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Meaningful image encryption algorithm based on compressive sensing and integer wavelet transform

Frontiers of computer science 2023年第3期17卷 161-175页

作者： Xiaoling HUANG Youxia DONG Guodong YE Yang SHI Faculty of Mathematics and Computer Science Guangdong Ocean UniversityZhanjiang 524088China School of Software Engineering Tongji UniversityShanghai 200092China

A new meaningful image encryption algorithm based on compressive sensing(CS)and integer wavelet transformation(IWT)is proposed in this *** of all,the initial values of chaotic system are encrypted by RSA algorithm,and then they are open as public *** make the chaotic sequence more random,a mathematical model is constructed to improve the random ***,the plain image is compressed and encrypted to obtain the secret ***,the secret image is inserted with numbers zero to extend its size same to the plain *** applying IWT to the carrier image and discrete wavelet transformation(DWT)to the inserted image,the secret image is embedded into the carrier ***,a meaningful carrier image embedded with secret plain image can be obtained by inverse ***,the measurement matrix is built by both chaotic system and Hadamard matrix,which not only retains the characteristics of Hadamard matrix,but also has the property of control and synchronization of chaotic ***,information entropy of the plain image is employed to produce the initial conditions of chaotic *** a result,the proposed algorithm can resist known-plaintext attack(KPA)and chosen-plaintext attack(CPA).By the help of asymmetric cipher algorithm RSA,no extra transmission is needed in the *** simulations show that the normalized correlation(NC)values between the host image and the cipher image are *** is to say,the proposed encryption algorithm is imperceptible and has good hiding effect.

关键词： image encryption algorithm compressive sensing integer wavelet transform Hadamard matrix

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An End-to-End Deep Learning QoS Prediction Model Based on Temporal Context and Feature Fusion

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IEEE Transactions on Services Computing 2025年第3期18卷 1232-1244页

作者： Zhang, Peiyun Fan, Jiajun Chen, Yutong Huang, Wenjun Zhu, Haibin Zhao, Qinglin Nanjing University of Information Science & Technology Engineering Research Center of Digital Forensics of Ministry of Education School of Computer Science Nanjing210044 China Nipissing University Department of Computer Science and Mathematics North BayONP1B 8L7 Canada Macau University of Science and Technology School of Computer Science and Engineering Taipa999078 China

Existing end-to-end quality of service (QoS) prediction methods based on deep learning often use one-hot encodings as features, which are input into neural networks. It is difficult for the networks to learn the information that is conducive to prediction. Aiming at the above problem, an end-to-end deep learning QoS prediction model based on a temporal context and feature fusion is proposed. In the proposed model, three blocks are designed for QoS prediction. Firstly, a user-service encoding conversion block is designed to convert the one-hot encodings of users and services into the latent features of users and services, which can make full use of the data in sparse matrices. Then a time feature extraction block is designed to extract time features based on the time-varying characteristics of QoS values. Finally, the time features are fused with the latent features of users and services to predict QoS values. The experimental results show that on existing datasets, the proposed model has better prediction accuracy than other advanced methods in response time and throughput. © 2025 IEEE.

关键词： Prediction models

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Regression analysis of squeezing-induced hybrid nanofluid flow in Darcy-Forchheimer porous medium

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Applied mathematics and Mechanics(English Edition) 2025年第1期46卷 193-208页

作者： K.MUHAMMAD M.SARFRAZ Department of Humanities and Sciences School of Electrical Engineering and Computer ScienceNational University of Sciences and TechnologyIslamabad 44000Pakistan Department of Mathematics Quaid-i-Azam UniversityIslamabad 44000Pakistan

This article presents a mathematical model addressing a scenario involving a hybrid nanofluid flow between two infinite parallel *** plate remains stationary,while the other moves downward at a squeezing *** space between these plates contains a Darcy-Forchheimer porous medium.A mixture of water-based fluid with gold(Au)and silicon dioxide(Si O2)nanoparticles is *** contrast to the conventional Fourier's heat flux equation,this study employs the Cattaneo-Christov heat flux equation.A uniform magnetic field is applied perpendicular to the flow direction,invoking magnetohydrodynamic(MHD)***,the model accounts for Joule heating,which is the heat generated when an electric current passes through the *** problem is solved via NDSolve in *** and statistical analyses are conducted to provide insights into the behavior of the nanomaterials between the parallel plates with respect to the flow,energy transport,and skin *** findings of this study have potential applications in enhancing cooling systems and optimizing thermal management *** is observed that the squeezing motion generates additional pressure gradients within the fluid,which enhances the flow rate but reduces the frictional ***,the fluid is pushed more vigorously between the plates,increasing the flow *** the fluid experiences higher flow rates due to the increased squeezing effect,it spends less time in the region between the *** thermal relaxation,however,abruptly changes the temperature,leading to a decrease in the temperature fluctuations.

