Reviewerly/exHarmony huggingface dataset:
Face Sketch to Photo Matching Through Dynamic Local Feature Extractions and Cascaded Static

By using a method of cascading static and dynamic local feature extraction, we attempt to deal with the identification of a photo from a face sketch effect of these effects in this process. To ensure that the feature vectors created are depending upon the appropriate patches. Along with that approach, we include the closest neighbours technique for matching sketches and photos, which concatenates the feature vectors obtained from local static extraction. Following the extraction of the image's characteristics, the most comparable images are narrowed down based on their closes neighbours. Eventually, feature vectors from the local dynamic extraction approach are used to rematch these images. The closest neighbours technique is used to match the feature vectors. The major goal of this technique is to increase the detection system's precision while simultaneously increasing recognition rates and feature stability.
Analysis of Fraud Detection on Credit Cards using Data Mining Techniques

Credit cards are widely used and accepted in the financial sector all over the world. The latest trend is to use electronic payments and to go cashless. Unfortunately, these credit card-based online transactions and cashless payments invite online fraudsters, who then attack all forms of online payment, including shopping sites and banking services. According to polls, approximately 4 billion people are presently affected by credit card fraud detection, and by 2025, that figure is projected to increase to 8 billion. Concern for its detection has increased as a result of this worrying pace. Both research scholars and industry experts have contributed their effort in this area for this goal. When considering the credit card detection method, its detection largely becomes a difficult problem. Due mostly to its unstable nature and dependence on customer behaviour, and secondarily because the dataset is readily available and easily accessible. This causes the dataset to become imbalanced, which makes it harder for a researcher to find instances of credit card fraud. Implementations of data mining algorithms are suitable for overcoming such difficulties. As a result, applying the proposed thesis necessitates using the random forest, decision trees, logistic regression, and Naive Bayes. The paper also proposes the use of a stacking algorithm, which integrates the basic theories of decision trees, logistic regression, and random forests, in addition to datamining techniques. According to experimental study of the aforementioned classifiers, the stacking algorithm produced an optimum model with exact precisions and generated the greatest accuracy of 97.78%.
Smart Contract Technology for Blockchain-based Systems: Design and Implementation Challenges.

The technology of smart contracts, which is an essential component of blockchain-based systems, has the potential to improve the efficiency, transparency, and safety of these systems. However, there are a number of challenges that need to be conquered in order to develop and implement smart contracts, particularly in regard to security, adaptability, interoperability, and meeting the standards imposed by legal and regulatory authorities. This paper examines ideas for designing and implementing smart contracts, as well as providing an outline of the challenges that have been encountered. In this article, we take a look back at 25 essential works that were published in the field between 2010 and 2019, and we discuss the impact that these works have had. According to the findings of our research, continual collaboration between the necessary stakeholders is required in order to fully realize the benefits given by smart contract technology while also limiting the risks associated with it.
Design of Convolutional Neural Networks Model for Fine-Grained Classification of Cervical Cells

One of the most prevalent cancers in the world and one of the most harmful to human life is cervical cancer (CC). This procedure was created to increase the diagnostic accuracy of CC in such dataset pictures in order to better detect and forecast illness. Therefore, we attempted to demonstrate a convolution neural network-based classification framework for CC that is intelligent and effective and has a comparatively basic architecture in comparison to others. Additionally, we offered a simple and useful technique for CC classification from cytological pictures using effective feature extraction or accurate cell image segmentation effort. The outcomes of automatic cell detection are contrasted with those of other cutting-edge cell detection methods as well as hand annotation of the underlying data. Many of the procedures for cell detection and categorization are still being worked on. This procedure's primary goals are to pinpoint the cell area and increase segmentation precision. With an F1 score of 0.96, the suggested approach has the highest cell detection accuracy.
Mixture of Gaussian Blur Kernel Representation for Blind Image Restoration

