Reviewerly/exHarmony huggingface dataset:
A proposed User-Based Approach for eBooks Recommendation Using a Weighted Nearest Neighbor Technique

Large book data stores were beneficial for our support systems but posed significant challenges for useful information retrieval. This issue was resolved by collaboratively filtering data depending on user needs. This study suggested a user-based methodology for recommending eBooks. The selected dataset was pre-processed, and Cross-validation was used to build a user-user similarity matrix. Three nearest neighbor algorithms (KNN Basic, KNN with Means and KNN with ZScore) were used, and weighted KNN was proposed for rating prediction. In this technique, the weight of each user was calculated based on its distance from the intended user. The evaluation process depends on the user-item matrix and user-user matrix for prediction. The proposed recommendation system was tested on the book-crossing dataset, and the results were evaluated using the root mean square error and the mean absolute value of error. The results show that the error rate of the proposed model is the lowest compared to the other methods used, specifically when using the Pearson-Baseline technique. Since the root mean square error is 1.647 and the mean absolute value of errors is 1.253. When using the cosine technique, the root mean square error is 1.742, and the mean absolute value of errors is 1.328.
Zero Knowledge Proof for SNAP (Standar Nasional OPEN API Pembayaran) in Indonesia

SNAP (Standar Nasional OPEN API Pembayaran) is an implementation of open banking for encouraging digital transformation in the banking industry. SNAP was submitted by several sub-working groups formed jointly by ASPI and the Bank of Indonesia. In the document Pedoman Tata Kelola (Bank of Indonesia, n.d.), there is already a customer data protection mechanism between the bank, the owner of Open API, and the user of Open API. However, there is no data protection process carried out by consumers so third parties, that use the Open API of the bank, do not need to know the customer's data. Based on the web3 protocol, users can store data and transmit only in encrypted form which can only be opened by calculating the data with a pre-agreed smart contract. Banks can work like a decentralized network on web3, where the process of calculating proof and witness is carried out by the bank. Proof and witness are calculated using a zero-knowledge proof protocol, making it difficult to duplicate. For this reason, we propose a new architecture using smart contracts between banks and customers using the ZK-SNARK method. Therefore, there is no significant performance difference between using ZK-SNARK and without ZK-SNARK in the API call process.
Classification of Public Sentiment on Fuel Price Increases Using CNN

The government's policy of changing fuel prices is carried out every year. The public gave responses to this policy categorized as positive, negative, or neutral sentiments. The community's response was conveyed through tweets on the Twitter application. Based on the public's response to the policy, sentiment classification can be done using data mining classification techniques. Some research has been carried out on classification techniques using deep learning and machine learning methods. In general, deep learning methods get better results, and this research will be approached using the CNN method. The system stages start from crawling data, labeling, and preprocessing, which consists of cleaning, case folding, tokenization, normalization, removing stopwords and stemming, classification using CNN, and evaluation using 10-Cross Validation. The dataset used is 17.270. The results show that the developed classification system is relatively high, with the highest accuracy of 87%, 93% recall, 93% precision, and 90% F1 score. An in-depth analysis of the classification results and an understanding of sentiment toward rising fuel prices can also provide valuable insights.
Smart Energy Management for a Microgrid System Based on Echo State Networks─Gray Wolf Optimizer Hybrid Algorithm

This work present a smart energy management system for a microgrid based on a hybrid intelligent adaptive algorithm composed of an echo state network and gray wolf optimizer techniques. The microgrid connected to the grid is composed of renewable sources such as photovoltaic and wind conversion associated with a hybrid battery hydrogen fuel generation storage system. An adaptive intelligent control unit is developed to manage the power flow between the hybrid power sources and other devices in the system. The controller chooses the optimal operating mode of power sources ensuring the continuous supply of the injecting power in the grid and maintaining the battery state of charge at acceptable levels as the hydrogen tank level. Complete mathematical modeling for the proposed system is implemented in MATLAB/Simulink to track the microgrid performance. Numerical and graphical results are presented to show the effectiveness of the proposed control system
DESIGNING OF OPEN CHANNELS AND CLOSED CHANNELS FOR DRAINAGE FACILITIES IN AIRSIDE

