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Tibyan corpus: balanced and comprehensive error coverage corpus using ChatGPT for Arabic grammatical error correction

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PeerJ Computer Science 2025年 11卷 e2724-e2724页

作者： Alrehili, Ahlam Alhothali, Areej Department of Computer Sciences Faculty of Computing and Information Technology King Abdul Aziz University Jeddah Saudi Arabia Department of Computer Science College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia

Natural language processing (NLP) augments text data to overcome sample size constraints. Scarce and low-quality data present particular challenges when learning from these domains. Increasing the sample size is a natural and widely used strategy for alleviating these challenges. Moreover, data-augmentation techniques are commonly used in languages with rich data resources to address problems such as exposure bias. In this study, we chose Arabic to increase the sample size and correct grammatical errors. Arabic is considered one of the languages with limited resources for grammatical error correction (GEC) despite being one of the most popular among Arabs and non-Arabs because of its close connection to Islam. Therefore, this study aims to develop an Arabic corpus called "Tibyan" for grammatical error correction using ChatGPT. ChatGPT is used as a data augmenter tool based on a pair of Arabic sentences containing grammatical errors matched with a sentence free of errors extracted from Arabic books, called guide sentences. Multiple steps were involved in establishing our corpus, including collecting and pre-processing a pair of Arabic texts from various sources, such as books and open-access corpora. We then used ChatGPT to generate a parallel corpus based on the text collected previously, as a guide for generating sentences with multiple types of errors. By engaging linguistic experts to review and validate the automatically generated sentences, we ensured they were correct and error-free. The corpus was validated and refined iteratively based on feedback provided by linguistic experts to improve its accuracy. Finally, we used the Arabic Error Type Annotation tool (ARETA) to analyze the types of errors in the Tibyan corpus. Our corpus contained 49% of errors, including seven types: orthography, morphology, syntax, semantics, punctuation, merge, and split. The Tibyan corpus contains approximately 600 K tokens. © 2025 Alrehili and Alhothali

关键词： Semantics

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Development of malware in Windows sandbox and Kali MSFvenom for hacking Android and Windows Operating Systems

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International Journal of Information Technology (Singapore) 2025年第3期17卷 1831-1838页

作者： Pandey, Bishwajeet Shukla, Man Mohan Pandey, Pushpanjali Bakar, W.A.W.A. GL Bajaj College of Technology and Management Greater Noida India Pranveer Singh Institute of Technology Kanpur India R & D Lab Gyancity Research Consultancy Greater Noida India Faculty of Informatics and Computing Universiti Sultan Zainul Abidin (UniSZA) Kuala Terengganu Malaysia

In this research, we developed seven malware programs utilizing Windows Notepad, NJRAT, and MSFvenom malware development environment. Two BAT viruses were created using Windows Notepad, one to simulate a Matrix effect and another for app bombing. We also developed three types of spyware using MSFvenom targeting Android, macOS, and Windows systems, designed to transmit information from these victims back to the Kali machine. When the spyware was deployed on a Windows system, it was capable of extracting password hashes, taking screenshots, uploading and executing payloads, downloading files, and performing various other exploits through the meterpreter shell. When the spyware was installed on an Android device, it allowed access to call logs, contacts, and geolocation, as well as the ability to send SMS messages and delete apps on the victim's device. When we executed spyware on a Mac machine, we got the process ID of all running applications on Mac, we were able to kill those running applications. The seventh spyware, built using the NJRAT Danger Edition, enabled a range of attacks, such as opening File Explorer and deleting specific folders to gain control over the victim's computer. The entire research and development process took place at the R&D Lab of Gyancity Research Consultancy, located at latitude 28.6519 and longitude 77.2315. We utilized an IP location tracker to obtain the geoinformatics coordinates based on the IP address of our Kali machine. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2025.