The use of blind image restoration, sharpness of edges may frequently be restored using previous information from a picture. De-blurring is the technique of taking out blurring flaws of the steady photographs, including motion or defocus aberration-related blur. the appearance of fast-moving the appearance of fast-moving entities flashing in still images flashing in a still photograph is known as motion blur. When an image is blurred using a Gaussian function, the result is a Gaussian blur. The employment of different sparse priors, either for the implicit photos or the motion blur kernels, contributes to the success of contemporary single-image approaches. De-blurring is the technique of taking out blurring flaws of the steady photographs, including motion or defocus aberration-related blur. The apparent flashing of quickly moving item in a static photograph is known as motion blur. When a picture is blurred utilizing a Gaussian function, the result is a Gaussian blur. The employment of different sparse priors, either for the latent photos or the motion blur kernels, contributes to the success of contemporary single-image approaches. On digital datasets, KSR also discovers effective kernel matrix approximation to hasten blurring and provide effective de-blur performances. The licence plate, which serves as the vehicle's distinctive identifier, is an important indicator of speeding or hit-and-run cars. However, the image of a fast-moving car taken by a security camera is usually blurred and not even humanly discernible.These observed plate pictures are frequently poor resolution and have significant edge information lost, which presents a significant challenge to the current blind deblurring techniques. The blur kernel may be thought of as a linear uniform convolution and parametrically modelled with angle and length for licence plate picture blurring brought on by rapid movement. This research suggests unique technique for locating the blur kernel based on sparse representation. We determine the angle of the kernel by looking at the sparse representation coefficients of the restored picture because the retrieved photo has the highest sparse representation when the kernel angle coincides with the real motion angle. Afterwards, using Radon transform in Fourier domain, we estimate the size of the motion kernel. Even when the licence plate is impossible for a person to read, our system handles big motion blur rather effectively. We assess our method using actual photographs and contrast it with a number of well-known cutting-edge blind image deblurring techniques. Experimental findings show that our suggested technique is superior in terms of efficiency and resilience.
Wavelet Transform Domain for Deep Image Compression Using High Frequency Sub-Band Prediction

This procedure demonstrates a Two Dimensional Discrete Cosine Transform (2D-DCT) system with quantization and zigzag layout. The JPEG picture compression approach uses this process as the foundation and main channel. Utilizing the 2D- DCT property, the calculation divides the overall process into compression and decompression. Even though the data in the images remained the same, the pixel was reduced predicated on the quantization and zigzag process. Division operation is used throughout the quantization process. As a result, this procedure minimizes data loss during decompression. The theoretical research offered in this study offers some fresh perspectives on how local variation behaves when JPEG compression is applied. Additionally, it might be utilised in a few aspects of image processing and analysis, including picture enhancement, image quality evaluation, and image filtering. MATLAB is used for this project, and improved vector quantization is used. Using the compression ratio, MSE, and PSNR, we can produce compressed pictures.
Secure Data Sharing in Cloud Computing Systems: Techniques and Applications

Secure data sharing in cloud computing systems is critical to protecting data from unauthorized access or manipulation. Conventional data security approaches are insufficient to assure total data protection in the cloud, and new strategies are necessary. Access control, data encryption, tokenization, digital signatures, data masking, data backup and recovery, virtual private networks, firewalls, intrusion detection systems, and cloud access security brokers are among the techniques investigated in this paper for secure data sharing in cloud computing systems. Furthermore, the study investigates safe data sharing applications in a variety of industries, including healthcare, banking, education, government, retail, manufacturing, legal, and non-profit. These examples show how secure data sharing can improve cooperation, efficiency, and trust in a variety of industries. Secure data sharing in cloud computing platforms is critical, and firms must employ adequate security measures to protect their data. Organizations may ensure that their data is secure, available only to authorized individuals, and used to promote cooperation, efficiency, and trust by implementing the methodologies and applications discussed in this paper.
Quantitative Evaluation of Sentiment Analysis on E-Commerce Data

With the emergence of technology and Big Data in the modern world, it is important to govern such vast amounts of data. Amazon and other e-commerce websites play a vital role in the delivery of goods services to the user. Nonetheless, such services are frequently accompanied by consumer reviews and ratings of the things they sell. Such reviews and ratings provided in the form of textual feedback serve to improve service delivery and product quality in the event that a consumer is dissatisfied. Thus, the process of evaluations and ratings is regarded as a significant component of the customer satisfaction process. Thus, it is vital to analyse them so that useful insights can be gleaned from them and the correct judgements can be made regarding the enhancement of the product. With emphasis on this concept, the authors of the proposed paper intend to develop a model capable of performing sentiment analysis on customer-provided product reviews and analysing products with positive and negative comments. On the basis of these reviews, three machine learning algorithms, primarily Decision Trees, XGBoost, and AdaBoost, are utilised to undertake sentiment analysis on products. It was noticed that XGBoost produced a greater detection accuracy of 84.45%.
An Efficient and Clinical-Oriented 3D Liver Segmentation Method

