Reviewerly/exHarmony huggingface dataset:
Stance Classification on FIFA World Cup Using Twitter Data

The widespread influence of social media has led to an increase in online discussions on varied topics. International sports events with a worldwide following bring in a lot of online engagement between people with various viewpoints. Analyzing the stances in different public discourses from social media posts has gained research attraction. In this paper, we have tried to identify the stance of people on the FIFA World Cup 2022 from Twitter. Natural language processing techniques have been used for text preprocessing and compared different machine learning approaches to classify the Twitter text content into three stance classes of FAVOR, AGAINST, and NONE. We have observed that for our approach, support vector machine (SVM) provides the best result with good accuracy. Our approach is performing well on benchmark datasets.
Digital Marketing Factors Affecting Purchasing Intentions for Delivery Services (A Field Study on Customers of Talabat Company at Zarqa City - Jordan)

The purpose of this study was to investigate how digital marketing factors influence the purchasing behavior of Talabat customers, with a focus on five independent variables: security, credibility, ease of use, efficiency, and modernization. The study also examined one dependent variable, which is the purchase intention for delivery services. The research was conducted on 473 Talabat customers in Zarqa city. The findings revealed that the digital marketing factors analyzed had a significant impact on customers’ purchase intention. The study found that safety, credibility, ease of use, efficiency, and modernization had positive effects on increasing customers’ intention to purchase from Talabat. As a result, the study suggests that Talabat can attract more customers and meet the growing demand for its services by offering these marketing factors. The study recommended that Talabat prioritize customer privacy by safeguarding their information and ensuring that customers have greater trust in commercial advertisements.
The Impact of Sustainability Principles on Organizational Commitments and the Mediating Effect of Digital Transformation Process

This study seeks to determine the impact of the five principles of sustainability (pollution reduction, recycling, renewable resources, green innovation, and ethical sustainability) on the types of organizational commitment (affective, continuance, and normative). It also seeks to explore the mediating effect of digital transformation on the relationship between sustainability principles and organizational commitment. A random sample of 202 respondents was taken from 4 Jordanian pharmaceutical companies. The results confirmed that there is positive effect of two principles (renewable resources, green innovation) on the three types of organizational commitment (affective, continuance, and normative). The results also showed that there was no significant effect of other three principles (pollution reduction, recycling, and ethical sustainability) on the three types of organizational commitment. The results of the study proved that the digital transformation had a significant positive impact on the relationship between the principles of sustainability and types of organizational commitment.
The Impact of Cost Leadership &amp; Product Differentiation on Profitability in the Industrial Companies Listed on the Amman Stock Exchange (ASE)

This article aims to demonstrate the impact of cost leadership and product differentiation on profitability in ASE-listed industrial companies. To achieve the research objectives, the descriptive-analytical method is adopted. The research population comprises all (33) ASE-listed industrial companies, exceptfor two companiesdue to the lack of necessary data, making the research sample in the final form (31) companies. Data is collected from the published lists of ASE-listed industrial companies between (2017) and (2021). The appropriate statistical methods are used through the (Smart-PLS) program to analyze the two research independent variables; cost leadership and product differentiation, and profitability as a dependent variable. The findings indicate a statistically significant impact of cost leadership and product differentiation on profitability in its dimensions: return on assets, return on equity, and return on invested capital in the ASE-listed industrial companies, demonstrating that the management policy in the surveyed industrial companies aims to reducing production costs and following a policy of a higher level of quality of goods. The research recommends working on raising the culture of differentiation between all administrative levels of workers in industrial companies and conducting much research work covering other economic sectors such as banks and the services sector.
A Hybrid GA-PSO based approach for Mining Top-Ranked Web Pages to Reorganize Websites

There are millions of websites in the world, and these are increasing gradually. Locating pertinent information from the Internet is a very challenging job. Most of the websites consist of several web pages. These web pages may reach from hundreds to thousands in a single website. It is a difficult task for website designer as well as owners of the website to organize these large number of web pages so that website becomes interactive, and navigation in the website becomes easier. At the same time, required information could be easily obtained to the users. It is possible only if we could bank bees web pages based on the parameters like access frequency, unique visitors to a web page, active time spent by the user, common keywords given by the user, hubs, and authority count values. In this paper, the problem of ranking these web pages has been addressed by the hybrid GA-PSO algorithm where the web pages are first ranked by GA algorithm, and thereafter, these ranked optimal web pages are further refined by the PSO algorithm. Experimental evidence has shown that the proposed hybrid approach allows to find out the top-ranked ‘K’ documents to reorganize the structure of website.
A Wrapper-based Feature Selection Approach Using Particle Swarm Optimization for Software Fault Prediction

