Reviewerly/exHarmony huggingface dataset:
Introducing a Fair Tax Method to Harden Industrial Blockchain Applications against Network Attacks: A Game Theory Approach

Industrial Internet of Things (IIoT) systems are enhancing the delivery of services and boosting productivity in a wide array of industries, from manufacturing to healthcare. However, IIoT devices are susceptible to cyber-threats such as the leaking of important information, products becoming compromised, and damage to industrial controls. Recently, blockchain technology has been used to increase the trust between stakeholders collaborating in the supply chain in order to preserve privacy, ensure the provenance of material, provide machine-led maintenance, etc. In all cases, such industrial blockchains establish a novel foundation of trust for business transactions which could potentially streamline and expedite economic processes to a significant extent. This paper presents an examination of “Schloss”, an industrial blockchain system architecture designed for multi-factory environments. It proposes an innovative solution to increase trust in industrial networks by incorporating a fairness concept as a subsystem of an industrial blockchain. The proposed mechanism leverages the concept of taxes imposed on blockchain nodes to enforce ethical conduct and discipline among participants. In this paper, we propose a game theory-based mechanism to address security and trust difficulties in industrial networks. The mechanism, inspired by the ultimatum game, progressively punishes malicious actors to increase the cost of fraud, improve the compensation system, and utilise the reward reporting capabilities of blockchain technology to further discourage fraudulent activities. Furthermore, the blockchain’s incentive structure is utilised to reduce collusion and speed up the process of reaching equilibrium, thereby promoting a secure and trustworthy environment for industrial collaboration. The objective of this paper is to address lack of trust among industrial partners and introduce a solution that brings security and trust to the forefront of industrial blockchain applications.
Implementation and Analysis of Image Steganography using Convolution Neural Networks

Steganography is an art of hiding the data which is used for communication purposes, and it is not visible to the human eye. By hiding information with the help of other information communication takes place. The hiding information is in different forms like text, image, audio, and video. Image Steganography is performed for images, and it takes two images as input, and they are combined by using an embedding algorithm and the data is decrypted to retrieve the original image. Image Steganography is classified into three types General Adversarial Network methods, Convolutional Neural Network (CNN) based, and traditional methods. The traditional method is based on Least Significant bit method it gives results in less time. But it is unprotected to do steganalysis, and it is not secure at all. For this purpose, we move to the Convolutional Neural Networks. In this CNN based deep learning Neural Networks are used. The comparison of input and output images is done by considering parameters like Histogram, MSE, SNR, and PSNR.
A Reinforcement Learning-Based Node Selection for PBFT Consensus

Blockchain is nowadays a flourishing technology that served many domains (finance, supply chain, smart cities, healthcare, etc.) thanks to the immutability of data stored on it. In fact, before validating a block and appending it to the blockchain, most of the system nodes should agree on it and reach consensus. One of the most used consensus protocols is the Practical Byzantine Fault Tolerance which tolerates byzantine nodes and works upon asynchronous systems. However, it is not scalable enough because of the increasing number of messages exchanged through the network. Our proposition uses reinforcement learning to adjust the number of participating nodes depending on their behavior and on the actual network state. The number of participating nodes is reduced without affecting the protocol's safety and liveness. Only honest and rapid nodes are included in the consensus set. Malicious nodes are removed and slow nodes are kept in a set until needed.
Exploring the Role of Artificial Intelligence in Internet of Things (IOT)

There is perfect role of artificial intelligence (AI) in Internet of things (IoT), and this get better result in the technological advancement processes. The major features of AI such as perception, deep learning, feature engineering, natural learning processing and robotics can make an effective development and establishment. There is major development can be featured with the technical process and this is possible with efficient application of AI in IoT. In this way business operation get feasible and this effective possible with the perfect intelligence. The bug data analytics and advanced techniques of technology can be maintained with the help of various new application which are especially be possible with proper management. Recent report has shown that revenue has been increased with the application of IoT. The main trends as also been used data driven which is combined with IoT to get better intelligence in major business conduction. This assist in maintaining huge growth in future development and also leads to the betterment in manufacturing procedure. Keyword : Artificial intelligence, Internet of things, Business operation
The degree to which faculty members in Palestinian universities possess digital competence in light of the Corona pandemic

