Reviewerly/exHarmony huggingface dataset:
Dropout Factors of English Mobile Learning among Chinese College Students based on UTAUT2 Model

Currently, mobile learning is highly developed, but China continues to struggle to retain learners and prevent them from dropping out. In light of this phenomenon, the present study employs the UTAUT 2 model to develop an dropout model of English mobile learning among Chinese college students. Through data analysis methods such as descriptive statistics, reliability/validity test, and structural equation modelling, the finings indicate that social influence has direct or indirect effects on each variable in the model, and that performance expectancy and facilitating conditions are the primary variables that influence Chinese college students' dropout of English mobile learning. Various grade of college students demonstrated significant differences in drop intention and facilitating conditions, and various usage experiences revealed significant differences in hedonic motivation, price value, habit and drop behaviour. Based on these findings, the present study provides recommendations for English mobile learning development and application strategies in China.
Research on Immersive Education Scenarios Based on X-Reality from the Perspective of Metaverse

The metaverse has brought novel concepts and models to education, which has the potential to rebuild the education industry. However, the study of the metaverse is currently in the theoretical stage and lacks empirical cases and XR(X-Reality) technology is more maturely developed in metaverse underlying technology. Furthermore, XR and mixed/remote teaching are considered advanced directions and new trends in the field of education. This article analyzes and reconstructs the educational operating mechanism from the perspective of the metaverse, constructs XR-based immersive, multimodal interactive educational scenes with Constructivist Learning Theory, and proposes proactive strategies to address potential issues in the development of XR-based education. This will enable XR-based educational scenarios to be smoothly applied and popularized, providing a reference for innovation and development of the education industry in the future.
Rhetorical structure and promotional language in baked product descriptions: An English-Spanish contrastive analysis

This study explores the relation between rhetorical communicative functions and grammatical categories and more specifically between persuasion and adjectives in online biscuit descriptions. Promotional genres make use of linguistic and non-linguistic strategies to achieve their persuasive function and among the linguistic ones, we find the use of positive adjectives. The purpose of this paper is twofold: (i) to describe the rhetorical structure of online descriptions and the distribution of adjectives in the different rhetorical sections of the genre, and (ii) to analyze the relationship between rhetorical communicative purposes and adjective frequencies to figure out how the communicative purpose of each move/step influences adjective distribution, in English and Spanish. The research is based on an English-Spanish comparable corpus of online baked descriptions, including cookies, cakes, muffins, pastries, crackers, and bread. The contrastive analysis was carried out in two stages, first the rhetorical level was described and then the grammatical one. The most frequent adjectives and their concordances were analyzed to classify them to establish common features that would explain their use in specific rhetorical moves and steps. There is an association between the frequency of positive adjectives and the different rhetorical moves of the genre in both languages. Objective moves and steps show a lower use of adjectives, whereas subjective ones show a greater frequency of adjectives in general and of positive evaluative adjectives in particular, which can be explained by the rhetorical function of the sections where they are used.
Car Modification Workshop Design With A High-Tech Architectural Approach In Medan City

The design of a car modification workshop in Medan is intended for car modification enthusiasts so they can develop their hobbies and are designed using a High-Tech Architectural design. Car users in the city of Medan have reached 42,397, so this figure can affect the number of car modification enthusiasts. In the city of Medan itself, there are not too many car modification workshops that provide multi-functional services with supporting facilities, so it is difficult to find a variety of modification options. The design of a car modification workshop in the city of Medan with high-tech architecture aims tobe able to meet the demand for vehicle sales services, engine servicing, modifications, and other facilities on a large scale due to the high market interest. This car modification workshop is specifically designed and has been limited according to the concept of the user, actor, type of activity, space requirements and building analysis. By using the basic High-Tech theme, the concept of structural configuration style is applied in such a way. It turns out that High-Tech Architecture is very much influenced by current scientific advances and innovations so that it has many functions that are useful for many people. This design is expected to be able to overcome the problems of the people in Medan to have the latest innovations and accommodation for technological advances because it has a futuristic impression along with the times and provides its own style to attract the attention of visitors.
Enhancing Phishing Website Detection via Feature Selection in URL-Based Analysis

