Reviewerly/exHarmony huggingface dataset:
The Effect of 6th graders' Self-esteem on Sense of Community: The Moderating Effects of Teachers’ Support and Friends’ Support

Objectives The purpose of this study was to examine the effects of 6th graders' self-esteem, teachers’ support and friends’ support on sense of community. Also, this study examined the moderating effects of teachers’ support and friends’ support. Methods Using data from 13th wave of ‘Panel Study on Korean Children’, 1,357 elementary school students(6th graders) were analyzed by SPSS and PROCESS macro model 1. Data were analyzed using descriptive statistics, correlations, and the regressions. Descriptive statistics, Pearson’s r and regression analysis were used to analyze the characteristics of the subjects, correlation among all variables and moderating effects. Results Self-esteem, teachers’ support and friends’ support had positive effects on sense of community. Teachers’ support and friends’ support moderated the relation between self-esteem and sense of community. Specifically, the higher the teachers’ support and friends’ support, the higher the influence of self-esteem on sense of community. Conclusions The effect of self-esteem on sense of community depends on teachers’ support and friends’ support level. This results indicated that 6th graders’ sense of community can be increased by promoting self-esteem, teachers’ support and friend’s support.
Video Super Resolution Method using Deformable Convolution based Alignment Network with Residual Dense Block

Although video super resolution(VSR) is one of the essential technologies in the area of video processing using deep neural network(DNN), it is difficult for the VSR network to utilize the temporal correlation between consecutive input video frames. Recently, convolutional neural networks(CNN) based VSR methods show a significant improvement to generate high resolution(HR) videos from low resolution(LR) videos. In this paper, we propose a VSR method using deformable convolution based alignment network with residual dense block. It enables to the proposed network to use the correlated information from the intermediate feature maps for the purpose of improving the quality of VSR. Compared to the previous methods, experimental results show that the proposed method achieves both PSNR and SSIM by as much as 0.23dB and 0.006 on average, respectively.
Design, Packaging, and Testing of Silicon Photonics JTC for Convolution Neural Network

Convolution Neural Networks are one of the most powerful types of Neural Networks. However, the 2D convolution task is computationally heavy. Here, we present an integrated Silicon Photonic chip that can perform the Joint Transfer Convolution, using the properties of light propagation.
Stable tracking for low contrast extended target based on edge feature point matching

To solve the tracking drift caused by rotation, uneven illumination, and beyond the field of view in the moving process of the target with low contrast extension, a method of edge feature points matching is proposed in this paper. Specifically, feature points are extracted from image edges to improve the stability of feature points matching. In addition, combined Non-Local algorithm and improved Contrast Limited Adaptive Histogram Equalization algorithms are used to enhance the image. Experimental results show that this method has satisfactory performance, has good anti-rotation characteristics, can be stably tracked when the target reappears in the field of view and basically meets the real-time performance.
Komparasi Jarak Euclidean dan Manhattan Pada Algoritma K-Nearest Neighbor Dalam Mendeteksi Penyakit Diabetes Mellitus

Diabetes Mellitus is a chronic disease. This disease is caused by an increase in blood sugar levels in the body, it can cause diseases such as heart disease, obesity, and eye, kidney, and nerve diseases. Detection of Diabetes Mellitus is usually carried out by laboratory tests, so that patients have to undergo several medical tests to provide input values to a computerized diagnostic system which has proven to be expensive and has long queue times. From these problems, an artificial intelligence system is needed to diagnose this disease more easily and quickly. Therefore, the researcher aims to use an intelligent system to produce the highest accuracy from the results of the classification test using the K-Nearest Neighbor (K-NN) method with Euclidean distance and Manhattan distance. The class classifications used were pregnancy calculations, blood sugar in blood, blood pressure, skin fold thickness, insulin, body weight, diabetes genealogy dysfunction, and age. The research data in the form of datasets amounted to 450 datasets and the data was divided into two to determine the highest accuracy of 80% test data and 20% for training data. The highest accuracy using Euclidean distance is 84% with a value of K=5, and secondly, the Manhattan distance has the highest accuracy of 82% with a value of K=7.
FACTORS INFLUENCING FRAUDULENT TRANSACTIONS FROM BIG DATA PERSPECTIVE

