Reviewerly/exHarmony huggingface dataset:
FORMATION OF CULTURAL VALUES OF STUDENTS IN THE PROCESSING OF RUSSIAN FOLK SONGS

The article proposes the author's version of the in-depth work with the folk original source, which contributes to the formation of cultural values of students in the course of the Arrangement course of the teaching course Academic Choir at the Tyumen State Institute of Culture. A distinctive feature of the course is the regional component – Russian folk songs of the Tyumen region and the region are used as musical material for student work. As part of the study, we identified the following contradictions: – between the prescription of the State educational standards on interdisciplinary disciplines, the student's ability to navigate in the diversity of culture and the competences offered by the curriculum for the discipline “Arrangement”, which are focused on creating a new creative product; – between the student’s constant need for creative self-realization and the insufficiently high level of his creative skills; – between installations of legislation in the field of education for the education of the individual with a high level of education, morality, and value installations that are incorrectly laid down by the mass culture. Therefore, we position the processing of a folk song not as an end in itself, but as a means of personal involvement of the student in the culture of his people, the transmission of sociocultural experience between generations. The goal of the “Arrangement” course is to create a value attitude of students to Russian culture in the process of creative work with a folk original source (Russian folk song) by expanding the course’s objectives to interdisciplinary and research (problem-searching, creative, heuristic, etc.), activating cognitive and creative activity of students. Formation of students' value attitude to the Russian folk musical culture in the process of creative work with a folk song, we propose to carry out at the following stages: search, motivational-incentive, research, analytical, creative, performing. Through contact with the sample of Russian song and its transformation, a student's interest in his origins, his pedigree, and cultural norms in the way of life and art is awakened. The effectiveness of the formation of cultural values of students through the Russian folk song is determined under the condition of positive motivation and initiative of students to learn cultural values, subject-subject interaction between the teacher and the student, enhancing independent cognitive activity, introducing the student to artistic creativity, creating conditions for self-discovery.
Study of Video Games as Alternative Teaching Media In Covid-19 Pandemic Era

Technology has a direct impact on human’s behaviors and lifestyles in Covid-19 pandemic era. Various sectors such as education and economy adapted technology-based remote work and study. With the existence of remote study, the educational world will still face some issues related to the study effectiveness which requires students’ initiatives in the process. This research’s aim is to explore video game's potential as an alternative education method to solve those issues. Also, it is to study the reason why videogames are considered as a solution for remote study’s challenges during this pandemic. A study method towards related literature was used in this research. Conclusion achieved from this research is that videogames are able to stimulate students’ initiatives in exploring learning materials. Video games gave play-like learning experience and thus, reduced the stress on the students during remote study. However, the result still needs further study to achieve realistic methods that can be applied in the field. &#x0D; Keywords: Game, Remote Study, Pandemic
To the question of modernity and corresponding representations of subjectivity: “dividual” and social organizations

This article is part of the extensive research of value characteristics of IT community and their potential for innovative development. In view of this, the author examines the problem of determination of the modern cultural situation and, as a result, deduction of the criterion that establishes what &amp;ldquo;modern&amp;rdquo; is. Based on the previously acquired conclusions (according to which, such criterion is the consideration of irreducible multiplicity, and the major risk is the loss of autonomy by individuals), the author aims to find the model of subjectivity that would be simultaneously multiple and autonomous. The search for the model that meets such criterion is carried out through referring to the control technologies developed within the framework of third-order cybernetics. The novelty of this article consists in peculiarity of the approach towards solution of the set task: the question in the form and method of determination of modernity has been first raised and&amp;nbsp; solved within the framework of the Russian academic philosophical community at&amp;nbsp; the previous stage of research, which determines the relevance at the current stage as well. Comparison of the developments of Russian and foreign authors reveals the parallelism between the development of cybernetics and psychoanalysis; demonstrates the conceptual intersections of the third-order cybernetics and schizoanalysis; establishes the conformity between the subjectivity of the individual and the type of organizations they are engaged in. The article offers the concept of &amp;ldquo;dividual&amp;rdquo; as a model suitable for solution of the set task. The conclusions is made that the existing potential of the IT community for modernization is neutralized in the context of transition towards the hierarchical structure of the organization.
A Method for Image Recognition of Insulator Jacket Defects under Small Sample Conditions

