Reviewerly/exHarmony huggingface dataset:
What is the Digital Competency Framework?

is part of the Digital Action Plan (DAP) for Education and Higher Education developed by Québec's Ministère de l'Éducation et de l 'Enseignement supérieur (2019).The plan promotes a vision of effective integration and optimal use of digital technologies in order to foster the success of all Quebecers and promote lifelong skills development and maintenance.The first measure of the DAP is structural: the establishment and implementation of a reference framework.The objective was to design an inter-level digital competency framework that spans preschool through higher education, including general education and vocational training.The result is the Digital Competency Framework.It sets out the key dimensions of learning and personal development for 21st-century learners, teachers, and non-teaching professionals.Elements specific to teaching will be addressed in more detail in an upcoming competency framework for the teaching profession.The framework is meant to provide the foundation for the development of digital competency.Digital competency is defined as a set of skills necessary to the confident, critical, and creative use of digital technologies to achieve objectives in the areas of learning, work, leisure, and inclusion or participation in society.Structurally, the framework is designed as a continuum of 12 dimensions (see Figure 1 below) that set out the scope of the development of digital competency.The elements of the dimensions are situated on a progress matrix.As they advance through the competency levels, individuals become increasingly autonomous in their use of digital technologies in educational, professional, and everyday life contexts.For example, they are able to select the best digital tools to perform particular tasks.Digital competency also means that individuals can adapt to technological innovations in years to come (including advances in artificial intelligence), assess these new technologies critically, and adopt and implement those that are useful.
Converting coherence based on positive-operator-valued measures into entanglement

Quantum resource theories provide a diverse and powerful framework for extensively studying the phenomena in quantum physics. Quantum coherence, a quantum resource, is the basic ingredient in many quantum information tasks. It is a subject of broad and current interest in quantum information, and many new concepts have been introduced and generalized since its establishment. Here we show that the block coherence can be transformed into entanglement via a block-incoherent operation. Moreover, we find that the coherence based on positive-operator-valued measures (POVMs) associated with block coherence through the Naimark extension acts as a potential resource from the perspective of generating entanglement. Finally, we discuss avenues of creating entanglement from POVM-based coherence, present strategies that require embedding channels and auxiliary systems, give some examples, and generalize them.
Autonomous stabilization of photonic Laughlin states through angular momentum potentials

We propose a method to stabilize Laughlin states of a large number of strongly interacting photons by combining a frequency-selective incoherent pump with a step-like potential in the angular momentum basis. Analytical expressions for the preparation efficiency and for the principal error sources are obtained. Direct extension of the preparation scheme to states containing single or multiple quasiholes is discussed.
Use of Blockchain Technology in Energy Banking and Electricity Markets

The power system reforms worldwide have commoditized electric energy and thus the electricity market has been developed. With this, trading of electric energy takes place in various time-domain like the day ahead, real-time, etc. These transactions take place through over the counter (OTC) or Power Exchange (Px) which provide to the market participants the required platform and payment security. The transactions on OTC and Px requires a third-party platform and guarantee for contract &amp; settlement, there incurs overhead cost. Since electric energy is a fungible commodity, it can be transacted very well with the old system like barter. Energy Banking is one such mechanism wherein one utility supplies the energy to another utility that need it more and in leisure, the energy can then be provided back. The requisite security of the transactions can be provided by blockchain technology. Energy banking is presently being done only on MW quantum basis with no price tag despite the cost being dependent on the demand-supply ratio. To ensure energy banking transactions in real-time and free from the perils of financial settlements, this article suggests the use of the Peer-to-Peer (P2P) model of blockchain technology for executing Smart Contracts mutually agreed upon by both parties and avoiding third parties overhead costs. Doi: 10.28991/HIJ-2021-02-03-03 Full Text: PDF
PCA-based dimensionality reduction for face recognition

In this paper, we conduct a comprehensive study on dimensionality reduction (DR) techniques and discuss the mostly used statistical DR technique called principal component analysis (PCA) in detail with a view to addressing the classical face recognition problem. Therefore, we, more devotedly, propose a solution to either a typical face or individual face recognition based on the principal components, which are constructed using PCA on the face images. We simulate the proposed solution with several training and test sets of manually captured face images and also with the popular Olivetti Research Laboratory (ORL) and Yale face databases. The performance measure of the proposed face recognizer signifies its superiority.
Multi-Task Learning for Medical Image Inpainting Based on Organ Boundary Awareness

