Reviewerly/exHarmony huggingface dataset:
The CEFR Impact on English Language Educators Teaching Engineering Programmes at a Private University in Malaysia

This paper presents some preliminary findings of a multi-dimensional and interdisciplinary research. It explores the experience of language educators on the newly implemented policy by the Ministry of Education, Malaysia. The implementation of the Common European Framework of Reference for Languages (CEFR) is a policy that develops through a whole continuum: from primary schools to universities. The paper fills the gap in terms of impact studies of the CEFR especially on engineering programmes where the desired CEFR levels are higher. This implementation affects language educators at university in various domains such as the required CEFR levels for language educators, teaching materials and support systems. The methodological framework used in this study is phenomenological study. It aims to explore the experience of the language educators. Interviews were conducted on three language educators teaching engineering programmes at a private university. Snowballing technique was used in identifying the research interviewees. Data gathered were analysed using Atlas.Ti, a qualitative analysis software. Preliminary result indicates that language educators were positive and welcome enthusiastically the change. However, challenges were immense and critical. This paper concludes that the implementation of the CEFR is a wise move for Malaysian students towards a higher level of proficiency in English.
Developing an E-Module Physics-Based Kvisoft Flipbook Maker to Enhance the Concept of Understanding for the Senior High School Student

This pilot paper aimed to develop a kvisoft flipbook maker-based e-module Physics for global warming symptomatic material as a physics supplementary teaching material for the eleventh-grade student.The research adopted the model of 4D development stood for Define, Design, Develop, and Disseminate.The subjects of this study were 31 students of Mathematics and Science of Senior High School of 1 PAKEM.The research instruments employed were preliminary study questionnaires, material expert validation survey form, media expert validation survey form.The methods of data collection employed were validation, observation, pretest, posttest, and study of documentation.Data Analyzing Technique utilized was the Sign Test.The results of the paper showed that 1) the KFM-based Physics e-module developed belonged to a valid category and was worth to use.2) Understanding students' concepts during learning activities has increased significantly.
The Mediating Role of Social Media in the Relationship between Perceived Leadership Support and Employee Engagement in Banking

Purpose:The present study endeavors to concentrate on the role of perceived leadership support in fostering employee engagement.Specifically, the study considers the social media contribution in this relationship.The mediating role of social media and the links between perceived leadership support and employee engagement are also discussed.Design/methodology/approach: The investigation begins with a review of literature in the context of organizations.The data are based on a cross-sectional structured questionnaire for a cohort of 167 respondents form the Polish and Sri Lankan banking sector and was used to verify the research hypotheses.The responses were examined using mediation analysis with bootstrapping.Findings: Results from the study indicate that social media partially mediates the relationship between perceived leadership support and employee engagement.The study demonstrates the growing need for better use of social media in developing a positive perception of leadership support to build employee engagement.Implications: Implications of these findings for organizations are discussed.The study contributes to the understanding and value of how leadership perception affects organizational outcomes.Originality/value: This study provides insight into the role of social media in facilitating leadership support to enhance employee engagement.
Research on Body Type Correction of FINA Diving Difficulty Coefficient Based on Rigid Body System

In order to reduce the influence of athlete’s body shape on the difficulty coefficient of diving, a more reasonable calculation method of body shape correction coefficient is proposed based on the original calculation rules of diving difficulty coefficient. First, the composition of the original diving difficulty coefficient and influencing factors is analyzed and the relationship between the various structural parts is fully clarified. Second, a coupled nonrigid body dynamics model is established and a 2-body model is used to simulate complex diving actions, and it is concluded that diving time is positively correlated with body shape. Finally, the air movement part and the water entry part of diving are discussed separately, the calculation model of the difficulty coefficient of body shape correction is established, and the original difficulty coefficient is corrected. The results show that the difficulty coefficient of each movement is obviously increased. This effectively avoids the influence of body shape on the diving difficulty coefficient.
FILTERING ALGORITHMS FOR DE-SPECKLE THE ULTRASOUND IMAGES OF BONE FRACTURE

