Reviewerly/exHarmony huggingface dataset:
IMPROVEMENT OF METHODICAL COMPETENCE OF MUSIC TEACHERS IN COMPREHENSIVE SECONDARY EDUCATION INSTITUTIONS

The article is devoted to the problem of improvement of methodological competence of music teachers in general secondary educational institutions through a well-thought-out system of school methodical work. The definition ofsuchconceptsas“professionalcompetenceofamusicteacher” and“methodicalcompetenceofamusicteacher” is proposed. Under the professional competence of a music teacher we understand the holistic characteristics of the personality of the specialist with the already-formed set of competencies that allow him to perform his professional functions effectively, to update and enrich his knowledge constantly, to develop new skills according to the level of science and technology, to be ready for social changes, to enjoy his professional activities. In its turn, the methodical competence of the teacher-musician is interpreted as an integral characteristic of personality, which determines the ability to solve professional problems in situations of real practical musical-educational activities, corresponds to the main types of methodical activities of music teachers and is a set of components such as orientational (professional orientation), cognitive (methodical knowledge), operational-active (methodical skills), personal (professionally significant personal qualities) and social-behavioral (experience). The functions of methodical work in the institution of general secondary education (planning, organizational, diagnostic, prognostic, modeling, corrective functions) and criteria of optimality of results of teacher’s methodical work (criterion of efficiency, criterion of rational spending time and economy, satisfaction of the teacher with the work, criterion of effective methodical work) are studied out. The characteristics of traditional and innovative methods, forms and technologies of improving the methodological competence of music teachers in general secondary education are given, which include concilium, round table, methodical ring, auction of pedagogical ideas, business game, week of pedagogical sciences, pedagogical workshop, master classes , pedagogical mosaic, creative laboratory, discussion-aquarium, debates, information and communication technologies, etc.
Design of hardware-software systems in the educational process with the use of virtual computer lab

The article describes an illustrative example of IoT-solutions that students can independently design and create using virtualization and containerization tools in a Virtual Computer Lab (VCL) environment. This article will allow you to more accurately represent the conditions and limitations that have to be overcome in traditional computer classes, as well as design requirements for a special VCL circuit, which will allow students to send requests for equipment configuration, inventory equipment, implement an intelligent queue and resource management system, and also verify correct connections of equipment based on machine earning with reinforcement. Learning the basics of designing IoT-solutions on Arduino and Raspberry Pi and creating management software allows you to train specialists who can contribute to the creation and development of high-tech advanced software and technology solutions.
On Multi-Session Website Fingerprinting Over TLS Handshake

Analyzing Internet users' traffic data and activities has a certain impact on users' experiences in different ways, from maintaining the quality of service on the Internet and providing users with high-quality recommendation systems to anomaly detection and secure connection. Considering that the Internet is a complex network, we cannot disintegrate the packets for each activity. Therefore we have to have a model that can identify all the activities an Internet user does in a given period of time. In this paper, we propose a deep learning approach to develop a multi-label classifier that can predict the websites visited by a user in a certain period. This model works by extracting the server names appearing in chronological order in the TLSv1.2 and TLSv1.3 Client Hello packets. We compare the results on the test data with a simple fully-connected neural network developed for the same purpose to prove that using the time-sequential information improves the performance. For further evaluations, we test the model on a human-made dataset and a modified dataset to check the model's accuracy under different circumstances. Finally, our proposed model achieved an accuracy of 95% on the test dataset and above 90% on both the modified dataset and the human-made dataset.
Drop-Bottleneck: Learning Discrete Compressed Representation for Noise-Robust Exploration

We propose a novel information bottleneck (IB) method named Drop-Bottleneck, which discretely drops features that are irrelevant to the target variable. Drop-Bottleneck not only enjoys a simple and tractable compression objective but also additionally provides a deterministic compressed representation of the input variable, which is useful for inference tasks that require consistent representation. Moreover, it can jointly learn a feature extractor and select features considering each feature dimension's relevance to the target task, which is unattainable by most neural network-based IB methods. We propose an exploration method based on Drop-Bottleneck for reinforcement learning tasks. In a multitude of noisy and reward sparse maze navigation tasks in VizDoom (Kempka et al., 2016) and DMLab (Beattie et al., 2016), our exploration method achieves state-of-the-art performance. As a new IB framework, we demonstrate that Drop-Bottleneck outperforms Variational Information Bottleneck (VIB) (Alemi et al., 2017) in multiple aspects including adversarial robustness and dimensionality reduction.
On the Convergence of Stochastic Gradient Descent with Bandwidth-based Step Size

