Reviewerly/exHarmony huggingface dataset:
An Empirical Study of the Relationship Between Covid-19 and Jordanian Banks' Profitability

Purpose: To investigate the impact of the COVID-19 pandemic on the profitability of Jordanian banks using financial disclosure data. Theoretical framework: The study focuses on the impact of external environmental factors such as the COVID-19 pandemic and non-performing loans on the profitability of Jordanian banks. Design/methodology/approach: The study uses a panel model to analyze financial disclosure data of 15 Jordanian banks between 2017 and 2021. The study employs both descriptive and inferential methods, utilizing a range of secondary sources including official reports, articles, electronic journals, and previous studies. Findings: The study reveals that the COVID-19 pandemic had a negative impact on the Return on Assets (ROA) of Jordanian banks. Additionally, the number of bank branches, earnings per share, and non-performing loans were significantly associated with ROA and Return on Equity (ROE). Research, Practical &amp; Social implications: The study highlights the sensitivity of banks to external environmental factors such as pandemics and economic shocks, which can impact their solvency and trigger a chain effect that puts the entire economy at risk. The findings can inform policy decisions and help increase bank resilience in the face of future crises. Originality/value: The study contributes to the literature on the impact of external environmental factors on the profitability of banks in emerging economies such as Jordan. The study's use of financial disclosure data provides a unique insight into the performance of Jordanian banks during the COVID-19 pandemic.
Pergerakan Non-Playable Character dalam Game Stealth Menggunakan Finite State Machine

Stealth is a game genre that relies on hiding skills and strategizing to complete missions. Challenges in the form of enemy Non-Playable Character (NPC) are one of the vital elements of this genre game. However, NPCs generally only move according to a predetermined line or path of movement, so that their movements can be estimated easily after the player has seen them several times. This causes the movement of NPCs to look monotonous and not challenging. Finite State Machine (FSM) is an artificial intelligence method that can simply model enemy behavior. FSM consists of states and transitions where the state is the current state of the NPC, while the transition is the state of a state. This study models the behavior of enemy NPCs in stealth games using FSM so that it is not easy to predict their movements by adjusting idle time, walking, and moving speed The results of this study indicate that NPC can have nine variations of behavior that can change according to these conditions.
PENGEMBANGAN E-MODUL PADA MATA PELAJARAN ADMINISTRASI INFRASTRUKTUR JARINGAN (AIJ) KELAS XI TKJDI SMK NEGERI 1 RAO SELATAN

The purpose of this study is to develop e-module-based learning media using the Sigit editor software. Development of learning media based on e-modules in AIJ subjects that are valid and practical. Based on the problems previously described, this type of research uses the R&amp;D (Research and Development) development research model which aims to produce application products and test the effectiveness of these products. The results of research on the development of E-modules in AIJ subjects that have been tested for the validity of developing E-modules in AIJ Class XI TKJ subjects at SMK Negeri 1 Rao Selatan, state that the e-module is very valid, namely material validity is 0.9 and media validity is 0.822 and the practicality test for the development of E-modules in the AIJ Class XI TKJ subject at SMK Negeri 1 Rao Selatan, declared practical by the teacher and with 100% practicality and 86.6% students.
Klasifikasi Masyarakat Penerima BPNT Program Sembako 2021 di Kelurahan Tiakar dengan Mengunakan Metode KNN Classifier

At the time of the implementation of the food program in the Tiakar Sub-District, there was no information available on the criteria that would make the head of a family eligible to be a recipient of assistance, even though this was very important so that the government's goal of fulfilling the need for nutritious food could be felt by those who really needed it. The purpose of this study was to classify the heads of families in Tiakar Subdistrict as eligible or not eligible to receive staple foods using the K-Nearest Neighbor method. This study uses interview data conducted with the heads of families in the Tiakar Village. The data analysis step is to divide the data into training data and test data by 80%:20%, determine the number of nearest neighbours, calculate the dissimilarity distance and choose a class, calculate the level of accuracy using the confusion matrix and then choose the optimal K. Based on the results of the study, it was found that the K value that was good to use in the classification of family heads in Tiakar Subdistrict was K=3 because it had an accuracy percentage of 95%.
PANDAS AI: A Step Towards GEN AI

