Reviewerly/exHarmony huggingface dataset:
Method for Predicting and Evaluating Post Earthquake Damage of Urban Buildings Based on Artificial Intelligence Algorithms

&lt;p&gt;This article mainly focuses on the damage assessment of buildings after earthquakes. Firstly, a structural damage model was established based on most reinforced concrete buildings and described using a function. Then, a BP neural network was used to solve the function. Traditional neural networks are prone to falling into local optima. Therefore, in order to improve the performance of neural networks, cross fusion with genetic algorithms is used to avoid falling into local optima, Improve the efficiency of the algorithm. Finally, through experimental verification, the proposed method can quickly evaluate the damage of building structures, with an accuracy rate of 97%.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
Scientific Approach E-Book on Science Content for VI Grade Elementary School Students

The use of technology is still lacking in learning, especially in the use of digital-based teaching materials which results in limited teaching materials that can be used by students in learning. This study aims to develop e-books with a scientific approach to science content for Class VI and to analyze the validity and effectiveness of e-books with a scientific approach to science content in Class VI. This research is development research and uses data collection techniques, namely questionnaires, pretest and post-test. Data collection is using questionnaires and tests. Data analysis techniques are using qualitative analysis, quantitative and inferential statistics. The results of this study are the results of validation by learning content experts obtained 95% with very good qualifications, validation results by learning design experts obtained 94.64% with very good qualifications, validation results by learning media experts obtained 95.31% with very good qualifications. The results of the individual trial review obtained 98.33% with very good qualifications, the results of the small group trial review obtained 96.66% with very good qualifications. The results of the effectiveness test of e-books with a scientific approach show significant differences in the results of learning science content before and after using e-book teaching materials. This shows that e-books with a scientific approach are effectively used due to the presentation design of material with a scientific approach, an attractive appearance, and practicality in learning so that they can motivate students in learning science.
Unveiling the Dynamics of Ethical Leadership: Empirical Insights and Impact on Organizational Outcomes

This article presents a research study that explores the relationship between ethical leadership, self-efficacy, and employee innovative behavior and knowledge sharing behavior in the textile industry of Pakistan. As a significant sector employing a substantial portion of the country’s workforce, understanding the interplay of leadership and employee behaviors in the textile industry is critical. Employing a quantitative research approach, primary data is collected through an adapted structured questionnaire using a 5-Likert scale to measure responses. The questionnaire assesses participants’ perceptions of their leaders’ ethical behavior, their self-efficacy, and their innovative and knowledge-sharing behaviors. The sample consists of 350 participants working in apparel outlets and textile firms in Islamabad and Rawalpindi, selected through convenience sampling. Statistical analyses in SPSS software include correlation, regression, and mediation analyses. The results reveal a positive impact of ethical leadership on self-efficacy in the textile industry of Pakistan, indicating that ethical leaders inspire self-confidence and self-belief in their employees. In turn, this self-efficacy drives innovative behavior and knowledge sharing among employees. Furthermore, the study finds that self-efficacy mediates the relationship between ethical leadership and employee innovative behavior and knowledge sharing. This suggests that self-efficacy acts as a mechanism through which ethical leadership influences employee behavior. The study concludes that ethical leadership is crucial in fostering innovative and knowledge-sharing behaviors among employees in the textile industry of Pakistan. These findings have practical implications for leaders, highlighting the importance of ethical leadership and its potential to inspire and motivate employees to engage in innovative and knowledge-sharing behaviors. This research sheds light on the significance of ethical leadership in promoting positive organizational outcomes in the textile industry and beyond. &nbsp
IMPLEMENTATION OF THE K-NN ALGORITHM TO DETERMINE PATTERNS OF TOURIST DESTINATION RECOMMENDATIONS

