Reviewerly/exHarmony huggingface dataset:
Designing an e-learning course in the context of professional training of future workers in the tourism industry

Electronic technologies and services, as well as digital educational services, are the key factors of the new economy. Today the generally recognized role of the digital economy as a driver of progressive changes and a tool for the qualitative modernization of the institution of education is obvious. In the context of digital restructuring of all spheres of human life, the most important place belongs to the formation of a digital educational environment as a tool for the flexible adaptation of educational institutions to permanently changing environmental conditions (requirements of Federal state educational standards, competitive struggle of universities in the educational space, requests from employers, etc.). One of the ways of education digitalization is the introduction of e-learning courses into the educational process as an innovative content for the organization of educational activities. In this regard, the problem of the research can be formulated as follows: what is the design technology and content of the e-learning course?The purpose of the research is to reveal the technology of designing an electronic training course in the context of professional training of future workers in the tourism industry. To achieve this goal, a complex of scientific methods has been used: the study of scientific and methodological literature, analysis, design, generalization.The research results are as follows: the concept of «electronic training course» has been disclosed, types of electronic training courses presented; the algorithm for developing an electronic training course described; an example of an electronic training course on the discipline «Program tourism and tourism safety» given; the advantages of using the e-learning course proposed by the author in the educational process of training future workers in the tourism industry described.Key findings are e-learning course is an independent learning unit, e-learning content is focused primarily on self-learning; there are three types of e-learning courses, differing in the degree of presentation of the materials of the work programs – from full compliance with the content of the work program of the discipline to a partial, fragmentary presentation of its individual elements at the discretion of the teacher; the algorithm for designing an e-learning course is represented by defining its structure in accordance with the requirements established by each university, preparing the relevant didactic materials, compiling an information map, presenting it in the e-learning management system, approval and coordination procedures by the relevant structures of a university; the electronic training course proposed by the author on the discipline «Program tourism and tourism safety» has a block structure, contains the parameters for presenting the material for each element of the block; own experience of introducing an electronic training course into the professional training of future employees of the tourism industry has showed its relevance in educational practice.
Future of money and central banking in the era of technological globalization

The article discusses the impact of digital currencies, blockchain technology, and decentralized finance (DeFi) on the traditional models of central banking and monetary policy predicting their future. The emergence of these technologies has disrupted the traditional approach to financial governance, forcing central banks to adapt their models to an increasingly complex and decentralized financial system. Therefore, this article has examined the technical and economic challenges that central banks face in the era of digital currencies and DeFi, such as the regulation of digital currencies, changing consumer's behavior, and geopolitical and economic factors. Moreover, the potential benefits and risks of Central Bank Digital Currencies (CBDCs), including their design, technical feasibility, and potential implications for central banks as well as the broader financial system, have been discussed. The overall objective of this paper is to provide a thorough evaluation of where money and central banking are headed in the future, contributing to the ongoing discussion among policymakers, economists, and financial professionals about how to manage increasingly complex and interconnected financial systems.
Self-Supervised On-Device Federated Learning From Unlabeled Streams

The ubiquity of edge devices has led to a growing amount of unlabeled data produced at the edge. Deep learning models deployed on edge devices are required to learn from these unlabeled data to continuously improve accuracy. Self-supervised representation learning has achieved promising performances using centralized unlabeled data. However, the increasing awareness of privacy protection limits centralizing the distributed unlabeled image data on edge devices. While federated learning has been widely adopted to enable distributed machine learning with privacy preservation, without a data selection method to efficiently select streaming data, the traditional federated learning framework fails to handle these huge amounts of decentralized unlabeled data with limited storage resources on edge. To address these challenges, we propose a Self-supervised On-device Federated learning framework with coreset selection, which we call SOFed, to automatically select a coreset that consists of the most representative samples into the replay buffer on each device. It preserves data privacy as each client does not share raw data while learning good visual representations. Experiments demonstrate the effectiveness and significance of the proposed method in visual representation learning.
A Perceptual Measure for Deep Single Image Camera and Lens Calibration

