Reviewerly/exHarmony huggingface dataset:
Development of E-Comic Teaching Materials for Social Studies Learning in Elementary Schools

The purpose of this research is to develop mobile learning-based e-comic social studies teaching materials to pique the interest of fifth-graders in elementary school. The students in this study are from the fifth grade at Riyadh El Jannah Islamic School. The ADDIE model is used to conduct research development or research and development in this study. However, it only reaches the development stage due to circumstances that prevent it from completing all of the stages. This research used a questionnaire for expert evaluation as well as interviews with teachers and students. This study employed descriptive quantitative and descriptive qualitative data analysis techniques. The quantitative descriptive method is used to process expert data and student response questionnaires, while the qualitative descriptive method is used to describe corrective comments and validators' inputs. The feasibility test for mobile learning-based e-comic teaching materials received an average percentage of 93.78 percent, making it a worthy category. The final scores for product trials on teacher and student responses were 92 percent and 94.57 percent, respectively. Based on the findings of the data analysis, it can be concluded that the developed mobile learning-based e-comic teaching materials are suitable for classroom use
An integrated multiple layer perceptron-genetic algorithm decision support system for photovoltaic power plant site selection

There is a need for non-renewable energy sources in generation of power for almost every domestic and commercial purposes. This source of energy helps in the development of a country. Because of the increasing usage of the fossil fuels and depletion of these resources, our focus has been shifted towards the renewable sources of energy like solar, water and wind. Therefore, in the present scenario, the usage of renewable sources has been increasing rapidly. Selection of a solar power plant (SPP) requires environmental factor, local terrain, and local weather issues. Thus, a large amount of investment is required for installation. Multi-criteria decision making (MCDM) is a method that identifies one in choosing the best sites among the other proposed options. This paper gives a detailed study of optimal ranking of SPP site using analytical hierarchy process (AHP), multiple layer perceptron (MLP) neural network trained with back propagation (BP) algorithm and genetic algorithm (GA). Three SPP sites of India were considered and various important criteria like local weather, geographical location, and environmental factors are included in our study as SPP site selection is a multi-criteria problem. A precise comparison of these three methods is listed in this paper.
Book Recommendation System Using Machine Learning

Abstract: Users can use book recommendation systems to search and select books from a number of options available on the web or elsewhere electronic sources. They give the user a little bit selection of products that fit the description, given a large group of objects and a description of the user needs. Our system will simply provide recommendations. Recommendations are based on previous user activity, such as purchase, habits, reviews, and likes. These systems gain lot of interest. In the proposed system, we have a big problem: when the user buys book, we want to recommend some books that the user can enjoy. Buyers also have a great deal of options when it comes to recommending the best and most appropriate books for them. User development privacy while placing small and minor losses of accuracy. Recommendations. The proposed recommendation system will provide user's ability to view and search the publications and using the Support Vector Machine (SVM), will list the most purchased and top rated books based on the subject name given as input. Keywords: Recommender System, Support Vector Machine (SVM), Machine Learning, Classification etc.
Dressed asymptotic states and QED infrared physics

The dressed state formalisms, which incorporate interactions of soft particles into an asymptotic state, are known as the prescriptions expected to solve the problem of infrared (IR) divergence in the quantum field theory (QFT). A particularly famous example is the dressed state formalism proposed by Kulish and Faddeev in quantum electrodynamics (QED). As pointed out by Hirai and Sugishita, however, this formalism has problems in gauge invariance and the IR divergence. These problems are mainly caused by the existence of ghosts or unphysical photon modes. Therefore, we start by studying the asymptotic states in the Coulomb gauge, which excludes ghosts and/or unphysical photon modes. In this paper, we propose a formalism to construct the asymptotic states directly from the interaction of the theory by setting a sufficiently large time scale $T$. In this dressed state formalism, we define the asymptotic interaction remaining at $|t|>T$ in terms of some fixed order of $1/T$, and we are performing all calculations according to that order. We study the asymptotic states in QED specifically, but we can formally apply the dressed state formalism proposed in this paper to any perturbative QFT. We show that, at least in QED, we can construct divergence-free and unitary $S$-matrix using dressed states proposed in this paper. Furthermore, we discuss the transition rate to show that we can predict experimental results. We also show that the $\mathrm{U}(1)$ gauge symmetry of $S$-matrix leads to the QED large gauge symmetry, and deviation of the expectation values of the vector potential between initial and final spacelike hypersurfaces emerges as a QED memory effect. The dressed state formalism in this paper may give a unified and new insight into IR physics, including asymptotic symmetries, memory effects, and unitarity of the state evolution.
4th INTERNATIONAL BALTIC SYMPOSIUM ON SCIENCE AND TECHNOLOGY EDUCATION “SCIENCE AND TECHNOLOGY EDUCATION: DEVELOPING A GLOBAL PERSPECTIVE (BalticSTE2021)”: SYMPOSIUM REVIEW