In this digital era, the recommendation system is an important tool in helping users to make decisions, including choosing tourist destinations. This study aims to implement the K-Nearest Neighbors (k-NN) algorithm in determining the recommendation pattern for tourist destinations. The k-NN algorithm was chosen because of its ability to classify based on the closest neighbor of a point, so that it can obtain recommendation patterns that are more personal and relevant to user preferences. This implementation method involves data collection, data preparation, model building, classification process, model evaluation, and finally implementation of the model in a recommendation system. The results of this study indicate that a recommendation system based on the k-NN algorithm is able to provide suggestions for tourist destinations that are more in line with user preferences, thus potentially increasing user satisfaction and making a positive contribution to the tourism industry. However, the effectiveness of this model is highly dependent on the quality and quantity of data used. Therefore, good data collection and preparation is very important in the implementation of this algorithm. This research opens opportunities for further research and development in the field of tourist destination recommendation systems using other algorithms or combining several algorithms to obtain more optimal results. The effectiveness of this model is highly dependent on the quality and quantity of data used. Therefore, good data collection and preparation is essential in the implementation of this algorithm. This research opens up opportunities for further research and development in the field of tourist destination recommendation systems by using other algorithms or combining several algorithms to get more optimal results. The effectiveness of this model is highly dependent on the quality and quantity of data used. Therefore, good data collection and preparation is very important in the implementation of this algorithm. This research opens up opportunities for further research and development in the field of tourist destination recommendation systems by using other algorithms or combining several algorithms to get more optimal results.
Blockchain-Powered Healthcare Systems: Enhancing Scalability and Security with Hybrid Deep Learning

The rapid advancements in technology have paved the way for innovative solutions in the healthcare domain, aiming to improve scalability and security while enhancing patient care. This abstract introduces a cutting-edge approach, leveraging blockchain technology and hybrid deep learning techniques to revolutionize healthcare systems. Blockchain technology provides a decentralized and transparent framework, enabling secure data storage, sharing, and access control. By integrating blockchain into healthcare systems, data integrity, privacy, and interoperability can be ensured while eliminating the reliance on centralized authorities. In conjunction with blockchain, hybrid deep learning techniques offer powerful capabilities for data analysis and decision making in healthcare. Combining the strengths of deep learning algorithms with traditional machine learning approaches, hybrid deep learning enables accurate and efficient processing of complex healthcare data, including medical records, images, and sensor data. This research proposes a permissions-based blockchain framework for scalable and secure healthcare systems, integrating hybrid deep learning models. The framework ensures that only authorized entities can access and modify sensitive health information, preserving patient privacy while facilitating seamless data sharing and collaboration among healthcare providers. Additionally, the hybrid deep learning models enable real-time analysis of large-scale healthcare data, facilitating timely diagnosis, treatment recommendations, and disease prediction. The integration of blockchain and hybrid deep learning presents numerous benefits, including enhanced scalability, improved security, interoperability, and informed decision making in healthcare systems. However, challenges such as computational complexity, regulatory compliance, and ethical considerations need to be addressed for successful implementation. By harnessing the potential of blockchain and hybrid deep learning, healthcare systems can overcome traditional limitations, promoting efficient and secure data management, personalized patient care, and advancements in medical research. The proposed framework lays the foundation for a future healthcare ecosystem that prioritizes scalability, security, and improved patient outcomes.
The Problem of Moral Education of the Individual in the Pedagogical heritage of K. V. Yelnitsky

The article is devoted to understanding the problem of moral education of the individual in the pedagogical heritage of K. V. Yelnitsky. The main views of the educator-educator concerning the purpose, content, principles, methods and means of moral education are analyzed. The purpose of the article is to analyze the views of K. V. Yelnitsky, directly affecting the issue of moral education of the individual. Methodology. The methodological basis of the study was culturological, anthropological and hermeneutic approaches. The work uses a set of theoretical methods: theoretical analysis of literary sources, interpretation, analysis, synthesis, generalization. The materials for writing the article were pedagogical articles by K. V. Yelnitsky and analytical articles by modern researchers. The results of the study. K. V. Yelnitsky adhered to the idea of multi-factority in the process of moral education of children. The main attention was paid by the teacher to the study of the principles, methods and means of moral formation of the child’s personality, the elimination of various negative effects that may prevail over positive ones in the process of education. Conclusion. The analysis allows us to conclude that the ideal and goal of moral education in the pedagogical concept of K. V. Yelnitsky was a virtuous person, a happy person, a good citizen, a true patriot, an educated and hardworking member of society.
Optimizing Voice Recognition Informatic Robots for Effective Communication in Outpatient Settings