This article focuses on fraudulent behaviour and patterns as well as ways of detecting such patterns by using Big Data. The study analyses scientific articles to examine types of financial fraud and their detection techniques as well as develops a model that is based on factors characterizing fraudulent credit card transactions made across USA. Regression analysis, correlation and descriptive statistics analysis is applied. Statistically significant results are found indicating a causal relationship between fraudulent transactions and transactions made in Alaska, during the month of October and on a Thursday. Although, the impact of these relationships is relatively small. Expanding the dataset with more numerical variables that could be used for identifying fraudulent transactions is advised for future research as to better the overall fit of the model.
Artificial Intelligence in Education

Education is an act of imparting information to the human brain with necessary exposures to solved methods, for example, practical as a repetitive process. Each teacher at various levels of class teaching adopts different strategies to make the students learn the concepts and expect 100% results from their students. However, few students with dedicated efforts are not able to clear the examinations on time and many students don’t find any interest in learning the concepts. The reason could be they are attracted by the lectures or students have no interest in the courses. &lt;b&gt;Aim:&lt;/b&gt; To make students show interest in learning the courses with little effort. &lt;b&gt;Methodology:&lt;/b&gt; To present the concepts in an easier way to the students and not make it a cumbersome topic. Include a video presentation for every subtopic with relevant attractive background music. &lt;ol&gt; &lt;li&gt;Use special and novel (artificial intelligence) techniques to bring the attention of the student’s brains in the classroom lectures.&lt;/li&gt; &lt;li&gt;Teachers should adopt an easy way to make students remember the concepts.&lt;/li&gt; &lt;/ol&gt; &lt;b&gt;Results:&lt;/b&gt; At least 95% of success in making the students learn the courses could be achieved
Genesis of the Fenghuangshan gold deposit, eastern Hebei, China: Insights from ore geology, fluid inclusion and H–O–S isotopes

The Jidong area is a vital gold province in North China, including plenty of metamorphic rock-hosted gold deposits. Whether ore-forming fluids are magmatic or metamorphic remains controversial. The genesis for gold mineralization in this gold province is ambiguous. The Fenghuangshan gold deposit lies on the eastern fragment of the Jidong gold province. In this paper, we present a detailed study on ore geology, fluid inclusions, and stable isotopes for the deposit to illustrate the characteristics of ore-forming fluids and ore genesis. The deposit occurs as gold-bearing quartz vein and disseminated- and veinlet-type mineralization. The main orebody named 7# is hosted in migmatitic granite, controlled by NE trending secondary fracture system. Four hydrothermal mineralization stages were identified, including: (Ⅰ) white quartz, (II) pyrite-quartz, (III) quartz-polymetallic sulfide, and (IV) calcite-chlorite-quartz. The Stage II and III belong to main gold mineralization stage. Fluid inclusions from different stages show that a lowering trend of fluid inclusion homogenization temperature was observed. They ranged from 222 °C to 407 °C (early-stage), 144 °C to 375 °C (main-stage), and 86 °C to 174 °C (late-stage), respectively. The salinities of the ore-forming fluids were typically low, ranging from 0.83 to 15.47 wt% NaCl equivalent. On the basis of Laser Raman analysis and fluid inclusion microthermometry, the ore-forming fluids belonged to a low- to moderate-temperature, low-salinity H2O-NaCl-CO2 system. Fluid immiscibility gave rise to rapid gold deposition. The early-stage δD and δ18OH2O values from quartzs ranged from –96.4‰ to –92.5‰ and from 1.74‰ to 2.44‰, whereas main-stage fluids had δ18OH2O values of –4.10‰ to –3.50‰ and δD values of –91.5‰ to –89.5‰. The early- and main-stage sulfide δ34S values varied from –2.0‰ to + 2.2‰, with average values of + 0.4‰. H-O-S-isotope data indicated that the primary ore-forming fluids were magmatic water mixing meteoric water during mineralization, and ore-forming materials were derived from a homogeneous magmatic source. Ore geology, fluid inclusions, and stable isotopes illustrate that the Fenghuangshan gold deposit is a low- to moderate-temperature magmatic-hydrothermal gold deposit. Future deep-seated and surrounding gold exploration should center on this area.
Request Relaxation Based-on Provider Constraints for a Capability-Based NaaS Services Discovery

