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Watermarking technique for document images using discrete curvelet transform and discrete cosine transform

Multimedia Tools and Applications 2024年第40期83卷 87647-87671页

作者： Singh, Balkar Sharma, M.K. Post Graduate Department of Computer Science Gujranwala Guru Nanak Khalsa College Punjab Ludhiana India School of Mathematics Thapar Institute of Engineering and Technology Punjab Patiala India

In this paper, a watermarking technique based on discrete curvelet transform and discrete cosine trans- form is proposed to protect the color document images. The six layers of the document image are created using the Discrete Curvelet Transform (DCuT). The sixth layer is chosen for the embedding process, while the remaining layers are discarded. Discrete Cosine Transform (DCT) is applied on the 8 × 8 blocks of the real part of sixth layer. DCT coefficients from low to medium are selected for the embedding process, excluding the first one. To choose the Direct Current (DC) coefficients of DCT to embed the watermark bits, a zigzag function is used. The same process is applied on the receiver side watermark image is extracted. Since this is a non-blind method, we require both a cover image and a watermarked image. DCT of watermarked image and the cover image are compared to extract the watermark image. The resistivity of the watermarked image against image processing attacks is measured using the Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), Mean Square Error (MSE), Normalization Correlation (NC), Bit Error Rate (BER) and Universal Quality Index (UQI). © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Mean square error

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A Spectral Convolutional Neural Network Model Based on Adaptive Fick’s Law for Hyperspectral Image Classification

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computers, Materials & Continua 2024年第4期79卷 19-46页

作者： Tsu-Yang Wu Haonan Li Saru Kumari Chien-Ming Chen School of Artificial Intelligence(School of Future Technology) Nanjing University of Information Science&TechnologyNanjing210044China College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266590China Department of Mathematics Chaudhary Charan Singh UniversityMeerutUttar Pradesh250004India

Hyperspectral image classification stands as a pivotal task within the field of remote sensing,yet achieving highprecision classification remains a significant *** response to this challenge,a Spectral Convolutional Neural Network model based on Adaptive Fick’s Law Algorithm(AFLA-SCNN)is *** Adaptive Fick’s Law Algorithm(AFLA)constitutes a novel metaheuristic algorithm introduced herein,encompassing three new strategies:Adaptive weight factor,Gaussian mutation,and probability update *** adaptive weight factor,the algorithmcan adjust theweights according to the change in the number of iterations to improve the performance of the *** helps the algorithm avoid falling into local optimal solutions and improves the searchability of the *** probability update strategy helps to improve the exploitability and adaptability of the *** the AFLA-SCNN model,AFLA is employed to optimize two hyperparameters in the SCNN model,namely,“numEpochs”and“miniBatchSize”,to attain their optimal ***’s performance is initially validated across 28 functions in 10D,30D,and 50D for CEC2013 and 29 functions in 10D,30D,and 50D for *** results indicate AFLA’s marked performance superiority over nine other prominent optimization ***,the AFLA-SCNN model was compared with the Spectral Convolutional Neural Network model based on Fick’s Law Algorithm(FLA-SCNN),Spectral Convolutional Neural Network model based on Harris Hawks Optimization(HHO-SCNN),Spectral Convolutional Neural Network model based onDifferential Evolution(DE-SCNN),SpectralConvolutionalNeuralNetwork(SCNN)model,and SupportVector Machines(SVM)model using the Indian Pines dataset and PaviaUniversity *** experimental results show that the AFLA-SCNN model outperforms other models in terms of Accuracy,Precision,Recall,and F1-score on Indian Pines and Pavia *** them,the Accuracy of the AFLA-SCNN model on India

关键词： Adaptive Fick’s law algorithm spectral convolutional neural network metaheuristic algorithm intelligent optimization algorithm hyperspectral image classification

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UAMP-Based Delay-Doppler Channel Estimation for OTFS Systems

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China Communications 2024年第10期21卷 1-15页

作者： Li Zhongjie Yuan Weijie Guo Qinghua Wu Nan Zhang Ji Department of Electronic and Electrical Engineering Southern University of Science and TechonologyShenzhen 518055China School of Electrical Computer and Telecommunications EngineeringUniversity of WollongongWollongongNSW 2522Australia School of Integrated Circuits and Electronics Beijing Institute of TechnologyBeijing 100081China School of Mathematics and Statistics Henan University of Science and TechnologyLuoyang 471000China

Orthogonal time frequency space(OTFS)technique, which modulates data symbols in the delayDoppler(DD) domain, presents a potential solution for supporting reliable information transmission in highmobility vehicular networks. In this paper, we study the issues of DD channel estimation for OTFS in the presence of fractional Doppler. We first propose a channel estimation algorithm with both low complexity and high accuracy based on the unitary approximate message passing(UAMP), which exploits the structured sparsity of the effective DD domain channel using hidden Markov model(HMM). The empirical state evolution(SE) analysis is then leveraged to predict the performance of our proposed algorithm. To refine the hyperparameters in the proposed algorithm,we derive the update criterion for the hyperparameters through the expectation-maximization(EM) algorithm. Finally, Our simulation results demonstrate that our proposed algorithm can achieve a significant gain over various baseline schemes.

