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Container Based Nomadic Vehicular Cloud Using Cell Transmission Model

computer Systems science & engineering 2023年第1期44卷 423-440页

作者： Devakirubai Navulkumar Menakadevi Thangavelu Department of Computer Science and Engineering Jayam College of Engineering and TechnologyDharmapuri636813India Department of Electronics and Communication Engineering Adhiyaman College of EngineeringHosur635109India

Nomadic Vehicular Cloud(NVC)is envisaged in this *** predo-minant aspects of NVC is,it moves along with the vehicle that initiates it and functions only with the resources of moving vehicles on the heavy traffic road without relying on any of the static infrastructure and NVC decides the initiation time of container migration using cell transmission model(CTM).Containers are used in the place of Virtual Machines(VM),as containers’features are very apt to NVC’s dynamic *** specifications of 5G NR V2X PC5 interface are applied to NVC,for the feature of not relying on the network ***-days,the peak traffic on the road and the bottlenecks due to it are inevitable,which are seen here as the benefits for VC in terms of resource availability and residual in-network *** speed range of high-end vehicles poses the issue of dis-connectivity among VC participants,that results the container migration *** the entire VC participants are on the move,to maintain proximity of the containers hosted by them,estimating their movements plays a vital *** infer the vehicle movements on the road stretch and initiate the container migration prior enough to avoid the migration failure due to vehicles dynamicity,this paper proposes to apply the CTM to the container based and 5G NR V2X enabled *** simulation results show that there is a significant increase in the success rate of vehicular cloud in terms of successful container migrations.

关键词： Vehicular cloud container migration cell transmission model 5G NR V2X PC5 interface

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A Secure Framework for WSN-IoT Using Deep Learning for Enhanced Intrusion Detection

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computers, Materials & Continua 2024年第10期81卷 471-501页

作者： Chandraumakantham Om Kumar Sudhakaran Gajendran Suguna Marappan Mohammed Zakariah Abdulaziz S.Almazyad School of Computer Science&Engineering Vellore Institute of TechnologyChennai CampusChennai600127India School of Electronics&Engineering Vellore Institute of TechnologyChennai CampusChennai600127India Department of Computer Sciences and Engineering College of Applied ScienceKing Saud UniversityRiyadh11543Saudi Arabia Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia

The security of the wireless sensor network-Internet of Things(WSN-IoT)network is more challenging due to its randomness and self-organized *** detection is one of the key methodologies utilized to ensure the security of the *** intrusion detection mechanisms have issues such as higher misclassification rates,increased model complexity,insignificant feature extraction,increased training time,increased run time complexity,computation overhead,failure to identify new attacks,increased energy consumption,and a variety of other factors that limit the performance of the intrusion system *** this research a security framework for WSN-IoT,through a deep learning technique is introduced using Modified Fuzzy-Adaptive DenseNet(MF_AdaDenseNet)and is benchmarked with datasets like NSL-KDD,UNSWNB15,CIDDS-001,Edge IIoT,Bot *** this,the optimal feature selection using Capturing Dingo Optimization(CDO)is devised to acquire relevant features by removing redundant *** proposed MF_AdaDenseNet intrusion detection model offers significant benefits by utilizing optimal feature selection with the CDO *** results in enhanced Detection Capacity with minimal computation complexity,as well as a reduction in False Alarm Rate(FAR)due to the consideration of classification error in the fitness *** a result,the combined CDO-based feature selection and MF_AdaDenseNet intrusion detection mechanism outperform other state-of-the-art techniques,achieving maximal Detection Capacity,precision,recall,and F-Measure of 99.46%,99.54%,99.91%,and 99.68%,respectively,along with minimal FAR and Mean Absolute Error(MAE)of 0.9%and 0.11.

