Reviewerly/exHarmony huggingface dataset:
Technological competence of future music teachers: diagnostic and formative methods

The study dwells upon the problem of technological competence of future music teachers, which is relevant to the current music and music-pedagogical education process in Ukraine and other countries. The objective of the article is to cover the results of the experimental research on methods of diagnosis and formation of future music teachers’ technological competence. As a matter of fact, with the purpose of studying the problem, the following scientific research methods have been applied: a set of theoretical, diagnostic, mathematical statistics methods. The essence of the concept of "technological competence" is revealed, which is an integral component of professional competence of a modern music teacher. The structure of the studied phenomenon is determined, which consists of the following components: axiological and cultural, epistemological and emotional; technological and operational; creative and professional. Moreover, the four groups of criteria are substantiated according: value-cultural, cognitive-emotional, technological-operational and creative-professional. Finally, a diagnostic experiment was carried out, in the process of which special diagnostic methods were used of the formative experiment lies in the gradual formation of the technological culture of future teachers-musicians according to the developed author's technique, which includes four pedagogical conditions and a set of interrelated methods.
From Actions to Events: A Transfer Learning Approach Using Improved Deep Belief Networks

In the last decade, exponential data growth supplied machine learning-based algorithms' capacity and enabled their usage in daily-life activities. Additionally, such an improvement is partially explained due to the advent of deep learning techniques, i.e., stacks of simple architectures that end up in more complex models. Although both factors produce outstanding results, they also pose drawbacks regarding the learning process as training complex models over large datasets are expensive and time-consuming. Such a problem is even more evident when dealing with video analysis. Some works have considered transfer learning or domain adaptation, i.e., approaches that map the knowledge from one domain to another, to ease the training burden, yet most of them operate over individual or small blocks of frames. This paper proposes a novel approach to map the knowledge from action recognition to event recognition using an energy-based model, denoted as Spectral Deep Belief Network. Such a model can process all frames simultaneously, carrying spatial and temporal information through the learning process. The experimental results conducted over two public video dataset, the HMDB-51 and the UCF-101, depict the effectiveness of the proposed model and its reduced computational burden when compared to traditional energy-based models, such as Restricted Boltzmann Machines and Deep Belief Networks.
A Combining AI Algorithm for the Restoration of Damaged Cultural Properties

Through the research of numerous researchers’ artificial intelligence imitates human language and visual expression with good performance and imitates human style in voice and picture. This ability although dependent on the data for learning artificial intelligence is more objective and based on numerical data than humans. We applied it to the restoration of cultural assets made in the past through artificial intelligence neural networks and we applied a general CNN a little differently for the purpose of restoration. Cultural properties contain various backgrounds from the era when they were created and for this reason there are many complications and difficulties in restoration. If it is simply regarded as noise and recovered the result is dependent on the learned data. To solve this problem the CNN was separated into full and detailed and the association was learned together and the damaged part was repaired through a generative competition network (GAN) based on this neural network. We trained a neural network that extracts visual features on a Korean "Pagoda" (mostly produced under the influence of Buddhism) and conducted a study to repair the damaged part based on the trained neural network. The features of the tower were extracted through a CNN-based neural network and the damaged part was repaired through a Generative Adversarial Network (GAN) based on the extracted features. It is thought that our research will be actively used for the restoration of cultural assets as well as the restoration of archaeological records in the future.
The Impact of Job Satisfaction in Achieving Strategic Agility through the Mediating Role of Knowledge Sharing

The main objective of this research is to identify the role of job satisfaction in influencing strategic agility through knowledge sharing. The researcher used the descriptive as well as the analytical approach in the completion of this research by collecting data by means of the questionnaire as the main tool on a sample of the General Company for Food Industries' employees, whose number reached (76) individuals. Moreover, some statistical methods were employed to process the data; including the arithmetic mean, Standard deviation, simple linear correlation coefficient (Pearson), simple linear regression, and the median variable test. It was represented that there is a significant and essential impact of job satisfaction in influencing strategic agility in the presence of active knowledge sharing. The outcome of this research can provide important information regarding how concern for the level of job satisfaction affects the creation of strategic lightness that will help enhance and increase the production flexibility of the General Company for Food Industries through knowledge-sharing processes that will contribute to increasing the skills of employees. It will also clarify the prominent role that the company can contribute to activating the private sector.
Solving for why

