Reviewerly/exHarmony huggingface dataset:
LISTEN–READ–DISCUSS (LRD) STRATEGY IMPROVE READING COMPREHENSION TO NURSING STUDENTS

Abstract&#x0D; This study sought to determine whether or not the listen-read-discuss (LRD) method can help students' reading comprehension. It was an in-class action research project. Both quantitative and qualitative data are used in the data collection. The results of the pre-test and post-test for the reading test were used to collect quantitative data. While this was happening, qualitative information was gathered from observation sheets of kids' and teachers' behavior. Tests and observation sheets were the instruments used. The outcome demonstrates that the tactic can enhance pupils' reading comprehension of report text. The difference between the students' pre-test, post-test one, and post-test two means may be seen. The mean pre-test score was 69.5 (35%), with seven students passing with the minimum passing grade. The mean post-test scores were 72.2 (60%), with twelve students passing with the minimum passing grade, and 78.25 (85%), with seventeen students passing with the minimum passing grade.
EdgeNets:Edge Varying Graph Neural Networks

Driven by the outstanding performance of neural networks in the structured Euclidean domain, recent years have seen a surge of interest in developing neural networks for graphs and data supported on graphs. The graph is leveraged at each layer of the neural network as a parameterization to capture detail at the node level with a reduced number of parameters and computational complexity. Following this rationale, this paper puts forth a general framework that unifies state-of-the-art graph neural networks (GNNs) through the concept of EdgeNet. An EdgeNet is a GNN architecture that allows different nodes to use different parameters to weigh the information of different neighbors. By extrapolating this strategy to more iterations between neighboring nodes, the EdgeNet learns edge- and neighbor-dependent weights to capture local detail. This is a general linear and local operation that a node can perform and encompasses under one formulation all existing graph convolutional neural networks (GCNNs) as well as graph attention networks (GATs). In writing different GNN architectures with a common language, EdgeNets highlight specific architecture advantages and limitations, while providing guidelines to improve their capacity without compromising their local implementation. An interesting conclusion is the unification of GCNNs and GATs -- approaches that have been so far perceived as separate. In particular, we show that GATs are GCNNs on a graph that is learned from the features. This particularization opens the doors to develop alternative attention mechanisms for improving discriminatory power.
IAG Membership Information

Geostandards and Geoanalytical ResearchVolume 47, Issue 2 p. 467-467 Membership InformationFree Access IAG Membership Information First published: 15 May 2023 https://doi.org/10.1111/ggr.12434AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL No abstract is available for this article. Volume47, Issue2June 2023Pages 467-467 RelatedInformation
Leadership Characteristics and Their Role in Marketing Strategic Planning in Commercial Banks Operating in Jordan

research also sets some recommendations, the most prominent of which is the need to confront the movements of competitors.Through the ability to innovate and lead among competitors and increase the awareness of society with its concepts, as well as creativity in the use of methods for analyzing and evaluating the surrounding environment.This is in addition to imposing its control on large fields in target markets, developing their products, and the need to make fundamental adjustments in the technology used, organizational structure, and working methods..
Performance Comparison of SVM, Naïve Bayes, and KNN Algorithms for Analysis of Public Opinion Sentiment Against COVID-19 Vaccination on Twitter

&#x0D; &#x0D; &#x0D; &#x0D; The emergence of the COVID-19 virus in 2020 has created a new breakthrough in the form of a vaccine as a solution to slow the spread of the virus. However, the COVID-19 vaccine is considered controversial and invites many people to express their views on various media, one of which is social media Twitter. Using Twitter data on the COVID-19 vaccine, sentiment analysis can be performed. Sentiment analysis aims to evaluate whether the tweet contains a positive sentence or sentiment. In this study, the analysis of sentiments on the COVID-19 vaccine on social media Twitter was carried out using the Support Vector Machine (SVM), Naïve Bayes, and k-Nearest Neighbor (KNN) algorithms. SVM has the advantage of being able to identify hyperplanes that maximize the margins between different sentiments. Meanwhile Naïve Bayes is an algorithm that is simple, fast and produces maximum accuracy with training. The KNN algorithm was chosen because it is superior to noise. The performance of the three classification algorithms will be compared, so that it can be seen which algorithm is better in classifying text mining. Sentiment classification results in this study consist of positive sentiment and sentiment classes. The resulting accuracy value will be a benchmark for finding the best test model in the case of sentiment classification. Based on ten tests, the final result of accuracy and best performance using the SVM algorithm with an accuracy value of 96.3% is obtained. Meanwhile, the Naïve Bayes and KNN algorithms have an accuracy of 94% and 91%, respectively. The high accuracy results are supported by the feature extraction TF-IDF the TextBlob library.&#x0D; &#x0D; &#x0D; &#x0D;
Cloud Computing Adoption Factors Affecting Academic Performance in UAE Public Universities

