Reviewerly/exHarmony huggingface dataset:
WordWall Application as an Interactive Learning Media in Mastering English Vocabulary at Elementary School

This study aims to determine the effect of using a wordwall application to improve English vocabulary mastery of elementary school students in Garut.The method used is an experimental research method using a quantitative approach.Because this research requires treatment to find out the effect of WordWall Media on Students' English Vocabulary Mastery.The data sources in this study is the fourth grade students of SDN 1 Kota Kulon, Garut that consist of 20 students.The researcher uses the type of one group pretest and posttest design, because this design is carried out on one group without a control or comparison group.Based on the results of the pre-test and post-test data calculations, the 2-tailed value was 0.000 with a significance level of 5% (α = 0.05).Meanwhile, hypothesis testing criteria: If the significance value (2-tailed) < 0.05, then H o is rejected and H a is accepted.Therefore Ho is rejected and Ha is accepted.This indicates that the students' mastery of English vocabulary is increasing.With 90% or 18 people who have not reached the KKM and 10% or 2 people who have reached the KKM at the time of empirical data collection.And after the authors conducted research using WordWall media there was an increase of 65% or 13 students who had reached the KKM and 35% or 7 students who had not reached the KKM.In other words, there is an influence of WordWall media on English vocabulary mastery of fourth grade students of SDN 1 Kota Kulon.
Plant Ecology Learning Modeling with Python Programming to Support Digital Argumentation Skills

The purpose of this research is to produce modelling in plant ecology courses based on python programming and to analyse whether the python programming model can encourage digital argumentation skills in problem-based learning through online learning.This research method uses research and development with ADDIE design (Analysis, Design, Development, Implementation, and Evaluation).Data collection techniques in this study are arguments written through tests taken from journals and student response questionnaires distributed after using the python programming application.The results of the preparation of modelling of plant ecology courses are arranged in a figma application in the form of a user experience based on a flow chart to match product features with user needs.The model design that has been compiled will be coded with the python programming language using visual studio code so that the code provided can be read by the system.The results of digital argumentation skills were carried out to 6 students with an average level of argumentation categories that varied, low (34%), moderate (50%), and high (16%).Small-scale trials using python resulted in an average percentage value of 94.66% which is included in the very good category and is feasible to use.
The use of digital technology in the selection and training of students in the transportation system

The article shows the possibility of using online testing with the help of a computer program in the selection of applicants for technical direction and digital tools for teaching students. It solves the following problems: to show the application of the computer program to determine professional aptitude of future applicants with the help of online testing on digital devices, as well as show the use of digital tools in the organization of work in the training of students. The methods and research tools were digital technologies (online board “Miro”; Edupuzzle – online service for creating visual material; Pear Deck – tool to involve students in the learning process by interactive; Mindomo – for creating mind maps; online testing with the “Wizard” tool; test questionnaire in Google form on the studied material on the “Interpersonal conflicts management” discipline; author’s questionnaire on using digital tools in the learning process of students). The article presents the author’s approach to recruit future students in different learning profiles, including technical, using a computer program written in the “JavaScript” programming language with the created testing interface and subsequent ability to process its results using digital devices such as phone, tablet, computer, etc.
Designing a System of Training and Methodology Support for Training Construction Industry Specialists in the Conditions of Digital Economy

The paper presents a concept for designing an information system that provides organisation and support of the educational process. The core of the concept is the individual approach to the formation of students’ educational trajectory. The individual approach is formed automatically on the basis of previous learning results and supposes complexity of the syllabus-grounded educational material, including as well a list of references depending on the factual level of assimilation of the instructional material. The information system concept involves storage and demonstration of course materials as well as third-party data from electronic databases of scientific, reference or educational sources. The system is able to predict the results of current, interim and final assessment in an automated fashion, depending on current results, in order to create additional recommendations for mastering the material and potential grade improvement. The designed system is capable of generating reports on learning outcomes in an automated mode. Such information system is a tool for the realisation of a hybrid form of education.
Value perception analysis of the educational product of the university in the conditions of digital transformation

In today’s realities, the issue of value development is of increased interest due to the transformational processes of digitalization, as well as the dynamically changing external environment, which contributes to shifts in the behavior in the educational process, changes in the perception of factors in the development of educational organizations, changing priorities, the formation of new approaches to the creation and promotion of educational products. Under these conditions, it becomes relevant to study the value perception of the university product by external and internal stakeholders to form an educational product that meets modern requirements.
AuthAttLyzer: A Robust defensive distillation-based Authorship Attribution framework