Ultrasound images are popularly known to contain speckle noise that degrades the quality of the images for good and fast interpretation in many areas of medicine, especially for bone fracture detection. This necessitates the need for robust de-speckling techniques for clinical practice. Therefore, a study was carried out to reduce speckle using filtering algorithms such as Wiener, Average, Median and Wavelets. This paper discusses the level of improvement obtained through these filtering algorithms using the peak signal to noise ratio (PSNR) as a measurement tool. The results of our work presented in this paper suggest that the combination of Daubechies–Wiener which we call as a hybrid technique, gave the best performance, which is a new contribution in this field. This de-speckling algorithm can be further developed and evaluated at a larger scale.
Program Pelatihan Gambar Teknik Menggunakan Aplikasi AutoCAD di SMK Muhammadiyah 3 Samarinda

Vocational students need some skills that will be used when students finish their studies at school. One of the expected skills is the ability to draw using the AutoCAD program. Where the final ability is expected of the students will be able to create engineering drawings in the form of 2-D models. The purpose of the training program is to develop and improve the technical drawing skills of the XII class students in using the AutoCAD program. Therefore, the students of class XII SMK have the ability to draw machine components and other engineering drawings in 2-D format. The method used in this training program through pre-test instrument, AutoCAD module, discussion, display through slide how to make machine image in 2-D formats and practice how to make the engineering drawing then evaluated with post-test. The result of this program is the improvement of the ability of grade XII students in drawing machine components and other engineering drawings in 2-D format.
Comparative analysis of augmented datasets performances of age invariant face recognition models

The popularity of face recognition systems has increased due to their non-invasive method of image acquisition, thus boasting the widespread applications. Face ageing is one major factor that influences the performance of face recognition algorithms. In this study, the authors present a comparative study of the two most accepted and experimented face ageing datasets (FG-Net and morph II). These datasets were used to simulate age invariant face recognition (AIFR) models. Four types of noises were added to the two face ageing datasets at the preprocessing stage. The addition of noise at the preprocessing stage served as a data augmentation technique that increased the number of sample images available for deep convolutional neural network (DCNN) experimentation, improved the proposed AIFR model and the trait aging features extraction process. The proposed AIFR models are developed with the pre-trained Inception-ResNet-v2 deep convolutional neural network architecture. On testing and comparing the models, the results revealed that FG-Net is more efficient over Morph with an accuracy of 0.15%, loss function of 71%, mean square error (MSE) of 39% and mean absolute error (MAE) of -0.63%.
Innovative Technologies Against the COVID-19’s Challenge: Education Issues

The intention of the paper is to consider the role of technologies used to overcome the COVID-19’s challenges in higher education. On investigating, it has been found that the digital strategies utilizing innovation technologies help students to adapt to pandemic consequences. This study enlightens the various technologies that assist systems of healthcare and education in diverse aspects to outlive against COVID-19. Furthermore, the technological shift of world economy that happened during the pandemic and its influence in the society is discussed. Besides the considered innovation technologies, this paper also deals with changes that have taken place in education system of Kazakhstan in pandemic circumstances. Alongside our suggested solution for further educational process issues is also presented.&#x0D; A detailed review of the literature is done on COVID-19, digital technologies in education, blended learning using appropriate keywords on SCOPUS, Springer, Science Direct, and Google Scholar. To get insights some relevant sites and blogs are also taken into account. We have identified technologies used in Kazakhstan that play an important role now.&#x0D; There have been chosen exploratory and conclusive research design. Fifty-nine students provided qualitative responses; ninety-eight students and graduates participated in quantitative survey. The results of study has shown students’ increased awareness concerning blended learning.&#x0D; The scientific novelty lies in the description of the experience gained by higher schools during the quarantine. We believe that in the future, blended learning should be used largely. The innovations in the learning process cover two areas. The first is related to software used. It is like creation of a virtual university that facilitates communication between students and teachers. The second area correlates with content, without which any innovation will fail.
Constructing decision rules from naive bayes model for robust and low complexity classification

A large spectrum of classifiers has been described in the literature. One attractive classification technique is a Naïve Bayes (NB) which has been relayed on probability theory. NB has two major limitations: First, it requires to rescan the dataset and applying a set of equations each time to classify instances, which is an expensive step if a dataset is relatively large. Second, NB may remain challenging for non-statisticians to understand the deep work of a model. On the other hand, Rule-Based classifiers (RBCs) have used IF-THEN rules (henceforth, rule-set), which are more comprehensible and less complex for classification tasks. For elevating NB limitations, this paper presents a method for constructing a rule-set from the NB model, which serves as RBC. Experiments of the constructing rule-set have been conducted on (Iris, WBC, Vote) datasets. Coverage, Accuracy, M-Estimate, and Laplace are crucial evaluation metrics that have been projected to rule-set. In some datasets, the rule-set obtains significant accuracy results that reach 95.33 %, 95.17% for Iris and vote datasets, respectively. The constructed rule-set can mimic the classification capability of NB, provide a visual representation of the model, express rules infidelity with acceptable accuracy; an easier method to interpreting and adjusting from the original model. Hence, the rule-set will provide a comprehensible and lightweight model than NB itself.
Ground Plane Estimation for Obstacle Avoidance During Fixed-Wing UAV Landing

