Reviewerly/exHarmony huggingface dataset:
Pengaruh Persentase Ampas Kopi Robusta (Coffea canephora) dan Suhu Pemanasan terhadap Karakteristik Body Scrub

Body scrub is one of the cosmetic products used for skin care. Body scrubs have coarse granules that function to remove dead skin cells and clean the skin optimally, which is called abrasive or sanding. This study aims to determine the effect of the percentage og coffee grounds and heating temperature and their interaction on the characteristics of the body scrub and to determine the percentage of coffee grounds and the right heating temperature so as to produce a body scrub with the best characteristics. This study used a factorial randomized block design with two factors. The first factor is the percentage of coffee grounds with 3 levels, namely 4, 6 and 8%, while the second factor is the heating temperature with 3 levels, namely 65, 70 and 75?C. The results showed that the percentage of coffee grounds had an effect on ph, viscosity, spreadability, adhesion, total phenolic compounds and overall body scrub acceptance. The heating temperature affects the viscosity, spreadability, adhesion, total phenolic compounds and overall acceptance of the body scrub but the heating temperature has no effect on the ph of the body scrub. The interaction between the percentage of coffee grounds and the heating temperature had no effect on ph, viscosity, spreadability, adhesion, total phenolic compounds and overall acceptance of the body scrub. The percentage of 4% coffee grounds and a heating temperature of 65?C is the best treatment of body scrub with characteristics of ph 5.93, viscosity of 7600 cp, dispersion of 5.6 cm, adhesion of 5.78 seconds, separation ratio = 1, total phenolic compounds 1.52 mg GAE/g and overall acceptance was 4.95 (regular to moderate).&#x0D; Keywords : body scrub, coffee ground, heating temperature
Authentication and Encryption algorithms for data security in Cloud computing: A comprehensive review

With the growth of data stored in cloud, data may become the target of attackers in the Internet.Therefore, the end users require high confidentiality, integrity and authentication in order to protect their data in cloud.In this paper, we aim at a comprehensive studying about the data security in cloud computing.The paper will discuss the details of cloud computing data security challenges and find out which are the most important challenges as well as the efficient solutions.The existing authentication and encryption algorithms are compared in terms of users' scenarios, outstanding features and the limitation.We also review the advantages and drawbacks of the algorithms for data security in terms of cloud computing services.
Cancer Prediction Using Cascade Generalization and Duo Output Neural Network

This paper proposes the combination of cascade generalization and duo output neural network based on feedforward backpropagation neural networks for cancer prediction.Duo output neural network is a neural network that is created based on two opposite targets in order to predict two opposite results.Cascade generalization is a technique that consists of a set of machines that are sorted together in which the predicted output produced from the previous machine plus the original training input are used for the creation of each machine.In this study, cascade generalization is organized in two levels: the base level and the meta level.In this research, duo output neural network is trained in each level of cascade generalization.Two outputs produced from the base level which are truth output and non-falsity output are averaged.The average result plus the original input are used for training a machine in meta level.The proposed technique is tested using two cancer datasets from UCI machine learning repository and found that our technique provides the best overall results when compared with three individual techniques.
Obstacles And Solution Toward English Learning During Covid-19 Pandemic

The process of online English learning during covid-19 pandemic brings both positive and negative impact. The positive impact could support learners to learn better. On the other side, the negative impacts could worsen the learning process. The present study was managed in order to answer three research questions mentioned as the problems faced by students and lecturers during English online learning in the pandemic of covid – 19, the most problem took place, and to find out possible solution to deal with the issue. The method implemented in this study was qualitative method with questionnaire and open-ended interview as the instruments. The result of the study pinpointed that atrocious internet was the problem occurred mostly during English online learning. The possible solution such as being focus in online learning, find places with good network, earlier material delivery and self-motivation were proposed by the students. Implementing blended learning was the solution recommended by the lecturers. To sum up, the obstacles in online English learning should be identified in order to be utilized as an input for the improvement of the learning process itself. Furthermore, the solutions could also become the factors to be considered as ways to enhance the process of English online learning.
Review on Frameworks Used for Deployment of Machine Learning Model

