Reviewerly/exHarmony huggingface dataset:
Integration Processes in Russian Education: The Eurasian Context

The article is devoted to the study of the current state of scientific and educational relations between Russia and the states of the Eurasian Economic Union. The current mechanisms of cooperation in this area are studied, conjunctural and systemic barriers to integration are identified, taking into account deep changes in the geopolitical and geo-economic sense, and the prospects for overcoming them are assessed. Special attention is paid to the study of promising cooperative projects that, according to the author, can not only give a “fresh” impetus to the general practice of scientific and educational interaction in the EAEU, but also bring the cooperation of the states of the region to a new level of interaction, provide the much-needed complexity to the entire project of Eurasian integration. In conclusion, the author attempts to build further projections of integration in the field of education, science and training in the Eurasian direction.
U-Net-based Pancreas Tumor Segmentation from Abdominal CT Images

There is no doubt that pancreatic cancer is one of the most deadly types of cancer.In order to diagnose and stage pancreatic tumors, computed tomography (CT) is widely used.However, manual segmentation of volumetric CT scans is a timeconsuming and subjective process.It has been shown that the U-Net model is highly effective for semantic segmentation, although several deep learning models have been proposed.In this study, we propose a U-Net-based method for pancreatic tumor segmentation from abdominal CT images and demonstrate its simplicity and effectiveness.Using the U-Net architecture, the pancreas is segmented from CT slices in the first stage, while tumors are segmented from masked CT images in the second stage.For validation set of NIH dataset and according to the proposed method's dice scores, the pancreas segmentation and tumor segmentation performance was outstanding, demonstrating its potential to identify pancreatic cancer efficiently and accurately.
Anti-Spoofing in Medical Employee's Email using Machine Learning Uclassify Algorithm

Since the advent of COVID-19, healthcare and IT cybersecurity have been an issue.Digital services and foreign labor have increased cyberattacks.July 2021 saw 260,642 phishing emails.94% of 12 countries' employees experienced epidemic cyberattacks.Phishing attacks steal sensitive data from spam emails or legitimate websites for profit.Phishing spam uses URL, domain, page, and content variables.Simple machinelearning methods stop phishing emails.This study discusses phishing emails and patient data and healthcare employee accounts cybersecurity.This paper covers COVID-19 email and phishing detection.This article examines the message's URL, subject, email, and links.Uclassify classifies content, spam, and languages and automates emails.Semi-supervised machine learning dominates healthcare.The Uclassify algorithm used multinomial Naive Bayesian classifiers.Document class is [0-1].This article compared Multinomial Naive Bayesian in two experiments with other algorithms.Experiment 1 achieved an MNB accuracy of 96% based on a database from Kaggle Phishing.Experiment 2 showed that the Multinomial Naive Bayesian system accurately predicted URL and hyperlink targets based on PhishTank data.96.67% of respondents correctly identified URLs, and 91.6% did so for hyperlinks.These two experiments focused on Tokenization, Lemmatization, and Feature Extraction (FE) and contained an internal feature set (IFS) and an external feature set (EFS).MNB is more exact than earlier methods since it uses decimal digits and word frequency.MNB only takes binary inputs.MNB can detect phishing and spoofing.
A Novel Software Quality Characteristic Recommendation Model to Handle the Dynamic Requirements of Software Projects that Improves Service Quality and Cost

The software is created and constructed to address particular issues in the applied field.In this context, there is a need to be aware of the crucial characteristics to assess the quality of software.But not all software requires checking all the quality-of-service parameters, resulting in effort loss and time consumption.Therefore, it is required to develop software quality characteristics recommendation model to address and resolve the issue.The proposed work involved in this paper can be subdivided into three main parts (1) a review of popular software quality models and their comparison to create a complete set of predictable, and ( 2) the design of an ML-based recommendation model for recommending the software quality model and software quality characteristics (3) performance analysis.The proposed recommendation system utilizes the different software quality of service attributes as well as the software attributes where these models are suitably applied to satisfy the demands.Profiling of applications and their essential requirements have been performed Based on the different quality of service parameters and the requirements of applications.These profiles are learned by machine learning algorithms for distinguishing the application-based requirement and recommending the essential attributes.The implementation of the proposed technique has been done using Python technology.The simulation aims to demonstrate how to minimize the cost of software testing and improve time and accuracy by utilizing the appropriate quality matrix.Finally, a conclusion has been drawn and the future extension of the proposed model has been reported.
Enhancing Facemask Detection using Deep learning Models