Detecting a phishing website accurately is crucial for ensuring the safety of online users, underscoring the importance of maintaining a secure digital environment. This research delves into the effectiveness of enhancing the detection of phishing websites through the application of a new dataset generation method. The method involves the transformation of a pure dataset obtained from Mendeley, by the utilization of regular expressions to extract the important features so that a detection process can be performed correctly with high performance. Based on the proposed features, we selected the best machine-learning algorithm. We performed a rigorous evaluation using Three prominent machine learning algorithms: Decision Trees, Support Vector Machines (SVM), and Random Forests, achieving 0.96% for Decision Tree Accuracy, 0.97% for SVM Accuracy, and 0.98% for Random Forest Accuracy. One of the critical contributions of this research is the deliberate selection of features. We have leveraged regular expressions to create a feature set that captures salient aspects of URLs and optimizes the algorithms' detection capabilities. This research has examined how feature selection affects the performance of each algorithm, highlighting its strengths and uncovering its weaknesses. Povzetek: glavni prispevek te raziskave je namerna izbira lastnosti. Izkoristili smo regularne izraze, da smo ustvarili nabor funkcij, ki zajame pomembne vidike URL-jev in optimizira zmožnosti zaznavanja algoritmov
Enhanced Cauchy Matrix Reed-Solomon Codes and Role-Based Cryptographic Data Access for Data Recovery and Security in Cloud Environment

In computer systems ensuring proper authorization is a significant challenge, particularly with the rise of open systems and dispersed platforms like the cloud. Role-Based Access Control (RBAC) has been widely adopted in cloud server applications due to its popularity and versatility. When granting authorization access to data stored in the cloud for collecting evidence against offenders, computer forensic investigations play a crucial role. As cloud service providers may not always be reliable, data confidentiality should be ensured within the system. Additionally, a proper revocation procedure is essential for managing users whose credentials have expired. With the increasing scale and distribution of storage systems, component failures have become more common, making fault tolerance a critical concern. In response to this, a secure data-sharing system has been developed, enabling secure key distribution and data sharing for dynamic groups using role-based access control and AES encryption technology. Data recovery involves storing duplicate data to withstand a certain level of data loss. To secure data across distributed systems, the erasure code method is employed. Erasure coding techniques, such as Reed-Solomon codes, have the potential to significantly reduce data storage costs while maintaining resilience against disk failures. In light of this, there is a growing interest from academia and the corporate world in developing innovative coding techniques for cloud storage systems. The research goal is to create a new coding scheme that enhances the efficiency of Reed-Solomon coding using the sophisticated Cauchy matrix to achieve fault tolerance
Steganography using AES and Edge Image

Steganography is the art of hiding messages in an image. This is a great way to send a secret message without drawing attention to it. Many technologies have been developed to ensure data security, and cryptography is considered one of the most important sciences used since the dawn of humanity. Using this proposed technique, we take the input text(128-bit) with key(128-bit), encrypt it using AES technology, and then hide out text(128-bit) inside the edges of the image free size. This is done by extracting the edges of the image and combining them with the ciphertext to make them disappear completely. Text data security is a good thing with this proposed new technology. It is also easy to extract text from the edges of the image and re-decode it. The resulting image is the same as the selected image, clear with raised edges, with text disappearing within the edges of the image indistinguishable from the naked eye. Therefore, this proposed method is considered the safest in hiding data inside the image.
Machine Learning-Based Approaches for Credit Card Fraud Detection: A Comprehensive Reviewz

The objective of data analytics is to discover hidden patterns and use them to guide wise judgements in a range of circumstances. Theft of credit cards has significantly grown as a result of modern technologies and has become a popular target for scam artists. Publicly available databases on credit card fraud are very unbalanced. As more people conduct business online, Fraud involving credit cards has grown to be a serious problem for both consumers and financial establishments. standard rule-based fraud detection strategies have shown to be insufficient to combat fraudsters' ever-evolving tactics. Machine learning algorithms have thus developed into a powerful tool for real-time unsupervised learning, and anomaly detection., is then explored in detail in order to accurately identify fraudulent transactions. Furthermore, we explore the various data features utilized by machine learning algorithms, including transaction history, transaction amounts, merchant information, and geographical locations. “For people, companies, and financial institutions, A significant financial danger is credit card fraud. In order to detect theft, robust methods for machine learning must be developed. researchers can help minimize financial losses associated with fraudulent activities”. In this research we will be using weighted product method. Taken as Alternative parameters is “Fraud detection using Game theory for M1, Hybrid Approach For Fraud Detection Using Svm And Decision Tree for M2, Fraud Detection Using Som &amp; Psofor M3, Dempster Shafer Theory Along With Bayesian Learning For Detecting Fraudfor M4, Cardwatchfor M5”. Taken as Evaluation parameters is “Sum of Squared Error, Mean Squared error, Root Mean Square error, Mean Absolute error, Root Mean Square Prediction Error, and Accuracy”. Model 1 outperformed the other 4 models when a machine learning algorithm was used to identify credit card frauds. With Weighted Product Method we are able to find the best way of detection of credit card frauds by machine learning algorithm which has been evaluated with various parameters and methodology.
Blockchain Technology in the Intrusion Detection Domain