Cloud computing helps in reducing costs and providing accessibility, reliability, and flexibility especially in educational sector. This able to provide the best educational resources and facilities to all level of students effectively and efficiently. It also seen as a creative technological innovation that provides stableand on-demand access to the available network. Hence, this study aimed to measure the cloud computing adoption factors affecting the academic performance in UAE university. The factors are clustered into five groups namely, knowledge sharing; knowledge application; learnability; perceived self-efficacy; and perceived enjoyment. A questionnaire survey was conducted with the university students to gauge their opinions on the level of significant of each factor affecting the academic performance using 5-points Likert scale. Out of 400 questionnaire sets distributed using random sampling, 382 valid responses were extracted as the collected data. This data was analysed descriptively. The results of the analysis found that that cloud computing adoption factors inperceived self-efficacy group are ranked first in term of its significance in affecting academic performance. The second rank is factors in learnability group; the third is perceived enjoyment; the fourth rank is knowledge application and finally, the fifth rank is knowledge sharing. The impact of this research will be felt simultaneously in the IT and education sectors, particularly through assisting students in utilising cloud computing to store and exchange information for both their academic and personal lives.
Deep learning inference time guarantee for aggregated images at the edge of the network

Image inference using deep learning (DL) has recently become popular in a variety of applications. In the near future, real-time inference for delay sensitive images will be widespread with the edge computing paradigm, where DL processing will be executed at the edge of the network. In this study, a DL inference method in near future edge computing is proposed to infer a large number of images, which are aggregated from various end devices toward an edge server and are temporarily stored in a queue, with different requirements on inference time. With the proposed method, the inference time including waiting time in a queue for a delay sensitive image is guaranteed and that for a delay tolerant image is minimized by adaptively partitioning the DL processing executed for delay tolerant images between an edge and a cloud server depending on the arrival conditions of images at an edge server. The effectiveness of the proposed method is evaluated via simulation.
Algorithm for evaluating distance-based entanglement measures

Quantifying entanglement in quantum systems is an important yet challenging task due to its NP-hard nature. In this work, we propose an efficient algorithm for evaluating distance-based entanglement measures. Our approach builds on Gilbert's algorithm for convex optimization, providing a reliable upper bound on the entanglement of a given arbitrary state. We demonstrate the effectiveness of our algorithm by applying it to various examples, such as calculating the squared Bures metric of entanglement as well as the relative entropy of entanglement for GHZ states, W states, Horodecki states, and chessboard states. These results demonstrate that our algorithm is a versatile and accurate tool that can quickly provide reliable upper bounds for entanglement measures.
Framework for facial recognition and reconstruction for enhanced security and surveillance monitoring using 3D computer vision

Workflows can be optimized with the use of three-dimensional (3D) computer vision (CV) by monitoring processes from many angles. The potential for increased efficiency and cost savings is clear here. Input scenarios are always being monitored by security and surveillance systems to recognize and classify items, human faces, and moving things. The input reconstruction in these surveillance systems is problematic because of the mapping from three dimensions to two dimensions in the form of pixels. We present a new 3D CV paradigm-based framework for facial recognition and reconstruction. In this setup, deep neural networks are used to distinguish between mapped and unmapped pixels during a 2D to 3D or 3D to 2D transformation. To use correlation analysis to recognize human faces, it is necessary to first examine the input image for textural properties. The mapping procedure continues with the extraction of textural elements using dimensional contours. Then, continuous contours are used to do the 3D mapping and reduce the number of false positives. Instead, this framework isolates individual contours of moving objects or faces to increase the number of training iterations. Finally, missing pixels in the dimension conversion are filled to rebuild the human face utilizing discrete and continuous outlines. Hence, the suggested framework minimizes the false rate (10%), increases the false positive rate as (13.93%), and minimizes the error by (9.89%) while maximizing the recognition accuracy for (13.93%) and the precision is (13.21%).
A new signal denoising algorithm for low-cost MEMS gyroscope

Inertial measurement unit (IMU) has been commonly used in measuring angles, displacement measurement and other fields. However, micro electro-mechanical system (MEMS) gyroscopes have little accuracy of measuring and enormous noise; real signals will be cloaked in the noise. A new denoising algorithm for low cost MEMS gyroscope is offeredin this paper. First, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm is used to decompose the initial-signal which can obtain a limited number of intrinsic mode functions (IMF). Next, use the power spectrum entropy to divide the IMF into noise signals, mixed signals and sound signals. Then, variational mode decomposition (VMD) is choosen to decompose the mixed signal, and using reptile search algorithm (RSA) optimization algorithm optimizes the crucial arguments of VMD. VMD algorithm appropriately dismantles the signal according to the optimized arguments to attain the IMF sections, divided into signal sections and noise sections using the Bhattacharyian distance. The signal sections are furthermore processed by lifting wavelet threshold denoising (LWT), and the valuable signal with denoising is obtained. Finally, all the valuable signals are amalgamated and reestablished into the ending-denoising signal. The effectuality of the algorithm is weighted by Matlab simulation. The static denoising experimentation of the three-axis turntable is put up in the experimental analysis, which exhaustively confirms that the algorithm has clear superiority in denoising and significantly increases the signal quality and the accuracy of the cheap MEMS gyroscope.
Heart Disease Diagnosis Using Tsukamoto Fuzzy Method