In the era of Generative Artificial Intelligence, there is a widespread emphasis on employing conversational methods to address and resolve issues. Conversational approaches to problem-solving have become the primary focus for everyone, regardless of the nature of the problem at hand. With the release of ChatGPT and chatbots like technologies, developers are also having high hopes of having such libraries and tools in future which can make their life easier. Conversational AI is a strategy enabling users to communicate with machines using language that is like that of humans. These programs will translate human language into relevant outcomes. Pandas AI has provided similar type of functionality for the most widely used Pandas library of Python. Pandas AI is a python library that adds human like conversational ability to Pandas and can be used for Data Analysis, cleaning, manipulation, transformations, insights generation etc. This library is used in conjunction with existing Pandas python library. Keywords—Pandas AI, Generative AI, Conversational AI, Artificial Intelligence
The Mordern Web 3.O in Blockchain Technology in Cryptographic

This research focuses on the potential of blockchain technology in the context of the modern web 3.0.The study explores the key characteristics of web 3.0, which includes the decentralized nature of the internet, enhanced user privacy and security, and the use of artificial intelligence and machine learning to improve the user experience.The research examines the potential of blockchain technology in meeting the requirements of web 3.0, which include transparency, immutability, and security It discusses the key components of a blockchain system, including distributed ledger technology, consensus algorithms, and smart contracts, and their relevance to web 3.0.The study also reviews the current state of blockchain technology, its limitations, and the challenges it faces in becoming a viable solution for web 3.0.It identifies key areas for further research, including scalability, interoperability, and regulatory issues.Overall, the research concludes that blockchain technology has the potential to play a critical role in the development of the modern web 3.0, but further research is needed to address the current limitations and challenges associated with its implementation.The most important aspect in web 3.0 examples such as Siri and Alexa voice assistants shows how machine learning can develop a new array of internet services.Other than the signs of introducing machine learning and connecting machines through IoT, the third generation of the internet would run on decentralized protocols Therefore, it is important to find a possible convergence of blockchain in web 3.0.
An Evaluation of the Lookahead Planning Function in Last Planner® System

Last Planner ® System (LPS) has been lauded as a critical improvement methodology for project execution.Best results accrue when all functions are utilised.However, in practice, due to lack of knowledge and appreciation of the LPS cycle and complementary interactions required, teams are not achieving optimal outcomes.Effective Lookahead planning that reduces variation and 'making-do' are primary concepts for facilitating better construction task execution.This study goes 'back to basics' and explores how improved Lookahead planning can enhance project delivery.It utilised a mixed-methods approach with case study design, encompassing interviews, project documentation, and existing research data.The case project utilised Visual Management, Takt concepts, Scrum, and Flow Walks to engage site supervisors proactively and collaboratively in diligent Lookahead planning.Findings demonstrate involvement of the trades persons in task breakdown and design of the operation ensured better activity and trade flow resulting in improved task execution.Proactive and diligent constraint screening and flow walks resulted in increased constraint identification and better on-time resolution, while also developing a workable backlog.Conducting a First-Run Study resulted in immediate productivity improvement.The basics of production planning and control are an essential component of Lean Project Delivery.The research highlights the value in practitioners exploring original literature in more depth to gain better knowledge and skills of the Lookahead planning function.
Visualization in the Context of Motivation Development Within the Framework of Corporate Training

The current stage of socio-economic development is characterized by the use of information and digital technologies in all spheres of human activity.The processes of production, exchange and consumption of goods, including knowledge and information, have changed.Intellectualization and digitalization of production systems are the main trends of economic development, which are supported in many countries.The functioning space of any organization is now increasingly facing difficulties in processing a large amount of data and new information to maintain competitiveness at the proper level.Employees' cognitive skills are also being transformed.According to our surveys, it is more difficult for employees to focus and keep their attention on all incoming information, process and analyze large amounts of data, present compressed data in reports so as to briefly and quickly explain the essence.Also, employees tend to experience stress because they do not have time to master new skills necessary for work.This is especially felt by representatives of creative professions (designers), as well as marketers, programmers, etc.Moreover, the modern volume of data should not just be perceived by employees, but analyzed, understood and used in work.All this leads to an increase in the volume of corporate training, which is not always of high quality and often includes traditional forms in the form of lectures, seminars.In the current situation, it is necessary to apply new approaches, techniques and methods to the education system at different levels in order to develop students' skills that are in demand in a modern technological environment.Modern educational practice is more effective when it uses visualization.
Distance Learning during the COVID-19 Pandemic: Stress Factors for Female School and University Teachers in Kazakhstan and Kyrgyzstan