Drainage facilities are one part of the airside facilities that are important to ensure the safety of the airside area when weather changes occur. This study aims to make drainage that can be carried out to ensure the safety of the airside area from stagnant water. The calculation of airside drainage refers to the planned drainage calculation rules so that the drainage channels can design accordingly so as not to interfere with the operation of other facilities. In the design of drainage work, the material requirements for making open and closed channels at Dewadaru Karimun Jawa Airport must be precisely designed so that the work takes place by the specified planning and quality standards. The research uses a mixed methodology that the exploratory study by conducting a literature study that compares observation. The analysis starts by finding the volume for each work item based on Standar Nasional Indonesia (SNI) 2052-2017, a national iron and concrete standard. The result shows the excavation of soil for making channels, the total volume required is 880.24 m3, the importance of sand is 40.76 m3, the book of substantial rebates of a mixture ratio of 1: 2: 3 is 23.82 m3, and the book of K300 Concrete works for U-Ditch type C channels (100 x 100) is 116.29 m3. For the needs of the required iron volume of 16,415.09 kg, formwork of 1,408.45 m2, stone river masonry size 100 x 100 by 139 m3, stone river masonry size 50x50 amounted to 42.05 m3, and the volume of plastering work of the channel amounted to 192.46 m2. The conclusion of this study is the accuracy of design required to calculate each work item's volume.
A no-reference panoramic image quality assessment with hierarchical perception and color features

The ultimate goal of no-reference omnidirectional image quality assessment (NR-OIQA) is to design a comprehensive perception method that can accurately assess the quality of damaged omnidirectional images without prior knowledge. However, most existing studies cannot attain credible accuracy because of lacking a non-neuroscience-based or non-biology-based model. Inspired by this, an original visual perception-based and neuroscience-based OIQA model by considering the hierarchical perception features of the HVS, which includes specific information, local saliency information, global information, and color information which is often ignored by researchers is proposed in this work. According to the hierarchical process in neuroscience, the high-frequency co-occurrence matrix (HFCM)-based and variance-based specific features are applied to perceive details that are first distorted in the frequency domain. The entropy-based combination of the paranormal saliency map(PSM) and superpixel segmentation with the simple linear iterative clustering (SLIC) algorithm is employed to emphasize the rich quality-aware local saliency information. The global panoramic statistical (GPS) model is utilized to express global semantics distortion as the high-level feature. Visual-aware color texture descriptor with the cross-channel local binary pattern(CCLBP) which effectively reflects the correlation and dependency of pixels between different color channels is employed to map color information. Finally, all above features have been extracted and combined with subjective scores to assess the objective quality scores by support vector regression (SVR). Experiments express that our method has more accuracy and stronger stability on CVIQD2018 and OIQA databases.
POLYVINYLIDENE FLUORIDE MEMBRANES WITH TIN (IV) DIOXIDE (SNO2) ADDITIVES: ENHANCING WATER TREATMENT FOR AIRPORT ECO GREEN

Water treatment is a major process of realizing Eco Airport in airport development that does not cause environmental damage, protects the environment, and seeks to minimize the impact of environmental pollution due to airport operations. The growth and development of the global economy make a significant contribution resulting in increasingly uncontrolled disposal of wastewater, resulting in a series of environmental problems related to the provision of clean water at airports. This study discusses water filtration using membrane technology with Polyvinylidene Fluoride (PVDF) polymer mixed with Tin (IV) Dioxide (SnO2). This study aims to analyze the microstructure formed through Scanning Electron Microscopy (SEM) testing and determine the mechanical properties and performance in water treatment of mixed PVDF and Tin (IV) Dioxide (SnO2) membranes. This research is a new method in water treatment. The research method used was quantitative with initial membrane specimen formation, membrane preparation, flat sheet method, and testing methods (tensile testing, Scanning Electron Microscopy (SEM) observations, and Clean Water Permeability (CWP) testing. The results of this study, namely the formation of mixed membranes, showed the corresponding results as an alternative to water filter membranes. PVDF and SnO2 mixed membranes subjected to tensile tests using ASTM D638 standards, the surface structure of mixed membranes using SEM showed significant differences at each concentration, and the performance of water treatment performed with CWP showed an increase with increasing PVDF concentration and SnO2 for the airport. The conclusion is that water treatment performance using Clean Water Permeability (CWP) increases with increasing concentrations of PVDF and SnO2, which are suitable for airports.
An Improved and Faster Software Fault Prediction Model Based on New Object-Oriented Features Using Machine Learning

