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An Innovative Approach Using TKN-Cryptology for Identifying the Replay Assault

computers, Materials & Continua 2024年第1期78卷 589-616页

作者： Syeda Wajiha Zahra Muhammad Nadeem Ali Arshad Saman Riaz Muhammad Abu Bakr Ashit Kumar Dutta Zaid Alzaid Badr Almutairi Sultan Almotairi Department of Computer Science Alhamd Islamic UniversityIslamabad44000Pakistan Department of Computer Science and Technology University of Science and Technology BeijingBeijing100083China Department of Computer Science National University of TechnologyIslamabad44000Pakistan Department of Electrical Engineering National University of TechnologyIslamabad44000Pakistan Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityRiyadh13713Saudi Arabia Department of Computer Science Faculty of Computer and Information SystemsIslamic University of MadinahMedinah42351Saudi Arabia Department of Information Technology College of Computer Sciences and Information Technology CollegeMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Natural and Applied Sciences Faculty of Community CollegeMajmaah UniversityMajmaah11952Saudi Arabia

Various organizations store data online rather than on physical *** the number of user’s data stored in cloud servers increases,the attack rate to access data from cloud servers also *** researchers worked on different algorithms to protect cloud data from replay *** of the papers used a technique that simultaneously detects a full-message and partial-message replay *** study presents the development of a TKN(Text,Key and Name)cryptographic algorithm aimed at protecting data from replay *** program employs distinct ways to encrypt plain text[P],a user-defined Key[K],and a Secret Code[N].The novelty of the TKN cryptographic algorithm is that the bit value of each text is linked to another value with the help of the proposed algorithm,and the length of the cipher text obtained is twice the length of the original *** the scenario that an attacker executes a replay attack on the cloud server,engages in cryptanalysis,or manipulates any data,it will result in automated modification of all associated values inside the *** mechanism has the benefit of enhancing the detectability of replay ***,the attacker cannot access data not included in any of the papers,regardless of how effective the attack strategy *** the end of paper,the proposed algorithm’s novelty will be compared with different algorithms,and it will be discussed how far the proposed algorithm is better than all other algorithms.

关键词： Replay attack malware message attack file encryption cryptology data security

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E2E-LANet: Unleashing the Potential of Linear Attention for High-Resolution Medical Image Segmentation via Pretraining Distillation 24

E2E-LANet: Unleashing the Potential of Linear Attention for ...

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5th International Symposium on Artificial Intelligence for Medicine science, ISAIMS 2024

作者： Tang, Hui Yu, Gang Cao, Lu School of Computer Science and Engineering Hunan University of Science and Technology Hunan Xiangtan China

ISBN: (纸本)9798400717826

Medical image segmentation plays an important role in computer-aid diagnosis. In the past years, convolutional neural networks, especially the UNet-based architectures with symmetric U-shape encoder-decoder structure and skip connection, have been widely used in a variety of medical imaging segmentation tasks. However, such methods are difficult to capture global context. In contrast, Vision Transformers (ViTs) utilize the self-attention mechanism to obtain long-range dependencies in images. Despite these advantages, the huge computational costs of the self-attention mechanism reduce the inference speed of models, thereby limiting their real-world applications. Meanwhile, hierarchical pixel-level decoding of U-shape architectures increases the complexity of large-size segmentation decoding. To address these limitations, in this paper we propose a linear attention-based end-to-end model for high-resolution medical image segmentation, named E2E-LANet. It consists of two modules: an image encoder based on constructed Efficient Vision Transformer (EVT) that reduces the high quadratic complexity of self-attention to linear complexity and a Query-Supported Mask Decoder (QSMD) that employs independent mask query for each type of medical images in segmentation mask predictions. It focuses on the detailed segmentation decoding of patch-level features to further improve the efficiency of large-size mask predictions. Especially, we leverage the advantages of pretraining distillation to improve the transferability of our model in various segmentation tasks. Experimental results on the nucleus segmentation task and the polyp segmentation task display the important role of pretraining distillation in improving the performance of our framework. E2E-LANet demonstrates superior performance compared to CNNs, ViTs, and latest Mamba-based U-shape architectures. © 2024 Copyright held by the owner/author(s).

