Reviewerly/exHarmony huggingface dataset:
Digitalization of Education: Models and Methods

The paper examines the possible effects of information and communication technologies (ICT) on educational institutions (four directions of impacts and four axes of measuring changespedagogy, technology, organization, and economics).The authors propose to use four classes of models to describe all forms of technology-based education and possible transformations of educational institutions under the influence of ICT to analyze the modern experience of digital transformation: a value chain model describing the primary and secondary activities of an educational institution; a diagram of added quality chains describing the sequence of actions carried out by an educational institution; a model of the routine loop of a teacher (employee) of an educational institution, describing his/her daily activities, taking into account the use of software products and computer and communication technology, displayed at the appropriate place in the value chain; the event chain of the process, detailing the description of the routine loops of teachers (employees) and trainees, describing the logic of their actions, incoming and outgoing information, information systems used, and all types of interactions in the educational process between a trainees and a teacher, a trainee and training material, and a trainee and his own himself.As a result, a tool has been created to describe and research all modern forms of technological education and their digital transformation.
A Novel FDCT-SVD Based Watermarking with Radon Transform for Telemedicine Applications

Digital Watermarking has been widely employed for content authentication by securely embedding patient private information within medical images.In this work, we present a novel watermarking, which employs hybrid Multiscale/Multiresolution frequency coefficients selection using the Fast Discrete Curvelet Transform (FDCT) in conjunction with Singular Value Decomposition (SVD).In order to add an extra layer of security, the Radon Transform (RT) is applied on the watermarks before embedding for the sake of robustness and security due to RT properties of capturing image directionality which is essential against various attacks.The proposed method attained promising results and has shown that the imperceptibility of watermarked medical images is higher than 55 dB for all medical images.In addition, the scheme has shown an improved robustness compared with existing related work.
Zero-Correlation Linear Cryptanalysis on SPARX-64

SPARX is a family of ARX-based block ciphers designed according to the long-trail strategy, which has 32-bit ARX-based SBoxes and has provable bounds against single-differential and single-linear cryptanalysis. Since its proposation, some third-party cryptanalysis methods have been presented. As far as we know, the best attacks against SPARX-64 covered 16 (out of 24) rounds. In this paper, we propose zero-correlation linear attacks on SPARX-64. At first, we construct some new zero-correlation linear distinguishers covering 14-round and 15-round SPARX-64. Then, 15,16,17 and 18-round versions can be attacked using multidimensional or multiple zero-correlation linear attack models, under DKP(distinct known plaintexts) settings. These are the best attacks against SPARX-64 up to now, regarding to the number of attacked rounds. Finally, we transform the zero-correlation distinguishers into integral ones using existing methods, which are also longer than the ones proposed by the designers.
Inverse Result of Approximation for the Max-Product Neural Network Operators of the Kantorovich Type and Their Saturation Order

In this paper, we consider the max-product neural network operators of the Kantorovich type based on certain linear combinations of sigmoidal and ReLU activation functions. In general, it is well-known that max-product type operators have applications in problems related to probability and fuzzy theory, involving both real and interval/set valued functions. In particular, here we face inverse approximation problems for the above family of sub-linear operators. We first establish their saturation order for a certain class of functions; i.e., we show that if a continuous and non-decreasing function f can be approximated by a rate of convergence higher than 1/n, as n goes to +∞, then f must be a constant. Furthermore, we prove a local inverse theorem of approximation; i.e., assuming that f can be approximated with a rate of convergence of 1/n, then f turns out to be a Lipschitz continuous function.
Untrained neural network for cryptanalysis of a phase-truncated-Fourier-transform-based optical cryptosystem

Optical cryptosystem based on phase-truncated-Fourier-transforms (PTFT) is one of the most interesting optical cryptographic schemes due to its unique mechanism of encryption/decryption. Several optical cryptanalysis methods using iterative phase/amplitude retrieval algorithm or deep learning (DL) have also been proposed to analyze the security risks of a PTFT-based cryptosystem. In this work, we proposed an innovative way to attack a PTFT-based cryptosystem with an untrained neural network (UNN) model, where the parameters are optimized with the help of the physical encryption model of a PTFT-based cryptosystem. The proposed method avoids relying on thousands of training data (plaintext-ciphertext pairs), which is an essential but inconvenient burden in the existing data-driven DL-based attack methods. Therefore, the plaintext could be retrieved with good quality from only one ciphertext without any training process. This novel UNN-based attack strategy will open up a new avenue for optical cryptanalysis. Numerical simulations demonstrate the feasibility and effectiveness of the proposed method.
A Specific Emitter Identification Algorithm under Zero Sample Condition Based on Metric Learning

