Reviewerly/exHarmony huggingface dataset:
AESTHETIC AND STYLISTIC FEATURES OF MUSICAL MODERN STYLE IN THE OPERA THE NIGHTINGALE BY I. F. STRAVINSKY

The article reveals the signs of musical modern manifested in Igor Stravinsky's opera The Nightingale . The modern style declared itself in the works of Russian composers of the turn of the XIX-XX centuries, including Stravinsky. The aesthetic principles of the modern style, such as the cult of beauty, the tendency to synthesize the arts, conventions, interest in orientalism, fairy-tale plots, originated in painting, found the expression in music. It is shown that the opera Nightingale becomes one of the samples of the modern style in Russian music, which is manifested in the structure of the work, its thematic invention, rhythm and texture. The composer's appeal to an exotic theme allows him to create a bright coloristic musical picture in which the visual component is of great importance. The fairy-tale plot puts an imprint of conventionality on the performance. The composition is dominated by three-part structures, which demonstrates the desire for concentricity. The decorative nature of the modern style is manifested in the appearance of ornamental features in the melodic line of the vocal part of the Nightingale, which is a combination of flexible, plastic lines. It is shown that the style of the opera changes due to a long break between the creation of the first and subsequent actions. The composer's words confirm the fact that the experience of creating his ballets, which participated in S. P. Diaghilev's Ballets Russes , influenced the formation of the musical canvas of The Nightingale . The stylistic heterogeneity of the opera does not contradict the aesthetic attitudes of the modern style. The opera symbolizes the transition from the composer's style, based on the experience of his predecessors, to the search for updated means of expression.
ANTINOMICITY OF PHOTOGRAPHY AS A VISUAL SEGMENT OF MYTH DESIGN IN SOCIAL NETWORKS

The object of the article is photos posted by a modern personality on social networks (for example, Odnoklassniki, VKontakte, etc.). Capturing an individual and undergoing technical processing, they will destroy him, transforming into a figurative simulacrum. The photo image of the personality turns out to be an artificial construct generated by a digital code. This fact testifies both modern individual's fear of himself and his desire to be seen and successful.
Study of digital branding as media strategic toward metaverse

Metaverse will be a testing ground for producers to face the changing dynamics of marketing and digital markets in the future.Rapid changes in consumer behavior affect the company's policy on promotion, especially on media strategy.Digital media provides unlimited access to all platforms flexibly.Several major brands have established themselves on the platform metaverse.This research aims to study brand strategies, especially in the digital era in positioning brands in the digital market through the metaverse platform.The AISAS model is also used in analyzing the activity of a brand.Observations are made through analysis of brand content based on the media space contained in the metaverse.A comparative study of several big brands as a sample of objective data on the metaverse platform is conducted.Brands have a great effect and involvement as a strategic form, especially in adapting to market trends swarmed by consumers.Although it has not yet become a form of consumer behavior, the existence of the metaverse platform will be one of the brand's strategies in anticipating changes in information technology today.
DEVELOPMENT OF DIGITAL SKILLS IN SECONDARY VOCATIONAL EDUCATION STUDENTS OF ENERGY FIELDS USING “1С: E-LEARNING” SOFTWARE

The article reveals the relevance of using state of the art software in teaching secondary vocational education students based on domestic software “1С: e-Learning”. The sequence of actions for doing the course of “Electrical and Electronics Engineering” developed on the basis of “1C: e-Learning” is given. One of such methods is the use of the e-course on “Electrical and Electronics Engineering”, which makes it possible to organize learning by doing. Interactivity and focus on construction of knowledge and skills in students when doing the e-course on “Electrical and Electronics Engineering” developed on the basis of “1C: e-Learning” are the features of the software. One of its specifics is that it allows students to obtain and develop knowledge and skills necessary to solve real-life and simulated issues as applicable to their future professions. This, in turn, contributes to the development of social skills and abilities to settle complicated issues at work, and teaches how to use the obtained knowledge in practice.
Computer-Aided Somatic Cells Counting on Three Different Milk Staining Condition

