Reviewerly/exHarmony huggingface dataset:
Digital marketing and its role in achieving customer’s happiness: Evidence Jordanian five-star hotels

The study aimed at bridging or reducing the knowledge gap between digital marketing di-mensions and customer’s happiness, by diagnosing both the level of digital marketing adoption and the level of customer’s happiness achievement, determining the nature of the relationship between digital marketing and customer’s happiness, and recognizing the level of effect and contribution of digital marketing dimensions in achieving customer’s happiness. This is based on the main idea that digital marketing activities are the main basis for achieving customer’s happiness, when designing digital services. Opinions of (360) customers in Amman five-star hotels were surveyed and viewed to achieve this direction, by designing and distributing a questionnaire. The most important results of the study showed that all digital marketing dimensions positively affect customer’s happiness, as well, the level of using dimensions of both digital marketing, and achieving customer happiness is lower than the required level, by five-star hotels in Amman. The most important recommendations were using of distinguished digital channels tools to respond to attract customers, such as the use of advertisements that accustom customers to use of products or services based on virtual reality or through the applications that use live streaming marketing system (photos or video) in order to bring customers close to what services the hotel offers. Using creative, and effective methods in communicating with customers through human emotions to create delicious marketing for customers, which makes them engaged with the offers they receive, whether through e-mail or SMS. Interest in providing real and effective content to digitally marketed service, in order not to create a gap between hotel and its customers as a result of the mismatch between what is advertised and what is actually provided. Determining success indicators of a hotel digital marketing campaigns by knowing customer’s feelings and their happiness (i.e. through customers’ positive feedback).
The mediating role of customer awareness to enhance the relationship between using social media tools and post-purchase behavior upon electrical devices buyers in Jordan

This study examined the effect of social media on post-purchase behavior on electrical device buyers in Jordan. Drawing on resource-based and knowledge-based previous studies, the mediating effects of customer awareness were also tested. Data were collected from 385 participants from the segment targeted group of customers in Jordan, and hypotheses were tested through partial least squares structural equation modeling using Smart PLS 4.0. The results showed that the mediating role of customer awareness influences enhancing the relationship between social media and promotion mix on the one hand, and post-purchase behavior (exit, voice, and loyalty) on the other hand. Our findings contribute to the existing literature by explaining and strengthening this relationship, which is also referred to as the black box through the mediation of customer awareness. Marketers should recognize the importance of this relationship to develop modern promotional tools in multiple social media to positively enhance the customers’ post-purchase behavior by giving them a competitive advantage.
Generative classifier pix2pix

Generative adversarial networks have been widely developed to generate new data, and they have been used for several different applications. Some networks have been developed to classify data at the discriminator level, either by modifying the loss function or by adding a classifier. In this paper, the generative classifier pix2pix, a classifier based on generative adversarial networks, specifically the pix2pix, has been introduced. The classification is done without the need to keep the discriminator or to add additional networks, only the generator is used to classify the data. This classification requires the preparation of a reference dataset. The generative classifier pix2pix was applied to the GC character recognition task using 50000 images for training, it achieved 99.36%. It was also applied to the ORL face recognition task using 360 images for training, and it achieved an average of 97.99%.
ChatGPT as a COBUILD lexicographer

Creating high-quality dictionary entries has been a meticulous and time-consuming process, but current developments in Artificial Intelligence (AI) could help to speed up this process. This study explores the use of ChatGPT to create COBUILD-style English monolingual entries and evaluates its ability to generate accurate and high-quality sense definitions and example sentences on a sample of fifteen verbs of communication. AI-generated entries are produced using ChatGPT Plus, and human experts evaluate them alongside COBUILD entries in a blinded randomized process. The quality of sense definitions, example sentences, and overall entries are assessed using a five-point scale. The results indicate that AI-generated definitions are comparable to those created by human lexicographers, while AI-generated examples and overall entries receive lower ratings. The study also explores the possibility of fine-tuning AI models based on expert feedback to enhance their performance.
Examining Digital Literacy towards ICT Among Students Based on Demographic Profile: A Descriptive Analysis Approach