Abstract: According to the current scenario, the use of machine learning is increasing in a variety of web applications and services. A good visual experience, fast performance, and easy to use framework is critical for developing and deploying your model. Working on a machine learning model is one thing but deploying a machine learning model to production can be another. Creating a Machine Learning model is one thing but deploying the model in real-time is the real challenge. For that purpose, many different technologies are available in the field. The simplest way to deploy a machine learning model is to create a web service or application. In this paper, we will discuss different frameworks for the deployment of the machine learning model on web applications or services. In this paper we will discuss Flask framework, Streamlit framework, Django Framework. Keywords: Flask, Streamlite, Django Framework, Model deployment, Web Framework
A novel independent job rescheduling strategy for cloud resilience in the cloud environment

Purpose Although proactive fault handling plans are widely spread, many unexpected data center outages still occurred. To rescue the jobs from faulty data centers, the authors propose a novel independent job rescheduling strategy for cloud resilience to reschedule the task from the faulty data center to other working-proper cloud data centers, by jointly considering job nature, timeline scenario and overall cloud performance. Design/methodology/approach A job parsing system and a priority assignment system are developed to identify the eligible time slots for the jobs and prioritize the jobs, respectively. A dynamic job rescheduling algorithm is proposed. Findings The simulation results show that our proposed approach has better cloud resiliency and load balancing performance than the HEFT series approaches. Originality/value This paper contributes to the cloud resilience by developing a novel job prioritizing, task rescheduling and timeline allocation method when facing faults.
Emergency Nurses' Knowledge and Practices Regarding Triage

Triage is the most important step in the emergency room. Triage is useful for ensuring that every patient gets rapid, precise, responsive and accurate treatment according to their condition based on ABCDE (airway, breathing, circulation, disability, environment) priorities. For this reason, it is necessary for nurses to be competent in the knowledge and skills related to triage. This study aimed to determine the association between knowledge and the implementation of triage by nurses at the Siloam Hospital in Kupang. This quantitative research used a correlative research design with a cross-sectional approach. 25 emergency nurses were recruited through saturation sampling. The instruments used in this study were a questionnaire, an observation sheet, and a checklist guided by the Service Operational Standards (SOP) for Triage Implementation. Data were analyzed using the Chi-square test. There was a significant relationship found between knowledge and the implementation of triage (p = 0.003). Therefore, it is important to optimize the nursing workforce, make arrangements related to their workload, enhance their motivation, and continue to improve their knowledge, skills and obedience in the emergency room through training programs and supervision.&#x0D; Keywords: knowledge, implementation, triage
Financial Products Innovation and Financial Performance of Commercial Banks in Rwanda

The purpose of this study was to examine the contribution of financial innovations on financial performance of commercial banks in Rwanda using a case of Investments and Mortgages Bank.The specific objectives were to analyze the effects of internet banking, debit and credit cards, and mobile banking on financial performance of Investments and Mortgages Bank in Rwanda.Findings are helpful to illuminate the correlation between financial innovation products and the financial performance for banking institutions.Results from the present study gave useful information with suggestion of related research areas.The study applied both descriptive and correlational research designs.A sample of 193 respondents was selected from a target population of 370 using Slovene's formula.Simple random sampling technique was used to select respondents.Questionnaire and interview guide were used to gather information, while descriptive and inferential statistics helped to analyze quantitative information, but qualitative information was analyzed using content analysis.Results on internet banking show a significant relationship between availability and simplicity of ICT application and the increase in net profits since test significance of statistics has a p value =0.017.The Regression analysis shows that an improvement in internet banking through availability and simplicity of ICT application can cause an increase in net profits by 0.173 units.Results on debit and credit cards show that restraint and record keeping were significantly affecting net profits with a level of significance of 0.033.The Regression analysis shows that an increase in debit and credit cards leads to an increase in net profits by 0.189 units.Results on mobile banking show that the increase in fund transfers between accounts and increase in deposits are significantly affecting the return on assets and operation cost with p=0.028 and P=0.000 respectively.The Regression analysis shows that an increase in mobile banking through transfers leads to the increase of return on assets by 0.162 units.This study recommends that more features of financial products innovations should be introduced to attract more customers in order to improve the performance.Future studies should be carried out using a deep qualitative approach in order to know costs of financial innovation products in commercial banks in order to make a comparison with the benefits.
Multiscale Anisotropic Morphological Directional Derivatives for Noise-Robust Image Edge Detection

