Reviewerly/exHarmony huggingface dataset:
Pedagogical ideas of Vitalii Ivanovych Hazinskyi in the context of professional training of music and pedagogical education applicants

The article summarizes the pedagogical ideas and stylistic features of the teaching activity of the outstanding representative of the Odesa Choir School, Professor Vitaly Ivanovych Gazinsky, in the context of training future music teachers and teachers of art disciplines for professional activity. Peculiarities of the artist's personal, professional, compositional, creative, performing qualities in achieving pedagogical achievements, high skill, gaining fame and recognition in a wide range of cultural environments of our country and abroad are noted. The historical significance of Vitaly Gazinskyi's multi-spectral activity in the development of the Podil choral school and national choral art in general is emphasized. On the example of the analysis of music-pedagogical, scientific and creative-performance activity of the conductor, the question of formation of personal and professional qualities of future music teachers is considered. The theoretical and practical aspects of the choirmaster's pedagogical activity, the formation of his humanitarian worldview and professional competences, outlined in the scientific and methodological works of Professor Vitaly Gazinskyi, are highlighted. The provisions of the educational and methodological manual "Educational Choir" regarding the use of the synthesis of scientific and creative activities in the system of musical and pedagogical education are substantiated. The content of the mentioned work is analyzed, which reveals the methodological foundations of the formation of professional skills and personal skills of future music teachers and teachers of artistic disciplines, in particular in the role of leaders of children's, youth creative teams, professional choirs. Emphasis is placed on the development of the artistic and aesthetic component of students' personality in the organization of the educational process using a wide range of disciplines of general training, as well as music-theoretical and vocal-choral disciplines. It is noted that the pedagogical, creative-performing, conducting and choral achievements of the great artist V. Gazinsky deserve the attention of teachers, choirmasters, musicologists and other circles of scientists.
BEHAVIORAL FACTORS IN DETERMINING INTENTION TOWARDS INDEBTEDNESS AMONG GOVERNMENT SERVANTS

Malaysian households’ indebtedness has nearly RM1.38 trillion worth which is a symptom as opposed to a cause. Undoubtedly, Malaysia's household debtto-GDP ratio of 89% is among the highest. Indeed, worsen debt trend among the government servants are significantly pop up this issue of borrowing. The purpose of this research is to look into the influence of behavioral factors on intention towards indebtedness by using Theory of Planned Behavior. Using the partial least square (SmartPLS) technique, a survey of 377 government employees in Northern Malaysia was conducted using the exploratory method. Respondents completed a self-administered questionnaire regarding behavioural factors. The findings validated two out of the three proposed hypotheses. Among those variables, intention towards indebtedness had an extremely significant relationship with both attitude towards indebtedness and subjective norm. The results may assist the government agencies in formulating saving strategy that will lessen Malaysian government servants’ risk of financial ruin. Previous studies on indebtedness have been conducted using survey approach among individuals by using self-constructed model. Meanwhile, this study focuses on the government servant by applying the theory of Planned Behaviour.
Pengaruh Edukasi Media Podcast Dan Video Animasi Terhadap Pengetahuan Gizi Lebih Remaja Usia 16-18 Tahun Di SMA PGRI 3 Bogor

&lt;p&gt;Background: The nutritional status of overweight and obesity worldwide has almost tripled since 1975. In 2016, more than 340 million children and adolescents aged 5-19 years experienced overnutrition and obesity. Most nutritional problems can be avoided if an individual has good knowledge about nutrition and regulates eating. Nutrition education is provided to increase the level of knowledge and use interesting media so that the educational material to be delivered can be received more easily. Objective: Knowing the difference in the level of knowledge about overweight in adolescents 16-18 years before and after being given education with videos and podcasts. Method : Quasy experimental study with pre-post-test group design involving 80 students in class X and XI which choosed by cluster random sampling. Knowledge data collected with online method. Result :there are a difference knowledge with the use of nutrition education media through podcasts (p=0.000) and animated videos (p=0.000). Conclusion: There are differences in the level of knowledge about overweight in adolescents 16-18 years before and after being given education with videos and podcasts&lt;/p&gt;&lt;p&gt; &lt;/p&gt;&lt;p&gt; &lt;/p&gt;
Large language models transition from integrating across position-yoked, exponential windows to structure-yoked, power-law windows.

