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A Reputation-Based AODV Protocol for Blackhole and Malfunction Nodes Detection and Avoidance

computers, Materials & Continua 2024年第8期80卷 1867-1888页

作者： Qussai M.Yaseen Monther Aldwairi Ahmad Manasrah Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityAjman346United Arab Emirates Department of Computer Information Systems Faculty of Computer and Information TechnologyJordan University of Science and TechnologyIrbid22110Jordan College of Technological Innovation Zayed UniversityAbu Dhabi144534United Arab Emirates Faculty of Information Technology and Computer Science Yarmouk UniversityIrbid21163Jordan Computer Information Systems Higher Colleges of TechnologySharjah341214United Arab Emirates

Enhancing the security of Wireless Sensor Networks(WSNs)improves the usability of their ***,finding solutions to various attacks,such as the blackhole attack,is crucial for the success of WSN *** paper proposes an enhanced version of the AODV(Ad Hoc On-Demand Distance Vector)protocol capable of detecting blackholes and malfunctioning benign nodes in WSNs,thereby avoiding them when delivering *** proposed version employs a network-based reputation system to select the best and most secure path to a *** achieve this goal,the proposed version utilizes the Watchdogs/Pathrater mechanisms in AODV to gather and broadcast reputations to all network nodes to build the network-based reputation *** minimize the network overhead of the proposed approach,the paper uses reputation aggregator nodes only for forwarding reputation ***,to reduce the overhead of updating reputation tables,the paper proposes three mechanisms,which are the prompt broadcast,the regular broadcast,and the light broadcast *** proposed enhanced version has been designed to perform effectively in dynamic environments such as mobile WSNs where nodes,including blackholes,move continuously,which is considered a challenge for other *** the proposed enhanced protocol,a node evaluates the security of different routes to a destination and can select the most secure routing *** paper provides an algorithm that explains the proposed protocol in detail and demonstrates a case study that shows the operations of calculating and updating reputation values when nodes move across different ***,the paper discusses the proposed approach’s overhead analysis to prove the proposed enhancement’s correctness and applicability.

关键词： AODV blackhole malfunction nodes pathrater reputation system wireless sensor networks(WSNs) watchdogs

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A fully flexible key delegation mechanism with efficient fine-grained access control in CP-ABE

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Journal of Ambient Intelligence and Humanized Computing 2023年第9期14卷 12837-12856页

作者： Porwal, Shardha Mittal, Sangeeta Department of Computer Science and Engineering and Information Technology Jaypee Institute of Information Technology Uttar Pradesh Noida India

Ciphertext Policy-Attribute Based Encryption (CP-ABE) is a secure one-to-many asymmetric encryption schemes where access control of a shared resource is defined in terms of a set of attributes possessed by a user. Key delegation service, where a user is able to temporally transfer its full or partial access rights, is an important requirement to make CP-ABE based systems practically useful. In existing CP-ABE delegation schemes, a delegator can delegate its entire or partial set of attributes such that all content accessible using those attributes becomes available to the delegatee also. Flexibility to delegate some specific content, which is a practical requirement, is not feasible with these schemes. Further, for a delegated resource, flexibility of fine grained delegation like delegating only a specific subset of read, write and update permissions is not yet available. In this work, CP-ABE key delegation has been enhanced to support specific content and specific permission delegation. The proposed scheme is fully flexible as it additionally supports limits on the number of times delegation can be done, delegatee revocation and traceability of forbidden delegation. Theoretical and practical analysis show that the proposed scheme achieves its objectives and is computationally efficient as well as secure against chosen plaintext and key abuse attacks. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Access control

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Adversarial attacks and defenses for digital communication signals identification

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Digital Communications and Networks 2024年第3期10卷 756-764页

作者： Qiao Tian Sicheng Zhang Shiwen Mao Yun Lin College of Computer Science and Technology Harbin Engineering UniversityHarbin150001China College of Information and Communication Engineering Harbin Engineering UniversityHarbin150000China Department of Electrical and Computer Engineering Auburn UniversityAuburnAL36849USA

