Reviewerly/exHarmony huggingface dataset:
Attitudes and Perceptions of Graduate ESL Students towards Mobile Assisted Language Learning in Pakistan

The present paper aims to describe perceptions and attitudes of graduate and undergraduate English-major ESL students towards the use, importance, benefits and challenges they face regarding Mobile Assisted Language Learning (MALL) apps.For this purpose, a quantitative study is conducted to record the respondents' responses on a dichotomous scale.Through the study, it was found that most of the language students were using MALL and were in favor of MALL, while using mobile phone and its apps for language learning, they recognized its importance and benefits, aspired to incorporate it in traditional learning method.The respondents assert that MALL not only makes them autonomous, but also saves their time.They also recorded numerous challenges, but the greatest challenges they declared are the language learning apps which require a significant time access to operate.The other challenge is the continually bugging advertisements they had to tolerate while using language learning mobile apps which disturb their tempo and waste their time.Considering its findings, the study highlights the need of uniform integration of MALL and formal teaching-learning mode by the government of Pakistan.This needs government's specialized care and budget for the smooth running.The study provides a careful estimate of situation of MALL in Pakistan which further stresses the speedy accommodation of MALL in education department to meet international standards.
SenSig: Practical IoT Sensor Fingerprinting Using Calibration Data

Sensing technologies are becoming ever more ubiquitous in society and increasingly finding their way into important and intimate aspects of our lives such as Industrial Internet of Things (IIOT) and Smart Homes. Accordingly, it's vital to fingerprint these sensors and devices enabling the detection of any malicious or malfunctioning sensors that may be present. The aim of this paper is to provide a simple and lightweight means of fingerprinting motion sensors, and by extension the devices these sensors reside in. To generate our fingerprints, we use the data produced by motion sensors (more specifically, the gyroscope) during the calibration process on start-up. Subsequently, we employ the use of a novel form of quantisation, and various signal processing techniques on this sensor data to generate our sensor fingerprints. Our results show that such calibration data is fingerprintable, and we demonstrate the effectiveness of a potential use case of our fingerprints: identification, where we are able to uniquely identify a sensor with a 0 % EER and 38-bits of entropy.
About One Method of Constructing a Code-Based Cryptosystem Based on Quasi-Cyclic Codes

This article discusses the problem of constructing a public-key code-based cryptosystem based on quasi-cyclic codes and the hard task of complete decoding. Experiments analyzing the correcting ability of the considered code system are presented. The ways of increasing the corrective ability of this code system are proposed.
Digital Behavioral Biometrics and Privacy: Methods for Improving Business Processes without Compromising Customer Privacy

To enable people to interact with online websites and systems, browsers capture a variety of events that occur on the page—such as how a person is moving the computer mouse, what a person clicks on, what a person types, and whether a person is scrolling. These events represent a user’s behavior on a page, referred to as the DOM or Document Object Model, and are recorded at a millisecond precision rate (e.g., the exact millisecond timestamp when a key goes down and when it comes back up). Research and practice alike have found that these behavioral events can provide powerful insight into the users’ experience, such as whether users are frustrated, and even help distinguish between legitimate and fraudulent users. In this paper, we present six best practices for responsibly collecting these digital behavioral biometric data to help protect user privacy as well as encourage proper interpretation. For each principle, we discuss its rationale and practical application.
Features of the Organization of Creative Activity in History Lessons at School in Grades 6–7

The article is devoted to the analysis of the problem of the organization of creative activity in history lessons in secondary school. Currently, the relevance of this methodology has increased significantly, which is associated with changes in standards in the field of education. Creative activity at school contributes to the development of personal qualities of schoolchildren in unity with professional skills of creative and inventive activity. The systematic work of a history teacher to develop the creative abilities of middle school students orients them to show interest in independent intellectual activity, the need for their own research of historical processes and phenomena, the desire to master intellectual skills and abilities, the need for active creative activity. The purpose of the article is to identify and characterize the features of the organization of creative activity in history lessons at school in grades 6-7. At the same time, the choice of age is due to the fact that, on the one hand, students already have a primary base of necessary skills and abilities, and on the other hand, they are still distinguished by sincere interest and willingness to realize their undiscovered creative potential. Methodology. The study was conducted on the basis of studying the modern educational environment. In the course of practical activity and theoretical generalization, an appeal was made to the technology of creative developing learning, to the method of artistic performance, which allows organizing joint activities of teachers and students on the basis of mastering artistic means of expressing their aesthetic understanding and attitude to historical reality. At the same time, creativity is considered in inseparable connection with educational and research activities. In conclusion, it is concluded that the implementation of the lesson with the use of creative activity is carried out through the use of various forms of creative activity, including such as problem-based learning, project activities, conducting non-traditional lessons.
Cloud Storage I/O Load Prediction Based on XB-IOPS Feature Engineering