As one of the leading causes of death in the world, heart disease needs special attention. Heart disease often causes sudden death because the signs of a heart attack are not easy to detect. However, early detection efforts can still be pursued and continue to be carried out, especially using information technology. This study aims to diagnose the risk level of heart disease using Tsukamoto method and involving 11 input variables such as cholesterol, blood pressure, ECG, and others. At the same time, the output variables include healthy, small, medium, large, and very large. The stages of the method consist of four main processes, namely literature review, fuzzy inference system design, applying of Tsukamoto fuzzy, and evaluation. The research concluded that the fuzzy logic of the Tsukamoto method can be used to diagnose the risk level of heart disease, although the model performance is still limited to an accuracy value of 58%.
Detection of the Use of Masks as an Effort to Prevent Covid-19 Using Gray Level Co-Occurrence Matrix (GLCM) Based on Learning Vector Quantization (LVQ)

Covid-19 is a disease caused by the SARS-CoV-2 virus. Transmission of Covid-19 can be through the flow of air (aerosol), splashes of liquid (droplets). One of the prevention efforts to break the chain of transmission is to use a mask when interacting with other people. Monitoring and controlling the use of masks will be safer and more efficient when implementing a mask detection system. This study will analyze GLCM for extraction method and LVQ for classification method. The results of GLCM successfully provide statistical features that represent image characteristics well. While the LVQ can provide classification results with a good percentage of accuracy. The results of the best percentage accuracy for the first rank are 83.15% in the composition ratio of 90: 10. Furthermore, the percentage of accuracy for the second rank is 76.03% at the composition ratio of 70: 30 and the third rank is 72.47% at the composition ratio of 80: 20. This indicates that the composition more training data does not guarantee the level of achievement of a higher percentage of accuracy. There is an optimal maximum number of epochs where the number of epochs that exceeds the optimal number of epochs will not experience a change in the percentage of accuracy. For each value the learning rate (alpha) can give the results of the percentage of accuracy with different graphic patterns and will stop at the optimal maximum number of epochs.
Improved Accuracy of Naïve Bayes Algorithm and Support Vector Machine Using Particle Swarm Optimization for Menstrual Cup Sentiment Analysis on Twitter

Menstrual cup is a menstrual hygiene sanitation tool that replaces disposable sanitary napkins for women that reaps many pros and cons in its use. From this, it is necessary to analyze the public's views regarding the use of menstrual cups, which is called sentiment analysis. Sentiment analysis is a process that aims to determine the polarity of the sentiment of a text. This paper performs a classification of menstrual cup sentiment analysis on Twitter using the Naïve Bayes and the Support Vector Machine algorithm. Particle Swarm Optimization is applied to improve the accuracy of both classification algorithms. The final result of the accuracy obtained by the Naïve Bayes algorithm is 92.72% and the Support Vector Machine algorithm is 96.13%. While the accuracy results after Particle Swarm Optimization is applied, for Naïve Bayes it produces an accuracy rate of 95.87%, and Support Vector Machine is 96.68%.
Prediction of Life Expectancy of Lung Cancer Patients Post Thoracic Surgery using K-Nearest Neighbors and Bat Algorithm

&#x0D; &#x0D; &#x0D; &#x0D; Lung cancer is one of the deadliest cancers, accounting for 11.6% of cancer diagnoses in the world. Death in lung cancer patients can occur in various ways and one of the treatments for lung cancer patients that can be done is thoracic surgery. Thoracic surgery is generally considered a medium risk procedure, but thoracic surgery has a high risk, one of the risks is that if the patient loses blood which will result in the death of the patient. In this study, the method used to implement predictive life expectancy in post-thoracic surgery patients is the bat algorithm for feature selection and the KNN algorithm for classifying data. The dataset used in this study was obtained from the UCI Machine Learning Repository, namely the thoracic surgery dataset which contains 470 data with 16 attributes. The results of the study in predicting the life expectancy of patients after thoracic surgery were carried out with 3 tests. The first test is testing the population with the best accuracy of 87.23%, the second test is convergent testing with the best accuracy of 87.23% and the third test is the comparison test of KNN which produces the best accuracy of 87.23%. The bat algorithm succeeded in increasing the accuracy of the KNN classification by 5.23% from 81.91%. &#x0D; &#x0D; &#x0D; &#x0D;
Effect of Spindle Speed of Bar-Plate Rotary Friction Welding Machine on Joint Interface Area and Hardness Value