In today's rapidly changing, technology-driven world, digital literacy has become increasingly important.As the use of technology continues to grow, it is vital that students possess the necessary skills to effectively navigate and utilize digital tools.Formal education students guided and assessed by instructors help develops a nation.Digital literacy requires creativity, security, and social awareness.This research's biggest problem is classifying students' digital literacy to meet the increased need for digitally proficient employment.To give students the skills they need to succeed in today's digital environment, it's important to know how to classify students by digital literacy.Thus, the goal of this study is to analyse digital literacy, determine skill and attitude levels, and examine the relationship between skill and attitude among UiTM students.The study sampled 364 students.Voluntary sampling to manage sample composition.Two types of analyses been conducted to achieved research objectives.Descriptive Analysis comes prior to Spearman Rank Correlation.The study assessed students' digital literacy by examining their computer and internet skills and attitude towards technology.50% of respondents had "Sometimes" to "Every time" application skills.The study also found a weak positive correlation (0.300) between computer and internet skills and technology attitude, indicating room for improvement in encouraging students to use technology.The correlation was significant at 0.01 (p=0.000).University students, mostly women, were surveyed on their digital skills and technology use.Most participants used computers daily and were competent.Students liked technology and preferred using their computers at home.The study indicated a weak positive correlation between digital skills and attitude, suggesting space for improvement in encouraging technology use.
Robustness of Sparsely Distributed Representations to Adversarial Attacks in Deep Neural Networks

Deep learning models have achieved an impressive performance in a variety of tasks, but they often suffer from overfitting and are vulnerable to adversarial attacks. Previous research has shown that dropout regularization is an effective technique that can improve model generalization and robustness. In this study, we investigate the impact of dropout regularization on the ability of neural networks to withstand adversarial attacks, as well as the degree of "functional smearing" between individual neurons in the network. Functional smearing in this context describes the phenomenon that a neuron or hidden state is involved in multiple functions at the same time. Our findings confirm that dropout regularization can enhance a network's resistance to adversarial attacks, and this effect is only observable within a specific range of dropout probabilities. Furthermore, our study reveals that dropout regularization significantly increases the distribution of functional smearing across a wide range of dropout rates. However, it is the fraction of networks with lower levels of functional smearing that exhibit greater resilience against adversarial attacks. This suggests that, even though dropout improves robustness to fooling, one should instead try to decrease functional smearing.
The impacts of task technology fit, transparency, and supply chain agility on the blockchain adoption by SMEs in Jordan

This study aims to find out how much the ways that blockchain technology helps in different parts of the supply chain affect how SMEs use blockchain technology. Moreover, the research examines the correlation between blockchain use and overall performance. The data was obtained from the SMEs' owners and managers, who have the authority to implement blockchain technology. Two follow-up phone calls yielded 145 usable replies from 500 prospective respondents, for a response rate of 29%, which was analysed employing structural equation modelling (SEM). The results demonstrated that the contributions of blockchain adoption to task technology fit, task characteristics, technology characteristics, operational supply chain transparency, and supply chain agility influenced the acceptance intentions of SME managers and that there is a significant positive correlation between blockchain adoption and overall performance. The results aid vendors and policymakers in formulating strategies and effective plans to accelerate blockchain adoption by SMEs. Furthermore, the findings provide owners/managers of SMEs with reason to believe that blockchain technology might be a source of competitive advantage.
Using cloud computing services to enhance competitive advantage of commercial organizations

Using advanced technology in business has created hyper-competition among organizations to satisfy customers' needs. Using advanced technology aims to provide customers with quality products/services at suitable prices in the right place better than competitors. Therefore, the current study's purpose is to explore the influence of cloud computing services on Jordanian commercial organizations’ competitive advantages, organizations which use cloud computing services. The study uses quantitative, cause-effect, and cross-sectional methods and uses a convenience sampling approach to collect the data by questionnaire from 111 managers and/or owners of commercial organizations. The collected questionnaires are examined and inserted into SPSS. The instrument validity, normal distribution, and reliability are verified, then descriptive analysis is performed, the relationship between independent and dependent variables is tested, and finally multiple regressions are used to test the hypotheses. The findings indicate that commercial organizations are concerned about cloud computing services as well as competitive advantage sub-variables. The results also show that there was a significantly strong correlation between cloud computing services and competitive advantage. Moreover, cloud computing services influence the dimensions of competitive advantages (quality, cost, reliability, innovation, and responsiveness) of commercial organizations, where cloud computing services have the most significant influence on quality followed by cost and responsiveness, respectively. However, cloud computing services do not significantly influence innovation and reliability. Finally, the study recommends doing comparable research on other sectors, and industries as well as in other countries to test the results' generalizability.
Impact of digital marketing on consumer behavior: A quantitative analysis on fast fashion industry in the KSA