Abstract Accurate identification of insulator jacket defect images requires a large number of samples for model training, and the actual defect image datasets available for model training is seriously insufficient. In order to solve the problems of the model cannot be trained, over-fitting and low accuracy caused by too few training samples, this paper proposes a new method for image recognition of insulator jacket defects under small sample conditions, which combines image enhancement technology and meta-learning technology to train the U-Net image segmentation network, and finally obtain the image recognition model of the insulator jacket defect. In this paper, the defect recognition models using meta-learning method and without meta-learning are compared experimentally, and the results show that the proposed method can achieve accurate recognition with a small-scale original data set.
The superdeterministic destiny, and its mathematical models within the neural network

The physical interactions among any number of elementary particles are governed by Schrodinger equation. The universe is a superdeterministic state machine which is formed by elementary particles. Mind’s “center stage”, which is a component of the mind, is imagined to exist as a real-time representation of all the elementary particles within the universe; the “center stage” only includes the physical objects perceived in the mind. A naïve cognitive researcher might incorrectly treat her mind’s “center stage” as the real world. It’s possible that the “center stage” doesn’t exist like “the ghost in the machine”. Otherwise, this “center stage” shouldn’t be able to impact the world line of any elementary particle. So, the human body is merely a fuzzy set of elementary particles, no matter the “center stage” really exist or not. The precondition of the “hard problem” of consciousness makes a mistake. Proving the precondition of the “hard problem”, is a “harder problem” of consciousness. The “harder problem” can’t be proved empirically. The conscious experience is actually the use of a mathematical model by a neural network within its low-level calculation. For example, when a neural network uses its 3D model of the reality, it feels like the subjective experience of being immersed within a topological structure.
Automated Personal Thesaurus TEZAL: Reconstructing the Psychosemantic Foundations of Interpersonal Relations

The present research featured the psychosemantic foundation of interpersonal relations, the latter represented as a three-level system. The psychosemantic foundation contains implicit models of interpersonal relations; the regulatory level regulates interpersonal relationships in various contexts; the behavioral level manifests itself in direct interpersonal behavior. The research was based on the method of semantic differential. However, the method proved too sensitive to various kinds of heterogeneity of the test subjects, which rendered it impossible to identify the factor structure. As a result, the semantic differential was converted to factorial personality systems through the Automated Personal Thesaurus TEZAL. In this research, the semantic differential vocabulary profile was converted to 16PF personality profile. The semantic differential and converted data underwent statistical and comparative analyses, which singled out the general structures of perception in the system of interpersonal relations. The procedure revealed two bipolar factors. The factor of social self-organization reflected the degree of orderliness of social activity and behavior. The factor of social interaction demonstrated the flexibility, activity and initiative of social behavior. A regression analysis of the psychosemantic foundations revealed a more complex system, when interpersonal relations are perceived through the role image of mother and friend. Thus, initial data conversion provided a more detailed reconstruction of the psychosemantic foundation of interpersonal relations.
Covid-19 Mask Detection

The corona epidemic poses a global health problem and therefore effective preventive measures are worn in public places,according to the World Health Organization (WHO). The COVID-19 epidemic has forced governments around the world to impose restrictions on the transmission of the virus. Reports show that wearing the right face while in public places and at work clearly reduces the risk of transmission. An effective and economical way to use machine learning is to create a safe environment for device setup. A hybrid model using the depth of the face mask detection machine will be introduced. The face mask detection databasecontains a mask and in addition to the facial images, we will use OpenCV to perform real-time facial detection from live streaming via our webcam. We will use the database to create a COVID-19 face mask detector from a computer view using Python, OpenCV, and Tensor Flow and Cameras. We aim to determine whether the person in the picture/video is wearing a face mask or not with the help of computer vision and in-depth reading and to show the same with caution. Steps to modeling are data collection, pre-processing, data classification, model testing, and modeling
Identification of Masked Face Using Deep Learning Techniques

Face detection is an artificial intelligence [AI]-based methodology, where a cascade function is prepared with a bunch of information and input data. It is prepared from a great deal of positive and negative pictures. It is then used to identify objects in different pictures. The proposed model’s classifier essentially increases the precision and strength of robustness search on faces with demeanor variation and vague forms. In the element extraction stage, this research work leverages a philosophy for increasing efficiency through the connection of two strategies: mathematical (geometrical) element-based approach and independent component analysis strategy. To implement face coordination step, this research work proposes a model that combines several neural networks to coordinate with the mathematical highlights of the human face. Since the proposed model connects several neural networks, it is termed as multi-artificial neural network. MIT + CMU information base is utilized for assessing the proposed strategies for face identification and arrangement. At last, the experimented results on Caltech dataset show the possibility of implementing the proposed model.
New hard-thresholding rules based on data splitting in high-dimensional imbalanced classification

