Reviewerly/exHarmony huggingface dataset:
Analysis of Query Processing on Different Databases

The most trustworthy method for categorizing data into updateable and searchable collections in modern times is database management systems. However, there are different DBMS systems available, and each has its own pros and cons regarding dependability, use, performance, and its security. The performance of the main DBMS systems is compared in this paper. Oracle, MS SQL Server, MS Access, MySQL and IBM DB2 are the prominent ones. The five DBMSs under examination will be subjected to a variety of SQL queries with varying levels of complexity. After the test, this would make it possible to create a comparison analysis among them that displays the average execution time of each DBMS.
SBM: A Time and Resource Efficient Single Backward Match Exact String Searching Algorithm

As in the current era, data is expanding daily on the web, where tremendous data banks with big data need to be searched by different users according to their data needs. This is when normal users cannot afford more waiting time searching the required data and indulge themselves in uncomfortable, lengthy, and complex computational processes. Moreover, search results will also play a vital role in extracting the desired output from the existing data bank. By keeping this common and consistent need of the normal user, the effort has been made to formulate a solution in the exact string searching algorithm. The algorithm's simplicity will add efficiency and speed up the processing time compared to the existing exact string-searching algorithms in terms of worst-case complexity. The proposed algorithm is compared with Boyer Moore Hoorspool, Knuth Morris Pratt (KMP), and Back and Forth Matching Algorithms to understand the results better.
The Role of Artificial Intelligence in Mitigating Cyber Security Issues and its Impact on FinTech

Given the status of cybersecurity today, it seems natural that researchers are seriously considering how artificial intelligence may help breakthroughs in conventional cybersecurity techniques. Some of the issues linked to cybersecurity have been mitigated by various technical developments. The use of Big Data, Blockchain technology, and behavioral analytics, to mention a few, can make these breakthroughs possible in financial sector. In order to measure the factors that assist in eliminating cyber security issues in financial technology this research was conducted. An empirical approach used to acquire the findings. Data were collected from 70 different branches of the banks (public and private) located in Dubai UAE. 201 valid respondents employed to test for statistical analysis. The research was purposive to measure primary data from FinTech sector where as secondary data used to authenticate the hypothesized model of the proposed research. Data was tested using SmartPLS 4.0 by applying, reliability, validity, discriminant validity and hypothesis testing. The analysis indicate that artificial intelligence has a greater propensity to resolve cyber security challenges in the financial sector.
Improving Security in IoT System Using a Blockchain and a Microservices Technologyy

In the Internet of Things (IoT), new inventions enhanced a demand for federation cloud layers through different smart organizations to exchange data and transaction. One problem with traditional security approaches is that they rely on a centralized entity. In this research paper, we present blockchain using microservices and smart contracts for federation cloud systems in an IoT scenario. Moreover, we demonstrate the validation system to allow transactions to be exchanged between heterogeneous blockchains used by various smart organizations. Finally, we present the proposed framework that enables interconnection and guarantees the security of sharing data for various IoT systems in a federated cloud.
Moderating and Mediating Roles of Users' Self-Efficacy and User Behavior amongst the Internet of Things and Search Engine Optimization

This study intends to examine how the Internet of Things (IoT) is affecting search engine optimization (SEO), while also considering the mediating function that user behavior plays. Based on a thorough examination of previous studies, a conceptual research model was created in accordance with UTAUT. An intentional sample of Jordanian workers working for telecom businesses was surveyed using a self-management questionnaire. Using Smart PLS 3.0, a dataset of 131 acceptable questions was examined. The IoT has an impact on user behavior and SEO, according to key findings. IoT also makes important predictions about user behavior, which mediates how it affects SEO, finally found that Users' self-efficacy moderated the link amongst Internet of Things (IoT) and user behavior. The outcomes highlight the significance of SEO as a cutting-edge, successful e-marketing technique for promoting consumer acceptance of new technology.
Impact of e-Human Resources Management on Public Safety with Mediating role of Occupational Health

