Reviewerly/exHarmony huggingface dataset:
Intelligent Transaction Techniques for Blockchain Platforms

This paper describes a new way to create a pre-authorized transaction on the blockchain so that the transaction is less likely to have errors. Currently, cryptocurrency transactions do not allow a sender to modify or cancel a transaction that has been sent to a wrong address or person. There is no way for the user to retrieve the funds, and the receiver may not return any coins or data. Thus, if a user sends a coin or data to a wrong or non-existent party the coin or data is permanently lost once the transaction is accepted in the blockchain. Current blockchain frameworks such as Bitcoin and Ethereum share this unidirectional technical standard. It is also possible to make it appear that a recipient is taking a bribe by sending them cryptocurrency without their permission. We define a new protocol of pre-authorization in which all parties agree and sign, and the blockchain ratifies the resulting agreement. While nothing will eliminate all risk of error and fraud, this proposal will prevent these kinds of losses.
Time-asymmetric quantum fluctuations in intensity-amplitude correlation for a driven cavity QED system

The intensity-amplitude correlation functions for a driven cavity QED system with two non-identical atoms are investigated in this paper. With the support of conditional homodyne detection, one can detect the time-dependent intensity-amplitude correlation functions experimentally. We find time-asymmetry in this correlation when the driving field is tuned to be resonant with the two-photon excitation state, which brings non-Gaussian fluctuations. The physical origin of these phenomena is the distinction of the third-order moment based on complete-collapse and partial-collapse, which corresponds to the measuring sequence of the intensity and amplitude. Finally, we also examined the nonclassical features of the system, which always exhibits photon bunching. The squeezing occurs in the region of weak driving and disappears with the increase of driving strength. Hence, a new classical inequality based on the technique of homodyne cross-correlation measurement is introduced to determine the nonclassicality of the non-Gaussian system in the region of unsqueezing.
A century of teacher education in India: 1883-1985

Teacher training in India has evolved from a circle system to normal schools and teacher training institutes/colleges. This progression was influenced by various events, debates and recommendations. With respect to the relevance of the teacher training institutions, ideas kept fluctuating, and several of the other adopted policy measures failed in the implementation phase. Initially, this led to the opening and closing of the normal schools, which later expanded into teacher training institutes/colleges. This paper attempts to present the historical developments in the field of teacher education around the axis of teacher training institutes, teachers’ qualifications, the teacher training course curriculum, and the status of teachers. For this paper, the government reports and reviews published in the periods both prior and subsequent to Independence have been studied using a historical method. It reveals that, in spite of 100 years of effort dedicated to improving teacher education, the availability of trained women teachers is far lower than the demand for them in schools. Unlike before, admission to any teacher training course for primary teachers requires at least 10 years of general education, and secondary school teachers need a degree. Though over these years the salaries of teachers have increased substantially, the deteriorating status of teachers and the teaching profession has been a source of constant concern for educationists and policymakers.
Missing data exploration in air quality data set using R-package data visualisation tools

Missing values often occur in many data sets of various research areas. This has been recognized as data quality problem because missing values could affect the performance of analysis results. To overcome the problem, the incomplete data set need to be treated or replaced using imputation method. Thus, exploring missing values pattern must be conducted beforehand to determine a suitable method. This paper discusses on the application of data visualisation as a smart technique for missing data exploration aiming to increase understanding on missing data behaviour which include missing data mechanism (MCAR, MAR and MNAR), distribution pattern of missingness in terms of percentage as well as the gap size. This paper presents the application of several data visualisation tools from five R-packges such as visdat, VIM, ggplot2, Amelia and UpSetR for data missingness exploration. For an illustration, based on an air quality data set in Malaysia, several graphics were produced and discussed to illustrate the contribution of the visualisation tools in providing input and the insight on the pattern of data missingness. Based on the results, it is shown that missing values in air quality data set of the chosen sites in Malaysia behave as missing at random (MAR) with small percentage of missingness and do contain long gap size of missingness.
Shearlet Convolutional Neural Network Approach for Age and Gender recognition