With the popularization of cloud computing and the deepening of its application, more and more cloud block storage systems have been put into use. The performance optimization of cloud block storage systems has become an important challenge facing today, which is manifested in the reduction of system performance caused by the unbalanced resource load of cloud block storage systems. Accurately predicting the I/O load status of the cloud block storage system can effectively avoid the load imbalance problem. However, the cloud block storage system has the characteristics of frequent random reads and writes, and a large amount of I/O requests, which makes prediction difficult. Therefore, we propose a novel I/O load prediction method for XB-IOPS feature engineering. The feature engineering is designed according to the I/O request pattern, I/O size and I/O interference, and realizes the prediction of the actual load value at a certain moment in the future and the average load value in the continuous time interval in the future. Validated on a real dataset of Alibaba Cloud block storage system, the results show that the XB-IOPS feature engineering prediction model in this paper has better performance in Alibaba Cloud block storage devices where random I/O and small I/O dominate. The prediction performance is better, and the prediction time is shorter than other prediction models.
The use of digital technologies for the development of pre-service teachers’ rhetorical skills: the experience of Ukraine

The authors characterize the typology of digital technologies for the development of pre-service teachers’ rhetorical skills: video content (speeches of prominent speakers of the past and present, fragments of movies, theater performances, television programs, speeches of masters of the word, lectures by national and foreign teachers, reports of famous teachers), audio content (podcasts, audiobooks), rhetoric courses on educational platforms, specialized rhetoric resources, specialized software in the field of rhetoric, social communities for on-line communication. The expert assessment was conducted to identify the most effective type of digital technology in the field of rhetoric for the development of pre-service teachers’ rhetorical skills. The use of video content (speeches by prominent speakers of the past and present), audio content and specialized software was found to be the most effective. The conclusions of the expert group were confirmed by the Kendall concordance coefficient and the chi-square test for estimating the probability of the result.
Design of an Artificial Neural Network-Based Model for Prediction Solar Radiation Utilizing Measured Weather Datasets

Forecasting solar radiation plays an important role in the field of energy meteorology, as it provides the energy value expected to be produced by the solar plants on a specific day and time of the year. In this paper, a new and reliable artificial intelligence-based model for solar radiation prediction is presented using Artificial Neural Network (ANN). The proposed model is built utilizing real atmospheric affecting measured values according to their locational weather station. In the training process, the Levenberg–Marquardt (LM), Bayesian Regularization (BR), and Scaled Conjugate Gradient (SCG) are used. The mean absolute error (MAE) and the root mean square error (RMSE) are used to evaluate the model accuracy. Results of the investigation show that the proposed model provides the lowest error rate when using the (BR) training algorithm for predicting the average daily solar radiation.
Forecasting Annual Solar Power Output From Geographic Location

Power determining of environmentally friendly power plants is an exceptionally dynamic examination field, as dependable data about the future power age takes into consideration a protected activity of the power framework and assists with limiting the functional expenses of these energy sources. AI calculations have demonstrated to be exceptionally strong in anticipating errands, like financial time series or discourse acknowledgment. Using random forest regression method, daily mean solar output generation can yield promising result rather than conventional NWP model for forecasting. Using that in practice also the goal was to create a user-friendly application, with easy access, to provide accurate forecasting regarding saving and conservation. This paper's goal was accomplished in three stages: To begin with, make an AI model that predicts the yearly energy result of an expected sun based establishment. From that point onward, make a model that predicts establishment costs. Carrying out these models on an easy to use web application that shows clients the amount they can hope to save money on their yearly energy bill by changing to sun oriented. The random forest model out-performs its other rivals and also conventional models, thus providing a better suited model to run with for forecasting.
STEM-Education and Features of its Implementation in Ukraine and the World