The ability of blockchain technology to improve security and transparency across a range of industries has receivedA great deal of attention has been garnered lately in correcting the sentence.. In the domain of intrusion detection, where the identification and mitigation of cyber threats are paramount, blockchain has emerged as a promising solution. This abstract examines how blockchain is used in intrusion detection systems and emphasizes its advantages. Blockchain technology improves the security and integrity of intrusion detection systems by using a decentralized and immutable ledger. It provides an immutable audit trail, distributed consensus, and increased resilience to attacks. Moreover, blockchain fosters trust, transparency, and collaboration among stakeholders, enabling faster threat detection and response. This research can explore novel approaches to integrating blockchain into intrusion detection systems, providing stronger protection against cyber threats.Immutable Audit Trail: In the context of intrusion detection, the capacity of blockchain to produce an unalterable and transparent audit trail is of enormous value. Research in this area can focus on developing techniques to leverage the blockchain's audit trail for effective incident response, forensic investigations, and attribution of cyberattacks. We will use theweighted product model in this study, which is a research approach that gives weights to various factors and combines them to make conclusions based on their relative relevance in a weighted way. Taken as alternative is“IDS1, IDS2, IDS3, IDS4, IDS5, IDS6, IDS7, and IDS8”.Detection Quality, Performance, Stability, User Interface, Profile update, ConvenienceThe By this we can see that IDS4 has 1 RANK and IDS5 has the 8th RANK.In conclusion, blockchain technology holds great potential in the intrusion detection domain. Its decentralized and immutable nature can enhance the security and reliability of intrusion detection systems by providing transparent and tamper-proof logs of network activity. Blockchain-based solutions can improve threat detection, facilitate secure information sharing among entities, and enhance the overall resilience of intrusion detection systems. As the technology continues to evolve, further research and development in integrating blockchain with intrusion detection will unlock new possibilities for combating cyber threats.
Unicode-driven Deep Learning Handwritten Telugu-to-English Character Recognition and Translation System

Telugu language is considered as fourth most used language in India especially in the regions of Andhra Pradesh, Telangana, Karnataka etc. In international recognized countries also, Telugu is widely growing spoken language. This language comprises of different dependent and independent vowels, consonants and digits. In this aspect, the enhancement of Telugu Handwritten Character Recognition (HCR) has not been propagated. HCR is a neural network technique of converting a documented image to edited text one which can be used for many other applications. This reduces time and effort without starting over from the beginning every time. In this work, a Unicode based Handwritten Character Recognition(U-HCR) is developed for translating the handwritten Telugu characters into English language. With the use of Centre of Gravity (CG) in our model we can easily divide a compound character into individual character with the help of Unicode values. For training this model, we have used both online and offline Telugu character datasets. To extract the features in the scanned image we used convolutional neural network along with Machine Learning classifiers like Random Forest and Support Vector Machine. Stochastic Gradient Descent (SGD), Root Mean Square Propagation (RMS-P) and Adaptative Moment Estimation (ADAM)optimizers are used in this work to enhance the performance of U-HCR and to reduce the loss function value. This loss value reduction can be possible with optimizers by using CNN. In both online and offline datasets, proposed model showed promising results by maintaining the accuracies with 90.28% for SGD, 96.97% for RMS-P and 93.57% for ADAM respectively.
Enhancing Network Security Through Blockchain Technology: Challenges And Opportunities