Aim/Objective Within the dynamic healthcare technology landscape, this research aims to explore patient inquiries within outpatient clinics, elucidating the interplay between technology and healthcare intricacies. Building upon the initial intelligent guidance robot implementation shortcomings, this investigation seeks to enhance informatic robots with voice recognition technology. The objective is to analyze users' vocal patterns, discern age-associated vocal attributes, and facilitate age differentiation through subtle vocal nuances to enhance the efficacy of human-robot communication within outpatient clinical settings. Methods This investigation employs a multi-faceted approach. It leverages voice recognition technology to analyze users' vocal patterns. A diverse dataset of voice samples from various age groups was collected. Acoustic features encompassing pitch, formant frequencies, spectral characteristics, and vocal tract length are extracted from the audio samples. The Mel Filterbank and Mel-Frequency Cepstral Coefficients (MFCCs) are employed for speech and audio processing tasks alongside machine learning algorithms to assess and match vocal patterns to age-related traits. Results The research reveals compelling outcomes. The incorporation of voice recognition technology contributes to a significant improvement in human-robot communication within outpatient clinical settings. Through accurate analysis of vocal patterns and age-related traits, informatic robots can differentiate age through nuanced verbal cues. This augmentation leads to enhanced contextual understanding and tailored responses, significantly advancing the efficiency of patient interactions with the robots. Conclusion Integrating voice recognition technology into informatic robots presents a noteworthy advancement in outpatient clinic settings. By enabling age differentiation through vocal nuances, this augmentation enhances the precision and relevance of responses. The study contributes to the ongoing discourse on the dynamic evolution of healthcare technology, underscoring the complex synergy between technological progression and the intricate realities within healthcare infrastructure. As healthcare continues to metamorphose, the seamless integration of voice recognition technology marks a pivotal stride in optimizing human-robot communication and elevating patient care within outpatient settings.
Development of additional professional education in Russia

The article provides an overview of current trends in the development of additional professional education. The article describes such trends in the development of education as a hybrid format, micro-qualification, training with the involvement of social networks and autonomy. Some factors hindering the development of the quality of vocational education are analyzed. The features of digital educational technologies, the use of which is relevant in the framework of the educational process, are highlighted
The Challenges of Internet Usage for Academic Activities Among Students of Delta State University, Abraka

This study examined the challenges of Internet usage for academic activities among Delta State University, Abraka students. Despite the numerous benefits students derived from internet usage, they are still faced with challenges which stakeholders need to address for an effective educational system. Three research questions were raised to achieve the objectives of the study. The study adopted a descriptive research design with a sample size of 120 students randomly selected from 100 to 400 levels. The data collected from respondents through structured questionnaire were analysed using descriptive and inferential statistics. The findings of the study revealed that the effect of Internet access affordability on academic activities among students of Delta State University Abraka included the cost of accessing the internet, high cost of internet devices and services, lack of access points and devices with limited capacity; lack of personal internet device and reliance on friends and families for internet access. It was also established that most of the students lack internet browsing skills and required skills to use digital devices and language barriers. The study concludes that the effects of safety and privacy concerns on students’ academic activities derived from fear of scams, stealing passwords, fake news and information, fraudster and distraction. It was therefore recommended among others that internet service providers should endeavour to eliminate or reduce internet challenges such as network problems and connection breaks.
Number Rinner Games To Improve 5-6 Year-Old Counting Ability

The purpose of this research is to develop educational game tools for improve children's cognitive. The application of APE (Educative Game Tool) was carried out on village children to be exact at Harapan Pertiwi 2 Kindergarten. The researcher used the R&amp;D research method (Research &amp; Development) which aims to determine the application of game tools educative windmill counting in developing the development of cognitive aspects of children. This study used a child subject, namely 4 children between the ages of 4-5 years. Technique data collection using observation, interviews, trials, expert validation, and documentation. Based on the results of the research on the application of the Counting Rabbit Educative Game Tool it is proven feasible to use and able to improve children's cognitive abilities. Children are very enthusiastic in trying the game. The results of the data obtained from the Material Expert found that the total rating score obtained was 63 out of the expected 80, after being converted in the presentation the result was 143.2% in the 'Very Good' category. In the assessment rubric it is known that there is a significant difference in the ability to recognize the concept of numbers in children aged 5-6 years in playing the Counting Rabbit Windmill. In the pre-test activity, an average score of 30 out of 80 was obtained. Meanwhile, in the post-test activity using developed media, an average score of 50 was obtained from the score of 85. The pre-test and post-test activities experienced an increase in the average score of 125.1. So that with the Counting Rabbit Wheel media it can improve children's cognitive abilities.
Design and Deploy a Recovery Solution