Public health and safety has recently shifted its attention from the environmental causes of disease to personal risk-taking behaviors. The approach of workplace public health has tended to downplay physical exposures and stressful working environments in favor of individual concerns. However, the people managing phenomenon catered the electronic networking in the workplace that may assist in finding the employees’ health and safety concerns through online portals and feedback. For the purpose to highlight these issues this research was conducted to explore the e-human resources and its impact on public safety with mediating effect of occupational health in the workplace. The research was objected to explore through online survey done within telecom sector Dubai, UAE. A quantitative approach was utilized with descriptive and exploratory research design. 206 employee’s responses were considered for the statistical analysis. SmartPLS 4.0 software used to assess the data by determining validity, reliability, discriminant validity and mediation through bootstrapping methods. The findings suggest an indirect positive influence of occupational health between the relationship of e-HR and public safety.
Using Gradient Boosting Algorithms in Predicting Student Academic Performance

Educational institutes carried out a huge research to investigate student academic performance and analyses the major factors that may affect student performance. Educational institutes care about how many students will continue their studies successfully and their appreciation. There is a crucial need to investigate and address the main challenges that may affect the student academic performance. In this paper, we propose a new approach to analyses student academic performance using Gradient Boosting algorithms. In particular, the proposed approach implements the XGBoost, CaBoost and LighGBM Gradient Boosting algorithms. The proposed approach consists of two prediction models: the student final status model and student appreciation model. The achieved results show that the CaBoost algorithm gets the best prediction results when it reaches 92.16% for student final status model and 86.89% for student appreciation model. On the other hand, the LightGBM gets the best results in terms of speed.
Wormhole Attack Detection Technques In MANET

MANET is a wireless medium, with no infrastructure, for nodes to communicate within a network. Movement of nodes is a lot more in this network than other networks due to lack of infrastructure (Wireless nature). In a mobile ad hoc network, each node functions as a router and finds the best path forwarding a packet via origin to destination. This network is important in places where building a framework is impossible, like in a military setting. A mobile ad-hoc network (MANET) is a temporary network created by unrestricted nodes that can move around and communicate without a centralized network. In a black hole attack, several or even more attacker node use a tunnel-like private channel to find a wormhole attack. Wormhole tunnel will then start collecting the data packets and transferring them to another location. The proposed work encouraged secure and effective routing in MANET. At that point, we selected 20 important attributes to create a dataset that was labelled with the aid of a unique node address. Apply two well-known machine learning classifiers that categorise data into two groups, specifically normal and malicious, to test sample data in this manner. For feature selection and SVM (Support Vector Machine) classification, respectively, we employ genetic algorithms. The demonstration of the technology was assessed on many quantifiable characteristics and compared to contemporary techniques.
Goldonomics: Cryptocurrency vs. Gold; Which is a Better Store of Value in the Global

Join the age-old debate over which asset is the superior store of value: Bitcoin or gold? These two heavyweight candidates have captured the interest of investors all across the world. Bitcoin's decentralized characteristics and limited supply make it an appealing alternative to existing fiat currencies, whereas gold has long been seen as a reliable hedge against inflation and currency depreciation. This paper goes into the benefits and cons of each asset, examining its ability to hold value over time. So, join us as we assess if Bitcoin or gold is the ultimate heavyweight champion of the global economy's store of value.
Post-Covid-19 Pandemic IT Project Management Skills and Challenges

Since December 2019, the COVID-19 pandemic has harmed social, financial, and work life. The novel coronavirus has caused problems in all business sectors, including Information Technology(IT). Many Projects have been stopped or delayed due to the impact of this pandemic. Most of the companies recognized the importance of IT to achieve competitive advantage and to survive. Companies are investing a lot of money in IT projects. More than 60% of IT project fail. Lack of professional IT project management is one of the main reasons for that fail. Managing IT projects is a complex problem. Crises such as COVID 19 and uncertainty are increasing the complexity and challenges in IT projects management. This paper investigates what are the IT project management Key success factors required during and after the Pandemic. Secondary data were collected from literature review in the field of IT project management. an online questionnaire was used to collect the primary data from 107 IT firms. 323 respondents were participated. It is concluded that the following Key success factors are required: e-communication, centralization of data, online project monitoring, designing new policies and guidelines for incorporating new work culture, gaining and establishing project risk management exercises, especially cybersecurity and data protection by gaining access to the latest tools, establishing a culture for strict usage of Personal Protective Equipment (PPEs) to help project managers complete projects safely, and top management support.
Electronic Image Processing for Writer-independent Offline Signature Recognition based upon Fourier Descriptors

