Reviewerly/exHarmony huggingface dataset:
Soft Skills Development by Means of Digital Pedagogical Cues

The article deals with the importance of identifying, developing and shaping soft skills that allow for success in professional activities. With the digitalization of adult education, including upskilling for school leaders, attention needs to be paid to the development of certain soft digital skills. This can be facilitated by the use of digital pedagogical cues, which allows working both individually and in a group. It has been experimentally proved that the obvious necessity of the modern educational paradigm is the use of interactive pedagogical tools, which include pedagogical cues, and digital ones as well. As part of the experiment described in the article, possible cues for the development of critical, systemic and creative thinking, as well as emotional intelligence (EQ), are shown. For each presented soft skill, three digital cues are given, which make it possible to understand the principle of selection of pedagogical tools for the development and formation of a certain skill.
Application of a Mind Mapping-Based Contextual Approach into an Individual Education of Engineers

This paper is intended to cover some major issues in the sphere of new educational technology contributing to visual and associative thinking development of students. The last two years of the pandemic have confirmed inevitable changes in educational approaches and teaching methods of engineers’ training. The student’s comprehension due to the big flow of information has changed a lot too. A key strand of this research concerns a mind mapping-based technology that can be efficiently used in the learning-teaching process of engineers at high establishments. The results obtained on the basis of experimental work on testing the mind mapping variable technology identified its competitive advantages for visualizing thinking and memory brainstorming. It reveals perspectives for different forms of intellectual work and focuses on the brain and imagination potential. Based on visual-figurative thinking this approach encourages the image and understanding of information and illustrates relevant perspectives as an effective psycholinguistic method.
Enhancing the Security of JSON Web Token Using Signal Protocol and Ratchet System

User Authentication is crucial for every application. Initially, session variables were used. If a session ID was compromised, lost, or stolen, the user’s identity was compromised. This resulted from session IDs having a long lifespan and the tendency of most users not to log out of their accounts. JSON web tokens (JWTs) Tokens were introduced to overcome this problem and are currently considered industry standards. JWTs are secured using base 64 encryption and session variables. They have a shorter lifespan as compared to session variables. Even then, it is highly susceptible to Man-in-the-Middle attacks, especially over unsecured networks. Internal attack vectors also pose a significant threat in this particular scenario. To counter this problem, we explore the possibility of using the Signal algorithm with a double ratchet system to encrypt our JWTs to add a new layer of security. The result is an algorithm capable of securing primary JSON payloads in an end-to-end manner. This research aims to increase the security of JWTs using a ratchet system.
A Study on Deep Learning Frameworks for Opinion Summarization

Opinion summarization has gained interest among researchers due to the usefulness and challenges in extracting insights from the unprecedented amount of opinionated data available on the internet. Deep learning has been employed in various natural language processing tasks and produced state-of-the-art results. In this paper, we present a comprehensive review of various deep learning approaches in the context of opinion summarization. This paper presents a review of the notable state-of-the-art models with recommendations for future research.
Retrospective Review on Object Detection Approaches Using Boundary Information

In computer vision, object detection is an approach for identifying and locating objects from theimages and videos. It can also be used to measure the number of objects present in a scene determining the precise location as well. A technique for detecting boundaries between two objects comes under semantic ontology. The semantic boundary and edge detection is a difficult task as assessment for an edge cannot be purely grounded on low-level features like a gradient. Semantic learning of classifiers involves the knowledge of edge-labels, which is a complex problem in image processing. Here we examine several levels of information in order to adopt a feasible method where all edges required pixels for the continuous detection of the objects. In this paper, we study the application of object detection and several recent approaches developed using boundary information in past decades. The associated drawbacks are also highlighted in this work to provide the scope of improvement in this research field.
Chi-Square Top-K Based Incremental Feature Selection Model for BigData Analytics