Automatic age and gender classification has become a very important field today, which is used in several applications (e.g. social networks). The key factors that influence clearly the age and gender recognition rate are the Feature Extraction Function (FEF) and the classification method. The need to further improve this rate continues to increase day by day. To try to further enhance the recognition rate, we propose in this contribution an approach based on: Discrete Shearlet Transform (DST) us a first step of manual feature extraction layer, and Deep Convolutional Neural Network (DCNN) us a second automatic feature extraction layer and classification step. The ability of the shearlet to extract significant features and of the neural networks to classify input data, will allow us to improve the age/gender recognition rate.
Scientists Scramble to Collect Data After Ridgecrest Earthquakes

Ground shaking in Southern California, including a magnitude 7.1 temblor, triggered a massive mobilization effort to collect seismological, geological, and geodetic data.
Atmospheric turbulence removal using convolutional neural network

This paper describes a novel deep learning-based method for mitigating the effects of atmospheric distortion. We have built an end-to-end supervised convolutional neural network (CNN) to reconstruct turbulence-corrupted video sequence. Our framework has been developed on the residual learning concept, where the spatio-temporal distortions are learnt and predicted. Our experiments demonstrate that the proposed method can deblur, remove ripple effect and enhance contrast of the video sequences simultaneously. Our model was trained and tested with both simulated and real distortions. Experimental results of the real distortions show that our method outperforms the existing ones by up to 3.8% in term of the quality of restored images, and it achieves faster speed than the state-of-the-art methods by up to 23 times with GPU implementation.
Rolling in the Deep Convolutional Neural Networks

Over the past years, convolutional neural networks (CNNs) have achieved remarkable success in deep learning.The performance of CNN-based models has caused major advances in a wide range of tasks from computer vision to natural language processing.However, the exposition of the theoretical calculations behind the convolution operation is rarely emphasized.This study aims to provide better understanding the convolution operation entirely by means of diving into the theory of how backpropagation algorithm works for CNNs.In order to explain the training of CNNs clearly, the convolution operation on images is explained in detail and backpropagation in CNNs is highlighted.Besides, Labeled Faces in the Wild (LFW) dataset which is frequently used in face recognition applications is used to visualize what CNNs learn.The intermediate activations of a CNN trained on the LFW dataset are visualized to gain an insight about how CNNs perceive the world.Thus, the feature maps are interpreted visually as well, alongside the training process.
Pedagogical conditions of preparation of future rehabilitation specialists to work in conditions of inclusive students 'training

The article substantiates pedagogical conditions of preparation of future rehabilitation teachers to work in the conditions of inclusive teaching of students. It is revealed that the realization of the first condition (formation of students of stable positive motivational orientation to inclusive learning through the use of interactive forms and active teaching methods) was ensured through the use of interactive forms (psychological-pedagogical practicum, lectures-discussions) , panel talk (panel debate), panel discussion, closed discussion in microgroups, small group work, psycho-gymnastics), active teaching methods (question-answer here, the “half-voice discussion” method, the clinic method, heuristic questions, the reproduction of the professional situation, business games, game therapy, relaxation exercises, selection and creation of individual portfolio, scenario method, brainstorming, gateway method a holistic view of students' level of motivation was used by the “Map of Social Motives Development”. socio-pedagogical work with children with special educational needs) provided for updating the content of disciplines ("Fundamentals of inclusive education" "Correctional psychopedagogy", "General pedagogy", "Psychological and pedagogical bases of correctional and educational work); use of organizational forms (seminar, discussions, debates, abstracts, research, pedagogical practice, out-of-class activities), methods (roleplaying games, analysis of pedagogical situations, performance of creative tasks, complex of psychological and pedagogical exercises, planning of rehabilitation) . It is proved that the realization of the third pedagogical condition (realization of quasi-professional activity aimed at mastering the ways and experience of performing specific professional actions during inclusive student learning) was carried out with the use of technologies: game technologies, technologies of correction and rehabilitation work, technology of formation of technology.
Choice of instructional design for the development of a learning environment in blended mode

This article is a part of the research process for choosing an instructional design model within an educational institution of Higher Education in Colombia. It analyzes and reflects on different models of instructional design and before that sustains a definition and historical evolution of the different systems that have evolved the term of instructional design. With all the data and information required and on a basis consistent with the mission and vision of the institution Minute University Corporation of God, response and choice is given to a coherent model.
Approximating Human Judgment of Generated Image Quality