Modern language models excel at integrating across long temporal scales needed to encode linguistic meaning and show non-trivial similarities to biological neural systems. Prior work suggests that human brain responses to language exhibit hierarchically organized "integration windows" that substantially constrain the overall influence of an input token (e.g., a word) on the neural response. However, little prior work has attempted to use integration windows to characterize computations in large language models (LLMs). We developed a simple word-swap procedure for estimating integration windows from black-box language models that does not depend on access to gradients or knowledge of the model architecture (e.g., attention weights). Using this method, we show that trained LLMs exhibit stereotyped integration windows that are well-fit by a convex combination of an exponential and a power-law function, with a partial transition from exponential to power-law dynamics across network layers. We then introduce a metric for quantifying the extent to which these integration windows vary with structural boundaries (e.g., sentence boundaries), and using this metric, we show that integration windows become increasingly yoked to structure at later network layers. None of these findings were observed in an untrained model, which as expected integrated uniformly across its input. These results suggest that LLMs learn to integrate information in natural language using a stereotyped pattern: integrating across position-yoked, exponential windows at early layers, followed by structure-yoked, power-law windows at later layers. The methods we describe in this paper provide a general-purpose toolkit for understanding temporal integration in language models, facilitating cross-disciplinary research at the intersection of biological and artificial intelligence.
Designing the “NCU Information Portal” — analysis of the mockups of the website based on the elements of user experience and information architecture

The study aimed to compare three mockups (static graphic designs) of the Information Portal of the Nicolaus Copernicus University in Toruń in terms of user experience and information architecture indicators and identify the best one to use for creating an active website. The research used an eye-tracking method, usability tests, observations and surveys. Most respondents indicated that mockup no. 3 was the best of the three proposed static website page designs. The respondents justified their choice, among other things, by the fact that it was clear and easy to use, had a functional layout of information and an appropriate colour scheme. The design chosen by respondents has been improved to reflect their suggested modifications. Its final version can be viewed at https://portal.umk.pl/.
The role of metallogenic research in predicting promising territories: From models to objects

The possibilities of academic basic metallogenic research in the forecast and search for various types of ore deposits are shown with the example of various academic institutes in the Siberian and Far East federal districts. Using a complex of mineralogical–geochemical and geological–structural methods, prospects for copper and gold in the Upper Indigirka and Kolyma regions and specific areas in Transbaikalia and the Bodaibo district of Irkutsk oblast have been studied. Problems in assessing and developing the mineral resource base of the regions are considered.
On the awarding of medals of the Russian Academy of Sciences with bonuses for young scientists and for students within educational programs of higher education based on the results of the 2022 competition

A number of young scientists were awarded medals of the Russian Academy of Sciences with bonuses of 50 000 rubles each; university students were awarded medals of the Russian Academy of Sciences with bonuses of 25 000 rubles each.
Causal Learning through Deliberate Undersampling.

Domain scientists interested in causal mechanisms are usually limited by the frequency at which they can collect the measurements of social, physical, or biological systems. A common and plausible assumption is that higher measurement frequencies are the only way to gain more informative data about the underlying dynamical causal structure. This assumption is a strong driver for designing new, faster instruments, but such instruments might not be feasible or even possible. In this paper, we show that this assumption is incorrect: there are situations in which we can gain additional information about the causal structure by measuring more slowly than our current instruments. We present an algorithm that uses graphs at multiple measurement timescales to infer underlying causal structure, and show that inclusion of structures at slower timescales can nonetheless reduce the size of the equivalence class of possible causal structures. We provide simulation data about the probability of cases in which deliberate undersampling yields a gain, as well as the size of this gain.
Mastering Stateful Application Management in Kubernetes: Strategies, Challenges, and Advances

The beginning of container technology, spearheaded by Docker and orchestrated by Kubernetes, has revolutionized the deployment of applications, offering remarkable agility, efficiency, and scalability.
Comprehensive Review on Optimizing Resource Allocation in Cloud Computing for Cost Efficiency

The cloud computing model has become very popular among organizations as they search for the ability to access computing resources on demand and pay-as-you-go with flexible and cost-efficient solutions. Fortunately, cloud environments are highly dynamic, but allocating resources efficiently can be difficult. Workloads may vary over time, and resources must be deployed to meet different performance targets while keeping within the stated budgetary allocations. This implies the tradeoff optimization of cost performance using predictive modeling, automation, and learning from diverse optimization methods. Thus, several solutions are given, such as criteria rules, reinforcement learning, metaheuristic, mathematical programming, and game theory. The aim is to choose the best resource mix that is economically viable and satisfies the threshold performance. There are plenty of benefits for the correct allocation, including less over-provisioning, better performance of applications, more efficient infrastructure use, and evidence-driven planning. The progress made is substantial. However, there still are significant difficulties around benchmarking strategies, uncertainties, coordination, business objectives, designs, and implementations that are robust and scalable. Further study is crucial to retrieve the economic profit of the cloud’s elasticity fully. This article reviews the most recent research on the optimal allocation of resources on cloud computing platforms and cost efficiency.
Self-Adaptive Differential Evolution with Hybrid Rules of Perturbation for Dynamic Optimization

