Reviewerly/exHarmony huggingface dataset:
Methodology of the cultivation of reflective thinking in Russian education: the experience of reflection

The paper focuses on the reflective analysis of one of the most powerful philosophical and methodological programs for the development of reflective thinking in Russia. This is the “Program for the Development of Regional Social Systems by Means of Education”, developed in the 90s of the last century by a team of young scientists on the basis of the theory of developing education of V.V. Davydov. The author studies the problems to be solved by the program as well as the main stages of its implementation. Such projects as “Metropolitan Education”, “Updating the content of education: meta-subjects, meta-subject technologies”, “Education of design activities: School of General Designers”, – are in the centre of analysis. The author discusses also the structures of solutions that have been aimed at the developing educational practices (experimental sites, innovative networks) and reflective thinking in the metropolitan education system (Expert Council). The results of the implementation of the program at each stage are summed up. The limitations in the application of the reflection method are analyzed and the path of further research is outlined.
Playing Blackjack Using Computer Vision

The field of computer vision is rapidly evolving, with a focus on analyzing, manipulating, and understanding images at a sophisticated level. The primary objective of this discipline is to interpret the visual input from cameras and utilize this knowledge to manage computer or robotic systems or to generate more informative and visually appealing images. The potential applications of computer vision are wide-ranging and include video surveillance, biometrics, automotive, photography, movie production, web search, medicine, augmented reality gaming, novel user interfaces, and many others. This paper outlines how computer vision technology will be utilized to achieve a winning outcome in the game of Blackjack. The game of Blackjack has long captivated the attention of enthusiasts and players worldwide. One area of particular interest is the development of a winning strategy that maximizes the player's chances of success. With the advent of sophisticated computer algorithms and machine learning techniques, there is enormous potential for research in this area. This paper explores the game-winning strategies for Blackjack, with a particular focus on utilizing advanced analytical methods to identify optimal plays. By analyzing large data sets and leveraging the power of predictive modeling, we aim to create a robust and reliable framework for achieving consistent success in this popular casino game. We believe that this research avenue holds enormous promise for unlocking new insights into the game of Blackjack and developing a more comprehensive understanding of its intricacies.
Improving Naturalness in Speech Synthesis Using Fuzzy Logic

The TTS (Text to Speech) synthesis systems have been developed for Indian languages for a few decades. Very little work has been done specifically for the Gujarati Language. The synthesized speech doesn’t sound as similar to human natural speech. Naturalness is the key parameter to achieving a natural-sounding effect in speech synthesis. This paper proposes a method for improving the naturalness of speech synthesis for the Gujarati language using fuzzy logic. The pause (silence) in-between words is also an important feature of a speech. The pause may not be the same after each word in a sentence. It is dependent upon the characteristics of the language and other parameters of the sentence. In the classic architecture of TTS, fuzzy logic is proposed as a new approach to calculate the pause to be applied after each word. The system takes a sentence or paragraph as an input which has the words Importance, Sentence Size, and Position in Sentence derived variables. The fuzzy logic produces the pause in seconds that can be applied after each word. The membership value of derived variables is calculated using straight-line formula. The developed TTS system is tested on a SARS-CoV-2 Covid-19 news dataset in the Gujarati language. The dataset is designed by collecting the news lines from websites of popular news channels in the Gujarati language. The fuzzy logic is proposed in solving the problem of naturalness in synthesized speech and aiming to achieve a more natural-sounding effect in generated speech. This paper describes the implementation of fuzzy logic in achieving naturalness in speech synthesis.
Logics for Epistemic Actions: Completeness, Decidability, Expressivity

We build and study dynamic versions of epistemic logic. We study languages parameterized by an action signature that allows one to express epistemic actions such as (truthful) public announcements, completely private announcements to groups of agents, and more. The language L(Σ) is modeled on dynamic logic. Its sentence-building operations include modalities for the execution of programs, and for knowledge and common knowledge. Its program-building operations include action execution, composition, repetition, and choice. We consider two fragments of L(Σ). In L1(Σ), we drop action repetition; in L0(Σ), we also drop common knowledge. We present the syntax and semantics of these languages and sound proof systems for the validities in them. We prove the strong completeness of a logical system for L0(Σ) and the weak completeness of one for L1(Σ). We show the finite model property and, hence, decidability of L1(Σ). We translate L1(Σ) into PDL, obtaining a second proof of decidability. We prove results on expressive power, comparing L1(Σ) with modal logic together with transitive closure operators. We prove that a logical language with operators for private announcements is more expressive than one for public announcements.
Research on Blockchain Data Availability and Storage Scalability

