Reviewerly/exHarmony huggingface dataset:
Combination of Empirical and Theoretical Knowledge as a Problem of Piano Pedagogy of Ukraine

Professional training of specialists in the field of musical art is an urgent task, as it contributes to the development of a performing and pedagogical culture, which is reflected in the understanding of a musical composition. High-quality music education ensures continuous professional and creative development. The purpose of the study is to determine the basic principles of combining empirical and theoretical knowledge in the educational process for training future musicians and pianists. The main methodological base of the study consists of: methods of theoretical (monographic, abstraction, and concretisation) and empirical (analysis, content analysis, generalisation of pedagogical experience, experiment) search. The study considers the main principles and trends in the development of modern piano pedagogy in Ukraine. The essence of the concepts of empirical and theoretical cognition in the context of the educational process is analysed and the features of their possible application in piano pedagogy are identified. An educational process model for training future pianists is proposed, which would allow providing favourable conditions for the development of a creative personality, better performing skills, and an independent professional performer. It is established that the specific features of piano pedagogy condition the need to adapt modern educational methods to the conditions of the educational process in the context of obtaining music education. As a result, during the application of the model for organising the teaching of piano art, which provides for the unity of theoretical and empirical cognition, the principles on which such a model should be based were identified. The practical value of the study lies in the fact that the development of a model for combining methods of theoretical and empirical cognition in the educational process of piano art students is proposed
UPAYA MENINGKATKAN KOMPETENSI GURU-GURU BAHASA JERMAN MELALUI PELATIHAN PENELITIAN TINDAKAN KELAS

Improving teacher competence is important in learning German. To improve the quality of learning German, the Devotion Team at the Department of German Language Education FBS-UNY held a Classroom Action Research training for German language teachers who are members of the Indonesian German Teacher Association (IGBJI). This Community Service Activity is realized in the form of a 32-hour training (workshop) which is divided into 3 meeting sessions. First session contains an explanation of the theories and methods of Classroom Action Research. Meanwhile, second session contains mentoring activities to find research ideas, and last Session contains presentations of research proposals from selected participants. This training activity is carried out entirely online using the Zoom application. Classroom Action Research training activities can be carried out properly and get the results as planned. The attendance rate of registered participants is in the good category. Average attendance of 50 people per session (69.4%) of the total 72 registrants. Participation of participants in the discussion is also good. At the end of the activity, 23 Classroom Action Research proposals were produced. Thus, German teachers are able to apply the results of this activity by observing the state of learning German in their respective schools and taking scientific steps to carry out action research.
Earthquake Early Warning System: Its Relevance for India

Earthquake early-warning system (EEWS) uses modern communication infrastructure and real-time seismology to estimate the size of the earthquake and gives warning to target cities well before arrival of damaging waves. For earthquakes originating from the central Himalayas, such a system can provide tens of seconds of warning to the adjoining plains and Delhi can get as much as 70 s of warning time. A successful EEWS, in the event of 7+ magnitude earthquake from the central Himalayas, can save millions of lives. This paper provides concept, methodology, algorithms, and other details of EEWS and its importance for India. The paper also presents details and performance of a pilot project of EEWS operational in Uttarakhand, India.
Educational poverty, digital and cultural divide: Some reflections from a case study

Abstract The pandemic has highlighted and accentuated some potential risks and critical issues already present in several educational contexts: the widening of educational poverty, the increase in the digital and cultural divide and the expansion of learning loss and of the implicit and explicit early school leaving. In this regard, the case study developed in a period just prior to the pandemic, on the one hand, has highlighted some critical issues of the teaching–learning relationship, while, on the other hand, it has deepened the strengths concerning the teaching–learning relationship in order to improve learning to learn and learning to teach for students and teachers in contemporary complexity. The results show that facing the current complexity means understanding the cultural aspects that guide today’s ways of learning and teaching based on technologies. The relevance of these aspects concerning meanings and practices of teaching and learning is understandable, taking into account the rapid and profound changes in the current world in which we live. In summary, the results of this educational research on the analysed teaching and learning practices can be useful both to treasure today’s lessons learned and to copy the unexpected changes that each crisis proposes like the current one.
Multiple AUUV cooperative area target searching method based on distributed model