As modern communication technology advances apace,the digital communication signals identification plays an important role in cognitive radio networks,the communication monitoring and management *** has become a promising solution to this problem due to its powerful modeling capability,which has become a consensus in academia and ***,because of the data-dependence and inexplicability of AI models and the openness of electromagnetic space,the physical layer digital communication signals identification model is threatened by adversarial *** examples pose a common threat to AI models,where well-designed and slight perturbations added to input data can cause wrong ***,the security of AI models for the digital communication signals identification is the premise of its efficient and credible *** this paper,we first launch adversarial attacks on the end-to-end AI model for automatic modulation classifi-cation,and then we explain and present three defense mechanisms based on the adversarial *** we present more detailed adversarial indicators to evaluate attack and defense ***,a demonstration verification system is developed to show that the adversarial attack is a real threat to the digital communication signals identification model,which should be paid more attention in future research.

关键词： Digital communication signals identification AI model Adversarial attacks Adversarial defenses Adversarial indicators

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CVTD: A Robust Car-Mounted Video Text Detector

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computers, Materials & Continua 2024年第2期78卷 1821-1842页

作者： Di Zhou Jianxun Zhang Chao Li Yifan Guo Bowen Li Department of Computer Science and Engineering Chongqing University of TechnologyChongqingChina College of Information and Engineering Jingdezhen Ceramic UniversityJingdezhenChina

Text perception is crucial for understanding the semantics of outdoor scenes,making it a key requirement for building intelligent systems for driver assistance or autonomous *** information in car-mounted videos can assist drivers in making ***,Car-mounted video text images pose challenges such as complex backgrounds,small fonts,and the need for real-time *** proposed a robust Car-mounted Video Text Detector(CVTD).It is a lightweight text detection model based on ResNet18 for feature extraction,capable of detecting text in arbitrary *** model efficiently extracted global text positions through the Coordinate Attention Threshold Activation(CATA)and enhanced the representation capability through stacking two Feature Pyramid Enhancement Fusion Modules(FPEFM),strengthening feature representation,and integrating text local features and global position information,reinforcing the representation capability of the CVTD *** enhanced feature maps,when acted upon by Text Activation Maps(TAM),effectively distinguished text foreground from non-text ***,we collected and annotated a dataset containing 2200 images of Car-mounted Video Text(CVT)under various road conditions for training and evaluating our model’s *** further tested our model on four other challenging public natural scene text detection benchmark datasets,demonstrating its strong generalization ability and real-time detection *** model holds potential for practical applications in real-world scenarios.

关键词： Deep learning text detection Car-mounted video text detector intelligent driving assistance arbitrary shape text detector

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A Deep Learning Approach to Industrial Corrosion Detection

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computers, Materials & Continua 2024年第11期81卷 2587-2605页

作者： Mehwash Farooqui Atta Rahman Latifa Alsuliman Zainab Alsaif Fatimah Albaik Cadi Alshammari Razan Sharaf Sunday Olatunji Sara Waslallah Althubaiti Hina Gull Department of Computer Engineering(CE) College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia Department of Computer Science(CS) College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia Department of Computer Information Systems(CIS) College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia

The proposed study focuses on the critical issue of corrosion,which leads to significant economic losses and safety risks worldwide.A key area of emphasis is the accuracy of corrosion detection *** recent studies have made progress,a common challenge is the low accuracy of existing detection *** models often struggle to reliably identify corrosion tendencies,which are crucial for minimizing industrial risks and optimizing resource *** proposed study introduces an innovative approach that significantly improves the accuracy of corrosion detection using a convolutional neural network(CNN),as well as two pretrained models,namely YOLOv8 and *** leveraging advanced technologies and methodologies,we have achieved high accuracies in identifying and managing the hazards associated with corrosion across various industrial *** advancement not only supports the overarching goals of enhancing safety and efficiency,but also sets a new benchmark for future research in the *** results demonstrate a significant improvement in the ability to detect and mitigate corrosion-related concerns,providing a more accurate and comprehensive solution for industries facing these *** CNN and EfficientNetB0 exhibited 100%accuracy,precision,recall,and F1-score,followed by YOLOv8 with respective metrics of 95%,100%,90%,and 94.74%.Our approach outperformed state-of-the-art with similar datasets and methodologies.