Blockchain adopts a chain data structure, and the characteristics of blocks that can only be added and cannot be deleted make the total number of blocks accumulate over time, forcing resource-constrained nodes to become degraded nodes in order to alleviate increasingly severe storage pressure. Degraded nodes only store partial blocks, although improving the scalability of blockchain storage and reducing data redundancy will lead to a decrease in data availability. To address the problem of storage scalability, quantitative research is needed on data availability. Based on a summary of the existing definitions of data availability, we propose a definition of data availability for blockchain. By analyzing the data synchronization process and the transaction lifecycle, key factors affecting data availability were extracted, and a data availability measurement model was constructed based on node types. On this basis, a relationship model linking data availability and storage scalability was constructed to find the range of data redundancy that meets the target data availability. The experimental results indicate that the data availability measurement model for blockchain can measure the data availability levels of different scalable storage schemes. The model of the relationship between data availability and storage scalability can guide the setting of data redundancy in scalable storage schemes.
THE IMPACT OF QUALITY OF EDUCATION ON SOCIOECONOMIC DEVELOPMENT OF THE KYRGYZ REPUBLIC IN A DIGITAL ECONOMY

The goal of this paper is to determine the impact of quality of education on socioeconomic development of the Kyrgyz Republic in a digital economy and to identify the prospects for enhancing this role in order to assist in the implementation of the Digital Transformation Concept "Digital Kyrgyzstan 2019-2023" and the Digital Agenda of the EEU for 2016-2019-2025.The methodology of regression analysis and forecasting based on the theory of probability using the Monte Carlo method was used to prove that the globalization of education in the Kyrgyz Republic in 2013-2019 hampered the social and economic development of the Kyrgyz Republic, and it is very likely that it will slow down the innovative development and economic growth of this country in 2020.This problem stems from the fact that the digital economy of the Kyrgyz Republic is in a formative stage thus far, and the significance of quality of education in it is contradictory.On the one hand, there is a growing demand for the teaching of digital competencies among the emerging information society, which cannot be met due to the retaining of traditional educational programs.On the other hand, the regulatory framework for digital business is still under development, which reduces the demand for training of digital personnel.In order to address the identified problem, it is recommended to develop an institutional environment and to promote business digitalization in the Kyrgyz Republic.This will make it possible to create a consistent demand for training of digital personnel from society, the State and business, and to promote the key role of quality of education in the digital economy of the Kyrgyz Republic.
Application of improved CNN-based face detection technology in public administration

In public management, intelligent face recognition detection technology plays a very crucial role, which can greatly improve the efficiency of public management and reduce the workload of staff. To address the shortcomings of traditional face detection algorithms such as low detection efficiency and easy overfitting, a face detection model based on convolutional neural network (CNN) was proposed in this study, and the structure of CNN was optimized to enhance the accuracy and efficiency of the proposed face detection model. To solve the face detection errors caused by illumination differences, a light compensation strategy was proposed to pre-process the data; meanwhile, a Gaussian curvature filtering algorithm was used to enhance the face image and improve the subsequent detection accuracy. On this basis, a face detection model based on improved CNN was designed in this study. Experiments showed that the accuracy of the model reached 99.86% with high accuracy and efficiency, indicating that such method can improve the efficiency of public management and has good application prospects in access control and check-in systems.
Development of an enhanced student identification system

Identification, verification, and student authentication to prevent impersonation during examinations are essential.This is a predominant issue in institutions in developing countries such as Nigeria.This paper designed and developed an enhanced student identification and authentication system capable of verifying and admitting only duly registered students into an examination hall in Nigerian higher educational institutions.The system uses radio frequency identification and biometrics for identification and comprises an Arduino microcontroller, a fingerprint reader, radio frequency identification (RFID) reader with a user-friendly desktop application.The registered students' data and fingerprints are captured and stored in the database.The verification and authentication of the student's identity are observed when the stored information matches the identity of the student presenting the RFID card or fingerprint.The enhanced identification system was rated as good with an average of 4.35 out of 5.0 based on the functionality, ease of use, efficiency and user interface.The developed system is more reliable and efficient than the existing systems in Nigerian institutions as it provides solutions for the identification and verification of students accurately.
The Reality of the Availability of Dimensions of Quality of Work Life in the Directorate of Electricity and Gas Distribution in the Wilaya of El Bayadh from the Viewpoint of Workers [In Arabic]