The aim of this study is to examine the impact of digital marketing on consumer behavior in the fast fashion industry in the Kingdom of Saudi Arabia (KSA). The fast fashion industry has been growing rapidly in the KSA, and digital marketing has played a significant role in changing consumer behavior in this industry. The study adopted a quantitative research design and used online surveys as the primary data collection method. The sample consisted of a convenient sample of participants who had purchased fast fashion products in the KSA. The data was analyzed using various statistical methods, including descriptive statistics, correlation analysis, and regression analysis. This study's findings shed new light on how digital marketing has affected customer behavior in the fast fashion sector in the KSA. Consumers in the KSA have a good reaction to digital marketing methods in the fast fashion industry, and this effect is significant.
An investigation into the effect of social support on job performance and job satisfaction in the Jordanian insurance industry

An increased sense of job performance and better job satisfaction can be achieved by receiving social support. In order to fulfil this purpose, this study aimed to determine the relationship between social support on the one hand and job performance and job satisfaction on the other hand for the Jordanian insurance industry employees. Survey data were gathered from 269 employees from the Jordanian insurance industry. The PLS 3.0 software was used to process data using the structural equation modelling method. The study’s findings revealed that all social support factors were positively and significantly related to job performance and job satisfaction, including manager support, peer support, friends support, and others’ support. Hence, job performance and job satisfaction in the Jordanian insurance industry can be predicted by studying the existence of social support. The study’s findings concluded that the higher the social support, the higher the job performance and job satisfaction.
THE ROLE OF REAP (READ, ENCODE, ANNOTE, AND PONDER) TECHNIQUE IN TEACHING READING COMPREHENSION AT ONE STATE SENIOUR HIGH SCHOOL OF SINTANG

Abstract: This research focused on solving the students’ difficulties in understanding the meaning and finding information from a text. The objective of this research was to recognize the improvement of the student’s participation and the student reading performance in teaching reading comprehension through REAP technique. The research design used was Classroom Action Research (CAR). The data was collected using; observation, interviews, tests, and documentation. The findings showed there was an improvement in student’s ability to comprehend a reading text. The student’s involvement in teaching and learning activities improved as well. Most of the students gave a positive response to the teaching-learning process. It could be concluded that the REAP technique was useful to be used in teaching and learning reading comprehension at one state senior high school. The students became active and began to think critically. REAP technique can be an alternative as a teaching medium specifically for reading comprehension. Keywords: Reap Technique, Reading comprehension, Classroom Action Research
An evaluation of elemental enrichment in rocks: In the case of Kısacık and its neighborhood (Ayvacık, Çanakkale/Türkiye)

In this study, the enrichment of the major oxide, trace element/heavy metal and rare earth element contents of the rocks outcropping in Kısacık and its vicinity (Ayvacık-Çanakkale/Türkiye) were investigated. The rocks in the field were handled in 5 groups, and whole rock analyses were carried out for 22 samples collected representing these rock groups and Element Enrichment Factor (EEF) of the major oxide, trace element/heavy metal and rare earth element contents of the rocks were calculated. As a result, it was determined that the Kısacık volcanics were enriched in SiO2, Fe2O3, K2O, Be, Co, Cs, Th, U, W, La, Eu, Tm, Yb, Lu, Mo, As, Cd, Sb, Bi and Hg elements at a rate of &gt;1 to &gt;150 according to the upper crust values, and the Fe2O3, MgO, CaO, TiO2, P2O5, MnO, Cr, Sc, Co, Nb, Sr, Mo, Cu, Ni, Cad, Sb, Bi, V, Cu and Cd concentrations of the Ophiolitic Mélange were enriched in ratios ranging from &gt;1 to &gt;36 according to the upper crust values. It has been also observed that the listvenitic rocks in the Ophiolitic Mélange are enriched in Cr, Co, Ni, As and Hg elements compared to the upper crust. As to Kazdağ Group, MgO, CaO, K2O, MnO, Cr, Co, Ta, U, W, Mo, Cu, Ni, As and Cd were enriched. Listvenite were enriched in SiO2, Fe2O3, MgO, Mn, Cr, Co, Ni, As, Sb and Hg at a rate of &gt;1 to &gt;32 according to the upper crust values. When the rocks in the area were evaluated together, some oxides (e.g., CaO, MgO, Fe2O3, TiO2) and elements (e.g., Cr, Ni, Co) were enriched due to parental rock, while some oxides (e.g., SiO2, K2O and MnO) and elements (As, Sb, Hg) were enriched due to epigenic processes such as hydrothermal alteration and weathering.
Bayesian Estimation of Attribute Hierarchy for Cognitive Diagnosis Models