The rapid proliferation of digital technologies has ushered in an era where network security is of paramount importance. Traditional security mechanisms have proven insufficient in protecting sensitive data and critical infrastructure from an ever-evolving landscape of cyber threats. Blockchain technology, originally designed to underpin cryptocurrencies like Bitcoin, has emerged as a promising solution for enhancing network security. Blockchain's core principles of decentralization, immutability, and transparency offer a unique approach to addressing vulnerabilities and mitigating risks in the digital realm. This paper examines how blockchain can enhance data integrity, authentication, and authorization processes, thereby fortifying the security posture of networks and systems.&#x0D; The paper discusses real-world applications and case studies where blockchain has been successfully implemented to bolster network security, such as supply chain management, identity verification, and secure communication protocols. These examples highlight the tangible benefits and opportunities that blockchain presents for organizations seeking to safeguard their digital assets and operations. In conclusion, this abstract underscore the pivotal role that blockchain technology can play in enhancing network security. By addressing challenges head-on and capitalizing on the opportunities it offers, organizations can build resilient, transparent, and secure digital ecosystems that protect against an ever-increasing array of cyber threats. The exploration of blockchain's potential in this context is critical for shaping the future of network security in our interconnected world.
Bidirectional LSTMs with Byte Pair Encoding in NMT for CLIR using English and Telugu Parallel Corpus

The Neural Machine Translation (NMT) is very crucial for Cross-Lingual Information Retrieval (CLIR). NMT is effective in translating English language queries to the Telugu Language. In this paper, we are translating English queries to Telugu. The NMT will utilize a parallel corpus for translations. Telugu is a resource-poor language, it is very difficult to supply large amounts of parallel corpus to NMT. So the NMT will have a problem called Out Of Vocabulary (OOV). To overcome this problem Byte Pair Encoding (BPE) is used along with Long Short Term Memory (LSTM), which segments the rare words into sub-words and tries to translate the rare words. It still faces problems like Named Entity Recognition (NER). Some problems of NER can be solved by utilizing bidirectional LSTMs in sequence-to-sequence models. The bidirectional LSTMs (BiLSTMs) will be helpful in training systems in both directions for recognizing the named entities. The accuracy parameters and a BLEU score show the translation quality of NMT with bidirectional LSTMs has slightly more accuracy than regular LSTMs which is considerable.
Enhancing the Security and Privacy of eHealth Records through Blockchain-based Management: A Comprehensive Framework

Progress in information technology is transforming the healthcare sector with the goal of enhancing medical services, diagnostics, and continuous monitoring through wearable devices, among other benefits, while also lowering expenses. This digital transformation enhances the convenience of computing, storing, and retrieving medical records, ultimately leading to improved treatment experiences for patients. Electronic health record systems have come under fire for centralized control, faults, and attack points with transferring data custodians. These systems are frequently utilized for the interchange of health information among healthcare stakeholders. The main objective is to overcome information asymmetry and data breaches commonly encountered in the Electronic Health Record (EHR) system. This study introduces a decentralized and trustless architecture aimed at securely storing patients' medical records and granting access to authorized individuals, including healthcare providers and patients themselves. The research primarily focuses on bolstering the security and privacy of healthcare data management systems using blockchain technology. To address the issue of blockchain scalability, an off-chain scaling approach is proposed, utilizing an underlying medium to store large volumes of data. This is achieved through the integration of Elliptic Curve Cryptography (ECC) and the Interplanetary File System (IPFS). The proposed system provides a secure and efficient method for storing and sharing sensitive healthcare data while ensuring confidentiality and data integrity.
Distribution of Heterogeneous Traffic Resources for 5G Cognitive Radio Networks based on Cooperative Learning and QOE

In addition to offering faster speeds and lower latency, 5G networks provide enhanced availability, significantly higher capacity, improved stability, and superior connectivity. The Mean Opinion Score (MOS), which quantifies the subjective quality of the user experience, has emerged as a widely accepted metric for assessing the quality of various types of network traffic. As we move towards the 5G era, measuring end-user quality has become increasingly important in the evolution of wireless communications. This paper presents a resource allocation approach for 5G cognitive radio networks that leverages cooperative learning and prioritizes Quality of Experience (QoE) for integrated heterogeneous traffic. The solution is based on a distributed underlay Dynamic Spectrum Access (DSA) system that utilizes MOS as a foundational metric for managing resource allocation across real-time video and data traffic, each with distinct characteristics. The proposed technique is designed to meet strict interference limitations for primary users while simultaneously maximizing the overall MOS. This is accomplished by employing reinforcement learning in a system where primary users and secondary users share the same frequency band of interest, ensuring efficient coexistence. Importantly, MOS serves as a standardized measure that allows for training across nodes carrying various types of traffic without compromising performance.
MULTIMEDIA EDUCATIONAL MATERIALS WITH DYNAMIC GRAPHICS

The paper discusses the peculiarities of using multimedia educational materials with dynamic graphics. The main types of multimedia educational material are presented. The approaches to the analysis of the quality of multimedia educational material are ch