Face detection and mask detection are critical tasks in the context of public safety and compliance with mask-wearing protocols.Hence, it is important to track down whoever violated rules and regulations.Therefore, this paper aims to implement four deep learning models for face detection and face with mask detection: MobileNet, ResNet50, Inceptionv3, and VGG19.The models are evaluated based on precision and recall metrics for both face detection and face with mask detection tasks.The results indicate that the proposed model based on ResNet50 achieves superior performance in face detection, demonstrating high precision (99.4%) and recall (98.6%) values.Additionally, the proposed model shows commendable accuracy in mask detection.MobileNet and Inceptionv3 provide satisfactory results, while the proposed model based on VGG19 excels in face detection but shows slightly lower performance in mask detection.The findings contribute to the development of effective face mask detection systems, with implications for public safety.
Dynamic Allocation Method of Incentive Pool for Financial Management Teaching Innovation Team Based on Data Mining

In order to reasonably allocate the amount of incentive pool and promote the unity of members of the financial management teaching innovation team, a dynamic allocation method of incentive pool for the financial management teaching innovation team based on data mining is proposed.This method constructs the incentive pool allocation index system by analyzing the principles of risk and income correlation, income and contribution consistency, individual and overall profit consistency, as well as the actual contribution of the financial management teaching innovation team, members' efforts and other factors that affect the allocation of incentive pool.After determining the index weight, the maximum entropy model is used to establish the incentive pool function of the financial management teaching innovation team project.The incentive pool scale decision model is established according to the prospect theory.After outputting the scale of the financial management teaching innovation team's incentive pool using the construction model, the incentive pool model of the financial management teaching innovation team is obtained.Based on the asymmetric Nash negotiation model, the allocation model for the incentive pool model of the financial management teaching innovation team is established, the improved artificial colony algorithm in the data mining algorithm is used to solve the model, and the dynamic allocation result of the incentive pool of the financial management teaching innovation team is obtained.The experiment shows that this method can effectively calculate the size of the incentive pool and allocate the incentive pool.The members of the financial management teaching innovation team have a high degree of satisfaction with the allocation result of the incentive pool, with allocation satisfaction consistently fluctuating around 96%.
Inverted Ant Colony Optimization Algorithm for Data Replication in Cloud Computing

Data replication is crucial in enhancing data availability and reducing access latency in cloud computing.This paper presents a dynamic duplicate management method for cloud storage systems based on the Inverted Ant Colony Optimization (IACO) algorithm and a fuzzy logic system.The proposed approach optimizes data replication decisions focusing on energy consumption, response time, and cost.Extensive simulations demonstrate that the IACO-based method outperforms existing techniques, achieving a remarkable 25% reduction in energy consumption, a significant 15% improvement in response time, and a substantial 20% cost reduction.By addressing the research gap concerning integrating IACO and fuzzy logic for data replication, our work contributes to advancing cloud computing solutions for large datasets.The proposed method offers a viable and efficient approach to improve resource utilization and system performance, benefiting various scientific fields.
Apache Spark in Riot Games: A Case Study on Data Processing and Analytics

This case study examines Riot Games' use of Apache Spark and its effects on data processing and analytics.Riot Games is a well-known game production studio.The developer Riot Games, best known for the well-liked online multiplayer game League of Legends, manages enormous volumes of data produced daily by millions of players.Riot Games handled and analyzed this data quickly using Apache Spark, a distributed computing technology that made insightful findings and improved user experiences.This case study explores Riot Games' difficulties, the company's adoption of Apache Spark, its implementation, and the advantages of utilizing Spark's capabilities.We evaluated the drawbacks and advantages of adopting Spark in the gaming sector and offered suggestions for game creators wishing to embrace Spark for their data processing and real-time analytics requirements.Our study adds to the increasing body of knowledge on the use of Spark in the gaming sector and offers suggestions and insights for both game producers and researchers.
Determinants of Medical Internet of Things Adoption in Healthcare and the Role of Demographic Factors Incorporating Modified UTAUT