In this paper an adaptive differential evolution approach for dynamic optimization problems is studied. A new benchmark suite Syringa is also presented. The suite allows to generate test-cases from a multiple number of dynamic optimization classes. Two dynamic benchmarks: Generalized Dynamic Benchmark Generator (GDBG) and Moving Peaks Benchmark (MPB) have been simulated in Syringa and in the presented research they were subject of the experimental research. Two versions of adaptive differential evolution approach, namely the jDE algorithm have been heavily tested: the pure version of jDE and jDE equipped with solutions mutated with a new operator. The operator uses a symmetric α-stable distribution variate for modification of the solution coordinates.
Four questions to guide decision-making for data sharing and integration.

This paper presents a Four Question Framework to guide data integration partners in building a strong governance and legal foundation to support ethical data use.While this framework was developed based on work in the United States that routinely integrates public data, it is meant to be a simple, digestible tool that can be adapted to any context.The framework was developed through a series of public deliberation workgroups and 15 years of field experience working with a diversity of data integration efforts across the United States.The Four Questions-Is this legal? Is this ethical? Is this a good idea? How do we know (and who decides)?-should be considered within an established data governance framework and alongside core partners to determine whether and how to move forward when building an Integrated Data System (IDS) and also at each stage of a specific data project. We discuss these questions in depth, with a particular focus on the role of governance in establishing legal and ethical data use. In addition, we provide example data governance structures from two IDS sites and hypothetical scenarios that illustrate key considerations for the Four Question Framework.A robust governance process is essential for determining whether data sharing and integration is legal, ethical, and a good idea within the local context. This process is iterative and as relational as it is technical, which means authentic collaboration across partners should be prioritized at each stage of a data use project. The Four Questions serve as a guide for determining whether to undertake data sharing and integration and should be regularly revisited throughout the life of a project.Strong data governance has five qualities: it is purpose-, value-, and principle-driven; strategically located; collaborative; iterative; and transparent.Through a series of public deliberation workgroups and 15 years of field experience, we developed a Four Question Framework to determine whether and how to move forward with building an IDS and at each stage of a data sharing and integration project.The Four Questions-Is this legal? Is this ethical? Is this a good idea? How do we know (and who decides)?-should be carefully considered within established data governance processes and among core partners.
Fairness-Aware Class Imbalanced Learning on Multiple Subgroups.

We present a novel Bayesian-based optimization framework that addresses the challenge of generalization in overparameterized models when dealing with imbalanced subgroups and limited samples per subgroup. Our proposed tri-level optimization framework utilizes local predictors, which are trained on a small amount of data, as well as a fair and class-balanced predictor at the middle and lower levels. To effectively overcome saddle points for minority classes, our lower-level formulation incorporates sharpness-aware minimization. Meanwhile, at the upper level, the framework dynamically adjusts the loss function based on validation loss, ensuring a close alignment between the global predictor and local predictors. Theoretical analysis demonstrates the framework's ability to enhance classification and fairness generalization, potentially resulting in improvements in the generalization bound. Empirical results validate the superior performance of our tri-level framework compared to existing state-of-the-art approaches. The source code can be found at https://github.com/PennShenLab/FACIMS.
Machine Learning for Predictive Observability - A Study Paper

Since artificial intelligence and machine learning have taken off over the last six years, predictive observability has progressed fast. This paper addresses the progress in machine learning technologies of leveraging observability data for anomaly detection, forecasting, and reliability prediction from 2016 to 2022. It centers on how machine learning developments have championed more proactive observation, distinguishing these from reactive observability. The review discusses unsupervised learning methods for anomaly detection, including isolation forests and autoencoders. The idea is to detect suspicious entities early when they have yet to cause harm to users. Likewise, LSTMs have shown effectiveness for forecasting critical time series of vital metrics to predict the capacity and performance issues to avoid them. Failure modeling methods like survival analysis secure the risk of failure and provide reliability improvement indicators. The transformers and the adversarial machine learning approaches are listed as the breakthroughs that lead to enhanced predictivity on noisy data. Accurate metrics quantified are 60-70% better tip-off early in the incident, a 25-50% decrease in user-affecting failures, and up to 30% shorter mean time to recovery. Companies that have already deployed probabilistic observability on a large scale, like Google, Stripe, IBM, and Alibaba, are reviewed. This summary concludes that in the past six years, applied machine learning has grown exponentially for predictive observability, giving teams proactive and preventive management capability. Advancement will occur in the future, along with the ability to harness new ML methods and deployment in real-world applications, which will see an impact on the field.
Risk management model for information security

