Reviewerly/exHarmony huggingface dataset:
Optimized multiscale deep bidirectional gated recurrent neural network fostered practical teaching of university music course

Music education has a rich historical background. Nevertheless, the introduction of modern teaching methods is relatively delayed. In recent years, there has been a remarkable acceleration in the advancement of music education. A promising tool that has emerged to revolutionize education as a whole is Virtual Reality (VR) technology, which offers immersive and interactive experiences across various disciplines. At the university level, integrating VR technology into music education opens up exciting opportunities to enhance practical teaching methods and provide students with enriched musical experiences. Virtual Reality together with Internet of Things (IoT) demonstrates its capabilities in various tasks, but its widespread availability in online learning remainders a pressing challenge that needs to be addressed. In pre-processing, it removes noise data using Dynamic Context-Sensitive Filtering (DCSF). VR technology creates an unparalleled learning environment, it transporting students to virtual concert halls, recording studios, or historical music venues. Hence the Multiscale deep bidirectional gated recurrent neural Network (MDBGNN) improves the practical teaching of music course concept, like Music theory, harmony, and rhythm can be visualized and experienced in VR. Finally, Dung Beetle Optimization Algorithm (DBOA) is employed to optimize the weight parameters of MDBGNN. The proposed MDBGNN-DBO-UMC-VRT is implemented in Python. The proposed method is analysed with the help of performance metrics, like precision, accuracy, F1-score, Recall (Sensitivity), Specificity, Error rate, Computation time and RoC. The proposed MDBGNN-DBO-UMC-VRT method attains 13.11%, 18.12% and 18.73% high specificity, 11.13%, 11.04% and 19.51% lower computation Time, 15.29%, 15.365% and 14.551% higher ROC and 13.65%, 15.98%, and 17.15% higher Accuracy compared with existing methods, such as Enhancing Vocal Music Teaching through the Fusion of Artificial Intelligence Algorithms and VR Technology (CNN-UMC-VRT), Exploring the Efficacy of VR Technology in Augmenting Music Art Teaching (BPNN-UMC-VRT) and Implementing an Interactive Music-Assisted Teaching System Using VR Technology (DNN-UMC-VRT) respectively.
TSRNet: A Trans-Scale and Refined Low-Light Image Enhancement Network

Retinex-based deep learning methods show good low-light enhancement performance and are mainstream approaches in this field. However, the current methods for enhancing low-light images are insufficient in accurately separating illumination and comprehensively restoring degraded information, especially in images with uneven or extremely low illumination levels. This situation often leads to the over-enhancement of bright regions, a loss of detail, and color distortion in the final images. To address these issues, we improved three subnetworks in the classic KinD network, and proposed a trans-scale and refined low-light image enhancement network. Compared with KinD, our method shows more precise image decomposition performance, enhancing the expressiveness of the reflection and illumination components in order to better depict image details, colors, and lighting information. For reflectance restoration, we use a U-shaped network for cross-scale denoising, incorporating attention mechanisms and a color saturation loss to restore image textures and colors. For light adjustment, we apply fine-grained light adjustment approaches to simultaneously enhance brightness in dark areas and prevent excessive enhancement in bright areas. The experimental results demonstrate that with the LOL dataset, the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) of TSRNet are improved by 2–31% and 5–34%, respectively, when compared with the mainstream methods.
Using iterative 2δ‐technique and 4δ‐outlier test for evaluating the geochemical background concentrations of trace elements in topsoil at Cao Bang province, Vietnam

Abstract This study investigated the geochemical background values for seven trace elements in topsoil samples collected from highland areas within Cao Bang province, Vietnam. Total concentrations of these elements were determined, revealing the following order of mean concentration: Zn &gt; Cr &gt; Pb &gt; Cu &gt; As &gt; Ni &gt; Co. The background values were calculated using two different methods including iterative 2δ‐technique and 4δ‐outlier test. The 4δ‐outlier test yielded the following background concentrations (dry weight) for the seven elements: As (24.41 ± 12.42 mg kg −1 ), Co (15.64 ± 10.57 mg kg −1 ), Cr (49.31 ± 33.41 mg kg −1 ), Cu (29.77 ± 27.14 mg kg −1 ), Ni (22.52 ± 13.33 mg kg −1 ), Pb (38.06 ± 31.48 mg kg −1 ), Zn (71.79 61.09 mg kg −1 ). The 2δ‐technique, on the other hand, resulted in the following background concentrations (dry weight) for the same elements: As (8.41 ± 8.57 mg kg −1 ), Co (5.06 ± 4.31 mg kg −1 ), Cr (26.72 ± 26.20 mg kg −1 ), Cu (25.31 ± 24.81 mg kg −1 ), Ni (8.02 ± 7.83 mg kg −1 ), Pb (28.12 ± 27.58 mg kg −1 ), Zn (51.91 ± 49.44 mg kg −1 ). These results provide valuable baseline data for future environmental assessments and monitoring programs in the region. They also highlight the potential impact of human activities, such as mining, on the geochemical landscape of highland areas.
Revolutionizing Supply Chain With Machine Learning and Blockchain Integration

