Reviewerly/exHarmony huggingface dataset:
A Study of Primary School Students in Johor: The Role of Temperament and Teacher-Student Relationships in Student Engagement

As students' engagement is a crucial component of their learning process, it is important to understand the factors that influence it.The COVID-19 pandemic has caused prolonged school closures and a shift to online learning, leading to learning loss.Student temperament and teacher-student relationships have been identified as two key factors that affect the quality of student engagement.This research aimed to examine the relationship between student temperament, teacher-student relationships, and student engagement in primary schools.The study employed a quantitative approach, using three questionnaires to collect data from 351 lower primary students selected through random sampling techniques.The analysis results showed that the respondents demonstrated moderate level of overall student temperament and high level of teacher-student relationship.The study results showed a weak positive correlation between student temperament and student engagement (r = 0.243, p < 0.01).Meanwhile, the study found a strong positive correlation between teacher-student relationship and student engagement (r = 0.740, p < 0.01).The findings indicate that teacherstudent relationship is a much stronger predictor of student engagement in primary school, as compared to student temperament.One of the key implications of this study is that teachers can now better understand the significance of building positive teacher-student relationship in increasing the level of quality student engagement in schools.
French Language Learning Strategies among Beginner-Level Learners of a Public University in Central Peninsula Malaysia During Covid-19

This study aims to describe the students' language learning strategies in learning French throughout the new learning norm due to COVID-19.A total number of 203 beginner learners from 9 different classes participated in this study.All classes were held online as part of the Standard Operating Procedure (SOP) to curb the spread of COVID-19.Oxford's (1990) SILL questionnaire (Strategy Inventory for Language Learning) version 7.0 was distributed online via Google Forms to observe learners' language learning strategies.The acquired data were analysed using the Statistical Package for the Social Sciences (SPSS) software and then classified to a certain level of SILL Profile.The result revealed that the Metacognitive Strategies are students' most frequently employed language learning strategies, with a mean score of 3.76.(classified as High level).Subsequently, with a mean score of 3.20, the students' least often used strategy is Compensation Strategies (classified as Medium level).Hopefully, this research will assist learners in learning French in different learning settings.The results of this study show that respondents supervise and plan their learning very well because they had prior experience learning Malay, English, and Arabic.Conversely, respondents lack the mastery to guess the meaning of words from context and struggle to perceive non-verbal communication to understand certain words or explanations because the online teaching and learning platform limits their eye contact during classes.Hopefully, students can improve their French language learning by incorporating more language learning strategies to make online learning more efficient.
Security-aware Mobile Application Development Lifecycle (sMADLC)

With the high mobile phone penetration and subsequent significant usage of mobile phone applications, mobile users have become prime targets of hackers.Secure Software Development (SSD) advocates incorporating security aspects at the initial stages of software development.This study proposes a novel Mobile Application Development Lifecycle by reviewing SSD concepts and incorporating these concepts into MADLC-a mobile-focused software development lifecycle to create a security-aware Mobile Application Development Lifecycle (sMADLC).The proposed development lifecycle, sMADLC, can potentially help mobile application developers create secure software that can withstand hacker aggression and assure mobile application users of the confidentiality, integrity and availability of their data and systems.
Assessing Similarity between Software Requirements: A Semantic Approach

The majority of projects fail to achieve their intended objectives, according to research.This could arise for a number of reasons, such as ensuring requirements are managed, excessive documentation of the code, or the difficulty in delivering software that includes all the requested features on time.An effort could be made to overcome such failure rates by establishing a proper management of requirements and concept of reusability.The correct requirements can be identified by checking similarity between the requirements received from the various stakeholders.A reusable software component can result in substantial savings in both time and money.It can be challenging to make a choice regarding the reuse of certain software components.A comparison of the requirements of a new project with those of previous projects prior to starting a new project or even at a later stage during development is useful for identifying reusable components.This paper proposes a framework (ReSim) for identifying software requirements' similarities, in an attempt to improve reusability and identify the correct requirements.A crucial component of ReSim is to measure similarity between software requirements.Different well-known similarity measurement techniques used by the researchers to evaluate the similarity between the software requirements.Some of the methods used to measure this include dice, jaccard, and cosine coefficients, but in this paper, we have used recently developed hybrid method which considers not only semantic information including lexical databases, word embeddings, and corpus statistics, but also implied word order information and produced significant improvements in the results related to the measurement of semantic similarity between words and sentences.As part of the experiments, the study used PURE dataset -in order to demonstrate the efficacy of the proposed framework.As a result, recently developed hybrid method of measuring the requirements similarity is more accurate than Dice, Jaccard, and Cosine, while Cosine is a better choice than Dice, and Jaccard is more accurate than Dice.Thus, ReSim outperforms existing approaches when tested on the PURE dataset, providing the most accurate results for both functional and non-functional requirements.
Linear Discriminate Analysis based Robust Watermarking in DWT and LWT Domain with PCA based Statistical Feature Reduction