We investigate the stochastic gradient descent (SGD) method where the step size lies within a banded region instead of being given by a fixed formula. The optimal convergence rate under mild conditions and large initial step size is proved. Our analysis provides comparable theoretical error bounds for SGD associated with a variety of step sizes. In addition, the convergence rates for some existing step size strategies, e.g., triangular policy and cosine-wave, can be revealed by our analytical framework under the boundary constraints. The bandwidth-based step size provides efficient and flexible step size selection in optimization. We also propose a $1/t$ up-down policy and give several non-monotonic step sizes. Numerical experiments demonstrate the efficiency and significant potential of the bandwidth-based step-size in many applications.
Optimization of Evacuation Route for Urban Traffic Congestion based on Particle Swarm Optimization

The traditional route optimization method updates the evacuation points less frequently, resulting in poor evacuation route connectivity. For this reason, an urban traffic congestion evacuation route optimization method based on particle swarm optimization is proposed. According to the different connectivity of the indicators, the layout of the evacuation route is selected. Finally, the evacuation points are updated by using particle swarm optimization algorithm to achieve the global optimization of the evacuation path. Experimental results show that the path reliability of the proposed method is greater than that of the traditional method, which reduces the blockage probability of evacuation paths.
Learning Enabled Adaptive Multiple Attribute-based Physical Layer Authentication

In this paper, we propose an adaptive multi-attributes based physical layer authentication framework for enhanced authenticity provisioning. Instead of optimizing the "threshold" for a preset PHY-layer signature, this paper resort to exploiting and selecting multiple historical better performed PHY-layer attributes for authentication enhancement. In particular, the authenticator of the proposed scheme is designed to be capable of recording the historically performance of each potential attribute. Based on which, the most effective PHY-layer attributes (MEA) would be chosen to improve the reliability of the PHY-layer authentication. This paper experimentally proves that the dimension extension on PHY-layer signature attributes effectively enhances authenticator's capability in signal discrimination. However, with more attribute to observe, it also complicates the predicting and authenticating procedure. Therefore, a learning-based search algorithm is then formulated to facilitate the MEA selection procedure. Both theoretical analysis and experiment results are given to demonstrate the efficiency and superiority of the proposed scheme.
A Discriminative Technique for Multiple-Source Adaptation

We present a new discriminative technique for the multiple-source adaptation, MSA, problem. Unlike previous work, which relies on density estimation for each source domain, our solution only requires conditional probabilities that can easily be accurately estimated from unlabeled data from the source domains. We give a detailed analysis of our new technique, including general guarantees based on R\'enyi divergences, and learning bounds when conditional Maxent is used for estimating conditional probabilities for a point to belong to a source domain. We show that these guarantees compare favorably to those that can be derived for the generative solution, using kernel density estimation. Our experiments with real-world applications further demonstrate that our new discriminative MSA algorithm outperforms the previous generative solution as well as other domain adaptation baselines.
Designing a Model of Professional Competence of Managers Based on the Development of Creativity and Innovation in Primary Schools

Designing a Model of Professional Competence of Managers Based on the Development of Creativity and Innovation in Primary Schools
The Value of Visual Disaster Records from Digital Archives and Films in Post-3/11 Japan

This paper explores the value of visual records from natural disasters in assisting reconstruction, including photography, single-shot videos, and documentary movies. It considers three types of visual records related to the Great East Japan Earthquake: 1) raw data, 2) edited educational videos, and 3) commercial documentary films. It also considers the nature of disaster records and their repository medium, including digital data archives, public educational websites, and commercial networks. Furthermore, the authors consider the overlaps between these categories. Raw video records certainly meet the needs of either digital archives or documentary movies. However, commercial documentary movies form a category of their own, as copyright and scripts constrain their exploitation and manipulation. In conclusion, this paper identifies the merit of each type of visual record and argues that both are necessary for the social remembering of disasters and to help reconstruct communities affected by such events. Keywords: digital archives; films; disaster
Host rock peculiarities and influence of major structures on gold and base metal sulfide mineralization in northern Pakistan