NIST cybersecurity incident recovery guidelines provide a framework for organizations to prepare, detect, respond to, and recover from cybersecurity incidents. The policies are designed to be flexible and adaptable to various organizations, regardless of size or industry. They provide a structured approach to incident response and recovery that can help organizations effectively manage cybersecurity incidents and minimize the impact of these incidents on their operations and reputation.
Penerapan Metode Discovery Learning untuk Meningkatkan Hasil Belajar IPS Materi Interaksi Sosial di Kelas VII B SMP Negeri 16 Mataram

The purpose of this study was to find out the increase in social studies learning outcomes in Social Interaction through the Application of the Discovery Learning Method to Improve Results in Class VII B of SMP Negeri 16 Mataram for the 2019/2020 academic year. The subjects of this study were Class VII.B students of SMP Negeri 16 Mataram in the odd semester of the 2019/2020 academic year, with a total of 32 students. The results showed that research using the Discovery Learning model could increase the activity of teacher and student learning outcomes. This was evident from the results obtained in cycle I was 74.06, cycle II was 82.71 and in cycle III was 88.18 for student learning outcomes . So it can be concluded that the application of the Discovery Learning method improves learning outcomes Social Interaction Social Studies Class VII B students of Mataram 16 Public Middle School in the 2019/2020 academic year.
Pengaruh Metode Drill terhadap Hasil Belajar Matematika Siswa Kelas IV SDN Curug Wetan II

This research is to find out the effect of drill methods on study mathematics results for IV grade student SDN Curug Wetan II. This research using experimental quasi research methods with research design Neonequivalent Control Group design. The population subject in this research its all students of IV grade SDN Curug Wetan II who have 58 students, with take a sample from two class who have 58 student from class IV A who have 30 students as a control class and class IV B who have 28 students as Experimental class. Data collection techniques using test question instruments, consisting of 14 valid and reliable questions. For test the hypothesis pretest In this study is using t-test, from the t-test results obtained calculated = -2.46 and table = 2.00, it can be concluded that there is no significant effect between the mean pretest scores of the control class and the experimental class. Meanwhile, for testing the posttest hypothesis, the t-test results obtained calculated = 8.52 and table = 2.00, it can be concluded that there is a significant effect between the average post-test scores of the control class and the experimental class. This can be interpreted that the results of learning mathematics IV grade students with using the drill method is higher than using the conventional learning method.
Meningkatkan Kemampuan Membaca Pemahaman melalui Penerapan Metode Reciprocal Teaching Masa Covid-19 Siswa Kelas VII-A SMPN 3 Masbagik Tahun Pelajaran 2020/2021

This study aims to find out that the use of the Reciprocal Teaching method can improve students' reading comprehension skills in grade VII-A during the COVID-19 period at SMPN 3 Masbagik, Wanasaba district, East Lombok district. The research was carried out for 3 months starting from October to December 2020. The form of this research was classroom action research. From the results of the first cycle, it can be seen that the students' reading comprehension ability averaged 63.5 while in the second cycle there was an increase to 77.73. This situation shows that the improvement of students' reading comprehension learning using the Reciprocal Teaching method can be said to be successful, although individual mastery has not been fully achieved but class mastery has increased from 67% in the first cycle to 90% in the second cycle.
Cyberbullying: A BERT Bi-LSTM Solution for Hate Speech Detection

With the growth of data on social networks, the semantic analysis of shared text has become essential for decision-making and knowledge extraction. It is also the case for the detection of abusive and hateful content. Most of the work done in this field so far proposed classical machine learning solutions. But recently, models based on deep learning have also been applied. In this paper, we propose a neural network model for hate speech classification using two imbalance datasets that were imbalanced, containing approximately 25 thousand and 10 thousand annotated text samples. The proposed approach is evaluated using the standard evaluation metrics, such as Accuracy and F-score. We compared our approach to the baseline results and it showed satisfying results. Moreover, our approach was able to detect more instances of the minority class compared to the baseline results.
Career guidance in the Belgorod region

Vocational guidance within the framework of educational activities is an extremely relevant area of work. This activity is regulated by both Federal Laws and regional regulations. In this paper, we analyzed the main methods for the development of career guidance activities. On the example of the Belgorod region, we determined which of these techniques work and what results they give. There are various vocational guidance centers in the region that assist schoolchildren and students in choosing a profession, providing information on the labor market, training and employment opportunities. However, despite the results achieved, there are still a number of challenges and problems that need to be addressed. One of these problems is the lack of information about the labor market in the region for specific professions, which makes it difficult to choose a future profession. The paper shows, taking into account trends in the labor market and the socio-economic situation in the Belgorod region, career guidance activities have great potential for development and improvement.