Time is running relentlessly fast. The Fourth International Symposium was held on June 21-22 this year. The topic of the Symposium was “Science and technology education: Developing a global perspective”. More than 60 scientists from 18 countries participated in the symposium. Comparing with the symposium which took place in 2019, the geographical distribution of the participants is wide and diverse. Ukrainian, Serbian, Moldavian, Malaysian, Latvian, Slovak etc. researchers participated in the symposium. Another important feature of the symposium is that it was held remotely/online due to the ongoing Covid-19 pandemic. One of this international symposium ideas is – to show that Baltic region countries also actively participate in natural science and technology education research, especially in STEAM context. The second idea is that Baltic countries as well as other small regions of the world need corresponding close communication and cooperation. During this symposium, these two essential ideas were developed and supported. The symposium was organised by scientific methodological centre “Scientia Educologica”, the associated member of Lithuanian Scientific Society. The main symposium partner was Publishing House Scientia Socialis, Ltd. It is significant that even in the conditions of a pandemic, the symposium was properly organized.
Super-Resolution of 3D Brain MRI With Filter Learning Using Tensor Feature Clustering

Surface-based analysis of magnetic resonance imaging (MRI) data of the brain plays an important role in clinical and research applications. To achieve accurate three-dimensional (3D) surface reconstruction, high-resolution (HR) MR image acquisition is needed. However, HR image acquisition is hindered by hardware limitations that result in long acquisition time and low spatial coverage. Single image super-resolution (SISR) can alleviate these problems by converting a low-resolution (LR) image to an HR image. However, unlike 2D SISR methods, conventional 3D methods incur a large computational cost and require abundant data. Further, 3D boundaries for surface reconstruction based on MR images have not been sufficiently investigated. We herein propose a cost-efficient novel regression-based framework for super-resolution of 3D brain MRI that directly analyzes 3D features by introducing a tensor using gradient information. We initially cluster features using tensors to create labels for both the training and testing stages. In the training stage, for each label, we collect LR patches and corresponding HR intensities to compute filters. In the testing stage, for each voxel, we construct a tensor to obtain a feature and predict the HR intensity using trained filters. We also propose a patch span reduction method by limiting patch orientation to reduce the orientation span and increase shape variety. Using only 30 masked T1-weighted brain MR volumes from the Human Connectome Project (HCP) 900 dataset, the proposed algorithm exhibited superior performance in terms of HR boundary recovery in the cerebral cortex as well as improved overall quality compared to conventional methods.
The Use of Cartoon Film Media in Narrative Writing Skills for Elementary School Students

This study was to describe the use of cartoon film media with character education to improve narrative writing skills and describe the constraints in implementing learning narrative writing materials and methods.This study used a classroom action research design consisting of two cycles.Each stage consists of planning, implementing, observing, and reflectingdata collection techniques in observation, field notes, documentation, and tests.The results showed that using VCD media for cartoon films can improve writing narrative essays.We were learning to write shows increased activity, enthusiasm, and attention of students in learning.Can be seen from the achievement of learning outcomes obtained during the implementation of the cycle I and cycle II.The result of narrative writing using cartoon film media has increased by 7.12, which in the initial condition (pre-cycle) the average class value of 67.74, rose to 74.87, and cycle II 82.26, whose initial state was 67.74, increased to 14.52, which was the initial condition of 67.74 which increased to 82.26.Constraints related to a less conducive classroom atmosphere, low student motivation to learn, less active students, and time management.The way to overcome this is with good classroom conditioning, using media in the learning process, and providing motivation to be more active and manage learning
Establishing the Influences of Cardinal Virtues on Employees’ Cyber Security Ethical Behavior in the Banking Sector in Uganda