Due to the vast variety of human differences in the morphologies of the liver and the variance in pixel intensity in the picture, automatic liver segmentation is challenging. Furthermore, the limits of the liver are unclear since it shares intensity distributions with neighbouring organs and tissues. We suggest a quick and accurate approach for segmenting the liver using contrast-enhanced computed tomography (CT) images in this methodology. We apply level-set speed photos to adopt the two-step seeded region growth (SRG) method to generate an initial liver boundary that is roughly defined. According to the gradient information and related component connectivity, this separates a CT picture into a collection of distinct objects. Our technique reduces computing time by reducing threshold propagation, which converges at the best segmentation result, with such an optimal estimation of the initial liver border. The limits of liver computer supported detection/diagnosis systems and potential strategies to enhance them will also be covered. We came to the conclusion in this research that, despite some potential for advancement, automatic liver segmentation approaches are now on level with human segmentation. However, it may be said that both automated and semi-automatic liver tumour segmentation approaches perform less well than anticipated. It is also clear that the majority of computer assisted detection/diagnosis techniques call for manual liver and liver tumour segmentation, which restricts the clinical use of these systems.
IOT based Manhole Detection and Monitoring System

Abstract: With the increasing Urban population, the need for Internet of Things (IoT) based smart city applications, such as monitoring manhole covers (MC), becomes crucial. However, current MC monitoring systems lack full automation, have a short lifespan, and face design challenges from an IoT device perspective. In this study, we explore the use of compressive sensing (CS) to reduce power consumption in the analog-to-digital converter (ADC) of an IoT-based MC monitoring system, aiming to extend its lifetime. We propose two low-power analog chaotic oscillators as an alternative to the digital pseudo-random number generator (PRNG) commonly used in CS-based ADC's. The proposed systems are validated through temperature measurements, showing promising results. This research addresses circuit design challenges and improves system performance for long-term and efficient operation of IoT-based MC monitoring systems.
Federal learning for security and privacy protection

Federated learning empowers different gatherings to coach a machine learning model together whilst not trading their local data, and give full play to the worth of data from all parties because of the inborn deficiencies of federated learning and the security issues of information stockpiling and correspondence, it still faces a variety of security and privacy threats in practical application scenarios the security attacks and privacy attacks looked by federal learning are described. Then, at that point, the most recent security defense mechanisms and privacy protection means are summed up for such run of the typical attacks, including defense against poisoning attacks, protection against adversarial attack, defense against Free-rider attack, and defense against Sybil attack. At last, by efficiently figuring out the current risks and comparing the defense method for federal learning, the application of existing privacy protection methods to federated learning is discussed and the future exploration difficulties and advancement headings of federal learning are predicted.
Task Demand evaluation with support from eye tracking

Recent developments in specialized hardware and software provide the ability to record and analyzephysiological measurements that can reliably detect and potentially predict changes in user behavior. This opportunity, in turn, makes it possible to imagine intelligent decision support systems that can do moreidentify and respond more effectively to users' needs at individual level [6,30]. The first step towards growthSuch adaptive decision support systems are the exploration of smart tools that can be reliable and discreetIdentify user needs in a reliable and discreet way. One such tool is a system that can automatically detect labor demand. Job demand gamesan important role in how people choose to process information and therefore has a significant impact on howIndividuals use computers to make decisions. Because computers can expand a person's capabilities. For information processing, it is often thought that they can help people improve decision-making.
Strategic decisions and eye tracking data

Recently, there has been a growing interest in the use of eye gaze. through a well-established gaze tracking method in psychology. The study of economic decision making. The purpose is to find Behavioral insights that are not basedly available Based only on observed choice data. At the same time, the opposite with expensive and complex procedures such as fMRI; Eye-tracking allows subjects to be tested under certain conditions. Standard tests showed similar conditions. Behavioral experiments.
Effective Tier 1 Reading Instruction for Elementary Schools: A Systematic Review

