Reviewerly/exHarmony huggingface dataset:
Framework Architecture for Designing High-Load Web Applications

The purpose of the article is to describe the architecture of the proprietary Minegraph framework for designing a simplified process of developing modern high-load web applications. The research methods are a review and analysis of modern technologies for developing architecture and designing frameworks. The novelty of the study is the analysis of frameworks for developing web applications, their features and applications depending on the tasks, and the analysis of their architecture. The article focuses on the problem of using off-the-shelf solutions when creating high-load and scalable web applications. The author’s own development of a framework for scalable and highly loaded applications of any level of complexity, the features of the proposed models are presented, and the architecture of the Minegraph framework is described in detail, the features of this framework are analysed, and its strengths and weaknesses are highlighted. Conclusions. The article analyses the existing frameworks for developing web applications. The author’s development of the Mineraph framework architecture is presented in detail, namely: drivers for interaction with databases, NoSQL databases; features of the model to allow adding or changing the basic logic for processing requests; models where the logic for processing requests is stored and its features; issues related to a flexible mechanism for debugging the basic logic of applications are highlighted. The created framework is a ready-made structural framework for the development of scalable and highly loaded applications of any level of complexity. It is important to note that development on this framework does not require in-depth analysis of web system architecture, but only basic knowledge of the development environment and the ability to navigate the documentation. This development framework takes into account the advantages of previous analogues and contains advanced technologies, which increases the productivity and efficiency of developing scalable and highly loaded web applications. The paper describes the features of the Minegraph framework model, analyzes the features of this framework, and identifies its strengths and weaknesses. The Minegraph framework goes beyond structural capabilities, seamlessly integrates and uses the latest technologies for data processing and storage. In addition, it also uses cloud solutions, which leads to a significant improvement in system functionality while reducing infrastructure management costs. The framework presented in this paper is not just a structural framework; it is an extremely versatile and adaptive tool, ready to accommodate applications of the highest complexity and scale. This framework has advanced technologies for data processing and storage and also uses cloud solutions that help improve the system’s functionality and reduce the cost of its infrastructure.
Epistolary of P. Kulish on the Websites of the Vernadsky National Library of Ukraine

The purpose of the article is to study and characterise the available sources and projects of digitisation and virtualisation of P. Kulish’s epistolary heritage on the websites of the Vernadsky National Library of Ukraine (hereinafter – VNLU) to preserve the historical and cultural heritage of Ukraine. Research methods. The work uses a set of scientific methods based on an interdisciplinary approach, which includes analytical (for analysing electronic resources and the Internet), historical (for tracing the process of digitising P. Kulish’s manuscript heritage), information and library science aspects. Scientific novelty. Kulish studies (a scientific discipline that studies the life and work of P. Kulish) are widely represented and introduced into scientific and information circulation thanks to the electronic resources of the VNLU. The electronic resources are filled with the digitized documentary heritage of P. Kulish: a small part of his epistolary works, fiction and scientific works, photographic documents, and VNLU reference books, which contain information about the funds where the writer’s paper documents are stored. The created digital objects of P. Kulish’s epistolary works are an integral part of the digital cultural heritage. Conclusions. P. Kulish’s epistolary heritage is not digitised to the full extent to which it is presented in paper form and requires an urgent digitisation process. Ukrainian society is deprived of the opportunity to freely use the national heritage in the form of P. Kulish’s epistolary heritage, which is a historical source and generally identifies Ukrainians as an original nation with an authentic cultural heritage. Today, the formation of an informative electronic environment aimed at highlighting national achievements is the main strategic task for the development of not only the leading library institutions of Ukraine but also public policy in general.
IoTMoF: A Requirements-Driven Modelling Framework for IoT Systems

The engineering of IoT systems brings about various challenges due to the inherent complexities associated with such heterogeneous systems. In this paper, we propose IoTMoF, a model-driven framework for requirements development, design and code generation of IoT systems. IoTMoF supports platform-independent modelling (PIM) with the ReqMIoT environment, which covers use case modelling and generation of IoT ARM compliant domain models. The use case model addresses exceptional behaviour by specifying exceptional situations along with handling functionality. The PIMs are mapped to platform-specific models, namely an IoT information model and a statechart model. The statechart defines both normal and exceptional behaviour of the system. These models form the basis for the subsequent generation of code for the IoT platform. A configuration model containing details of the IoT devices is used to generate wrapper code for deployment. Our work is demonstrated with the use of a smart lights system.
Task Models as a Mean to Identify and Justify Automations in Development Tasks