Image editing and compositing have become ubiquitous in entertainment, from digital art to AR and VR experiences. To produce beautiful composites, the camera needs to be geometrically calibrated, which can be tedious and requires a physical calibration target. In place of the traditional multi-image calibration process, we propose to infer the camera calibration parameters such as pitch, roll, field of view, and lens distortion directly from a single image using a deep convolutional neural network. We train this network using automatically generated samples from a large-scale panorama dataset, yielding competitive accuracy in terms of standard l2 error. However, we argue that minimizing such standard error metrics might not be optimal for many applications. In this work, we investigate human sensitivity to inaccuracies in geometric camera calibration. To this end, we conduct a large-scale human perception study where we ask participants to judge the realism of 3D objects composited with correct and biased camera calibration parameters. Based on this study, we develop a new perceptual measure for camera calibration and demonstrate that our deep calibration network outperforms previous single-image based calibration methods both on standard metrics as well as on this novel perceptual measure. Finally, we demonstrate the use of our calibration network for several applications, including virtual object insertion, image retrieval, and compositing.
LTE Device Radio Frequency Fingerprints Blind Extraction Based on Temporal-Frequency Domain PRACH Signals

A frequency-domain cepstrum correlation and temporal transient filtered radio frequency fingerprints (RFF) blind extraction method is proposed for long term evolution uplink device authentication in this work. With the merits of the constant-modulus nature of the Zadoff-Chu (ZC) sequence and the cyclic prefix (CP) structure of the physical random access channel (PRACH) frame, the device RFF integrated in both the frequency-domain ZC sequence and the transient ramp-on/ramp-off sequences are extracted. For the frequency-domain RFF extraction, the cepstrum of the ZC sequence is calculated to enlarge the gap between the constant RFF and the random fading effects, and the correlation between two cepstrum fragments is then calculated to mitigate the random fading components. For the temporal RFF extraction, the ramp-on and ramp-off features induced by the device-specific power amplifier are extracted by exploiting the CP structure of the PRACH frame, where the CP part and the duplicate part are adaptively filtered and the final adapted weight of the least mean square filter is used as RFF. No prior PRACH parameter knowledge is required in RFF extraction and both the frequency-domain and the temporal RFF are combined as the input of the convolutional neural network to enhance the classification accuracy. With four-months sampling of PRACH signals emitted from universal software radio peripheral devices, the experimental results show that the authentication accuracy of the proposed combined RFF can reach as high as 99.9% in the line-of-sight (LOS) scenario and 99.6% in the non-LOS scenario.
EdgeCompress: Coupling Multidimensional Model Compression and Dynamic Inference for EdgeAI

Convolutional neural networks (CNNs) have demonstrated encouraging results in image classification tasks. However, the prohibitive computational cost of CNNs hinders the deployment of CNNs onto resource-constrained embedded devices. To address this issue, we propose EdgeCompress, a comprehensive compression framework to reduce the computational overhead of CNNs. In EdgeCompress, we first introduce dynamic image cropping (DIC), where we design a lightweight foreground predictor to accurately crop the most informative foreground object of input images for inference, which avoids redundant computation on background regions. Subsequently, we present compound shrinking (CS) to collaboratively compress the three dimensions (depth, width, and resolution) of CNNs according to their contribution to accuracy and model computation. DIC and CS together constitute a multidimensional CNN compression framework, which is able to comprehensively reduce the computational redundancy in both input images and neural network architectures, thereby improving the inference efficiency of CNNs. Further, we present a dynamic inference framework to efficiently process input images with different recognition difficulties, where we cascade multiple models with different complexities from our compression framework and dynamically adopt different models for different input images, which further compresses the computational redundancy and improves the inference efficiency of CNNs, facilitating the deployment of advanced CNNs onto embedded hardware. Experiments on ImageNet-1K demonstrate that EdgeCompress reduces the computation of ResNet-50 by 48.8% while improving the top-1 accuracy by 0.8%. Meanwhile, we improve the accuracy by 4.1% with similar computation compared to HRank. The state-of-the-art compression framework. The source code and models are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/ntuliuteam/edge-compress</uri> .
Noncontact High-Precision Aperture Detection Based on Distortion Compensation and Adaptive Mean Blur Algorithm