This study aims to determine the effect of the rotational speed parameter on the joint interface weld area and the value of hardness on similar materials. Welding parameters used the rotational speed of 2,484 rpm, 2,613 rpm and 4,335 rpm. Test method used in this research was liquid penetrant, macro and micro-observation and hardness test. The results of the liquid penetrant test showed no defects on the surface of the connection. In macro-observation, where there is a fairly large void at rotational speed of 2,484 rpm, which has length is 2,69 mm, then shrinks at 2,613 rpm to 1,52 mm, then at 4,335 rpm there is no visible void. In micro-observations showed that the weld metal area had a finer grain structure than the HAZ (Heat Affected Zone) and base metal areas this affect hardness value. The results of the hardness test are that the higher the rotational speed the higher the hardness value produced.
Resource Provisioning in Cloud Computing using Fuzzy Logic Control System: An adaptive approach

The need for cloud computing has been increasing widely in this era since last two decades due to its continual benefits include security, disaster recovery, increased collaboration, cost saving and virtualization etc. This innovation introduces an enormous transformation of conventional applications in business industry. However, cloud users indulge themselves not acquiring a sufficient amount of computing resources caused by resource confliction. A challenging and significant problem in cloud computing is Resource Provisioning in virtualization environment that relates with selecting, deploying and run time management of software and hardware resources. On the contrary, such situations also exist where resources are found in virtual servers either under or over-utilization. In order to deal with these problems, proposed framework in this paper is demonstrated using Fuzzy Logic Control System towards characterization the processes’ priorities by means of employing input parameters (workload, processing time, and process age) keeping in view of the fact that expert judgment is required for taking into account these parameters.
Transfer Learning for Hypersonic Vehicle Trajectory Prediction

Hypersonic glide vehicles (HGVs) introduce challenges in terms of predicting their future flight behavior because they fly at high speeds and maneuver during flight. Furthermore, there are limited examples of actual HGV flights, which impedes the development of prediction methods that model HGV trajectories. In this paper, we investigate transfer learning for HGV trajectory prediction to evaluate how stochastic grammars trained on a limited number of HGV trajectories perform on new, unseen HGV trajectories. Our analysis includes two datasets containing HGV trajectories that exhibit different maneuvers. One dataset contains trajectories that exhibit vertical maneuvers, which are behaviors related to changes in altitude. The second dataset exhibits both horizontal and vertical maneuvers, where horizontal maneuvers refer to changes in crossrange and downrange. The vertical dataset is also significantly smaller than the second dataset (i.e., the dataset that exhibits horizontal and vertical maneuvers). We develop a prediction method using the smaller, less complicated dataset to model HGV trajectories. Specifically, we use an unsupervised machine learning method based on stochastic grammars. Then, we demonstrate that the learned model can be used to predict HGV behavior and engageability for trajectories from the larger, more complicated dataset. Our results show that transfer learning improves prediction performance (even on unseen trajectory maneuvers) and can be used in limited data scenarios.
A Modified Wavelet Threshold Approach for Reducing Various Noise with Statistical Results

Different types of noise play a role in reducing the details of the images and blur the features which are important in many purposes. Physical and medical images are fractal in nature and especially the ones that are taken from experiments related to a medical field. This work investigates several noise reduction methods and reducing the fractional Brownian motion noise in physical and medical images using a modified wavelet threshold approach. The performance of this approach is analyzed and compared with some other approaches by using PSNR (Peak Signal to Noise Ratio), MSE (Mean Square Error), and other criteria for the validity of the method.
The empirical research on university instructors’ competencies in virtual reality environment from the perspective of students’ learning experience

The integration of virtual reality (VR) and education is a key way to realize the modernization of higher education. Instructors’ competencies directly affect students’ learning experiences and achievements. Based on the IBSTPI competency standards compiled by the International Board of Standards for Training, Performance, and Instruction (IBSTPI), this study developed an instructors’ competencies scale suitable for the VR environment from the perspective of students’ learning experience and conducted an empirical research on the current situation of instructors’ competencies in VR environment. The scale categorizes of instructors’ competencies included six dimensions: establishing and maintaining learning motivation competencies, expressing competencies, questioning competencies, giving feedback competencies, promoting knowledge consolidation competencies, and facilitating knowledge transfer competencies. The results showed that the competencies of university instructors in VR environments have been in the upper middle level and there were differences in the level of instructors’ competencies in different dimensions. Necessary is for universities and instructors to optimize the cultivation of instructors’ competencies in VR environment.