关键词： Deep learning intrusion detection fuzzy rules feature selection false alarm rate accuracy wireless sensor networks

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Does Partner Asymmetry Facilitate Innovation Speed? Organizational Learning and Social Exchange Perspectives

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IEEE Transactions on engineering Management 2024年 71卷 4144-4159页

作者： Hsiao, Yung-Chang Wu, Ming-Ho National University of Tainan Department of Business and Management College of Management Tainan700301 Taiwan National Kaohsiung University of Science and Technology Department of Computer Science and Information Engineering College of Electrical Engineering and Computer Science Kaohsiung824005 Taiwan

The right partner and high innovation speed are crucial for a successful research and development (R&D) alliance in the high-tech industry. Does homogeneity or heterogeneity between partners benefit innovation speed? This study examined the links between partner asymmetries (technological heterogeneity and network resource asymmetry) and innovative speed from both economic and social dual perspectives by focusing on biopharmaceutical industries. The results revealed an inverse U-shaped relationship between technology heterogeneity and innovation speed. In addition, a positive linear relationship exists between network resource asymmetry and innovation speed for 1991-2000 alliances and for biotech-biotech partner-type alliances;significantly negative moderating effects of time to market occurred on these linear relations. Finally, this study makes important theoretical and practical contributions to partner selection literature on R&D alliances in the biopharmaceutical industry. © 1988-2012 IEEE.

关键词： Behavioral research

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Improved Chinese Few-Shot relation extraction using Large Language Model for Data Augmentation and Prototypical Network

Improved Chinese Few-Shot relation extraction using Large La...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Haoran, Xu Lou, Kechen Chan, Sixian College of Electronics and Information Engineering Tongji University Shanghai China College of Computer Science and Technology Zhejiang University of Technology Hangzhou China

ISBN: (纸本)9781665410205

Chinese few-shot relation extraction aims to effectively identify relationships between entities in text using limited data, which is crucial for information extraction and knowledge construction and assists in extracting critical information from medical cases to support personalized rehabilitation treatment plans. However, due to the limited number of samples, existing methods struggle to capture sufficient relational features from limited data, resulting in poor extraction performance. Therefore, we propose an Improved Chinese Few-Shot Relation Extraction Using Large Language Model for Data Augmentation and Prototypical Network to address this issue. Specifically, we establish the Chinese few-shot relation extraction datasets DUIE Few and SanWen Few. Notably, we introduce a framework based on large language models for dataset augmentation, which effectively alleviates the problem of feature extraction due to insufficient data and improves task performance. Finally, we present baselines for prototype networks, Siamese networks, and the CFSRE model based on relational category information. Experimental results show that the CFSRE model improves accuracy, recall, and F1 score under few-shot conditions, particularly as the sample size decreases. In summary, the method we propose demonstrates promising results in Chinese few-shot relation extraction tasks and holds the potential to advance medical rehabilitation research. © 2024 IEEE.

关键词： information retrieval

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Analysis of environmental impact factors in urban road traffic accidents: logistic regression model

Advances in Transportation Studies

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Advances in Transportation Studies 2024年第SPECIAL ISSUE期3卷 39-50页

作者： Zhao, X.J. Li, S.Y. Guan, H.B. College of Mathematics and Information Science Anyang institute of Technology Anyang455000 China School of Computer Science and Information Engineering Anyang Institute of Technology Anyang455000 China College of Mathematics and Information Science Zhengzhou University of Light Industry Zhengzhou450002 China

The traditional methods for analyzing environmental impact factors in urban road traffic accidents suffer from issues such as low recall, poor precision, and time-consuming processes. To address these challenges, a novel analysis method based on the logistic regression model is proposed. This method utilizes the MIFP-Apriori algorithm to collect urban road traffic accident data and incorporates the EM algorithm for data filling and processing. Principal component analysis is then employed to screen the environmental impact factors in urban road traffic accidents. These selected factors are input into the logistic regression model to conduct a comprehensive analysis. The results reveal that environmental impact factors like road conditions and meteorological factors have a significant influence on traffic accidents. Experimental results show that the proposed method achieves an average recall rate of 96.98%, an average precision rate of 97.66%, and the analysis time for environmental factors ranges from 0.12s to 0.52s. © 2024, Aracne Editrice. All rights reserved.