Source Code Authorship Attribution (SCAA) is the technique to find the real author of source code in a corpus. Though it is a privacy threat to open-source programmers, it has shown to be significantly helpful in developing forensic-based applications such as ghostwriting detection, copyright dispute settlements, catching authors of malicious applications using source code, and other code analysis applications. Recent advances in SCAA techniques have performed exceptionally well on varied datasets. However, recent works on gradient-based attacks and universal perturbations can adversarially modify source code to reduce the accuracy of state-of-the-art classification techniques based on deep neural networks to as low as 10%. In this paper, we derive inspiration from recent advances in cyber-security and propose using the concept of defensive distillation to create a new architecture for source code authorship attribution with increased robustness against such adversarial attacks (reduce sent size). We empirically show that our approach, defensive distillation, and varied feature selection reduce miss-classification on perturbed source code files for Google code Jam and GitHub database while maintaining a 95% accuracy on legitimate source code files.
MURAS: An Efficient Multi-Value-Proof-Based Remote Attestation Scheme for Cloud Platform

An efficient multi-value-proof-based remote verification scheme in the cloud environment, MURAS, is proposed to address the inefficiency of multiple component verification for virtual machines and alleviate the TPM bottleneck. We use the virtual machine introspection technique to perform semantic reconstruction outside the virtual machine, transparently capture metric logs in the virtual machine, and store virtual machine component information with the help of an unbalanced hash tree structure. Meanwhile, MURAS utilizes a multi-value proof method to complete trusted verification of multiple components based on their core authentication paths and then combines trusted proof of the virtual machine and host environment to achieve efficient verification of the integrity of the cloud platform. By verifying all evidence data at once, it can reduce frequent operations on TPM and alleviate TPM bottlenecks. By verifying all evidence data at once, it can reduce frequent operations on TPM and alleviate TPM bottlenecks. In the QEMU-KVM environment, many experiments show that with the continuous increase of the cloud platform scale, MURAS will have a more prominent efficiency of cloud platform trusted state verification.
Hardware Implementation of SPONGENT Lightweight Hash Algorithm

Security and Privacy at the end nodes are of prime concern as the IoT paradigm is increasing with more connected physical devices at the end nodes. The end nodes are resource-constrained devices and hence the primitives addressing the security and privacy concerns at these nodes need to have a smaller footprint and even have better performance, thus making it a bottleneck to meet both the tradeoffs. SPONGENT-88 variant is a lightweight sponge-based hash algorithm (authentication primitive) deployable at end nodes with moderate security bounds on pre-image, secondary pre-image, and collision resistance which would be apt for any trivial application where the security can be forfeited in the pursuit of better efficiency. This work focuses on realizing the Spongent-88 architecture with better performance and reduced area on target FPGAs.The proposed architecture is designed to have a balance between sequential and combinational logic required to realize the hash algorithm. The proposed work achieves a 19% increase in the throughput performance with LUT-6 technology FPGA with works reported. ASIC flow is being carried out for the design on the TSMC180nm node and the area occupied reports 1204GE.
Detecting IMAP Credential Stuffing Bots Using Behavioural Biometrics

Credential stuffing has seen a great uptick in use and is now one of the most common types of cyberattacks. Legacy email protocols like the Internet Mail Access Protocol (IMAP) are particularly vulnerable to this kind of attack as they do not support multi-factor authentication (MFA). We propose a supervised learning system that detects credential stuffing bots using two kinds of behavioural biometrics: mouse and keystroke dynamics. The system records a user’s mouse and keystroke events while they complete three tasks in a graphical user interface (GUI) application. We also developed two types of bots: a simple bot and an advanced bot, the latter of which uses techniques to simulate human-like mouse and keyboard motions. We evaluated our system using the Random Forest (RF), decision tree (DT), support vector machine (SVM), and k-nearest neighbors (KNN) algorithms and compared them against two data sets: Simple containing human and simple bot data, and Advanced containing human and advanced bot data. The highest accuracy against the Simple and Advanced data sets were both 96.95% achieved by the KNN and RF classifiers, respectively. The RF classifier showed the best overall performance, achieving the highest precision and mean AUC against the Simple data set and the highest scores across all metrics against the Advanced data set.
Idei K.D. Ushinskogo i sovremennoe patrioticheskoe vospitanie studentov