Testing the software after development plays a significant role in developing defect-free and quality software but consumes almost half the time and resources allocated to develop it. To reduce development time and cost, efficient allocation of resources is necessary, and software defect prediction models can help by giving prior knowledge about defective modules. Many software defect prediction models based on features of code and machine learning (ML) algorithms have been proposed previously; however, no model is perfect. This research article proposes a software defect prediction (SDP) model based on new object-oriented features using the best-suited machine learning algorithm for proposed features. Proposed features are designed to capture the class-level properties such as coupling, cohesion, complexity, inheritance, overloading, overriding, and abstraction in a better way compared to existing features which are based on the trivial count of the code properties. The performance of machine learning (ML) algorithms depends heavily on training datasets, so it is necessary to select the best-suited machine learning algorithm with proper hyperparameter tuning to develop a good software defect prediction model. We select the machine learning algorithm based on the nine compared algorithms' AUC and accuracy performance metrics. Hyperparameters of each machine learning algorithm are tuned properly using the grid search algorithm. Our proposed defect prediction model is compared with those developed based on existing object-oriented features. Experimentation is done on 18 open-source, object-oriented software whose code and defect information are openly available, and the results show that the proposed model is better and faster. Finally, the cost and time of the testing team saved by the proposed model are compared with existing models, and the results show that the proposed model gives better cost and time benefits with less false omission rate.
Transient Response of Ventilated Room Enclosure Subjected to Solar Radiation Load

The effect of incident solar radiation ray’s penetration through glassed windows of duct - ventilated room enclosure air is investigated assuming constant solar intensity. The room is supplied with cold dry air through rectangular duct and the air is distributed through two grid ceiling fans located at the duct exit. The effect of fixed solar intensity penetration with variable fans rotational speed at specified time intervals is investigated upon the room temperature distribution and is simulated using the ANSYS CFX package. The results showed the variation in room temperature distribution at a specified time period (1, 2, 3 minutes) with increased fan speed becomes more efficient, also the transient room temperature distribution response to the solar radiation is significantly present in the simulation noticing fans effect on accelerating heat transfer rate through the domain.
Combine SVM and KNN classifiers for Handwriting Arabic Word Recognition based on Multifeatures

This paper presents a proposed system for recognizing the handwritten Arabic words. The proposed system recognized the Arabic word as one entity without using segmentation stage, which converted the word into parts. A proposed method for feature extraction stage used two groups of feature extraction techniques. First group combines two techniques and the second group used single technique. First group combines gradient (directional) feature method with the Run Length Count method and second group based on Discrete Cosine Translation technique. Classification stage is based on combined SVM with KNN classifiers. A standard data set which is AHDB database is used to simulate the proposed system. The recognition accuracy for the experimental results of the proposed system is 97.11 %.
A Comprehensive Evaluation of the Rivest-Shamir-Adleman (RSA) Algorithm Performance on Operating Systems using Different Key Bit Sizes

In today's digital world, practically everyone uses the Internet for various purposes.Most data sent over the Internet contains personal or private information that people desire to keep hidden.There are numerous encryption techniques available for concealing data.However, none of the previous research has thoroughly examined different bit sizes of RSA algorithms on Windows and Linux.According to previous studies, there is a range of factors, such as operating systems, compilers, and environmental conditions that affect how well cryptographic algorithms function.This study investigates the various key bit sizes used in the RSA technique (512, 1024, 2048, and 4096).The time it took to generate the following: private keys, public keys, signature blocks, and verification processes utilized in the RSA method was the basis for this experiment.Two virtual machines, one running Windows and the other running Linux, were used for the experiment.The experiment was conducted on three HP laptops, each equipped with a 3.38GHz Intel Core i5 processor, 12GB of RAM, and a 1TB SSD.The experiments were repeated three times on each laptop, and the average times were recorded for both virtual machines.It was demonstrated in this investigation that the Linux operating system outperforms the Windows operating system in terms of overall performance.According to the results, both RSA private and public key generation were faster on Linux than on Windows.Furthermore, the test for digital signature and verification throughput indicated that total signatures and verifications per second were higher on Linux than on Windows for all RSA key bit sizes.Finally, increasing the RSA key bit reduced throughput for both digital signatures and verification in both operating systems.
Opportunities and Risks of Individual Educational Trajectories (IET)

Individual educational trajectory (IET) is a concept that has been relevant in the field of pedagogy and education organization throughout history. The idea behind IET is to center educational processes around the unique abilities, interests, and needs of each student. This article discusses some facts from the history of IET, its principles and prospects for Russian education, as well as problems associated with its implementation in education practice and possible ways to overcome them. In modern society, where the speed of change and technological development is constantly increasing, it is critical to consider the individual needs of each student. IET is an effective tool that can qualitatively improve the efficiency of education in modern conditions and help students develop comprehensively. However, the implementation of IET in education practice is accompanied by certain risks. One of them is the possibility for students to choose only those subjects that interest them, which can lead to a lack of necessary knowledge and skills. Another risk is the possibility of forming scattered knowledge that will not meet systemic requirements and will not be interconnected. Nevertheless, IET is undoubtedly a promising concept that can give impetus to innovative changes in education.
A web-based Probabilistic Medical Computation platform based on Bayesian network