The exponential rise in advanced software computing, internet technologies, and humongous data has given rise to a new paradigm called BigData, which requires an allied computing environment to ensure 4Vs aspects, often characterized as varieties, volume, velocity, and veracity. In sync with these demands, most of the classical commutating models fail, especially due to large unstructured features of gigantically huge volume. To alleviate this problem, feature selection can be a viable solution; provided it guarantees minimum features with optimal accuracy. In this reference, the proposed work contributed a first of its kind solution which could ensure minimum features while ensuring expected higher accuracy to meet 4V demands. To achieve it, in this paper, a robust Chi-Squared Select-K-Best Incremental Feature Selection (CS-SKB-IFS) model is developed that achieved a minimum set of features yielding the expected accuracy. Subsequently, over the selected features, the CS-SKB-IFS model is used for further classification using the Extra Tree classifier. Thus, the strategic amalgamation of the CS-SKB-IFS model achieved the accuracy of (91.02%), F-Measure (91.20%), and AUC (83.06%) than the other state-of-art methods. In addition to the statistical performance, CS-SKB-IFS exhibited significantly smaller computational time (1.01 s) than the state-of-art method (6.74 s).
A Group Recommendation Algorithm Based on Dividing Subgroup

Abstract With the rapid development of Internet technology and social networks, traditional personalized recommendation systems have been unable to provide effective recommendation services for groups composed of multiple users. In order to content the needs of group users, the current group recommendation algorithm mainly uses users’ consensus preferences instead of personal preferences for group recommendation, which can only content the preferences of most users and ignore the preferences of a few cold users. In order to improve the fairness of group recommendation, a group recommendation algorithm based on dividing subgroups is proposed. The algorithm introduces the comprehensive trust between users to divide the group into different subgroups, and obtains the initial subgroup recommendation list according to the average strategy. The PageRank idea is used to set the weight of subgroups and assign the number of recommended items to subgroups to generate the group recommendation list. The experimental results show that the algorithm in this paper can content the user's personalized recommendation needs to the greatest extent under the premise of improving the group fair recommendation service.
A weakly supervised approach for recycling code recognition

Waste sorting at the household level is a virtuous process that can greatly increase material recycling and boost the circular economy. To this purpose, waste must be differentiated by material (e.g., PVC, Polyethylene, Paper, Glass, Aluminum, etc.), a task that can be simplified by printing a recycling code on the product case. Unfortunately, the large number of recycling codes printed on products makes this process unfriendly for many users. In this work, we propose a vision-based mobile application to support users in recognizing recycling codes for proper waste sorting. The proposed system combines a dual-head CNN with an image processing pipeline (based on domain knowledge) in order to improve: (i) the reliability of symbol detection/classification and (ii) the weakly-supervised labeling of new samples during iterative training. Our experimental results prove the feasibility of developing effective applications with minimum effort in terms of data collection and labeling, which is one of the main obstacles to successfully applying deep-learning techniques to real-world problems.
A Phenomenological Study on the Korean Cuisine Research Class in High School Credit System Applied STEAM Education Model

The purpose of this study is to establish meanings, values, and implications of the STEAM education by empirically researching on the Korean cuisine STEAM education in high school credit system. The results of this study present followings. Firstly, as a result of the investigation on students’ cognition of the Korean cuisine STEAM education in high school credit system, the educational demands on ambivalent emotions and association with industry has been appeared. Secondly, changes in cognitive, affective, and behavioral areas have been shown from students who took the educational experience of the Korean cuisine STEAM education in high school credit system. Thirdly, cooperative changes and implications of the educational community through the Korean cuisine STEAM education in high school credit system have been drawn. In this study, academic implications were deduced from pedagogic meanings and values what the Korean cuisine STEAM education in high school credit system provides, and the connection between education and industry and the necessity of STEAM education were proposed as practical implications.
A Study on the Importance and Satisfaction of Selection Attributes of Coffee Vocational Schools Using IPA

This study conducted a study on the selection attributes of coffee vocational academies targeting students of coffee vocational training institutes. In order to achieve the purpose of this study, it was attempted to derive practical implications through IPA analysis by deriving 18 selection attribute items, each of importance and satisfaction, from the students who used the coffee vocational training institute. For data collection, a questionnaire was conducted targeting students who used coffee vocational training institutes located in Daegu and Gyeongbuk. The survey period was conducted from May 1 to May 30, 2022, and after distributing 250 copies, 232 copies of the collected questionnaires were used for analysis, excluding 18 questionnaires in which responses were insincere or incomplete. The conclusion is as follows. As a result of examining the importance of each selection attribute of the coffee vocational training institute independently, there were 11 results that were higher than the average (3.1077), and 6 results showed that the satisfaction was higher than the average (3.0967).
A Multidimensional Adaptive Entropy Cloud-Model-Based Evaluation Method for Grid-Related Actions

