Reviewerly/exHarmony huggingface dataset:
Encrypting of Digital Banking Transaction Records: An Blockchain Cryptography Security Approach

Increased use of digital banking poses a risk to the security of its transactions.As result the system more vulnerable risks faced by digital banking stemmed of cyber-attacks.This study reveals blockchain technology algorithms that encrypted through RSA cryptography and SHA-256 hashes, to enhance digital banking security and reduce the risk of cyberattacks over the banking transaction.The level of blockchain data accuracy and completeness tested through the mathematics permutation method to find out which data-combination that may lead to vulnerable.However, this study is very promosing.The most significant parameter in influencing the validity of the transaction is Value with an accuracy percentage value of 88%, and then followed by Sender Address with an accuracy percentage value of 70% and the last is the Recipient Address with a percentage value.an accuracy of 62%.In other hand, the study reveals that the use of key pairs improves system reliability by implementing of SHA-256 hash on the blockchain.
The Use of Electronic Courses in the Implementation of Educational Programs “Physical Education and Sport”, “Applied Physical Education and Sport” for Students of the Special Educational Department

The article provides statistical data on the number of SibFU students with disabilities (in 2013, 2014 and 2020), as well as the number of the 1st-year students enrolled in the Special Educational Department of the Department of Physical Education based on the results of a medical examination. The main conditions that ensure access to higher education for students with disabilities are reflected. As a practical experience of educational activities in the educational programs “Physical Education and Sport”, “Applied Physical Education and Sport” with students with disabilities, temporarily exempted from physical education, as well as during the period of restrictive measures during the pandemic, the structure of the electronic course is described and the methodological and practical material posted on the e.sfu-kras.ru platform is presented. The organization of educational activities for students of the Special Educational Department in the subjects under consideration with the use of an electronic course over the past 3 years has proved its effectiveness. This material will be of interest to teachers of physical education of various specializations, special educational departments, as well as specialists in the field of physical education and adaptive physical education
Big Data and Democracy

One of the biggest questions in the digital age pertains to the relevance of democracy in the era of big data. Unquestioningly, the digital revolution is growing at a rapid pace and many are being caught unaware by its impact in various avenues. The amount of data available has been doubling year on year, and the conditions of usage have been evolving at a pace faster than the policies are being provided to ensure proper usage. Many have started looking for ways of turning big data into big money, an aspect that is coming at the expense of the democratic values countries have upheld for generations. Instead, it is becoming a huge problem as the history of humankind is becoming more documented now and messages are easier to send compared to a century ago. Understanding the impact of big data on democracy can help accentuate the best way of improving democratic institutions and their ability to overcome the pressure coming from evolving technology.
Professional Creativity as A Component of The Effective Activity of a Teacher

The article considers the role and influence of professional creativity on the professional activities as an expression of the teacher’s creative abilities. The purpose of the article is a theoretical and empirical study of the influence of professional creativity on the effectiveness of a teacher’s professional activity. Attention is paid to the place and importance of creativity in the activities of a teacher. Based on an analysis of current literary sources, the phenomenon of professional creativity of a teacher is highlighted, a definition of this concept is given, and its structure and components are considered. The research methodology and its algorithm include a complex psychological and pedagogical experiment, diagnosing the stated parameters and statistical analysis of the results based on Spearman's rank correlation coefficient. The study of teachers’ creativity was carried out on the basis of the Institute of Foreign Languages of the Federal State Autonomous Educational Institution of Higher Education “The Peoples’ Friendship University of Russia” (RUDN) at the Center for Continuing Professional Education. For research purposes, an experimental and a control group were formed. Each group consisted of 35 teachers. The sample was homogeneous by age and gender. The results of the study allow us to conclude that there is a strong relationship between the professional creativity of a teacher and the effectiveness of the professional performance, which is confirmed by the results of statistical processing of the data obtained.
Towards a Model for Work Distribution to Overcome Communication Barrier in Global software development

According to competitive and complex market situation, to fulfill customer needs the companies required to provide more functionality and higher quality software faster. Companies should be used existing resources as efficiently as possible, and they also need to utilize resources on a global scale from different sites within the company as well as associate companies throughout the world. All these provide in GSD that increase the functionality and quality of software. The main problem that GSD team face are: communication, coordination and work distribution. A proper mechanism for work distribution and communication has overcome the challenges that effect the project goal. To solve the GSD issues. We proposed a model that helps to mitigate these problems. We use site/work dependency and work/site characteristics criteria for the work distribution model. We also implement phased base configuration and work distribution model that helps in the work distribution mechanism. And use NEOStation software to overcome the barriers of communication, coordination and project management. The model has been developed to provide reliable communication between team members and provide an approach to the distribution of work as support for managers.
Construction of Parallel Corpus for English Translation Teaching Based on Computer Aided Translation Software

