Reviewerly/exHarmony huggingface dataset:
Convergent Model of Digital Hygiene in Modern Upbringing

Based on the study and synthesis of scientific sources, the author of the article presented a methodologically developed theoretically grounded convergent model of digital hygiene in education. The purpose of the article is to demonstrate the resource capabilities of the convergent model of digital hygiene in the modern education of students and to fill the scientific gap in knowledge about the importance of the convergent approach for a qualitatively new level of successful socialization in the education of the competitive younger generation. Methodology. The study was carried out on the methodological basis of the principles of convergent approach (methodology of erasing interdisciplinary boundaries as an integration tool: merging, convergence, convergence, unification, mutual enrichment, mutual influence, interpenetration): 1) interdisciplinary and supra-disciplinary synthesis; 2) designing self-organization in various activities; 3) flexible network communication. The convergent model of digital hygiene in modern education is a hybrid algorithmized intellectual model of safe (interesting, relevant, effective) education, where the tools and mechanisms of digital hygiene in modern education are defined and described: 1) combination of interdisciplinary and applied approaches; 2) development of critical thinking; 3) development of research competencies; 4) group and team work skills. Results. The developed model, which is based on the components: factors, personality qualities, characteristics-grounds, is a safety system for children and adolescents (demobilization of stressful states; the ability to preserve oneself under the destructive effects of the digital environment; the state of protection of vital interests of the individual). Conclusion. The author’s contribution consists in the theoretical development and description of the components of the convergent model of digital hygiene in modern education.
Resources for Upbringing Implementation as a State Priority

Introduction to the problem. Education is a state priority that requires certain resources for its realization. The aim of the article. Theoretical analysis of modern problems of upbringing, with substantiation of resources of various subjects of upbringing for full realization of upbringing as a state priority. Results of the study. Among the resources that should contribute to the implementation of education as a state priority can be attributed: the system of professional development of teachers as educators, including the principles of its organization; mentoring in the education system; the implementation of a multisubject strategy of education, the activities of the scientific school in the field of education. Conclusion. The allocation of education as a state priority is important because it creates conditions for focusing attention on the most important phenomena in this sphere, to consider them from different positions, to identify their new characteristics and educational potential.
Phenomenological Analysis of School Curriculum

The article considers school curricula as a pedagogical phenomenon, which is understood as a general reflection of theoretical ideas about the content of education, fixed in a normative document and maintaining relative stability over a long period of time. The purpose of the article is to describe the main characteristics of curricula that determine the features of the school curriculum as a pedagogical phenomenon. The main research method is phenomenological analysis, auxiliary methods are historical and pedagogical analysis and expert analysis of documents. The analysis of curricula created over 100 years in Soviet and Russian schools is carried out in the logic of a systematic approach. As a result of the study, the author identified a set of qualitative characteristics of curricula: focus on achieving learning outcomes, objectivity, integrativity, consistency, variability, stability (sustainability), richness, flexibility, cultural conformity. Among them, a “core” of permanent characteristics has been singled out, which have been characteristic of curricula for decades in an almost unchanged form and which ensure the functionality of this type of pedagogical documentation. In addition, the author additionally highlights additional qualities that, in different periods of the development of the education system, are manifested in curricula to varying degrees, from the minimum to the maximum possible. The identified complex of characteristics reveals the value-semantic basis of curricula and their deep non-linear connections with the content of education.
Practice-oriented Tasks as a Means of Forming Professional Competencies of a Future Teacher at a Pedagogical University

The article actualizes the formation of professional competencies of a future teacher at a pedagogical university through practice-oriented tasks. The process of practice-oriented training of a future biology teacher at a pedagogical university can be carried out with the use of various means. One of these tools is practice-oriented tasks. Under them, the authors consider a special type of teaching assignment of a teacher to students, which contains a set of requirements to perform intellectual and (or) applied educational and cognitive actions for optimal mastering of professionally-oriented content. The article presents competencies and provides examples of tasks for each of them. The purpose of the article. Presentation of practice-oriented tasks as one of the means of forming professional competencies of a future teacher at a pedagogical university. Research methodology. The priority methodological basis is a competence–based approach that determines the use of practice-oriented tasks in the aspects of “know”, “be able”, “own” in the process of preparing a future teacher. Research methods. Methods of the theoretical level – analysis of published sources on the stated topic, systematization and generalization of research material. Methods of the empirical level – pedagogical experiment, diagnostics of the state of the ability of students – future teachers to perform practice-oriented tasks, elementary mathematical calculations of the data obtained and their interpretation. Conclusion. The original tasks proposed by the authors, developed on the basis of a competence–based approach, are acceptable for professionally oriented training of students – future teachers at a pedagogical university.
Human Capital in Russian and International Research

