Reviewerly/exHarmony huggingface dataset:
THE SIGNIFICANCE OF METHODOLOGICAL WORK AT THE PRESENT STAGE OF DEVELOPMENT OF THE EDUCATIONAL SYSTEM

In the context of modernization of education in Kyrgyzstan, the most important task of the methodological service of an educational institution is to ensure the implementation of new priorities of educational policy, to assist teachers in achieving higher education. Scientific and methodological support of the level of professional activity, innovation processes. Especially relevant is the development of new methods for building a model of methodical work at school aimed at improving the competence of teachers. According to research data, there is still a need to organize methodological work at school, and this need is still relevant, but there is a need to revise the content and forms of work taking into account existing scientific directions. The modern methodological service of an educational institution (hereinafter referred to as MSU) is a qualitatively new stage of development in this area.
GAME TECHNOLOGIES IN MATHEMATICS LESSONS

The main goal of mathematical gaming technologies is to create a full-fledged motivational basis for the formation of activity skills, depending on the working conditions of an educational organization and the level of development of children. All considered components of the game determine the technological structure of the game. The game hasbeen used since ancient times as a method of teaching and passing on the experience of older generations to younger ones. Educational (didactic) game as a learning technology has long attracted the interest of scientists and practitioners. As a pedagogical technology, the game is interesting because it creates an emotional upsurge, and the motives of game activity are aimed at understanding the meaning of this activity.
MOTIVATION THROUGH GAME TECHNOLOGIES IN TEACHING MATHEMATICS

Modern requirements of the state educational standard of the latest generation require professional pedagogical competence from the teacher. This directly depends on the purposeful use of pedagogical technologies by the teacher. Game technologies occupy a leading place among pedagogical technologies in shaping students as worthy individuals capable of self-development and self-education. The article pays special attention to business games among gaming technologies, their role in the formation of competencies in the subject of mathematics is noted. An example of the use of business games in the development of a particular topic is given from a fragment of the lesson. With the help of gaming technologies, the performance of students during the month was analyzed, and their dynamic growth was shown using a diagram.
SIGNIFICANCE AND PLACE OF PORTFOLIO IN PEDAGOGICAL ASSESSMENT

In this article, attention is paid to the portfolio as the latest form of pedagogical assessment in the activity of the competence-based approach in higher professional education. Based on the competence-based approach, the article considers the sequence, reality and formation of students’ achievements. The concepts in pedagogical assessment are analyzed, such as the pedagogical test and its significance, pedagogical diagnosis, monitoring as a concept inherently close to assessment, as well as the difference between the comparative characteristics of the portfolio from the traditional one. Proposals have been made to the portfolio structure, its sections and headings. As you know, group curators face certain difficulties in compiling a database of students, in this case, the portfolio plays an important role as the best form of compiling a database of students’ achievements in the process of training and education.
FORMS OF SHORT WRITING OF EDUCATIONAL MATERIALS AND THEIR PRACTICAL SIGNIFICANCE

The article discusses the forms of the summary of educational materials and their practical significance, definitions, basic requirements, methods of its compilation based on the analysis of pedagogical-psychological and scientific-methodological literature. An example of purposeful use of forms for writing summaries of educational materials in mathematics is the proposed tasks.
Being Agile as a Freelancer

When I began working a freelancer last year, I knew I wanted to use an agile approach to build apps for my clients, even if they were small projects. As there are limited resources for agiling independently, I thought I would write about my experience for others to use. First of all, some terms.
METHODS OF ORGANIZATIONAL CHECK OF KNOWLEDGE OF STUDENTS AND METHODS OF DISTANCE EDUCATION

The sudden transition of the higher education institutions around the world to e-learning in the time of COVID-19 pandemic has raised significant challenges. A particular challenge in the context of remote learning has been the search for valid online assessment tools, measuring student knowledge and skills and organizing student examinations on line. Particular attention is paid to the revision and improvement of control and measuring materials, the need to use flexible assessment regimes and increase the importance of continuous monitoring, as well as the introduction of alternative forms of assessment aimed at developing creativity and critical thinking of students. For example, project work, digital portfolio, feedback tools, etc.
IMPLEMENTATION OF THE MODEL OF THE METHODOLOGICAL SYSTEM FOR TEACHING STUDENTS OF PHYSICAL AND MATHEMATICAL AREAS OF TRAINING IN THE BASICS OF CORRELATION ANALYSIS

A theoretical model of a methodological system for teaching students of physical and mathematical areas of training in correlation analysis and its implementation on the basis of pedagogical requirements formulated in the Federal State Educational Standard of Higher Education is considered. Specific examples show the features of the created educational and methodological complex.
Container Basics Using Docker

