Reviewerly/exHarmony huggingface dataset:
Secure Data Sharing in a Cyber-Physical Cloud Environment

Cloud computing plays a significant role in digital workplaces and has become an integral part of everyday life. However, the security issues associated with cloud computing remain a major concern that hinders the wider adoption of this technology. In a cyber-physical cloud environment, achieving secure and efficient file storage remains a tough goal. This is particularly the case owing to the wide variety of devices that are being utilised to access the various services and data. Thus, the employment of a secure data sharing protocol is one of the essential techniques to better protect the shared data. A formal agreement between entities or organisations in exchanging personal or business data is called a data sharing protocol. A secure data sharing protocol ensures that data is encrypted and secured when being transferred. There are many different encryption algorithms and protocols in use to devise various secure and efficient data sharing protocols. This paper first reviews the state of the art of some existing data sharing protocols and subsequently implements a secure and efficient data sharing protocol as an application in a cyber-physical cloud environment. The performance analysis conducted on the developed secure data sharing protocol application shows positive results.
Additional Education in The New Social Conditions: Factors of Transformation

Introduction. The article analyzes the factors of additional education transformation in new social conditions. They are understood as a need actualized by the transition to the stage of information society, obtaining lifelong learning. Information obsolescence, the need for permanent updating of knowledge, skills and abilities determine the new status of additional education, which becomes equal to higher professional education. The degradation of additional education in higher education, the spread of online learning and transprofessionalization are considered as factors transforming its status. Purpose of the article: to formulate and describe the main factors of additional education transformation in the new social conditions. Methods. The study is built using general scientific methods of analysis and synthesis, comparative method, predictive approach used to assess the establishment of vectors of additional education under the impact on it transformational factors. Results and its discussion. As a result of the study we came to the conclusion that additional education is currently undergoing transformational processes. This is associated with the transition of its social significance in the status of education equal to that received in classical universities. This status is conditioned by a new stage of social development, the transition to the stage of information society, in which the updating of knowledge, skills and abilities received in higher education is required throughout life, and much more often and in greater volume than it was in the industrial society. Supplementary education is not just a superstructure on the basis of professional education, obtained in higher education, it provides the ability to meet the current requirements of the modern labor market.
Educational needs of high school students as an indicator of readiness for online learning

Introduction. The article analyzes high school students’ readiness for learning activities in the conditions of online learning. The indicator of verification is educational needs. The review of scientific literature has led to the conclusion that the presence of developed and well-reflected educational needs is one of the most important conditions for the transition of high school to online learning. The aim of the article: to identify the level of development of educational needs in high school students. Methods. Two theoretical approaches formed the methodological basis of the research: general scientific model of social research developed by A.V. Lubsky (the idea of methodological construct and division of scientific search into logically complete fragments), and the concept of youth by Yu.A. Zubok and V.I. Chuprov, based on the idea of youth as a social group with unstable, labile system of needs. The empirical basis of the research was formed on the basis of mass public opinion poll, which was conducted in January–February, 2022. The research involved 860 high school students, 1246 parents and 636 teachers living in Rostov-on-Don. The results and its discussion. As a result of the study we came to the conclusion that the level of development of educational needs of high school students does not allow transferring high schools to online learning, because there is a high probability of reducing the quality and intensity of learning activities. The assumptions of supporters of online education about high school students as highly motivated students who clearly understand why they study and have clear goals in the subject of education look a bit far-fetched. But we also cannot ignore the fact that among high school students there must obviously be a certain part of students who are intellectually and socially ready for a full transition to digital learning. And look for it, mainly, in the group of the most motivated to learning activities.
THE ASYMPTOTIC STABILITY OF THE SIMPLEST HYBRID SYSTEMS

The linear autonomous discrete-continuous systems (also known as hybrid systems) with uncertain coefficients is considered. We consider the class of hybrid systems, that consist of difference and ordinary differential equations. We obtain a stability domain for hybrid systems from this class. Two illustrative examples are presented
DEEP AND MACHINE LEARNING FOR IMPROVING BREAST CANCER DETECTION

