Reviewerly/exHarmony huggingface dataset:
Trustworthy VANET: Hierarchical DAG-Based Blockchain Solution with Proof of Reputation Consensus Algorithm

The new paradigm edge computing demonstrates significant advantages in quality of service including low latency and bandwidth efficiency when deployed in autonomous vehicle applications. However, deploying edge computing in the context of a widely used vehicular ad hoc network (VANET) leads to potential security issues. Blockchain is a promising technology to solve security and trust issues with the identically distributed structure as edge computing and VANET, but its efficiency and scalability limitations constrain the usage in VANET. This paper proposes a hierarchical Directed Acyclic Graph (DAG)based blockchain architecture to overcome these integration challenges. The hierarchical architecture divides the whole system into multiple local chains according to geographical regions to improve scalability while also maintaining a global chain to facilitate security. The DAG-based blockchain structure can better support simultaneous operations to further improve scalability and efficiency. In addition, in recognition of VANET’s unique requirements, we design a reputation-based consensus for tip selection algorithm (TSA) to replace the widely used Proof-of-Work TSA in DAG, which ensures security in VANET without heavy computational burden. Our experimental simulations reveal that our proposed scheme not only fortifies security but also elevates scalability and efficiency.
Integration of Nonlinear Dynamics in Blockchain Security Protocols

Because of its ability to completely revamp current blockchain security methods, this connection is crucial. An effective safeguard against complex assaults, Nonlinear Dynamics (ND) adds a living, breathing component to consensus methods and cryptographic primitives. There is an urgent need for creative, nonlinear methods to strengthen blockchain security in light of present challenges including increasing attack vectors and risks posed by quantum computing. The suggested Dynamic Chaos-based Blockchain Security (DC-BS) system in this paper makes use of the chaotic dynamics present in ND to strengthen various aspects of blockchain security. Adaptive threat detection systems, dynamic consensus methods, and chaos-based encryption are all newly introduced in DC-BS. Validation of DC-BS's efficacy in preventing various attack scenarios through simulation studies demonstrates its advantages in reducing vulnerabilities and responding to new attack types. Various decentralized systems can benefit from DC-BS, including as supply chain management, the Internet of Things (IoT), conventional blockchain networks, and decentralized finance (DeFi). To strengthen the security of various decentralized applications, DC-BS works to increase trust, transparency, and resilience. The effectiveness of DCBS is confirmed by thorough simulation analyses that cover a wide range of attack scenarios, including double-spending assaults, Sybil attacks, and eclipse attacks. Based on the results of the simulations, DCBS is much more effective than conventional blockchain security procedures at reducing these risks. Showcased as well is the technique's capacity to react to changing attack techniques, highlighting its capacity to provide strong security even in dynamic settings.
Analyzing Patterns of Revision in Writing Assignments Using ChatGPT

This study derived educational implications by analyzing the question types, answer acceptance patterns, and revising patterns in high school students’ revising using ChatGPT.&#x0D; We found that, first, learners asked ChatGPT many content-related questions and accepted the answers based on critical awareness. Second, the ability increasing groups clearly recognized the problems in the first draft and revised it at a macro level. Third, learners with increased ability accurately identified problems and actively restructured their drafts, rather than simply accept ChatGPT’s suggestions.&#x0D; The implications are as follows. First, revising answers in classes using ChatGPT can significantly impact learners’ writing skills. Second, macro-level question creation education and critical judgment education using ChatGPT need to be strengthened. Third, revising education using ChatGPT needs to be subdivided depending on the learner’s writing level.
Pure Literature Controversies before and after Liberation

This study examined the controversy over pure literature before and after liberation. Ideology was internalized by Japanese imperialism in the time before liberation. During this period, the pure literature debate was linked to the generational debate and was accomplished with the participation of Jin-oh Yoo, Dong-ri Kim, and others. However, owing to the limitation of being an extension of the latter, the former itself could not be greatly deepened or expanded. However, the concept of pure literature discussed by Yu Jin-oh and others is significant in that it inherited the humanism debate and is connected to the pure literature debate after liberation.&#x0D; After liberation, the debate began amidst ideological conflict between the left and the right. Purity was divided into the logic of left- and right-wing literature, depending on whether purity was understood in relation to the situation of the times or as an attitude toward the literary spirit itself and toward literature itself. As it was developed based on the political logic, it was able to deepen and expand the discussion with the participation of many commentators. However, this debate failed to reach an agreement and ended in an emotional fight, which caused disappointment. The above discussion is significant in that it comprehensively considers the pure literature debate before and after liberation.
Program Dekonvolusi Blind Berbasis Matlab untuk Mempertajam Citra Akibat Simulasi Efek Buram Lensa