Generative models have made immense progress in recent years, particularly in their ability to generate high quality images. However, that quality has been difficult to evaluate rigorously, with evaluation dominated by heuristic approaches that do not correlate well with human judgment, such as the Inception Score and Fréchet Inception Distance. Real human labels have also been used in evaluation, but are inefficient and expensive to collect for each image. Here, we present a novel method to automatically evaluate images based on their quality as perceived by humans. By not only generating image embeddings from Inception network activations and comparing them to the activations for real images, of which other methods perform a variant, but also regressing the activation statistics to match gold standard human labels, we demonstrate 66% accuracy in predicting human scores of image realism, matching the human inter-rater agreement rate. Our approach also generalizes across generative models, suggesting the potential for capturing a model-agnostic measure of image quality. We open source our dataset of human labels for the advancement of research and techniques in this area.
Using the Subtask Methodology in Student Training for Demonstration Examination in “Web Design and Development” Skill

The final state attestation in the demonstration examination format becomes obligatory in 2020 for the educational organizations providing students with the working professions included in the list of the most demanded and perspective on the labor market. In this regard, it is necessary to develop the most effective practices to complete the demonstration exam tasks. This article discusses the approach of using subtasks and their relation to the corresponding actions in organizing students’ training for the demonstration examination “Web design and development” and its successful passing. The approach described can also be applied to training for WorldSkills standards on different levels both for “Web design and development” and other skills. The conclusions and solutions formulated in the study are the basis for the creation of the information system to support students’ training for the demonstration exam.
Algorithm for optimal allocation of limited resources based on the game iteration method

The article proposes an algorithm for solving a linear programming problem (LPP) based on the use of its representation in the form of an antagonistic matrix game and the subsequent solution of the game by an iterative method. The algorithm is implemented as a computer program. The rate of convergence of the estimates of the solution to the actual value with the required accuracy has been studied. The software implementation shows a high speed of obtaining the LPP solution with acceptable accuracy in fractions or units of seconds. This allows the use algorithm in embedded systems for optimal control.
Classification of 3D Film Patterns with Deep Learning

Researches on pattern recognition have been tremendously performed in various fields because of its wide use in both machines and human beings.Previously, traditional methods used to study pattern recognition problems were not strong enough to recognize patterns accurately as compared to optimization algorithms.In this study, we employ both traditional based methods to detect the edges of each pattern in an image and apply convolutional neural networks to classify the right and wrong pattern of the cropped part of an image from the raw image.The results indicate that edge detection methods were not able to detect clearly the patterns due to low quality of the raw image while CNN was able to classify the patterns at an accuracy of 84% within 1.5 s for 10 epochs.
Network traffic identification of several open source secure proxy protocols

Summary Secure proxy technique has a wide range of applications especially in the area of VPN to protect the private communication from being surveilled. However, the adversaries may also take advantage of the VPN services for other purposes. Thus, knowing the characteristics of the corresponding security protocols will benefit the administration to narrow down the targets. Also, given the various VPN services on the market, we also want to know whether there exists some security flaws from the cryptographic protocol's point of view, so that can be used to improve analyzing the protocol. In this paper, we first target several widely used open source secure proxy protocols and provide their weaknesses through the static analysis. Then we apply the deep learning technique to train all the related network traffic, and our results showed that all the secure proxy traffics can be successfully distinguished with high accuracy.
Initiation as a Tool for the Development of Leadership Qualities of Socially Active High School Students: the case of the School of Leaders "The Restless Hearts of Kuzbass" (Kemerovo State University)

The article considers initiation as a means of developing leadership qualities of socially active high school students. The research featured the development of leadership skills in socially active high school students in the School of Leaders organized by Kemerovo State University. The paper reviews theoretical ideas about leadership as an object of psychological and pedagogical research. The author determined the content characteristics of social competence as the basis of leadership manifestation and described the features of the development of leadership qualities in socially active high school students. The article describes various socialization projects implemented in Russian universities and considers the phenomenon of initiation employed in "The Republic of Restless Hearts". The initiation project is based on a forming experiment and proved effective in the local School of Leaders. The research results can help to improve the process of leadership development of socially active high school students in various youth organizations. The results were implemented in practice to develop leadership skills of socially active high school students during the Regional Professional Camp of youth organizations "The Republic of Restless Hearts" and can be of practical use to other youth organizations.
RECEPTIVE-INTERPRETATIVE SPACE OF LITERARY TEXT: PROBLEMATIC PARADIGM