Introduction to the problem. Social and economic transformations associated with the transition to the information society are constantly taking place in the modern world, which places a number of relevant issues for researchers. Human capital is a specific resource of the information society. The formation and development of human capital, the use of its potential becomes important on a par with the development of engineering, technology, industry as a whole. The aim of our research presented in the article is to define the concept of “development of human capital of a teacher training college student” through the analysis of theoretical representations, approaches to the development of “human capital” in domestic and foreign scientific researches. The article considers the concept of human capital in theoretical concepts, domestic and foreign researchers’ approaches. The stages of studying the concept of “human capital” in the context of historical periods of social and economic development of society are defined. Interdisciplinary relations in the formation of the definition of human capital under the influence of new emphases, trends of its understanding and development are presented. Interdisciplinary approach served as the methodological basis of the study, theory of human capital development under social transformation conditions. Research results. The analysis of research of domestic and foreign authors made it possible to identify the structure and significant components of the concept of human capital, which contributed to the definition of the concept “development of human capital of a teacher training college student”. Conclusion. The definition of the concept “development of human capital of a teacher training college student” makes it possible to take a new look at professional pedagogical education, identify opportunities for development of human capital of a teacher training college student, and determine the factors influencing this process.
Hierarchical Vision Transformers with Shuffled Local Self-Attentions

Vision Transformers have reached breakthrough improvements in addressing computer visual fields, for instance, object classification, bounding box localization, semantic/instance pixel-wise predictions, single/multiple tracking, and generative AI models such as GPT-4, SAM, and UniAD. The key success of the Transformers is derived from the flexibility in fulfilling long-range dependencies from raw data and the generalization capability of input-dependent weight adaption. With these properties, Transformer models operated with the self-attention heart and without inductive biases become the new paradigm in processing multiple-modality data. However, the main bottle-neck of the Transformer is that global multi-head self-attention layers have high computational costs with the input lengths, e.g., quadratic complexity. When exploiting Transformer-based models on pixel-wise predictions, the cost is not affordable. To deal with this issue, recent methods try to calculate attention weights in local non-overlapped areas and require extra designs that exchange information across windows, for example, window shifting, window expanding, and window sliding. Although these strategies improve accuracy, their implementation is unfriendly and produces additional inference time. Following a line of this research, this paper introduces a new block that consists of non-overlapped local self-attention and overlapped local self-attention. Non-overlapped local self-attention learns interactions inside each window and overlapped local self-attention captures relationships among non-overlapped windows to boost receptive fields and modeling abilities. To be more efficient, both layers are performed in parallel in which each half of the heads is assigned to each layer. Therefore the diversity of the model is enhanced since conventional methods treat all heads equally. Experimental results are conducted and evaluated on the medium dataset, ImageNet-1K. As a result, the proposed approach achieves 77.2% Top-1 accuracy at 5.1M parameters and 0.5 GFLOPs, surpassing lightweight models by clear rooms.
FINANCIAL INCLUSION, INSTITUTIONAL EFFICIENCY AND UNDERNOURISHMENT IN SUB-SAHARA AFRICA

The governments of Sub-Saharan African (SSA) countries and international organizations have undertaken a variety of undernourishment-targeting initiatives, such as financial inclusion, humanitarian interventions, social reengineering and institutional changes, in line with the United Nations' top priority of eradicating undernourishment as articulated in the Sustainable Development Goals (SDGs). We have investigated the crucial role that institutional quality plays in diminishing the prevalence of undernourishment through financial inclusion in sub-Saharan Africa. With a time, scope of 18 years and a cross-sectional scope of 29 SSA nations, we used the system generalized Methods of Moments (system-GMM) in a panel data setting for our analysis. For the linear system-GMM, we discovered a strong positive self-replicating effect of prevalence of undernourishment, diverse effects of financial inclusion, and an unremarkable role of institutions. However, in the context of a nonlinear system-GMM method, the role of institutions strengthens the impact of financial inclusion. Therefore, we stressed the need for relevant authorities to develop informed policy alternatives that are based on instantaneous surveillance of changes in prevalence of undernourishment in the SSA region.
Enhancing security and ensuring secure performance: A performance evaluation of consensus algorithms in a distributed healthcare blockchain system