A risk management model makes it possible to explore the organizational factors and risk management practices that affect or delay the achievement of the objectives that are considered strategic. The purpose of managing risks is to develop a detailed analysis of the organization, its operations, assets, processes and their existing interrelationships in order to establish a complete list of risks, which implies identifying, analyzing and providing alternative treatment to risks. actual and potential. Therefore, a risk management model obtains too much importance when focusing on the needs of the organization in a specific way, since it is not only about copying norms or policies of one organization to mitigate the risks of another, but each of these has different scenarios or contexts.
Traditional Techniques and Emerging Technologies in Observability

Identity and access management is the bedrock of cybersecurity. Identity access to digital resources is governed by its techniques, procedures, and rules, which also define the breadth of identity permission over those resources. Some new cyberattack or data breach pops up in the news every week. Many data breaches occur due to inadequate security measures, software flaws, human mistake, malevolent insiders, or the abuse of access and privileges. An improved access control system is possible with the use of AI methods. In order for organisations to better handle authentication and access control in order to reduce cyber risks and other IAM difficulties, studies into artificial intelligence in IAM are necessary. With an eye towards AI’s potential uses in identity and access management - more especially in the areas of privilege monitoring, administration, and control - this research investigates the nature of the connection between AMIS and AI. To better understand how AI works in minimising recognised IAM issues, this study aimed to present evidence from the relevant literature. This study’s results show how AI reinforces identity and access management, which helps with automating procedures, keeping up with technology advances, and reducing the prevalence of cyber threats. One way to accomplish this is by using a binary classification system for security access control, which takes the PDP problem and turns it into a yes/no question. In order to create a distributed, effective, and accurate policy decision point (PDP), a vector decision classifier is also built using the supervised machine learning technique. Kaggle-Amazon access control policy dataset evaluated performance by comparing the proposed mechanism to previous research standards in terms of performance, duration, and flexibility. Given that the PDP is not in direct contact with the PAP, the proposed approach accomplishes a high level of secrecy in relation to access control requirements. In conclusion, PDP-based ML can manage massive access requests, execute many major policies simultaneously, and have a 95% accuracy rate, all without policy conflicts, with a response time of about 0.15 s. The security of access control can be enhanced by making it more responsive, flexible, dynamic, and dispersed.
Enhancing Cyber Security in the Banking Domain: Innovative Problem Resolution

As cyber threats continue to evolve, the banking sector faces escalating challenges in safeguarding sensitive information and maintaining the integrity of financial systems. This paper explores innovative solutions to address these challenges and enhance cybersecurity in the banking domain. Beginning with an overview of the current cybersecurity landscape, we delve into the unique vulnerabilities that financial institutions encounter. Through an extensive literature review, we identify existing gaps and limitations in current cybersecurity approaches, setting the stage for the introduction of novel ideas. The proposed innovations encompass advanced technologies and methodologies aimed at fortifying security measures within the banking sector. Drawing on real-world case studies, we illustrate successful implementations of these innovative solutions, emphasizing their impact on mitigating cyber threats. An implementation strategy is outlined, accompanied by a discussion of potential challenges and recommended mitigation strategies. The paper concludes by highlighting the importance of continuous improvement in cybersecurity for banks and suggesting avenues for future research and development. The presented work aims to provide a comprehensive framework for securing the banking domain against the evolving landscape of cyber threats.
Enhancing Regression Tree Predictions with Terminal-Node Anomaly Detection

This paper presents a framework for detecting outliers found in the predictions made by regression tree models. The idea is to build a regression tree using a training dataset, and then automatically label predictions that are significantly different from the mean prediction within each terminal node, as outliers. The training inputs along with the automatically derived outlier labels are then used to construct an anomaly detector. To evaluate the framework, we send the test input data to the regression tree and anomaly detector for estimations and anomaly detections, respectively. For each test instance that has been predicted as an outlier, the corresponding value estimated by the regression tree is marked for further treatments. In our case, we simply choose to discard all estimated values that are flagged as outliers. The results show significant reductions in the Mean Absolute Errors of regression tree predictions when the approach is applied to four datasets. This method is potentially useful when a proportion of suspicious predictions can be discarded without affecting the overall quality of a system. Our analyses also provide some insights into why the approach works, as well as caveats when applying this approach.