A focus on causality is promising to help researchers overcome shortcomings that have bedeviled more traditional approaches to artificial intelligence.
Performance of K-means algorithm based an ensemble learning

K-means is an iterative algorithm used with clustering task. It has more characteristics such as simplicity. In the same time, it suffers from some of drawbacks, sensitivity to initial centroid values that may produce bad results, they are based on the initial centroids of clusters that would be selected randomly. More suggestions have been given in order to overcome this problem. Ensemble learning is a method used in clustering; multiple runs are executed that produce different results for the same data set. Then the final results are driven. According to this hypothesis, more ensemble learning techniques have been suggested to deal with the clustering problem. One of these techniques is "Three ways method". However, in this paper, three ways method as an ensemble technique would be suggested to be merged with k-mean algorithm in order to improve its performance and reduce the impact of initial centroids on results. Then it was compared with traditional k-means results through practical work that was executed using popular data set. The evaluation of the hypothesis was done through computing related metrics.
Feature Selection for Fuzzy Neural Networks using Group Lasso Regularization

In this paper, a Group Lasso penalty based em-bedded/integrated feature selection method for multiple-input and multiple-output (MIMO) Takagi-Sugeno (TS) fuzzy neural network (FNN) is proposed. Group Lasso regularization can produce sparsity on the widths of the modified Gaussian membership function and this can guide us to select the useful features. Compared with Lasso, Group Lasso formulation has a Group penalty to the set of widths (weights) connected to a particular feature. To address the non-differentiability of the Group Lasso term, a smoothing Group Lasso method is introduced. Finally, one benchmark classification problem and two regression problems are used to validate the effectiveness of the proposed method.
Laser Curve Extraction of Wheelset Based on Deep Learning Skeleton Extraction Network

In this paper, a new algorithm for extracting the laser fringe center is proposed. Based on a deep learning skeleton extraction network, the laser stripe center can be extracted quickly and accurately. Skeleton extraction is the process of reducing the shape image to its approximate central axis representation while maintaining the image's topological and geometric shape. Skeleton extraction is an important step in topological and geometric shape analysis. According to the characteristics of the wheelset laser curve dataset, a new skeleton extraction network, a hierarchical skeleton network (LuoNet), is proposed. The proposed architecture has three levels of the encoder-decoder network, and YE Module interconnection is designed between each level of the encoder and decoder network. In the wheelset laser curve dataset, the F1_score can reach 0.714. Compared with the traditional laser curve center extraction algorithm, the proposed LuoNet algorithm has the advantages of short running time, high accuracy, and stable extraction results.
The Role of Elementary School Teachers in the Implementation of Online Learning during the COVID-19 Pandemic

This study aims to determine the role of teachers, obstacles and solutions in online learning during the COVID-19 pandemic. This type of research is qualitative research with a fixed case study design. Data were collected through interviews, observation and documentation techniques. Data were analysed using interactive model analysis. The results showed that the role of elementary school teachers in online learning during the COVID-19 pandemic was compiling learning materials taken from the internet (Google), preparing a laptop device with an internet network connection using WhatsApp media, using video media in delivering material who need a concrete example, assess online learning by not burdening students, teachers participate in various online learning training both and independent learning, involving parents of students to be able to provide learning assistance for their children. Constraints faced by elementary school teachers in online learning include: difficulty getting an internet signal, lack of internet quota, students cannot access WhatsApp because not all students or parents have cellphones, parents do not assist their children, students do not understand the material given. Solutions to overcome these obstacles include: changing cellular card providers that have good signals, finding sources of learning materials in schools that have better signals, subsidising internet quotas from schools, communicating and motivating.
A Robot Maker for Health Sector

