation systems, also aims to inform several scholars who are interested in recommendation systems by looking at the various technologies and kinds of recommender systems applied in the service sector.
Installing Docker Desktop

Docker Desktop is available as a free download from . Linux users can install Docker Desktop or Docker Engine, a native environment for running Docker containers.
Customizing Images

Something I love about working with Oracle (and databases in general) is the variety. Each day brings something new, and problems often have multiple solutions. Every shop has its way of doing things, and solutions vary from customer to customer based on industry practices, security, and conventions.
Performance comparison of two non-parametric classifiers for classification using geometric features

This study aims to examine and compare the performances of Random Forest (RF) and k-Nearest Neighbor (k-NN) algorithms used for classification based on certain geometric features. For the purpose of the analysis, the Breast Cancer Wisconsin (BCW) public dataset is used. BCW dataset contains features like area, perimeter, radius, compactness, and symmetry computed from 357 benign, and 212 malignant breast images, respectively. Three different experiments related to the size of training and testing datasets for classification are conducted and different accuracy values are obtained. The best accuracy of 91.9% for RF and 91.3% for kNN, respectively, are reached when 30% of the entire dataset is used as testing dataset. For all experiments, the RF classifier outperformed the kNN.
The level of IT-business strategic alignment and its impact on achieving a sustainable competitive advantage as mediated by the net benefits of information systems

This study aims to identify the direct impact of IT-business strategic alignment on achieving a sustainable competitive advantage within the context of Jordanian mining sector (JMS). In addition, it aims to investigate the impact of IT-business strategic alignment on achieving a competitive advantage in the case of involving net benefits of information systems as a mediator variable. The study uses a descriptive-analytical approach to collect data via an online survey. Analysis of data was made using SPSS and AMOS software packages (version 26). The study sample includes 462 employees from the JMS companies. Results confirm the high level of IT-business strategic alignment in JMS. In addition, results show a significant impact of IT-business strategic alignment on achieving a sustainable competitive advantage and a partial effect of the net benefits of information systems on the correlation between IT-business strategic alignment and achieving a sustainable competitive advantage.
Dixon’s Framework Model of Think Verbs in English and Wawonii Language

The aim of the current study is to examine the employment of the Think verbs in English and Wawonii language, employing Dixon’s framework model of semantic and grammatical relations. In analyzing the phenomenon of the Think verbs in English and Wawonii, this study uses a descriptive qualitative methodology. The data for English is taken from a corpus of contemporary American English (COCA), including the definition of the conceptual meaning of the Think verbs in a thesaurus dictionary and Journals. The Wawonii language data is obtained through depth online interviews. The participants are 10 indigenous Wawonii people in Kabupaten Konawe Kepulauan, Southeast Sulawesi, Indonesia. Several procedures are applied to analyze the data; first, the data for the English language from COCA is constructed with the data of the Wawonii language that had been collected. Then, presenting both of the data based on Dixon’s framework model. The final step is concluding the data reflecting the main point of the analysis relying on Dixon’s framework model. This study discovered that (1) There are three Thinking verbs of the Think subtype in English they are Think of, Think over, and Think about, while in the Wawonii language, there are six Think verbs; Mepikiri Kanaampe, Pepikiri kida, Tepikiri kio, Kohawa-hawa Kanaampe, Momaana, and Mompatudu. (2) Dixon’s five framework model of Think verbs is employed in both languages. This study contributes to the language and society to enrich a variety of works of literature that can be used to learn English and the Wawonii language.
Q-Learning with the Variable Box Method: A Case Study to Land a Solid Rocket

Some critical tasks require refined actions near the target, for instance, steering a car in a crowded parking lot or landing a rocket. These tasks are critical because failure to comply with the constraints near the target may lead to a fatal (unrecoverable) condition. Thus, a higher resolution action is required near the target to increase maneuvering precision. Moreover, completing the task becomes more challenging if the environment changes or is uncertain. Therefore, novel approaches have been proposed for these problems. In particular, reinforcement learning schemes such as Q-learning have been suggested to learn from scratch, subject to exploring action–state causal relationships aimed at action decisions that lead to an increase in the reward. Q-learning refines iterative action inputs by exploring state spaces that maximize the reward. However, reducing the (constant) resolution box needed for critical tasks increases the computational load, which may lead to the tantamount curse of the dimensionality problem. This paper proposes a variable box method to maintain a low number of boxes but reduce its resolution only near the target to increase action resolution as needed. The proposal is applied to a critical task such as landing a solid rocket, whose dynamics are highly nonlinear, underactuated, non-affine, and subject to environmental disturbances. Simulations show successful landing without leading to a curse of dimensionality, typical of the classical (constant box) Q-learning scheme.
The “Comédie-Française” Registers as Linked Open Data: From Heterogeneity to Quantitative RDF Data