The article considers the basic problems of reception and interpretation of literary text within the classical scientific paradigm. The paper aims to identify the main factors influencing artistic reception and multi-variant interpretation of literary text. Artistic reception is simultaneously a final stage of artistic communication and its starting point in the context of the recipient’s motivation for interpretative activity. The ontological status of literary text is conditioned not only by the author’s intention but in many ways by the recipient’s ability, readiness for interaction with the author.
Moving towards an Open Archaeology: projects, opportunities and challenges

Generally, open science practices are only slowly having an impact on mainstream archaeological practice. An exception is the open access to publications, which, together with open data and open methodologies may represent those practices most relevant for archaeological researchers. This article introduces a selection of archaeology projects that embrace and facilitate open science practices. Finally there will be a discussion of some of the questions and challenges the discipline is facing in its move towards an Open Archaeology.
optimizing electrical energy consumption in cloud data center

The cloud data Center uses more and more computers leading to the need for new electrical installations each year, increasing thus power consumption. Many studies on optimizing energy consumption have recently been conducted. As a result, many techniques aimed at reducing energy consumption have been adopted. Efficient VM management can reduce energy consumed. In this paper, we investigate the energy minimization problem while taking into account of VM management. We introduce an algorithm which reduces the energy consumption by decreasing the number of active PMs, while also preserves quality of service (QoS). We began with a review of the various techniques used for minimizing the energy consumed in the data center by analysing them. We propose an approach based on a virtual machine (VM) migration technique. To evaluate our approach, we used the CloudSim simulator. We finally made simulations whose results allow us to say that our approach can improve the energy gain while preserving the quality of service.
The Humanitarian Component in Modernizing the Engineering Education under the Conditions of the Fourth Industrial Revolution

The new avenues in the innovative development of the education system are determined by the challenges of the fourth industrial revolution. A strategy aimed at transforming the National Research University Moscow Power Engineering Institute (NRU MPEI) into a higher school corresponding to the University 3.0 concept, is being consistently implemented at the NRU MPEI, which combines scientific, educational, and technological innovations in its activities. The importance of the problem is reflected in the decisions of the Academic Council meeting held on February 15, 2019 and in the report presented by the NRU MPEI rector N.D. Rogalev. The key figure in the Fourth Industrial Revolution, also termed as Industry 4.0, an organizational and technical system intended for achieving technological breakthroughs, is an engineer. The training of engineers is a strategic objective of utmost importance; its successful solution is a necessary condition for making the state competitive and able to ensure national security. A graduate from an engineering and technical higher school who will be in demand at a neoindustrial production facility must have universal competences of both professional and general cultural content. In this regard, the role of the humanitarian component in the engineering training of students is invariably increasing, because it is only humanities that form a new thinking pattern in a specialist who strives for constant self-improvement and self-education. The purpose of the article is to study the leading theoretical, methodological and practical aspects of modern approaches to humanitarian training of engineers under the conditions of shaping the Industry 4.0 system. Engineering activities in the post-industrial world are becoming increasingly integrated, comprehensive, and innovative in nature. It is exactly through the system of engineering education that highly qualified personnel are trained for the Industry 4.0 system, which determines the growth of its importance. Professional competences are of top priority for the future specialist, but the dynamics of their obsolescence is very high. In the modern realities of neoindustrialism, the creative and projective abilities of students, which are least prone to devaluation, are of utmost importance. The article presents a comparative analysis of foreign and domestic experience and discusses ways avenues of imparting more humane and humanitarian features to engineering education under the conditions of forming the University 3.0 model and on the basis of implementing universal competences according to the Federal State Educational Standard of Higher Education 3++.
Methods and Approaches for Privacy-Preserving Machine Learning

One of the main problems of machine learning is the need for a large amount of memory and a long learning time. To solve this problem, many companies prefer to store their data and training models on remote servers. However, not all data and models can be stored in the plaintext without any protection. In many areas (for example, banking or medical), the privacy of data and models is very important. To ensure confidentiality, a privacy-preserving machine learning application is a good solution. This article discusses two main approaches to privacy-preserving machine learning (cryptographic and perturbation), describes methods for ensuring privacy, which they include, and provides examples of using of some methods in practice.