With aiming to design a novel image watermarking technique, this paper presents a novel method of image watermarking using lifting wavelet transform, discrete wavelet transform, and one-dimensional linear discriminate analysis.In this blind watermarking technique, statistical features of the watermarked image have been incorporated for preparing the training set and testing set.After that, the principal component analysis is applied to reduce the obtained feature set, so the training time is reduced to the desired level and accuracy is enhanced.The one-dimensional linear discriminate analysis is used for binary classification as it has the ability to classify with good accuracy.This technique applies discrete wavelet transform and lifting wavelet transform in two different watermarking schemes for the image transformation.Both transformations give higher tolerance against image distortion than other conventional transformation methods.One of the significant challenges of a watermarking technique is maintaining the proper balance between robustness and imperceptibility.The proposed blind watermarking technique exhibits the imperceptibility of 43.70 dB for Lena image in case of no attack for the first scheme (using LWT) and 44.71 dB for the second scheme (using DWT+LWT).The first watermarking scheme is tested for robustness, and it is seen that the given scheme is performing well against most of the image attacks in terms of robustness.This technique is compared using some existing similar watermarking methods, and it is found to be robust against most image attacks.It also maintains the excellent quality of the watermarked image.
The effectiveness of e-book in learning process

This study aims to analyze the effectiveness of e-books in the last three years and to analyze what types of e-books are most appropriate for current learning. The methodology used is based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Articles related to the use of e-books from 2020-2022 were searched through the published or perish article search application that focuses on Scopus-indexed articles. The results obtained indicate that the existence of e-books is still widely used to support students to obtain even better learning outcomes. Although several studies have found no significant difference between the use of e-books and regular printed books, the results also show that e-book users got higher learning outcomes. For now, the use of interactive e-books is the most effective e-book of other types, considering that this type of e-book has been tested on users of various ages and different educational backgrounds, with different subjects and different places of implementation.
Prediksi Deret Waktu Manajemen Lalu Lintas

Optimizing customer service for the number of drivers in an area through real-time transportation service industry online to scale up. In this paper, the dataset used is traffic management accompanied by attributes such as level 6 geohash, day, timestamp, and demand. The dataset used is a sample from geohash6 coded qp0991, containing online transportation demands from 01/04/2018 until 31/05/2018 (61 days). The training datasets are from the qp0991 code sample, starting from 01/04/2018 until 10/05/2018 and the remaining datasets are used as the testing datasets. The percentages for training and testing are respectively 67% and 33%. The methods applied to construct the objective function are three different forecasting methods, namely the Naïve approach, auto-regressive integrated moving average (ARIMA), and simple exponential smoothing. The results of this study indicate that the simple exponential smoothing method is better than the naïve approach and auto-regressive integrated moving average based on the root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). The simple exponential smoothing has an accuracy rate of 98.7% for the RMSE value, 98.9% for the MAE value, and 88.81% for the MAPE value.&#x0D; Keywords: time series, traffic management
HUMANITARIAN EDUCATION IN TRAINING PERSONNEL OF CULTURE AND ART (ON THE EXAMPLE OF ESSIC)

The article considers the condition of humanitarian education in ESSIC. The role of humanitarian education in forming the workers of culture and art is shown. The features of humanitarian education in training such specialists are analyzed.
FUNDAMENTALS OF IMPROVING THE PEDAGOGICAL MECHANISM OF DIRECTING THE CHOICE OF PROFESSION ON THE BASIS OF AN INTEGRATIVE APPROACH