Underwater unmanned vehicle cluster cooperative area target searching is an inexpensive and efficient effort for salvaging and vigilance at sea. In order to realize the autonomous area searching of the attacking underwater unmanned vehicle (AUUV) cluster, a multiple AUUV cooperative area target searching method is put forward and simulated in this paper. The proposed method combines the AUUV with an underwater environment to create an environmental model, an AUUV motion model, a target probability and update model, and an object optimization function. Moreover, the global-best difference-mutation brain storm optimization algorithm is adopted to test the multiple AUUV cooperative area target searching path planning. The results of the simulation experiment verify the effectiveness of the proposed method and models. Hence, a new scheme is advanced to realize multiple AUUV cooperative area target searching and expand multiple AUUV applications.
Robust reputation independence in ranking systems for multiple sensitive attributes

Ranking systems have an unprecedented influence on how and what information people access, and their impact on our society is being analyzed from different perspectives, such as users' discrimination. A notable example is represented by reputation-based ranking systems, a class of systems that rely on users' reputation to generate a non-personalized item-ranking, proved to be biased against certain demographic classes. To safeguard that a given sensitive user's attribute does not systematically affect the reputation of that user, prior work has operationalized a reputation independence constraint on this class of systems. In this paper, we uncover that guaranteeing reputation independence for a single sensitive attribute is not enough. When mitigating biases based on one sensitive attribute (e.g., gender), the final ranking might still be biased against certain demographic groups formed based on another attribute (e.g., age). Hence, we propose a novel approach to introduce reputation independence for multiple sensitive attributes simultaneously. We then analyze the extent to which our approach impacts on discrimination and other important properties of the ranking system, such as its quality and robustness against attacks. Experiments on two real-world datasets show that our approach leads to less biased rankings with respect to multiple users' sensitive attributes, without affecting the system's quality and robustness.
Node Embedding and Classification with Adaptive Structural Fingerprint

Graph attention network (GAT) is a promising framework for message passing on graphs, but how to exploit rich, high-order structural information in the attention mechanism is still an open challenge. Furthermore, increasing the attention range to more than one-hop neighbors can negatively affect the performance of GAT, reflecting the over-smoothing risk of graph neural networks in general. In this paper, we propose an “adaptive structural fingerprint” model to fully exploit complex graph topology in graph attention networks. The key idea is to contextualize each node with its “structural fingerprint” that can automatically adjust to the local graph topology and edge connections in the neighborhood of the node. By doing this, structural interactions between the nodes can be evaluated more accurately and better confined to relevant neighbors, thus contributing to an improved attention mechanism and clearer cluster boundary. Furthermore, our approach provides a useful platform for different subspace of node features and various spatial scale of graph structures to “cross-talk” with each other through multi-head attention, which is more flexible than existing attention mechanism using a fixed, minimal spatial attention scale. Encouraging results are observed on a number of benchmark data sets including citation and social networks.
READING AND HOW TO TEACH IT

Reading is a basic process of constructing meaning in written texts, gaining information, and learning about the wide world. It makes serious problem if someone has difficulties in reading because it is of the most frequent language skill used in daily life. Reading comprehension is the skill to understand what we are reading. In teaching, the main outcome of reading strategies is students have the ability to comprehend reading materials. Reading comprehension is a challenge for students because most of them have various problems in understanding the texts, specifically in unfamiliar words and sentences correlation. This study aims to propose teachers consider that reading comprehension plays a vital role in reading. The study applied library research. Teachers should be sensitive to learn what students’ characteristics are in reading and choose the best strategy in it, build up students’ confidence, provide suitable materials and exercises, and also give more time to explore what students are reading. Further, teachers are expected to put forward comprehension and bring proper strategy to students understand the texts comprehensively. If students can improve their reading comprehension skills, then the purpose of teaching reading has been successfully achieved.
On Neural Architecture Inductive Biases for Relational Tasks

Current deep learning approaches have shown good in-distribution generalization performance, but struggle with out-of-distribution generalization. This is especially true in the case of tasks involving abstract relations like recognizing rules in sequences, as we find in many intelligence tests. Recent work has explored how forcing relational representations to remain distinct from sensory representations, as it seems to be the case in the brain, can help artificial systems. Building on this work, we further explore and formalize the advantages afforded by 'partitioned' representations of relations and sensory details, and how this inductive bias can help recompose learned relational structure in newly encountered settings. We introduce a simple architecture based on similarity scores which we name Compositional Relational Network (CoRelNet). Using this model, we investigate a series of inductive biases that ensure abstract relations are learned and represented distinctly from sensory data, and explore their effects on out-of-distribution generalization for a series of relational psychophysics tasks. We find that simple architectural choices can outperform existing models in out-of-distribution generalization. Together, these results show that partitioning relational representations from other information streams may be a simple way to augment existing network architectures' robustness when performing out-of-distribution relational computations.
An Assessment of Blockchain and Artificial Intelligence as Transformational Technologies in Marketing