The automatic crash-landing of a fixed-wing UAV is challenging due to high velocity of approaching aircraft, limited manoeuvrability, lack of ability to hover and low quality of textural features in common landing sites. Available algorithms for ground plane estimation are designed for automatic cars, robots, and other land-bound platforms, where textural features can be easily compared due to low altitude, or for rotor-wing UAV that can hover. Their usefulness is limited when quick manoeuvre is needed to avoid collision with obstacles. Due to developments in parallelised, mobile computational architectures, an approach based on dense disparity estimation becomes available assuming proper constraints on ground plane transformation phase space. We propose an algorithm utilising such constraints for ground plane estimation on the often prohibitively time-consuming task of disparity calculation as well as plane estimation itself using a pyramid-based approach and random sample consensus in order to discard pixels belonging to obstacles. We use Inertial Navigation System, commonly available in fixed-wing UAVs, as a source of such constraints and improve our estimation in subsequent frames allowing for stable flight trajectory as well as detection of obstacles protruding from the ground plane.
Learning Games from Videos Guided by Descriptive Complexity

In recent years, several systems have been proposed that learn the rules of a simple card or board game solely from visual demonstration. These systems were constructed for specific games and rely on substantial background knowledge. We introduce a general system for learning board game rules from videos and demonstrate it on several well-known games. The presented algorithm requires only a few demonstrations and minimal background knowledge, and, having learned the rules, automatically derives position evaluation functions and can play the learned games competitively. Our main technique is based on descriptive complexity, i.e. the logical means necessary to define a set of interest. We compute formulas defining allowed moves and final positions in a game in different logics and select the most adequate ones. We show that this method is well-suited for board games and there is strong theoretical evidence that it will generalize to other problems.
Método auditivo visual para el aprendizaje del idioma inglés en los estudiantes de turismo

This article presents the results obtained from the work carried out in the application of a visual auditory method that allowed strengthening the learning of the English language in tourism students.The objective proposed in this research is: to determine the influence of the visual auditory method to strengthen the teaching of English language learning in students at the School of Tourism.For the development of this work, a questionnaire prepared and validated by experts was applied as an evaluation instrument, the design of the research is quasi-experimental in which there was a population of 210 students and a sample of 54 students from the school of tourism.The results were analyzed with the application of the Student's t test in which it is demonstrated that the significance of the paired differences, at the 95% confidence interval of the difference, is less than 0,05.It is concluded that the visual auditory method strengthens in significant levels the teaching of English language in the students at the Tourism School of the National University of Tumbes.
Optimal dual quantizers of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e189" altimg="si6.svg"><mml:mrow><mml:mn>1</mml:mn><mml:mi>D</mml:mi></mml:mrow></mml:math> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e197" altimg="si7.svg"><mml:mo class="qopname">log</mml:mo></mml:math>-concave distributions: Uniqueness and Lloyd like algorithm

We establish for dual quantization the counterpart of Kieffer’s uniqueness result for compactly supported one dimensional probability distributions having a log-concave density (also called strongly unimodal): for such distributions, Lr-optimal dual quantizers are unique at each level N, the optimal grid being the unique critical point of the quantization error. An example of non-strongly unimodal distribution for which uniqueness of critical points fails is exhibited. In the quadratic r=2 case, we propose an algorithm to compute the unique optimal dual quantizer. It provides a counterpart of Lloyd’s method I algorithm in a Voronoi framework (see [14] and [15]). Finally semi-closed forms of Lr-optimal dual quantizers are established for power distributions on compacts intervals and truncated exponential distributions.
Project Reporting Management System with AI based Assistive Features for Text Summarization