With the development of information technology in modern military confrontation, specific emitter identification has become a hot and difficult topic in the field of electronic warfare, especially in the field of electronic reconnaissance. Specific emitter identification requires a historical reconnaissance signal as the matching template. In order to avoid being intercepted by enemy electronic reconnaissance equipment, modern radar often has multiple sets of working parameters, such as pulse width and signal bandwidth, which change when performing different tasks and training. At this time, the collected fingerprint features cannot fully match the fingerprint template in the radar database, making the traditional specific emitter identification algorithm ineffective. Therefore, when the working parameters of enemy radar change, that is, when there is no such variable working parameter signal template in our radar database, it is a bottleneck problem in the current electronic reconnaissance field to realize the specific emitter identification. In order to solve this problem, this paper proposes a network model based on metric learning. By learning deep fingerprint features and learning a deep nonlinear metric between different sample signals, the same individual sample signals under different working parameters can be associated. Even if there are no samples under a certain kind of working parameter signal, it can still be associated with the original individual through this network model, so as to achieve the purpose of specific emitter identification. As opposed to the situation in which the traditional specific emitter identification algorithm cannot be associated with the original individual when the signal samples of changing working parameters are not collected, the algorithm proposed in this paper can better solve the problem of changing working parameters and zero samples.
A separation logic for heap space under garbage collection

We present SL♢, a Separation Logic that allows controlling the heap space consumption of a program in the presence of dynamic memory allocation and garbage collection. A user of the logic works with space credits, a resource that is consumed when an object is allocated and produced when a group of objects is logically deallocated, that is, when the user is able to prove that it has become unreachable and therefore can be collected. To prove such a fact, the user maintains pointed-by assertions that record the immediate predecessors of every object. Our calculus, SpaceLang, has mutable state, shared-memory concurrency, and code pointers. We prove that SL♢ is sound and present several simple examples of its use.
Computer Science beyond Coding: Partnering to Create Teacher Cybersecurity Microcredentials

Computer science, cybersecurity education, and microcredentials are becoming more pervasive in all levels of the educational system. The purpose of this study was partnering with precollegiate teachers: (1) to investigate the self-efficacy of 30 precollegiate teacher participants towards computer science before, during, and after three iterations of a cybersecurity microcredential, and (2) to make changes to the cybersecurity microcredential to improve its effectiveness. The authors explored what teachers need in a microcredential. The first Cohort (n = 5) took the microcredential sequence over 28 days in the summer of 2020, the second Cohort (n = 16) took it over 42 days in the fall of 2020, and the third Cohort (n = 9) took it over 49 days in the summer of 2021. The authors investigated three research questions and used a systems thinking approach while developing, evaluating, and implementing the research study. The researchers used quantitative methods in the collection of a self-efficacy subscale survey to assess whether the precollegiate teachers’ beliefs about computer science changed, and then used qualitative methods when conducting semi-structured teacher participant interviews to address the research questions. The findings show that the precollegiate teachers’ self-efficacy scores towards computer science increased, and that there are areas in need of attention, such as resources and implementation, when creating microcredentials. The implications of this research include the importance of purposefully crafting microcredentials and professional developments, including aspects of creating effective partnerships.
Hierarchical Modified Fast R-CNN for Object Detection

In object detection there is high degree of skewedness for objects' visual separability. It is difficult to distinguish object categories which demand dedicated classification. The deep convolutional neural networks (CNNs) are trained as N-way classifiers. As such considerable work is required towards leveraging hierarchical category structures. We present here Modified Fast region-based CNN (Mod Fast R-CNN) and Hierarchical Modified Fast region-based CNN (HMod Fast R-CNN) with deep CNNs being embedded considering categorical hierarchy. The easy classes are separated through coarse classifiers. The difficult classes are classified by fine classifiers. HMod Fast R-CNN is trained by initial components training which follows fine-tuning globally using multiple group discriminant analysis. The regularization is done using coarse category consistency. For large-scale recognition tasks, scalability is done considering conditional execution of fine category classifiers and layer parameters compression. Using MS-COCO (benchmark) CIFAR100 and VisualQA datasets we obtain good results. We build several different HMod Fast R-CNN versions where standard CNNs top-1 error is reduced significantly. HMod Fast R-CNN's performance superiority with other object detectors on PASCAL VOC 2007 and VOC 2012 datasets are also highlighted.
modern problems and prospects of music formation and art education development during COVID-19

