Reviewerly/exHarmony huggingface dataset:
Identification of Stage Action in the Process of Creation of a Role Image Score by an Artist

The purpose of the work. Highlight and characterise the identifier of the stage action, its specific diversity in the process of the artist's creation of the score of the image of the role in accordance with the personification of the dramatic work, taking into account all the characteristic signs of ideological-emotional verbal and physical expressiveness. The research methodology consists in the application of the functional method to consider the complementarity of verbal and stage action, physical correspondence of posture and gesture, general plasticity of body culture, levels of creative potential, categories of psychophysical action; the art method for understanding the interpretation of the image of a character in a dramatic work in the process of creating stage role-playing material by the performer on the basis of a creative reinterpretation of a number of events, implementing various options for the transformation of extra-artistic facts with the help of one's own emotional memory; a systematic method for revealing and understanding the essence of the mechanism of formation of the general picture of the image of the role in the process of stage action, a set of rehearsals, creativity of the individual performer; analytical method for the analysis of the characteristic features of the formula of the visual-spectacle model of the performance, the classification reference point of certain stage actions according to the genre plan of the performance. The novelty of the research consists in considering the procedural essence of the artist's creative activity, a consistent analysis of the comparability of the stage action and the dramaturgical series of events, the justification of the main means and ways of expressiveness of acting. Conclusions. An actor is an imaginative and creative substance of the formation of theatrical art, the main category of which is directly the stage action, the essentiality of which is determined by dramatic circumstances, quality, relationships, relationships between characters, a series of events, space-time dimension and possession of the inherent properties of the artistic image of the role. The artistic creativity of the masters of the stage spectacle as a living organism exists thanks to new ideas of interpretation of the score, implementation of the idea of the performance and original modern embodiment, and a priori cannot exist without the latest techniques and methods of artistic performance.&#x0D; Keywords: theatre, opera house, performance, series of events, actor, stage action, means of expression, emotional memory.
Anomalously acting agents: the deployment problem

Abstract The detection of intentionally antagonistic behavior in robot swarms brings about challenges that exceed identifying merely erroneous behavior. We investigate a data-based approach to recognize antagonistic behavior in robots executing a deployment task. The task requires a robot swarm of variable size and start positions to optimally distribute within an arbitrary convex surveillance area. Combining a long short-term memory neural network and a normalizing flow, our approach learns to approximate the probability of a robot action. Thus, actions with low probability density values can be categorized as anomalous. The applicability of the proposed approach is validated on simulated runs containing benevolent, antagonistic, and erroneous robots. Both antagonistic and erroneous robots are detected with an accuracy of more than 90 percent.
Communication as an evolutionary factor of knowledge, from primordial organisms to complex collective systems

The intrinsic characteristic of material substances is their ability to modify their physical state and their energy content in response to the interactions and stresses they receive. And it is the capacity that crosses transversely all living matter including man, with the difference that the man solicited by external events becomes aware of his own uniqueness and his own destiny. We can say that man acquires human characters when he learns to re-know his fellow men and the world around him and experiences the wonder that this experience transmits him. And we will show how the whole of the multiplication of contacts and interactions between men produces the corpus of knowledge of peoples. Starting from this hypothesis, in this work we are going to describe how the transformations produced by the reciprocal interactions between material substances, and between these and their environment, are acquired in the form of knowledge. The analysis of the events we describe of the cognitive process follows the logical procedure, which will lead us to the conclusion that knowledge is the intrinsic manifestation of matter and how it originates and takes shape from it. With the advent of man, the highest expression of the organization of matter and knowledge, matter itself embodied in man can investigate knowing itself and its intimate essence. Thus, knowledge can be conceived as the virtuous ontological circuit that originates from matter and returns to it modified in form and enriched with content.
Performance Evaluation of LSTM Based Solar Irradiance Prediction Models without Long-term Local Weather Data

Prediction of solar irradiance that influences the building load and production of new and renewable energy is crucial in the predictive control of buildings. However, most solar irradiance prediction models are developed using weather data measured over a long term in the target region. This study proposed a solar irradiance prediction model based on a hybrid layer that combined a long short-term memory layer and a bi-directional long short-term memory layer. Next, the solar irradiance of the following day was predicted using only the hourly irradiance measured on site one day before the prediction day using a model trained with the weather data of another region. The proposed solar irradiance prediction model showed an RMSE of 69.5 W/m2, a level suitable for the predictive control of buildings. Moreover, although training data from a different region was used, the performance of the proposed model was similar to that of a model trained with long-term data measured in the target region.
LEADING PRINCIPLES OF MENTORING LEARNERS