The main aim of feature selection (FS) is to determine and select the optimal number of features from a big dataset to help us find more accurate predictions with high efficiency and less computation power. Particle Swarm Optimization (PSO) is a wrapper-based feature selection algorithm which is inspired by flocking of bird and schooling of fishes. The primary goal here is to put forward a way for earlier software fault prediction using PSO-based feature selection. Through an early detection of a software fault, we not only speed up the future software development process but also save a lot of time and resources. In this article, we have also attempted to show a brief comparison of accuracy between different classifiers namely—K-nearest neighbors, Naïve Bayes and decision tree when used with PSO. Our study shows a promising increase in accuracy when PSO with all three above-mentioned classifiers was used for various datasets.
Handling Class Imbalance Problem Using Support Vector Machine

The class imbalance problem makes it difficult to use a classification model. The model may not be trained appropriately due to the availability of a lower number of objects of a minority class. When we need real-time or critical prediction, this problem can be precarious. So the model, when needed for real-time or critical prediction, the class imbalance problem may play a significant role in the hazard regarding the training and subsequent functioning of the model. To avoid such a hazard, we need to balance the dataset. The proposed model uses a support vector machine (SVM) to determine the outliers. Those outliers deal with the class imbalance problem. Our study analyzes different classification algorithms employed to predict stroke or non-stroke classes based on health diagnosis data collected from Kaggle. After balancing the data, we conducted several experiments to test the model’s functioning using various traditional classification models.
Analytical Comparison of Deep Learning Frameworks for Semantic Segmentation with Pixel-Level Understanding

In recent years, semantic segmentation has expanded enormously. Yet, a significant number of pixel-to-pixel-level annotations are crucial to the enjoyable performance. On numerous difficult datasets for semantic segmentation, contemporary deep learning architectures produce incredibly accurate results. The state-of-the-art approaches are not readily convertible to real-time applications, since the simple adaption of such systems to cut the processing cost produces a significant drop in accuracy. The model’s erroneous predictions have the potential to have a significant impact on this strategy. Visual comprehension of intricate urban street sceneries is a necessary component for a variety of applications. In the context of deep learning, object detection offers an exceptional advantage over large-scale datasets. Semantic segmentation’s main issue has been model accuracy and model loss. To get grip on the problems, this paper comes with an optimized comparison of different deep learning frameworks for semantic segmentation. This paper has analyzed certain deep learning frameworks for precise semantic segmentation on a dataset. This work is pulled off using UNet, ResUNet, DeepLab V3+, and Gated SCNN, resulting in a preferable desired output. Implementing this algorithm required a deep understanding of the frameworks. The experimental result and analysis are done with different semantic segmentation frameworks on a cityscape dataset to verify the efficiency of the models. The frameworks have been compared with each other to establish reliability and accuracy.
SBGAN: Sequential Bengali Word Image Generation Model

The rapid growth and usage of camera-based mobile devices have increased the number of camera-captured documents; these documents may be printed or handwritten. In a nation like India, multi-script recognition from document images is a critical step before choosing a suitable script-specific optical character recognition (OCR). The handwritten script identification problem is more complex than that of printed scripts due to the unequal variations among the authors, regions, topics, timing, etc. In contrast, the lack of datasets with several diverse images for various regional languages is a constraint for these challenges. Bengali is the second-most spoken language in India after Hindi and is most affected by the lack of dataset availability in the public domain for the development of a recognition system. The letters of the Bengali language are arranged in a family of complex scripts, either succeeding or following one another. Expanding the sequential training words through innovative techniques is necessary because acquiring data is expensive and challenging. To address this data shortage, a method for creating artificially handwritten Bengali sequence words is proposed in our research. For this, a sequential base generative adversarial network (SBGAN) model that draws inspiration from the generative adversarial network (GAN) is created to produce synthetic handwritten Bengali sequence words in order to address this data limitation. In this effort, the results of visual and quantitative examination show that the generated samples were realistic in nature and utilised to expand Bengali sequence words datasets.
Digital Watermarking Using Visual Cryptography