Medical Internet of Things (mIoT) is the IoT subset with vast potential in healthcare.However, the adoption of eHealth solutions such as mIoT has been a critical challenge in the health sector of the Kingdom of Saudi Arabia.Therefore, this study was conducted to explore the mIoT adoption determinants in Saudi public hospitals.Methods: A total of 271 participants were recruited from public hospitals in Riyadh, and a modified UTAUT model named UTAUT-HS was developed in this study to test its relevance with respect to mIoT adoption.Results: Ten path relationships were tested in this study, out of which six showed significant results.Similarly, three variables (Computer and English Language Self-efficacy or CESE, Performance Expectancy or PE and Social Influence or SI) showed a significant direct relationship with the behavioural intention to adopt mIoT.Furthermore, CESE showed the strongest relationship and emerged as a major sub-set of Effort Expectancy (EE) for mIoT adoption.However, moderator analysis showed substantial variations between different study demographic groups.In particular, the current study findings unravelled a comparatively novel relevance of Perceived Threat to Autonomy (PTA) for mIoT adoption for clinical and nonclinical and for older and younger participants.Conclusion: The study concludes that UTAUT-HS is an adequate model to explain the mIoT adoption in healthcare.However, it also suggests conducting future large-scale studies in KSA and elsewhere to validate the relevance of UTAUT-HS in other contexts and with much more confidence.
Leveraging Big Data and AI in Mobile Shopping: A Study in the Context of Jordan

This study investigates the current state of mobile shopping in Jordan and the integration of big data and AI technologies in this context.A mixed-methods approach, combining qualitative and quantitative data collection techniques, utilized to gather comprehensive insights.The survey questionnaire distributed to 105 individuals engaged in mobile shopping in Jordan.The findings highlight the popularity of mobile shopping and the preference for mobile apps as the primary platform.Personalized product recommendations emerged as a crucial factor in enhancing the mobile shopping experience.Privacy concerns regarding data sharing were present among respondents.Trust in AI-powered virtual assistants varied, indicating the potential for leveraging AI technologies.Respondents recognized the potential of big data and AI in improving the mobile shopping experience.The study concludes that businesses can enhance mobile shopping by utilizing AI-powered virtual assistants and prioritizing data security.The findings contribute to understanding mobile shopping dynamics and provide guidance for businesses and policymakers in optimizing mobile shopping experiences and driving economic growth in Jordan's digital economy.Future research and implementation efforts are encouraged to harness the potential of big data and AI in the mobile shopping landscape.
Unsupervised Document Binarization of Engineering Drawings via Multi Noise CycleGAN

The task of document binarization of degraded complex documents is tremendously challenging due to the various forms of noise often present in these documents.While the current state-of-the-art deep learning approaches are capable for the removal of various noise types in documents with high accuracy, they employ a supervised learning scheme which requires matching clean and noisy document image pairs which are difficult and costly to obtain for complex documents such as engineering drawings.In this paper, we propose our method for document binarization of engineering drawings using 'Multi Noise CycleGAN'.The method utilizing unsupervised learning using adversarial and cycle-consistency loss is trained on unpaired noisy document images of various noise and image conditions.Experimental results for the removal of various noise types demonstrated that the method is able to reliably produce a clean image for any given noisy image and in certain noisy images achieve significant improvements over existing methods.
An Intelligent Evaluation Path for English Teaching Quality: Construction of an Evaluation Model Based on Improved BPNN

The current intelligent evaluation methods for English teaching quality are inefficient and have poor evaluation accuracy for effective assessment.The paper suggests an evaluation model based on an upgraded Back Propagation Neural Network to overcome the aforementioned issues.First, the principal component analysis is utilized to lessen the dimensionality of the index system as we build an English teaching quality evaluation index system with reference to the results of the existing study.Then, we adopt a multi-strategy improved dragonfly optimization algorithm to evaluate Back Propagation Neural Network for its defects; an algorithm to improve it.Finally, to increase the efficacy and objectivity of English teaching quality evaluation, an intelligent evaluation model based on IDA-BPNN is developed.The experimental results demonstrate that the IDA-BPNN model has an evaluation accuracy of 98.96%, an F1 value of 0.950 on the training set and 0.968 on the test set, a Recall value of 0.948 on the training set and 0.966 on the test set.The aforementioned indicators are all superior to the most recent state-of-the-art approaches for evaluating teaching quality.The aforementioned findings thus demonstrate that the model suggested in the study has high performance and can successfully improve the accuracy and efficiency of English teaching quality evaluation, which has a positive impact on the development of English teaching careers.
Innovating Art with Augmented Reality: A New Dimension in Body Painting