The article reveals the current ideas of K.D. Ushinsky about the education of patriotism and how these ideas are implemented in the education of students (as a methodological basis, the author of the article uses a personal-activity approach, the foundations of which are laid by K.D. Ushinsky). It is considered how patriotic education of students is organized during the educational process, how educational projects and tasks are based on digital, Internet technologies characteristic of the youth subculture for its effectiveness, attractiveness for young people (according to Ushinsky's idea, a person should be educated according to the &amp;quot;spirit of the times&amp;quot;, the social environment, culture). The criteria for evaluating the results of patriotic education and the results of work (increasing the social activity of students, etc.) are disclosed.
Testing and Evaluation of a Model for Optimizing the Process of Information Interaction of Users with Distributed Digital Learning Resources Based on the Chatbot Software

The main features of information interaction in the information and educational environment are considered. The transformation of the traditional structure of interaction between the participants of the teaching and learning process and digital learning resources is presented. It is proposed to include an additional component of the chabot in this scheme. To illustrate the proposed model the DFD methodology is used. The chatbot presented in this study was beta tested in a web development technologies course. The results of testing the functionality within the framework of the proposed model are shown. Assessment results for the experimental and control groups are presented.
Technology Associated with the Formation of the Students‘ Mathematical Language at the Technical University

Requirements to the educational process in universities is quite high. The volume of knowledge that is necessary for a person and society is growing rapidly. Sufficiently well-developed speech of a student is one of the decisive conditions for orientation in a huge flow of information. Activation of the educational process and the use of intensive teaching methods contribute to the high development of students’ speech. The paper deals with the initial studies for the development of students’ mathematical language.
Design of New Single-Electron Reaction-Diffusion Circuit with Ability to Generate and Control Plane Waves

We propose a new single-electron (SE) reaction-diffusion (RD) circuit that can express traveling plane waves. While SE circuits have advantages such as nonlinearity and stochastic behavior, a suitable information processing method has not yet been established for them. Thus, we focus on the behavior of a photo-responsive (PR) Belousov-Zhabotinsky (BZ) reaction, one of RD systems, to develop a suitable information processing method. Reportedly, the PR-BZ reaction can generate plane waves, and the waves can be used to represent logic gates. Here, we designed the new SE-RD circuit that can generate the desired plane waves. In simulation results, we found our circuit can operate as desired. We believe this circuit can be used to construct a logic gate device as same as the PR-BZ reaction.
Fast and Accurate Detection of Audio Adversarial Examples

Neural networks have become an attractive choice for audio applications, but they are known to suffer from adversarial examples. In this work, we propose a method for detecting adversarial examples of a keyword spotting system. We design convolutional neural networks for metric learning to map the internal representation of each layer of an input audio to a low-dimensional feature space. We then extract the distance information from the feature space of each layer and feed it into an LSTM network to determine whether the input audio is clean or adversarial. Promising experimental results are shown to support our detector.
Some Elementary Labs for a Neural Networks and Data Science Introduction

Neural networks and data science become more and more popular. Though regardless of the late appearance of new advanced libraries permitting neural network creation and training seemingly “in one code-line”, there remains a starting-point problem, when new-coming pupils cannot understand the subject before they create the working code, but they see an interface of black box laboratory works. The author having dealt with universal and advanced applied mathematics courses practiced a clear and concise neural networks introduction, reflecting state of the art before the 2012 year “Big Bang” when current top-used convolution, LSTM, and other Transformer-like networks become super-effective and super-popular. Instead, the presented course consists of approximately 5 of the very classical labs adopted to mainly standard Excel (without VBA) and in 1-2 cases to C#, and, more important we solve a problem of building a neural network or a model from the white sheet (including corresponding experiments) in a standard 4 academic hour time.
Training Acceleration of Frequency Domain CNNs Using Activation Compression

Reducing the complexity of training convolutional neural networks results in lower energy consumption expended during training, or higher accuracy by admitting a greater number of training epochs within a training time budget. During backpropagation, a considerable amount of temporary data is offloaded from GPU memory to CPU memory, increasing training time. In this paper, we address this training time overhead by introducing an activation compression technique for frequency domain convolutional neural networks. Applying this compression technique on frequency domain AlexNet results in activation compression of 57.7%, and a reduction of training time by 23%, with a negligible effect on classification accuracy.
SHORT-TERM PHOTOVOLTAIC GENERATION FORECASTING ENHANCED BY SATELLITE DERIVED IRRADIANCE