Smart grid training system needs to evaluate actions during power grid operations in order to complete training for relevant personnel. The commonly used action evaluation methods are difficult for evaluating fine actions during power grid operations, and the evaluation results are subjective. The use of an effective method to evaluate the actions of the power grid operation is important for improving the smart grid training system, enhancing the skills of the trainers, and ensuring the personal safety of operators. This paper proposes a cloud attention mechanism and an evaluation method of grid-related actions based on a multidimensional adaptive entropy cloud model to complete the evaluation of fine actions in the grid’s operation process. Firstly, the OpenCV technique is used to obtain the data related to hand actions during grid operation and to extract the action features to complete the construction of multiscale date sets; then, the adaptive entropy weight matrix at different scales is constructed based on multiscale data sets using the cloud attention mechanism, and the basic cloud model is generated from original hand-action feature data; finally, the multidimensional adaptive entropy cloud model is constructed by the adaptive entropy weight matrix and the basic cloud model, and the multidimensional adaptive entropy cloud model obtained is compared with the multidimensional adaptive entropy cloud model generated based on the standard action features in the same space to obtain the evaluation level of the hand action. The results show that the evaluation method of grid-related actions based on the multidimensional adaptive entropy cloud model can solve the mutual mapping problem between quantitative indicators and qualitative evaluation results in the evaluation of grid operation processes relatively well, and it effectively solves the subjectivity of the weight assignment of evaluation indicators, which can be used for the evaluation of fine actions in the grid’s operation processes.
Exploring the impact of mass migration in Jin Dynasty by psycholinguistic analysis on Classical Chinese legacy text

The first mass migration in China took place at the end of the Western Jin, which resulted in the southward transfer of the Central Plains Culture and brought about huge social changes. Such social changes exerted significant impacts on the gentry of the Jin Dynasty. This paper used a huge volume of Classical Chinese legacy text of Jin gentry members. We used CC-LIWC to calculate frequencies of different word categories used in these text contents and conducted an analysis of variance to measure significant differences between the three groups. We found 16 categories of words with significant differences and calculated their effect sizes, such as tense markers (tensem), F = 3.588, P < 0.05, η2 = 0.034; modal particles (modal_pa), F = 3.468, P < 0.05, η2 = 0.053; words for affective processes (affect), F = 3.096, P < 0.05, η2 = 0.028; words for cognitive processes (cogproc), F = 3.308, P < 0.05, η2 = 0.031; words for perceptual processes (percept), F = 7.137, P < 0.05, η2 = 0.06. Combining the psycholinguistics of the 16 categories of words and researches of historians on the Jin Dynasty, we then analyzed the direct and indirect, immediate and long-lasting psycholinguistic impacts of this mass migration on the gentry themselves and their descendants.
Organizational and managerial problems of blended and hybrid learning

The COVID-19 pandemic has changed the world; the educational system has altered too. The pandemic instantaneously transformed the traditional form of organizing and implementing education. The previously existing approaches to distance learning which involved using article-based learning resources have been replaced with a digital format. This shift required all participants involved in learning activity to have both appropriate digital competencies and specialized communication platforms in conjunction with libraries of digital resources. For the first time in history, education was delivered completely outside educational institutions, which required considerable effort on the part of educational organizations, parents, and students themselves to organize a remote workplace. For the first time in history, students and teachers were stratified in terms of their digital competencies levels and the feasibility of carrying out learning activity remotely. At the end of the pandemic, it is obvious that using only distance learning in any educational stage in impossible. This is due to not only a complete absence of approaches to regulation and requirements for a remote workplace, but also a lack of funding sources for its creation and functioning. Under these conditions, educational institutions have realized that the learning environment is not exclusively confined to their walls. Moreover, students and their places of study, stay, and residence are its cornerstones. All these factors require modern education to make fundamentally different efforts to ensure the operation of the system and going beyond its framework. First and foremost, the focus ought to be on a particular student’s needs and capabilities.
Sensorless position detection of reluctance spherical motors based on inductive characteristics

The rotor attitude detection of spherical motors usually requires the installation of position sensors on the rotor shaft or stator to achieve, which occupy a large space and cause negative effects for the practical installation and application of spherical motors. In this paper, a sensorless position detection method for reluctance spherical motors based on the structure and operating characteristics of reluctance spherical motors is proposed. By detecting the stator coil inductance data at different rotor attitude angles, the position prediction model is constructed using the extreme learning machine algorithm. Through comparison and validation, it is demonstrated that the rotor attitude angle obtained by the method is highly accurate, providing a feasible idea for the control and application of reluctance spherical motors.
A SWOT analysis of smart governance applications amid the COVID-19 pandemic