The authors analyze the concept of STEM education and describe its implementation in educational institutions in Ukraine and around the world. The results are based on a content analysis of scientific findings published in journals indexed in Scopus and Web of Science in the last five years. A retrospective analysis of teaching mathematics, physics, and computer science in Ukrainian schools over the past 20 years is presented. The possibility of introducing STEM education through interdisciplinary projects using special software, including cloud services, is substantiated. Features of realization of one of such projects using the dynamic mathematics software GeoGebra are described.
Power Side-Channel Leakage Assessment of Reference Implementation of SABER Key Encapsulation Mechanism

Traditional public-key cryptographic schemes are soon going to be replaced with Post-Quantum Cryptographic (PQC) schemes to ensure security guarantees in a Quantum Computing-enabled world. While Quantum Computing will help solve many hard problems intractable by classical computing paradigm, it will also compromise the hard problems that traditional cryptographic schemes are built upon. Among the National Institute of Standards and Technology (NIST) finalist PQC schemes, SABER Key Encapsulation Mechanism (KEM) is the only one based on Module Learning With Rounding (LWR). In this work, we have investigated the decryption procedure of SABER KEM to identify leakages in power consumption traces for the reference implementation running on an ARM Cortex-M4 microcontroller by correlating the trace samples with the decrypted message bytes, and by performing a Test Vector Leakage Assessment (TVLA). Both assessment techniques indicate that the incremental-storage steps in the reference implementation might allow an adversary to reveal information about the message and/or secret key.
Neural networks and their application for image recognition

This paper will explain what an artificial neural network is and how it works in theory. To make it easier to explain how an artificial neural network works, the first part will explain the terminology, the idea behind neural networks and their role in machine learning. In the continuation of the paper, the development of neural networks will be presented, as well as the principle of operation of the most important models that will be used through examples, and special attention will have focus to explain the concepts of neural networks. Through various applications in the examples, we will analyze the architecture of neural networks on image recognition using web applications and explain the concepts of convolutional neural networks.
Digital transformation - New Approaches and Challenges in Education

In early 2020, the world faced a pandemic of the COVID-19 disease that suddenly changed the way we learn, live and work. It was necessary to ensure the continuity of teaching and learning literally overnight. The university management, teachers and academic staff, students and all administrative services had to adapt to the new situation.Although the impact of the pandemic on higher education institutions was stressful and in most cases there was no contingency plan, higher education systems ensured continuity of the educational process. The system required the reorganization of teaching and learning, significant teacher involvement and additional time, advanced organization and continuous training of teachers.The lessons learned so far should serve as a guide for the future development of education as a whole and through the analysis and application of good solutions to put in the forefront a recovery program and increase investment in education. At the same time, digital transformation is becoming one of the strategic goals of the development strategy of most universities. Paper will present what necessary steps should be taken to achieve digital transformation in education, why continuous teacher training is important and what are the trends and further steps of development.
PV Energy Prediction in 24 h Horizon Using Modular Models Based on Polynomial Conversion of the L-Transform PDE Derivatives in Node-by-Node-Evolved Binary-Tree Networks

Accurate daily photovoltaic (PV) power predictions are challenging as near-ground atmospheric processes include complicated chaotic interactions among local factors (ground temperature, cloudiness structure, humidity, visibility factor, etc.). Fluctuations in solar irradiance resulting from the cloud structure dynamics are influenced by many uncertain parameters, which can be described by differential equations. Recent artificial intelligence (AI) computational tools allow us to transform and post-validate forecast data from numerical weather prediction (NWP) systems to estimate PV power generation in relation to on-site local specifics. However, local NWP models are usually produced each six hours to simulate the progress of main weather quantities in a medium-scale target area. Their delay usually covers several hours, further increasing the inadequate operational quality required in PV plants. All-day prediction models perform better, if they are developed with the last historical weather and PV data. Differential polynomial neural network (D-PNN) is a recently designed computational method, based on a new learning approach, which allows us to represent complicated data relations contained in local weather patterns to account for irregular phenomena. D-PNN combines two-input variables to split the partial differential equation (PDE), defined in the general order k and n variables, into partition elements of two-input node PDEs of recognized order and type. The node-determined sub-PDEs can be easily converted using operator calculus (OC), in several types of predefined convert schemes, to define unknown node functions expressed in the Laplace images form Application of the inverse L-transformation formula to the L-converts results in obtaining the prime function originals. D-PNN elicits a progressive modular tree structure to assess one-by-one the optimal PDE node solutions to be inserted in the sum output of the overall expanded computing model. Statistical modular models are the result of learning schemes of preadjusted day data records from various observational localities. They are applied after testing to the rest of unseen daily series of known data to compute estimations of clear-sky index (CSI) in the 24 h input-delayed time-sequences.
Intelligent Personnel Assistant for Field Researchers