Blurred image due to lens defocus can be simulated mathematically as a convolution result between sharp image and Point Spread Function of blur (unfocus) lens. Image sharpening is the opposite of image blurring which can be done by deconvolution. This research was conducted to simulate digital image sharpening using the Blind deconvolution method. The Point Spread Function of the lens effect is used as the deconvolution Kernel function. This Point Spread Function is modeled mathematically with the Gaussian function approach. Convolution results between digital images from photos of an object are convoluted with the point spread function to produce a blurry image. The blurry image is then sharpened using the Blind convolution method. The slight difference between the deconvolution result image and the original object photo image indicates that the program is running well. Peak Signal to Noise Ratio (PSNR) is used to determine image sharpening recovery. Optimal sharpening recovery from deconvolution iterations is obtained at the maximum PSNR value
Exploring the Interplay of Cybersecurity and cybercrime in Pakistan's Digital Landscape

This study delves into the intricate dynamics between cybersecurity and cybercrime in Pakistan's rapidly advancing digital landscape. As the nation witnesses technological progress and heightened connectivity, the interaction between these two forces becomes pivotal. The research assesses the current state of cybersecurity measures employed by individuals, businesses, and government entities, evaluating their efficacy against the escalating wave of cyber threats. Concurrently, it investigates the tactics employed by cybercriminals, analyzing the nature and scope of prevalent cyber threats in Pakistan. The research involves an in-depth examination of cybersecurity measures and cyber threats in Pakistan. It employs a multi-faceted approach, including surveys, interviews, and data analysis, to comprehensively understand the evolving digital security landscape. The findings highlight the complex and diverse cybersecurity environment in Pakistan, shaped by the convergence of technology, government, and international connections. The research underscores the changing nature of cyber threats and their substantial impact on political stability, emphasizing the necessity for robust cybersecurity measures. This study contributes valuable insights into the evolving intersection of cybersecurity and cybercrime in Pakistan. It takes into account the historical context, societal challenges, and technological advancements, providing a nuanced understanding of the complex digital security landscape.
RESEARCH OF INFORMATION SYSTEMS IN WEATHER FORECASTING

Weather forecasting plays a crucial role in numerous industries and activities, ranging from agriculture and energy to tourism and transportation. In recent years, information technologies have significantly enhanced the capabilities of weather forecasting, providing more accurate and timely data. This article explores innovative information technologies employed in weather forecasting and their impact on modern practices. It highlights the utilization of cloud computing and data storage for managing vast amounts of meteorological data, enabling the use of more precise forecasting models. Additionally, the article discusses the integration of Internet of Things (IoT) and sensor networks, which facilitate the collection of weather data from diverse sources and contribute to localized and real-time weather predictions. Artificial intelligence (AI) and machine learning techniques are also examined for their ability to analyze large datasets, identify patterns, and improve forecast accuracy. Finally, the article emphasizes the importance of advanced data visualization techniques in effectively conveying weather information to end-users. By harnessing these information technologies, weather forecasting continues to advance, empowering various industries and enhancing decision-making processes. Keywords: Cloud computing, Data storage, Internet of Things (IoT), Artificial intelligence (AI), Machine learning, Forecast accuracy, Data visualization, Decision-making processes.
Cytological Evaluation of Fine Needle Aspiration of Breast Lesions Using IAC Yokohama Classification System for Reporting Breast Cytopathology

Introduction: Breast cancer is rapidly emerging as the leading cause of cancer in Indian women. Cytopathology and histopathology services are required to tackle this growing burden. The use of International Academy of Cytology (IAC) Yokohama Reporting System of breast cytology by classifying them into five categories insufficient, benign, atypical, suspicious and malignant. (IAC) Yokohama Reporting System offers structured protocols, are expected to improve breast cytopathology reporting. Objective : The main objectives of our study were to classify breast fine needle aspirates according to the IAC Yokohama system and assess the risk of malignancy, sensitivity, specificity, positive predictive value, negative predictive value, and accuracy. Methodology: All breast FNAs done in the above period were retrieved and classified into five categories according to the Yokohama system. Histopathological diagnosis was also retrieved wherever available. Results: The new “International Academy of Cytology (IAC) Yokohama system” was used to categorize 205 patients into five categories based on the cytologic diagnosis. That C1: insucient material (7.8%), C2: benign (64.3%), C3: atypical (7.3%), C4: suspicious of malignancy (7.8%), and C5: malignant (12.6%). Risk of malignancy (ROM) was calculated which was 100% in Yokohama category I, IV and V. In category II ROM was 00% and in category III ROM was 55.5%. FNAC correlation with histopathology was significant (P Value=&lt;0.01 &amp; Chi square value 40.7). Conclusion: The IAC Yokohama system is an excellent system for accurately diagnosing breast fine needle aspirate. Keywords: Histopathology, International Academy of Cytology Yokohama System, Fine­ Needle Aspiration Biopsy Cytopathology
A Real-Time System for Classification of Pakistan Sign Language using Machine Learning

