Reviewerly/exHarmony huggingface dataset:
Practical methods of listening to music in the course of musical literature at the music college

The purpose of the study is to determine the optimal ways to solve the pedagogical problem of activating auditory perception in students studying the subject "Musical literature" in professional music educational institutions. The scientific novelty consists in identifying the components of the educational process that help to control the process of sensory reflection of reality that arises in the imagination or in memory based on the acquired experience as a result of interaction with the musical art: information and communication component, emotional and intellectual development, self-determination competencies. As a result of the study, based on the principle that true comprehension of music is possible only with the unity of the emotional and intellectual, methods and techniques that develop imagination, creative thinking, emotional and sensory perception of music, as well as education of self-expression confidence were selected. The typology for these types of activities is given.
Space-Efficient Data Structure for Next/Previous Larger/Smaller Value Queries

Given an array of size n from a total order, we consider the problem of constructing a data structure that supports various queries (range minimum/maximum queries with their variants and next/previous larger/smaller queries) efficiently. In the encoding model (i.e., the queries can be answered without the input array), we propose a $$(3.701n + o(n))$$ -bit data structure, which supports all these queries in $$O(\log ^{(\ell )}n)$$ time, for any positive integer $$\ell $$ (here, $$\log ^{(1)} n = \log n$$ , and for $$\ell > 1$$ , $$\log ^{(\ell )} n = \log ({\log ^{(\ell -1)}} n)$$ ). The space of our data structure matches the current best upper bound of Tsur (Inf. Process. Lett., 2019), which does not support the queries efficiently. Also, we show that at least $$3.16n-\Theta (\log n)$$ bits are necessary for answering all the queries. Our result is obtained by generalizing Gawrychowski and Nicholson’s $$(3n - \Theta (\log n))$$ -bit lower bound (ICALP, 15) for answering range minimum and maximum queries on a permutation of size n.
OPTIMIZATION OF METHODS OF TEACHING HIGHER EDUCATION IN HIGHER EDUCATION

In the work the transformation of the educational environment with the purpose of realization of the concept of sustainable development is considered. Advantages and disadvantages of distance form of study are given. It is noted that the teacher gives the
Improved Convolutional Neural Network based Feature Extraction Method

Deep learning algorithms based on convolutional neural networks have been widely researched and developed in the field of images. This helps in more accurate classification and recognition of images. In order to improve the recognition accuracy of convolutional neural network and optimize the learning performance of neural network, an improved dynamic adaptive pooling algorithm is proposed. First, an overview of the basic structure of convolutional neural networks, convolutional layers and pooling layer operations. Second, build a convolutional neural network model, study and compare different network pooling models. Finally, an improved dynamic adaptive pooling model is constructed for the case where the existing algorithm has a slow convergence speed. Experiment on handwritten database. The simulation results show that as the number of iterations continues to increase, the mean square error continues to decrease, and the recognition accuracy of the model continues to improve. The improved pooling method not only makes the feature extraction of the convolutional neural network more accurate, but also improves the convergence speed and model accuracy, and achieves the purpose of optimizing the network learning performance. This approach can be further extended to other models related to convolutional neural networks.
Métodos de Lyapunov

Métodos de Lyapunov"It is a truth universally acknowledged that stability requires far more effort to restore than to disrupt.." -
CaptchaGG: A linear graphical CAPTCHA recognition model based on CNN and RNN

This paper presents CaptchaGG, a model for recognizing linear graphical CAPTCHAs. As in the previous society, CAPTCHA is becoming more and more complex, but in some scenarios, complex CAPTCHA is not needed, and usually, linear graphical CAPTCHA can meet the corresponding functional scenarios, such as message boards of websites and registration of accounts with low security. The scheme is based on convolutional neural networks for feature extraction of CAPTCHAs, recurrent neural forests A neural network that is too complex will lead to problems such as difficulty in training and gradient disappearance, and too simple will lead to underfitting of the model. For the single problem of linear graphical CAPTCHA recognition, the model which has a simple architecture, extracting features by convolutional neural network, sequence modeling by recurrent neural network, and finally classification and recognition, can achieve an accuracy of 96% or more recognition at a lower complexity.
Estimation of Memory Resource Utilization with Time Series Analysis

Time series forecasting is one of the important branches of big data analysis and in this analysis, future time data is estimated from past or present time data with one or more variables. Resource estimation of machines in data centers is a growing area for research in time series analysis. Resource utilization estimation is an important consideration in achieving optimum resource provisioning in cloud computing. Due to the existence of long-range dependency in cloud workloads, traditional methods are not sufficient to develop predictive models. In this study, RNN, LSTM, BiLSTM and BiLSTM-RNN models for the estimation of resource usage in cloud workloads were analyzed with 2 different data decomposition methods as data-based and cluster-based, and the results were compared. As a result of the tests, the most successful result was obtained with the RNN model using the cluster-based separation method.
Global Semantic Information-based Text Detection and Recognition Networker