With the continuous development of science and technology, more and more technologies such as computers have been put into actual teaching work.Li's computer-assisted music teaching model can just make up for the single teaching model of exam-oriented education.Based on computer-assisted teaching technology, this article carries out the design and development of a digital music course teaching system.The system can easily digitally process the recorded sound and present it in the form of sound waves on the computer.It can visually edit the audio and save the specific training status in the database comprehensively, providing more information for teaching and training.Intuitive visual information and all parameters transform the theory and methods of skills into vivid image data information.At the same time, analysis and discussion on computer technology are carried out.Using computers, music production can be operated in an intuitive and visual environment, clarifying the theme of music, developing the structure, and post-production.It has been proved through practice that this system can not only provide for teaching and training More intuitive and vivid visual information and all parameters, which transform skill theory and methods into vivid image data information, can also significantly expend efficiency of music learning and the training of professional skills, which is worthy of vigorous promotion and practice.
Advanced supervised learning in multi-layer perceptrons to the recognition tasks based on correlation indicator

The article deals with the problem of recognition of handwritten digits using feedforward neural networks (perceptrons) using a correlation indicator. The proposed method is based on the mathematical model of the neural network as an oscillatory system similar to the information transmission system. The article uses theoretical developments of the authors to search for the global extremum of the error function in artificial neural networks. The handwritten digit image is considered as a one-dimensional input discrete signal representing a combination of "perfect digit writing" and noise, which describes the deviation of the input implementation from "perfect writing". The ideal observer criterion (Kotelnikov criterion), which is widely used in information transmission systems and describes the probability of correct recognition of the input signal, is used to form the loss function. In the article is carried out a comparative analysis of the convergence of learning and experimentally obtained sequences on the basis of the correlation indicator and widely used in the tasks of classification of the function CrossEntropyLoss with the use of the optimizer and without it. Based on the experiments carried out, it is concluded that the proposed correlation indicator has an advantage of 2-3 times.
Sentiment Analysis On YouTube Comments Using Word2Vec and Random Forest

Purpose: This study aims to determine the accuracy of sentiment classification using the Random-Forest, and Word2Vec Skip-gram used for features extraction. Word2Vec is one of the effective methods that represent aspects of word meaning and, it helps to improve sentiment classification accuracy.Methodology: The research data consists of 31947 comments downloaded from the YouTube channel for the 2019 presidential election debate. The dataset consists of 23612 positive comments and 8335 negative comments. To avoid bias, we balance the amount of positive and negative data using oversampling. We use Skip-gram to extract features word. The Skip-gram will produce several features around the word the context (input word). Each of these features contains a weight. The feature weight of each comment is calculated by an average-based approach. Random Forest is used to building a sentiment classification model. Experiments were carried out several times with different epoch and window parameters. The performance of each model experiment was measured by cross-validation.Result: Experiments using epochs 1, 5, and 20 and window sizes of 3, 5, and 10, obtain the average accuracy of the model is 90.1% to 91%. However, the results of testing reach an accuracy between 88.77% and 89.05%. But accuracy of the model little bit lower than the accuracy model also was not significant. In the next experiment, it recommended using the number of epochs and the window size greater than twenty epochs and ten windows, so that accuracy increasing significantly.Value: The number of epoch and window sizes on the Skip-Gram affect accuracy. More and more epoch and window sizes affect increasing the accuracy.
Distributed Key Generation for SM9-Based Systems

Identity-Based Cryptography (IBC) is a useful tool for the security of IoT devices, but securely deploying this cryptographic technique to the IoT systems is quite challenging. For instance, a leakage of the master secret key will result in the leakage of all IoT devices’ private keys. SM9 is the only approved IBC algorithm standard in China. It is critical to have mechanisms to protect the SM9 master secret keys. In this work, to reduce the risk of the master secret key leakage, we propose a (t, n)-threshold distributed private key generation scheme for SM9 with some techniques from multiparty computation. Our scheme is compatible with all the three SM9 sub-algorithms (i.e., the encryption, signature and key agreement). It is also provably secure and completely eliminates the single point of failures in SM9 that is concerned by the industry. The experimental analysis indicates that the proposed scheme is efficient, e.g., up to 1 million private key generation requests can be handled per day.
Online Public Shaming Approach using Deep Learning Techniques