In binary classification, imbalance refers to situations in which one class is heavily under-represented. This issue is due to either a data collection process or because one class is indeed rare in a population. Imbalanced classification frequently arises in applications such as biology, medicine, engineering, and social sciences. In this paper, for the first time, we theoretically study the impact of imbalance class sizes on the linear discriminant analysis (LDA) in high dimensions. We show that due to data scarcity in one class, referred to as the minority class, and high-dimensionality of the feature space, the LDA ignores the minority class yielding a maximum misclassification rate. We then propose a new construction of hard-thresholding rules based on a data splitting technique that reduces the large difference between the misclassification rates. We show that the proposed method is asymptotically optimal. We further study two well-known sparse versions of the LDA in imbalanced cases. We evaluate the finite-sample performance of different methods using simulations and by analyzing two real data sets. The results show that our method either outperforms its competitors or has comparable performance based on a much smaller subset of selected features, while being computationally more efficient.
Manufacturing crafts and trades of Togul district (based on the materials of the historical and ethnographic expedition Altai State Museum of Local Lore 2021)

The article gives a brief description of pimokat, woodworking, leather crafts and trades in the Togul district in the 1930s–1950s. A general description of technical and technological processes is presented.
Analysis of Flood Potential and Risk on Agricultural Land in Pacitan Regency

Flood is a national disaster in Indonesia. Some of those factors, landform driven factors and non-driven factors in the form of land use management. Pacitan Regency has an alluvial landform that is vulnerable to flooding. BNPB states that the floods in 2018 continued on 07 March 2019, as a massive flood resulted in losses reaching &gt; 600 billion. This study analyzed the potential and risk of flood in Pacitan Regency in 2018, in the past (1998 and 2008), and used it to projected future floods (2030). The research focused on land use change and its impact on flood potential and hazards. The potential and risk of flooding were analyzed using Paimin’s method. The parameter was analyzed from Landsat 5, 7, and 8 images in an unsupervised. The trend will be used for Business As Usual (BAU) analysis in 2030. As a comparison, land use analysis carried out based on Land Ability Class (KKL) and Spatial Planning (RTRW). Data validation using confusion matrix overall accuracy. As a result, there had been an increase of potential floods in high and very high levels (1998 to 2018) around 263.04 ha and 368.99 ha. This continues until 2030 (BAU), around 191.61 ha and 172.8 ha. Land use management with RTRW will increase the potential flooding at a very high level in 2030 + 1088.63 ha. The best land management is the KKL application which reducing the flood potential at a very high level + 1973.39 ha. Accuracy tests conducted at 100 points in 2018 showed that 88 model points matched the flooding event ( 88% accuracy).
Trajectory library generation with solvability and computational-load bound guarantees for warm-starting trajectory optimization

In nonlinear-optimization-based online trajectory planning, warm-started replanning using a good initial solution speeds up solving time significantly. However, the computational load required to do such online replanning and even solvability are unknown to a user. Such a lack of guarantees hinders robots to be applied to real-life tasks. In this paper, we focus on the fact that in many daily life settings, manipulation is done with known environments. Taking advantage of this observation, we propose a method to generate a trajectory library with which computational load’s upper bound and solvability in the online replanning phase is guaranteed. Note that the trajectory library generation takes place offline. We applied our proposing method to reaching tasks into the refrigerator. We showed that offline trajectory generation is properly done. Finally, we performed a real-robot demonstration of an online replanning using the generated trajectory library.
Hybrid Context-Based Recommendation for Media

There are many recommendation engines present that work on providing users with recommendations based on a single media. This is our attempt to create a recommendation system that works on relationships between different media elements and provides users with the best new media item to choose from. “Hybrid context-based recommendation for media” works on pre-context filtering and a hybrid approach of content and collaborative-based filtering and given a media element as input and recommends the same or different media element that the user needs or demands. The given model has been prepared using the MovieLens 100 K Data set and Goodreads Book Data set. This model can be further enhanced through a systematic consideration of demographic attributes in the future.
Implementasi Algoritme C4.5 untuk Prediksi Penanaman Cabai Merah