Network as a Service (NaaS) enables cloud customers to connect their distributed services across multiple clouds without relying exclusively on their infrastructures. The discovery of NaaS services remains challenging not only because of their scale and diversity but also because of the hidden constraints that cloud providers impose on these services at the networking layer. NaaS services are usually offered in the form of service bundles containing underlying services and constraints not requested by the customers. This creates undesirable dependencies and constraints that hamper portability, compatibility and interoperability across providers. The problem of service discovery becomes more challenging when these constraints are the main and first cause that prevents a customer’s request from being fulfilled. Without a mechanism that enables customers to identify these constraints and to adjust their requests accordingly, existing service discovery solutions are likely to fall short. We propose to complement existing service discovery solutions by not only identifying unmatched constraints but also recommending relaxing discovery requests to retrieve optimal and compliant services.
Analisis Sentimen Komentar Youtube Tentang Relawan Patwal Ambulance Menggunakan Algoritma Naïve Bayes dan Decision Tree

The presence of the ambulance patrol is sometimes considered disturbing by the community, many people's opinions are still pro and contra about the actions carried out by civilian ambulance patrol volunteers because they are considered illegal and sometimes also arrogant. In this study. the researchers wanted to know the opinions and responses of the public about the actions taken by civilian ambulance patrol volunteers. The method used in this study is to perform sentiment analysis with data mining techniques to find the polarity of sentences in a document using the Naïve Bayes and Decision Tree algorithms. The initial steps in this research were collecting comment data by scraping YouTube comments using API Key Youtube V3, followed by manual data labeling, data cleaning, data preprocessing, and word weighting using TF-IDF. From the overall results of testing with 600 training data, the Naïve Bayes algorithm has a higher accuracy value of 66.72% while the Recall value is 64.98%. Testing with the Decision Tree algorithm in this study has a higher Recall compared to Naïve Bayes. %. From the results of the YouTube comment dataset used in this study, it can be concluded that the Naïve Bayes algorithm has a higher accuracy value than the Decision Tree algorithm, so it can be concluded that Naïve Bayes has the best accuracy in the YouTube comment dataset used in this study.
Finding Harmony in FAIRness

The lack of consistency among different approaches for assessing how well digital data objects meet the FAIR principles makes it difficult to compare assessment results across tools and systems.
FRACTURE-FAULTDETECTIONUSING DEEP LEARNING WITH STEPWISE ELIMINATION FROM SATELLITE IMAGES IN DJIBOUTI

Accurate estimation of groundwater flow is crucial in arid regions where permanent surface water is absent.In several groundwater simulation models, an important parameter for identifying areas with high potential for groundwater resources is the accurate fracture-fault detection.In the present study we propose a deep learning approach to detect fracture-fault structures in the Ali Faren sub-catchment of Ambouli Wadi in Djibouti.Our deep convolutional neural network (Deep-CNN) model is trained on high-spatial resolution multispectral satellite images using wadi streamline as labels.Fracture-fault structures are extracted using stepwise elimination based on geological characteristics observed in relief images derived from PALSAR-1/2 data.Our results demonstrate that the proposed Deep-CNN model accurately detects fracture-fault lines, achieving a validation accuracy of 0.9684, precision of 0.9124, recall of 0.9701, and F1 of 0.8997.The proposed model has the potential to identify potential areas for groundwater resources across the country, contributing to sustainable water management and improving Djibouti's water security.Our study highlights the potential of deep learning techniques in addressing challenges related to sustainable water management in arid regions.
GBGP 시스템에 기반한 다변량 동적 공정 모델을 이용한 KNR 생물학적 고도처리공정의 성능예측