Attribute hierarchy, the underlying prerequisite relationship among attributes, plays an important role in applying cognitive diagnosis models (CDM) for designing efficient cognitive diagnostic assessments. However, there are limited statistical tools to directly estimate attribute hierarchy from response data. In this study, we proposed a Bayesian formulation for attribute hierarchy within CDM framework and developed an efficient Metropolis within Gibbs algorithm to estimate the underlying hierarchy along with the specified CDM parameters. Our proposed estimation method is flexible and can be adapted to a general class of CDMs. We demonstrated our proposed method via a simulation study, and the results from which show that the proposed method can fully recover or estimate at least a subgraph of the underlying structure across various conditions under a specified CDM model. The real data application indicates the potential of learning attribute structure from data using our algorithm and validating the existing attribute hierarchy specified by content experts.
Transformer-based Text Classification on Unified Bangla Multi-class Emotion Corpus

Abstract Due to its importance in studying people’s thoughts on various Web 2.0 services, emotion classification is a critical undertaking. Most existing research is focused on the English language , with little work on low-resource languages, e.g., Bangla. In recent years, sentiment analysis, particularly emotion classification in English, has received increasing attention, but little study has been done in the context of Bangla (one of the world’s most widely spoken languages). In this research, we propose a complete set of approaches for identifying and extracting emotions from Bangla texts. We provide a Bangla emotion classification for six classes, i.e., anger, disgust, fear, joy, sadness, and surprise, from Bangla words using transformer-based models, which exhibit phenomenal results in recent days, especially for high-resource languages. The Unified Bangla Multi-class Emotion Corpus (UBMEC) is used to assess the performance of our models. UBMEC is created by combining two previously released manually labelled datasets of Bangla comments on six emotion classes with fresh manually labelled Bangla comments created by us. The corpus dataset and code we used in this work are publicly available.
File Fragment Type Identification Based on CNN and LSTM

In digital forensics, file carving is the process of recovering files on a storage media without any file system information. Note that when a file is deleted, the file system does not zero-out the corresponding data blocks because their content will be overwritten by other new files later. Due to a deleted file may be divided into different parts or successive but partly occupied by a new file, evidence may be found in deleted file fragments. Therefore, identifying the type of a file fragment is a necessary step for effective file carving. In this paper, we proposed a file fragment type identification network architecture based on CNN (convolutional neural networks) and LSTM (Long Short-Term Memory). Specifically, we first use a trainable embedding layer to convert sparse binary file fragment into compact real-valued representations. Then, successive convolutional modules are utilized to learn higher level representation of file fragments. Finally, the obtained features are fed into LSTM for classification. Our proposed deep network architecture was trained and tested on the largest public file fragment dataset FFT-75. Experimental results show that we can achieve average accuracy of 66.5% and 78.6% for 512-bytes and 4096-bytes file fragments, respectively, which are higher than existing work.
Literature Review: Multiparadigm Accounting Research

The objective of this paper is to explore all related literature with multiparadigm accounting research approaches. The literature search strategy begins by looking at literature from sources in the forms of books or sources originating from several databases or search engines including open knowledge maps, google scholar, and z-library. The keywords used for the search are “accounting: science with various paradigms, multiparadigm accounting research, accounting paradigm and paradigm accounting”. From the results of searching and reviewing the literature, including books, journal articles and other electronic sources, various kinds of multiparadigm accounting research were obtained. By knowing the benefits of each of the paradigm viewpoints that have been described, it is good if we take a mixed paradigm approach in accounting research so that research results are of higher quality because they are not only based on one paradigm. Even though in one research using one accounting paradigm, those of us who strongly believe in a certain paradigm still have an open mind towards other paradigms thus enriching our views so that in studying accounting all paradigms are believed to complement one another. This research will greatly contribute to academics in viewing accounting from various perspectives. This literature review classifies multiparadigm accounting research based on more specific groups.
Modern Educational Technologies in a Fractal Approach Implementation in the Math Lessons (on the Example of Learning a Probability-Statistical Line Elements)

The article aims to reveal the didactic potential of modern educational technologies used within the framework of the fractal approach in teaching stochastics to learners, to show the effectiveness of fractal approach technologies in practice experimentally. In the course of the scientific research, the authors employed scientific analysis of literary sources on philosophical and methodological problems associated with the introduction of a fractal approach in teaching and informatization of education; systematization and generalization of the principles of fractal pedagogy; study, analysis, and concretization of advanced pedagogical experience in the use of modern educational technologies in the educational process; observation and analysis of the results of educational activities of seventh graders; and pedagogical experiment. This research allowed for identifying a group of modern educational technologies in the implementation of the fractal approach in mathematics lessons and identifying their didactic potential and possibilities of using, which is reflected in Table 1 of the main text of this publication. At the same time, it was found that the technologies of the fractal approach in teaching are quite useful: the experimental group received the best result.