Multiple empirical studies have been proposed to support developers' tasks by means of automation. These automations exhibit different levels of granularity ranging from low-level tasks to much higher-level tasks. However, proposed automation focusses more on the functional aspects of the development tools rather than the user aspect (e.g. goals and work) leaving the supported tasks under specified. We propose to use and extend the HAMSTERS task-modeling notation to describe developers' goals and tasks. We demonstrate on simple examples how the task models can be used to identify good candidates for automation and to assess the benefits and drawbacks brought by automation.
Towards Understanding and Analyzing Rationale in Commit Messages Using a Knowledge Graph Approach

Extracting rationale information from commit messages allows developers to better understand a system and its past development. Here we present our ongoing work on the Kantara end-to-end rationale reconstruction pipeline to a) structure rationale information in an ontologically-based knowledge graph, b) extract and classify this information from commits, and c) produce analysis reports and visualizations for developers. We also present our work on creating a labelled dataset for our running example of the Out-of-Memory component of the Linux kernel. This dataset is used as ground truth for our evaluation of NLP classification techniques which show promising results, especially the multi-classification technique XGBoost.
UPDATING THE POTENTIAL OF SUBJECT TRAINING OF FUTURE TEACHERS FOR THE LABOR EDUCATION OF PRIMARY SCHOOLCHILDREN

We have theoretically studied the potential of subject training of future teachers for the labor education of primary schoolchildren. It is noted that the labor skills that are acquired in educational and extracurricular activities of schoolchildren are one of the most important factors in the formation of activity. Together with other manifestations of social activity, we note such an important manifestation as collectivism, although we do not single it out ourselves. A preliminary testing of the methodology we developed for determining the level of presentation of pedagogical activity in the field of labor education of primary schoolchildren was carried out. Based on the fact that this definition is familiar to schoolchildren, we decided to establish what meaning they attach to the concept of social activity, what knowledge about social activity characterizes them. Depending on the number of manifestations named by students, knowledge was conditionally distributed into levels: 1 — complete knowledge of the main manifestations of labor activity — no; 2 — incomplete knowledge of manifestations of labor activity — 2 people; 3 — fragmentary knowledge — 13 people; knowledge about this phenomenon was not found — 13 people. The obtained material allowed us to classify the ideas of pedagogical activity in the field of labor education of primary schoolchildren from the point of view of its ideological, moral and active essence. Naturally, the essay provided richer material than the questionnaire, since the students saw the diverse, interesting activities of a particular person. It was easier for the students to grasp and identify manifestations characteristic of a worker, which are generally difficult for schoolchildren to talk about.
Decision theoretic bootstrapping

The design and testing of supervised machine learning models combine two fundamental distributions: (1) the training data distribution (2) the testing data distribution. Although these two distributions are identical and identifiable when the data set is infinite, they are imperfectly known when the data is finite (and possibly corrupted), and this uncertainty must be taken into account for robust Uncertainty Quantification (UQ). An important case is when the test distribution is coming from a modal or localized area of the finite sample distribution. We present a general decision-theoretic bootstrapping solution to this problem: (1) partition the available data into a training subset, and a UQ subset (2) take $m$ subsampled subsets of the training set and train $m$ models (3) partition the UQ set into $n$ sorted subsets and take a random fraction of them to define $n$ corresponding empirical distributions $\mu_{j}$ (4) consider the adversarial game where Player I selects a model $i\in\left\{ 1,\ldots,m\right\} $, Player II selects the UQ distribution $\mu_{j}$ and Player I receives a loss defined by evaluating the model $i$ against data points sampled from $\mu_{j}$ (5) identify optimal mixed strategies (probability distributions over models and UQ distributions) for both players. These randomized optimal mixed strategies provide optimal model mixtures, and UQ estimates given the adversarial uncertainty of the training and testing distributions represented by the game. The proposed approach provides (1) some degree of robustness to in-sample distribution localization/concentration (2) conditional probability distributions on the output.
Freshness and Security-Aware Cache Update in Blockchain-Based Vehicular Edge Networks