Biological wastewater treatment systems are characterized by large temporal variability of inflow, variable concentrations of components in the incoming wastewater to the plant, and highly variable biological reactions within the process. This paper proposes an automatic process model induction system using an evolutionary computional intelligence, called grammar-based genetic programming (GBGP), that is specially designed to automatically discover multivariate dynamic process models that best fit observed process data. This automatic process model induction system combines an evolutionary self-organizing system of genetic programming paradigm with various mathematical functions for a multivariate nonlinear model evolution using a grammar system via the mechanism of genetics and natural selection. In the GBGP, a four-step modelling procedure was performed: (1) initialization: generatr an initial population of P models using contex-free grammar. generation K=0; (2) optimize each model (variables and constants) in the population; (3) execute and evaluate the fitness of each model in the population; (4) genetic loop: repeat until termination criterion is met (maximum generation Kmax). The results demonstrate that the multivariate dynamic process modelling technique presented using a grammar-based genetic programming (GBGP) provides a valuable tool for predicting the out puts with high levels of accuracy and identifying key operating variables for the full-scale KNR biological nutrient removal process. These modells are derived automatically in the form of understandable mathematical formulas that enable engineers to extract important knowledge hidden in the data and develop better operation and control strategies.
Penerapan Clustering Terhadap Segmentasi Zonasi Gangguan Layanan Pelanggan Dengan Menggunakan Analisis RFM

The service area and the wide distribution of customers often require quite a long time in terms of handling disturbances, which can reduce the level of customer trust and loyalty to using the network services provided. Customer segmentation by region can help make it easier to analyze disruptions that occur to customer service in customer area zoning so that customer trust and loyalty can be maintained. This research was conducted to understand the characteristics of disturbances that occur in several customer zoning areas. In this study, RFM (Recency, Frequency, Monetary) analysis was performed. Clustering using the K-Means modeling technique, and also performance checking using Cluster Distance Performance to determine the performance of each cluster performed. The results of this study indicate that K-Means Clustering with a value of K=2 is the with the smallest Davies Bouldin value. It means that K=2 has the best performance when compared to other clusters formed.
Architectural Distortion-Based Mammograms Classification Using Adaptive Contrast Enhancement and Residual Network

A mammogram classification method based on adaptive contrast enhancement combined with residual network is proposed to address the problem that the image features of architectural distortion on mammography images are not obvious and easily overlap with breast glandular tissue and background structures. Firstly, the dataset samples are increased by data augmentation to avoid the overfitting problem caused by insufficient samples in deep learning. Secondly, the adaptive contrast enhancement algorithm is used to improve the image contrast and enhance the image defects. Finally, the residual network model is used for training, testing and validation. The experimental results show that the method has good classification effect, with accuracy and sensitivity reaching 0.8704 and 0.8782, respectively.
WePaMaDM-Outlier Detection: Weighted Outlier Detection using Pattern Approaches for Mass Data Mining

Weighted Outlier Detection is a method for identifying unusual or anomalous data points in a dataset, which can be caused by various factors like human error, fraud, or equipment malfunctions. Detecting outliers can reveal vital information about system faults, fraudulent activities, and patterns in the data, assisting experts in addressing the root causes of these anomalies. However, creating a model of normal data patterns to identify outliers can be challenging due to the nature of input data, labeled data availability, and specific requirements of the problem. This article proposed the WePaMaDM-Outlier Detection with distinct mass data mining domain, demonstrating that such techniques are domain-dependent and usually developed for specific problem formulations. Nevertheless, similar domains can adapt solutions with modifications. This work also investigates the significance of data modeling in outlier detection techniques in surveillance, fault detection, and trend analysis, also referred to as novelty detection, a semi-supervised task where the algorithm learns to recognize abnormality while being taught the normal class.
Offensive Social Network Posts Classification using Apache Spark Platform