Distorted medical images can significantly hamper medical diagnosis, notably in the analysis of Computer Tomography (CT) images and organ segmentation specifics. Therefore, improving diagnostic imagery accuracy and reconstructing damaged portions are important for medical diagnosis. Recently, these issues have been studied extensively in the field of medical image inpainting. Inpainting techniques are emerging in medical image analysis since local deformations in medical modalities are common because of various factors such as metallic implants, foreign objects or specular reflections during the image captures. The completion of such missing or distorted regions is important for the enhancement of post-processing tasks such as segmentation or classification. In this paper, a novel framework for medical image inpainting is presented by using a multi-task learning model for CT images targeting the learning of the shape and structure of the organs of interest. This novelty has been accomplished through simultaneous training for the prediction of edges and organ boundaries with the image inpainting, while state-of-the-art methods still focus only on the inpainting area without considering the global structure of the target organ. Therefore, our model reproduces medical images with sharp contours and exact organ locations. Consequently, our technique generates more realistic and believable images compared to other approaches. Additionally, in quantitative evaluation, the proposed method achieved the best results in the literature so far, which include a PSNR value of 43.44 dB and SSIM of 0.9818 for the square-shaped regions; a PSNR value of 38.06 dB and SSIM of 0.9746 for the arbitrary-shaped regions. The proposed model generates the sharp and clear images for inpainting by learning the detailed structure of organs. Our method was able to show how promising the method is when applying it in medical image analysis, where the completion of missing or distorted regions is still a challenging task.
Classification of User Comment Using Word2vec and Deep Learning

This study will classify the text based on the rating of the provider application on the Google Play Store. This research is classification of user comments using Word2vec and the deep learning algorithm in this case is Long Short Term Memory (LSTM) based on the rating given with a rating scale of 1-5 with a detailed rating 1 is the lowest and rating 5 is the highest data and a rating scale of 1-3 with a detailed rating, 1 as a negative is a combination of ratings 1 and 2, rating 2 as a neutral is rating 3, and rating 3 as a positive is a combination of ratings 4 and 5 to get sentiment from users using SMOTE oversampling to handle the imbalance data. The data used are 16369 data. The training data and the testing data will be taken from user comments MyTelkomsel’s application from the play.google.com site where each comment has a rating in Indonesian Language. This review data will be very useful for companies to make business decisions. This data can be obtained from social media, but social media does not provide a rating feature for every user comment. This research goal is that data from social media such as Twitter or Facebook can also quickly find out the total of the user satisfaction based from the rating from the comment given. The best f1 scores and precisions obtained using 5 classes with LSTM and SMOTE were 0.62 and 0.70 and the best f1 scores and precisions obtained using 3 classes with LSTM and SMOTE were 0.86 and 0.87
Evaluation of Student Satisfaction in Using the Learning Management System for Online Learning at XYZ University

This research is to analyse what factors that influence the student satisfaction with the learning management system and how much influence do these factors have on student satisfaction with the use of the learning management system. This research aim was to find if there is a direct influence from the system quality, information quality, service quality, and perceived ease of use into the user satisfaction and whether there is an indirect influence from System use to the User satisfaction toward learning management system. The analytical method for analyse regression with the estimation of PLS Method, this research took sample of 99 students at XYZ University in Information system online learning, major study for undergraduate students. The results show that the indirect effect through system use are not significant; however the perceived ease of use and system quality turn out significant affecting the student satisfaction. Perceived ease of use is the most high factor for affecting the satisfaction of the students to use Learning Management System(LMS) as the media for online learning.
Post-Hartree–Fock method in quantum chemistry for quantum computer

Quantum computational chemistry is a potential application of quantum computers that is expected to effectively solve several quantum-chemistry problems, particularly the electronic structure problem. Quantum computational chemistry can be compared to the conventional computational devices. This review comprehensively investigates the applications and overview of quantum computational chemistry, including a review of the Hartree–Fock method for quantum information scientists. Quantum algorithms, quantum phase estimation, and variational quantum eigensolver, have been applied to the post-Hartree–Fock method.
High-Efficiency Parallel Cryptographic Accelerator for Real-Time Guaranteeing Dynamic Data Security in Embedded Systems