The article describes the essence of improving the pedagogical mechanism related to the foundations of an integrative approach to eliminating theoretical and practical problems that stand on the basis of the educational system for directing students to the choice of profession.
FORMATION OF PEDAGOGICAL CREATIVITY AMONG PRIMARY SCHOOL TEACHERS

This article is about the formation of pedagogical creativity among primary school teachers. The article suggests some ways and means of developing creativity in primary school teachers, as well as describes the psychological and pedagogical conditions for the development of creativity, capable of influencing the need–motivational and emotional spheres of the student's personality: the regular reinforcement of creative manifestations by the teacher, which consists in “centering” the student on the self–achieved result; providing students with freedom of choice in their pedagogical activity when demanding responsibility for it; the evaluation and promotion of creative initiative and high–quality work.
Business Mapping in Context of Web 3.0 using Blockchain Technology

Abstract: The foundation of e-commerce is based on conducting transactions and transferring goods online without the need for physical travel. However, cyber threats can pose a risk when it comes to transferring transactional data. Our primary goal is to develop a system that can protect against such mishaps during the process of transmitting transactional data. Additionally, we aim to implement an automated system that ensures error-free transactions. To achieve these objectives, we will leverage the latest technologies such as blockchain and smart contracts. Blockchain technology provides a decentralized and immutable digital ledger that can securely store and transfer data across a network. E-commerce can use blockchain technology to process transactions safely, securely, and rapidly. By using blockchain technology, we can enable peer-to-peer transaction systems and data encryption, ensuring the safe transfer of sensitive financial information. Moreover, we can use blockchain technology to transfer transactional data securely. A smart contract is a specialized program that functions as an agreement and runs on the Ethereum blockchain. Smart contracts provide transactional security, and when combined with blockchain technology, they can transform e-commerce. By utilizing blockchain technology to ensure data security and user privacy and smart contracts to maintain the transaction protocol, our proposed system aims to offer a solution for transactional data privacy and transparency. In conclusion, our system will provide a secure and transparent platform for conducting e-commerce transactions. With the use of blockchain technology and smart contracts, we can create a system that ensures safe transfer of data and error-free transactions.
Analytical Optimization of X-ray Mammography for Increased Benefits and Safety; using: Data Analytics, Electronics Engineering and Artificial Intelligence

Objective: To analyse, understand and investigate common X-ray Mammography techniques, terminologies and properties; and how the X-ray Mammography systems can be optimized for a safer, more effective, more efficient, more energy efficient procedure for breast cancer diagnosis; using Deep Learning (DL)/ Artificial Intelligence (AI), Analytical, Electronic Engineering, tools and Methodologies. Methods: Using data and breast cancer image datasets from validated open source data stores; investigative and comparative analyses were carried out on common X-ray Mammography techniques in relation to breast cancer diagnosis and treatments for Women; as well as optimization analyses using Electronic engineering principles and Artificial Intelligence principles on the quality, intensity, radiation photon energy, HVL(Half Value Layer), Tube current-time product, Metal filters properties (e.g. K-edge energy cut off), and classification validation accuracy of the X-ray Mammography properties, procedure, processes and systems. The methodical and data-driven analyses were carried out using the following Data, Artificial Intelligence (AI) and Electronic Engineering, methodologies and algorithms: Data Analytics, Convolutional Neural Networks (CNN) in Machine Learning (ML) Engineering, and X-ray-Impedance Circuit Composite System Analysis
Radio signal detection based on spectrogram

In this paper, a radio signal detection algorithm is proposed. This algorithm detects the radio signal from complex electromagnetic environment to obtain the center frequency, bandwidth and starting and ending time. First, to detect the time and frequency information simultaneously, the I/Q data in time domain is transformed into spectrogram. Then, the noise threshold is calculated by equidistant binning statistics, and the spectrogram is binarized based on the threshold. A kind of filter operator is designed for binary image denoising. Then we use DBSCAN clustering algorithm to cluster signal points in binary spectrogram. Finally, after separating the overlapping signals and filtering the false signals, the center frequency, bandwidth, starting and stopping time of each signal can be extracted. For experimental evaluation, Average Precision (AP) and average Distance-DIOU (ADIOU) are used as evaluation indexes. Radio signal data with center frequency near 2.4GHz and bandwidth 40 MHz is collected. And 420 segments of test data are extracted for manual labeling. Detection experiments on the 420 test samples reveal that the algorithm we proposed can achieve an AP value of 0.839, and the ADIOU value reaches 0.674. The result shows that the algorithm can detect radio signals accurately.
Childrearing culture from the agrarian era to the digital age