Breast cancer is the most common type of cancer in the world, as the number of people infected with it reached 2.2 million women in 2020, and World Health Organization reports indicated that the incidence of it is 1 to 12 women, that is, one woman out of every woman. every 12 women. As a result, it is crucial to have high cancer-predictive accuracy to update patient survival criteria and treatment options. Research on machine learning and deep learning, whether using traditional neural networks or using convolutional neural networks, has spread widely and has proven to be a useful technology. It can be very helpful in early detection and prognosis of breast cancer. According to the six machine learning algorithms used in this study and based on the Wisconsin breast cancer diagnostic dataset, they are as follows: Naive Bays (NB), K-Nearest Neighbors (KNN), Random Forest (RF), Decision Tree (DT), Logistic Regression (LR), and Support Vector Machine (SVM), we reached an accuracy of 99.1% with SVM that surpassed all competitors and achieved the highest accuracy. As for deep learning, we have reached an accuracy of up to 99.9%, and this is a reliable result for analysis purposes. In the presented work, the Anaconda environment (Jupyter platform) was used, which uses the Python programming language in all work.
EDUCOLOGICAL ASPECTS OF VIDEO GAME ACTIVITIES IN THE MODERN EDUCATIONAL ENVIRONMENT

Based on educational ideas about the complex, integrated nature of the education system, the article suggests the author's vision of the place and role of video games as an important element of the educational process, which affects the formation of a modern educational environment. The article says that the video game, as a type of game activity, is a complex socio-cybernetic phenomenon, a specific phenomenon in the modern life of society which requires in-depth scientific research in order to clarify its methodological potential in the education system. It has been emphasized that in modern conditions, especially on online learning platforms, the use of video game methods in the educational process is of great importance, as they allow learning new knowledge, acquiring skills and abilities much more effectively and without any external coercion. The innovative ways of forming education seekers’ creativity, interactivity, motivational activity during learning by means of video games has been considered. The examples of the use of genre video games in education have been provided, allowing increasing motivation significantly, form skills of cooperation, creativity, competition, and mutual assistance in the participants of the educational process.
MODEL OF FORMATION OF DIAGNOSTIC COMPETENCE OF FUTURE TEACHERS-PSYCHOLOGISTS IN THE UNIVERSITY EDUCATION SYSTEM

Аnnotation. The article presents a model of the formation of diagnostic competence of future teachers-psychologists in the university education system when studying the disciplines of the psychological and pedagogical cycle, as well as a set of complementary approaches that determine the choice of structural components of the model. criteria, indicators, levels of formation of diagnostic competence of a teacher-psychologist are determined. the complex of psychological and pedagogical conditions ensuring the success of the model implementation is revealed. On the basis of theoretical research, the concept of diagnostic competence of a teacher-psychologist is clarified. Key words: formation, diagnostic competence, educational environment, university education.
Analysis of User Satisfaction of Academic Digital Library: An Empirical Model with Mediating Effects

The role of Academic Digital library (DL) in our educational system has becoming of paramount important in bringing education knowledge and information closer and faster to academic users. Moreover, through the fast-changing development of the information and communication technology, it is further positively proliferated the sphere of information diffusion. This paper aims to investigate the academic digital library effectiveness towards selected public university students in fulfilling their study needs. A total of seven latent variables proposed in building PLS model in examining the relationships between Information Quality, Service Quality, System Quality and DL Collection, and Student Satisfaction. Furthermore, it was hypothesized that the mentioned relationships were mediated by Perceived Ease of Use and Perceived Usefulness. A survey method is opted and a total of 200 students were randomly selected. The PLS model results indicated that about 76%, a satisfactory percentage of variance accounted for in the DV, is explained by the IVs. However, Service Quality was the only IV that showed no direct effect on Student Satisfaction. Moreover, findings also revealed that mediating effects might not be critical in describing the posited relationships however it might offer a new point of view in examining DL effectiveness (due to some significant results shown) and could make meaningful contribution to the literature in other research setting.
A metric learning-based method for biomedical entity linking

Biomedical entity linking task is the task of mapping mention(s) that occur in a particular textual context to a unique concept or entity in a knowledge base, e.g., the Unified Medical Language System (UMLS). One of the most challenging aspects of the entity linking task is the ambiguity of mentions, i.e., (1) mentions whose surface forms are very similar, but which map to different entities in different contexts, and (2) entities that can be expressed using diverse types of mentions. Recent studies have used BERT-based encoders to encode mentions and entities into distinguishable representations such that their similarity can be measured using distance metrics. However, most real-world biomedical datasets suffer from severe imbalance, i.e., some classes have many instances while others appear only once or are completely absent from the training data. A common way to address this issue is to down-sample the dataset, i.e., to reduce the number instances of the majority classes to make the dataset more balanced. In the context of entity linking, down-sampling reduces the ability of the model to comprehensively learn the representations of mentions in different contexts, which is very important. To tackle this issue, we propose a metric-based learning method that treats a given entity and its mentions as a whole, regardless of the number of mentions in the training set. Specifically, our method uses a triplet loss-based function in conjunction with a clustering technique to learn the representation of mentions and entities. Through evaluations on two challenging biomedical datasets, i.e., MedMentions and BC5CDR, we show that our proposed method is able to address the issue of imbalanced data and to perform competitively with other state-of-the-art models. Moreover, our method significantly reduces computational cost in both training and inference steps. Our source code is publicly available here .
SYSTEMATIZATION OF METHODS FOR DETECTING INSIDERS’ CYBER THREATS IN BANKS