关键词： Convolutional neural networks

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Applications and prospects of functional oligosaccharides in pig nutrition:A review

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Animal Nutrition 2023年第2期13卷 206-215页

作者： Nian Liu Haibo Shen Fan Zhang Xiang Liu Qinrui Xiao Qian Jiang Bie Tan Xiaokang Ma College of Animal Science and Technology Hunan Agricultural UniversityChangsha410128China

Oligosaccharides are low molecular weight carbohydrates between monosaccharides and poly-saccharides,which consist of 2 to 20 monosaccharides linked by glycosidic *** have the effects of promoting growth,regulating immunity,improving the structure of intestinal flora,and are anti-inflammatory and *** the comprehensive implementation of the antibiotic prohibition policy in China,oligosaccharides as new green feed additive have been paid more ***-charides can be divided into the following 2 categories according to their digestive characteristics:one is easy to be absorbed by the intestine,called common oligosaccharides,such as sucrose and maltose oligosaccharide;the other is difficult to be absorbed by the intestine and has special physiological functions,called functional *** common functional oligosaccharides include mannan oligosaccharides(MOS),fructo-oligosaccharides(FOS),chitosan oligosaccharides(COS),xylo-oligosaccharides(XOS)and so *** this paper,we review the types and sources of functional oligo-saccharides,their application in pig nutrition,and the factors limiting their efficacy in recent *** review provides the theoretical basis for further research of functional oligosaccharides,and the future application of alternative antibiotics in pig industry.

关键词： Functional oligosaccharides Pig nutrition Growth performance Intestinal health Antioxidants Immune performance

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The joint Laplace transforms for killed diffusion occupation times

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Applied Mathematics(A Journal of Chinese Universities) 2024年第3期39卷 398-415页

作者： LI Ying-qiu CHEN Ye School of Mathematics and Statistics Changsha University of Science and TechnologyChangsha 410114China College of Mathematics and Computational Science Hunan University of Arts and ScienceChangde 415000China

The approach of Li and Zhou(2014)is adopted to find the Laplace transform of occupation time over interval(0,a)and joint occupation times over semi-infinite intervals(-∞,a)and(b,∞)for a time-homogeneous diffusion pr... 详细信息

关键词： time-homogeneous diffusion process occupation time joint occupation time Laplace transform Brownian motion with drift

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Identification for the Low-Contrast Image Signal with Regularized Variational Term and Dynamical Saturating Nonlinearity

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Journal of Systems science & Complexity 2023年第3期36卷 1089-1102页

作者： ZHANG Ning MA Yumei PAN Zhenkuan HUANG Baoxiang WANG Dongcheng College of Computer Science&Technology Qingdao UniversityQingdao 266071China

In recent years,image processing based on stochastic resonance(SR)has received more and more *** this paper,a new model combining dynamical saturating nonlinearity with regularized variational term for enhancement of low contrast image is *** regularized variational term can be setting to total variation(TV),second order total generalized variation(TGV)and non-local means(NLM)in order to gradually suppress noise in the process of solving the *** addition,the new model is tested on a mass of gray-scale images from standard test image and low contrast indoor color images from Low-Light dataset(LOL).By comparing the new model and other traditional image enhancement models,the results demonstrate the enhanced image not only obtain good perceptual quality but also get more excellent value of evaluation index compared with some previous methods.