关键词： Environmental impact factors Logistic regression model Mifp-apriori algorithm Principal component analysis method Traffic accident Urban road

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Medical Reports Simplification Using Large Language Models 23th

Medical Reports Simplification Using Large Language Models

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23rd International Conference on Intelligent Systems Design and Applications, ISDA 2023

作者： Almezhghwi, Khaled Hassan, Morad Ali Ghadedo, Adel Belhaj, Fairouz Shwehdi, Rabei Electrical and Electronics Engineering College of Electronics Technology Tripoli Tripoli Libya Computer Science Bani Waleed University Bani Waleed Libya Computer Science College of Electronics Technology Tripoli Tripoli Libya Computer Information Systems Higher Institute of Science and Technology Tripoli Libya Electrical and Electronics Engineering College of Electronics Technology Tripoli Tripoli Libya

ISBN: (纸本)9783031648120

Making medical reports easily understandable for a wider audience is a significant endeavor, and the recent advancements in deep learning and large language models offer a promising solution. In our research, we have introduced a fine-tuned Text-to-Text Transformer (T5) model to efficiently summarize these medical reports. Our model is trained and assessed on the publicly accessible Indiana Dataset. To gauge its effectiveness, we employ the ROUGE metrics. The outcomes we've achieved hold promise for the potential of this approach in enhancing medical report summarization. By harnessing the power of cutting-edge technology, we aim to make medical information more accessible and comprehensible to everyone. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Deep learning

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Fault Detection of Automotive Engine System Based on Canonical Variate Analysis Combined With Bhattacharyya Distance

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Journal of Dynamic Systems, Measurement and Control 2025年第4期147卷 041004页

作者： Cheng, Zhang Yun-Fei, Wang Yu-Yu, Lao Yuan, Li College of Science Shenyang University of Chemical Technology Liaoning Shenyang110142 China College of Computer Science and Technology Shenyang University of Chemical Technology Liaoning Shenyang110142 China College of Information Engineering Shenyang University of Chemical Technology Liaoning Shenyang110142 China

Aiming at the nonlinear and dynamic characteristics of data in automotive engine systems, a fault detection method based on canonical variate analysis combined with Bhattacharyya distance (CVA-BD) is proposed in this paper. First, CVA is utilized to calculate the state space of the system data. Second, a sliding window is introduced in the state space to quantify the difference in data distribution within the window using Bhattacharyya distance, thereby constructing a novel statistical indicator. Finally, the control limit for statistical indicator is determined to achieve process monitoring of automotive engine systems. CVA-BD effectively enhances the performance of process monitoring by capturing the sequential correlation of data through CVA and eliminating the nonlinear impact between samples using similarity measurement metrics. Simulation experiments are conducted using a numerical case and experimental data from turbocharged spark ignition (TCSI) engines. The simulation results further confirm that, compared with principal component analysis (PCA), dynamic principal component analysis (DPCA), canonical variable analysis (CVA), dissimilar canonical variable analysis (CVDA), auto-encoder (AE), and stacked auto-encoder (SAE) CVA-BD has demonstrated an improvement of at least 41%. Copyright © 2025 by ASME.

关键词： Fault detection

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Reversible data hiding in encrypted images with block-based bit-plane reallocation

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Multimedia Tools and Applications 2024年第37期83卷 84911-84932页

作者： Liu, Li Chen, Chaofan Wu, Yingchun Chang, Chin-Chen Wang, Anhong College of Electronic Information and Engineering Taiyuan University of Science and Technology Taiyuan030024 China Department of Information Engineering and Computer Science Feng Chia University Taichung40724 Taiwan

As cloud storage and multimedia communication continue to evolve, the preservation of image privacy is becoming increasingly important. Reversible data hiding in encrypted images (RDHEI) is an effective method for enhancing the protection of personal privacy. To improve the embedding capacity, this paper introduces a novel scheme for RDHEI using block-based bit-plane reallocation. First, an effective optimized prediction method is utilized to predict the value of each pixel, followed by the calculation of prediction errors (PEs). Then, block-based bit-planes of PEs are extracted, and ten different bit-plane types are categorized to facilitate bit-plane reallocation, thereby freeing up redundant space. Finally, an adaptive encryption procedure is employed to encrypt the reallocated bit-plane, excluding the flag-bits. Experimental results demonstrate that the proposed scheme achieves average embedding rates (ER) of 3.584 bpp, 3.501 bpp, and 2.985 bpp on the three experimental datasets, all of which are higher than those of other state-of-the-art RDHEI schemes while ensuring both reversibility and security. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Cryptography