Marketing practice is changing in this era of digital disruption and revolutionary technologies. Transformation technology is a difficult task, but the adoption of blockchain and AI will make it easier. Regardless of the number of partners (internal, external, or both) participating in a given commercial transaction, a system in which numerous electronic parties can safely communicate, collaborate, and transact without human intervention is highly adaptive and efficient. The authors of this study propose a methodology for reviewing numerous situations of marketing through the use of transformative technologies and their relevant consequences for enterprise, consumer, and academic research to assess blockchain and AI as transformational.
Denoising and feature extraction in photoemission spectra with variational auto-encoder neural networks

In recent years, distinct machine learning (ML) models have been separately used for feature extraction and noise reduction from energy-momentum dispersion intensity maps obtained from raw angle-resolved photoemission spectroscopy (ARPES) data. In this work, we employ a shallow variational auto-encoder neural network to demonstrate the prospect of using ML for both denoising of as well as feature extraction from ARPES dispersion maps.
Toward Emancipation Through a Regenerative Digital Economy

Let us imagine a different future. Not one where a handful of corporations control and exploit our data—or own the machines and the algorithms that render our jobs obsolete. Not one where the current state of digital servitude becomes actual economic servitude of the medieval kind. Not the sci-fi dystopias of social wastelands, environmental degradation, and abject poverty. Instead, let us consider the possibility that we the people, the workers, the communities, and the citizens exercise our inalienable right to control our destiny. And that, instead of letting a small minority enjoy the bountiful rewards of an automated future, we claim that future for ourselves too.
Summary of Research on Application of Deep Learning in Image Recognition

With the rapid development of technology, artificial intelligence is gradually applied in the field of image processing. Traditional image processing and recognition takes human extraction of features as a means to describe and characterize low-level edge information in images, but a large number of features extraction causes certain troubles to image recognition, and it is difficult to extract deep-level information. In recent years, deep learning, as an important emerging technology in the field of image recognition, has broad application prospects, and the research on image recognition technology has important theoretical value and practical significance to promote the development of computer vision and artificial intelligence. It opens up a whole new direction in image processing research, thus meeting the demand for efficient image processing. This paper reviews the applications of deep learning in image recognition, analyzes the classical convolutional neural network model, and summarizes the applications of deep learning in the fields of license plate recognition, face recognition, and medical image recognition.
Quantum Simulation of the Two-Dimensional Weyl Equation in a Magnetic Field

Quantum simulation of 1D relativistic quantum mechanics has been achieved in well-controlled systems like trapped ions, but properties like spin dynamics and response to external magnetic fields that appear only in higher dimensions remain unexplored. Here we simulate the dynamics of a 2D Weyl particle. We show the linear dispersion relation of the free particle and the discrete Landau levels in a magnetic field, and we explicitly measure the spatial and spin dynamics from which the conservation of helicity and properties of antiparticles can be verified. Our work extends the application of an ion trap quantum simulator in particle physics with the additional spatial and spin degrees of freedom.
LA-ICP-MS ANALYSIS OF TRACE ELEMENTS IN SILICATE MINERALS ON ICP-MS NEXION 300S MASS SPECTROMETER WITH NWR 213 ATTACHMENT FOR LASER ABLATION: METHODOLOGICAL ASPECTS

Inductively coupled plasma mass spectrometry (ICP-MS) and laser ablation (LA) are widely used to study the trace element composition of minerals, including silicates; nevertheless, methodological activities in this area aimed at increasing the sensitivity and locality and reducing composition error, remain relevant. The paper describes methodological approaches for studying the trace element composition of a number of silicate minerals using NexION 300S quadrupole ICP-MS with an NWR 213 LA attachment, obtained on standard synthetic glasses NIST SRM 612 and 610, as well as a number of interlaboratory zircon standards Mud Tank, GJ-1, 91500, Plesovice, Temora-2 and clinopyroxene samples 1636 and 1780 from clinopyroxenite of the Nizhniy Tagil massif (Urals). Presented here are the metrological characteristics of the analysis technique (the accuracy in the determination of elements from Li to U) and variations in sensitivity for different elements, obtained with a crater diameter of 13, 20, 25, 50 and 100 microns. The MS sensitivity drift analysis has been performed through an 8-hour analytical session; a comparison has been made between the results obtained and the literature data; a satisfactory agreement of the results has been observed. The analytical errors allow the use of methods for conducting research in solving various geochemical problems.
A Relief Based Feature Subset Selection Method