This research aims to Identified to the extent to which faculty members in Palestinian universities possess digital competence in light of the Corona pandemic, as a stratified sample of (300) faculty members was selected, and the questionnaire that he made (Sangeetha and Saileela, 2021) was developed and used to detect Digital competence, and the results showed that the degree of possession of digital competencies by faculty members in Palestinian universities came to a medium degree, and they use e-mail and browsing, social media and to direct students to follow the course, and their ability to prepare electronic lecture files and upload documents and videos necessary for the lecture. On the other hand, it was also found that there were no statistically significant differences attributable to the variables of sex and university, while it was found that there were statistically significant differences due to the variable of academic rank and in favor of the highest rank, and in the variable of years of service and in favor of the least years of service.
Inductive Graph-based Knowledge Tracing

The rise of virtual education and increase in distance, partly owing to the spread of COVID-19 pandemic, has made it more difficult for teachers to determine each student’s learning status. In this situation, knowledge tracing (KT), which tracks a student’s mastery of specific knowledge concepts, is receiving increasing attention. KT utilizes a sequence of studentexercise interactive activities to predict the mastery of concepts corresponding to a target problem, recommending appropriate learning resources to students and optimizing learning sequences for adaptive learning. With the development of deep learning, various studies have been proposed, such as sequential models using recurrent neural networks, attention models influenced by transformers, and graph-based models that depict the relationships between knowledge concepts. However, they all have common limitations in that they cannot utilize the learning activities of students other than the target student and can only use a limited form of exercise information. In this study, we have applied the concept of rating prediction to the studentexercise knowledge tracing problem and solved the limitations of the existing models. Our proposed Inductive Graph-based Knowledge Tracing (IGKT) designed to integrate structural information and various unrestricted types of additional information into the model through subgraph sampling, has been found superior over the existing models across two different datasets in predicting student performances.
Increasing the Resolution and Spectral Range of Measured Direct Irradiance Spectra for PV Applications

The spectral distribution of the solar irradiance incident on photovoltaic (PV) modules is a key variable controlling their power production. It is required to properly simulate the production and performance of PV plants based on technologies with different spectral characteristics. Spectroradiometers can only sense the solar spectrum within a wavelength range that is usually too short compared to the actual spectral response of some PV technologies. In this work, a new methodology based on the Simple Model of the Atmospheric Radiative Transfer of Sunshine (SMARTS) spectral code is proposed to extend the spectral range of measured direct irradiance spectra and to increase the spectral resolution of such experimental measurements. Satisfactory results were obtained for both clear and hazy sky conditions at a radiometric station in southern Spain. This approach constitutes the starting point of a general methodology to obtain the instantaneous spectral irradiance incident on the plane of array of PV modules and its temporal variations, while evaluating the magnitude and variability of the abundance of atmospheric constituents with the most impact on surface irradiance, most particularly aerosols and water vapor.
Factors affecting e-supply chain management systems adoption in Jordan: An empirical study

Recently, there have been a growing number of articles focusing on the benefits of adopting e-SCM systems and the value of such systems in supply chain performance. However, less academic research was devoted to understanding factors affecting the adoption intention of such systems. This study uses the technology, organization, and environment (TOE) framework to examine factors that affect the adoption of e-SCM systems in Jordan, where limited research has been conducted in this country. Through an online survey filled by 251 participants via the LinkedIn website, the study shows that perceived relative advantage, financial resources, employee competency, top management support, competitive pressures, and customer pressure positively impact the adoption intention of e-SCM systems. The findings confirm the association between variables embedded in the TOE framework and the adoption intention of innovative supply chain systems and solutions and support earlier findings. According to the study findings, e-SCM systems providers should focus on the relative advantage these systems offer to increase the likelihood of their adoption.
Impact of strategic agility on the financial performance of commercial banks in Jordan