Scene text recognition has long been a research hotspot in computer vision, and the detection and recognition of incidental and diverse scene text is a worthy challenge. For scene text with low quality, blurred images, illumination effects and character defects. We propose a text detection recognition networker based on global semantic information for simultaneous detection and recognition, and a network optimization combined with stochastic gradient descent with fractional order momentum, which can robustly recognize low-quality scene text. Extensive experiments show that the proposed algorithmic framework is not weaker than other algorithms and achieves advanced results on several benchmark datasets.
Assessment of the quality of educational activities in the context of digital transformation

The relevance of the study of the issues of assessing the quality of educational activities is due to the digital transformation of education, increasing requirements for the level of quality of training, complicating the conditions for organizing the educational process, which determines the need to search for new approaches and technologies. Assessment of the quality of educational activities in the conditions of digital transformation requires the creation of the necessary infrastructure, the formation of an appropriate regulatory, educational, and methodological base. When considering this problem, methods of systematization and generalization were used. The practical significance of the research results is determined by the fact that the conclusions drawn and the proposed scientific and theoretical provisions can be useful to the heads of educational organizations and have the ability to adapt to the conditions of the educational process.
Theoretical Concepts of Credit Scoring

The foundation of statistical methodology, which later was applied in credit scoring, was developed by Ronald Fisher in 1936. Fisher introduced linear discriminant analysis for the classification of groups based on the independent variables. Fisher’s research defined the discriminant function that was based on a linear combination of predictors that discriminates the predicted variable.
Graduate education in acoustics at a distance from Penn State

The Graduate Program in Acoustics at Penn State has been providing access to graduate level education in Acoustics for remote students across the country and around the world for more than 35 years. This poster summarizes the distance education Acoustics program from Penn State by showcasing student demographics, capstone paper topics, enrollment statistics and trends, and the success of our graduates. Our distance education program is offered in conjunction with our resident graduate program—course lectures are broadcast as a live stream over Zoom from a hybrid multimedia classroom allowing remote students to engage with faculty and students during live lectures; archived recordings are available for offline viewing afterward. Courses offered for distance education students include: fundamentals of acoustics and vibration, electroacoustic transducers, signal processing, acoustics in fluid media, sound and structure interaction, digital signal processing, aerodynamic noise, acoustic measurements and data analysis, ocean acoustics, architectural acoustics, noise control engineering, nonlinear acoustics, outdoor sound propagation, computational acoustics, flow induced noise, spatial sound and 3D audio, marine bioacoustics, and acoustics of musical instruments. Distance Education students can earn the M.Eng. in Acoustics degree remotely by completing 30 credits of coursework and writing a capstone paper.
Pengukuran Pengalaman Pengguna Aplikasi Platform Pembelajaran dan Konferensi Video Menggunakan Framework UEQ+

This study aims to measure and evaluate the user experience of the Microsoft Teams application as a learning and video conferencing platform using the UEQ+ framework, which is the development of the UEQ method. Through the UEQ+ framework, questionnaires can be designed by customizing user experience variables according to the application to be measured, thus the research results are expected to be more accurate and relevant. The scale of user experience measured in this questionnaire includes: efficiency, perspicuity, dependability, trust, usefulness, intuitive use, trustworthiness of content, quality of content, and clarity. After the questionnaires were distributed, 149 data were obtained which could be processed using data processing tools which being provided by UEQ+ called Data Analysis Tools. In conclusion, respondents have positive impression of Microsoft Teams as a video conferencing application and learning platform. The most important scale that represents the quality of Microsoft Teams is usefulness, clarity, trustworthiness of content, and quality of content.
Contextual Policy Transfer in Meta-Reinforcement Learning via Active Learning

In meta-reinforcement learning (meta-RL), agents that consider the context when transferring source policies have been shown to outperform context-free approaches. However, existing approaches require large amounts of on-policy experience to adapt to novel tasks, limiting their practicality and sample efficiency. In this paper, we jointly perform off-policy meta-RL and active learning to generate the latent context of the novel task by reusing valuable experiences from source tasks. To calculate the importance weight of source experience for adaptation, we employ maximum mean discrepancy (MMD) as the criterion to minimize the experience distribution distance between the target task and the adapted source tasks in a reproducing kernel Hilbert space (RKHS). Integrating source experiences based on active queries with a small amount of on-policy target experience, we demonstrate that the experience sampling benefits the fine-tuning of the contextual policy. Then, we incorporate it into a standard meta-RL framework and verify its effectiveness on four continuous control environments, simulated via the MuJoCo simulator.
Improving Self-supervised Lightweight Model Learning via Hard-Aware Metric Distillation