Different types of noise interference lead to low accuracy of image edge detection and severe loss of feature extraction details. A new noise-robust edge detection method is proposed, which uses a set of multiscale anisotropic morphological directional derivatives to extract the edge map of an input image. The main advantage of the method is that high edge resolution is maintained while reducing noise interference. The following five parts form the whole framework of this paper. First, multiscale anisotropic morphologic directional derivatives (MSAMDDs) are proposed to filter and obtain the local gray value of the image. Second, the edge strength map (ESM) is extracted by using spatial matching filters. In the third stage, multiscale edge direction maps (EDMs) based on the directional matched filters are fused, and the new EDM is constructed. Fourth, edge contours are obtained by embedding the ESM and the EDM into the standard route of Canny detection. Finally, the precision-recall curve and Pratt’s figure of merit (FOM) are used to evaluate the proposed method against eight state-of-the-art methods on three data sets. The experimental results show that the proposed method can perform better for noise-free (F-measure value of 0.776) and Gaussian noise (FOM value of 95.75%) and attains the best performance in impulse noise images (highest FOM value of 98.90%).
Implementation of Deep Learning for Classification Type of Orange Using The Method Convolutional Neural Network

Orange is a type of fruit that is easily found in Sambas Regency. The types that are widely sold are Siam oranges, madu susu and susu. Each type of orange has a different quality and a different price. The price difference often results in fraud committed by traders against buyers to the detriment of the buyer. This is because differentiating types of oranges based on the appearance of the fruit does not have a standard. Therefore, in this study, a citrus fruit classification system was created based on images by implementing deep learning. The method of deep learning used in this research is Convolutional Neural Network (CNN) with AlexNet architecture. The types of oranges that will be observed are madu oranges, madu susu, and siam. The data used are 2250 images of oranges with each class totaling 750 images with a size of 227x227 pixels. The training data is 1575 images and the test data is 675 images. The training is carried out with a total of 10 epochs and each epoch will produce a model. System testing is carried out based on the model generated in the training process. Each model will be observed results in the form of accuracy which is calculated using a confusion matrix. The most optimal model was generated from training in epoch the 9th which resulted in an accuracy of 94.81%.
Pembelajaran Berhitung Permulaan Melalui Permainan Balok Bergambar pada Anak TK: Studi Kasus TK Sukma Sejahtera Leuwiliang dan TK Islam Fatahillah Depok

&#x0D; Learning outcomes are influenced by several factors including the use of teaching aids by educators in the teaching and learning process. This study aims to analyze the ability to count, literacy, and creativity using educational games using number blocks at Sukma Sejahtera Leuwiliang Kindergarten, Bogor and Fatahilan Islamic Kindergarten, Depok. The research method used is a qualitative descriptive analysis method on the results of observations and assessments of class actions. Learning tools through educational games with number blocks are proven to be able to improve the mastery of numeracy comprehension in early childhood A in Sukma Sejahtera Leuwiliang Kindergarten, Bogor and Fatahilah Islamic Kindergarten. Relatively, the ability of teachers and students in Cycle II (TK Islam Fatahillah) was relatively better than the ability of teachers and students in Cycle I (TK Sukma Sejahtera). Literacy, creativity and innovation towards new methods are more important than institutional age..&#x0D; Keywords: Counting, game tools, learning to count
Improving students’ reading comprehension by using mind mapping technique through Mimind Application as android based mobile learning at the first grade of SMAN 1 Sumberejo in academic year 2020/2021

The objectives of this research are: (1) to find out the significant difference of using mind mapping through miMind Application in improving students’ reading comprehension of narrative text; and (2) to know the students’ responses toward miMind Application as android based mobile learning. This is a quantitative study which had intact group pre-test and post-test design. Therefore, the effectiveness of this study can be seen through the significant difference before and after the implementation of mind mapping technique through miMind Application as revealed on the students’ scores. This research was conducted at the first year of SMAN 1 Sumberejo in academic year 2020/2021. The sample of this research was X MIPA 1 consisted of 36 students which was selected by using cluster random sampling. The research data were collected by using reading test and questionnaire. The results show that students’ mean scores in the pre-test was 61.18 and post-test was 76.81. It can be seen that the students’ mean score of post-test increased about 15.63 point after being taught by using mind mapping technique through miMind Application. Then, the results were computately analyzed using Paired Samples T-Test through SPSS 20 Program, which showed that t-value (6.780) was higher than t-table (2.0301) at level of significance 0.05. Based on this result, H0 was rejected, while H1 was accepted. In other words, there is significant difference between the students’ reading comprehension in narrative text before and after the implementation of mind mapping technique through miMind application in SMAN 1 Sumberejo. It was also supported by the students’ responses in questionnaire. From the result, there were 89% students gave positive response toward mind mapping through miMind Application. It can be concluded that mind mapping technique through miMind Application is effective to improve students’ reading comprehension of narrative text. Keywords: Reading Comprehension, Mind Mapping Technique, miMind Application, Android, Mobile Learning
Incremental Learning With Adaptive Model Search and a Nominal Loss Model