This article aims to assess the problems and prospects of the development and formation of music and art education given the impact of the pandemic COVID-19. Methodology. The research formed a model for the creative development of pupils and students of music and art education based on the Organization for Economic Co-operation and Development Skills for Jobs database. Results. Creativity is shaped by the hands-on activities of students, their music, and art education. Basic creativity skills are best formed through interdisciplinary collaboration toward STEM-education. Research shows that math skills, science, and engineering have a positive effect on students' creativity in hands-on product creation during art courses. Technical skills are important, but they stimulate creativity when students independently decide to use them in a specific, student-defined task. The novelty effect of technology is temporary in creativity stimulation, whereas the student's independent decision to use specific technical skills accordingly to a self-defined problem and task stimulates creativity in the long run. COVID-19 pandemic has exacerbated long-standing challenges and barriers for arts and music education as well as ways to solve those challenges.
Hyper-Heuristic Based on ACO and Local Search for Dynamic Optimization Problems

Hyper-heuristics comprise a set of approaches that are motivated (at least in part) by the objective of intelligently combining heuristic methods to solve hard optimization problems. Ant colony optimization (ACO) algorithms have been proven to deal with Dynamic Optimization Problems (DOPs) properly. Despite the good results obtained by the integration of local search operators with ACO, little has been done to tackle DOPs. In this research, one of the most reliable ACO schemes, the MAX-MIN Ant System (MMAS), has been integrated with advanced and effective local search operators, resulting in an innovative hyper-heuristic. The local search operators are the Lin–Kernighan (LK) and the Unstringing and Stringing (US) heuristics, and they were intelligently chosen to improve the solution obtained by ACO. The proposed method aims to combine the adaptation capabilities of ACO for DOPs and the good performance of the local search operators chosen in an adaptive way and based on their performance, creating in this way a hyper-heuristic. The travelling salesman problem (TSP) was the base problem to generate both symmetric and asymmetric dynamic test cases. Experiments have shown that the MMAS provides good initial solutions to the local search operators and the hyper-heuristic creates a robust and effective method for the vast majority of test cases.
use of synchronous and asynchronous teaching methods in pedagogical education in COVID-19 terms

The purpose of the academic paper lies in determining the ratio of synchronous and asynchronous teaching methods in modern pedagogy on the example of the analysis of the activity of higher education institutions within the conditions of quarantine restrictions. The sociological survey was conducted in two stages. Stage I – July – September 2021 (stage of information collection: opinion poll). The second stage is October 2021 (analytical). The advantages of synchronous teaching and learning have been determined, namely: easy interaction; real-time mode; questioning, instant answers, motivation, and help, immediate feedback from the teacher, group classes, high concentration. The disadvantages of synchronous teaching and learning have been identified, namely: physical compliance with the schedule, poor implementation of individualization, appropriate training, poor content of the digital learning platform, poor organization of various communication channels, and low level of digital alternative. The list of benefits of asynchronous learning has been revealed as follows: flexibility, antilocalization, economy, time mobility, one’s own working pace, the schedule convenience, point definition of the purposes. It has been found that the drawbacks of asynchronous learning are as follows: a decrease in an academic discipline, a decrease in socialization, feedback difficulty, limited contact, isolation, the need for self-discipline.
The Optimum Site Selection for Solar Energy Farms using AHP in GIS Environment, A Case Study of Iraq

Recently, renewable energy (RE), such as solar energy, sources have proven their importance as an alternative source of fuel. The utilizing of solar energy can contribute to move the world towards relying on clean energy to curb global warming. However, the placement of solar farms is a major priority for planners as it is a critical factor in the succession energy project. This study combines one of the multi-criteria decision-making techniques Analytic Hierarchy Process (AHP) and Geographic Information System (GIS) to assess the suitability of land for establishing solar farms in Iraq. Numerous climatic, geomorphological, economic, and environmental criteria and some exclusionary constraints have been adopted in modeling process. It is supported by expert knowledge and a comprehensive literature review. The results showed that approximately 19% of the study area are optimal areas for installing solar farms. The southern, southeastern, and a few western regions obtained the largest part of the suitable lands. Furthermore, this approach can be adapted easily to cover different criteria and different weights in order to assist planners in deciding solar farm locations.
Scrum Framework and Health Solutions: Management and Results