Mentorship, as one of the types of mentoring activities in the educational sphere, is currently a promising direction of pedagogical influence, expanding the possibilities of personal development of students and optimizing the professional competence of teachers. Mentorship aims all subjects of this process at non-directive, co-evolutionary interaction, which creates an effective developmental environment for obtaining/updating knowledge, skills and abilities, expands the range of self-development of each individual. This circumstance can act as a powerful trigger for expanding the classical format of educational impact on students and stimulates the growth of professional skills of teachers. In this regard, it is important for the scientific community to study in depth the specifics of the principles used in the process of mentoring students. An important point in building a successful, effective mentor’s (teacher’s) work with mentees (students) seems to us to be the nuances of the accents of interaction, which are based, of course, on the basic principles of the personality-oriented and activity-based approaches applicable to the educational sphere. At the same time, it is the nuance, the specificity of the principles realized in mentoring that determines their peculiarity, which affects the effectiveness of the mentor’s work with students. The purpose of this article is to analyze the peculiarities of the leading principles of mentoring interaction, to emphasize the vector of the direction of mentoring influence in the system “teacher-students”. The methods of obtaining theoretical data presented in the article were the general ways of analyzing and synthesizing the information available in scientific practice concerning the problem of leading principles of mentoring. The article presents scientific data related to the substantiation of the peculiarities of the leading principles of mentoring. This information will help to expand the understanding of the essence of mentoring work of a teacher with students.
THE INNOVATIVE POTENTIAL OF THE MODEL-BASED APPROACH IN SHAPING STUDENTS' DIGITAL SKILLS

The content of the model-based approach in forming digital skills of students is revealed. The principles (systemacy, nonlinearity, universality, scalability) of the model-based approach implementation in the training of future specialists are characterized. The article focuses on the innovative potential of this approach, ensuring its application in the formation of digital skills required by specialists in various fields: science, production, application of information technologies. The resources (intellectual, resources of the information space of a higher educational organization, professional creativity), necessary for teachers to solve problems on preparation of competitive specialists, are described. The significance of mastering digital skills by students, which allow them to demonstrate reflexive and research skills in class, decision-making algorithm capabilities and predict their consequences, perform tasks on modeling processes and systems, using modern information technology is revealed. The scientific novelty of the article lies in revealing the content of the process of forming digital skills on the basis of the model-based approach in teaching students. The effective use of the model-based approach in the formation of digital skills in higher education students is justified, the conclusion of the need for teachers to use its innovative potential is made. The article is of interest for teachers of higher educational institutions, graduate students, applicants.
Acute Leukemia Detection using Deep Learning Techniques

Leukocytes, which are created in the bone marrow comprise one percent of all blood cells. When these white blood cells grow uncontrollably it gives rise, to the development of blood cancer. The proposed research presents an approach, for categorizing One of the three kinds of Multiple Myeloma (MM) and Acute Lymphoblastic Leukaemia (ALL) are the two diseases that make use of the SN- AM dataset. the malignancy known as acute lymphoblastic leukaemia (ALL), to start with in which an excessively large number of lymphocytes are pro- duced by the bone marrow. Secondly, Multiple myeloma (MM) is a type of can- cer that results in the accumulation of malignant cells in bone marrow, rather than their release into the bloodstream. Hence, the growth of blood cells is to be resist and prevent. Beforehand, the procedure was carried out manually and evaluated by experienced hematologists. The proposed methodology totally eliminates the chance of human mistake through using deep learning methods, particularly convolutional neural networks. A total of 89 ALL patients 3256 smears of peripheral blood (PBS) pictures were acquired from an online portal. The model undergoes training using modified convolutional neural networks that has been optimized and its ability to predict which type of malignancy is present in the cells is determined. In 96 out of 100 cases, the algorithm strongly replicated every measurement that corresponded to the samples. The accuracy of the system was found to be 97.6%, which is more appropriate- ate than modern techniques like Decision Trees, Random Forests, Naive Bayes, Support Vector Machines (SVMs), VGG16, VGG19, AlexNet, Google-Net, Mobile-NetV2. The work showcases that Modified CNN performs more accurately.
Exploring individual variation in constructional schematicity using random effects