For decades, the streams and rivers in northern areas of Pakistan are being explored for gold but the source rock for primary gold is yet to be located. Recent geological surveys have identified several areas for follow-up study for Cu-Au mineralization in Chitral and Gilgit-Baltistan regions. The rocks exposed at various localities are variably altered and/ or mineralized and intensely deformed, which mostly include diorite, granodiorite, greenschists, limestones and undeformed volcanic rocks and granitoids of the Kohistan island arc. The results of petrographic and X-ray diffraction (XRD) analyses of a variety of representative rock samples from a number sites have revealed that mineralization in the Chitral and Gilgit-Baltistan regions is dominantly in the form of pyrite, tetrahedrite, chalcopyrite and galena with subordinate amounts of magnetite, malachite and azurite. The associated/ gangue minerals include albite, quartz, actinolite, biotite, chlorite, epidote, calcite, garnet, illite and titanite (sphene). Diffraction patterns of glycolated samples did not show the existence of swelling clay (e.g., smectite). Sericitic, chloritic and carbonatic alterations have been observed in the studied altered rocks. Mass balance calculations indicate that most of the sulfide-bearing samples are enriched in Cu, Au, Sb, As and Pb and depleted in the rest of the metals reflecting differences in elements mobility during the ore-forming processes. Some of the mineralized rocks especially limestone have experienced a significant volume change due to mobilization of some of the major element oxides e.g. CaO, Fe2O3, SiO2 and ore-forming components e.g. Cu and Pb. The mineralization is of epithermal type in most of the investigated sites, however a few areas show ore formation though orogenic processes and very rarely the ore deposit displays features of the volcanogenic massive sulfide type. Some of the areas are quite promising with respect to their ore element contents and the highly mineralized zones lie in close proximity to major structures in the region. Besides, results from the current study indicate that the mineralization shows close spatial association and hence a strong genetic link with strike slip faults especially where they bend and merge together.
INTEGRATED APPROACH TO MORAL EDUCATION

In article process of moral education, his importance in the modern world is considered, it is analyzed questions of deepening and enrichment of content of moral education, formation of new approaches to moral education on the basis of integration of knowledge of such spheres as the biosphere, a noosphere and also moral education is treated both social, and personal needs.
Orphanage Helping System

An orphanage is a residential institution devoted to the care of orphan-children whose parents are unwilling or unable to take care of them. The problem of orphan is acute due to urbanization and industrialization. Need for each of the orphanages varies in different categories such as food, money, clothes, medicine. Most of the people would like to help the orphanages but the major problem is that they have any idea on how to approach these orphanages. The main objective of this application is to develop a centralized site for orphanages. In our proposed system we try to create an interface between Orphanages\Old age Homes which are in need of charity and people/Volunteers who are willing to help these people. Volunteers can also approach the Orphanage managers regarding the help that they can afford. People can donate through Internet Banking or Online Money Payment applications.
Bayesian Bonus-Malus Premium with Poisson-Lindley Distributed Claim Frequency and Lognormal-Gamma DistributedClaim Severity in Automobile Insurance

The traditional automobile insurance bonus-malus system (BMS) merit-rating depends on thenumber of claims. An insured individual who makes a small severity claim is penalized unfairly compared to aninsured person who makes a large severity claim. A model for assigning the bonus-malus premium wasproposed. Consideration was based on both the number and size of the claims that were assumed to follow aPoisson-Lindley distribution and a Lognormal-Gamma distribution, respectively. The Bayesian method wasapplied to compute the bonus-malus premiums, integrated by both frequency and severity components based onthe posterior criteria. Practical examples using a real data set are provided. This approach offers a fairer methodof penalizing all policyholders in the portfolio.
A Concept for Context Awareness in Smart Environments

Abstract All aspects of daily life increasingly include digitization. So-called „smart home“ technologies, as well as „wearables“, are gaining attention from more and more dwellers. Therefore, sensor-based, individualized, AI-based services for improved post-intervention monitoring and therapy accompaniment will become feasible and possible if these systems offer a related context-awareness. This paper provides an approach on how to sense and interpret specific contexts with the help of wearables, smartwatches, smart home sensors, and emotion detection software.
Survey of Lattice to Design Post Quantum Cryptographic Algorithm Using Lattice

Objective: Quantum algorithms are stronger and more secure than classical computers because they run on faster, harder ones and require fewer steps.With Quantum computers, the attackers have high computing power, and with a quantum, the algorithm can easily break the cryptographic system.Lattice is a regularly spaced grid of points stretching to infinity.Quantum safe security algorithms are resistant to both attacks caused by quantum computers and attacks caused by classical computers.Lattice-based cryptography is the postquantum cryptographic standards resistant to the attacks from quantum computers, hence having the advantage of strong security and high efficiency.The paper's main objective is to study Lattice, lattice properties, Latticebased cryptographic algorithm to design new Latticebased cryptographic algorithms that are quantum resistant in the future.Methods: In this paper, lattice-based cryptography is discussed right from its seminal work to its efficient cryptographic schemes.Paper discusses Lattice, lattice properties, lattice problem, the algorithmic solution to lattice problem, and lattice-based cryptography.Findings: After studying post-quantum cryptographic algorithms using Lattice, lattice-based post-quantum cryptographic algorithms are resistant to quantum computer attacks.Novelty: The paper discusses Lattice, properties of Lattice in a simple way.Widely used cryptographic algorithms like RSA, Diffie-Hellman Key exchange, Elliptic Curve Cryptography are not resistant to quantum computer attacks.Paper discusses the importance of a post-quantum algorithm using Lattice that is resistant to quantum computer attacks.
Neighborhood Attentional Memory Networks for Recommendation Systems