关键词： Kali MSFvenom Notepad Sandbox Spyware Virus Windows

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A Multi-Modal Assessment Framework for Comparison of Specialized Deep Learning and General-Purpose Large Language Models

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IEEE Transactions on Big Data 2025年第03期11卷 1001-1012页

作者： Nadeem, Mohammad Sohail, Shahab Saquib Madsen, Dag Oivind Alzahrani, Ahmed Abrahim Ser, Javier Del Muhammad, Khan Aligarh Muslim University Department of Computer Science Aligarh India VIT Bhopal University School of Computing Science and Engineering Bhopal-Indore Highway Kothrikalan Madhya Pradesh Sehore466114 India University of South-Eastern Norway Norway King Saud University Computer Science Department Community College Riyadh11437 Saudi Arabia TECNALIA Derio48160 Spain Department of Communications Engineering Bilbao48013 Spain Department of Applied Artificial Intelligence School of Convergence College of Computing and Informatics Seoul03063 Korea Republic of

Recent years have witnessed tremendous advancements in Al tools (e.g., ChatGPT, GPT 4, and Bard), driven by the growing power, reasoning, and efficiency of Large Language Models (LLMs). LLMs have been shown to excel in tasks ranging from poem writing and coding to essay generation and puzzle solving. Despite their proficiency in general queries, specialized tasks such as metaphor understanding and fake news detection often require finely tuned models, posing a comparison challenge with specialized Deep Learning (DL). We propose an assessment framework to compare task-specific intelligence with general-purpose LLMs on suicide and depression tendency identification. For this purpose, we trained two DL models on a suicide and depression detection dataset, followed by testing their performance on a test set. Afterward, the same test dataset is used to evaluate the performance of four LLMs (GPT 3.5, GPT 4, Google Bard, and MS Bing) using four classification metrics. The BERT-based DL model performed the best among all, with a testing accuracy of 94.61%, while GPT 4 was the runner-up with accuracy 92.5%. Results demonstrate that LLMs do not outperform the specialized DL models but are able to achieve comparable performance, making them a decent option for downstream tasks without specialized training. However, LLMs outperformed specialized models on the reduced dataset. © 2015 IEEE.

关键词： Contrastive Learning

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Improvement of the Teaching Process Using the Genetic Algorithm 8th

Improvement of the Teaching Process Using the Genetic Algori...

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8th EAI International Conference on Future Access Enablers of Ubiquitous and Intelligent Infrastructures, FABULOUS 2024

作者： Šimić, Goran Jevremović, Aleksandar Strugarević, Danilo School of Electrical and Computer Engineering at Academy of Technical and Art Applied Studies Belgrade11000 Serbia Faculty of Informatics and Computing Singidunum University Belgrade11000 Serbia Academy of Applied Preschool Teaching and Health Studies Kruševac37000 Serbia

ISBN: (纸本)9783031723926

Teaching is a process that requires permanent observation and improvement. With the rapid development of e-learning, there was a need to review, improve and optimize the process of evaluating students’ performance. The main objective of this study is to develop and implement the procedure for automatized generation of assessment tests. For this purpose, the proposed solution includes the application of a genetic algorithm. The procedure is developed on a sample of 29 existing test cases of "Internet programming" course. The optimization resulted in better selection and reduced number of questions used for evaluation of students` performance. The results of the study and the topicality of the issue point to the need for further research, both in the aspect of students` performance assessment, as well as in other aspects of higher education. © ICST Institute for Computer Sciences, Social informatics and Telecommunications Engineering 2025.

关键词： Conceptual framework e-learning Genetic algorithm Higher education Students` performance

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PaintingScapeVR: Exploring Gamified Interaction in Virtual Galleries 1st

PaintingScapeVR: Exploring Gamified Interaction in Virtual ...

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1st International Conference on Extended Reality, ICXR 2024

作者： Cai, Dongning Ren, Le Chang, Yaqi Li, Xiaodan Lin, Yuxin Yao, Junfeng Center for Digital Media Computing School of Film School of Informatics Xiamen University Xiamen361005 China Institute of Artificial Intelligence Xiamen University Xiamen361005 China Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Ministry of Culture and Tourism New Taipei City Taiwan School of Informatics Xiamen University Xiamen361005 China

ISBN: (纸本)9789819636785

In reality, galleries usually display paintings in a protective manner, partly to delay the damage to the original artwork and ensure the long-term preservation of the artwork. On the other hand, it also hinders visitors’ deep appreciation and learning of the artwork. In the face of this challenge, our research aims to provide users with immersive viewing and entertainment, as well as interactive gaming experiences with artwork, through the use of virtual reality technology. So the paper proposes PaintingScapeVR, which is based on a virtual gallery and reproduces the three-dimensional scenes corresponding to different paintings. Visitors can freely explore in this virtual environment and gain a deeper understanding of the meaning and artistic value of artworks through interaction with them. Our results indicate that the reproduction of 3D scenes and gamified interactive design can not only significantly enhance visitors’ appreciation of painting art, but also leave a deeper learning impression. This immersive experience stimulates visitors’ desire to appreciate and explore, prompting them to actively delve into the background and techniques of art works, thereby enhancing their artistic appreciation abilities. At the same time, this work also demonstrates its potential in enhancing visitors’ understanding of painting works, allowing more people to explore the joy of painting and art. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Interactive gamification Three-dimensional displays Virtual reality

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A Smart Farming System for Rubber Nursery Management in Monitoring Plant Growth Performance Using IoT Technology 1

A Smart Farming System for Rubber Nursery Management in Moni...