关键词： channel estimation hidden Markov model(HMM) orthogonal time frequency space(OTFS) unitary approximate message passing(UAMP)

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Predicting Interpersonal Influence from Conversational Features 10

Predicting Interpersonal Influence from Conversational Featu...

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10th IEEE International Conference on Behavioural and Social Computing, BESC 2023

作者： Gassmann, Luke McConville, Ryan Edwards, Matthew University of Bristol School of Computer Science School of Engineering Mathematics and Technology Bristol United Kingdom

ISBN: (纸本)9798350395884

Interpersonal influence has a radical impact on the dissemination of information in online social media. Methods for measuring this influence between online conversation partners are often over-reliant on platform-level features, rendering them inoperable in other settings. We propose a novel and transferable solution using BERT to derive features of conversations that indicate influence. In an evaluation across a diverse discussion dataset, we show that our framework competes with existing large language models, being able to predict both social and behavioural measures of influence accurately, and at different levels of resolution, with a Macro-F1 above 0.91 in all cases of social influence. © 2023 IEEE.

关键词： Neural networks

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ESM-LSTM based optimisation model for weather prediction 5

ESM-LSTM based optimisation model for weather prediction

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5th International Conference on Big Data and Artificial Intelligence and Software engineering, ICBASE 2024

作者： Luo, Guilan Wang, Long Zeng, Yongwang Chen, Zijie Wen, Shuzhen Zhang, Mei Dali University School of Mathematics and Computer Science Yunnan Dali China Dali University School of Engineering Yunnan Dali China

ISBN: (纸本)9798331506612

This study introduces an optimized prediction model based on the integration of the Event Synchronization Method (ESM) and Long Short-Term Memory (LSTM). The primary objective is to address the limitations inherent in traditional forecasting models, particularly in understanding the intricate mechanisms of complex weather systems and handling uncertainty factors. The proposed model aims to enhance both prediction accuracy and true positive rates. The research methodology involves an initial rough-graining process applied to rainfall data, followed by the construction of a complex rainfall network using ESM. Subsequently, this network is seamlessly integrated with an optimized LSTM model, complemented by a rough K-nearest neighbors classifier for comprehensive weather predictions. The results of the comprehensive evaluation indicate a notable 20% improvement in rainfall true positive rates, while maintaining accuracy levels comparable to those of traditional models. This optimized model introduces innovative perspectives and methodologies to the field of weather prediction, providing valuable insights for researchers in related domains. © 2024 IEEE.

关键词： Prediction models

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Deep Learning-Powered Camouflaged Object Recognition Model 2

Deep Learning-Powered Camouflaged Object Recognition Model

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Tomar, Riya Chauhan, Naman Chauhan, Chhavi Tomar, Yash Singh, Soumyendra Chauhan, Nishant Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India University of Petroleum and Energy Studies School of Computer Science Dehradun India Cmr University Department of Computer Science and Engineering Bengaluru India Amrita School of Engineering Amrita Vishwa Vidyapeetham Department of Mathematics Chennai India Amity School of Engineering and Technology Amity University Department of Computer Science and Engineering Noida India

ISBN: (纸本)9798350317558

This research paper delves into the intersection of advanced technology and military strategies, specifically focusing on the imperative task of camouflaged object detection within the Indian Paramilitary forces. Leveraging cutting-edge DL Frameworks, like CNNs, Faster RCNN, and YOLOv8, our methodology aims to enhance the precision and efficiency of identifying concealed weapons and camouflaged objects in real-world scenarios. The collaboration with the Indian Paramilitary has enabled the creation of a dataset tailored to the challenges faced on the ground, reflecting a commitment to addressing contemporary threats. The study explores the global positioning of India in military technology, emphasizing the nation's role as a key player in adopting sophisticated solutions for defense. Beyond technological innovation, this research underscores a noble cause the protection of lives and the fortification of national security. The fusion of advanced technology and a profound sense of duty forms the core of this work, exemplifying a commitment to the greater good. © 2024 IEEE.

关键词： Camouflaged object detection Convolutional Neural Networks (CNNs) Deep Learning Defense Strategies Faster RCNN Indian Paramilitary Military Technology Object Recognition Situational Awareness YOLOv8

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Distributed Online Optimization with Long-Term Constraints and Bandit Feedback: An Event-Triggered Approach 7th

Distributed Online Optimization with Long-Term Constraints ...