The Signature Recognition system collects and compares a set of distinctive features that are derived from the writer's typical patterns of behaviour throughout the signature creation process. One signature verification process's goal is to confirm or reject a sample, while another's seeks to identify the sample's author. In this work, we offer efficient algorithms for signature recognition based on Fourier Descriptors. The Fourier Transform is a highly effective technique for object recognition. Fourier transforms of shape signatures produce FDs. The following is a boundary tracing of the signature image. Through the application of morphological processes, we are able to encompass the full signature within a closed curve that is a good fit for it. The generated curve is unique for each signature and so may be efficiently utilised to calculate FDs in the context of form recognition. The FAR is a metric used to measure the success and exactness of a system through calculating the number of times it incorrectly verifies inaccurate patterns. In this study, a local data of 60 test sign patterns was utilized, and it was discovered that 10% of those patterns were mistakenly validated, resulting in a False Acceptance Rate (FAR) of 0.175.
Triple-Entry Accounting (TEA) and Blockchain Implementation in Accounting and Finance - A Survey

Accounting practices are based on their underlying accounting principles, and currently, the Double-Entry Accounting (DEA) is used in both manual and computer-based Accounting Information Systems (AIS). DEA has been used for centuries according to the rules that were set when it was invented. At this stage, there is no further room to expand its functionality, but a logical expansion of DEA is possible. This new dimension is called Triple-Entry bookkeeping in which a third accounting entry is passed in a shared ledger that is distributed in nature and publicly visible. Similarly, Blockchain is a form of shared ledger distributed on a peer-to-peer network, and all transactions in the ledger are available to view. Blockchain emerged in 2008 as an underlying record-keeping and data storage technology for Bitcoin. It is believed that TEA is the idea behind the Blockchain. In this research study, we review the evolution of TEA and Blockchain from the existing academic literature, and the findings of the study will deepen the researchers' understanding of these two concepts and help them identify new opportunities.
Integrating Artificial Intelligence in Improving Educational System: The Mediating role of Smart Learning

One of the contemporary developments in educational technology is artificial intelligence (AI) in the education. The use of AI will lead to a significant change in governance across the board for UAE institutes of learning. The potential use of AI in education includes an examination of the educational consequences, including how teachers would be enriched, how students would learn, and how fast and accurate judgements could be made in education institutions. Given this situation, AI assistance is absolutely necessary. Despite the fact that it has been around for close to 30 years, educators are still confused of how to use it pedagogically on a broader scale and how it may actually have a big impact on teaching and learning in higher education. Through an empirical review, this research aims to present an empirical analysis of the application of artificial intelligence in educational system with mediating effect of smart learning. This research carried out empirical evidence from 28 educational institutions based in UAE. The managerial and professors were approached in order to answer the online questionnaire. 195 valid responses were considered for data analysis using SmartPLS. Model assessment done on the basis of various tests, for instance, reliability, discriminant validity, PLS-SEM. The findings suggest a significant relationship between artificial intelligence and educational system, and mediating role of smart learning is predicted to be significant. Hence, the intention to use artificial intelligence with smart learning can enhance the academic learning outcomes.
Increase the Degree of Accuracy by Employing A More Accurate Classification Approach

Traditional artificial neural networks and machine learning have traditionally been used to classify images; however, these approaches have not only struggled to keep up with the processing demands of such datasets but have also performed inefficiently and with poor classification accuracy due to the processing demands of enormous image datasets during feature extraction and model training. Large photo collections make it challenging for machine learning and traditional artificial neural networks to keep up with their processing demands. Deep learning significantly surpasses other, more traditional techniques for photo classification. Another difficulty was that when dealing with really large pictures, standard artificial neural networks were forced to exceed their data storage limits. As a direct result of their research, the authors of the study developed a deep-learning model for image tagging. This model was developed as a tool to aid in the identification and categorization of pictures on a large scale. Following a brief explanation of neural network theory, the study's focus shifted to an examination of the various convolutional neural network types and the general strategy for employing such networks for picture categorization. The present model of a convolutional neural network was utilized to minimize noise and alter the parameters used in the feature extraction operation. This was supposed to improve the results' dependability. An upgraded convolutional neural network was used to develop the model's architecture. The resulting deep learning model improved operational effectiveness and data categorization accuracy significantly. To achieve this purpose, the structure must be modified so that it can work as effectively as possible. Experiments were carried out to evaluate if the number of times an image classification network model is put through its training process influences the level of accuracy obtained by the model. This was done to see how effectively the proposed deep learning model could categorize different types of photos. The results demonstrate that, when compared to previous models, the classification accuracy of the model developed for this study has significantly improved. Prior to and during model tweaking, we compared the deep learning model's classification accuracy on the training set to that of the test set. The purpose of this study was to find any distinguishing characteristics that existed between the two groups. According to the findings of this study, some type of optimization would be extremely advantageous to increase the model's capacity to effectively categorize photographs. This is illustrated by the fact that classifications get more exact with time.
Real-Time Human Body Sensors Data Classification using Supervised Machine Learning