The maximum amount of somatic cell in a millilitre of milk defined by National Standardization Agency is 4x105 cells/ml. The condition of milk will be excluded from a decent quality if the amount of somatic cell is greater than it. Microscopic image of somatic cells counting is a conventional method which utilized by farmer or researcher to count the number of somatic cells from dairy milk sample. The problems of this method are: 1) The counting process is manually conducted which prone to miscalculation, 2) The different colour of staining (purplish, bluish, and greenish) may complicate the calculation. In this research, a computer-based approach is proposed to calculate the number of somatic cells from sample of milk and to eliminate the difficulty of different colour due to staining technique. Image processing knowledge i.e. erosion, dilation, colour conversion, and BLOB Analysis are involved and utilized to achieve the objective. Overall, the correctness of the system in detecting the number of somatic cells is 94%. From this study, a comprehensive system to calculate the number of somatic cells from cow’s milk will be closer to be implemented.
COMPARISON OF FEATURE SELECTION BASED ON COMPUTATION TIME AND CLASSIFICATION ACCURACY USING SUPPORT VECTOR MACHINE

The goal of this research to compare Chi-Square feature selection with Mutual Information feature selection based on computation time and classification accuracy. In this research, people's comments on Twitter are classified based on positive, negative, and neutral sentiments using the Support Vector Machine method. Sentiment classification has the disadvantage that it has many features that are used, therefore feature selection is needed to optimize a sentiment classification performance. Chi-square feature selection and mutual information feature selection are feature selections that both can improve the accuracy of sentiment classification. How to collect the data on twitter taken using the IDE application from python. The results of this study indicate that sentiment classification using Chi-Square feature selection produces a computation time of 0.4375 seconds with an accuracy of 78% while sentiment classification using Mutual Information feature selection produces an accuracy of 80% with a required computation time of 252.75 seconds. So that the conclusion are obtained based on the computational time aspect, the Chi-Square feature selection is superior to the Mutual Information feature selection, while based on the classification accuracy aspect, the Mutual Information feature selection is more accurate than the Chi-Square feature selection. The recommendations for further research can use mutual information feature selection to get high accuracy results on sentiment classification
Study on the standard components of digital forensics laboratory

Abstract Recently, digital forensics has become increasingly important as it is used by investigation agencies, corporate, and private sector. To supplement the limitations of evidence capacity and be recognized in court, it is essential to establish an environment that ensures the integrity of the entire process ranging from collecting and analyzing to submitting digital evidence to court. In this study, common elements were extracted by comparing and analyzing ISO/IEC 17025, 27001 standards and Interpol and Council of Europe (CoE) guidelines to derive the necessary components for building a digital forensic laboratory. Subsequently, based on 21 digital forensic experts in the field, Delphi survey and verifications were conducted in three rounds. As a result, 40 components from seven areas were derived. The research results are based on the establishment, operation, management, and authentication of a digital forensics laboratory suitable for the domestic environment, with added credibility through collection of the opinions of 21 experts in the field of digital forensics in Korea. This study can be referred to in establishing digital forensic laboratories in national, public, and private digital forensic organizations as well as for employing as competency measurement criteria in courts to evaluate the reliability of the analysis results.
Motion Decoupling Network for Intra-Operative Motion Estimation Under Occlusion

In recent intelligent-robot-assisted surgery studies, an urgent issue is how to detect the motion of instruments and soft tissue accurately from intra-operative images. Although optical flow technology from computer vision is a powerful solution to the motion-tracking problem, it has difficulty obtaining the pixel-wise optical flow ground truth of real surgery videos for supervised learning. Thus, unsupervised learning methods are critical. However, current unsupervised methods face the challenge of heavy occlusion in the surgical scene. This paper proposes a novel unsupervised learning framework to estimate the motion from surgical images under occlusion. The framework consists of a Motion Decoupling Network to estimate the tissue and the instrument motion with different constraints. Notably, the network integrates a segmentation subnet that estimates the segmentation map of instruments in an unsupervised manner to obtain the occlusion region and improve the dual motion estimation. Additionally, a hybrid self-supervised strategy with occlusion completion is introduced to recover realistic vision clues. Extensive experiments on two surgical datasets show that the proposed method achieves accurate motion estimation for intra-operative scenes and outperforms other unsupervised methods, with a margin of 15% in accuracy. The average estimation error for tissue is less than 2.2 pixels on average for both surgical datasets.
Deep-learning-based digital signal modulation mode identification

In modern electronic warfare, in order to correctly identify the intercepted enemy signals, it is first necessary to identify the modulation system used by the enemy signals and correctly demodulate them in order to obtain the information transmitted by the enemy and seize the opportunity in informationized warfare. Aiming at the low recognition rate of digital signal modulation recognition under low signal-to-noise ratio, the recognition model suitable for signal constellation diagram is improved on the basis of the classical convolutional neural network GoogLeNet network, and a recognition model suitable for signal constellation diagram is constructed. Experimental results show that when the signal-to-noise ratio SNR=5dB, the average classification accuracy of the 12 hybrid modulation types can reach 95.28%, and the classification accuracy of the improved GoogLeNet is increased by 1.1%.
Dynamic Scale-free Graph Embedding via Self-attention