This paper details a proof-of-concept system called Project Reporting Management System (PRMS) to manage the project reporting process in a typical research centre where the process can be manual for many centres. In fact, it is general enough to be scaled up and deployed for a large department or scaled down for a smaller setup in any organization which needs a simple and efficient project progress reporting system but does not entail the kind of complexity and cost of commercial project management systems. Using a research centre scenario, the progress of the individual projects has to be tracked through the periodic submission of progress reports by the Principal Investigator (PI) of the project. The centre will need to consolidate these individual reports manually into a consolidated report and an executive summary for higher management. PRMS automates the tracking of individual projects and reporting deadlines, sends reminders and allows online submission of reports by the PIs. PRMS also incorporates assistive and automated features exploiting Machine Learning (ML) and Natural Language Processing (NLP) techniques to generate the consolidated report and rank sentences of verbose report for assistive text summarization to facilitate the manual process of producing an executive summary.
ARTIFICIAL NEURAL NETWORKS IN MEDICAL DIAGNOSIS

Artificial neural networks are finding many uses in the medical diagnosis application. The article examines cases of renopathy in type 2 diabetes. Data are symptoms of disease. The multilayer perceptron networks (MLP) is used as a classifier to distinguish between a sick and a healthy person. The results of applying artificial neural networks for diagnose renopathy based on selected symptoms show the network's ability to recognize to recognize diseases corresponding to human symptoms. Various parameters, structures and learning algorithms of neural networks were tested in the modeling process.
Monitoring social-distance in wide areas during pandemics: a density map and segmentation approach

With the relaxation of the containment measurements around the globe, monitoring the social distancing in crowded public spaces is of great importance to prevent a new massive wave of COVID-19 infections. Recent works in that matter have limited themselves by assessing social distancing in corridors up to small crowds by detecting each person individually, considering the full body in the image. In this work, we propose a new framework for monitoring the social-distance using end-to-end Deep Learning, to detect crowds violating social-distancing in wide areas, where important occlusions may be present. Our framework consists in the creation of new ground truth social distance labels, based on the ground truth density maps, and the proposal of two different solutions, a density-map-based and a segmentation-based, to detect crowds violating social-distancing constraints. We assess the results of both approaches by using the generated ground truth from the PET2009 and CityStreet datasets. We show that our framework performs well at providing the zones where people are not following the social-distance, even when heavily occluded or far away from the camera, compared to current detection and tracking approaches.
A coercion-resistant blockchain-based E-voting protocol with receipts

We propose a decentralized e-voting protocol that is coercionresistant and vote-selling resistant, while being also completely transparent and not receipt-free.We achieve decentralization using blockchain technology.Because of the properties such as transparency, decentralization, and non-repudiation, blockchain is a fundamental technology of great interest in its own right, and it also has large potential when integrated into many other areas.We prove the security of the protocol under the standard DDH assumption on the underlying prime-order cyclic group (e.g. the group of points of an elliptic curve), as well as under standard assumptions on blockchain robustness.
Технології формування природничо-екологічної компетентності молодших школярів

International scientific and practical conference «Pedagogy and Psychology in the Modern World: the art of teaching and learning» : conference proceedings, February 26–27, 2021. Riga, Latvia : «Baltija Publishing». 444 pages.
I-LEARN

The I-LEARN model—Identify, Locate, Evaluate, Apply, Reflect, kNow—both describes the process of learning with information and provides a learning sequence that can be taught. Grounded in research and theory from information science and instructional design, it is also based on the author’s own research and writing. The model reflects an inquiry approach built on the three-part information-literacy paradigm that underlies many instructional activities in library media centers: accessing, evaluating, and using information. It expands that paradigm to focus specifically on the use of information as a tool for learning. Plans to validate the model in a digital library and in face-to-face environments are currently underway.
Ontology-Based Semantic Retrieval for Durian Pests and Diseases Control System

In Southeast Asia, durian is affectionately called the king of fruit. Durian is the most popular crop planted in eastern and southern of Thailand. The total crop is around 600,000 tons per year; among this, 500,000 tons of the total production were exported worldwide. In Thailand, the knowledge of durian production is based on experience from generation to generation, especially the knowledge of durian pests and diseases control. This paper presents the ontology knowledge based for durian pests and diseases retrieval system. The major contributions of the system consist of 1) the stored knowledge of durian pests and diseases and 2) the diagnosis of durian diseases and the suggestions for the treatments. The ontology knowledge consists of 8 main classes: 1) diseases, 2) pests, 3) cultivars, 4) symptoms of bunch, 5) leaf area symptoms, 6) symptoms of the branches and trunk, 7) symptoms of fruit, and 8) symptoms of root and growth. The experimental results yielded 100% of precision, 88.33% of recall, and 93.8% of overall performance.