The performance of self-supervised learning (SSL) models is hindered by the scale of the network. Existing SSL methods suffer a precipitous drop in lightweight models, which is important for many mobile devices. To address this problem, we propose a method to improve the lightweight network (as student) via distilling the metric knowledge in a larger SSL model (as teacher). We exploit the relation between teacher and student to mine the positive and negative supervision from the unlabeled data, which captures more accurate supervision signals. To adaptively handle the uncertainty in positive and negative sample pairs, we incorporate a dynamic weighting strategy to the metric relation between embeddings. Different from previous self-supervised distillers, our solution directly optimizes the network from a metric transfer perspective by utilizing the relationships between samples and networks, without additional SSL constraints. Our method significantly boosts the performance of lightweight networks and outperforms existing distillers with fewer training epochs on the large-scale ImageNet. Interestingly, the SSL performance even beats the teacher network in several settings.
Design and Simulation of a Novel UAV Combined Fixed Wing and Quadrotor

Vertical takeoff and landing fixed-wing UAV is a special kind of UAV, that is, the fixed-wing UAV can take off and land vertically without using the runway.Compared with conventional UAVs, fixed-wing UAVs have the advantages of flexible takeoff and landing mode, convenient launch and recovery, good mobility and so on.Compared with multi-rotor UAVs, fixed-wing UAVs have the characteristics of longer endurance, faster speed and higher flight height.At present, the research and development of vertical takeoff and landing fixed-wing UAVs is still a hot research topic home and abroad.In this paper, a vertical take-off and landing fixed-wing UAV is designed according to the flight system of the quadrotor, including the overall design and structure design.The model of the vertical take-off and landing UAV based on aerodynamics, which is established in SolidWorks, is used for the simulation test carried out to simulate the vertical take-off and landing process of the UAV and the related functions of path tracking.Finally, the relevant parameters are obtained to verify the feasibility of the scheme.
PRACTICAL EXPERIENCE OF DEVELOPING THE CREATIVE POTENTIAL AND CREATIVITY OF FUTURE TEACHERS OF TECHNOLOGIES IN THE CONDITIONS OF DISTANCE LEARNING

Ukrainians continue to live under martial law, defending their sovereignty, independence, and territorial integrity. The related events taking place in front of people cause mental health disorders for many: they become less productive, have problems with motivation and concentration. The rapid introduction of information technologies, rapid technological development against the backdrop of increasing instability and uncertainty dictate certain requirements for the formation of professional qualities of modern youth, on which the scientific, technical and socio-economic development of Ukraine, its stability and level of well-being depend. At present, in times of rapid change, the skills of positive thinking, optimistic perception of any situation and the level of creative activity of young people are becoming the most in demand and important. Despite the catastrophic consequences of hostilities, obtaining education and knowledge is a very relevant topic. Modern domestic education determines the need for creative individuals. The preparation of students of pedagogical specialties should be based on the content of education adapted to modern changes in the social environment. And this requires future graduates to be able to create a creative environment, think outside the box, creatively, proactively, respond flexibly to professional challenges, and also have the tools of psychological and pedagogical influence that would help them prepare their students for real life. In this turbulent time, almost every teacher is revising their values, scientific developments, educational and methodological support of disciplines and is trying to find additional opportunities to improve the quality and efficiency of the educational process. The article presents the experience of organizing the educational process for future technology teachers in the context of distance learning, which consists in a creative approach to teaching professional disciplines. This is useful for those who seek to develop, gain new experience, grow professionally, teach in a new way and keep abreast of current trends in education. The development of the scientific foundations of technical creativity, the development of a methodology for activating the creative process, teaching the basics of engineering and creativity in higher education institutions is a necessity in our time. Therefore, the study was conducted on the example of teaching the new discipline “Engineering and Technical Creativity”, introduced into the educational program for training students of the Engineering and Pedagogical Faculty of the National Pedagogical Dragomanov University educational level “bachelor”, specialty 014 Secondary education: (Labor training and technology) in 2021.
Design of a Conveyor Belt Manipulator for Reposition of Boxes in Logistics Centers