This paper addresses an incremental learning problem, in which tasks are learned sequentially without access to the previously trained dataset. Catastrophic forgetting is a significant bottleneck to incremental learning as the network performs poorly on previous tasks when it is trained on a new task. We propose an adaptive model search method that uses a different part of the backbone network depending on an input image to mitigate catastrophic forgetting. Our model search method prevents forgetting by minimizing the update of critical parameters for the previous tasks while learning a new task. This model search involves a trainable network that selects the model structure for an input image. We also propose a method for approximating the loss function of previous tasks without the previous dataset. The critical parameters for the previous tasks can be found, considering the relationship between the approximated loss function and the parameters. The proposed framework is the first method of model search that can consider the performance of both current and previous tasks in the incremental learning problem. Empirical studies and theoretical analysis show that the proposed method outperforms other competitors for old and new tasks while requiring less computation.
Evaluation of the educational administration program in light of the academic accreditation standards from the point of view of the faculty staff members of the Universities of Jordan in Irbid

The aim of this study was to evaluate the degree of the evaluation of the educational management program in light of the academic accreditation standards in the universities of Jordan in Irbid from the point of view of the faculty members. The researchers used the descriptive analytical approach and the study sample consisted of (17) faculty members specialized in educational administration. The results showed that the degree of evaluation educational administration program in the light of academic accreditation standards from the viewpoint of faculty members was average (3.47), and a mean of (0.46) and the results showed that there were no significant in estimates of faculty members to the degree of evaluation of the educational administration program due to variables (gender, academic rank, and experience), the recommendations include working on improving the educational administration program in light of the academic accreditation standards in terms of the content and goals, study plan.&#x0D; &#x0D; Keywords: Evaluation, Educational Administration Program, standards, Academic Accreditation, universities of Jordan.
Neuroscience can contribute to change management: STREAP-Be model

In our world of volatile, uncertain, complex, and ambiguous (VUCA), a capacity for change and adaptation is vital.However, changing successfully has been a challenging task for both individuals and organizations.Taking into account the insights of neuroscience, this chapter introduces a framework of change management called STREAP-Be.The acronym represents seven factors that could significantly influence the effect of change: safety, trigger, reward, emotion, alignment, people, and behavior.1
USING IMMERSIVE TECHNOLOGIES FOR LEARNING JAPANESE BY STUDENTS OF PHILOLOGICAL SPECIALITIES

The article deals with the problem of using immersive technologies for teaching future philologists of the Oriental language. It considers the ways to implement tasks aimed at fast, active, correct and convenient studying by students of the Oriental languages by means of immersive technologies, such as technologies of augmented and virtual reality. The main classifications of types of augmented and virtual realities have been provided for understanding the principles of their use in the educational process, in particular for learning languages. The article describes the scientists’ studies and their main results regarding selection and application of immersive technologies for students who study the Oriental languages at philological departments of higher education institutions. The advantages of using immersive technologies for teaching future translators of the Oriental languages, in particular of Japanese, at the Education and Scientific Institute of Philology, Taras Shevchenko National University of Kyiv, at lecturers and seminars for “Japanese Kanji Characters” have been revealed and substantiated. It has been found out that among these approaches, students prefer an approach based on the creation of educational materials by themselves using augmented reality, which is due to their motivation for creating such examples of hieroglyphs with augmented reality that will interest other students and illustrate the most difficult cases in the language practice while translating from the corresponding (Oriental) languages.
Efficient Density Estimation for High-Dimensional Data

Multivariate density estimation methods typically work well in low dimensions and their extension to data analytics in high dimensions domain has proven challenging. For density estimation in high-dimensional big data domains, the non-parametric Bayesian sequential partitioning (BSP) algorithm provides an efficient way of partitioning the sample space, based on Bayesian inference. In this paper, we present a detailed analysis of BSP and provide a computationally efficient copula-transformed data structure and algorithm for use in density estimation for data analytics in high dimensions. Using the copula-transformed data structure, we implement the density estimation for marginals in both BSP and kernel density estimation (KDE) methods. The data structures and algorithm are suitably designed for most efficient rendering into parallel processing paradigms of open multi-processing (OPENMP <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> ) and message passing interface (MPI).