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7th Chinese Conference on Swarm Intelligence and Cooperative Control, CCSICC 2023

作者： Zhang, Difeng Feng, Zhangcheng Yang, Shaofu Xu, Wenying School of Mathematics Southeast University Nanjing211189 China School of Computer Science and Engineering Southeast University Nanjing21189 China

ISBN: (纸本)9789819733231

This paper considers the distributed online optimization problem (DOO) with long-term constraints (LTC) and bandit feedback under a time-varying communication network, where the cost function is time-varying and only two specific values are disclosed to agents subsequent to their decision-making process. The primary objective of this paper is to develop a bandit distributed online algorithm that not only ensures sublinear growth of regret and cumulative absolute constraint violation (CACV) with respect to the total time T, but also addresses computation and communication burdens. By considering long-term constraints and introducing an event-triggered (ET) strategy, we design a novel two-point bandit distributed online algorithm. Then, we establish upper bounds for the regret and CACV of the proposed algorithm at O(Tmax{c,1-c}) and O(T1-c2), respectively. These bounds align with the state-of-the-art results for online algorithms, even under full information feedback without any communication constraint. Finally, the effectiveness of the algorithm and the influence of ET threshold are verified by studying a numerical example of distributed online regularization linear regression. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Cost functions

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Cut-and-choose bilateral oblivious transfer protocol based on DDH assumption

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Journal of Ambient Intelligence and Humanized Computing 2024年第2期15卷 1327-1337页

作者： Jiang, Han Xu, Qiuliang Liu, Changyuan Zheng, Zhihua Tang, Yi Wang, Mingqiang School of Computer Science and Technology ShanDong University Jinan China ShanDong Dazhong News Group Jinan China School of Information Science and Engineering Shandong Normal University Jinan China Department of Mathematics Guangzhou University Guangzhou China School of Mathematics ShanDong University Jinan China

In secure two-party computation protocols, the cut-and-choose paradigm is used to prevent the malicious party who constructs the garbled circuits from cheating. In previous realization of the cut-and-choose technique on the garbled circuits, the delivery of the random keys is divided into multiple stages. Thus, the round complexity is high and the consistency of cut-and-choose challenge should be proved. Based on DDH assumption, we build a so-called cut-and-choose bilateral oblivious transfer protocol, which transfers all necessary keys of garbled circuits in one process. Specifically, in our oblivious transfer protocol, the sender inputs two pairs (k01,k11), (k02,k12) and a bit τ;the receiver inputs two bits σ and j. After the protocol execution, the receiver obtains kτ1,kσ2 for j=1, and k01,k11,k02,k12 for j=0. The protocol inherit the cut-and-choose OT protocol in Lindell and Pinkas (Proceedings of the 8th conference on theory of cryptography, Springer, 2011), and can be applied into the state-of-the-art cut-and-choose secure two party computation protocol without any obstacles. By the cut-and-choose bilateral oblivious transfer protocol, the cut-and-choose challenge j is no need to be opened anymore, therefore the consistency proof of j is omitted, and the round complexity of secure two-party computation protocol can be decreased. © Springer-Verlag GmbH Germany, part of Springer Nature 2018.

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Fuzzy control design for time-delay systems under adaptive event-triggered mechanism

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Neural Computing and Applications 2024年第9期36卷 4727-4739页

作者： Du, Zhenbin Qu, Zifang Kao, Yonggui Wu, Zhaojing School of Computer and Control Engineering Yantai University Shandong Yantai264005 China School of Mathematics and Information Science Shandong Technology and Business University Shandong Yantai264005 China Department of Mathematics Harbin Institute of Technology Weihai264209 China School of Mathematics and Information Sciences Yantai University Shandong Yantai264005 China

This manuscript studies the fuzzy H∞ control design problem for nonlinear time-varying delay systems in fuzzy form via adaptive event-triggered mechanism. The fuzzy model and an adaptive event-triggered controller are connected in closed-loop form. A looped-functional is established to conduct stability analysis, and the main feature of this functional can fully reflect the sampling feature of control systems. Thus, the achieved results are less conservative. The Wringter inequality is also used to further relax stability analysis. Finally, sufficient conditions are achieved for the existence of event-triggered fuzzy controller, and the resulting closed-loop system reaches an H∞ control performance. Two examples verify the efficiency of the presented control strategy. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023.

关键词： Time delay

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A Key Node Identification Algorithm in Social Networks Based on Global Betweenness Entropy 5

A Key Node Identification Algorithm in Social Networks Based...

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5th International Conference on Big Data and Artificial Intelligence and Software engineering, ICBASE 2024

作者： Luo, Guilan Wen, Shuzhen Wang, Caikui Wang, Long Zeng, Yongwang Chen, Zijie Dali University School of Mathematics and Computer Science Yunnan Dali China Dali University School of Engineering Yunnan Dali China

ISBN: (纸本)9798331506612

As social networks continue to develop, accurately identifying key nodes has become crucial. Existing centrality algorithms often rely on local network structures and are susceptible to changes in the degree of individual nodes, making it difficult to identify truly important nodes from a global perspective. This paper proposes an algorithm based on an improved Global Betweenness Degree Entropy (GBDE) to identify key nodes in complex networks. The algorithm combines closeness centrality and the eigenvector centrality of neighboring nodes, iteratively selecting and optimizing the node set to ultimately identify nodes that are critical to the network structure and the propagation process. This algorithm not only considers the network position of the nodes but also takes into account their relationships with adjacent nodes, thereby improving the accuracy and effectiveness of key node identification. Experimental results on five real-world networks demonstrate that the GBDE algorithm outperforms BDE, DC, BC, LE, and ME algorithms in the task of identifying key nodes. © 2024 IEEE.

关键词： Consensus algorithm

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