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1st Engineering and Science International Conference 2024, ESIC 2024

作者： Sadikan, S.F.N. Aziz, M.L.H.A. Umor, M.A.S. Mahzan, S. Marjudi, S. Salamat, M.A. Dunne, R.S. Faculty of Plantation and Agrotechnology Universiti Teknologi MARA Merlimau Melaka77300 Malaysia College of Computing Informatics and Media Universiti Teknologi MARA Merlimau Melaka77300 Malaysia Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Batu Pahat86400 Malaysia Faculty of Communication Visual art and Computing Universiti Selangor Kuala Selangor45600 Malaysia

Rubber plantations are an integral part of economy in Malaysia. However, inadequate soil moisture poses a significant problem for rubber trees as it can lead to water stress, ultimately hindering their growth, leaf development, and latex production. Water stress occurs when the trees are unable to obtain sufficient water from the soil, which resulting in stunted growth, leaf development, leading to fewer and smaller leaves. There is needed an effective water management strategy to ensure optimal growth of rubber trees for latex production. Thus, this project is aim to design and build an active soil monitoring system based on the Internet of Things (IoT). This system is made up of two major components: soil moisture and light intensity. The first component is a soil moisture sensor, which determines the soil's moisture content;light intensity sensor, which detects the intensity of the light and ANSELF Brushless DC Pump connected to an Arduino. This system provides sustainable solutions to increase crop output, reduce waste, and develop a greener plant cultivation. This smart monitoring plant growth system prototype is important in agriculture and plant production. It has potential to promote healthy plant growth and increase agricultural production. © 2025 Institute of Physics Publishing. All rights reserved.

关键词： Soil moisture

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Optimizing Machine Learning for Malicious Blockchain Node Detection Using Modified Metaheuristic

Optimizing Machine Learning for Malicious Blockchain Node De...

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AIML-Applications for Engineering & Technology (ICAET), International Conference on

作者： Nebojsa Bacanin Marko Vićentijevic Luka Jovanovic Angelina Njegus Branislav Radomirovic Miodrag Zivkovic Faculty of Informatics and Computing Singidunum University Belgrade Serbia Faculty of Technical Sciences Singidunum University Belgrade Serbia

ISBN: (数字)9798350355611

ISBN: (纸本)9798350355628

The decentralized architecture of blockchain tech-nology introduces various challenges, particularly in Proof of Stake (PoS) networks, where malicious nodes can undermine the consensus process and threaten the integrity of the network. Nodes with significant stakes may disproportionately influence validation, weakening the network's decentralization. Despite ongoing advancements in blockchain security, there remains a notable gap in research on the integration of artificial intelli-gence (AI) and optimization techniques for enhancing blockchain security. This paper proposes an approach, utilizing a CatBoost classifier optimized with a modified Particle Swarm Optimization (PSO) algorithm, designed specifically for this study. A compar-ative analysis, performed on a real-world dataset, demonstrates the effectiveness of the proposed method, with the most accurate models achieving an accuracy of 87.13%. The findings highlight the potential of AI -driven optimization in improving blockchain security. Future research will focus on the application of AI and optimization techniques to further refine blockchain protocols, enhance detection and prevention of security threats, and improve overall network performance.

关键词： Proof of stake Analytical models Accuracy Prevention and mitigation Metaheuristics Machine learning Linear programming Security Particle swarm optimization Convergence

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End-to-End Optimized Ensemble Model for Alzheimer's Disease Detection

End-to-End Optimized Ensemble Model for Alzheimer's Disease ...