Social networking has become one of the most significant aspects of everyday lives since it allows us to communicate with many people. Social networking is used for many purposes besides communication. Though social networking platforms provide abundant benefits, there is no proper mechanism to check if only legitimate users are using the platforms. This happens to be the reason for many users to use those platforms to make offensive and vulgar comments about others. This research study intends to classify those social media messages as offensive or not offensive using Machine Learning Models available in the Apache Spark framework using Scala Programming Language. The dataset used for the classification is of size 43 GB, and the clustering mechanism has been implemented using the Apache Spark platform.
Pengaruh variasi konsentrasi terhadap sifat listrik lapisan tipis CoFe2O4

A thin layer is a very thin layer of material (organic, inorganic metal or a mixture of organic metals) which has the properties of a conductor, a semiconductor, a superconductor or an insulator. CoFe2O4 nanoparticles are very popular materials to be developed in the field of electricity and magnetism because they have cubic magnetocrystalline properties and high coercivity properties, as well as medium saturation magnetization. Sol-gel is one of the techniques in the manufacture of thin films that involves chemical processes in the form of hydrolysis and condensation reactions. Sol-gel dip coating is a technique for making a thin layer that has several advantages, namely a simple method, easy, few precursors so that it saves production costs and does not damage the environment. XRD test results of a 0.20 M CoFe2O4 thin layer found a peak in the 2Ө 18.1152 region with a diffraction pattern (111). The highest UV-Vis absorbance peak at a wavelength of 285 nm was 2.5423 A and the lowest bandgap value was 2.79 eV. The resistance value is 6.46 and the resistivity value is 0.00016225 m.
The Possibility of Using the Electronic Control at Jordanian Public Universities and the Obstacles that Hinder its Use from the Point of View of the Auditors of the Jordanian Audit Bureau

The study aimed to identify the possibility of using electronic control in the Jordanian public universities and the obstacles that impede its use from the point of view of the auditors of the Jordanian Audit Bureau. The study was conducted on auditors of the Audit Bureau at public universities. In order to measure the variables of the study, a questionnaire was designed and arbitrated by academics in Jordanian universities, and the descriptive analytical approach was used. Due to the small size of the study population of (40) auditors, the comprehensive enumeration method was used, and the number of valid questionnaires for analysis was (34), the study concluded that there is a significant possibility to apply (dimensions of financial, accounting, and administrative electronic control) arranged according to statistical importance in auditing processes, and that there are statistically significant obstacles to the electronic control. The dimensions of electronic control came to a high degree (financial control, accounting control, administrative control) arranged according to statistical importance and obstacles to apply electronic control to all fields to a moderate degree, and that one of the most important obstacles to the application of the electronic control is the universities’ fear of disclosing the financial data. The study recommended the need to apply the electronic control in its all fields in public universities and to remove the obstacles that hinder its application.&#x0D;
Automatic Flood Detection Using CNN

Floods are catastrophic natural disasters that can cause extensive damage to infrastructure and human lives.Early and accurate flood detection is crucial for effective disaster response and mitigation.In recent years, Convolutional Neural Networks (CNNs) have emerged as a powerful tool for image and analysis.This research paper proposes an approach for flood detection using CNNs, aiming to enhance detection accuracy and speed while addressing existing limitations.The research paper concludes by discussing potential applications of the proposed flood detection approach, such as disaster management systems, early warning systems, and urban planning.It also highlights future research directions, including the integration of additional data sources such as remote sensing and weather data to further enhance flood detection performance.