Graph neural networks (GNNs) have recently become increasingly popular due to their ability to learn node representations in complex graphs. Existing graph representation learning methods mainly target static graphs in Euclidean space, whereas many graphs in practical applications are dynamic and evolve continuously over time. Recent work has demonstrated that real-world graphs exhibit hierarchical properties. Unfortunately, many methods typically do not account for these latent hierarchical structures. In this work, we propose a dynamic network in hyperbolic space via self-attention, referred to as DynHAT, which leverages both the hyperbolic geometry and attention mechanism to learn node representations. More specifically, DynHAT captures hierarchical information by mapping the structural graph onto hyperbolic space, and time-varying dynamic evolution by flexibly weighting historical representations. Through extensive experiments on three real-world datasets, we show the superiority of our model in embedding dynamic graphs in hyperbolic space and competing methods in a link prediction task. In addition, our results show that embedding dynamic graphs in hyperbolic space has competitive performance when necessitating low dimensions.
Applied research on BIM + GIS + IoT multi-source heterogenous data fusion

In general, modern construction projects are characterized by large construction scale, high construction difficulty, and complex project management and data information analysis. Hence, various technologies should be fused and integrated to comprehensively and effectively manage the project construction. In this paper, a multi-source heterogeneous data fusion technology, which combined building information modelling (BIM), geographic information system (GIS), and Internet of Things (IoT) (BIM + GIS + IoT Multi-Source Heterogenous Data Fusion), was systematically explored to achieve a total factor, data sensing, and heterogeneous fusion concept. Then, the BIM + GIS scene-based 3D modelling technology was discussed from three aspects: total factor information 3D modelling, model tiled loading, and ultra-large model lightweight processing. With a tunnel construction project combined, a tunnels Internet-based construction management platform was developed on the basis of BIM + GIS + IoT multi-source heterogeneous production data fusion, thereby realizing the organic integration of virtual model information and total factor production information. Furthermore, the platform provided the capability of monitoring core indexes such as gas, geology, personnel, and progress involved in the tunnel construction, which facilitated the dynamic visual management of the construction process and enhanced management efficiency.
Smart Living Solution to Optimize Building Systems for Efficient Energy Usage and Prediction

In this era of new technologies with the ever growing need for reliable ecological energy supplies, monitoring and reducing the energy consumption of buildings becomes a very crucial concern. Improved healthcare institutions available in the city, more employment opportunities, high standards of living, along with increase in population, has led to rapid urbanization resulting in development of a huge number of buildings. Buildings have become one of the most important contributors to energy consumption, which are responsible for around one-third of energy that is consumed in cities. This makes it very important to monitor and analyze the energy usage by such territories in a meaningful manner to further save energy and even help in cutting down financial costs. The proposed system provides various features as a solution to conserve energy, monitor the power consumption and water usage along with real time monitoring. Smart living allows you to have greater control of your energy usage, all while automating things like adjusting devices based on weather conditions, turning on or off appliances based on occupancy of the room, etc. It provides insights into energy use that can help you become more energy efficient and mindful of ecological factors.
Nonlinear absorption of 2D materials and their application in optical neural networks

In recent years, optical neural network (ONN) research has blossomed due to the outstanding advantage of energy consumption and computing property. Regrettably, nonlinear processing in the optical domain remains a huge challenge. The optical characteristics of 2D material, particularly related to saturable absorption (SA), have enabled nonlinear operation. Here, we discuss the SA models with various categories and their application in ONNs. A feedforward artificial neural network was built for handwritten digit recognition to illustrate the feasibility of SA features as nonlinear mapping. For comparison, ONNs without the assistance of the activation function were used as a benchmark to examine the capability of the nonlinear models. A simulation shows that the accuracy of digit classification ranged from 86% to 95%, depending on the nonlinearity of the mediums. This work offers an optical nonlinear unit selection guideline to explore ONNS.
The Construction of Dynamic Model of English Blended Learning Based on Data Mining