The registers of the Comédie-Française theatre company (Paris, France), kept since the 17th century, record the company’s expenses. This data is particularly interesting for historical purposes. There is however currently no other way to study these expenses other than analysing the registers manually. This lack of easy access motivated us to create a database from the expenses registers transcription. The main objective is to address the registers’ heterogeneity in order to translate it into a schema. The second challenge is to align these data with other transcription databases in order to obtain a single SPARQL endpoint, which can then be used to obtain quantitative information from initially heterogeneous data.
Coin Sum Counter from an Image

Abstract: The purpose of our paper is to recognize the coins which exist in the picture taken by the following user, which will be of Indian National Rupees. Almost every sacred place or some tourist place present in India has a charity/donation box. So the partition or segmentation of such coins requires a lot of people or manpower and therefore for that purpose the whole process must be automated so that it can be more efficient as well as reduce time consumption. We will organize and classify them based on the following classes specified by the user while making the model and then calculate the accumulated or total sum of the INR coins. Features of newer Indian coins are also considered. The proposed approach is to use the image classification model to classify the INR coins from the image and use Edge Detection, Gaussian Blur, and other techniques to correctly identify the edges of the coins as well as to find out the denominations of the coins. The implementation of this classification is written in Python and the dataset is taken from Kaggle.
Analysis of Sounding Rocket Dispersion Using Monte-Carlo Simulation

In this paper the Monte-Carlo method was used to investigate the sounding rocket dispersion. It was assumed that the rocket is equipped with a control unit composed of 32 solid propellant lateral thrusters. The 6 degree-of-freedom model was developed and implemented in the MATLAB/Simulink environment. Several factors affecting the projectile dispersion were taken into account, including uncertainties in rocket’s inertial and aerodynamic parameters, magnitude and direction of wind velocity as well as uncertainties in initial conditions. In addition, the influence of the malfunction of the rocket fins and the time of flight of its occurrence on the mean impact point was investigated.
Evolving U-Nets Using Genetic Programming for Tree Crown Segmentation

The U-Net deep learning algorithm and its variants have been developed for biomedical image segmentation, and due to their success gained popularity in other science domains including remote sensing. So far no U-Net structure has been specifically designed to segment complex tree canopies from aerial imagery. In this paper, a handcrafted convolutional block is introduced to replace the raw convolutional block used in the standard U-Net structure. Furthermore, we proposed a Genetic Programming (GP) approach to evolving convolutional blocks used in the U-Net structure. The experimental results on a tree crown dataset show that both the handcrafted block and the GP evolved blocks have better segmentation results than the standard U-Net. Additionally, the U-Net using the proposed handcrafted blocks has fewer numbers of the learning parameters than the standard U-Net. Also, the proposed GP approach can evolve convolutional blocks used in U-Nets that perform better than the handcrafted U-Net and the standard U-Net, and can also achieve automation.
StencilTorch: An Iterative and User-Guided Framework for Anime Lineart Colorization

Automatic lineart colorization is a challenging task for Computer Vision. Contrary to grayscale images, linearts lack semantic information such as shading and texture, making the task even more difficult. Modern approaches train a Generative Adversarial Network (GAN) to generate illustrations from user inputs such as color hints. While such approaches can generate high-quality outputs in real-time, the user only interacts with the pipeline once at the beginning of the process. This paper presents StencilTorch, an interactive and user-guided framework for anime lineart colorization motivated by digital artist workflows. StencilTorch generates illustrations from a given lineart, color hints, and a mask allowing for iterative workflows where the output of the first pass becomes the input of a second. Our method improves previous work on both objective and subjective evaluations.
Semi-automated red blood cell core detection in blood micro-flow

In microcirculation, red blood cells (RBCs) tend to migrate toward the centre of the vessel leaving a region of a cell depleted layer or cell-free layer (CFL) at the vessel wall and a core of RBCs at the centre. This heterogenous distribution of cells has an effect on the blood apparent viscosity and the exchanges of gases and nutrients between the RBCs and the vessel. Understanding the formation of the CFL and obtaining accurate measurement of it is paramount for furthering development of devices such as drug administration. This paper presents a general semi-automatic method to quantify the thickness of the CFL for different channel geometries and image quality. It enables the use of a method based on intensity, a method using the gradient of the intensity, or a method based on spatiotemporal variation. The main features are reported, the performance is demonstrated on experimentally obtained image sets and accuracy is validated using synthetic images with known CFL thickness. A pure automatic detection is limited by the most visually correct using the spatiotemporal method, however proposed thresholding through automatic detection allows for quality controls through manual adjustments. With a semi-automatic approach RBC core variability between 3 % to 8 % was found when the test user was tasked with repeating the analysis of the same set. The presented method provides, users without programming ability with a user-friendly interface that can extract CFL automatically with quality control through manual adjustments.
Comparative analysis of Cloud, Grid and Distributed Computing on working perspective