Public shaming on social media platforms like Twitter / Instagram / Facebook etc. have recently increased from the past years. This results in affecting an individual’s social, political, mental and ﬁnancial life. The impact can range from mild bullying to severe depression. With the growing leniency on these social platforms, many people have started misusing the opportunity by turning to online bullying and hate speech. When something is posted online, it stays there forever and it becomes extremely hard taking something out of the digital world. Manually locating and categorizing such comments is a lengthy procedure and just cannot be relied upon. To solve this challenge, automation was performed to identify and classify the shamers. This has been done using the classic SVM model which worked on a given quantity of data. To identify the negative content being posted and discussed online, this paper further explores the deep learning system which can successfully classify these content pieces into proper labels. The text-based Convolution Neural Network (CNN) is the proposed model in this paper for this analysis.
Blind deconvolution using bilateral total variation regularization: a theoretical study and application

Blind image deconvolution recovers a deblurred image and the blur kernel from a blurred image. From a mathematical point of view, this is a strongly ill-posed problem and several works have been proposed to address it. One successful approach proposed by Chan and Wong consists in using the total variation (TV) as a regularization for both the image and the kernel. These authors also introduced an Alternating Minimization (AM) algorithm in order to compute a physical solution. Unfortunately, Chan's approach suffers in particular from the ringing and staircasing effects produced by the TV regularization. To address these problems, we propose a new model based on Bilateral Total Variation (BTV) regularization of the image keeping the same regularization for the kernel. We prove the existence of a minimizer of a proposed variational problem in a suitable space using a relaxation process. We also propose an AM algorithm based on our model. The efficiency and robustness of our model are illustrated and compared with the TV method through numerical simulations.
Aula invertida en tiempos emergentes covid-19

Horizontal communication in virtuality requires synchronous and asynchronous processes that benefit students and teachers by fulfilling the activities inherent to their role. But, in atypical circumstances, it is necessary to propose the flipped classroom as an accompaniment strategy for the advancement of the construction of the monograph work of the students of the International Baccalaureate in Spanish Category 1. Through the systematization of professional experience. The purpose of the teaching accompaniment guide is to systematize the progress of the construction of the teaching work through the flipped classroom based on a schedule of activities and the tools necessary for exploration as a didactic strategy to improve the teaching-learning process. A learning systematization sheet was applied, which showed that students have difficulties to carry out their synchronous activities, therefore, the use of the flipped classroom is proposed as an alternative solution to the problem addressed. Keywords: flipped classroom, communication, teaching-learning, skills.
Relational-grid-world: a novel relational reasoning environment and an agent model for relational information extraction

Reinforcement learning (RL) agents are often designed specifically for a particular problem and they generallyhave uninterpretable working processes. Statistical methods-based agent algorithms can be improved in terms ofgeneralizability and interpretability using symbolic artificial intelligence (AI) tools such as logic programming. Inthis study, we present a model-free RL architecture that is supported with explicit relational representations of theenvironmental objects. For the first time, we use the PrediNet network architecture in a dynamic decision-making problemrather than image-based tasks, and multi-head dot-product attention network (MHDPA) as a baseline for performancecomparisons. We tested two networks in two environments -i.e., the baseline box-world environment and our novelenvironment, relational-grid-world (RGW). With the procedurally generated RGW environment, which is complex interms of visual perceptions and combinatorial selections, it is easy to measure the relational representation performance ofthe RL agents. The experiments were carried out using different configurations of the environment so that the presentedmodule and the environment were compared with the baselines. We reached similar policy optimization performanceresults with the PrediNet architecture and MHDPA. Additionally, we achieved to extract the propositional representationexplicitly -which makes the agent's statistical policy logic more interpretable and tractable. This flexibility in the agent'spolicy provides convenience for designing non-task-specific agent architectures. The main contributions of this studyare two-fold -an RL agent that can explicitly perform relational reasoning, and a new environment that measures therelational reasoning capabilities of RL agents.
Pedagogical Design of the Content of Professional Training of Teachers of General Technical Disciplines and Methods of Teaching Technology