Digital watermarking is a crucial information hiding or steganographic technique that can be infused with other security mechanisms, such as cryptography, to provide more robust security to data. The bit substitution method is primarily employed on a cover image to hide a secret watermark. However, they suffer from lossy compression, cropping, and other issues. In this work, we have proposed a two-out-of-two visual cryptography model that creates only two shares. These two visual secret shares cannot divulge any information on their own, but when used together, they reveal a secret image or watermark. Additionally, a random grid and an XOR operation are used for constructing these shares. A grayscale image is employed for embedding the watermark text in it. Later, the modified image undergoes the message extraction phase to reveal the watermark. Six transformations on the modified image have been done to highlight the behavior of the extracted watermark. A performance analysis is given for each such transformation. Moreover, a performance comparison of the developed model with another exisiting approach is also presented.
Method-Level Code Smells Detection Using Machine Learning Models

Code smell detection is critical for calculating system quality and identifying issues that require more work and development. The technique of finding wrongly developed code components and implementing them is known as code smell detection. In this study, we used two method-level code smell datasets: the long parameter list and the switch statement, for detecting the code smells. A SMOTE class balancing approach is utilized to deal with the issue of class imbalance in datasets. A wrapper-based feature selection approach is used to choose the best features from each dataset. We applied three ensemble learning-based machine learning methods. To validate the model's accuracy, we utilized a fivefold cross-validation technique with five performance measurements (precision, recall, F-measure, AUC_Score, and accuracy). Using the max voting dataset, we obtained the best accuracy of 97.12% for the long parameter list dataset.
Multi-player evolutionary game of federated learning incentive mechanism based on system dynamics

Federated learning has emerged as a new way of data sharing. The participants in the federation tend to choose different strategies based on their benefits, which is formalized into an evolutionary game model. Existing techniques can limit the malicious behavior of participants by detecting betrayers or weakening their influence. The problem that whether there is an incentive mechanism which makes participants spontaneously choose to cooperate honestly and maintains the stability of the federated learning system is urgent. In this paper, we develop a multi-player evolutionary game model in federated learning. We model the federated learning process by evaluating the payoffs of the central server, internal clients, and external clients. The stability of the federated learning system in the long-term dynamics process is assessed by seeking the evolutionarily stable equilibrium solutions. In this paper, mathematical reasoning and computer simulation are combined to analyze the impact of reward and punishment strategies in incentive mechanisms on the game process and game equilibrium. An incentive mechanism is designed to achieve evolutionarily stable equilibrium while make most clients join the federation spontaneously and cooperate honestly. Finally, the effectiveness, stability, and generalizability of this incentive mechanism are verified by sensitivity analysis and Lyapunov stability theory.
Impact of Information Technology Security System on Patronage of Private Beaches in Eti-Osa Local Government of Lagos State

Tourism is a significant contributor to the economic growth and cultural enrichment of Lagos State, particularly in the Eti-Osa Local Government area. Private beaches play a crucial role in coastal tourism development, attracting millions of visitors each year for business, leisure, and recreational purposes. However, the area faces several challenges such as over-congestion during peak periods, insufficient infrastructural facilities, pollution, and occasional flooding, which can affect the safety and security of tourists. To address these issues and ensure a safe and secure environment for visitors, the integration of robust information technology security systems is essential. This study aims to examine the relationship between IT security measures and the patronage of private beaches by exploring the perceptions and experiences of beachgoers and operators. A total of 150 valid questionnaires were collected from respondents of the two major beaches in the area, representing an average number of customers over time. The data were analyzed using percentages and descriptive statistics. The study found that most respondents agreed that adequate security systems would strengthen the security of the beach and increase their patronage, leading to referrals to the destination. The study recommended the monitoring and transmission of information using gadgets like Closed Circuit Television (CCTV), Walkie Talkies, and Electric Security Alarms.
Self-Proficiency of Essential Technology Skills of Undergraduate Students at a Coastal University in Ghana

One of the numerous challenges facing education today is that of preparing students and staff for globalization and the information and communication revolution. Policymakers, educationists, non-governmental organizations, academics, and ordinary citizens are increasingly concerned with the necessity to make their societies competitive in the evolving information economy. Globalization and advancements in technology have led to an increased utilization of ICTs in all sectors, and education is no exception. Uses of ICTs in education are widespread and are repeatedly growing worldwide. Thus, this study investigated undergraduate students’ self-proficiency of essential technology skills in the University of Cape Coast, Ghana on the use of technology for various educational purposes. The study adopted the descriptive survey design using 318 randomly selected respondents from three different programmes in the university. The quantitative data collected were analyzed using descriptive statistics. The study found that students possessed technical abilities needed to perform some specific tasks such as changing the default settings of tech tools to meet specific needs and also make configurations to connect computers to a network. However, respondents were unable to perform advanced formatting functions such as customized tabs, insert page breaks, query databases and sort the results, and using styles as well as unable to create and manage classroom webpages.
H1B Visa Analysis using GANs