This study includes evaluation of three of the most well-known computer paradigms in the world. These three paradigms of computing are cluster, grid, and cloud. All three paradigms are defined, the structure is taken into account, the regions of each package are investigated, and the advantages and disadvantages are stated. At the conclusion of the studies, some aspects are planned to distinguish between the three different paradigms; these kinds of elements are thoroughly expressed earlier in the studies. In this instance, the research comes to the conclusion that while there are many parallels and differences between the three paradigms, there was no clear winner.
DEPARTMENT OF HISTORY OF SCIENCE AND TECHNOLOGY URAL STATE UNIVERSITY - UPI (1999-2017)

The article analyzes the experience of creation and development of the Department of History of Science and Technology at Ural Federal University. The article by the head of the Department of History of Science and Technology UGTU - UPI - UrFU Professor V.V. Zaparia and his colleagues from the department, docents V.A. Doroshenko and E.V. Zaytseva, analyze and summarize the almost 20-year history of the department. The article reveals the achievements of the department staff and its problematic moments, as well as considers the history and methods of teaching the main discipline of the department - the history of science and technology. The author describes the creation of methodological works in the form of textbooks, teaching aids and reading-books, multimedia courses. The authors not only summed up the results of the department, but also showed its place in the Russian school of historical research and analyzed the main directions of scientific work of the teaching staff.
By the Rivers of Babylon

An algorithm for computing square roots of option smiles is remarkably similar to a method invented nearly 4 millennia ago
Inertial sensor-based movement classification with dimension reduction based on feature aggregation

In this paper, a novel feature aggregation method is proposed for dimension reduction in inertial sensor-based pattern recognition applications. The method is utilized in a movement classification algorithm, which applies measurements of wearable sensor units attached to the wrists of the subjects. The feature aggregation is realized separately for each feature type by using a linear combination of the feature values extracted for each sensor axis. The weights are computed using genetic algorithm-based optimization, which uses the classification efficiency provided by the k-nearest neighbor (kNN) method to compute the fitness value. After the weights are determined for all feature types and the aggregated inputs are computed for both the training and validation data, a multi-layer perceptron (MLP) classifier is trained, which can be used during real-time classification. The algorithm was tested with multiple datasets based on features extracted using different processing window sizes and used sensor combinations. The achieved results show that the proposed weighted aggregation provides significantly, mostly 5-6%, higher recognition rates on validation data than when equal coefficients are utilized during aggregation. The method in the case of some datasets with the same configuration gives even higher classification efficiencies than when using the features extracted for the sensor axes separately.
Analysis of time series data for anomaly detection

The integration of sensors in our everyday lives and in industry presents a serious challenge to data analysis professionals. Since the use of smart devices has exponentially increased the amount of data collected in all areas, we must not only store these data, but also extract valuable information from those using some data analysis method. In many cases, the increased amount of data also causes problems for data analysis algorithms, so we need to be continuously updated and specialized for fundamental purposes. In the article, we will present some data analysis techniques, starting from the simplest statistical methods to more complex techniques using machine learning and presenting one of their possible applications. In our study, we will use the KMeans clustering algorithm and examine its effectiveness in time series sensor data analysis.
The Classification Model of Learners Based on Diagnostic Assessment Results in E-learning With Machine Learning

The development of technology and artificial intelligence, especially in the era of Industrial Revolution 4.0 almost covers all fields, one of which is e-learning. In its development, it is very important to determine the factors to produce the quality of e-learning. Machine learning can help to find various solutions in the real world, especially to solve problems for which a specific algorithm has not been found. The implementation of e-learning is often ineffective and not on target which has an impact on learning achievement that is not achieved, this is due to several things namely the learning model is applied in general, the learning materials and weights are applied equally to all users. Seeing these problems, it is necessary to have a model to classify students according to their abilities, talents, and interests before starting learning to achieve their goals to be achieved. This paper aims to create a machine learning approach model to determine the level of ability and needs of students before starting learning which can reduce disparities between students and follow up learning materials. With the machine learning based on learning model, e-learning can provide teaching materials according to the abilities, and interests of students, sustainably and improve the quality of students.