Dynamic data security in embedded systems is raising more and more concerns in numerous safety-critical applications. In particular, the data exchanges in embedded Systems-on-Chip (SoCs) using main memory are exposing many security vulnerabilities to external attacks, which will cause confidential information leakages and program execution failures for SoCs at key points. Therefore, this paper presents a security SoC architecture with integrating a four-parallel Advanced Encryption Standard-Galois/Counter Mode (AES-GCM) cryptographic accelerator for achieving high-efficiency data processing to guarantee data exchange security between the SoC and main memory against bus monitoring, off-line analysis, and data tampering attacks. The architecture design has been implemented and verified on a Xilinx Virtex-5 Field Programmable Gate Array (FPGA) platform. Based on evaluation of the cryptographic accelerator in terms of performance overhead, security capability, processing efficiency, and resource consumption, experimental results show that the parallel cryptographic accelerator does not incur significant performance overhead on providing confidentiality and integrity protections for exchanged data; its average performance overhead reduces to as low as 2.65% on typical 8-KB I/D-Caches, and its data processing efficiency is around 3 times that of the pipelined AES-GCM construction. The reinforced SoC under the data tampering attacks and benchmark tests confirms the effectiveness against external physical attacks and satisfies a good trade-off between high-efficiency and hardware overhead.
Radio spectrum awareness using deep learning: Identification of fading channels, signal distortions, medium access control protocols, and cellular systems

Radio spectrum awareness, including understanding radio signal activities, is crucial for improving spectrum utilization, detecting security vulnerabilities, and supporting adaptive transmissions. Related tasks include spectrum sensing, identifying systems and terminals, and understanding various protocol layers. In this paper, we investigate various identification and classification tasks related to fading channel parameters, signal distortions, Medium Access Control (MAC) protocols, radio signal types, and cellular systems. Specifically, we utilize deep learning methods in those identification and classification tasks. Performance evaluations demonstrate the effectiveness of deep learning in those radio spectrum awareness tasks.
A learning-based view extrapolation method for axial super-resolution

Axial light field resolution refers to the ability to distinguish features at different depths by refocusing. The axial refocusing precision corresponds to the minimum distance in the axial direction between two distinguishable refocusing planes. High refocusing precision can be essential for some light field applications like microscopy. In this paper, we propose a learning-based method to extrapolate novel views from axial volumes of sheared epipolar plane images (EPIs). As extended numerical aperture (NA) in classical imaging, the extrapolated light field gives re-focused images with a shallower depth of field (DOF), leading to more accurate refocusing results. Most importantly, the proposed approach does not need accurate depth estimation. Experimental results with both synthetic and real light fields show that the method not only works well for light fields with small baselines as those captured by plenoptic cameras (especially for the plenoptic 1.0 cameras), but also applies to light fields with larger baselines.
Assessing Visual Exploratory Activity of Athletes in Virtual Reality Using Head Motion Characteristics

Maximizing performance success in sports is about continuous learning and adaptation processes. Aside from physiological, technical and emotional performance factors, previous research focused on perceptual skills, revealing their importance for decision-making. This includes deriving relevant environmental information as a result of eye, head and body movement interaction. However, to evaluate visual exploratory activity (VEA), generally utilized laboratory settings have restrictions that disregard the representativeness of assessment environments and/or decouple coherent cognitive and motor tasks. In vivo studies, however, are costly and hard to reproduce. Furthermore, the application of elaborate methods like eye tracking are cumbersome to implement and necessitate expert knowledge to interpret results correctly. In this paper, we introduce a virtual reality-based reproducible assessment method allowing the evaluation of VEA. To give insights into perceptual-cognitive processes, an easily interpretable head movement-based metric, quantifying VEA of athletes, is investigated. Our results align with comparable in vivo experiments and consequently extend them by showing the validity of the implemented approach as well as the use of virtual reality to determine characteristics among different skill levels. The findings imply that the developed method could provide accurate assessments while improving the control, validity and interpretability, which in turn informs future research and developments.
Repurposing Case-Based Learning to a Conversational Agent for Healthcare Cybersecurity

A crucial factor for successful cybersecurity education is how information is communicated to learners. Case-based learning of common cybersecurity issues has been shown to improve human behaviour for prevention. However, some delivery methods prevent realistic critical appraisal and reflection of awareness. Conversational agents can scaffold healthcare workers’ understanding and promote deterrence strategies. The challenges of repurposing material to create a case-based agent were explored, and the ASPIRE process was modified. Heuristic evaluation from 10 experts in innovative educational technology resulted in the desired outcomes of usability, however Natural Language Understanding improvements were needed. Discussion of best practice when repurposing into conversational agents suggested modification of the ASPIRE process is feasible for future use.
The Effect of Latent Space Dimension on the Quality of Synthesized Human Face Images