Blockchain is an emerging technology in today’s real-time applications and database transaction systems—the primary motto is to determine transactional clarity to end-user without any fluctuation. According to the basic block policy, it is immutable or does not allow updating the existing transaction, which automatically maintains the security and transaction clarity. This paper proposes a system performance evaluation of various consensus algorithms in a distributed environment with a peer-to-peer network. The four different consensus algorithms have been evaluated in four peer nodes. Healthcare application has been successfully tested with the entire system. The first blockchain implementation has done in a Java environment with the custom hash generation, mining strategy, and smart contract for each level. Each algorithm provides different time complexity accordingly. The system evaluates the performance of four consensus algorithms sequentially and demonstrates the effectiveness and utilization of each algorithm according to the requirements.
Securing healthcare data management using machine learning and blockchain technology: A comparative performance evaluation of support vector machine and conventional classifiers

As access to healthcare has become more central to people’s daily lives, the amount of medical big data has grown exponentially. Wearable Internet of Things (IoT)-reliant technology is gaining popularity in the medical field as a means to improve patient care and reduce wait times. In recent years, billions of sensors, devices, and machines have been hooked up to the web. One such technology, remote patient monitoring, is increasingly used in modern patient care and treatment. In addition, these developments pose substantial security issues about the recording of transaction data and the transmission of information itself, and pose considerable dangers to users’ privacy. Concerns about the privacy of a patient’s medical records have the ability to discontinue treatment, putting the patient’s life in threat. Thus, a system is proposed using machine learning in combination with blockchain technology to allow secure management as well as analysis of large amounts of healthcare data. With the help of machine learning, it is feasible to sort through all of the data and extract out only the most pertinent information. This is accomplished with the help of trained methodologies. After this data has been saved, the next issue will be the exchange of data and ensuring its trustworthiness. The concept of blockchain is introduced at this point. The Blockchain technology relies on consensus to ensure that all data is accurate and that all transactions are conducted in a safe manner. The management of healthcare is one area where blockchain technology offers the ability to have a huge impact by placing patients at the center of the system and improving the privacy and portability of health records. This study is primarily concerned with finding solutions to issues relating to the administration of healthcare data by utilizing Blockchain technology and incorporating some crucial characteristics developed with Machine Learning. The performance evaluation of proposed Support Vector Machine is compared with the other conventional machine learning classifiers. It is observed from the experimental finding that performance accuracy of Support vector machine is 98% which is better as compared to other traditional machine learning classifiers.
Futures literacy laboratory playbook. An essentials guide for co-designing a lab to explore how and why we anticipate

Futures literacy is an essential skill for the 21st century. By understanding how we think about the future, we can reshape our view of our place in it and the choices we make. Over the last decade, UNESCO has deployed a special tool for supporting the multiple ways humans imagine the future. A Futures Literacy Laboratory (FLL) enables anyone to become more futures literate by exploring their own capacity to imagine the future. These labs have been extensively field tested by a wide range of organisations worldwide, and UNESCO has run over 115 of these labs with its global network. This essentials guide describes all the elements you need to co-design and implement a Futures Literacy Laboratory in your own community. This guide combines all setup material in one place, including sample agendas, activity examples, planning tips, facilitator scripts, and more. UNESCO Catno: 0000385485 https://doi.org/10.54678/KSWO4445 https://unesdoc.unesco.org/ark:/48223/pf0000385485
Investigating Factors Contributing to the Reading Challenges among Intermediate Learners of San Rafael-Agpo Elementary School

In the pursuit of the objectives, a qualitative case study is used to identify the factors that affect struggling readers of San Rafael-Agpo Elementary School. A total number of 11 struggling readers who met the criteria set in the study were interviewed in semi-structured interview using an open-ended interview guide questions about their experiences, and common problems they encounter that affect reading skills. The study identified the following factors: reading motivation, lack of parental involvement and support, school-based violence and bullying, lack of reading materials at home, and reading instructions that cause reading difficulties among intermediate grade pupils. Hence, the study propose that teachers should provide and implement more interactive teaching strategies to encourage reading motivation, inform parents of their child’s excessive number of missed classes and set up frequent meetings with parents, implement strong policies to address bullying and school-based violence, and to differentiate instruction to cater different learning needs.
Room-Object Entity Prompting and Reasoning for Embodied Referring Expression