The H1B visa is a non-immigrant visa that enables foreign employees to enter the country and work there for a set amount of time. The purpose of the study is to analyze the 5 years of data on H1B Visa petitions from 2011-2016 and record the findings. Machine Learning algorithms will be employed to predict if the H1B Application Case status. The project also applies the functionality of Generative Adversarial Networks (GANs) to augment the training data. Tabular GANs are applied to the H1B Visa data and after augmentation, to test it on regular GANs, it is converted to images. These are fed to the generator network of a regular GAN model tested on MNIST data. The results show that GANs decrease the accuracy and increase the randomness of the data: Logistic Regression (before GAN=87.18%, after=73.8%), Random Forest (before=80.5%, after=70%), Gradient Boosting Ensemble (before=87.87%, after=71.3%), KNN (before=85.8%, after =71.4%), ANN (Error score Before GAN = 0.656, After GAN=1.033). Images formed by GANs to match the MNIST data showed satisfactory results after training for 5000+ epochs.
Inquisition of Vision Transformer for Content Based Satellite Image Retrieval

Content based satellite image retrieval is among the most vital technologies for military application. Huge volumes of satellite imagery get generated day by day. Management of huge archives of satellite image requires intelligent image retrieval system. In this paper, we have carried out study using SOTA Vision Transformer and other deep CNN based features in conjunction with various matching and indexing methods. We conducted exhaustive experiments by employing various deep features and different variants of Vision Transformer with variety of feature vector matching techniques. Experimental results conducted on various benchmark satellite image retrieval dataset demonstrate the promising capability of the Vision Transformer in conjunction with Ball tree. ResNet & DenseNet have outperformed on two benchmark datasets namely RS19, & PatterNet. ViT has outperformed on RSSC7N dataset & has performed on par with ResNet & DenseNet on UCM dataset.
Deep Learning Models for Automatic De-identification of Clinical Text

In health care, clinical narratives play an essential part in the diagnosis and treatment of patients. In recent times, many organizations have stored these clinical narratives as electronic health records (EHR). The EHR is an organized collection of patient health information in digital format. These EHR’s are an extensive collection of medical information, and they provide novel and rich data for clinical research. EHR also contains the patient’s identities, such as name, address, mobile number, etc. These patient identities must be de-identified before use for clinical research to protect the patient’s privacy. Clinical De-identification intends to detect and eliminate Protected Health Information (PHI) from medical data to facilitate data exchange and publication. This study aims to elucidate how the research community interprets and defines the terms like de-identification and anonymization and the steps involved in the process. A thorough review has been made to provide information on how deep learning approaches are used and how reliable they are.
Neural Tangent Kernel in Implied Volatility Forecasting: A Nonlinear Functional Autoregression Approach

Implied volatility (IV) remains a pivotal yet intricate component of financial markets, posing continuous challenges for accurate forecasting. We present a Nonlinear Functional Autoregression framework tailored to the series of implied volatility surfaces-a dynamic domain influenced by moneyness and maturity. This approach, designed for European put and call options, adeptly navigates the nonlinear and asymmetric temporal and spatial dependencies intrinsic to IV. Central to our approach is the functional Neural Tangent Kernel (fNTK) estimator. Grounded in the Neural Tangent Kernel parameterization, this estimator presents a modern statistical solution, parsing the intertwined dependencies found within the projections on the covariance operator of the IV surfaces’ time series. A methodological contribution lies in establishing the nexus between fNTK and kernel regression, emphasizing fNTK’s place in contemporary nonparametric statistical modeling. Transitioning from methodology to empirical evidence, we showcase the framework’s real-world utility via an analysis of the S&P 500 index spanning January 2009 to December 2021. The fNTK not only stands out in forecasting accuracy, achieving an average improvement of 4.54% to 39.44% in RMSE forecast accuracy for 5 to 20-day-ahead forecasts but also paves the way for practical trading strategies. When underpinned by our model, straddle trading yields a remarkable Sharpe ratio between 3.72 and 5.41, culminating in a staggering 107.79% to 528.36% relative enhancement in trading results. This convergence of methodological rigor and empirical results offers both a statistical advancement and a beacon for practitioners in the options market.