In recent years Generative Adversarial Networks (GANs) have achieved remarkable results in the task of realistic image synthesis. Despite their continued success and advances, there still lacks a thorough understanding of how precisely GANs map random latent vectors to realistic-looking images and how the priors set on the latent space affect the learned mapping. In this work, we analyze the effect of the chosen latent dimension on the final quality of synthesized images of human faces and learned data representations. We show that GANs can generate images plausibly even with latent dimensions significantly smaller than the standard dimensions like 100 or 512. Although one might expect that larger latent dimensions encourage the generation of more diverse and enhanced quality images, we show that an increase of latent dimension after some point does not lead to visible improvements in perceptual image quality nor in quantitative estimates of its generalization abilities.
DEVELOPMENT OF INTERACTIVE LEARNING MEDIA EDUGAME USING ADDIE MODEL

In line with the district development program of Dharmasraya regency, namely the Dharmasraya Smart City Program and the implementation of Dharmasraya Smart Education, Edugame is currently one of the favourite innovations being used in the teaching and learning process for the primary school students. This study was aimed at creating a learning innovation using Edugame as a media for conveying information to students as well as inviting students to play. In addition, the approach method used was in the form of media development using the ADDIE (Analysis Design Develop Implementation and Evaluation) development method. This development method was started by conducting the field observations, then designing the Edugame interface to evaluate the shortcomings of the Edugame. From the results of the whiteboard test, it was proven that this application had succeeded in delivering learning materials to primary school students of SDN 08 Pulau Punjung, Dharmasraya Regency, West Sumatra. In sum, the teachers are assisted by the Edugame since the students learn independently to explore each Edugame and try to get the highest score.&#x0D; Keywords: Learning Media; Edu-Game; Learning Innovation; ADDIE.
The digital divide at three disadvantaged secondary schools in Gauteng, South Africa

In this study, we aimed to explore the extent of the digital divide at three quintile 1 secondary schools in Sedibeng West, Gauteng, South Africa. The rapid diffusion of information and communication technology (ICT) has changed the education sector along with others in societies around the globe. Technology, and, in particular, the internet, has affected, for instance, the ways in which learners communicate with teachers, learn, and work. With the advent of the internet, teaching and learning is no longer confined to classrooms. However, the digital divide, exposed by the closing of schools because of Covid-19 has now become more apparent. We collected data from 48 teachers and 3 school principals through focus group discussions and in-depth face-to-face interviews, respectively. We conclude that the three schools whose principals we interviewed, suffer from the inadequacy of ICT that creates digital, information, and knowledge divides between schools of different quintiles. Furthermore, these schools cannot run online classes during school closures related to Covid-19. We recommend training teachers and learners on how to use technology in teaching and learning since this would help to overcome difficulties during the Covid-19 pandemic.
Handwritten Digits Recognition Using SVM, KNN, RF and Deep Learning Neural Networks

This article discusses several classification algorithms of recognizing numbers from photographic images or with manual input, namely: support vector machine (SVM), Knearest neighbors (KNN), random forest (RF) and several variants of neural networks.The success rates of the algorithms in the field of handwriting recognition were compared.Six variants of recognition technology were analyzed and tested: using classifier from Scikitlearn package and using deep learning neural networks.To construct and train neural networks or train classifiers, a well-known and rather complete base of handwritten digits MNIST was chosen.Two types of neural networks were considered: sequential and convolutional.The training of neural networks was carried out using a variable number of steps (epochs).Recognition images were scaled to a size of 28x28 (784 cells in onedimensional representation).Preliminary processing of images (filtering, scaling, etc.) was carried out using the OpenCV library.For recognition, each image of a digit was converted to a 28x28 size and fed to the input of a pre-trained neural network.A technique to select the area of interest in photographs containing hand-written digits for further recognition has been devised.For handwritten digit recognition, the best recognition accuracy is provided by a convolutional neural network, as 97.6% of car ladle digits were recognized correctly with it.To improve the recognition accuracy for handwritten digits, it is necessary to perform two additional stages of image preprocessing and dataset transformation.After building recognition models using all the algorithms mentioned above, the recognition accuracy of all handwritten digits on the test program turned out to be within 98-100%.For industrial images regardless of the used neural network version, the recognition accuracy was 96-98%.