In a context of natural resources depletion, weather-dependent renewable energy sources play an increasingly important role in the energy mix. Yet, high shares of renewables can jeopardise the safe operation of power grid due to their variable nature. To address this challenge, it is essential to know the future amount of energy produced to balance production and consumption. This paper aims at investigating photovoltaic generation short-term forecasting and particularly spatio-temporal approaches. These approaches permit to exploit the spatial dependency of weather variables to provide valuable information regarding cloud movements. Thus, it is possible for a power producer to take advantage of dense PV plants networks by considering spatially distributed units as remote sensors. For low-density network, satellite-derived information observed in the vicinity of the power unit location offers an interesting alternative. To reduce the computational burden induced by this data source, feature-selection approaches are implemented. Usually, a correlation score is used to measure the dependence between lagged satellite-based time-series with the target feature (i.e. power production observations). However, this approach tends to provide redundant information (i.e. highly correlated pixels). To address this issue, we implement a minimal-Redundance Maximal-Relevance framework. Performance comparisons with state-of-the-art approaches are also performed.
Kaniadakis’s Information Geometry of Compositional Data

We propose to use a particular case of Kaniadakis' logarithm for the exploratory analysis of compositional data following the Aitchison approach. The affine information geometry derived from Kaniadakis' logarithm provides a consistent setup for the geometric analysis of compositional data. Moreover, the affine setup suggests a rationale for choosing a specific divergence, which we name the Kaniadakis divergence.
STEM Preservice Teachers’ e-Readiness for Online Multimodal Teaching Methods Usage in Pietermaritzburg, South Africa: Analysis Through the Adapted TPACK Framework

Education institutions globally, are increasingly driven to integrate innovative technologies in teaching spaces. While many studies have been conducted on e-learning, blended learning and m-learning, only a few studies have concentrated on the STEM preservice teachers’ e-readiness for using online multimodal teaching methods in developing economic contexts. Therefore, the purpose of this paper is to examine the interplay of experiences, attitudes and technological pedagogical content knowledge (TPACK) self-efficacy as they influence STEM preservice teachers’ e-readiness for implementing online multimodal teaching. This paper employed a descriptive online survey method and a stratified random sampling technique to collect data from 204 final-year STEM preservice teachers on a teacher training programme in South Africa. Statistical tools of confirmatory factor analysis, exploratory factor analysis, bivariate and correlation analysis were employed on the collected data. The results revealed that STEM preservice teachers have positive attitudes towards multimodal online learning. However, STEM preservice teachers do not feel confident in using multimodal online resources in their teaching. In addition, this paper found that experiences of online multimodal teaching impacted on preservice teachers’ attitudes and resultantly, STEM preservice teachers’ TPACK self-efficacy. The results and discussions inform the design for improvement in online multimodal teaching methods e-readiness and usage by preservice teachers. Recommendations include resource support, alignment of teacher training curricula and development of practicum practices for overall improvement of preservice teachers’ experiences.
Designing for Interactive Rehabilitation in Virtual Reality

Virtual reality (VR) offers excellent prospects for rehabilitation. The central concept of VR rehabilitation systems is to provide immersion and motivation to patients in the VR environment while they perform rehabilitation exercises in the physical world. However, existing VR-based rehabilitation solutions rely on conventional interaction devices, such as VR controllers. Specifically, they do not provide custom hardware or tailor the interactions in the physical world to the patients’ physical therapy requirements. This study focuses on designing an interactive VR device to help rehabilitate musculoskeletal elbow injuries or similar conditions. In such cases, the treatment involves exercises for the wrist and hand. The developed custom controller is implemented in a physical object with sensors to track rehabilitation performance. The object used for rehabilitation is represented and interacted with in a dedicated game-like VR environment. This environment targets user engagement, experience, and motivation. In this way, we realize rehabilitation-customized physical and virtual object interactions instead of the interactions that employ existing VR controllers. In the experiment, participants were asked to perform two rehabilitation exercises that involved interacting with the designed VR rehabilitation system. A series of questions were asked as part of the user experience evaluation. The results showed a positive participant experience. Based on the data from the sensors in the rehabilitation object, we can quantify the rehabilitation performance. In this way, the rehabilitation outcomes can be judged by the patient or the therapist. The approach described in this study allows for designing custom interactions tailored to specific rehabilitation cases.