Usage-based constructionist approaches see language as an inventory of constructions at different levels of schematicity learned from the input. If so, personal construct-i-cons should vary as a function of usage. Repeated use and chunking/entrenchment of specific instances should lead to reanalysis of their internal structure and change in the level of schematicity. Emancipation of such instances can be viewed as idiosyncratic node creation in the network of personal constructions. This paper exploits the variation between the full and the reduced form of it is to detect instances undergoing reanalysis in a 1.75-million-word diachronic corpus of a single blogger. Occurrence of the reduced form is modelled as an effect of time, constructional entropy and unidirectional word-to-construction and construction-to-word associations. Including lexically specified instances of different constructions as random effects reveals variation in schematicity (intercept variance) and change in schematicity over time (slope variance for the effect of temporal order).
Exploring Key Variables Related to Adolescent’ Academic Engagement using Explainable Artificial Intelligence

Recently, the importance of academic engagement, an important key to overcoming under-achievement and academic-related stress, has been highlighted. The purpose of this study was to explore key variables related to adolescents’ academic engagement. For this purpose, data from the 1st year (7th graders) and 4th year (10th graders) of the Korean Children and Youth Panel Survey 2018 were used. A comparison was made of the predictive performance of random forest, gradient boosting, and XGBoost to select the best machine learning technique for predicting academic engagement. Additionally, the SHAP index, which is an explainable artificial intelligence technique, was used to identify the relationship between key variables and academic engagement. The main research results are as follows. It was found that gradient boosting had the best prediction performance among the three machine learning techniques compared. The key variables commonly derived from middle and high schools are self-study time (weekends, weekdays), grade level for all subjects the previous semester, life satisfaction, grit, creative personality (cleverness), and teacher-student relationship, which had a positive relationship with academic engagement. However, academic helplessness (lack of learning motivation, lack of positive emotions) was found to have a negative relationship with academic engagement. The commonly derived key variables were in the areas of daily living time, intellectual development, social/emotional, and school. In addition, major variables were derived in the activity area for middle school and in physical development, media, career, and family areas for high school. Based on these results, educational implications regarding adolescents’ academic engagement are discussed.
An Analysis of Research Trends on ‘Infant-Teacher Interaction’ : Focusing on domestic journal articles after free childcare policy implementation (2012)

The purpose of this study was to comprehensively analyze the research trends regarding 'Infant-Teacher Interaction' published in domestic academic journals, in order to provide foundational data for the activation of research on infant-teacher interaction. To achieve this, a total of 93 papers published in domestic academic journals from 2013 to 2022, following the implementation of free infant care, were selected as the subjects of analysis. The study analyzed trends based on the periods of research, target subjects, methods employed, and content. The research findings are as follows First, research related to 'Infant-Teacher Interaction' has been consistently published in various academic journals, with the exception of 2017. Second, the research subjects predominantly focused on infants aged 2 years and younger. Third, in terms of research methods, survey research utilizing questionnaires held a prominent position, surpassing the majority of quantitative studies. Fourth, concerning the content of the research, studies related to teachers, individual factors, and teacher leadership overwhelmingly dominated. Based on these research outcomes, future research directions can be proposed. Research on 'Infant-Teacher Interaction' should not only focus on teacher-related variables but also explore various ecological aspects, including infant and parent-related factors. Additionally, a qualitative research approach is recommended to actively delve into the profound and internal explorations of human variables.
The Development and Validation Study of The Korean Version of Teacher Efficacy Scale for Early Childhood Teachers

The purpose of this study is to develop a Korean version of the teacher self-efficacy scale for early childhood teachers by adapting Bandura's (2006) teacher self-efficacy scale (TSES) and considering the contextual suitability of early childhood education in Korea, and to verify its validity and reliability. In the preliminary investigation, the constructs and questions of the Korean version of the teacher efficacy scale for early childhood teachers were explored through literature analysis, translation-back-translation, and content validity verification. As a result of the preliminary investigation, a preliminary draft of 6 constructs and 28 questions was developed. In main survey, exploratory factor analysis, confirmatory factor analysis, and reliability analysis were conducted on 305 early childhood teachers to verify the scale goodness of the Korean version of the teacher efficacy scale for early childhood teachers. As a result of the study, the Korean version of the teacher efficacy scale for early childhood teachers was developed with 6 constructs and 28 questions, and the Cronbach’s α coefficient was found to be .96. Therefore, the Korean version of the teacher efficacy scale for early childhood teachers developed in this study was found to be an appropriate scale for measuring the teacher efficacy of early childhood teachers.
Approaches to teaching and learning middle school music using technology