关键词： Deep learning YOLOv8 EfficientNetB0 CNN corrosion detection Industry 4.0 sustainability

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Chinese Medical Named Entity Recognition Using Pre-Trained Language Models and an Efficient Global Pointer Mechanism

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第19期49卷 97-114页

作者： Zhang, Xu Li, Feihong Li, Chenlong Yu, Xufeng College of Software Engineering Xiamen University of Technology Xiamen361000 China College of Computer and Information Engineering Xiamen University of Technology Xiamen361000 China

Medical Named Entity Recognition (MNER) is a critical task in medical text mining, serving as a foundation for intelligent diagnosis, disease prediction, and related tasks. However, Chinese medical texts present unique challenges, such as diverse entity classifications, varying entity lengths, and complex entity nesting, which hinder NER performance. To address these issues, we propose a Chinese MNER method leveraging pre-trained models and the efficient global pointer (EGP). Our approach incorporates three key innovations: (1) data augmentation techniques, including easy data augmentation and remote supervision with a medical entity dictionary, to address imbalanced entity types distribution and varying entity lengths;(2) a character-word fusion strategy that integrates RoBERTa-generated character vectors and Word2Vec-generated word vectors, enhancing semantic representation;and (3) an improved decoding layer based on EGP, enabling efficient recognition of both nested and flat entities while reducing computational costs. Experimentals show that our method achieves F1 scores of 75.87% and 92.77% on the CMeEE-V2 and CCKS2020 datasets, respectively, outperforming the RoBERTa-BiLSTM-CRF baseline by 3.06% and 4.38%, respectively. © 2025 Slovene Society Informatika. All rights reserved.

关键词： data augmentation EGP MNER RoBERTa Word2Vec

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An Experimental Study on Dynamic Lifelong Learning with GPT for Mitigating Catastrophic Forgetting in Aspect-Based Sentiment Analysis

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IEEE Access 2025年 13卷 90316-90332页

作者： Huang, Huang Asemi, Adeleh Universiti Malaya Faculty of Computer Science Information Technology Department of Software Engineering Kuala Lumpur Malaysia

GPT is widely recognized as one of the most versatile and powerful large language models, excelling across diverse domains. However, its significant computational demands often render it economically unfeasible for individuals and small businesses, underscoring the need for efficient, domainspecific alternatives. In Aspect-Based Sentiment Analysis (ABSA), existing models are typically optimized for single domains, facing challenges in performing effectively across multiple domains. A key issue, known as "catastrophic forgetting," arises when models trained on one domain lose previously learned knowledge upon exposure to new domain data. This leads to two significant problems: limited cross-domain generalization and difficulty in retaining prior knowledge while learning domain-specific information. To address these challenges, we introduce the Dynamic Lifelong Learning Aspect-Based Sentiment Analysis GPT (DllaGPT), a model designed to handle multiple domains while mitigating catastrophic forgetting. Leveraging datasets from four ABSA domains—Laptops, Restaurants, Tweets, and Finance—this study fine-tunes a pretrained GPT model from HuggingFace sequentially across domains. DllaGPT employs a mechanism to retain real data from earlier domains during new domain training, effectively preserving prior knowledge. Experimental results highlight that DllaGPT achieves an average accuracy of 0.85 and a Backward Transfer (BWT) score of -0.09 across the four domains, showcasing its high accuracy and robust lifelong learning capabilities. © 2025 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Contrastive Learning

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Monitoring COVID-19 environment: a real-time facial mask detection using YOLO models

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Research on Biomedical engineering 2025年第1期41卷 1-18页

作者： Pandiyan, P. Thangaraj, Rajasekaran V, Balasubramanian V. K, Manavalasundaram S, Balakrishnan Department of Electrical and Electronics Engineering KPR Institute of Engineering and Technology Coimbatore India Department of Computer Science and Engineering Nandha Engineering College Erode India Department of Information Technology Velalar College of Engineering and Technology Erode India Department of Computer Science and Engineering Aarupadai Veedu Institute of Technology Chennai India Department of Computer Science and Engineering Sri Ramakrishna Institute of Technology Coimbatore India