Efficient supply chain management has emerged as a crucial determinant of organizational performance in the contemporary dynamic corporate environment. The incorporation of nascent technology, such as machine learning and blockchain, is revolutionizing how enterprises manage their supply chain operations. By examining extensive datasets, machine learning algorithms can predict future demand, optimize inventory levels, and improve the planning of routes. By discerning regularities and deviations within datasets, these algorithms facilitate enterprises in making well-informed choices and managing potential hazards. Additionally, the utilization of machine learning facilitates the automation of monotonous jobs, hence mitigating the occurrence of human fallibility and augmenting the overall efficacy of supply chain operations. The utilization of blockchain technology, renowned for its decentralized and unalterable ledger system, effectively tackles several critical issues encountered in the realm of supply chain management.
The Financial Dynamics of AI-Enhanced Supply Chain Management

This chapter explores the evolving landscape of AI-enhanced supply chain management, emphasizing the pivotal role of artificial intelligence (AI) in optimizing supply chain operations. It commences with an introduction that defines AI-enhanced supply chain management and underscores the importance of AI in this context. Subsequently, the chapter delves into the multifaceted role of AI in supply chain management, elucidating its diverse benefits. These advantages encompass enhanced supply chain efficiency, streamlined inventory and transportation management, and improved revenue generation through superior customer service and data-driven demand forecasting.
Synthetic Hard Negative Samples for Contrastive Learning

Abstract Contrastive learning has emerged as an essential approach in self-supervised visual representation learning. Its main goal is to maximize the similarities between augmented versions of the same image (positive pairs), while minimizing the similarities between different images (negative pairs). Recent studies have demonstrated that harder negative samples, i.e., those that are more challenging to differentiate from the anchor sample perform a more crucial function in contrastive learning. However, many existing contrastive learning methods ignore the role of hard negative samples. In order to provide harder negative samples for the network model more efficiently. This paper proposes a novel feature-level sample sampling method, namely sampling synthetic hard negative samples for contrastive learning (SSCL). Specifically, we generate more and harder negative samples by mixing them through linear combination and ensure their reliability by debiasing. Finally, we execute weighted sampling of these negative samples. Compared to state-of-the-art methods, our method can provide more high-quality negative samples. Experiments show that SSCL improves the classification performance on different image datasets and can be readily integrated into existing methods.
Utilizing Machine Learning in Legal Management to Enhance Supply Chain

Innovative legal and supply chain complexity solutions include machine learning in legal management and intelligent supply chain governance. Complex laws and supply chains necessitate data-driven initiatives. Effective supply chain governance needs transparency, accountability, and risk management. Complex data-rich legal administration requires machine learning. Legal research, document analysis, and predictive analytics benefit from machine learning. Supply chain governance requires compliance and risk management. Machine learning principles demonstrate lawyers can switch careers. Innovation in legal tech comes from AI, blockchain, and cybersecurity. This novel machine learning strategy for legal and change and training management requires careful planning and implementation.
Worst-Case Analysis of Heapsort, Exactly

Abstract This paper completes analysis of the worst-case running time of $ {\tt{Heapsort}}$ measured by the number of comparisons of keys performed while sorting. A derivation of an exact formula for the maximum number of comparisons of keys performed by $ {\tt{Heapsort}} $ on any array of size $ N \geq 2 $ is presented. It is equal to $$ \begin{align}&amp; \nonumber 2(N-1)\lceil \lg N \rceil - 2 ^{\lceil \lg N \rceil +1} - 2 s_2(N) - e_2(N) + \min (\lceil \lg N \rceil, 3) + 5 + c, \end{align} $$ where $s_2(N)$ is the sum of digits of the binary representation of $N$, $e_2(N)$ is the exponent of $2$ in the $N$’s prime factorization and $ c $ is a binary function on the set of positive integers defined by $$ \begin{align}&amp; \nonumber c = \left\{ \begin{array}{@{}ll} 1 \text{ if} \; N \leq 2 ^{\lceil \lg N \rceil} - 4 \\[4pt] 0 \text{ otherwise}. \end{array} \right. \end{align} $$ The above formula allows for deciding, in $ O(N \log N) $ time, if any given $ N $-element array is a worst-case array for $ {\tt{Heapsort}} $. Its proof yields an algorithm for construction, in $ O(N \log N) $ time, of worst-case arrays of arbitrary sizes $ N \geq 2 $ for $ {\tt{Heapsort}} $.
Quantifying the age peaks, age ranges and weights of detrital ages based on the EM algorithm