The hybrid learning mode based on data mining technology is to establish a system that can meet students' personalized needs and targeted teaching goals by sorting and analyzing the relevant information of online courses on the basis of computer network. The model takes English as the research object. This paper first constructs a hybrid learning dynamic mode from the theory, then designs the corresponding strategies to realize the system functions according to the actual situation, and tests the performance of the learning dynamic mode. Finally, the test results show that the English hybrid learning dynamic model based on data mining has short data processing time and small delay time, which can well meet students' English learning needs.
Second order relation graph embedding neural network for rational drug use prediction

Rational drug use prediction, which is used to estimate whether the drug use is reasonable in clinical medical treatment, is of great significance in combating excessive medical treatment. The feature interaction model based on electronic medical records is widely used in the field of medical behavior prediction. However, the features recorded in electronic medical records are very complex, and medical behavior has an obvious long-tail effect. The problem of feature sparseness makes traditional prediction models based on feature interaction impossible. It works well, we propose a novel business domain-based second-order relational graph embedding neural network model (SORGE-NN), which can distinguish the scope of features and mine the implicit propagation relationship, through the residual-multi-head attention layer , and perform high-order weighted combination of features, which effectively alleviates the challenges brought by feature sparse and complex features. We conduct experiments with real datasets, and the experimental results show that our proposed SORGE-NN achieves better results than current state-of-the-art prediction models.
The “7 Wonders of the World-APP” Augmented Reality-based Media to Improve Elementary School Students’ Conceptual Understanding

Elementary school students cannot visualize virtual objects or images on some social studies materials in a real environment. This study aims to develop 7 Wonders of the World-APP-based Augmented Reality media to improve elementary school student's mastery of concepts. This study used the four-D method and used a pretest-posttest control group design. Methods of data collection using questionnaires and tests. Validity includes the validity of teaching materials, design validity, and learning media validity from two experts. Data analysis techniques using descriptive qualitative analysis, quantitative, and inferential statistics. The results showed that the proportions of the validity test of learning materials, the validity test of the learning media design, and the validity test of the learning media got a very good category. The results of the t-test show significant differences in students' mastery of concepts. Media 7 Wonders of the World-APP, based on Augmented Reality, is very suitable and effective for learning media in elementary schools for social studies.
An Investigation on the Implementation of Early Childhood Education: A Case Study of Selected Schools in Petauke District

This study investigated the implementation of early childhood education among schools in Petauke District. Despite a high enrolment rate, the children entering primary school have low levels of reading and writing skills. The study considered the benefits of ECE; the associated challenges and alternative approaches for effective implementation. Using the mixed research design, the study drew a sample of 90 respondents from 10 schools which included teachers, school administrators and parents. Data was collected by means of semi-structured questionnaires, an interview guide and focus group discussions. A combination of statistical descriptive analysis and thematic analysis was adequate for data analysis. The study found that ECE was beneficial for development of reading and writing skills; acquisition of knowledge and continuity of education to higher levels. The major challenges in ECE implementation were poor teachers’ understanding of ECE curriculum; negative attitude of parents towards ECE; poor water and sanitation; lack of ECE trained staff; poor infrastructure for ECE centres; and lack of teaching and learning materials. The alternative measures put in place included: parental sensitization and school feeding program. The overall conclusion is that the challenges and inadequacies faced by primary schools to implement ECE arise from the gaps in policy implementation and extend to institutional and domestic factors. The study contributes to the understanding that the rolling out of ECE in rural areas lags behind and that much can be done by involving the local communities and utilising available materials.
Enhancing IoT-Based Intelligent Video Surveillance through Multi-Sensor Fusion and Deep Reinforcement Learning

Currenlty, wireless communication that is successful in the Internet of Things (IoT) must be long-lasting and self-sustaining. The integration of machine learning (ML) techniques, including deep learning (DL), has enabled IoT networks to become highly effective and self-sufficient. DL models, such as enhanced DRL (EDRL), have been developed for intelligent video surveillance (IVS) applications. Combining multiple models and optimizing fusion scores can improve fusion system design and decision-making processes. These intelligent systems for information fusion have a wide range of potential applications, including in robotics and cloud environments. Fuzzy approaches and optimization algorithms can be used to improve data fusion in multimedia applications and e-systems. The camera sensor is developing algorithms for mobile edge computing (MEC) that use action-value techniques to instruct system actions through collaborative decision-making optimization. Combining IoT and deep learning technologies to improve the overall performance of apps is a difficult task. With this strategy, designers can increase security, performance, and accuracy by more than 97.24 %, as per research observations.