This study aimed to identify the availability of dimensions of quality of work life at the Directorate of Electricity and Gas Distribution in the Wilaya of El Bayadh from the viewpoint of workers, To achieve the study's objectives a questionnaire was developed, which consisted of 36 items and was divided into 5 dimensions. And the results were as follows: The level of quality of work life in the Directorate of Electricity and Gas Distribution in the Wilaya of El Bayadh was average. There were no statistically significant differences between the responses of the sample members about the quality of work life due to personal and occupational characteristics (gender, age, scientific qualification, job classification and duration of work in the institution).
PENTINGNYA MANAJEMEN AKUNTANSI SUMBER DAYA MANUSIA

Accounting still seems to be growing more widely until now. One thing which is disputed is the Human Resource Accounting. Human Resources Accounting (HRA) was born because some experts feel that the human resources is an asset to the company and not a burden as in conventional accounting. Human resources is an enforcer for the company which has the most important role for a company. Because of human resources, the company will be able to get high profits, the operation go smoothly, and can get other corporate goals. Therefore, human resources ought to be used as a corporate asset so companies need to implement Human Resources Accounting.
The Effect of Electronic Marketing on International Trade: case study on Algerian International Companies

This study investigates the influence of e-marketing mix components on the success of international firms in Algeria, as well as the relationship between e-marketing mix parts and individual performance. First, A model was built and hypotheses were supplied to meet the study's objectives. A standardized questionnaire prepared expressly for this study was used to collect data from Algerian enterprises' marketing and management teams. A Multiple regression study revealed that e-marketing mix elements have a significant influence on business performance. Simple regression analysis revealed that each component of the e-marketing mix has a significant influence on the performance of international companies doing business in Algeria. Also, The elements of the e-marketing mix (promotion, product, location, and pricing) were shown to have a positive influence on the operations of international companies operating in Algeria. Therefore, Algerian companies are advised to apply this technology in their international market to cope with the international advancement in technology and the internet. Finally, research conclusions, results, and recommendations are outlined
The role of Yemeni commercial banks in achieving sustainable development - an applied study

The study aimed to identify the role of Yemeni commercial banks in achieving sustainable development and to arrive at a general standard model that can be used to evaluate the role of commercial banks in sustainable development and to test the efficiency of the standard model. The descriptive approach was used to conduct the study, and the questionnaire was used as a tool to collect data through a simple random sample of workers in the higher departments of the sample banks. A set of descriptive and inferential statistical methods were used to analyze the data, including the structural modeling approach using the structural equation in building a general standard model to evaluate the role of commercial banks in sustainable development. The model was also applied to and evaluated Yemeni commercial banks. The study found a set of results, the most important of which are: The standard model that has been reached which includes a working structure consisting of five factors: the economic role, the administrative role, the legal role, the social role, and the environmental role can be used to assess the role of commercial banks in sustainable development, The results also showed that Yemeni commercial banks rely to a greater extent on the economic role to achieve sustainable development, followed by the administrative, environmental, social and legal role, respectively. The study also concluded with a set of recommendations.
The impact of intellectual capital disclosure in supporting theorganizational performance

Intellectual Capital has become one of important tools to achieve organizations success in thebusiness world. Therefore, it is important to consider the intellectual capital in order to determine theeconomic value of any organizations. The disclosure of intellectual property and its basic components isessential to show the Organization independency as it has direct impact on its performance. This objective ifthe study is to measure the impact of the disclosure of intellectual capital and subsequently on theorganizational performance in selected Iraqi organizations. The study has hypothesized that there is a directimpact the disclosure of intellectual capital on the organizations performance in selected organizations ofIraq. The study has employed the descriptive approach and secondary source of data from previous studiesand primary source of date through the questionnaire based survey, which formed the conceptual frameworkof the study. The results of the study showed that there is negative impact of the on the intellectual capital ifthe organization has not adapted the disclosure.
IOT Based Health Monitoring System