The Jordanian banking sector has been able to achieve remarkable growth rates at various levels. This sector is based on a robust infrastructure and is subject to the supervision of the Central Bank, which guarantees the likes of banks operating to financial reporting standards in order to maintain good financial soundness indicators. Therefore, this study aimed to examine the impact of strategic agility on the financial performance of commercial banks in Jordan. The study population was represented by senior managers. The purposeful sampling method was used to collect primary data from (188) respondents who constituted (81.74%) of the sent questionnaires. Structural Equation Modeling (SEM) was applied to test the study's hypotheses. The results showed that strategic agility had a positive impact on financial performance, as the greatest impact was strategic sensitivity, followed by resource fluidity, and finally leadership unity. This study contributed to the development of a logical framework supported by empirical evidence about the possibility of developing financial performance in dynamic environments. Hence, senior managers recommended focusing on making rational decisions derived from studying the reality of the work environment and adjusting organizational structures to become more flexible in response to the volatile business environment.
The impact of human resources employment strategy in achieving competitive advantage: Zain Jordan Telecom company

Organizations in all sectors seek to achieve their goals and achieve competitive advantage in order to reach profits, which is the main goal of organizations. They work to determine their needs of human resources, and try to attract the largest possible number of job seekers, in order to choose the best elements among them and then plan the workforce, through which the full and good description of all the jobs to be filled and the specifications of those who occupy them are determined, and once this process is completed, the next step begins, which is to search for the most suitable people for these jobs and try to attract and attract the most qualified to work in the organization and entice them to stay in it. The aim of this paper is to identify the effect of the human resources employment strategy in achieving competitive advantage in Zain Jordan Telecom company. The descriptive and analytical approach was relied on. The study community consisted of all (1200) employees of Zain Jordan Telecom company, and a random sample of (180) employees was taken. The study concluded that there was a positive impact of the strategy of employing human resources in achieving competitive advantage, and the presence of a positive impact of the strategy of human resources in achieving the dimensions of competitive advantage (quality, cost, and innovation). The paper contributes to the development of literature related to the relationship between the strategy of employing human resources and achieving competitive advantage by providing field indicators on the nature of the between these two variables in the work environment.
Impact of Leader’s State of Core Self-Evaluation on Task Complexity: A Quantitative Analysis

This study aims to explore the association between a leader's State of Core Self-Evaluation and the complexity of tasks assigned to them. Previous research on this topic has established a strong foundation for the conceptual framework and hypotheses used in this study. A quantitative approach was employed, utilizing an adapted questionnaire to gather data from 141 organizational leaders holding various positions in the banking sector. Statistical analysis of the data demonstrates that a leader's core self-evaluation is positively linked to task complexity. This implies that when leaders believe they possess the necessary abilities and skills to handle a task, their cognitive skills improve, leading to efficient task completion. Conversely, when leaders perceive a task as complex, they may exhibit avoidance behaviour and refrain from attempting to complete the task. These findings are useful in assessing leaders' performance under different levels of task complexity, particularly in a demanding work environment. Policymakers can also benefit from this research in understanding what triggers leader behaviour when task complexity increases. The results of this study align with previous research, indicating that a leader's State of Core Self-Evaluation and task complexity are crucial considerations in the field of management.
Score-based Bayesian network structure learning algorithms for modeling radioisotope levels in nuclear power plant reactors

Radioactive corrosion products released into the primary coolant loop dominate the final shutdown radiation fields of pressurized water reactors. Thus, reducing the concentration of these corrosion products is a paramount duty in the optimization process of the reactor performance. However, the complexity and uncertainty present in this process make it difficult to predict their evolution in a theoretical way. We propose the application of structural learning of Bayesian networks to discover the complex relations between the corrosion products and the most relevant variables in the primary loop, giving rise to probabilistic models that obtain accurate and reliable predictions of the corrosion products. Our analysis of 5 power plants demonstrates that our approach results in simpler and more reliable models. Additionally, we conclude that the learned structures may represent an interpretable tool for power plant technicians since they reveal useful information that can be directly employed to improve the reactor operation.
Design of An Adaptive E-learning Model Based on Artificial Intelligence for Enhancing Online Teaching