Industries 4.0 and 5.0 have ushered in a transformative era for the financial sector, particularly within the realm of banking. This evolution revolves around the integration of intelligent technologies, automation, and data-driven decision-making. While these advancements offer considerable benefits, they also expose the financial sector, including banks, to an elevated risk of cyber threats. The increasing interconnectivity of banking systems, driven by the integration of devices and the adoption of the Industrial Internet of Things (IIoT), broadens the avenues through which cyber threats can infiltrate. This connectivity extends to IoT devices and sensors utilized for real-time data collection, which, despite their advantages, often lack robust security measures, rendering them susceptible to exploitation.
How Riau Malay Culture is Integrated into The English Textbook for Class Iv Elementary School?

Objective: This research aims to address the deficiency in English textbooks used in the fourth-grade elementary schools of Pekanbaru, which currently lack the integration of Riau Malay Culture (RMC) and do not adequately consider the specific needs, context, and characteristics of the students. The primary objective is to develop an English textbook that incorporates RMC into its content.&#x0D; &#x0D; Theoretical Framework: The research is based on the ten-step model of development research outlined by Borg and Gall. This model includes stages such as research information gathering, planning, draft development, expert testing, expert test revision, limited trials, revision of limited trial results, broad trials, revision of broad trial results, and dissemination or socialization. The research framework considers input from principals, teachers, and students, with trials conducted in one and five selected schools involving 10 and 30 participants, respectively.&#x0D; &#x0D; Method: The development research method involves various stages of data collection, textbook development, expert evaluations, limited trials, extensive trials, and result analysis. It includes the participation of principals, teachers, and students to ensure a comprehensive assessment of the textbook's suitability.&#x0D; &#x0D; Result and Conclusion: The research findings indicate that the existing Teaching Book lacks an emphasis on the importance of RMC in English language learning. Consequently, new textbooks have been developed, including teacher instruction manuals and student workbooks. Students have shown enthusiasm for learning English and demonstrated recognition of the presented vocabulary. The Text Book has proven to be suitable for fourth-grade students in Pekanbaru Private Elementary School, Riau.&#x0D; &#x0D; Originality/Value: This research contributes to addressing the gap in English language education by developing textbooks that incorporate RMC, enhancing the learning experience for elementary school students in Pekanbaru. It provides a valuable resource for educators and curriculum developers looking to integrate local culture into English language teaching materials.
Neural network methods for vowel classification in the vocalic systems with the [ATR] (Advanced Tongue Root) contrast

The paper aims to discuss the results of testing a neural network which classifies the vowels of the vocalic system with the [ATR] (Advanced Tongue Root) contrast based on the data of Akebu (Kwa family). The acoustic nature of the [ATR] feature is yet understudied. The only reliable acoustic correlate of [ATR] is the magnitude of the first formant (F1) which can be also modulated by tongue height, resulting in significant overlap between high [-ATR] vowels and mid [+ATR] vowels. Other acoustic metrics which had been associated with the [ATR], such as F1 bandwidth (B1), relative intensity of F1 to F2 (A1-A2), etc., are typically inconsistent across vowel types and speakers. The values of four metrics – F1, F2, A1-A2, B1 – were used for training and testing the neural network. We tested four versions of the model differing in the presence of the fifth variable encoding the speaker and the number of hidden layers. The models which included the variable encoding the speaker achieved slightly higher accuracy, whereas the precision and recall metrics of the three-layer model were generally higher than those with two hidden layers.
Supervision is one of the critical aspects of doctoral education