Smart governance technologies are emergent information and communication technologies (ICT), which amid the COVID-19 pandemic have been widely used to govern COVID’s impacts. However, very few studies have provided insight into the strengths, weaknesses, opportunities, and threats (SWOT) of smart governance technologies under COVID-19. This prevents the widespread usage of smart governance technologies across cities and countries and impedes their usefulness during emergencies. This study combines knowledge of emergency management theory and planning support systems (PSS) to frame the functional usefulness of smart governance technologies in handling practical tasks amid the COVID-19 state of emergency into a task-technology fit model. By employing qualitative content analysis to collect views and insights from the international news-based cases (n= 69), we identify SWOT of smart governance technologies under COVID-19. Results indicate that usefulness of smart governance technologies is reflected in task-handling in the ex-post emergency phases of response (e.g., provide e-health service, tracing and tracking) and recovery (e.g., improve e-government services, enhance collaboration for renewal), whereas few applications focus on tasks in the ex-ante emergency phases of mitigation (e.g., hazard analysis, vulnerability assessment) and preparedness (e.g., planning, training and exercises). Main findings lead to a conclusion that hidden capacities of smart governance technologies can be fully achieved if policy makers and practitioners can focus on the full range of tasks and demands before, amid, or after emergencies and identify collaborative opportunities for technological development.
Are the Russian Higher Educational Institutions Ready to Export Education?

The article substantiates the necessity to hold regular research of the foreign students’ and trainees’ opinion about the degree of their satisfaction with the conditions of study in Russian higher educational institutions. The criteria to assess their satisfaction degree with the study conditions have been highlighted. The results of research held in 2021 have been provided, the main difficulties and issues the respondents are facing have been described. The organizational aspects of study in Russian higher educational institutions attracting foreign students have been defined. The issues of studies during the restrictive measures caused by COVID-19 have been raised, and the acceptable forms of educational process organization during the pandemic have been presented. The expressed proposals of the respondents to improve the conditions of the educational services provision have been systematized.
Adaptive Batch Size Selection in Active Learning for Regression

Training supervised machine learning models requires labeled examples. A judicious choice of examples is helpful when there is a significant cost associated with assigning labels. This article improves upon a promising extant method – Batch-mode Expected Model Change Maximization (B-EMCM) method – for selecting examples to be labeled for regression problems. Specifically, it develops and evaluates alternate strategies for adaptively selecting batch size in B-EMCM.&lt;br/&gt; By determining the cumulative error that occurs from the estimation of the stochastic gradient descent, a stop criteria for each iteration of the batch can be specified to ensure that selected candidates are the most beneficial to model learning. This new methodology is compared to B-EMCM via mean absolute error and root mean square error over ten iterations benchmarked against machine learning data sets.&lt;br/&gt; Using multiple data sets and metrics across all methods, one variation of AB-EMCM, the max bound of the accumulated error (AB-EMCM Max), showed the best results for an adaptive batch approach. It achieved better root mean squared error (RMSE) and mean absolute error (MAE) than the other adaptive and nonadaptive batch methods while reaching the result in nearly the same number of iterations as the non-adaptive batch methods.
Post-COVID-19 Neural Cryptographic Onset of Homeopathy Psychiatry Medicine's Transmission in "New Normal Form"

In telepsychiatric treatments, the medical data of various types of patients' data are to be communicated in a well-protected way between the patient and the psychiatrists. Homeopathy drugs play a significant role in treating such psychiatric patients. The objective of this manuscript is to develop a strong session key with the help of neural cryptography. Neural cryptography is the extensive usage of the artificial neural networks for the cryptographic functions. E-medicines were encrypted here by the classical methods and the proposed session keys of 128 bits long. The length has been selected to 128 bits in order to have less memory space needed. Alzheimer's disease, Dementia, Insomnia, Stress, OCD, Brain restlessness, Depression, etc are the most clinical mental complications observed in this post COVID-19 situations. This is the era of digital health to deal the remote patients who are non-invasive in nature. In this manuscript, a session key has been proposed by the Key Distribution Centre (KDC) with the aid of artificial neural networks. Strong session keys were derived of 128 bits length which in turn used for AES 128 encryption. To avoid the Man-InThe-Middle attacks, it has been designed that the server machine shall accumulate all the needful symptoms of the psychiatric patients.