Coronavirus infections have once again highlighted the need to accelerate the companies, especially in health care to adopt automation. Countless robots have been made since last more than 50 years, but still we do not find their ubiquitous presence. Consequently, countless doctors, nurses and healthcare workers have risked, are currently risking and may continue to risk their precious lives, while tending the dependents. Countless families have suffered as their lone breadwinner succumbed to a risky act while tending coronavirus-affected patients. The paper suggests that it is high time now that companies delegate only the risky tasks to robots. The industry is currently in version 4.0, which is digitalization, and a universal need is felt to accelerate the companies toward adopting intelligent automation. While automation is the next version in industrialization of the world, the question still arises as to whether the state-of-the-art technologies are available to usher the companies toward the new era of automation or not. There are innumerable researches on robotics, and even the kids are being prepared to construct a robot at some places. However, it seems to be an aimless proliferation of robotics undertaking the ordinary or cliché jobs so much so that the whole activity appears clichéd. It is high time that a focused research is undertaken to construct a universal robot that can help to make robots that are secure and harmless to humans and replace preferably risky tasks. This not only will prepare the world in moving in right direction, but would also ensure safety and security of human race as the automation could be directed to replace only the risky tasks. This work is therefore aimed at constructing a universal “automaton” to understand any requirement and assist in creating safe and secure robots with the help of 3D printers. A prototype to this effect was developed and is available for further research and development in this area.
Multiple-structure Attentional Network for Click-through Prediction in Recommendation System

In recent years, the total amount of data generated by people has increased exponentially, which makes it a great problem to recommend for users, so recommendation system (RS) plays an important role in this process, and it has been widely used in many online service fields such as e-commerce system and science and technology (S & T) service platform. As an important part of the RS, CTR prediction has been studied in depth, from the beginning, machine learning methods include logistic regression (LR), factorization machines (FM) and field-aware factorization machines (FFM) models, to deep learning methods such as multi-layer perceptron (MLP), product-based neural network (PNN), deepFM and xdeepFM models, and later attention mechanism based methods such as AFM and DIN models, they all lack the application of S & T service platform. The multiple-structure attention (Mul-AN) network model is proposed in this paper integrates the low-order as well as explicit and implicit feature interactions without any manual feature engineering for the data, and introduces the attention mechanism into the embedding layer of the model to distinguish the importance of the interaction of different feature, and will be finally applied it to the Hainan Comprehensive S & T Service Platform. Extensive experiments verify that the model can improve the performance and accuracy of S & T service platform CTR prediction.
Virtual university and educational transformation in the context of the pandemic

The abrupt change of the pandemic implied the transformation of traditional education to a virtual one, with numerous shortcomings, both in the organization, methodology, strategies, and instruments used. The article's objective was to deepen the knowledge found on the impact of the virtual university on educational transformation during the Covid-19 pandemic. The methodology used was a literature review. Different searches of reliable and recently published sources were carried out in the Scopus database. The following 4 points were prioritized: global context, ICT, e-learning, the emotional aspect of students and teachers. The results indicate that the adaptation to an online education highlighted the significant gaps in the sector, affecting both students and teachers, who presented a lack of motivation in their academic work. It is concluded that virtual education is in total development. Therefore, institutions must improve their entire organization to adapt to the new form of teaching.
A New Power Forecasting Method for Photovoltaic Plants under Hazy Conditions

Power forecasting has become more and more important for the safe and economic operation of Photovoltaic (PV) plants. In practice, the power generation of PV plants may be affected by haze conditions. To improve the power forecasting accuracy of PV plants under hazy conditions, the Air Quality Index (AQI) is adopted for the power forecasting modeling in the paper. The relationship between the AQI and solar irradiance is analyzed firstly, which indicates that the AQI and solar irradiance have a significant negative correlation. It reveals the weakening effect of haze on solar irradiance. A clear boundary exists in the scatter diagram of the AQI and solar irradiance under different haze conditions which represents the maximum solar irradiance which the PV plant can receive at a moment. Then, a new method is proposed in which the AQI is adopted to correct the solar irradiance from Numerical Weather Prediction (NWP). The corrected NWP solar irradiance serves as an input variable for the Artificial Neural Network (ANN) model. Finally, the measured data is used to compare the different power forecasting methods. The results show that the proposed method can effectively improve the power forecasting accuracy of PV plants under hazy conditions.
AUGMENTED REALITY IN TECHNOLOGY-ENHANCED LEARNING: SYSTEMATIC REVIEW 2011-2021