Technology arrives aiming at bringing benefits to society, with aspects applied to health such as Information and Communication Technologies (ICTs). Developing a technology solution requires methodological rigor and systematic instructional design.This experience report method used the Agile methodology for management and control of results in the development of Information and Communication Technologies.The Agile methodology was adopted using the Scrum framework, involving people, artifacts, and ceremonies. The Scrum team was consisted of multidisciplinary professionals and undergraduate students. Artifacts are comprised of product backlog, sprints, sprint backlog, time-box, ready concept, and release burn-down.The adoption of the Scrum methodology helps with the integration of roles and knowledge to a project. Moreover, it efficiently helps carrying out activities by allowing careful analysis of each stage with regard to quality, technology, and implementation.
Bitcoin price as one of basic cryptocurrencies in relation to the basic stock market's indicators

During 2020, factors such as the global pandemic, financial uncertainty and the US election saw an increase in the correlation between bitcoin and gold and the stock market.Both bitcoins and gold rose sharply during 2020, thanks to the relentless press of fiat money by governments and central banks that intend to keep their economies afloat as a result of the coronavirus pandemic.As digital gold, BTC appeals to the cashless Internet economy largely for its features, which include continuous price transparency and a lack of restrictions, disruptions or third-party oversight.The paper focuses on the analysis and identification of the relationship between the value of basic cryptocurrencies and gold price movements, as well as the research hypothesis that the value of the most known and used cryptocurrency bitcoin (BTC) increases with the rising price of gold -a special commodity that serves several functions of the world economy.This issue is currently gaining more and more attention also in context of risk management which is the identification, evaluation, and prioritization of risks followed by coordinated and economical application of resources to minimize, monitor, and control the probability or impact of unfortunate events or to maximize the realization of opportunities and mobile commerce in the aspect of the impact of modern technologies and mobile communication platforms on consumer behavior and consumer preferences.Innovatively, we have made our observations of the price of bitcoin in relation to price fluctuations in the gold markets in order to develop a predictive model for simulating the development of the price of bitcoin.The mathematical model was developed on the basis of statistical analysis of the observed data.
Digital Technologies in Education: Possibilities and Trends of Application

The purpose of the article is to identify the positive and negative consequences of education digitalization, as well as to find out the prospects for the introduction of advanced technologies in the educational process. The research methodology is based on the application of a systematic approach and dialectical method, the choice of which is due to the culturological approach to the study of education digitalization in the development of modern society and the widespread introduction of information and communication technologies. The scientific novelty of the obtained results is to clarify the main directions of education digitalization and its main contradictions. Conclusions. Attention is paid to the transformation of methods of providing quality education, the system of working remotely under the influence of the rapid and large-scale spread of digital technologies. The need to adapt the education system to the requirements of the time and the expectations of young people through the massive and effective use of innovative educational technologies and didactic models based on modern information and communication technologies is emphasized. The positive and negative consequences of education digitalization have been revealed, the prospects of the introduction of advanced technologies in the educational process have been clarified. It has been concluded that information and communication technologies are changing the field of education at different speeds in the direction of organizing the educational process and transforming the infrastructure of the educational institution. It is emphasized that a modern educational institution must undergo a path of digital transformation, otherwise, it will not meet market demands. The transition to a digital educational institution involves the use of flexible processes, taking into account the formation of an adaptive corporate culture and optimization of educational and social processes. The process of digitalization of education can be successful if the educational process based on the use of information and communication technologies, focused on specific users, has a specific meaning, based on the right methodology and approaches, interesting content, good motivation of students (learners), well-established program and technical base.
Distributed Remote E-Voting System Based on Shamir’s Secret Sharing Scheme

A number of e-voting systems have been proposed in the last decades, attracting the interest of the research community. The challenge is far from being fully addressed, especially for remote systems. In this work, we propose DiverSEC, a distributed, remote e-voting system based on Shamir secret sharing, operations in Galois field and mixnets, which enables end-to-end vote verification. Parties participate as nodes in the network, protecting their interests and ensuring process integrity due to the conflicting interests. The threat model is very conservative, not letting even the most privileged actors to compromise votes privacy or integrity. Security in depth is implemented, overlapping different mechanisms to offer guarantees even in the most adverse operating conditions. The main contributions of the resulting system are our proposal for secret-sharing among the political parties, which guarantees that no party can compromise the integrity of the ballot without being detected and identified in real time, and the computational and architectural scalability of the proposal, which make it easy to implement.