Purpose: The rapid spread of COVID-19 has resulted in significant harm and impacted tens of millions of people globally. In order to prevent the transmission of the virus, individuals often wear masks as a protective measure for themselves and others. Coronavirus protection guidelines have been published by the World Health Organization (WHO). According to WHO standards, COVID-19 can be prevented by wearing a mask in public places and congested regions. In these places, it is very difficult to personally check to see if people are wearing face masks or not. Methods: The objective of this research work is to build a powerful, efficient, and real-time approach for detecting people not wearing masks. Three cutting-edge object identification models, namely YOLOv4, Tiny-YOLOv4, and YOLOv5, are employed in this study for the identification of masked faces. Result: The proposed YOLOv5 model is evaluated using real-time images captured using a smartphone or tablet. The test images include both single and multiple people with and without masks. The YOLOv5 model achieved recognition accuracy of 88.90% with an average detection speed of 0.0316 s per image, whereas the YOLOv4 and Tiny-YOLOv4 produced recognition accuracy of 82.24% and 74.80% with an average detection speed of 0.0530 s and 0.0541 s per image, respectively. Conclusion: The comparative performance suggests that the YOLOv5 model has a maximum recognition accuracy of 88.90% in face mask identification tasks compared to other models such as the YOLOv4 and Tiny-YOLOv4. © The Author(s), under exclusive licence to The Brazilian Society of Biomedical engineering 2025.

关键词： COVID-19

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Transient Characteristics and Accommodative Current Limiting Strategy for Bidirectional Interlinking Converters in Hybrid AC/DC Microgrids

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CSEE Journal of Power and Energy Systems 2024年第4期10卷 1575-1588页

作者： Xia Shen Zhikang Shuai Wen Huang Chao Shen Yang Shen Z.John Shen College of Electrical and Information Engineering Hunan UniversityChangsha 410082China Department of Electrical and Computer Engineering Illinois Institute of TechnologyChicagoIL 60616 USA

Bidirectional interlinking converter(BIC)is the core equipment in a hybrid AC/DC microgrid connected between AC and DC ***,the variety of control modes and flexible bidirectional power flow complicate the influence of AC faults on BIC itself and on DC sub-grid,which potentially threaten both converter safety and system *** study first investigates AC fault influence on the BIC and DC bus voltage under different BIC control modes and different pre-fault operation states,by developing a mathematical model and equivalent sequence ***,based on the analysis results,a general accommodative current limiting strategy is proposed for BIC without limitations to specific mode or operation *** amplitude is predicted and constrained according to the critical requirements to protect the BIC and relieving the AC fault influence on the DC bus *** with conventional methods,potential current limit failure and distortions under asymmetric faults can also be ***,experiments verify feasibility of the proposed method.

关键词： Bidirectional interlinking converter current limiting fault characteristic hybrid AC/DC microgrid variation/oscillation of DC bus voltage

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Mining user's navigation structure by filtering impurity nodes for generating relevant predictions

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International Journal of Cognitive Computing in engineering 2023年第1期4卷 248-258页

作者： Jindal, Honey Sardana, Neetu Vidyarthi, Ankit Gupta, Deepak Mahmud, Mufti Department of Computer Science & Engineering and Information Technology Jaypee Institute of Information Technology Noida India Department of Computer Science and Engineering Maharaja Agrasen Institute of Technology Delhi India United Kingdom

Web Navigation Prediction (WNP) has been popularly used for finding future probable web pages. Obtaining relevant information from a large web is challenging, as its size is growing with every second. Web data may contain irrelevant noise, and thus cleaning such data is essential. In this paper, we emphasize the identification and elimination of noisy information from user-navigated web pages. The pruning of user browsed sessions by removing noisy webpages and their relations can help in the development of high-performing prediction models with fewer prediction errors. To minimize the prediction errors, we have proposed four pruning models namely PM3ER, PM3EN, PM3GI, and PM3EP which remove noisy web pages and their relations from the model consisting of varied simple and complex navigations. The study reveals that PM3ER is effective in prediction for websites with complicated structures resulting in complex navigations, and PM3EN is effective in predicting websites with a simple tree-like structure resulting in simple navigations. PM3ER has shown an improvement in predictions by up to 3% whereas PM3EN has attained improvement up to 5.45%. © 2023

关键词： Forecasting

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