In this chapter, we will expand from the previous chapter by learning more about containers, where to find, download, run, and more importantly configure containers so that we can access the services the containers provide for us. For example, in the previous chapter, we ran the command docker run nginx. What happened behind the scenes? Why did we have to map port 80:80? What if I wanted to run a different application? Where do I find container images for that? We will take a closer look at these topics here.
Container Hello-World

Continuing from where we left off in the VM world, the goal of this chapter is to set up container technology in our workstation and run our first container, hello-world, and nginx web server, using docker with the intent to learn the basics of containers.
DeepSegNet: An Innovative Framework for Accurate Blood Cell Image Segmentation

Image segmentation plays a crucial and indispensable role in computer vision, as it allows the partitioning of an image into meaningful regions or objects. Among its numerous applications, image segmentation holds particular significance in the domains of medical diagnosis and healthcare. Its vital role in this field stems from its ability to extract and delineate specific anatomical structures, tumors, lesions, and other critical regions from medical images. In medical diagnosis, accurate and precise segmentation of organs and abnormalities is paramount for effective treatment planning, disease monitoring, and surgical interventions. Blood cell image segmentation is highly valuable for medical diagnosis and research, particularly in the domains of hematology and pathology. Precisely segmenting blood cells from microscopic images is essential, as it offers critical insights into various blood-related disorders and diseases. Although deep learning segmentation models have exhibited promising results in blood cell image segmentation, they suffer from several limitations. These drawbacks encompass scarce data availability, inefficient feature extraction, extended computation time, limited generalization to unseen data, challenges with variations, and artifacts. Consequently, these limitations can adversely impact the overall performance of the models. Blood cell image segmentation encounters persistent challenges due to factors like irregular cell shapes, which pose difficulties in boundary delineation, imperfect cell separation in smears, and low cell contrast, leading to visibility issues during segmentation. This research article introduces the innovative DeepSegNet framework, a powerful solution for precise blood cell image segmentation. The performance of widely-used segmentation models like PSPNet, FPN, and DeepLabv3+ is enhanced through the use of sophisticated preprocessing techniques, improving generalization capability, data diversity, and training stability. Additionally, the incorporation of diverse dilated convolutions and feature fusion further contributes to the improvement of these models. The Improved PSPNet, Improved FPN, Deep Lab V3, and Improved Deep Lab V3+ achieved 98.25%, 99.04%, 98.23%, and 99.31% accuracy, respectively, and the Improved Deep Lab V3+ model outperformed well and produced a Dice Coefficient of 99.32% and Precision of 99.38%. The proposed DeepSegNet framework improves overall performance with an increased accuracy of 8.91%, 3.72%, 17.73%, 22.83%, 7.96%, 9.61%, 17.36%, 6.22%, 13.32%, and 14.32% compared to the existing models. This framework, which can be applied to accurately identify and quantify different cell types from blood cell images, is instrumental in diagnosing a variety of hematological disorders and diseases.
Curated Datasets for Use in Automated Media Monitoring and Feedback System: “News Classification System” Dataset, “Government News Classification” Dataset

Online journalism in India, a growing field that involves news websites and Digital media, connects with the Press Information Bureau (PIB), a government agency dedicated to sharing accurate information about government policies and initiatives with journalists. While various news outlets publish diverse articles and opinions on these topics, the government seeks to leverage Artificial Intelligence and Machine Learning for gathering feedback in multiple languages. To develop such a system, a notable obstacle is the lack of a readily accessible standard dataset is required. To address this, two datasets are developed named, 'NCS' and 'GNC,' consisting of information from 2020 to 2023 and collected through web scraping tools like Parsehub and manually scrapping. NCS represents News Classification system dataset and GNC represents Government News Classification. The 'NCS' dataset includes Indian news in Hindi, Marathi, and English with categorization of Indian news as government-related or not. Then, a Machine Learning model called "Government News Classifier" to sort news articles using the 'NCS' dataset into either government-related or non-government-related categories. The objective is to use this model to figure out if a news source is discussing topics related to the government or not. Using this model, we created the 'GNC' dataset, which contains only news articles related to government schemes and policies in Hindi, Marathi, and English. In GNC dataset, Human experts manually classify each news source into three categories: "government favourable," "government non-favourable," or "neutral." In essence, this research emphasizes the importance of having access to a large dataset, which can stimulate more advanced prediction models in this complex field.
Transmission Channels of Effects of Inequality in Income Distribution on Economic Growth in the Group of Countries with Lower Levels of Inequality and Economic Development than Average - Suggestions for Vietnam