Detrital geochronology fundamentally involves the quantification of major age ranges and their weights winthin an age distribution. This study presents a streamlined approach, modeling the age distribution of detrital zircons using a normal mixture model, and employs the Expectation-Maximization (EM) algorithm for precise estimations. A method is introduced to automatically select appropriate initial mean values for EM algorithm, enhancing its efficacy in detrital geochronology. This process entails multiple trials with varying numbers of age components leading to diverse k-component models. The model with the lowest Bayesian Information Criterion (BIC) is identified as the most suitable. For accurate component number and weight determination, a substantial sample size (n > 200) is advisable. Our findings based on both synthetic and empirical datasets confirm that the normal mixture model, refined by the EM algorithm, reliably identifies key age parameters with minimal error. As a kind of probability density estimator, the normal mixture model offers a novel visualization tool for detrital data and an alternative foundation for KDE in calculating existing similarity metrics. Another focus of this study is the critical examination of quantitative metrics for comparing detrital zircon age patterns. Through a case study, this study demonstrates that metrics based on empirical cumulative probability distribution (such as K-S and Kuiper statistics) may lead to erroneous conclusions. The employment of the Kullback–Leibler (KL) divergence, a metric grounded in probability density estimation, is proposed. Reference critical values, simulated via the Monte Carlo method, provide more objective benchmarks for these quantitative metrics. All methodologies discussed are encapsulated in a series of MATLAB scripts, available as open-source code and a standalone application, facilitating wider adoption and application in the field.
"Virtual reality" as a tool for global manipulation of socio-cultural identity

The subject of the article is the philosophical and cultural methodology of digital "virtual reality", comparing the declarations of developers with the practical possibilities and social consequences of using such technologies. The developers presented projects of online digital content services for all five senses using special equipment (glasses, headphones, interactive gloves, joysticks, costumes, printers of smells and tastes, etc.). It was assumed that virtual reality would surpass the reliability of previous multimedia content and interactive computer games, and the persuasiveness and attractiveness of traditional technical means of art and the press. Failure to fulfill these promises has led to significant losses since 2022 for leading companies developing digital virtual reality technologies Meta (recognized by the court as an extremist organization on 03/21/2022, activity is limited in the territory of the Russian Federation), Microsoft HapticLinks, Cave, AlloSphere, Teslasuit, VRealizer. There was a need to assess how and to what extent the methodology used and the predicted capabilities of digital virtual reality were correct. Philosophical and cultural analysis as a research method reveals: the developers of digital virtual reality promised the methodologically impossible, reducing human perception to the "sum" of the external "five senses" out of connection with the "self", inner sensitivity, bodily self-perceptions. A person is only partially amenable to algorithmized digital interaction; a critical barrier to digital virtuality is the socio-cultural identity of society and personality. The novelty of the results of the analysis lies in the conclusion: digital virtual reality in many respects is fundamentally inferior in effectiveness to traditional technical means of sensory and emotional impact and cognition, used both for creative and destructive purposes. In terms of creative potential, digital virtual reality is inferior to the technical means of art, science, education, upbringing, etc., and in terms of destructive capabilities, it is already used in the press and other mass communications technologies for manipulating public and individual consciousness, as well as psychotropic substances that cause addiction. National regulation of the development of digital "virtual reality" is recommended, aimed at protecting and developing the socio-cultural identity of Russian citizens and society, as well as for cognitive and educational purposes. It is recommended to limit the remote use of such developments to trusted domestic Internet services.
Technology for the formation of digital competencies of civil servants

The subject of the research is the study of algorithms for the formation of competencies. The author examines various process structures of competence formation. The aim work is to develop a unified algorithm formation of digital competencies for civil servants. The specifics study will be focused on government civil servants who use digital technologies in their official activities. Special attention is paid to the issue of training and development of public civil servants. Author considers such an aspect of the topic as the impact of digital knowledge, skills and abilities on the digital economy of the Russian Federation. Author discusses various training technologies aimed at improving the qualification level of a civil servant. Author considers certain algorithmization of the formation of digital competencies in public administration. Features influence of educational activities on the creation of a special model of digital competencies of a civil servant. The following methods were used in the study: traditional analysis of documents (normative legal acts, scientific articles), modeling. As a result of the research author developed technology for the formation of digital competencies, aimed at developing the professional level of government civil servants and focused on the introduction of modern training models in public authorities. The area of possible implementation of the research results in such state bodies as the Government of the Russian Federation, the Administration of the President of the Russian Federation, the Ministry of Digital Development, Communications and Mass Media of the Russian Federation, etc. The novelty research is the development of completely new process of training and development of civil servants. The use of algorithmic technology for the formation of digital competencies will contribute to improving the qualification level of civil servant, improving public administration in a particular public authority, increasing measures to train and develop digital literacy of civil servant.
China Virus, Kung Flu, and MAGA: Countervalues and sociological fractionation on Twitter as evidenced by pro- and anti-Trump discourses in relation to Covid-19