With the knowledge economy playing such a critical role in global economic development, increasing the number of Doctor of Philosophy degree (PhD) graduates in higher education institutions worldwide has been imperative. The knowledge economy emphasises the importance of critical thinkers and researchers, as 'doctoral education cultivates thinkers and researchers’ and equips them to participate in development. African-identifying females account for 30% of doctoral graduates in sub-Saharan African countries, even when PhD education is viewed as an entry into academia and a gateway to careers in research and other disciplines. There is a paucity of research regarding PhD graduates who are specifically women in Africa; as a result, there is little information about their attributes to success in PhD studies, what opportunities and freedoms they have, and what challenges they face during their PhD journey, along with their motivation to overcome these challenges so that they can achieve their goals. It is essential for women in Africa to complete their PhD education to contribute to the development of their countries as some scholars believe no nation can grow without women’s participation. Thus, modern civilisation is the result of males and females cooperating in modern society. Even though some women complete their PhD studies, there is scant research on what strategies enabled their success. Instead, the reasons for their attrition are known. This book aims to contribute to PhD education scholarship, specifically for women in Africa. It allows women in Africa to narrate their PhD experiences through resilience theory and the capabilities approach lenses.
Bitcoin price forecasting using ARIMA model

The Bitcoin price was chosen as the research subject, and the observation period was set from January 2015 to September 2023. An ARIMA time series model was constructed to forecast the trading price. The results indicate that the optimal model for fitting the trading price is ARIMA (3, 2, 8). This model takes into account trends, seasonality, and other factors that may impact the price of Bitcoin. By analyzing the historical data, the model was able to accurately predict the short-term fluctuations in Bitcoins trading price. Based on this, short-term predictions were made for Bitcoins trading price in the next year. Recommendations were then provided by combining the forecast results with the economic development situation in the post-pandemic era. The recommendations suggest that Bitcoin has become a low-quality asset and is no longer suitable for diversifying ones investment portfolio, but rather focus on the development of physical industries and adjust ones investment portfolio in a timely manner.
Bitcoin price and return prediction based on LSTM

This paper focuses on the prediction of Bitcoin prices and returns based on the Long Short Term Memory (LSTM) neural network model, to better consider the impact of time factors. Since Bitcoin has long dominated the digital currency trading market, many researchers have completed many Bitcoin prediction results, including the screening of optimal features, comparison of prediction models and classification of prediction problems. Based on previous work, this article adds a Bitcoin revenue forecast section, presenting the results in the form of charts and data to provide more intuitive trends and more accurate performance. This paper uses LSTM as the experimental model, and uses the Bitcoin transaction history data set with timestamps as the original input. After a specific normalization method, the original model is trained, and then the subsequent transaction data is predicted. Compare it with the real value in the data set to get the final experimental results show that in this prediction problem, the performance of LSTM is slightly better than Autoregressive Integrated Moving Average (ARIMA) and eXtreme Gradient Boosting (XGBoost); on the other hand, compared with price prediction based on real values for prediction, the prediction fluctuations of return are more obvious and more realistic, providing better reference value.
API Architecture Trends in 2023

Today, APIs are the cornerstone of all digital transformation initiatives across the industry. APIs are driving innovations and have become the backbone for new customer experiences. However, customer demands, economic forces, and industry trends are reimagining how APIs should be used to grow the business and remain competitive. This chapter covers some of the top trends in the industry that are adopted as API technologies evolve and enterprises accelerate their APIs and digital transformation journey.
API Governance

API governance provides a policy-driven approach that defines and enforces standards and checkpoints throughout the API lifecycle. It encompasses not only the API runtime but also design through development processes. It includes the guidelines, standards, and processes for API identification, interface documentation, development, testing, deployment, running, and operation. Standards and principles defined by API governance provide API quality assurance, such as security, availability, scalability, and reliability. It ensures that the APIs are standardized, reliable, and secure, which is critical for successful adoption.
A Study to assess the effectiveness of Iron supplementary ball on Haemoglobin level among adolescent girls with Nutritional deficiency Anemia at selected Nursing Colleges, Thanjavur district

A study was conducted to assess the effectiveness of Iron supplementary ball on haemoglobin level among adolescent girls with nutritional deficiency anemia at selected Nursing colleges, Thanjavur. An experimental research design was used for this study. Samples were selected by using simple random (lottery method) sampling technique. The investigator assessed the haemoglobin level by using Sahli’s method in both experimental and control group. Then from day 2 to day 31 Iron supplementary ball intervention was given to the experimental group sample after breakfast under the supervision of the researcher for a period of 30 days. After 30 days the investigator assessed the posttest level of haemoglobin in both groups. The mean value 10.27 with standard deviation 1.91 in experimental group was higher than the mean value 9.49 with standard deviation 1.38 in control group. The ‘t’ test calculated Value 10.933 was higher than the Table Value 2.05 (CV&gt; TV) which is significant at 0.05 level. The statistical analysis reveals that there was a significant differences in posttest levels of haemoglobin among adolescent in experimental group in comparison with control group. So the given Iron supplementary ball was effective in proving haemoglobin level among the adolescent girls.