The research subject is specialized intelligent personal assistants as tools designated to facilitate and automate the activities of scientists and researchers engaged in fieldwork. Most researchers still follow traditional data collection and processing methods, taking notes on paper and then digitizing them long after the fieldwork is completed. Another common practice is using several digital devices such as video cameras, ultrasound detectors, and various sensors in combination with paper and laptops. These practices often cause long delays, data transfers between analog and digital media often lead to technical errors, and valuable notes or observations may be lost during the period from information gathering to digitization. Field researchers who use software systems in their work do not have a tool designed especially for this type of activity but are forced to adapt and combine different existing solutions.A specialized intelligent personal assistant is offered, which serves as a unified system for collecting, storing, and processing field data. The aim is to improve the quality of the collected field data and optimize the analysis. Good cooperation in gathering information is crucial for research processes.
Introduction to Information Security: From Formal Curriculum to Organisational Awareness

Many organisations responded to the recent global pandemic by moving operations online. This has led to increased exposure to information security-related risks. There is thus an increased need to ensure organisational information security awareness programs are up to date and relevant to the needs of the intended target audience. The advent of online educational providers has similarly placed increased pressure on the formal educational sector to ensure course content is updated to remain relevant. Such processes of academic reflection and review should consider formal curriculum standards and guidelines in order to ensure wide relevance. This paper presents a case study of the review of an Introduction to Information Security course. This review is informed by the Information Security and Assurance knowledge area of the ACM/IEEE Computer Science 2013 curriculum standard. The paper presents lessons learned during this review process to serve as a guide for future reviews of this nature. The authors assert that these lessons learned can also be of value during the review of organisational information security awareness programs.
Mr. Turtle (From “The Tortoise and the Hare” Fairy Tale)

This chapter explores the potential application of SC by individuals vis-à-vis the improvement of their own learning methods. The author recaps the main characteristics of his own learning processes, and he (re-)introduces the second data-set of the study in earnest, i.e., a corpus of data- and cognitive-science articles. Altogether, the results indicate that individuals can utilize SC and cognitive-science principles in the improvement of their learning methods. The author identifies five main steps that individuals can use via SC, before and during learning processes. And for cognitive science, the author identifies three main aspects that summarize how that interdisciplinary field can be used to improve learning.
Outdoor Perception Space Navigation Experimental Procedures

This paper proposes methods to compare two different navigation frameworks: move base—Potential Gap and Global Path Follower—Potential Gap in outdoor environments. Initially, we plan to utilize Gazebo and RViz simulators to first run test scripts in a simulated map. To assess outdoor navigation performances, we decide to use success rate, navigation time, trajectory length, and velocity smoothness as our evaluation metrics. Then we will move into real-world experiments. For outdoor testing, we decide to run trials on an open space platform and a narrow alleyway and plan to implementing Monte Carlo Localization to pinpoint the location of TurtleBot by combining GPS and robot odometry data. After completing all the trials, the final results will be analyzed and included in future works.
18‐3: 3D Image‐Quality Evaluation Method Based on Image Comparison Metrics

We propose a method for evaluating the 3D image quality with a peak signal to noise ratio (PSNR) and structural similarity index (SSIM), which are image comparison metrics. Compare light field display (LFD) quality level with existing 3D X‐Talk evaluation and depth range evaluation method. And we proceed verification of image comparison metrics through 2D image. After creating a pattern dedicated to 3D evaluation for 3D application of image comparison metrics, 3D image quality evaluation is carried out. In summary, the present paper proposes a method for performing 3D image quality evaluation through image comparison metrics using only 3D images.
Quantity vs. Quality of Monolingual Source Data in Automatic Text Translation: Can It Be Too Little If It Is Too Good?

Monolingual data, being readily available in large quantities, has been used to upscale the scarcely available parallel data in order to train better models for automatic translation. Self-learning, where a model is made to learn from its output, is one approach of exploiting such data. However, it has been shown that too much of this data can be detrimental to the performance of the model if the available parallel data is comparatively extremely low. In this study, we investigate whether the monolingual data can also be too little and if this reduction, based on quality, have a any affect on the performance of the translation model. Experiments have shown that on English-German low resource NMT, it is often better to select only the most useful additional data—based on quality or closeness to the domain of the test data—than utilizing all of the available data.