A broad literature exists reviewing approaches for students struggling to learn to read (Inns, Lake, Pellegrini, &amp;amp; Slavin, 2018; Scammacca, Roberts, Vaughn, &amp;amp; Stuebing, 2015; Slavin, Lake, Davis, &amp;amp; Madden, 2011; Wanzek &amp;amp; Vaughn, 2007; Wanzek et al., 2016, 2013; Wanzek, Wexler, Vaughn, &amp;amp; Ciullo, 2010). These reviews focus on Tiers 2 and 3 of a multi-tiered model, on students who need additional support. Tier 1 is crucial for elementary reading, because it touches all students, and because successful Tier 1 instruction can reduce the need for far more expensive Tier 2 and 3 intervention.Fewer reviews have focused on Tier 1 literacy instruction in the elementary grades (Dietrichson, Bøg, Filges, &amp;amp; Klint Jørgensen, 2017; Graham &amp;amp; Hebert, 2011; Graham et al., 2017; Same et al., 2018; Slavin, Lake, Chambers, Cheung, &amp;amp; Davis, 2009; Swanson et al., 2017). The current review provides this, building upon these prior reviews in several ways.
Does corruption foster income inequality in sub-Saharan African countries?

Abstract The aim of this paper is to analyse the effect of corruption on income inequality for Sub-Saharan African (SSA) countries. To do so, the paper uses panel data covering the period 2002–2017, the Fractional Regression Method (FRM) as well as the Generalized Moment Method (GMM-two stage system) to highlight the effect of corruption on income inequality for 44 SSA countries. The results of the estimations show that with the FRM method the corruption perception index (CPI) is statistically significant and positively affects the GINI index which represents the income inequality indicator, and with the GMM method that better governance of states in terms of controlling corruption significantly reduces income inequality. The main contribution of this research is both academic and political. On the academic side, it fills the gap in the literature on the link between corruption and income inequality, particularly in SSA. On the policy side, the paper suggests that controlling the level of corruption in the public sector by the relevant institutions should be considered one of the most effective weapons in reducing income inequality in SSA countries. JEL Classification : O10 O43 P37
A New Literacy: Addressing the Barriers to Digital Literacy Through Public Libraries and Peer-To-Peer Learning

&lt;p&gt;In an increasingly digital world, spurred forward even faster by the COVID-19 global pandemic, people are being left behind. Social connection and learning have all been pushed online. Governments of all levels are asking for citizen input and engaging with citizens through digital means. However, the digital divide is growing and valuable perspectives are being left out of the decisions shaping communities. Society is developing in the digital sphere and that requires that people within that society are digitally literate. Public libraries could be the key to addressing the barriers leading to digital inequality. This study worked to understand digital literacy, the role public libraries are currently playing in the development of digital literacy skills, as well as the barriers libraries face in this endeavour and found that while there are many digital resources available for individual learners to increase their literacy, efforts could be improved by using a connectivist approach to learning and focusing on peer to peer level support.&lt;/p&gt;
Using Web Augmented Reality to add Visual Interactions to Contactless Restaurant Menus in Response To COVID-19

&lt;p&gt;Businesses that are slow to adapt to the digital world risk getting left behind. This research project explores how restaurants can adopt emerging technologies such as web-based augmented reality (WebAR) to improve the customer’s ordering experience during the reopening phases of the economy from COVID-19 and beyond. Many restaurants that were permitted to reopen had to limit their menu offerings, adopted single-use paper menus, or asked customers to scan a QR code to view their digital menus. Standard menus are often engineered to influence customer purchases through clever content placement, visuals and other psychological tactics that increases a restaurant’s profitability. This project demonstrates how WebAR can be used in three different ways to enhance contactless menus to reestablish trust and build purchase confidence for customers. The overall goal was to illustrate how WebAR can reduce the barriers for restaurants to create augmented brand experiences through integration with their existing digital presence and the ease of access to these experiences for their target customers without the need to download apps. &lt;/p&gt;
Recommendation System for Boarding House Selection using Simple Additive Weighting Method

The selection of boarding houses for work or study often leaves potential residents uncertain about choosing the right boarding house to meet their daily personal needs. Due to the varying prices and facilities offered by each boarding house, potential residents need to consider the prices and various facilities provided by each boarding house. Therefore, a recommendation system is needed to assist potential residents in deciding on the right boarding house according to their daily needs. This system is created using the Simple Additive Weighting method, which can help potential residents in decision-making through ranking obtained by multiplying the matrix of each criteria weight with the available alternative values. The development of this recommendation system uses MYSQL database, HTML, PHP, and JavaScript programming. The testing of the recommendation system using a Likert scale resulted in an average total interpretation score of 76.3%, indicating that users have a positive response to this recommendation system.