According to international studies, teachers experience more stress than representatives of other professions and are included in the group of occupations with a high presence of stress factors.This is due to the hard work of teachers in terms of high responsibility, motivational and personal involvement in pedagogical work, as well as significant participation in communication with school children or students that determine the stressful nature of the teacher's activity.Difficulties were especially noticeable when teachers switched to distance education in the context of the COVID-19 pandemic.This study examines the predictors of stress affecting the mental health of female teachers in schools and universities in Central Asia (Kazakhstan and Kyrgyzstan) during distance (online) learning at the time of the pandemic.In addition, according to the results of the study, it was found that in the conditions of COVID-19, the impact of the level of material and technical security/access on mental health is important when female teachers have a good social climate at work.The study also confirmed that the impact on mental health is most noticeable when female teachers are more actively involved in the decision-making process with specific organizational goals in accordance with a participatory management policy, and female teachers often receive feedback about their work.Female teachers at universities and schools were surveyed using a random stratified sampling method, and an anonymous online survey was conducted.Responses from school and university women teachers/lecturers were collected in Google Forms, which subsequently led to a comparative analysis of stress factors affecting their mental health.A total of 748 female teachers took part in the survey.Empirical research is based on a quantitative research method and uses a survey data collection methodology.Stress factors among female educators were tested by T-test analysis using SPSS.22 software.
Multi-View Graph Convolutional Networks with Differentiable Node Selection

Multi-view data containing complementary and consensus information can facilitate representation learning by exploiting the intact integration of multi-view features. Because most objects in the real world often have underlying connections, organizing multi-view data as heterogeneous graphs is beneficial to extracting latent information among different objects. Due to the powerful capability to gather information of neighborhood nodes, in this article, we apply Graph Convolutional Network (GCN) to cope with heterogeneous graph data originating from multi-view data, which is still under-explored in the field of GCN. In order to improve the quality of network topology and alleviate the interference of noises yielded by graph fusion, some methods undertake sorting operations before the graph convolution procedure. These GCN-based methods generally sort and select the most confident neighborhood nodes for each vertex, such as picking the top- k nodes according to pre-defined confidence values. Nonetheless, this is problematic due to the non-differentiable sorting operators and inflexible graph embedding learning, which may result in blocked gradient computations and undesired performance. To cope with these issues, we propose a joint framework dubbed Multi-view Graph Convolutional Network with Differentiable Node Selection (MGCN-DNS), which is constituted of an adaptive graph fusion layer, a graph learning module, and a differentiable node selection schema. MGCN-DNS accepts multi-channel graph-structural data as inputs and aims to learn more robust graph fusion through a differentiable neural network. The effectiveness of the proposed method is verified by rigorous comparisons with considerable state-of-the-art approaches in terms of multi-view semi-supervised classification tasks, and the experimental results indicate that MGCN-DNS achieves pleasurable performance on several benchmark multi-view datasets.
Social, Psychological, Professional and Academic Features of the Use of Social Media in the Activities of Higher Education Institutions

In addition to being an important part of modern public consciousness, social media are seen as the basis of information and educational activities in higher education institutions.However, social media have not only social and academic, but also psychological impact (which is important for foreign students during the acculturation process).Furthermore, the Article states that certain social media (e.g.LinkedIn) contribute to the formation of professional and academic directions because they provide interaction with the teaching staff of an educational institution.Thus, the issue of the practical application of social media by universities as part of their activities is relevant.This allowed us to formulate the purpose of our study, i.e. the theoretical substantiation of social, psychological, professional and academic features and analysis of the practice of using social media in the activities of universities in Ukraine (V.N.Karazin Kharkiv National University) and the US (New York University).The Article outlines the criteria for selecting countries and universities for the study as well as the methods used such as analysis (when studying the practical application and concept of social media content), synthesis (to aggregate the data obtained and interpret the Outcome (Competencies) category) and statistical method (aimed at working with information and digital data).Having analysed the content of social media such as Facebook, Instagram, Twitter, Telegram, YouTube, LinkedIn, TikTok, we concluded that the Content category is represented in the selected universities by materials of an informational, socio-educational and lecture nature.It should be emphasised that professional video content of New York University is more effective due to its thematic diversity.In turn, the Outcome (Competencies) category is determined by the professional development of students, building emotional and psychological resilience when adapting to a new social or social and cultural (for foreign students) environment.
THE EFFECTS OF TRANSFORMATIONAL, TRANSACTIONAL, AND LAISSEZ-FAIRE LEADERSHIP STYLES ON EMPLOYEE MOTIVATION: A QUANTITATIVE STUDY CONDUCTED ON THE PRIVATE SECTOR IN MOROCCO