Purpose: Cyber security threats emanating from employees’ incorrect behavior have escalated in the banking sector. Yet formal policies and technical solutions have failed to solve the problem. Virtue ethics may be a method that can handle this concern. This research aimed at enhancing Cyber security by confirming through statistical analysis the applicability of cardinal virtues related to cyber security ethical behavior.&#x0D; Methodology: The quantitative survey method utilizing an online questionnaire was used. Confirmatory factor analysis determined causal patterns in the variables and assessed them for validity and reliability. Partial Least Squares Structural Equation Modeling was then used to test casual relations between the study’s constructs. &#x0D; Findings: The results reveal that there is a positive but an insignificant effect of prudence Beta 0.094. p = 0.277 on cyber security ethical behavioral Intentions; there is a positive and significant influence of Temperance; Beta 0.255, p = .000; a positive and significant influence of courage; Beta 0.247, p = .001; on cyber security ethical behavior, that there is a positive and significant influence of Justice; Beta 0.452, p = .000; on cyber security ethical behavioral Intentions. The results further showed that a positive change in ethical behavior intentions leads to a positive change in cyber security ethical behavior.&#x0D; Unique contribution to theory and practice: This research makes a theoretical contribution to Cyber security ethics by promoting virtue ethics as a framework for moral investigations into Cyber security. Results can be utilized to improve methods, instruments, and tools to assess the employees’ cyber security ethical behavior. Policy makers can develop virtue ethical based training programs with dedicated, continuous learning and teaching undertakings geared towards monitoring and enhancing the overall ethical behavior towards cyber security in the banks. The results are a platform to the Government of Uganda through the Ministry of ICT in generating appropriate national policies related to Cyber security ethics as a strategy aimed at improving Cyber security in the banking sector.
Upaya Peningkatan Kemampuan Membaca Melalui Buku Cerita Bergambar Pada Anak Usia Dini

Early reading is a unique and important skill for children’s language development, as well as a means of communication for humans. Individuals will acquire new information, knowledge and new experiences through reading. The main and fundamental problem in this study is that many children are not able to read fluently. This study is aimed at early childhood with reading delays. This study aims to determine the improvement of reading comprehension through the method of reading picture story books. The method used in this research is descriptive and qualitative methods, the data is collected through observation and documentation. Based on the results of study, it was found that there was an increase in reading comprehension with picture story books.
Adaptive Image Denoising Method Based on Diffusion Equation and Deep Learning

Effective noise removal has become a hot topic in image denoising research while preserving important details of an image. An adaptive threshold image denoising algorithm based on fitting diffusion is proposed. Firstly, the diffusion coefficient in the diffusion equation is improved, and the fitting diffusion coefficient is established to overcome the defects of texture detail loss and edge degradation caused by excessive diffusion intensity. Then, the threshold function is adaptively designed and improved so that it can automatically control the threshold of the function according to the maximum gray value of the image and the number of iterations, so as to further preserve the important details of the image such as edge and texture. A neural network is used to realize image denoising because of its good learning ability of image statistical characteristics, mainly by the diffusion equation and deep learning (CNN) algorithm as the foundation, focus on the effects of activation function of network optimization, using multiple feature extraction technology in-depth networks to study the characteristics of the input image richer, and how to better use the adaptive algorithm on the depth of diffusion equation and optimization backpropagation learning. The training speed of the model is accelerated and the convergence of the algorithm is improved. Combined with batch standardization and residual learning technology, the image denoising network model based on deep residual learning of the convolutional network is designed with better denoising performance. Finally, the algorithm is compared with other excellent denoising algorithms. From the comparison results, it can be seen that the improved denoising algorithm in this paper can also improve the detail restoration of denoised images without losing the sharpness. Moreover, it has better PSNR than other excellent denoising algorithms at different noise standard deviations. The PSNR of the new algorithm is greatly improved compared with the classical algorithm, which can effectively suppress the noise and protect the image edge and detail information.
A Data Augmentation Method for Vertical Federated Learning

Federated learning (FL) enables various organizations to jointly train one single model without revealing their private data to each other. The FL can be classified as horizontal federated learning (HFL) and vertical federated learning (VFL) according to the distribution of overlap samples and overlap features in the dataset. VFL allows various organizations to share machine learning based on the overlap samples, each one of which has the same identity. However, VFL suffers from insufficient number of overlap samples among all participants. Hence, the shortage of overlap data results in a worse performance of the global model. In this article, we propose a data augmentation method, FedDA, which is based on the generative adversarial network (GAN) to increase the number of training data. We generate more overlap data by learning the features of finite overlap data and many locally existing nonoverlap data, which expand the availability for training the overlap dataset. A series of experiments were executed on both MNIST and CIFAR-10. The results show that FedDA can efficiently utilize nonoverlap samples to enhance the effect of the data augmentation. It can generate high-quality overlap samples and expand the set of overlap samples. Thus, when the VFL is short of overlap samples, FedDA can provide abundant training data to improve the performance of the VFL model.
Wearable Accelerometer Layout Optimization for Activity Recognition Based on Swarm Intelligence and User Preference