Chili has been the most widely grown plant in Indonesia because it can bear fruit and grow in the highlands and the lowlands. Every year the market demand for chili continues to increase, even the annual price of chili tends to increase as a result of reduced chili supply and less fulfilled market demand. Besides, t he crop failure experienced by chili farmers cause inflation growth in 2020 as much as 0.31%, resulting from rising prices in the food sector, including the price of cayenne pepper and red chili. The fluctuations in chili prices are strongly influenced by weather, harvest season, trade policies and the accompanying momentum. According to the background, the purpose of this study is to predict chili planting using the C4.5 algorithm. First, the researchers collect weather and price data taken from DISPERINDAG, BMKG , use chili price data on the market as a dataset to be later preprocessed to eliminate missing values, data outliers and imbalance data and then make a model that can predict chili planting. The prediction results using the rule from the decision tree have an accuracy rate of 97.8% based on the calculation of the prediction using validation data as much as 95 data
Discovering the arrow of time in machine learning

Machine learning (ML) is increasingly useful as data increases in both volume and accessibility.Broadly, ML uses computational methods and algorithms to learn to perform tasks, such as categorisation, decision making or anomaly detection through experience and without explicit instruction.ML is most effective in situations where non-computational means or conventional algorithms are impractical or impossible, such as when the data are vast, complex, highly variable and/or full of errors [1,2].Thus, ML is useful for analysing natural language, images, or other types of complex and messy data that are now available in ever-growing and impractically large volumes.Some ML methods are suitable for analysing explicitly ordered or timedependent data, although these tend to be less tolerant of errors or data asymmetry.Nevertheless, most data has at least an implicit order or temporal context.Selecting an appropriate ML algorithm depends on the properties of the data to analyse and the aims of the project as algorithms vary in the supervision needed, tolerable error levels, and ability to account for order or temporal context, among many other things.Using non-temporal ML algorithms may obscure but not remove the order or temporal features of the data, potentially allowing the hidden 'arrow of time' to affect performance.This research takes the first step in exploring the interaction of ML algorithms and implicit temporal representations in training data.Thus, this research addresses the suitability of ML for analysing the kind of data that is accumulating daily from every social media platform, Internet of Things device, businesses report, transport tracker or other modern data source.Two supervised ML algorithms are selected and described before experiments are run to train those ML algorithms to perform automatic classification tasks under a variety of conditions that balance volume and complexity of data.In this way, the experiments explore whether more data is always better for ML models or whether implicit temporal features of data can influence performance.The research shows that ML algorithms can be sensitive to subtle or implicit temporal context with consequences for the accuracy in classification tasks.This means that researchers should carefully consider the implications of time within their data when selecting appropriate algorithms, even when the algorithms of choice are not expected to explicitly address order or temporal context.
DEVELOPMENTAL POTENTIAL OF THE PEDAGOGICAL UNIVERSITY EDUCATIONAL ENVIRONMENT IN THE CULTURE FORMATION OF A FUTURE TEACHERS SAFE LIFESTYLE

The increase in the crisis phenomena of modern life determines the social order for pedagogical universities to establish a culture of a safe lifestyle of the future teacher, who is capable of timely and appropriate actions to meet the ever-increasing challenges of ensuring safety in education and raising a viable generation. The fruitful actualization and realization of the developing potential of the educational environment of the pedagogical university to achieve the effectiveness of the process under study is in demand. The purpose of our theoretical research was to substantiate the developing potential of the educational environment of the pedagogical university in the formation of a culture of a safe lifestyle of the future teacher and the experience of its implementation. The concept of “developing potential of the educational environment of a pedagogical university” is considered by us as a set of resources and opportunities. They can be successfully implemented by the subjects of pedagogical education in the selected areas to ensure the necessary and sufficient pedagogical, didactic, organizational conditions for the formation of a culture of a safe lifestyle of the future teacher. The resources and capabilities of the developing potential of the educational environment of a pedagogical university are classified by us as available means that determine the achievement of the effectiveness of the process under study. What is fundamentally new in the results of our research is the allocation of normative-cultural, didactic-professional and interactive-productive spheres in the developing potential of the educational environment of a pedagogical university, within which the allocated opportunities and resources are implemented. Thus, it is advisable to structure the developing potential of the educational environment in the totality of resources, opportunities, and areas in which the effectiveness of the formation of a culture of a safe lifestyle of the future teacher is achieved. Resources and opportunities can be demanded by university teachers for a socially determined, pedagogically accompanied process of changing the professional and personal quality of a teacher.