This paper describes the design of a conveyor belt manipulator for repositioning boxes to locate the bar code side in the right position to be sensed in a logistic center. The general rule is that a few pairs of hands touch the package as possible in parcel logistics centers, ideally just three. First for loading, second for unloading the containers, and third for a worker has to place the packages on the conveyor logistic system in the right position, i.e., the bar code must be visible by the vision system. The correct position of the bar code is essential for the vision systems to check the delivery information and manage the package automatically through the correct conveyor in the logistics centers. That process needs to be automated using a collaborative robot (COBOT) and a specific manipulator design for that task. In this work, we present a reposition boxes methodology with a novel conveyor belt manipulator. Our novel design has been achieved by analyzing the manipulation characteristics needed to control, rotate, and flip boxes in a real logistic industry. Then, a prototype was built and tested in the laboratory.
Virtual Acoustics, better than the real thing? Considering the creative side

Once room acoustical reflections data are extracted from a physical space or a model, and are encapsulated in a 3D impulse response, they can be used to render immersive sound fields in real time. A range of possibilities then opens for creative use of acoustics in music. A skilled virtual acoustics designer-engineer may rebalance digital signals representing the room response to situate player and listener on the stage or at the back of the auditorium, may modify and arrange temporal segments to re-imagine the aural dimensions of the space, and apply gain and directional placement to shape the impression of immersive presence, adapting acoustics to musicians’ creative needs. In the process of building an idealized acoustical environment for the music, techniques of sound reinforcement and of rendering room acoustics are combined to balance presence with ambience and to deliver a sensation of acoustical power with lift-off. The means exist to move beyond acoustical realism into fictionalized acoustics.
Hybrid Data Fragmentation Using Genetic Killer Whale Optimization-Based Clustering Model

Effective storage structure is essential in distributed data processing systems as it determines the users' consistent, scalable and reliable data accessibility.A flexible distributed database system's major aspects are data fragmentation and subsequent allocation.An efficient data fragmentation technique must support both the horizontal and vertical categorization of the tuples so that the frequency and type of queries for the data statistics are learned along with the data pattern and the attributes that impact these patterns.However, most existing fragmentation methods are based only on the query frequency and type for the data statistics, leading to less effective analysis of the data patterns and attributes in large databases.To avoid the data loss and imperfect grouping of large databases, this paper presents a hybrid data fragmentation model using an advanced optimization-based clustering model for performing horizontal and vertical fragmentation.This proposed model, called Genetic Killer Whale Optimization based Clustering (GKWOC), is developed by integrating the Killer Whale Optimization (KWO) and Genetic Algorithms (GA) into the standard subspace clustering algorithm.This optimization-based hybrid fragmentation model performs fragmentation in terms of the tuples and the attributes.Since the databases can be high dimensional, subspace-based clustering can improvise good fragments for distributed databases and reduce the data access time for the users.This proposed model showed better fragmentation performance than the state-of-the-art methods during the evaluations, thus suitable for large distributed databases with competent access time and reduced error rates.
Option-based Multi-agent Exploration

Effective exploration is essential to cooperative multi-agent reinforcement learning (MARL). However, existing exploration MARL algorithms remain two challenges: enormous exploration space, and partial observability constraints. To address these challenges, we propose a method called option-based multiagent exploration (OMAE): we introduce the concept of option to reduce the number of decisions, where options are defined as policies with a termination condition. Option-based exploration improves learning efficiency because the option space is much smaller than the original policy space. We use a dual-policy framework to overcome partial observability constraints where the global state is not available in execution. Our framework separates the exploration and the exploitation policies to ensure that the exploitation policy is accessible to the state information without explicitly taking the options as input. We further introduce a likelihood estimation to solve the distribution shift problem between two policies. Experimental results show that the OMAE improves the coordinated ability in comparison with the baseline methods in most of the tasks in the StarCraftII environment(SMAC).
Edge Restoration of Video Stabilization Based on Panoramic Stitching

Video stabilization is widely used in various fields of life. After video stabilization, due to the offset of video frames, it often brings the problem of edge missing. Traditional electronic video stabilization algorithms deal with the problem of missing video edge information through cropping. The clipping amplitude is determined by the intensity of video jitter and video smoothing algorithm. To solve this problem, this paper proposes a method to repair the video edge based on panoramic stitching. Use the reciprocity of jitter to segment the video; combined with the video motion trajectory, the extraction range of feature points is reduced; perform homography transform on each frame in the segmented interval, and then stitch it into an image containing panoramic information; finally, using the panoramic image, each frame after offset is stitched to repair the video with missing edge information. The experimental results show that using the real scene information to restore the missing part of the video, the filling is natural, the effect is stable, and can be applied to a variety of complex environments.