Given a high-level instruction, the task of Embodied Referring Expression (REVERIE) requires an embodied agent to localise a remote referred object via navigating in the unseen environment. Previous vision-language navigation methods utilise the provided fine-grained instruction as step-by-step navigation guidance to conduct strict instruction-following, while REVERIE aims to achieve efficient goal-oriented exploration according to the high-level command. In this work, we propose a Cross-modal Knowledge Reasoning (abbreviated as CKR+) framework, which incorporates the prior knowledge as decision guidance to learn the navigation scheme comprehensively. Specifically, we design a Room-Object Aware (ROA) mechanism to explicitly decouple the room- and object-related clues from instruction and visual observations. Moreover, we propose a Knowledge-enabled Entity Relation Reasoning (KERR+) module to leverage the structured knowledge from the knowledge graph explicitly and unstructured knowledge from pre-trained model implicitly, to learn the internal-external correlations among room- and object-entities for the agent to make proper decisions. We devise an Entity Prompter (EP) that embeds in the KERR+ module, which utilises the navigation history and visual entities as prompts to transfer knowledge from the pre-trained CLIP model. In addition, we develop a Reinforced End Decider (RED) to learn the stopping scheme specifically, which is achieved by a customised reinforcement learning strategy and knowledge enhanced matching. Two techniques are also introduced to improve navigation efficiency further. Extensive experiments conducted on the REVERIE benchmark demonstrate the effectiveness and superiority of our proposed methods, which boosts the key metrics, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , SPL and REVERIE-success rate, to 14.46% and 13.81% respectively.
Finite-Time Extended Dissipativity Analysis for Generalized Neural Networks With Discrete and Distributed Time-Varying Delays

This paper investigated the finite-time extended dissipativity for generalized neural networks with discrete and distributed time-varying delays via the improved Lyapunov-Krasovskii functional (LKF).We constructed an appropriate LKF by employing more neural network information and consisting of quadratic functions.By combining the proposed LKF, Jensen's integral inequality, orthogonal polynomialsbased integral inequality, and extended Wirtinger's integral inequality, new delay-dependent conditions are achieved in the form of linear matrix inequalities (LMIs), which can be verified via MATLAB's LMI toolbox.In addition, we concentrate on the extended dissipative analysis problem, which is a unified formulation of L 2 -L ∞ , H ∞ , passivity, and dissipative performance.This paper is less conservative delay bound than some recently published literature by stability criteria.In addition, we presented seven numerical examples to illustrate the effectiveness of the obtained results.
Music Teachers' Opinions on Block (Visual) Based Coding in Digital Literacy

This case study was conducted to determine whether there is a need for coding training in undergraduate education and in-service trainings by taking the opinions of music teachers about block (visual) based coding. The population of the research consists of music teachers in Turkey, while the sample consists of 170 music teachers who voluntarily participated in the research. An interview form was prepared for the teachers participating in the study. This form was finalized by taking expert opinion and prepared on “Google forms”. The results of content analysis revealed that music teachers did not receive any training and did not have any knowledge about the use of block (visual) based code. In this context, it has been determined that music teachers’ demand for block (visual) based coding training is quite high.
ChatGPT v.s. media bias: A comparative study of GPT-3.5 and fine-tuned language models

In our rapidly evolving digital sphere, the ability to discern media bias becomes crucial as it can shape public sentiment and influence pivotal decisions. The advent of large language models (LLMs), such as ChatGPT, noted for their broad utility in various natural language processing (NLP) tasks, invites exploration of their efficacy in media bias detection. Can ChatGPT detect media bias? This study seeks to answer this question by leveraging the Media Bias Identification Benchmark (MBIB) to assess ChatGPT's competency in distinguishing six categories of media bias, juxtaposed against fine-tuned models such as Bidirectional and Auto-Regressive Transformers (BART), Convolutional Bidirectional Encoder Representations from Transformers (ConvBERT), and Generative Pre-trained Transformer 2 (GPT-2). The findings present a dichotomy: ChatGPT performs at par with fine-tuned models in detecting hate speech and text-level context bias, yet faces difficulties with subtler elements of other bias detections, namely, fake news, racial, gender, and cognitive biases.
A new interpolation algorithm based on Hibbard-Laroche algorithm and its superiority

In order to optimize the possible problems and improvements in the existing color image restoration interpolation algorithms, we conduct research based on the existing bilinear interpolation method, cok algorithm and Hibbard-Laroche algorithm. Our method is to use our own comparison method to compare different types of images through three algorithms to find the advantages and disadvantages and to some extent combine the advantages of bilinear interpolation and Hibbard-Laroche algorithm to try to innovate a new algorithm to compare with the existing three algorithms. The results show that the existing three algorithms have their own advantages in different scenarios, and the new algorithm is superior to the existing algorithms in terms of clarity and color restoration accuracy in most scenarios. However, due to the large computational complexity, the operation speed is slow.