Deep learning systems have been phenomenally successful in the fields of computer vision, speech recognition, and natural language processing. Recently, researchers have adopted deep learning techniques to tackle collaborative filtering with implicit feedback. However, the existing methods generally profile both users and items directly, while neglecting the similarities between users’ and items’ neighborhoods. To this end, we propose the neighborhood attentional memory networks (NAMN), a deep learning recommendation model applying two dedicated memory networks to capture users’ neighborhood relations and items’ neighborhood relations respectively. Specifically, we first design the user neighborhood component and the item neighborhood component based on memory networks and attention mechanisms. Then, by the associative addressing scheme with the user and item memories in the neighborhood components, we capture the complex user-item neighborhood relations. Stacking multiple memory modules together yields deeper architectures exploring higher-order complex user-item neighborhood relations. Finally, the output module jointly exploits the user and item neighborhood information with the user and item memories to obtain the ranking score. Extensive experiments on three real-world datasets demonstrate significant improvements of the proposed NAMN method over the state-of-the-art methods.
Learning Unsupervised Cross-domain Image-to-Image Translation using a Shared Discriminator

Unsupervised image-to-image translation is used to transform images from a source domain to generate images in a target domain without using source-target image pairs. Promising results have been obtained for this problem in an adversarial setting using two independent GANs and attention mechanisms. We propose a new method that uses a single shared discriminator between the two GANs, which improves the overall efficacy. We assess the qualitative and quantitative results on image transfiguration, a cross-domain translation task, in a setting where the target domain shares similar semantics to the source domain. Our results indicate that even without adding attention mechanisms, our method performs at par with attention-based methods and generates images of comparable quality.
A Generative Model for Hallucinating Diverse Versions of Super Resolution Images.

Traditionally, the main focus of image super-resolution techniques is on recovering the most likely high-quality images from low-quality images, using a one-to-one low- to high-resolution mapping. Proceeding that way, we ignore the fact that there are generally many valid versions of high-resolution images that map to a given low-resolution image. We are tackling in this work the problem of obtaining different high-resolution versions from the same low-resolution image using Generative Adversarial Models. Our learning approach makes use of high frequencies available in the training high-resolution images for preserving and exploring in an unsupervised manner the structural information available within these images. Experimental results on the CelebA dataset confirm the effectiveness of the proposed method, which allows the generation of both realistic and diverse high-resolution images from low-resolution images.
Strongly Connected Topology Model and Confirmation-based Propagation Method for Cross-chain Interaction

Cross-chain interaction is among different blockchains. When the number of blockchains increases, it is difficult for blockchains to form a single star topology or a fully connected topology. Meanwhile, different from legacy networks, the propagation method is required to keep the data validity. Thus, the blockchain topology and associated propagation methods are two key issues, which should be ensured during the propagation. In this paper, we first propose the confirmation-based propagation method to keep the validity of the cross-chain data. The confirmation method is to seal the cross-chain transaction synchronized from other blockchains. Second, we point out that a valid topology requires blockchains to be strongly connected. With this requirement, we propose several topologies, which match different connection scenarios. The simulation results show that the proposed methods can securely propagate the cross-chain data and the connection way is flexible.
Road Performance Evaluation of Modified Steel Slag Asphalt Mixture based on Chaos Optimization Neural Network

The road performance evaluation method, determining the asphalt mixture inaccurate evaluation index, resulting in a stable and freeze-thaw splitting strength variation inaccurate assessment before and after immersion, is proposed for this neural network based on chaos optimization slag Modified Asphalt Mixture Pavement performance evaluation methods. Analysis of Modified slag asphalt mixture, the performance index of the composition is determined slag modified asphalt road performance evaluation index; full Chaotic optimization neural network convergence speed fast convergence and high accuracy, the establishment of road performance evaluation model; calculating road pavement structural strength performance index and a coefficient, road assessment modified asphalt with slag porous. Experimental results show that the modified bitumen slag study Mixes performance evaluation method when evaluating slag modified asphalt road performance, stability and freeze-thaw splitting strength change before and after immersion of the right path performance, evaluate the results more accurate.