Supervised learning algorithms have recently been brought to the classification of medical data in an effort to boost diagnostic throughput and eliminate human-caused diagnostic errors. This measure was taken to improve diagnostic precision. To be carried out, the fully supervised learning process needs a large amount of accurately labelled data. Annotating data material, on the other hand, demands a large expenditure of time, effort, and resources. Using the self- supervised learning approach to solve this challenge has the potential to provide a variety of beneficial outcomes for everyone. When there is no direct supervision, pretext projects are regularly employed in independent study scenarios. This is done so that massive volumes of unsupervised data may be utilized to obtain supervised knowledge. Furthermore, by utilizing specialized, supervised information, the network is prepared to obtain representations that will be beneficial for still-growing professions. The two-step CPC-based classification framework (TCC) model is built completely using contrastive predictive coding and is divided into two distinct phases that are carried out in sequential order. Making use of the contrastive predictive coding (CPC) model, the initial step is to build a fictional position. It must be completed as quickly as possible. This activity must utilize the encoder to extract appropriate features from a broad variety of classes in order to achieve the goal that it has set out to achieve. The encoder will employ the skills they learned while working on the pretext in the second half, which will take place in a training setting under the guidance of an expert teacher. Finally, we assess the proposed framework by comparing it to the most recent and innovative research in the field. This allows us to highlight the framework’s success by displaying how well it compares to other cutting-edge and current industry efforts. Experiments will be conducted to compare the efficacy of the proposed framework to that of supervised learning in order to determine which technique is superior. These studies were created with the assumption that there would be a broad range of labelled data to examine, and this premise was taken into account when creating the investigations.
Impact of Open Banking Strategy and Fintech on Digital Transformation

Digitalization in a number of financial sectors has forced the financial system to adapt to new standards of technological change. The current regulatory structure also made the financial system reevaluate its business strategies and its interactions with the market. These factors led to the creation of a new type of ecosystem-wide competition in the banking industry that had never been witnessed before. There is a lack of a comprehensive framework for the new ecosystem of banks and financial institutions, which would clarify strategic decisions and organizational structures within the new ecosystems as well as highlight the development lines of open innovation in the banking industry. The objective of this research was to evaluate the open banking strategy and fintech impacts on digital transformation. Empirical evidences collected from banking sector located in Dubai UAE. 102 bank branches were approached to collect data using a descriptive research. A valid sample of 201 employees used for data analysis. SmartPLS 4 software used to measure the hypothesized model. Significant relationships between the variables were found in the results. Additionally, this research is regarded as a fantastic resource for financial knowledge for experts, bankers, professionals, and academics interested in this sector.
Cyber Security Threats on Multifunctional Devices and Mitigation Techniques

The risks discussed in this document are related to smart printers/ fax/ photocopiers/ or e-mail-enabled smart devices, also known as "document centers,". These devices can be purchased outright or rented every month. When an item reaches its end of life, it is either donated to a non-profit organization or returned to the manufacturer (OEM) or supplier at the end of a lease arrangement. These network-interconnected devices have an inherent susceptibility that most IT operations teams or information security professionals are unaware of. This effort will raise awareness of the dangers of losing a company's data. This discussion will focus on reported vulnerabilities, previous discussions, and given the author's view on the potential danger. Create instructions for potential users on how to use and disassemble these devices safely to prevent losing sensitive data. Lastly, the technological structure of two potential attack vectors against a document center of an organization will be demonstrated in this study. The study was conducted in government as well as the private sector, medium-sized enterprises (300 employees of each entity) settings, outside of normal business hours, with special authorizations and engagement, limiting the scope to a minimal set of devices.