This research explores the influence of transformational, transactional, and laissez-faire leadership styles on employee motivation within the private sector in Morocco.The study reveals that while transformational and transactional leadership styles significantly enhance employee motivation, the impact of laissez-faire leadership is less consistent.The article underscores the complexity of motivation, suggesting leaders need to adapt their style to the unique needs and aspirations of their employees.
Power generation at highways using vertical windmill and efficient solar system

This project proposes using vertical windmills and efficient solar systems for power generation along highways.The compact vertical windmills capture wind energy from all directions.High-efficiency solar panels are mounted alongside the windmills.Advanced monitoring and control systems optimize energy production.The generated power is stored in batteries for continuous supply.This project promotes sustainable energy solutions and reduces carbon emissions.It harnesses the open spaces and consistent airflow along highways for efficient power generation.
A Multimodal Discourse Analysis of Five Selected Online Bank Advertisements

Language remains an indispensable tool used in the advertisement world to communicate. Advertisements as forms of communication are goal oriented; they are used to manipulate the viewers or listeners’ emotions not for the sole aim of entertainment but designed to achieve their persuasive goal of selling goods and services. The covid 19 outbreak had a big impact on several industries and the banking sector is not an exception. It is an undeniable fact that current advertising landscape poses a challenge to agencies, all round the world; hence businesses can still experience great result with digital advertising values for those who stay at home. Thus, the problem still remains that people do not look beyond the fascinating coated language, images, colour, gestures, and facial expression among others which have in –depth meaning. This paper investigates the different multimodal elements employed by Banks in the advertisement of their products and services on Facebook. Five screen-shot advertisements of five banks from Facebook were sampled and subjected to descriptive analysis. The Banks are, Access Bank, United Bank for Africa (UBA), First City Monument Bank (FCMB), First Bank of Nigeria and Guarantee Trust Bank (GT). The study is hinged on Kress and van Leeuwen (2006) theoretical approach to multimodal discourse which is heavily anchored on the systemic functional linguistic orientation of Halliday’s (1975) view of language as a social semiotic inspired studies on multimodal socio semiotic. The analysis revealed that bank advertisements are characterized by verbal and visual resources which include images, bold prints, capitalization, gothic writing, figures of speech, phrases, lexical acronym, quotes, punctuation marks and other graphological devices. It also revealed that colour, gesture, facial expression among others is also significant for meaning making of online Bank adverts. The paper concludes that some of these multimodal elements are embedded in the advert and they have persuasive effects on the audience even during the lock down situation.
REACT: A New Technical Committee for Earth Observation and Sustainable Development Goals [Technical Committees]

Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.
Data Storage and Research on Distance Education Platform

Distance education resources have the characteristics of large amount of data and rapid growth.Low-cost storage and content-based efficient retrieval of these massive data resources are a major problem in the construction of distance education cloud platforms.Based on Hadoop, this paper designs a set of storage and retrieval methods for the massive resources of distance education, which solves this problem.Distribute the load of video and audio streams to the network.For this reason, this paper proceeds from the reality of the application of the distance teaching platform, fully considers the human factors of the user's order and the technical environment of the video service system and conducts in-depth research on key technologies such as data storage, database update, and resource synchronization in the resource service system.And put forward the following improvements and innovations: use of cloud storage technology; automatic update of educational information in the database; automatic download of resource updates; synchronous update technology, which greatly reduces the workload of managers.
Java Implementation of Intelligent Platform of Computer Distance Education Based on Real-time Information Interaction Algorithm

An interactive two-level fusion algorithm based on evidence theory is proposed.The data from different information sources obtained by the data fusion system are classified in detail, and the different roles of non-real-time information and semi-real-time information in the early and middle stages of data fusion are clarified.This paper designs and implements a value-added platform based on Java language, using SSM framework, AngluarJS framework and Shrio security framework.Analyze the teaching organization form, teaching evaluation method, teaching management mode and other elements of the blended teaching model, emphasize the learner-centered constructivist education concept, rely on the smart online teaching platform to collect teaching process data, and provide the basis for teaching assessment and teaching improvement .While reducing the use cost of the resource sharing platform, it can eliminate resource heterogeneity and provide services on demand, providing a reference for further optimizing the distance education resource sharing system.