Vehicular Edge Computing (VEC) integrated with blockchain technology holds great promise for delivering secure temporal data services. However, ensuring data freshness in edge nodes remains a difficult task due to the temporal nature, heterogeneity, and privacy concerns associated with the data. Secondly, the dynamic VEC environment degrades blockchain performance, resulting in low transaction throughput or excessive energy consumption. As a result, we formulate the problem of Blockchain-based Edge Cache Update (BECU), which aims at maximizing both edge cache benefit and blockchain performance by optimizing cache decisions and critical blockchain parameters, including primary node, block size, and block interval. Furthermore, we develop the Contextual Multi-Armed Bandit for Caching Update (CMAB-CU) algorithm for online cache decision-making, which evaluates rewards by training a linear function based on mobility features and temporal data characteristics. Additionally, we design the Deep Q-learning Network for Blockchain Parameter Optimization (DQN-BPO), which dynamically determines blockchain parameters to strike the balance between transaction throughput and energy consumption. Finally, we conduct simulations using realistic vehicular traces, demonstrating that the proposed algorithms outperform the UCB and FBI algorithms in terms of edge cache benefit and blockchain performance by 95.56% and 144.93%, respectively.
Exploring and forecasting of solar radiation with machine learning methods

Machine learning has recently advanced to the point that a wide range of solar predicting works have been produced. Specifically, one of the most widely used methods at the moment for hourly solar forecasting is machine learning. However, it appears that there is a misconception regarding forecast accuracy—almost all study articles assert to be better than others. However, it appears that there is a misconception regarding forecast accuracy—almost all study articles assert to be better than others. It is illogical for solar forecasters to place their initial wager on a single model for any new forecasting project. Only commercially available versions of these algorithms are employed, and no hybrid models are taken into account to guarantee an equitable comparison. Additionally, the package's automatic tuning algorithm is used to train each model. Overall results show that tree-based methods consistently yield good results. Reliable generation forecasts are becoming more and more necessary for grid operation as distributed renewable power grows in penetration. In order to produce the most accurate day-ahead hourly irradiance forecasts, the current work combines cutting edge Weather Research and Forecasting (WRF) model implementations with machine learning techniques.
Automated Detection and Symbolic Replacement of Abusive Language using Deep Learning in Online Platforms

Abstract: This research addresses the pressing challenge of curbing abusive language in online platforms through the implementation of advanced deep learning techniques. Focused on Natural Language Processing (NLP), this study aims to develop a robust automated censorship system capable of swiftly detecting and mitigating abusive content. By leveraging the prowess of deep learning algorithms, particularly in neural network architectures, the proposed system aims to proactively identify and censor abusive language across various online platforms. Key components involve training models to comprehend contextual nuances, enabling accurate recognition of abusive language patterns. Through this approach, the research aims to significantly contribute to online moderation mechanisms, ensuring a safer and more respectful online environment. The integration of deep learning methodologies within automated censorship systems represents a pivotal step towards mitigating the spread of abusive language, thereby fostering healthier and more inclusive online communities.
Business Email Compromise (BEC) Fraud and How to Prevent it

Cybercrime is on the rise both at the national and cross-border levels. The latest mode of cybercrime is fraud using Business Email Compromise (BEC). A qualitive analysis method with literature study is applied to discuss two key questions of this paper. First, how does the BEC scheme occur?. Second, how an organization/company can prevent/mitigate the risk of BEC fraud. This paper concludes that BEC can be executed in the form of phishing emails sent by perpetrators (both internal and external actors of the organization) to the target victim (organization’s employees) in order to deceive and obtain financial gain. Various efforts can be made by an organization/company to prevent the risk of BEC fraud, among others in the form of implementing a risk management system, implementing an information security management system, and increasing the organization’s internal awareness.
Peningkatan Kemampuan Mendengarkan Siswa SMK Negeri 1 Air Putih Melalui Media Audio Visual

Learning English, there are four language components, including: listening, reading, writing, and speaking, where these four components are the main factors in teaching English as a foreign language. These components will develop students' communication skills either orally or in writing. This research was conducted using classroom action research in 5 meetings. 1 initial meeting is used for the initial ability test, then 4 meetings are used for the learning process which consists of two cycles of learning in the classroom. The population in this study were all students of class X KI SMK Negeri 1 Air Putih, totaling 36 people, consisting of 28 female students and 8 male students. 9 students have relatively good ability in listening to English, 22 students have medium ability, and 5 students have less ability. The results of this study were 1) from the initial data, almost all students scored below the KKM, then in the first cycle it decreased to 8 students and in the second cycle, all students' scores were above the standards set at the school; 2) from the initial average of 48 it rose to 63 in cycle I and in cycle II it rose to 79; 3) from the initial data there were no students who passed while in the first cycle there were more, namely 4 students and in the second cycle all students achieved completeness.
MSR18 Modelling Challenges in Geographic Atrophy