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International Conference on Ubiquitous Information Management and Communication (IMCOM)

作者： Abdelrahman Hassan Tamer Abuhmed Shaker El-Sappagh Faculty of Computer Science and Engineering Galala University Egypt College of Computing and Informatics Sungkyunkwan University South Korea

ISBN: (数字)9798331507817

ISBN: (纸本)9798331507824

Alzheimer's disease (AD) is a progressive neurodegenerative disorder with an increasing prevalence among the elderly, making early and accurate diagnosis critical for effective intervention and management. This paper introduces an end-to-end machine learning pipeline optimized for detecting AD using multimodal data from the Alzheimer's Disease Neuroimaging Initiative (ADNI). The study proposes an ensemble model that integrates key modalities-demographics, cognitive scores, neuropsychological assessments, and MRI data-via early fusion to enhance AD detection. Various machine learning models, including Logistic Regression, Gradient Boosting, AdaBoost, XGBoost, Support Vector Machine, and Neural Networks, are compared, and feature selection is optimized through Recursive Feature Elimination, ANOVA F-value, and mutual information methods, with mutual information showing the most efficacy. With further hyperparameter tuning, the highest accuracy is achieved at 95.85%, with Gradient Boosting as the top-performing model. Additional experiments assess and evaluate individual modality contributions, underscoring the predictive value of cognitive scores and neuropsychological features. To enhance clinical interpretability, we incorporate model explainability using LIME and SHAP, which identify key features influencing model predictions across different AD stages. Explainable AI (XAI) is essential in healthcare for providing transparency and supporting clinician confidence by highlighting which biomarkers, such as MMSE, CDRSB, and PTAU, contribute most to diagnostic outcomes. This study demonstrates classical machine learning's potential in AD diagnostics, offering a pathway to cost-effective and clinically viable models for neurodegenerative disease management.

关键词： Support vector machines Accuracy Biological system modeling Neural networks Predictive models Boosting Feature extraction Alzheimer's disease Tuning Optimization

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Drone-Based Public Surveillance Using 3D Point Clouds and Neuro-Fuzzy Classifier

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Computers, Materials & Continua 2025年第3期82卷 4759-4776页

作者： Yawar Abbas Aisha Ahmed Alarfaj Ebtisam Abdullah Alabdulqader Asaad Algarni Ahmad Jalal Hui Liu Faculty of Computing and AI Air UniversityIslamabad44000Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Information Technology College of Computer and Information SciencesKing Saud UniversityRiyadh12372Saudi Arabia Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Department of Computer Science and Engineering College of InformaticsKorea UniversitySeoul02841Republic of Korea Cognitive Systems Lab University of BremenBremen28359Germany

Human Activity Recognition(HAR)in drone-captured videos has become popular because of the interest in various fields such as video surveillance,sports analysis,and human-robot ***,recognizing actions from such videos poses the following challenges:variations of human motion,the complexity of backdrops,motion blurs,occlusions,and restricted camera *** research presents a human activity recognition system to address these challenges by working with drones’red-green-blue(RGB)*** first step in the proposed system involves partitioning videos into frames and then using bilateral filtering to improve the quality of object foregrounds while reducing background interference before converting from RGB to grayscale *** YOLO(You Only Look Once)algorithm detects and extracts humans from each frame,obtaining their skeletons for further *** joint angles,displacement and velocity,histogram of oriented gradients(HOG),3D points,and geodesic Distance are *** features are optimized using Quadratic Discriminant Analysis(QDA)and utilized in a Neuro-Fuzzy Classifier(NFC)for activity ***-world evaluations on the Drone-Action,Unmanned Aerial Vehicle(UAV)-Gesture,and Okutama-Action datasets substantiate the proposed system’s superiority in accuracy rates over existing *** particular,the system obtains recognition rates of 93%for drone action,97%for UAV gestures,and 81%for Okutama-action,demonstrating the system’s reliability and ability to learn human activity from drone videos.

关键词： Activity recognition geodesic distance pattern recognition neuro fuzzy classifier

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Bittersweet Snapshots of Life: Designing to Address Complex Emotions in a Reminiscence Interaction between Older Adults and a Robot 25

Bittersweet Snapshots of Life: Designing to Address Complex ...

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Proceedings of the 2025 CHI Conference on Human Factors in computing Systems

作者： Long-Jing Hsu Manasi Swaminathan Weslie Khoo Kyrie Jig Amon Hiroki Sato Sathvika Dobbala Kate Tsui David Crandall Selma Sabanovic Informatics Indiana University Bloomington Bloomington Indiana USA Indiana University Bloomington Indiana USA Luddy School of Informatics Computing and Engineering Indiana University Bloomington Bloomington Indiana USA Informatics/Luddy/R-Lab Indiana University-Bloomington Bloomington Indiana USA Toyota Research Institute Cambridge Massachusetts USA School of Informatics Computing and Engineering Indiana University Bloomington Indiana USA

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