This study investigates the fusion of augmented reality (AR) and body painting as a novel concept for artistic expression.By combining the immersive capabilities of AR with the creative potential of body painting, this research explores individuals' perceptions and attitudes towards this innovative artistic approach from an HCI perspective.Drawing upon the Technology Acceptance Model (TAM) and the Diffusion of Innovation Theory (DIT), the study examines the factors influencing individuals' acceptance and intention to engage in AR-integrated body painting.Additionally, the research explores the mediating role of artistic expression in understanding the impact of these factors on the actual outcomes of this merged concept.A sample of 212 respondents participated in an online survey to accomplish the research objectives.The survey comprehensively measured participants' perceptions of innovativeness, social system support, perceived usefulness, perceived ease of use, artistic expression, and behavioral intention towards AR-integrated body painting.Rigorous data analysis was conducted using Partial Least Squares Structural Equation Modeling (PLS-SEM) to examine the intricate relationships between the variables.The findings underscore the significant impact of factors such as Innovativeness, social system support, perceived usefulness, and perceived ease of use on individuals' acceptance and intention to engage in AR-integrated body painting from an HCI perspective.Moreover, the study reveals the mediating role of artistic expression in connecting these influential factors with the actual outcomes of this merged concept.These empirical insights substantially contribute to our understanding of the fundamental mechanisms driving the adoption and utilization of AR in artistic practices, particularly within the domain of body painting, from both an artistic and HCI standpoint.
Web Phishing Classification Model using Artificial Neural Network and Deep Learning Neural Network

Phishing is an online crime in which a cybercriminal tries to persuade internet users to reveal important and sensitive personal information, such as bank account details, usernames, passwords, and social security numbers, to the phisher, usually for mean purposes.The target victim of the fraud suffers a financial loss, as well as the loss of personal information and reputation.Therefore, it is essential to identify an effective approach for phishing website classification. Machine learning approaches have been applied inthe classification of phishing websites in recent years.The objectives of this research are to classify phishing websites using artificial neural network (ANN) and convolutional neural network (CNN) and then compare the results of the models.This study uses a phishing website dataset collected from the machine learning database, University of California, Irvine (UCI).There were nine input attributes and three output classes that represent types of websites either legitimate, suspicious, or phishing.The data was split into 70% and 30% for training and testing purposes, respectively.The results indicate that the modified ANN with Rectified Linear Unit (ReLU) activation function model outperforms other models by achieving the least average of root mean square error (RMSE) value for testing which is 0.2703, while the CNN model produced the least average RMSE for training which is 0.2631.ANN with Sigmoid activation function model obtained the highest average RMSE of 0.3516 for training and 0.3585 for testing.
A Novel 2D Deep Convolutional Neural Network for Multimodal Document Categorization

Digitized documents are increasingly becoming prevalent in various industries.The ability to accurately classify these documents is critical for efficient and effective management.However, digitized documents often come in different formats, making document classification a challenging task.In this paper, we propose a multimodal deep learning approach for digitized document classification.The proposed approach combines both text and image modalities to improve classification accuracy.The model architecture consists of a convolutional neural network (CNN) for image processing and a recurrent neural network (RNN) for text processing.The output features from the two modalities are then merged using a fusion layer to generate the final classification result.The proposed approach is evaluated on a dataset of digitized documents from various industries, including finance, healthcare, and legal fields.The experimental results demonstrate that the multimodal approach outperforms single-modality approaches, achieving high accuracy for document classification.The proposed model has significant potential for applications in various industries that rely heavily on document management systems.For example, in the finance industry, the proposed model can be used to classify loan applications or financial statements.In the healthcare industry, the model can classify patient records, medical images, and other medical documents.In the legal industry, the model can classify legal documents, contracts, and court filings.Overall, the proposed multimodal deep learning approach can significantly improve document classification accuracy, thus enhancing the efficiency and effectiveness of document management systems.
The application of iterative hard threshold algorithm based on nonlinear optimal compression sensing and electronic information technology in the field of automatic control

Abstract To improve the accuracy effect of the iterative hard threshold, an improved iterative hard threshold (IHT) method is proposed. The specific contents of this method include the principle of an IHT algorithm based on compression sensing (nonlinear optimization), weighted least squares improvement, the establishment of an IHT algorithm model based on weighted least squares improvement, and the experimental research of traditional algorithms and improved algorithms on one-dimensional signal reconstruction. The results show that the improved IRLSIHT algorithm takes 8.37, 29.63, and 30.86 s when the sampling rate is 0.2, 0.5, and 0.8, respectively, and the signal-to-noise ratio is 20.11, 27.47, and 31.82 dB, respectively. Compared with the traditional IHT algorithm, it takes a long time, which is a deficiency, but the signal-to-noise ratio is the largest, and the improved algorithm improves the accuracy. It has been proven that combining the method proposed in this article with automatic control can significantly save time and increase industrial output.