Nowadays, teaching involves more and more the usage of digital tools to accompany face-to-face learning. Such a system is being referred to as hybrid teaching. These digital tools include mainly distance learning platforms based on LMS (Learning management systems). LMS help to create on-line digital spaces to store and organize teaching material while providing a pedagogical learning process to connect educational communities. Despite the many forms of content that can be implemented on these platforms, from traditional file-based to interactive and animated content, these systems remain both passive and generic. They lack the ability to adapt to the learner in terms of learning skills, preferences, languages, intellectual abilities, learning patterns and rhythms. This paper proposes a design and modeling of an intelligent and dynamic adaptive learning system based on artificial intelligence with the main objective of identifying and providing personalized learning environments adapted to the learner needs.&#x0D;
Combined E-Learning Course Recommender System

In this paper, we propose learners course recommender system of the E-Dirassa platform. This system, which distinguishes between new and active learners, adopts three complementary recommendation approaches: content-based recommendation as well as course-based collaborative filtering for active learners, and static profile-based collaborative filtering for new learners. Let us also note the use of multi-criteria decision aid (MDA) techniques for the choice of the textual similarity measure to be used in the first approach of content-based recommendation.
Fragments Inpainting for Tomb Murals Using a Dual-Attention Mechanism GAN with Improved Generators

As the only underground mural in the collection, the tomb murals are subject to damage due to temperature, humidity, and foundation settlement changes. Traditional mural inpainting takes a long time and requires experts to draw it manually. Therefore, the need for digital inpainting is increasing to save time and costs. Due to the scarcity of samples and the variety of damage, the image features are scattered and partially sparse, and the colors are less vivid than in other images. Traditional deep learning inpainting causes information loss and generates irrational structures. The generative adversarial network is, recently, a more effective method. Therefore, this paper presents an inpainting model based on dual-attention multiscale feature aggregation and an improved generator. Firstly, an improved residual prior and attention mechanism is added to the generator module to preserve the image structure. Secondly, the model combines spatial and channel attention with multiscale feature aggregation to change the mapping network structure and improve the inpainting accuracy. Finally, the segmental loss function and its training method are improved.The experimental results show that the results of using signal-to-noise ratio (PSNR), structural similarity (SSIM), and mean square error (MSE) on epitaxial mask, crack mask, random small mask, and random large mask are better than other methods. It demonstrates the performance of this paper in inpainting different diseases of murals. It can be used as a reference for experts in manual inpainting, saving the cost and time of manual inpainting.
Faculty Equity Issues and Challenges: Analysis of Problems and Obstacles that Outstanding Engineering Faculty Face

Equity is defined as “fairness and impartiality based on even-handed treatment” (Nace, 2021, Unitedwaynce, 2021). Current engineering education problems are the improper selection of faculty members, educational leaders, and administrators. Equity approaches will alone sustain the fast growth of attributes of graduates, and service to society through creating human and knowledge capital. The National Education Policy 2020 focuses on the vibrant growth of engineering education. This research paper assesses the prevailing equity culture, getting feedback from the senior faculty members, and suggests ways and means of implementing equity, norms, and standards in recruiting outstanding and wellaccomplished faculty members, and training and developing them. Best practices in ethics, areas that require major change and development, the impact of ethical practice, and the responsibilities of maintaining ethics in an institute are presented. Keywords: Ethics, the desired scenario of engineering institutes as per NEP 2020, Best practices in ethics, the impact of ethical practice, maintaining ethics, soft solutions, and implementation strategy.
Examining The Effect of Different Networks on Foreign Object Debris Detection

Foreign Object Debris (FOD) at airports poses a risk to aircraft and passenger safety. FOD can seriously harm aircraft engines and injure personnel. Accurate and careful FOD detection is of great importance for a safe flight. &#x0D; According to the FAA's report, FOD types are aircraft fasteners such as nut, safety; aircraft parts such as fuel blast, landing gear parts, rubber parts; construction materials such as wooden pieces, stones; plastic materials, natural plant and animal parts. For this purpose, in this study, the effect of different networks and optimizer on object detection and accuracy analysis were examined by using a data set of possible materials at the airport. AlexNet, Resnet18 and Squeezenet networks were used. Application is applied two stages. The first one, 3000 data were divided into two parts, 70% to 30%, training and test data, and the results were obtained. The second one, 3000 data were used for training, except for the training data, 440 data were used for validation. Also, for each application, both SGDM and ADAM optimizer are used. The best result is obtained from ADAM optimizer with Resnet18, accuracy rate is %99,56.