Wearable intelligent systems that recognize the daily activities of humans have significantly contributed to many useful applications. However, in practical wearable applications, the target individuals may have different demands in terms of sensor wearing. Sensors may return different information depending on their placement, which to some extent determines the quality of a recognition system. Obtaining the optimal sensor positions with the highest recognition accuracy plays a significant role in activity recognition design. To contribute to a flexible and user-friendly wearable sensor layout, this paper designed a multistage and multiswarm discrete particle swarm optimization algorithm to explore the best sensor combinations and accuracy trends corresponding to different requirements of sensor numbers. The proposed optimization scheme is applied to investigate the influences that different numbers and placements of wearable accelerometers have on an activity recognition system. Furthermore, to address the issue of user preference regarding the sensor position, a relevant sensor layout can also be designed based on the demands and physical condition of the subject. The proposed method can determine the best sensor position (combinations) with lower computational cost for various activity recognition systems.
Introduction of the EU Digital COVID Certificate in Slovenia: Technological and Process Aspects

In early 2021, the European Commission presented a proposal to introduce an EU Digital COVID Certificate, which should enable safe border crossings for citizens within the EU during the COVID-19 pandemic. Subsequently, all EU Member States successfully introduced the EU Digital COVID Certificate by 1 July 2021. This article focuses on a review of the technological and process aspects identified in the introduction of the EU Digital COVID Certificate in Slovenia. The research applies a case study framework, including focus group discussions, as the primary data collection method. The research findings expose the technological and process complexities related to the dispersed data sources and fairly intricate and copious business rules used for the creation of the EU Digital COVID Certificate. Moreover, the study implies that the ad hoc introduction of such demanding and sensitive digital solutions in the future will not be possible without the establishment of effective national health information infrastructures across the EU.
A General Framework for Sorting Large Data Sets Using Independent Subarrays of Approximately Equal Length

Designing an efficient data sorting algorithm that requires less time and space complexity is essential for computer science, different engineering disciplines, data mining systems, wireless networks, and the Internet of things.This paper proposes a general low-complex data sorting framework that distinguishes the sorted or similar data, makes independent subarrays approximately in equal length, and sorts the subarrays' data using one of the popular comparison-based sorting algorithms.Two frameworks, one for serial realization and another for parallel realization, are proposed.The time complexity analyses of the proposed framework demonstrate an improvement compared to the conventional Merge and Quick sorting algorithms.Following complexity analysis, the simulation results indicate slight improvements in the elapsed time and the number of swaps of the proposed serial Merge-based and Quick-based frameworks compared to the conventional ones for low/high variance integer/non-integer data sets, in different data sizes and the number of divisions.It is about (1 -1.6%) to (3.5 -4%) and (0.3 -1.8%) to (2 -4%) improvements in the elapsed times for 1, 2, 3, and 4 divisions, respectively for small and very large data sets in Mergebased and Quick-based scenarios.Although these improvements in serial realization are minor, making independent low-variance subarrays allows the sorted components to be extracted sequentially and gradually before the end of the sorting process.Also, it proposes a general framework for parallelizing conventional sorting algorithms using non-connected (independent) or connected (dependent) multi-core structures.As the second experiment, the numerical analyses that compare the results of the parallel realization of the proposed framework to the serial one in 1, 2, 3, and 4 divisions, show a speedup factor of (2 -4) for small to (2 -16) for very large data sets.The third experiment shows the effectiveness of the proposed parallel framework to the parallel sorting based on the random-access machine model.Finally, we prove that the mean-based pivot is as efficient as the median-based and much better than the random pivot for making subarrays of approximately equal length.
AUTOMATED ARABIC ESSAY SCORING BASED ON HYBRID STEMMING WITH WORDNET

Schools, universities, and other educational institutions have been forced to close their doors because of the coronavirus outbreak. E-learning has become an option and has long been discussed about the need to integrate it into the educational process-learning uses a variety of evaluation methods, one of which is the essay. This research introduces a new model for Arabic Automated Essay Grading (AAEG) that has been developed to reduce human bias mistakes and costs while saving time. However, (AAEG) is still in its infancy. The model relies on new hybrid stemming with Arabic WordNet (AWN). The primary goal of stemming is reducing inflectional forms of words to root words. The hybrid method is based on different techniques: Extended Light Stemmer, ISRI, and looking at tables (AWN). Data used in this study consists of 3050 words with their roots were retrieved from (AWN) and then stemmed using algorithms (Light10, ISRI, Hybrid...). For evaluation, the metrics used were accuracy, precision, recall, and F1-score. While comparing the performance of the different stemming algorithms, the hybrid stemming method had the greatest results, therefore the (AAEG) will improve with Hybrid Stemming.