Sign language is used by the deaf to interact with hearing individuals in the community. Despite the fact that sign language is often used, the general population is unaware of it, and only hearing-impaired people are familiar with its usage. In this article, we have created a system for real-time sign language recognition in order to communicate with folks who are deaf or hard of hearing by using Pakistan Sign Language (PSL) as its sign language. Ten types or indications of PSL are differentiated in this study. Flex sensors in the glove can detect the position of certain fingers. The MPU-6050, a micro-electro-mechanical device is used to capture the unique characteristics of the hand's movement in a 3D space. In this setup, a Raspberry Pi 3B serves as the microcontroller. MPU-6050 and Flex sensors performance are integrated with additional elements including mean, energy, variance, and correlation for classification. In our investigation, we have collected information from over 3,000 movement feature samples from different people. To increase the precision of the proposed methodology, we have incorporated several machine learning classification algorithms.
Automated Simulation and Virtual Reality Coupling for Interactive Digital Twins

While there are many efforts to simulate technical systems in virtual environments and provide a visual interaction for applications such as training, authoring and analysis, the process of generating applications still requires a lot of manual work. This is particularly critical in the context of interactive Digital Twins for resilience, where uncertain events can occur and every malfunction or mistreatment of any part of the system needs to be modeled. This paper presents an approach to model such systems in a modular way by automating the generation of its components for a game engine and simulators based on a common specification. Component instances are then synchronized bidirectionally across applications to achieve interaction between the game engine and simulators. An example hydraulic system is implemented and tested to demonstrate our approach, which needs minimal manual work by using predefined components. The solution can be extended by integrating more components and simulations.
Four Years of Not-Using a Simulator: The Agent-Based Template

With steadily increasing performance of computers, agent-based modeling has evolved from an analysis method for qualitative phenomena to strategy for quantitative decision support. With this orientation, however, the modeler faces new challenges during implementation. In particular, an appropriate simulation tool must feature the combination of data and model flexibility, process reproducibility, performance and portability. While existing simulators often do not sufficiently cover these features, it is also not sustainable to generally implement models from scratch. In this work, we want to present the idea of simulation templates as a compromise between the two strategies. We show, on the example of our Agent-Based Template and two use cases, the importance of the described challenges and how the simulation template concept supports solving them. We aim to generally promote the idea of developing a customized template, which, as a layer between simulator and from-the-scratch implementation, combines the advantages of both approaches.
Curriculum Recommendations Using Transformer Base Model with InfoNCE Loss and Language Switching Method

The Curriculum Recommendations paradigm is dedicated to fostering learning equality within the ever-evolving realms of educational technology and curriculum development. In acknowledging the inherent obstacles posed by existing methodologies, such as content conflicts and disruptions from language translation, this paradigm aims to confront and overcome these challenges. Notably, it addresses content conflicts and disruptions introduced by language translation, hindrances that can impede the creation of an all-encompassing and personalized learning experience. The paradigm's objective is to cultivate an educational environment that not only embraces diversity but also customizes learning experiences to suit the distinct needs of each learner. By proactively identifying and addressing these issues, the paradigm strives to pave the way for a more inclusive and responsive educational landscape, ensuring that learning opportunities are equitable and tailored to individual learners' requirements. To overcome these challenges, our approach builds upon notable contributions in curriculum development and personalized learning, introducing three key innovations. These include the integration of Transformer Base Model to enhance computational efficiency, the implementation of InfoNCeLoss for accurate content-topic matching, and the adoption of a language switching strategy to alleviate translation-related ambiguities. Together, these innovations aim to collectively tackle inherent challenges and contribute to forging a more equitable and effective learning journey for a diverse range of learners. Competitive cross-validation scores underscore the efficacy of sentence-transformers/LaBSE, achieving 0.66314, showcasing our methodology's effectiveness in diverse linguistic nuances for content alignment prediction.
Predicting the Veracity of News Articles Using Multimodal Embeddings and NLP-Based Features