Feature selection methods are used as a preliminary step in different areas of machine learning. Feature selection usually involves ranking the features or extracting a subset of features from the original dataset. Among various types of feature selection methods, distance-based methods are popular for their simplicity and better accuracy. Moreover, they can capture the interaction among the features for a particular application. However, it is difficult to decide the appropriate feature subset for better accuracy from the ranked feature set. To solve this problem, in this paper we propose Relief based Feature Subset Selection (RFSS), a method to capture more interactive and relevant feature subset for obtaining better accuracy. Experimental result on 16 benchmark datasets demonstrates that the proposed method performs better in comparison to the state-of-the-art methods. DUJASE Vol. 6 (2) 7-13, 2021 (July)
Image Security Barrier (ISB): Hide valuable information in image using machine learning

People have a addictions of sharing their daily activities on story, posts, chatting or snap in form of an image on social media. Sometimes image content information includes the date of birth, addresses, phone no, Aadhar no, PAN no, or QR code.if someone shares too much information, then hackers can harvest this information and use it for illegal purposes and like government and Businesses secret information leak, due to lack of security or maliciously, causes big costs in money, a distinction of business, and national security. In this paper, we present software/tool for hiding such kind sensitive/private data of users while shearing on the world of internet. ISB is a software/tool that creates a barrier between hackers and public it recognizes our sensitive information from image/document and hide or encrypt them. An ISB provides secured image with 95.5% of accuracy The final output is completely secure for sharing anywhere on Internet/social media platforms.
Privacy Risk Perception of Online Medical Community Users Based on Deep Neural Network

This paper studies the privacy risk perception of online medical community users based on deep neural network. Firstly, this paper introduces privacy protection based on deep neural network and users’ privacy risk perception in online medical community. Then, using the fuzzy neural network to deal with highly complex and nonlinear data, we can better obtain the accurate evaluation value, and use the improved gravity search optimization algorithm to optimize the fuzzy neural network evaluation model and improve the convergence puzzle of the model. Finally, using the experimental method of questionnaire survey, and the questionnaire is composed of three parts. The first part investigates the basic personal information of the subjects, including gender, age, educational background, physical condition, physical examination frequency, Internet use experience, long-term residence, etc.; The second part is the measurement items of each variable in the theoretical model, including nine variables: service quality, personalized service, reciprocal norms, result expectation, material reward, perceived risk, trust in doctors, trust in websites, and willingness to disclose health privacy information. The experimental results show that the correlation coefficient between the interaction items of personalized service and reciprocal norms on material reward is positive (β = 0.072, P &amp;lt; 0.01), and the correlation coefficient between sexual service and material reward was positive (β = 0.202, P &amp;lt; 0.01), then reciprocal norms positively regulate the relationship between personalized service and material reward.
Gender Based Access Control System

Gender classification finds out whether the given facial image belongs to male or female. Face detection is feature based approach, the apparent property of the face such as skin color and face geometry are used. Feature based face detection method depends on feature derivation and analysis to gain the required knowledge about face. This recognized gender through classification can be used to give the authorized access at some public spaces or some specific places where particular gender people are expected to come and have access. The system is developed using simple edge computing device Raspberry pi interfaced with camera and solenoid latch. The openCV and python based Artificial Neural Network model is used to classify the gender. This paper discuss the need and implementation of such system with its parametric case study for future implementation.
How might we evaluate co-design? A literature review on existing practices

In the last 30 years, co-design has seen exponential adoption in response to complex socio-technical transitions.However, the appraisal around co-design still lacks development, making its quality, effect, and value ambiguous.This study aims to encourage more research and practice in co-design evaluation by summarizing existing attempts.We first conduct an integrative literature review in Scopus by analyzing and comparing existing methods used for assessing co-design.Then we iteratively code the reviewed articles in ATLAS.tifollowing grounded theory.Through critical reflection and synthesis of the codes, we form higher-level themes describing different aspects of assessment.Based on these, we propose a co-design evaluation framework with five steps guiding the practice.An overview of the methods, values, challenges, and suggestions for co-design assessment has been presented, and an assessment framework is proposed to generate more interest and practice in this area.