关键词： Dynamical saturating nonlinearity image enhancement low contrast regularized variational term stochastic resonance(SR)

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Evaluation Model for Energy Efficiency of Factory Workshop Based on DSR and Fuzzy Borda

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Energy Engineering 2025年第3期122卷 1073-1092页

作者： Shengjie Yang Zijian Zhu Xu Ouyang HDHL Electrical and Information Technology Co.Ltd.Changsha410205China College of Computer Science Hunan University of Technology and BusinessChangsha410205China College of Electrical and Information Engineering Hunan UniversityChangsha410082China

In the context of advancing towards dual carbon goals,numerous factories are actively engaging in energy efficiency upgrades and *** accurately pinpoint energy efficiency bottlenecks within factories and prioritize renovation sequences,it is crucial to conduct comprehensive evaluations of the energy performance across various ***,this paper proposes an evaluation model for workshop energy efficiency based on the drive-state-response(DSR)framework combined with the fuzzy BORDA ***,an in-depth analysis of the relationships between different energy efficiency indicators was *** on the DSR model,evaluation criteria were selected from three dimensions-drive factors,state characteristics,and response measures-to establish a robust energy efficiency indicator ***,three distinct assessment techniques were selected:Grey Relational Analysis(GRA),Entropy Weight Method(EWM),and Technique for Order Preference by Similarity to Ideal Solution(TOPSIS)forming a diversified set of evaluation ***,by introducing the fuzzy BORDA method,a comprehensive energy efficiency evaluation model was developed,aimed at quantitatively ranking the energy performance status of each *** a real-world factory as a case study,applying our proposed evaluationmodel yielded detailed scores and rankings for each ***,post hoc testing was performed using the Spearman correlation coefficient,revealing a statistic value of 10.209,which validates the effectiveness and reliability of the proposed evaluation *** model not only assists in identifying underperforming workshops within the factory but also provides solid data support and a decision-making basis for future energy efficiency optimization strategies.

关键词： DSR model fuzzy Borda method combined evaluation energy efficiency evaluation

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Adaptive Control of a Flexible Manipulator With Unknown Hysteresis and Intermittent Actuator Faults

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 148-158页

作者： Shouyan Chen Weitian He Zhijia Zhao Yun Feng Zhijie Liu Keum-Shik Hong School of Mechanical and Electrical Engineering Guangzhou University the School of Control Science and Engineering Shandong University IEEE the College of Electrical and Information Engineering Hunan University the National Engineering Research Center for Robot Visual Perception and Control Technology Hunan University the School of Intelligence Science and Technology University of Science and Technology Beijing (USTB) the Institute for Future the School of AutomationQingdao University the School of Mechanical Engineering Pusan National University

In this study, we consider a single-link flexible manipulator in the presence of an unknown Bouc-Wen type of hysteresis and intermittent actuator faults. First, an inverse hysteresis dynamics model is introduced, and then the control input is divided into an expected input and an error compensator. Second,a novel adaptive neural network-based control scheme is proposed to cancel the unknown input hysteresis. Subsequently,by modifying the adaptive laws and local control laws, a fault-tolerant control strategy is applied to address uncertain intermittent actuator faults in a flexible manipulator system. Through the direct Lyapunov theory, the proposed scheme allows the state errors to asymptotically converge to a specified interval. Finally,the effectiveness of the proposed scheme is verified through numerical simulations and experiments.

关键词： Adaptive control flexible manipulator intermittent actuator faults inverse hysteresis dynamics vibration control

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BI²Net: Graph-Based Boundary-Interior Interaction Network for Raft Aquaculture Area Extraction From Remote Sensing Images

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IEEE Geoscience and Remote Sensing Letters 2024年 21卷 1-5页

作者： Lu, Yan Zhao, Yuchao Yang, Mingkai Zhao, Yanli Huang, Ling Cui, Binge Shandong University of Science and Technology College of Computer Science and Engineering Qingdao266590 China