High-precision apertures are of great significance for the manufacture and assembly of precision instruments. There are currently two common types of methods for aperture measurement: manual measurement methods and computer vision-based methods. The computer vision-based methods work by using industrial cameras and morphological operators and image interpolation algorithms, and can dramatically reduce human errors. However, these methods still suffer from two problems. First, common morphological operations would damage the edge information of the image and fail to capture the accurate edge of the hole. Second, although the magnification algorithm based on image interpolation has an obvious edge positioning effect, it tends to make the edge jagged and blurred. To reduce the influence of image distortion on the error of subsequent image processing, we perform distortion compensation on the image, and use the grid intersection and pixel information in the vicinity for fitting and analysis, and calculate the lens distortion coefficient to correct the image distortion. Then, to obtain more accurate edge information, we improve the Canny operator by using the mean blur with an adaptive threshold that preserves the edge in place of the Gaussian blur and adding a new convolution kernel to smooth the image to provide a more accurate profile for subsequent aperture measurements. With these efforts, we propose a non-contact high-precision aperture measurement method based on a distortion compensation and adaptive mean blur algorithm. The proposed method has a measurement error no greater than ±0.03 mm, and achieves a 100% pass rate. The method solves the problem that the edge contour of the hole cannot be accurately extracted due to edge blurring, and allows for high-precision detection of apertures in industrial fields. The method is expected to be used with a number of precision instruments for aperture size measurement.
Virtual Art Exhibition to Encourage Traditional Culture Knowledge for Generation-Z

Indonesia as a country that has a lot of traditional cultures, requires attention especially among generation-Z to maintain its sustainability. In many cases, various Indonesian cultures are often claimed by other countries as part of their culture, sometimes for the political purposes, economic, or even industrial motives. Thus, it is important to introduce and strengthen the traditional cultures knowledge, especially to generation-Z as the nation's successors. This study was conducted by creating a virtual gallery exhibition as a manifestation of new media forms that can adapt to the trends from generation-Z. The virtual gallery contains traditional attractions, culinary, dance, tourism, music, folklores, and ethnic houses. Some contents in this virtual gallery applies augmented reality technology with image tracking based. This research used mix method with qualitative and quantitative approach to explore the generation-Z expression. Results, virtual gallery gives an effective penetration to increase the traditional culture knowledge among generation- Z.
Digital Language Use in Bangka as Contribution to Digital Culture and Heritage

This research aims to find out how the bilingual speakers of Khek and Malay in Bangka communicate using the messaging app, find out regarding what shifts that could be identified in the use of the languages, and find out what the attitudes of the Bangkanese people are towards Khek and Malay. The method is approaching a descriptive qualitative design which involves some screenshots of WhatsApp chat to analyze as well as a questionnaire for the participants to answer. The results show that they indeed use the certain codes while performing a digital communication such as language shift, code switching, code mixing, and unique words. Moreover, they have a positive attitudes toward both Khek and Malay for some reasons. The most important part is that through the digital communication have they spread the cultures from both Chinese-Indonesian and Malays by using Khek and Malay as well as the fact that the languages used during the process of digital communication have exposed their heritage and identity. This research needs to be further investigated about the language preservation due to the fact that some people cannot speak one of those particular languages actively. With further investigation will reveal the current condition of those languages.
Is Instagram recommended to be used in learning?

Social media has the potential to enhance the learning process. Nowadays, students frequently use social media, including Instagram. However, most teacher mentions still doubt using Instagram for learning. Therefore a literature review is needed to provide a current overview of this platform. Based on the characteristic of social media, which rapidly develops and changes, the researchers consider that rapid review is the exact method to answer the questions raised. The articles were selected from Wiley, ERIC, Taylor, and IEEE databases, with requirements: 1) published from 2020 onwards; 2) passed a peer review process, 3) written in English and Indonesian. The article answers several questions: 1) What is Instagram, and how to use the features for learning? 2) What are the strategies to optimize Instagram for learning? and 3) What is the student's perception of Instagram for learning? The results show Instagram has been used in learning, and the students tend to respond positively.
Exploring the Effective Classroom Teaching Model Based on Cloud Platform - Linux Course as an Example

The traditional experimental learning environment that is used to teach Linux courses in schools like colleges and universities has a number of drawbacks that can be mitigated by the creation of a cloud platform, which ensures the continuity and safety of experiments performed by students during the course of their education. In addition, the novel design and implementation of the teaching method for the Linux course that is housed on the cloud platform is made possible by the integration of the post-course race certificate and the logic of the Civics course. Its purpose is to cultivate talents with high levels of competence and quality in the areas of operation and maintenance.
How artificial intelligence promotes the education in China