Using a fixed effects model for processed panel data to increase data homogeneity, the article found that in the group of countries with lower levels of inequality and economic development than average, increased income inequality will stimulate economic growth. This positive effect is the combined result of many different effect transmission channels, of which 6 channels were found including fiscal policy channel, imperfect capital market channel, consumer behavior channel, labor motivation channel, access to education channel and domestic demand for goods and services channel. The article has not found evidence about socio-political instability channel, fertility decision channel, investment savings channel and access to health service channel. From the estimation results, the study has proposed a number of solutions for Vietnam towards harmoniously resolving the relationship between income inequality and economic growth.
A Systematic Study on the Development of Public Cloud Systems

The term "cloud computing" refers to a group of resources and services made available online. Internet-based computing lets devices and computers access shared resources, software, and information. Google applications, a product of Google and Microsoft Share Point, are a typical example of cloud services. A collection of information technology services known as "cloud computing" are offered to a client across a network on a leased basis with the flexibility to scale up or down the level of service needed. In order to demonstrate how cloud computing will make the corporate world easier, more efficient, and more specialized, its qualities will be presented with a number of examples in this article. We'll also define different cloud computing settings, cloud computing style, etc. The goal of this study was to ascertain if cloud computing might be used for learning and what advantages it offers in the creation of learning systems. Information technology advancement is now a creative, dynamic, and financially successful answer. Information technology has the answers to the problems and challenges the educational industry is now experiencing. Cloud computing is changing the way that information technology services are provided and made available to institutions so that they can have access to scientific and educational data. With several benefits over traditional systems, cloud computing technology offers a new paradigm in the delivery of computer services. This technology has recognized the significance of the service-oriented idea and developed a new system in the computer industry thanks to its impact and benefits. The potential of cloud computing has allowed us to advance the IT industry by one step. Large and well-known companies are now using cloud computing for their processing and storage. In this essay, we present a general overview of cloud computing and call attention to its advantages.
Teaching Evaluation System on Engineering for Applied University in China

This research presents a novel Teaching Evaluation System model designed to enhance the quality of education in Chinese applied universities with a focus on engineering programs. The objectives included synthesizing relevant theories related to teaching evaluation systems in applied university engineering and developing a comprehensive system. A population of 9 experts from China was selected through purposive sampling, all qualified and with a minimum of five years' experience as Assistant Professors at the same university. SPSSPRO was employed for data assessment, ensuring questionnaire reliability with a Cronbach's ? of .823, indicating excellent reliability. The research result, with an average score of 4.6358, SD. = 0.4655, CV = .1008, IQR = 1, demonstrated a strong agreement among expert opinions. Utilizing the Analytic Hierarchy Process (AHP) for weighting analysis and assessing matrix consistency, CR = .039 &lt; .1, suggested sufficient consistency for decision-making. The research employed focus groups to discuss the evaluation system for Chinese applied university engineering education, utilizing the CIPP model, modifying the evaluation index system, and employing AHP for indicator ranking. A case study at Sichuan University of Science &amp; Engineering, using the new evaluation index system, revealed program issues, and offered recommendations. The index system comprises four first-level, 12 second-level, and 52 third-level indexes, providing a reflective evaluation of the actual relationships among indicators and serving as a valuable reference for assessing engineering education quality.
Imbalance analysis of regional economic law laws incorporating improved PPO algorithms

Abstract This paper first analyzes the status quo of regional economic law coordinated development and draws out the mechanism and characteristics of regional economic law legal imbalance under the perspective of game theory. Then, on the basis of the proximal strategy optimization algorithm, the generative adversarial network is introduced, and according to the auxiliary role of the subjective metric constituted by the generator in the generative adversarial network as the dominance function to provide the real metric, the GAN-PPO algorithm based on the generative adversarial network is constructed to improve the GAN-PPO algorithm. Finally, after evaluating the performance of the improved PPO algorithm, the mechanism and degree of imbalance in regional economic law laws are studied using the GAN-PPO algorithm. The results show that the GAN-PPO algorithm reaches the peak gain at 1,250,000 steps, the peak value of the average reward obtained is 885, and the speed of convergence is improved by 0.42 in the sensitive environment, which realizes the acceleration of the algorithm. In the regional economic law imbalance, the degree of regional economic imbalance is between [0.1,0.48], the degree of industrial imbalance is between [0.08,0.43], the degree of capacity imbalance is between [0.10,0.45], and the degree of information imbalance is between [0.05,0.38], and the regional economic law legal imbalance is obvious, and it needs to be reasonably regulated.
‘And all the men and women merely players’: theatre training in VR