With the raise of COVID-19 pandemic in 2020, the traditional teaching-learning process became inefficient. Technology-Enhanced Learning (TEL) research has increasingly focused on emergent technologies such as Augmented Reality (AR). It became one of the technologies that have received great attention and interest in the last decade. In this paper, we conducted a systematic review that describes the current state of using AR as a learning tool. Taking into consideration the needs of all students including those with a disability, in different levels of education. It is done through the analysis of the following factors: AR in learning system, AR in levels of education and categories of educational AR applications. A total of 103 studies between 2011 and 2021 were analyzed through searching in four interdisciplinary databases: Springer, IEEE Xplore, ResearchGate, and Google Scholar. This analysis helped to see in which direction AR systems for education are heading and how it will be designed to fit the students' needs and improve their learning. Further research and development will make AR a more promising learning tool.
A comprehensive study of blockchain technology and its uses

Large data management is a major worry issue for academics in today's digital era. Several applications, such as big data, data analysis, image processing, and data mining, require large-scale data processing. The Blockchain is a distributed ledger technology that securely saves data across many systems to allow peer-to-peer transactions by establishing a trustworthy source of truth and eliminating the need for so-called trust intermediaries. A blockchain is a method for creating distinct kinds of distributed databases made up of immutable blocks of data, each with a list of transactions and a peerless reference to the block before it. Blockchain-based applications are surfacing in a variety of areas, including reputation management, the Internet of Things (IoT), big data, and financial services, among others. This method uses a shared, distributed ledger to make recording transactions and monitoring assets in a corporate network simpler. We wanted to provide a close-up perspective of Blockchain technology in this paper. We also go through the basics of Blockchain design, how various Block chains vary, and how Blockchain operates. We believe that, given its rapid growth and development, Blockchain will soon become a very widespread and well-known phenomena.
Intelligent Classification Method of Basic Elements of Chinese Ink Painting Based on a Convolution Neural Network

Facing the problem that current experience-based expert, physical and chemical analysis and identification methods lack in-depth learning of samples, resulting in inaccurate classification results, an intelligent classification method of basic elements of Chinese ink painting based on a convolutional neural network is proposed. The convolutional neural network structure is designed, and the basic elements of the seal, house, moss point, Figure and branch are analyzed. Combined with the deep learning algorithm, the activation function is initialized to avoid the saturation effect and improve the convergence speed. According to the clustering principle based on the deep learning algorithm, samples with membership less than 1 in clustering are selected to avoid the adverse impact of disordered elements on classification. Combined with the loss function curve, the classification results are output. Taking the world art database as an example, this paper makes an example analysis. According to the analysis results, the highest classification accuracy of this method is 0.9856, which has accurate classification results.
Path-aware optimistic optimization for a mobile robot

We consider problems in which a mobile robot samples an unknown function defined over its operating space, so as to find a global optimum of this function. The path travelled by the robot matters, since it influences energy and time requirements. We consider a branch-and-bound algorithm called deterministic optimistic optimization, and extend it to the path-aware setting, obtaining path-aware optimistic optimization (OOPA). In this new algorithm, the robot decides how to move next via an optimal control problem that maximizes the long-term impact of the robot trajectory on lowering the upper bound, weighted by bound and function values to focus the search on the optima. An online version of value iteration is used to solve an approximate version of this optimal control problem. OOPA is evaluated in extensive experiments in two dimensions, where it does better than path-unaware and local-optimization baselines.
A influência do estresse na incidência de infarto em indivíduos jovens durante a pandemia / The influence of stress on the incidence of heart attack in young individuals during the pandemic

A influência do estresse na incidência de infarto em indivíduos jovens durante a pandemiaThe influence of stress on the incidence of heart attack in young individuals during the pandemic
Dataset for studying gender disparity in English literary texts

Recent discourse has highlighted significant gender disparity in many aspects of economic, social and cultural life. With the advent of advanced tools in Artificial Intelligence (AI) and Natural Language Processing (NLP), there is an opportunity to use computational and digital tools to analyze corpora, such as copyright-expired literature in the pre-modern period (defined herein as books published approximately between 1800 and 1950) from the Project Gutenberg corpus. Nevertheless, there are challenges in using such tools, especially for maintaining high-enough quality to explore interesting hypotheses. We present a dataset and materials that illustrate how modern processes in NLP can be used on the raw text of more than 3,000 literary texts in Project Gutenberg to (i) extract characters and pronouns from the text with high quality, (ii) disambiguate characters so that they are not overcounted, (iii) detect the gender of each character. Furthermore, we also used manual labeling to determine the genders of authors who have published these texts, and published the labels as part of the dataset to facilitate future digital humanities research.