This paper seeks to investigate the indexical links (Silverstein, 2003) to social values activated by terms like "covid" and "virus" in tweets from users with opposing political leanings. Our data comes from a corpus of 12,607 tweets collected in both May and August 2020. We focus on tweets containing "Trump", as these occurred frequently throughout the corpus, to assess the ways in which Twitter users engage with discourses surrounding Covid-19 relative to the then-US President. Focusing on the local contexts of the tweets we, first, demonstrate the contrasting social values indexed by specific keywords and hashtags. We refer to these as countervalues (Bearth, 2005) that illustrate the multiple and competing valorisations of terms for Covid-19 and which lead to the reproduction of two main contrasting discourses. The first illustrates that Covid-19 is "fake", "a hoax", and is explicitly linked indexically with China and tends to appear in tweets by pro-Trump users. A second set of discourses emerges in opposition to the pro-Trump tweets where categorisations of the virus as fake or a hoax are described by users as moronic, links to China are described as racist, and users demonstrate an explicitly anti-Trump ideology. We conclude that the recirculation of these discourses is evidence of sociological fractionation (Agha, 2007), as we see the pro-Trump group resisting the scheme of values put forward by the anti-Trump group. To this end, we contribute to the body of research that sheds light on the participatory frameworks enabled by social media affordances.
Extension of the kinematics problem about the meeting of two bodies

Abstract The well-known problem about the meeting of two bodies starting from two different points and moving along their line is extended by the general case when the bodies move in a plane at a non-zero angle to the given line. The task is solved graphically, analytically and using GeoGebra. This approach makes it possible to use IT technologies close to students in both standard and distance education.
Innovative technologies of physical and mathematical education implemented by using of fractal approach

Abstract The implementation of the fractal approach to the teaching of physical and mathematical disciplines using computer modeling is substantiated.The formation of a fractal structure is established and iterations are defined that reflect the integrity and spontaneity of information presentation. The involvement of students of related specialties from two universities – University of Presov (Faculty of Humanities and Natural Sciences) and Uzhhorod National University (Faculty of Physics) – demonstrates the feasibility of using the fractal approach in training future teachers and scientists of natural sciences in higher educational institutions.
Mentoring as a way to the professional development of in-service physics teachers

Abstract Nowadays education is facing new challenges based on the changes in the world and society. The generation of pupils has changed and their needs are different to what the teachers were used to. The change not only in teaching and learning methods but also in the way of thinking is necessary. To help teachers to find a different point of view on education and implement the changes, the Regional teachers’ support centres were established. The first two launched in 2021 and within three years there should be 40 centres across Slovakia. The main idea is to provide individual support for primary and lower secondary teachers as well as training in five main topics -i) school curriculum, ii) assessment and evaluation of pupils’ learning outcomes, iii) educational process and teaching style, iv) personalization and inclusive education, v) digitalization. Individual support is provided by a mentor in the form of class observations and mentoring conversations. The physics teachers can be involved and work on professional self-development in the topics they feel they want to improve. The paper reports on the mentors’ activities with the main focus held on the possibility of the in-service physics teachers’ professional development.
The entry profile of a pre-service physics teachers and the development of their professional competences

Abstract In the paper, we reflect on various factors of motivation of pre-service physics teacher. Based on the data obtained from the entrance questionnaires of incoming students for the previous four years and a structured interview with current students studying in all grades, we create a professional entry profile of a future physics teacher student. Based on international standards, current trends, current and future needs of school practice, we characterize the professional competences of a physics teacher graduate. In the conditions of the Slovak educational space, we are looking for a breakthrough with the updated description of the field of study teaching and pedagogical sciences and standards for study programs. We present the goals and key elements of the university training of future physics teachers, educational methods, educational content and links to pedagogical practice.
Affordances of technology-supported trails in education of pre-service primary teachers in physics

Abstract Outdoor activities focused on solving problems related to concrete objects, supported by technology, also known as trails, offer opportunities to develop not only content knowledge, but also the soft-skill of learners. The learner can be a pupil or a pre-service teacher, student of initial teacher education programme. Current students of initial teacher education programme are usually members of Generation Z and their education should be tailored to the needs of this generation. In this paper we designed an interdisciplinary trail connecting mathematics and science, particularly physics, knowledge and compared the solution process of pre-service teachers for primary schools and the primary pupils. The strategies almost did not differ, but pre-service teachers demonstrated deeper understanding of concept of measure.