Click to increase image sizeClick to decrease image size Notes1 Consider recent examples from industry: Pilot Theatre’s Traitor VR (2019), developed as part of the Digital Catapult/Arts Council England funded Creative XR programme1; The Under Presents… Tempest (2020), which saw live actors performing Shakespeare’s The Tempest in virtual reality; the RSC’s recent Dream (2021) project; and the National Theatre’s XR adaptation of All Kinds of Limbo (2022).2 This was not conceived of as a game by us but was branded as such by the games engine.3 https://vimeo.com/4863699624 Grotowski refers to the audience as the unprepared ensemble.5 https://vimeo.com/559920279/8a781339d3Additional informationNotes on contributorsKlaus KruseKlaus Kruse is a Senior Lecturer in Theatre and Acting at Falmouth University and co-founder of the experimental theatre collective Living Structures. https://livingstructures.co.ukCiaran ClarkeDr. Ciaran Clarke is a Lecturer in Acting and Performance at Falmouth University and part of the core team for Living Structures theatre collective. https://www.falmouth.ac.uk/research/centre-pedagogy-futures/varyon-vr
Recommendation and sentiment classification on E-Commerce reviews

Due to the improvement of online shopping mode, an increasing number of customers rely on reviews displayed on online shopping websites to choose products, and there are also more and more sellers taking consumers' text reviews into consideration to modify their products. Therefore, understanding and analyzing these reviews are getting increasingly significant. This study utilized natural language processing on E-Commerce Reviews. First, I used the Nave Bayes model and Support Vector Machine to classify whether a reviewer recommends the reviewed product; the accuracies are both 87%. Then I used the random forest to classify the reviewer's positive, neutral, and negative sentiment on each review, which gave 86% precision.
Approximation and interpolation with neural network

In this paper we show that multilayer feedforward networks with one single hidden layer.and certain types of activation functions can approximate univariant continuous functions defined on a compact set. arbitrarily well. In particular, our results contain some usual activation functions such as sigmoidal functions, RELU functions and threshold functions. Besides, since interpolation problems are highly related to approximation problem, we demonstrate that a wide range of functions have the ability to interpolate and generalize our results to functions which are not polynomial on R. Compared to existing results by numerous work, our methods are more intuitive and less technical. Lastly, the paper discusses the possibility of combining interpolation property and approximating property together, and demonstrates that given any Riemann integrable functions on a compact set in R, with several points on its graph, the finite combination of monotone sigmoidal functions can pass through these points and approximate the given function arbitrarily well with respect to L^1 (dx) (in the sense of Riemann integral) when the number of points getting large.
An iterative loss correction method for deep learning with noisy labels

Noisy labelling is a prevalent issue in real-world data, often causing deep neural networks (DNNs) to overfit. Prior research in this area primarily relies on the accurate estimation of noise transition matrices, which is contingent on identifying anchor points in the clean data domain. However, current methods typically estimate the anchor points using information from the noisy labels, potentially resulting in poor estimation. In contrast, our novel method aims to enhance precision by developing an estimator that jointly learns the transition matrices and anchor points through iterative learning. Our approach is validated on the IMDB and MNIST datasets, proving to be more precise and effective than previous methods.
Research on improvements of fraud detection system: basing on improved machine learning algorithms

Nowadays, commercial fraud behaviors commonly occur in many industries. However, due to obstacles like concept drift, imbalanced dataset and uneven distribution of fraud entries, Fraud Detection System (FDS) fails to identify such behaviors. Among the problems mentioned above, most research focus on dealing with skewed dataset. This paper first presents common application scenarios of FDS which consist of credit card fraud, insurance fraud and supply chain fraud. Then, this study introduces five representative methods in dealing with problems mentioned above, which are K Nearest Neighbors-Synthetic Minority Oversampling Technique-Long Short-term Memory Networks (kNN-SMOTE-LSTM), Generative Adversarial Nets-AdaBoost-Decision tree (GAN-AdaBoost-DT), Wasserstein GAN-Kernel Density Estimation-Gradient Boosting DT (WGAN-KDE-GBDT), Time-LSTM (TLSTM) and Adaptive Synthetic Sampling-Sequential Forward Selection-Random Forest (ADASYN-SFS-RF). KNN-SMOTE-LSTM adopts KNN as an identifying classifier so as to only retain true samples. GAN-AdaBoost-DT generates new samples without referring to real transactions. WGAN-KDE-GBDT uses Wasserstein Distance as distance measurement instead, and thus improves training speed and guarantees successful generation. TLSTM tires to consider the weights of different time intervals and measures the similarity between the simulated behavior and the genuine behavior. ADASYN-SFS-RF employs SFS algorithm, basing on RF, to only reserve optimal subsets of features. Finally, result metrics prove that those improved algorithms do improve the overall performance of FDS, even if with limitations at some indicators.