Artificial intelligence is a unique term that only exists in the human world. It is also one of the important signs that distinguish human beings from other intelligent creatures. It is recognized as one of the three cutting-edge technologies in the 21st century. In the past two decades, artificial intelligence has also been widely used in the field of education. From the change of students' teaching methods to the update of educational technology, it is full of intelligent, virtual and real-time elements. This paper will sort out the development track of artificial intelligence, and its relations with education in China, and discuss the practical application, Dilemma and future development of artificial intelligence in education in China.
Research on the Design and application of deep Learning Teaching Model of English Listening and Speaking in Primary School under the Information Technology Environment

In order to promote the in-depth learning of primary school English listening and speaking course, this paper constructs the teaching model of the in-depth learning of primary school English listening and speaking under the information technology environment. It consists of six parts: teaching preparation, setting up situation, activating prophet, acquiring new knowledge, deep processing of knowledge, teaching evaluation and reflection. Three rounds of action research were carried out on this model in the experimental class, which was verified from four aspects: pre and post test of deep learning characteristics, evaluation of students' learning effect, classroom observation and interview between teachers and students. The results show that the deep learning teaching model of primary school English listening and speaking can effectively improve students' learning effect, students' learning attitude and motivation, knowledge connection and integration, knowledge transfer and application. Problem solving ability, knowledge construction and reflection, the average of five deep learning ability indicators were all improved; Both teachers and students gave positive attitude to this model. In addition, in order to further improve the effect of in-depth learning in English listening and speaking classroom, this paper finally gives some suggestions for English listening and speaking classroom teaching based on the research results.
A Study on the Influence of Task-driven Instruction on Learning Outcomes in Online Learning under COVID-19

Interest in online learning is increasing due to its advantages and pedagogical potential. However, few studies have investigated the effects of task-driven instruction on learning outcomes. This study examines the effectiveness of the application of task-driven instruction as a means of verifying that the use of task-driven instruction in online learning is effective by comparing changes in students' grades, intrinsic motivation, perceived social presence, and perceived cognitive load before and after the application of the method. Eighty high school students (33 males) were recruited for this experiment. Prior to the experiment, the purpose and steps of the study were explained frankly and candidly, problems and risks that might arise from participation in the study were pointed out, the benefits that would result from participation in the study were explained, and the possibility of voluntarily withdrawing from the study at any time was clearly communicated and approved by the study subjects or guardians. They were divided into experimental group I and control group II, with 40 students in each group. The results of the study showed that after the implementation of the instruction, the experimental group I performed significantly better than the control group II. In addition, the experimental group II outperformed the control group II in terms of perceived intrinsic motivation, social presence, and cognitive load.
Ready for What?—Digital Readiness in Teacher Education: A Case Study of Professional Partnership in Northern Ireland

Internationally, education systems make considerable investments in ICT resources and infrastructure to support digital learning in schools. Since 1997, Northern Ireland has undertaken an ambitious, regional partnership initiative, providing ICTs to support schools in both pedagogy and school management. Over the first decade of this rollout, an estimated £500 million was spent in a country with a school population of around 330,000. In partnership, Initial Teacher Education (ITE) providers across Northern Ireland are impacted as they prepare pre-service teachers for teaching in relatively ICT-rich schools and recommendations have been made that the online education skills of teachers should be developed. Whilst this sounds promising for digital skills’ preparedness in the teaching workforce, the strategy was not systematically enacted. COVID-19 precipitated a move to emergency home schooling across Northern Ireland, as elsewhere, and this highlighted systemic weaknesses, including in partnerships between schools and ITE and their capability to support online learning fully. This chapter will examine the partnership between ITE in universities and the schools they work alongside highlighting some challenges to that partnership, particularly exposed during the pandemic. This particularly highlighted the need for a reset to a vision of digital readiness within education.
Sampling Techniques

Sampling is a statistical method to select a subset or sample from a population for the purpose of making certain observations to draw inferences regarding the population under study. It is difficult to study an entire population for an attribute, say a disease condition. Reducing the number of individuals in any study reduces the cost, time, and workload, besides providing high-quality information that can be extrapolated for the entire population. At times, it becomes difficult to locate and/or reach the entire population. Therefore, a representative group of individuals, i.e. a sample, is selected from the population to make any observation and analysis. It is of utmost importance that the sample is a true representation of the population so that the inferences arrived at after analysis can be applied to the population from which it has been drawn. Any bias leading to improper/non-representative drawing of the sample will give a result that cannot be applied to the population under study. Using sampling techniques eliminates any bias in choosing the subset.