Geographic atrophy (GA) is the advanced form of dry age-related macular degeneration (AMD) and manifests as central visual loss and impairs health-related quality-of-life. Our objective was to review existing literature for guidance on developing cost-effectiveness (CE) models for GA.
THE SPECIFICS OF CONTINUOUS TRAINING FOR THE IT INDUSTRY IN THE “SCHOOL — UNIVERSITY — ENTERPRISE” SYSTEM

Currently, Russian scientists and specialists face important tasks related to the process of digitalization of the economy and the need to accelerate the rate of development of high-tech industries, increase the competitiveness of domestic goods and services, and ensure the technological independence of the country. To solve these problems, it is necessary, first of all, to ensure continuous advanced training of qualified personnel for the modern labor market within digital economy. The paper reflects the current issues of modernization of the Russian education system taking into account the challenges of digital transformation. The problems of training qualified IT specialists and possible ways to make up for personnel shortfall in the IT industry are outlined. The activities carried out in educational institutions for the purpose of mastering relevant digital competencies by students are described. An innovative model of continuous training “school — university — enterprise” in the areas of digital economy has been developed and tested. The image of an IT-class graduate is revealed, representing a mobile, constantly self-improving personality with a set of digital competencies necessary for full participation in socially significant activities in a dynamically changing information society. The tasks are considered and the results of career guidance work with students of educational institutions are presented. The conclusion is made about the effectiveness of the implemented educational project for the development of the digital economy.
Studying and investigation of the Semantic Agent Case Study (SemanSearch)

Semantic Web can be defined on the Internet that it was able to describe things in a certain way to allow all computers understand it. Tim Berners-Lee, inventor of the World Wide Web, defines the Semantic Web as "An extension of the current Web in which information is given well-defined meaning, enabling computers and people to work in better cooperation"&#x0D; This paper describes both architecture and a prototype of SemanSearch, a semantic agent that helps user to get more relevant results when searching for information using a keyword-based search engine. SemanSearch is implemented using Jena (a java frame work) with the help of ontology that developed for education domain. SemanSearch also includes the Arabic meaning of concepts to get documents that contain the needed meaning but in Arabic. A comparative study compares keyword-based search via Google with semantics-based search via the SemanSearch prototype is used for evaluation.
Information quality and academic heads of departments’ effectiveness in public universities in South-West, Nigeria

The study examined the influence of information quality on effectiveness of academic heads of departments (HODs) in public universities in South-West, Nigeria. Managerial effectiveness is the academic heads of departments’ ability to achieve desired results and goals of the departments and universities. Studies in the past revealed that managerial effectiveness of academic heads of departments in public universities in Nigeria has not been encouraging. Factor such as information quality is perceived may influence managerial effectiveness of academic heads of departments in public universities in South-West, Nigeria. Survey research design which included the collection of primary data from the respondents was used for the study. The study population comprised 770 academic heads of departments in public universities of South-West Nigeria. Proportional sampling technique was used to arrive at 309 academic heads of departments by the researcher to capture the entire academic heads of departments in the public universities in South-West, Nigeria. The result proves that the regression analysis result revealed that information quality (F (1,245) = 101.771, p&lt;.05, R2 = 293) has significant influence on effectiveness of academic heads of departments. The study confirmed that information quality is highly necessary in effective management of various tasks to achieve university academic goals by academic heads of departments. The study recommended that university administrative system should emphasis quality of information by academic heads of departments to achieve strategic goals and objectives in the university. Also information quality for effective management of academic heads of departments needed improved ICT facilities and other information services.
S3EF-HBCAs: Secure and Sustainable Software Engineering Framework for Healthcare Blockchain Applications

Blockchain applications in healthcare have grown rapidly and it includes record keeping, clinical trials, medical supply chains, patient monitoring, etc. where Blockchain characteristics are needed to improve safety, privacy, and security. Blockchain Technology is one of the biggest disruptive technologies today. However, Porru, et. al (2017) have reported it lacks processes, tools, and techniques. Therefore, this paper provides a systematic framework for a secure and sustainable software engineering framework for healthcare blockchain applications. Secure and Sustainable Software Engineering Framework for Healthcare Blockchain Applications (S3EF-HBCA) significant contribution includes requirements engineering for healthcare, business process modelling for healthcare, domain modelling for healthcare, a reference architecture for healthcare, and validation by case study on Electronic Healthcare Record Management System (EHR), and simulation with Business Process Modelling Notation (BPMN) tools. The simulation shows it has taken 10.45 minutes to process 100 instances of real-time data and service requests. The overall result shows encouragement in terms of process, tools, standards, and testing.