Abstract Objective The objective of this study was to develop a medical service platform utilizing Bayesian networks for medical modeling and risk inference, with minimal configuration requirements. The platform was designed to provide accurate and efficient disease diagnosis and treatment plans for medical professionals. Methods The platform consists of four layers: database layer, Bayesian network construction layer, Bayesian network inference layer, and client layer. The database layer accepts user data uploads. The Bayesian network construction and inference layers are implemented for Bayes network learning and inference based on the bnlearn package of Python. The client layer allows users to define Bayesian network learning parameters and presents inference results in the form of bar charts. Results The code for our prototype system is available on Github (https://github.com/rose1203/BayesNet-platform-for-medical-computation.git). Our user-friendly and flexible platform allows professionals and IT experts to easily configure appropriate parameters for network structure and parameter learning. User-specified Bayesian networks can be saved for inference. Additionally, the platform supports data visualization for medical staff, which provides doctors with an intuitive understanding of patients' potential disease progression. Based on this information, doctors can formulate corresponding treatment plans and care measures. Conclusion Our interactive online platform, which is based on Bayesian networks, provides healthcare professionals and researchers with a valuable tool to make informed decisions. Combined with models based on real-world data and individual patient cases, our platform can promote personalized healthcare and enhance the quality of healthcare services.
Pengaruh Blended Learning, Jigsaw, Direct Instruction Terhadap Pemahaman Akuntansi Pada Matakuliah Teori Akuntansi

This research aims to examine differences in students' understanding of accounting taught with Blended Learning, Jigsaw, and Direct Instruction learning models. The research was conducted at the University of ABC. The study population numbered 264 students, then the sampling technique used cluster random sampling, and the number of students being sampled was 60. This type of research is quasi-experimental. Data collection techniques using learning outcomes tests. The data analysis technique used one ANOVA. The results showed (1) There was a significant difference in the understanding of accounting taught by the Blended Learning model, Jigsaw, and Direct Instruction, (2) Students who were taught with Blended Learning had a significantly higher understanding of accounting than students taught with Jigsaw, (3) Students who were taught with Blended Learning had a higher understanding of accounting than students who were taught with Direct Instruction, (4) Students who were taught with Jigsaw had a lower understanding of accounting than students who were taught with Direct Instruction. In conclusion, Blended Learning, Jigsaw, and Direct Instruction learning models positively affect understanding accounting. The most effective learning model in this research was Blended Learning. Blended Learning, Jigsaw, and Direct Instruction learning models can be used to teach accounting; however, to maximize the effect of Blended Learning, Blended Learning requires a powerful internet connection.
Faktor-Faktor yang Memengaruhi Sikap Mahasiswa terhadap Profesi Akuntansi: Pendekatan Studi Kasus

The popularity of the accounting profession in several countries shows concern caused by the declining number of students majoring in accounting which has a direct impact on the supply of accounting graduates for the profession. Therefore, this study aims to find out the factors that influence the attitude of students majoring in accounting towards the accounting profession. This research also explores the interest of students majoring in accounting towards the accounting profession. This study uses a qualitative method with a case study approach. Data was collected through in-depth interviews. Next, the researcher conducts data analysis starting from processing and preparing the data, carrying out the coding process up to interpreting the data. The results showed that the factors driving students' interest in the accounting profession were educational background, accounting learning experience, lecturer teaching methods, interest in accounting, and students' ability to understand the basic concepts of accounting and the accounting profession. This study also found that students majoring in accounting do not absolutely choose an interest in the accounting profession, but they also have an interest in career choices outside the accounting profession.
Implementasi Big Data Analytics dan Opini Audit terhadap Kualitas Audit dengan Audit Delay Sebagai Variabel Mediasi

This research is based on the relationship between Big Data Analytics and its impact on the quality of audit evidence and the level of assurance felt by end users of financial statements. Big Data Analytics potentially play an important role in innovative auditing practices. Big Data Analytics can increase the efficiency and effectiveness of financial statement audits, but additional competencies and technological capabilities are required to implement Big Data Analytics. With Big Data, auditors can minimize the risks posed by sampling, and therefore, provide a reasonable level of assurance. This study aims to examine Big Data Analytics variables and audit opinion whether these two variables are antecedent variables of audit delay and affect audit quality. This study uses secondary data in the form of the company's annual financial report. The population in this study are all companies listed on the Indonesia Stock Exchange (IDX). The population in this study were 100 companies in the telecommunications, transportation and banking sub-sectors that met the criteria and had been determined to be related to problems during the 2016 to 2020. The data analysis technique in this study was to use the Structural Equation Model to examine the mediating effect. The implementation of big data analytics has no effect on the audit delay variable. Based on the research results, there is sufficient evidence that audit opinion has a significant positive effect on audit delay. However, there is not enough evidence that BDA has a significant positive effect on audit delay