The prevalence of false news in today's culture has brought to light the necessity for automated systems that can recognize and categorize news stories according to their validity with accuracy. The validity of news items is predicted using multimodal embeddings and characteristics based on natural language processing (NLP) in this work. In this method, the textual and visual content of news articles are jointly modeled using multimodal embeddings, and NLP-based features like sentiment, readability, and subjectivity are extracted. A machine learning model is then trained using these features to categorize news articles as true or false. The performance of the suggested method has been assessed against a number of cutting-edge algorithms for the identification of false news using a huge dataset of news items. The experimental findings demonstrate the efficacy of multimodal embeddings and NLP-based features by showing that it produces greater accuracy and F1 scores in comparison to other techniques. The suggested method offers a potential technique for the automatic identification and categorization of news stories based on their truthfulness, which might have significant effects on the fight against the spread of false information in modern society.
Enhanced Extremist Reviewer Group Detection in Online Product Reviews using RNN in comparison with CNN

The study aims to improve the accuracy of detecting extremist reviewer groups in e-commerce product reviews by utilizing algorithmic techniques. This research utilizes Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) with various grounding categories to predict the detection of extremist reviews The article uses data from CSV files that are available on the Kaggle website. A GPower test was conducted, resulting in an 80% power level using α=0.05 and power=0.80 as the parameters Predicting the extremist reviewer groups in e-commerce with improved accuracy rate is performed by Recurrent Neural Network (RNN) whereas samples count (N=10) and Convolutional Neural Network CNN where count of sample iteration values (N=10). The Recurrent Neural Network has demonstrated greater potential for increased accuracy value of 94.89% compared to the Convolutional Neural Network (CNN) at 93.33%, as determined by independent sample-t-test significance value the obtained p-value of 0.01 (p<0.05) It suggests a noteworthy variation between the groups being compared, backed by statistical significance. The Recurrent Neural Network yields a better accuracy rate than Convolutional Neural Network.
Thematic Event Extraction Based on Event-Related Sentence Detection

Existing works in event extraction typically extract event arguments within the sentence scope. However, besides the sentence level, events may also be naturally presented at the document level. A document-level event usually reflects, to some extent, the theme (i.e., the main content) of the document (e.g., electronic medical records and news articles), which is thus referred to as the thematic event. Thematic Event Extraction (TEE) aims to extract the arguments of thematic events. TEE faces a major challenge, i.e., the sparsity and dispersion of arguments, which means that the arguments of a thematic event are dispersed in different sentences of the document. To overcome this challenge, we propose an Event-related Sentence Detection based TEE model, called ESDTEE, which first detects the sentences related to the thematic event and then extracts the arguments only within these detected sentences using existing models. Extensive experiments with comprehensive analyses demonstrate the effectiveness of ESDTEE.
The effect of employee training and perceived organizational support on succession planning: The mediating role of employee commitment

Purpose- This research aims to look at the practices of succession planning in private banks in Quetta. Focusing on the succession planning theory (Ivan &amp; Joseph, 1994), this study establishes and tests a conceptual model to elucidate how organizational attributes (Employee training, perceived organizational support, Employee commitment) relate to succession planning.&#x0D; Research Methodology- The primary data of the research work were gathered through a cross-sectional survey, for this purpose, sixteen major private banks of Quetta, Pakistan were targeted. Convenience sampling was used due to the access and the information limitations.328 questionnaires were obtained out of 500 questionnaires distributed. A few statistical methods (confirmatory factor analysis &amp; the PROCESS MACRO etc.) were applied to test expressed hypotheses and conclude results.&#x0D; Findings- Results signified positive relations among all the study variables (Employee training, perceived organizational support, Employee commitment, Succession planning) and that the positive association between employee training &amp; succession planning was mediated by employee commitment. On the other hand, opposing hope’s employee commitment didn’t mediate the relationship between Perceived organizational support and succession planning.&#x0D; Implications- Along with the theoretical involvement, this study gives new ways for research to scholars and practitioners in human resource management and organizational development pathways.&#x0D; Originality/value- The results of this work offer a lot of support for the managerial practices that may assist financial institutions in attaining their internal successors through training and supporting the employees. Additionally, this study offers data-based plans to the banking management to accomplish their objective for employee development in the financial sector of Pakistan.
Research on Intelligent English to Chinese Translation Based on Transformer Model

The application of the Transformer model represents a pivotal advancement in machine translation, owing to its utilization of a self-attention mechanism that excels particularly in the translation of extensive textual content. The primary objective of this research is to delve into and execute an English-to-Chinese translation model based on the Transformer architecture, with the intention of refining the precision and fluency of machine translation. By seamlessly integrating attention mechanisms and pre-training on extensive parallel corpora, the model adeptly captures the intricate interrelationships and subtle semantic nuances that exist between sentences. This model has exhibited superior BLEU scores and translation quality in the English-to-Chinese translation task, thereby affirming the transformative potential of the Transformer model in the domain of machine translation. This study holds considerable implications for the enhancement of cross-lingual communication efficiency and cultural exchange, while also providing invaluable insights for the continued advancement and exploration of Transformer-based translation models.