Childrearing culture from the agrarian era to the digital age Chris Girard, an Associate Professor of Sociology in Florida International University, looks at childrearing culture from the perspective of coevolving informatics. During the 20th and 21st centuries, growth in mass schooling represents a rupture with thousands of years of home-based socialization of children. The spatiotemporal dynamics of expanding children's formal education, outside of the home, coevolve with more learning-centred culture and greater specialization in energy sources (e.g., fossil fuels, electricity). This three-way interaction between socializing information flows, space, and energy is investigated by coevolving informatics. A key insight is that greater depth in information flows overcomes spatiotemporal and energy-conversion barriers (Girard 2020).
Making fair data a reality for photons and neutrons

Making fair data a reality for photons and neutrons Andy Götz, the coordinator of PaNOSC for ESRF, looks at FAIR data – an initiative to deliver research data to the scientific community to use. The scientific world is undergoing a major revolution with the widespread adoption of the so-called FAIR principles for research data. FAIR stands for Findable, Accessible, Interoperable and Reusable and was first published in a paper in Nature in 2016(1). The catchy mnemonic was originally proposed at a Lorentz Workshop in Leiden, The Netherlands, in January 2014, named: Jointly designing a Data FAIRport. In a nutshell, the FAIR principles were proposed to ensure research data are made available to the scientific community in such a way that they can be found, downloaded, understood and reused.
Secure and Privacy-Preserving Federated Learning-Based Resource Allocation for Next Generation Networks

&lt;p&gt;This paper surveys recent research on Federated Learning-based resource allocation for next-generation networks with the aim of identifying research gaps and potential directions for future work. We start by outlining the main challenges and requirements for secure and privacy-preserving resource allocation in these networks. The existing solutions and techniques proposed in literature are then reviewed. The strengths and limitations of these solutions are analyzed and trade-offs in terms of security, privacy, efficiency, and scalability are discussed. The research gaps and promising directions for future research are identified, such as integrating multiple solutions (e.g., Game theory or optimization techniques with Federated Learning) and developing new models and algorithms for AI-based resource allocation that addresses specific challenges and requirements of next-generation networks. The goal is to inspire further research in this critical and rapidly evolving field. &lt;/p&gt;
Service composition in satellite network based on the self-adaption genetic algorithm

For the service composition under the multi-resource and multi-service constraints of the satellite network, without considering the problem that service quality and service priority lead to poor Quality of Service (QoS) and low service priority matching degree, the priority inverse number of service sequence is defined, the approximation is established aiming at the greatest QoS and the smallest priority inverse number of service sequence, and an adaptive genetic algorithm is proposed to solve the model. The algorithm improves the selection mechanism based on elite-reservation and roulette strategy and designs adaptive crossover and mutation operators. It solves the shortcoming that standard genetic algorithm is easy to fall into local optimum and avoids the loss of optional solution. The simulation results show that the proposed method increases the QoS of service execution by 13.461% and reduces the priority inverse number by 26.77%. It effectively solves the service composition problem with multi-resource and multi-service constraints in satellite network.
An image registration method based on SIFT and ORB

For the SIFT algorithm having great computational complexity caused by too many feature points and complicated descriptors, an advanced image registration method is proposed. At first it uses SIFT to extract the feature points from two images and eliminate edge points. Next it introduces image entropy into feature point threshold to select feature points with better quality and reduce the number of feature points. Then it uses ORB algorithm to describe the feature points and Hamming distance nearest neighbor method to match the feature points. Lastly it uses the advanced voting law based on the constraint relations of feature points to purify the matching points and the improved PROSAC algorithm to calculate the optimal transformation matrix. Experiments verify the performance of the algorithm.