Abstract: In India, close to regarding two hundredth of the whole population loses their lives because of interrupted health observance system i.e., in most of the hospitals, doctor visits patients either in morning shift or in evening shift or in each shift. What happens if patient's health becomes important in between that interval or once a doctor isn't on the market with a patient. the solution is a patient could lose her\his life. thus, to avoid this important situation; we tend to area unit proposing a sensible embedded system device that monitors patients’ health ceaselessly. This project introduces the structure and usage of an IoT based patient observing framework, utilizing different sensors and NodeMCU as the MCU (Master Control Unit). The framework can be utilized to constantly screen the wellbeing parameters of a patient. ECG Monitoring with AD8232 ECG Sensor and MAX30102 BPM sensor is used forsensing temperature and heartbeat rate can be measured from anyplace on the globe utilizing IoT (Internet of Things). IoT is executed, utilizing an ESP-8266, which enables the data to be transmitted flawlessly over the web and mobile app
The Performance Comparison of a Brute-Force Password Cracking Algorithm using Regular Functions and Generator Functions in Python

Python is used extensively in research, including algorithm testing. Python is a multi-paradigm programming language and supports both object-oriented programming and functional programming. In the functional side, it supports both regular functions and generator functions. This study tests both approaches in terms of usability cases and performance. A password-cracking algorithm is used for this tryout.
Human stability assessment and fall detection based on dynamic descriptors

Abstract Fall detection systems use a number of different technologies to achieve their goals. This way, they contribute to better life conditions for the elderly community. The artificial vision is one of these technologies and, within this field, it has gained momentum over the course of the last few years as a consequence of the incorporation of different artificial neural networks (ANN's). These ANN's share a common characteristic, they are used to extract descriptors from images and video clips that, properly processed, will determine whether a fall has taken place. These descriptors, which capture kinematic features associated with the fall, are inferred from datasets recorded by young volunteers or actors who simulate falls. Systems based on this concept offer excellent performances in tests which use that kind of datasets. However, given the well‐documented differences between these falls and the real ones, concerns about system performances when processing falls of elderly people are raised. This work implements an alternative approach to the classical use of kinematic descriptors. To do it, for the first time to the best of the authors’ knowledge, the authors propose the introduction of human dynamic stability descriptors used in other fields to determine whether a fall has taken place. These descriptors approach the human body in terms of balance and stability; this way, differences between real and simulated falls become irrelevant, as all falls are a direct result of fails in the continuous effort of the body to keep balance, regardless of other considerations. The descriptors are determined by using the information provided by a neural network able to estimate the body centre of mass and the feet projections onto the ground plane, as well as the feet contact status. The theory behind this new approach and its validity is studied in this article with very promising results, as it is able to match or over exceed the performances of previous systems using kinematic descriptors employing available data and, given the independence of this approach from the conditions of the fall, it has the potential to have a better behaviour than classic systems when facing falls of elderly people.
Prediction of Microsatellite Instability From Gastric Histological Images Based on Residual Attention Networks With Non-Local Modules

Gastric cancer can be classified into different subtypes according to their genetic expression. Microsatellite instability (MSI) is one of these subtypes and an important clinical marker for prognosis and consideration for immunotherapy. Since genetic testing is relatively expensive and laborious, this study tackles the challenge of using deep neural networks (DNNs) to identify MSI based on analyzing histomorphologic features of gastric whole-slide images (WSIs). A two-stage patch-wise framework was proposed, which first differentiates the tumor regions from normal, then predicts MSI status from the tumorous patches. The proposed deep learning architecture enhances the residual attention network with non-local modules and visual context fusion modules, thereby allowing both local fine-grained details and coarse long-range dependencies to be captured. Image post-processing procedures were also proposed to better align the region segmentation with pathologist annotations. The model was applied to a three-way classification task, namely normal tissue, microsatellite stable (MSS), and MSI, using a private dataset gathered by Chang Gung Memorial Hospital and achieved 91.95% slide-wise accuracy. We also studied the feasibility of transfer learning by fine tuning on the TCGA-STAD public dataset, where we attained a high accuracy of 96.53% and an AUC of 0.99, outperforming previous literature.