With the rapid pace of digital transformation and technological advancements, there is a clear direction in education to enhance students' digital media literacy, starting with the 2022 National Curriculum; this is also an important task in music education. In line with the trend, this study investigates the types and functions of technology that can be utilized in middle school music education. Additionally, it has developed practical examples of music teaching and learning processes that make use of these technologies. To achieve this, an analysis of the 2015 and 2022 music National Curriculum was conducted, along with discussions on the necessity of technology integration in music curricula; the current middle school music textbooks were analyzed, and literature reviews, website surveys, and interviews with music education technology experts were conducted. As a result, a total of 24 technologies suitable for music activities in the areas of performance, listening, and composition at the middle school level were identified, along with their usage instructions, advantages, and disadvantages. Furthermore, utilizing those technologies, the researchers developed practical teaching and learning guides for the performance and composition areas based on the content of the 2022 music National Curriculum. As a conclusion of the research process, the researchers suggested the importance of developing music technologies that are suitable for the Korean curriculum and school environment, the necessity of learner-driven and critically receptive approaches in utilizing artificial intelligence music composition technologies, and the need for music teachers to take a leading role in the development of music education technologies.
The Impact of a Music-Centered STEAM Education Program through Inquiry-Based Learning (IBL) on the Self-Directed Learning Ability, Music Content Knowledge, and Music Teaching Efficacy of Pre-Service Early Childhood Teachers

The purpose of this study was to examine the effects of a music-centered STEAM education program through inquiry-based learning for pre-service early childhood teachers on self-directed learning ability, music content knowledge, and music teaching efficacy. This study was conducted for 15 weeks with 44 second-year students (experimental group: 22, comparison group: 22) of early childhood education department at C university located in B city. Differences between groups were examined through covariate analysis using self-directed learning ability tools by Lee et al. (2003), music teaching content knowledge tools by Nam (2014), and music teaching efficacy tools by Bang and Park (2005). The findings indicated that pre-service teachers in the experimental group, who experienced an inquiry-based learning approach within a music-centered STEAM education program, demonstrated significant improvements compared to the control group. Notable enhancements were observed in self-directed learning abilities—learning planning, execution, overall self-directed learning—and in music teaching content knowledge, specifically in knowledge about young children, music teaching methods, music teaching environment, and comprehensive content knowledge. Moreover, within music teaching efficacy, there were substantial improvements in belief in teaching abilities, result expectations, and overall efficacy. This study is significant for introducing innovative music education methods that align with the Revised Nuri Curriculum through creative and problem-solving play for pre-service early childhood teachers.
Analysis of Domestic Music Education Research Trends Using Text Mining: Focused on the Impact of the Fourth Industrial Revolution on Music Education

The purpose of this study is to understand the impact of the Fourth Industrial Revolution on music education by analyzing research trends in the field. To this end, we extracted research papers from the Research Information Sharing Service (RISS) using keywords such as 'Fourth Industrial Revolution,' 'Artificial Intelligence,' 'Big Data,' 'Virtual Reality,' 'Augmented Reality,' and 'Deep Learning.' After collecting the titles, abstracts, and keywords of these papers, we performed text-mining analyses, including keyword analysis, network centrality analysis, and LDA topic modeling. The results of this study are as follows: First, in the TF and TF-IDF analyses, 'Artificial Intelligence' and 'Music' were identified as the most influential keywords, and 'Program-Development' emerged as the most frequently co-occurring keyword combination. Second, the centrality analysis revealed that the keywords 'Music,' 'Education,' and 'Artificial Intelligence' had nearly equal importance. Third, the topics extracted through LDA topic modeling included subjects such as AI-based music education programs, content development, and the future direction of music education in relation to AI platform technologies.Further research is needed to explore the measures and effectiveness of practical applications that link various technologies of the Fourth Industrial Revolution with teaching and learning.