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A NAS-Based TinyML for Secure Authentication Detection on SAGVN-Enabled Consumer Edge Devices

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IEEE Transactions on Consumer electronics 2024年第1期71卷 303-313页

作者： Chen, Xingchi Zhang, Shuanglong Li, Qing Zhu, Fa Feng, Ansong Nkenyereye, Lewis Rani, Shalli Pengcheng Laboratory Shenzhen518000 China Nanjing Forestry University College of Information Science and Technology China The College of Information Engineering Shenyang University of Chemical Technology Nanjing Shenyang China Nanjing Forestry University College of Information Science and Technology College of Artificial Intelligence Nanjing China Shenyang University of Chemical Technology College of Information Engineering Shenyang China Sejong University Department of Computer and Information Security Korea Republic of Chitkara University Department of Chitkara University Institute of Engineering and Technology Punjab 140401 India

Traditional authentication methods utilize a uniform model to verify all users, which has been proven to be ineffective for the authentication on consumer edge devices of space-air-ground integrated vehicular networks (SAGVN). On the other hand, consumer edge devices in SAGVN have limited resources to load large models for authentication. In order to handle these issues, this paper proposes a Neural Architecture Search-Tiny Machine Learning (NAS-TinyML) method for authentication on SAGVN-enabled consumer edge devices. The NAS-TinyML integrates a feature extraction module derived from NAS and a classification module constructed using Spiking Recurrent Neural Networks (SRNN). It can combine the flexible architecture search capability of NAS, the robustness of SRNN in handling time-series data, and a macro-level search mechanism to enhance model search efficiency. The validation on various scenario datasets shows that NAS-TinyML is superior to several state-of-the-art methods, including GeneticNAS, Drnas, NASP, NetAdapt, SMASH, and SNAS. Furthermore, the deployment of pre-trained NAS-TinyML on different consumer edge devices demonstrates its effectiveness. © 1975-2011 IEEE.

关键词： Adversarial machine learning

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RoadFormer: Duplex Transformer for RGB-Normal Semantic Road Scene Parsing

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年第7期9卷 1-10页

作者： Li, Jiahang Zhan, Yikang Yun, Peng Zhou, Guangliang Chen, Qijun Fan, Rui College of Electronic and Information Engineering Tongji University Shanghai China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong

The recent advancements in deep convolutional neural networks have shown significant promise in the domain of road scene parsing. Nevertheless, the existing works focus primarily on freespace detection, with little attention given to hazardous road defects that could compromise both driving safety and comfort. In this article, we introduce RoadFormer, a novel Transformer-based data-fusion network developed for road scene parsing. RoadFormer utilizes a duplex encoder architecture to extract heterogeneous features from both RGB images and surface normal information. The encoded features are subsequently fed into a novel heterogeneous feature synergy block for effective feature fusion and recalibration. The pixel decoder then learns multi-scale long-range dependencies from the fused and recalibrated heterogeneous features, which are subsequently processed by a Transformer decoder to produce the final semantic prediction. Additionally, we release SYN-UDTIRI, the first large-scale road scene parsing dataset that contains over 10,407 RGB images, dense depth images, and the corresponding pixel-level annotations for both freespace and road defects of different shapes and sizes. Extensive experimental evaluations conducted on our SYN-UDTIRI dataset, as well as on three public datasets, including KITTI road, CityScapes, and ORFD, demonstrate that RoadFormer outperforms all other state-of-the-art networks for road scene parsing. Specifically, RoadFormer ranks first on the KITTI road benchmark. Our source code, created dataset, and demo video are publicly available at ***/RoadFormer. IEEE

关键词： Semantics

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