Accurate monitoring of raft aquaculture areas (RAAs) is particularly important for the protection of marine ecosystems. However, existing semantic segmentation methods are often degraded by severe shrinkage when extracting inapparent RAAs caused by natural factors such as tide-level changes and human activities including laver harvesting. In this letter, a graph-based boundary-interior interaction network (BI2Net) is proposed for laver RAA extraction. Graph convolution based on soft clustering is introduced to capture the global distribution patterns among RAAs. For the network structure, we designed the RAA-boundary branch and RAA-interior branch, one for locating the boundaries and the other for extracting the RAAs. Importantly, unlike traditional dual-branch methods that only use additional information from the auxiliary tasks to assist with the primary task, BI2Net introduces a graph interaction module (GIM) that implements reasoning about the relationship between different distribution patterns, which sufficiently and comprehensively exploits mutual benefits between RAA extraction and boundary detection. After the addition of GIM, precision, recall, F1, and IoU were improved by 2.0%, 8.2%, 0.060%, and 8.2%, respectively. Extensive experiments have shown that our proposed BI2Net outperforms existing methods in terms of the consistency and completeness of RAA extraction, with precision, recall, F1, and IoU reaching 93.0%, 90.6%, 0.916%, and 84.7%, respectively. © 2004-2012 IEEE.

关键词： Data mining

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Improved Simulation of Summer Heavy Rainfall over Beijing and Henan by the YHGSM with Updated Subgrid Orographic Parameters

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Journal of Meteorological Research 2024年第3期38卷 504-529页

作者： Yingjie WANG Jianping WU Kaijun REN College of Computer Science and Technology National University of Defense TechnologyChangsha410600China College of Meteorology and Oceanography National University of Defense TechnologyChangsha410600China

In numerical weather prediction(NWP),the parameterization of orographic drag plays an important role in representing subgrid orographic *** subgrid orographic parameters are the key input to the parameterization of orographic ***,the subgrid orographic parameters in most NWP models were produced based on elevation datasets generated many years ago,with a coarse resolution and low *** this paper,using the latest high-quality elevation data and considering the applicable scale range of the subgrid orographic parameters,we construct the orographic parameters,including the subgrid orographic standard deviation,anisotropy,orientation,and slope,that are required as input to the orographic gravity wave drag(OGWD)***,we introduce the newly constructed orographic parameters into the Yin-He Global Spectral Model(YHGSM),optimize the description of the orographic effect in the model,and improve the simulation of two typical heavy rainfall events in Beijing and Henan.

关键词： subgrid orographic parameters orographic drag heavy rainfall Yin-He Global Spectral Model(YHGSM) parameterization

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A Software Defect Prediction Method Using a Multivariate Heterogeneous Hybrid Deep Learning Algorithm

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computers, Materials & Continua 2025年第2期82卷 3251-3279页

作者： Qi Fei Haojun Hu Guisheng Yin Zhian Sun College of Computer Science and Technology Harbin Engineering UniversityHarbin150001China Information Technology Research Department Jiangsu Automation Research InstituteLianyungang222062China School of Computer Science and Technology Harbin Institute of TechnologyShenzhen518055China

Software defect prediction plays a critical role in software development and quality assurance processes. Effective defect prediction enables testers to accurately prioritize testing efforts and enhance defect detection efficiency. Additionally, this technology provides developers with a means to quickly identify errors, thereby improving software robustness and overall quality. However, current research in software defect prediction often faces challenges, such as relying on a single data source or failing to adequately account for the characteristics of multiple coexisting data sources. This approach may overlook the differences and potential value of various data sources, affecting the accuracy and generalization performance of prediction results. To address this issue, this study proposes a multivariate heterogeneous hybrid deep learning algorithm for defect prediction (DP-MHHDL). Initially, Abstract Syntax Tree (AST), Code Dependency Network (CDN), and code static quality metrics are extracted from source code files and used as inputs to ensure data diversity. Subsequently, for the three types of heterogeneous data, the study employs a graph convolutional network optimization model based on adjacency and spatial topologies, a Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) hybrid neural network model, and a TabNet model to extract data features. These features are then concatenated and processed through a fully connected neural network for defect prediction. Finally, the proposed framework is evaluated using ten promise defect repository projects, and performance is assessed with three metrics: F1, Area under the curve (AUC), and Matthews correlation coefficient (MCC). The experimental results demonstrate that the proposed algorithm outperforms existing methods, offering a novel solution for software defect prediction.

关键词： Software defect prediction multiple heterogeneous data graph convolutional network models based on adjacency and spatial topologies CNN-BiLSTM TabNet

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