Professional training of masters in higher education institutions, its effectiveness is largely determined by the level of pedagogical design of the training content of future teachers of general technical disciplines and teaching methods of technology, since it is the content that is designed to ensure the formation of a personality that has not only a system of special knowledge and professional actions, but also differs in formation professionally important competencies, the appropriate level of qualifications, taking into account world and European quality standards while maintaining national achievements and priorities, as well as capable of fruitful teaching activities in modern conditions. According to the results of the study it can be stated: when designing the content of training masters of technological education it is necessary to take into account the general principles of building the content of education and the principles of updating the content of training to the requirements of time; designing the content component of integrated modules of psychological-pedagogical and special-subject training involves determining their goals and objectives, selection of innovative content for each topic of the content module, selection of forms, methods and technologies of its implementation, determination of evaluation criteria and system of diagnostics of success of undergraduates; the content component of professional training of masters of technological education must contain knowledge and experience of innovative psychological-pedagogical and technical-technological activity of a teacher in the system of higher educational institution; the effectiveness of the content of professional training of future teachers of general technical disciplines and methods of teaching technology is determined by the effectiveness of professional activity, which can be characterized as the formation of professional and pedagogical competence, ability to professional and pedagogical self-development and self-improvement.
Universal Discourse Representation Structure Parsing

Abstract We consider the task of crosslingual semantic parsing in the style of Discourse Representation Theory (DRT) where knowledge from annotated corpora in a resource-rich language is transferred via bitext to guide learning in other languages. We introduce Universal Discourse Representation Theory (UDRT), a variant of DRT that explicitly anchors semantic representations to tokens in the linguistic input. We develop a semantic parsing framework based on the Transformer architecture and utilize it to obtain semantic resources in multiple languages following two learning schemes. The Many-to-One approach translates non-English text to English, and then runs a relatively accurate English parser on the translated text, while the One-to-Many approach translates gold standard English to non-English text and trains multiple parsers (one per language) on the translations. Experimental results on the Parallel Meaning Bank show that our proposal outperforms strong baselines by a wide margin and can be used to construct (silver-standard) meaning banks for 99 languages.
Differentiable Architecture Search Based on Coordinate Descent

Neural architecture search (NAS) is an automated method searching for the optimal network architecture by optimizing the combinations of edges and operations.For efficiency, recent differentiable architecture search methods adopt a one-shot network, containing all the candidate operations in each edge, instead of sampling and training individual architectures.However, a recent study doubts the effectiveness of differentiable methods by showing that random search can achieve comparable performance with differentiable methods using the same search cost.Therefore, there is a need to reduce the search cost even for previous differentiable methods.For more efficient differentiable architecture search, we propose a differentiable architecture search based on coordinate descent (DARTS-CD) that searches for optimal operation over only one sampled edge per training step.DARTS-CD is proposed based on the coordinate descent algorithm, which is an efficient learning method for resolving large-scale problems by updating only a subset of parameters.In DARTS-CD, one edge is randomly sampled, in which all the operations are performed, whereas only one operation is applied to the other edges.Weight update is also performed only at the sampled edge.By optimizing each edge separately, as in the coordinate descent that optimizes each coordinate individually, DARTS-CD converges much faster than DARTS while using the network architecture similar to that used for evaluation.We experimentally show that DARTS-CD performs comparably to the state-of-the-art efficient architecture search algorithms, with an extremely low search cost of 0.125 GPU days (1/12 of the search cost of DARTS) on CIFAR-10 and CIFAR-100.Furthermore, a warm-up regularization method is introduced to improve the exploration capability, which further enhances the performance.
Pandemic Era, Education Support Technology: The Potential for Augmented Reality in Science Learning

Currently, all sectors of life are limited, working from home to schooling from home due to the spread of the coronavirus or covid-19.Many victims have died due to contracting this virus.Therefore, all activities that create crowds are tightened and reduced, all students from elementary level to college level are learning online through the help of technological sophistication.From the existing prolems, in the article, we provide a systematic literature review that analyzes the use of AR technology concerning the natural science context to highlight the importance of the role for students, therefore it can be used as learning support during the Covid-19 pandemic.We use the approach developed by Tranfield to conduct a systematic literature review, which followed the selection of articles that fit the keyword and analyzed in depth.The resulting development of AR can be used in learning as a means of making it easier for students to understand concepts that are considered abstract, so that it can improve knowledge, motivation, achievement, and experience for students.