关键词： convective boundary condition Darcy-Forchheimer medium hybrid nanofuid Joule heating magnetohydrodynamic(MHD) numerical solution squeezing flow regression analysis

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JPEG Reversible Data Hiding via Block Sorting Optimization and Dynamic Iterative Histogram Modification

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IEEE Transactions on Multimedia 2025年 27卷 3729-3743页

作者： Li, Fengyong Wang, Qiankuan Cheng, Hang Zhang, Xinpeng Qin, Chuan Shanghai University of Electric Power College of Computer Science and Technology Shanghai201306 China Fuzhou University School of Mathematics and Statistics Fujian Fuzhou350108 China Fudan University School of Computer Science Shanghai200433 China University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai200093 China

JPEG reversible data hiding (RDH) refers to covert communication technology to accurately extract secret data while also perfectly recovering the original JPEG image. With the development of cloud services, a large number of private JPEG images can be efficiently managed in cloud platforms by embedding user ID or authentication labels. Nevertheless, data embedding operations may inadvertently disrupt the encoding sequence of the original JPEG image, resulting in severe distortion of the host image when it is re-compressed to JPEG format. To address this problem, this paper proposes a new JPEG RDH scheme based on block sorting optimization and dynamic iterative histogram modification. We firstly design a block ordering optimization strategy by combining the number of zero coefficients and the quantization table values of non-zero coefficients in a DCT block. Subsequently, a dynamic iterative histogram modification scheme is proposed by considering the local features and embedding capability of histograms generated from different texture images. According to the given payloads, we introduce different parameters to control the iterations of two-dimensional histogram and then adaptively generate the optimal histogram modification mapping, which can realize low JPEG file size increments by guaranteeing most of the AC coefficients unchanged as much as possible. Numerous experiments have shown that our scheme can achieve an effective balance among embedding capacity, visual quality, file size increment, computational complexity, and outperforms the state-of-the-arts in terms of the above metrics. © 1999-2012 IEEE.

关键词： Embeddings

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Chinese Relation Extraction Based on Cross-Attention and Multi-feature Perception

Journal of Network Intelligence

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Journal of Network Intelligence 2024年第3期9卷 1837-1853页

作者： Xu, Shiao Sun, Shuihua Zhang, Zhiyuan Zhou, Huan School of Electronics and Information Engineering Jingchu University of Technology Jingmen448000 China School of Computer Science and Mathematics Fujian University of Technology Fuzhou350118 China

Relation extraction aims at extracting semantic relations between given pairs of entities from unstructured textual data and is a critical task in information extraction. A relation extraction model based on cross-attention and multi-feature perception is proposed to address the limitations of Chinese relation extraction models that rely solely on characters, which struggle to capture rich semantic information, and the inability to ful ly utilize the textual structural information during neural network feature extraction. First, the model uses BERT to encode sentences and entities to obtain character vector sequences and word vector sequences of sentences, and uses word cross-attention mechanism to capture the key word and word information in sentences to form character and word cross-fused sentence sequences. Second, uses graph convolutional network and deep separable convolutional network to capture syntactic information between nodes and multi-granularity local semantic information in sentence sequences, respectively, and forms sentence features for classification by attention mechanism. Final ly, combines with Softmax classifier for relation classification. Experimental results on the Chinese SanWen dataset demonstrate the superiority of the proposed method over mainstream neural network-based relation extraction methods, with an F1-score of 71.57%. © 2024, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Convolutional neural networks

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Mutual Retinex: Combining Transformer and CNN for Image Enhancement

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IEEE Transactions on Emerging Topics in Computational Intelligence 2024年第3期8卷 2240-2252页

作者： Jiang, Kui Wang, Qiong An, Zhaoyi Wang, Zheng Zhang, Cong Lin, Chia-Wen Harbin Institute of Technology School of Computer Science and Technology Harbin150001 China Wuhan University National Engineering Research Center for Multimedia Software School of Computer Science Wuhan430072 China Wuhan Polytechnic University School of Mathematics and Computer Science Wuhan430024 China National Tsing Hua University Department of Electrical Engineering Institute of Communications Engineering Hsinchu30013 Taiwan

Images captured in low-light or underwater environments are often accompanied by significant degradation, which can negatively impact the quality and performance of downstream tasks. While convolutional neural networks (CNNs) and Transformer architectures have made significant progress in computer vision tasks, there are few efforts to harmonize them into a more concise framework for enhancing such images. To this end, this study proposes to aggregate the individual capability of self-attention (SA) and CNNs for accurate perturbation removal while preserving background contents. Based on this, we carry forward a Retinex-based framework, dubbed as Mutual Retinex, where a two-branch structure is designed to characterize the specific knowledge of reflectance and illumination components while removing the perturbation. To maximize its potential, Mutual Retinex is equipped with a new mutual learning mechanism, involving an elaborately designed mutual representation module (MRM). In MRM, the complementary information between reflectance and illumination components are encoded and used to refine each other. Through the complementary learning via the mutual representation, the enhanced results generated by our model exhibit superior color consistency and naturalness. Extensive experiments have shown the significant superiority of our mutual learning based method over thirteen competitors on the low-light task and ten methods on the underwater image enhancement task. In particular, our proposed Mutual Retinex respectively surpasses the state-of-the-art method MIRNet-v2 by 0.90 dB and 2.46 dB in PSNR on the LOL 1000 and FIVEK datasets, while with only 19.8% model parameters. © 2017 IEEE.

关键词： Image enhancement

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Feature-preserving color pencil drawings from photographs

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Computational Visual Media 2023年第4期9卷 807-825页

作者： Dong Wang Guiqing Li Chengying Gao Shengwu Fu Yun Liang College of Mathematics and Informatics South China Agricultural UniversityGuangzhouChina School of Computer Science&Engineering South China University of TechnologyGuangzhouChina School of Data and Computer Science Sun Yat-sen UniversityGuangzhouChina

Color pencil drawing is well-loved due to its rich *** paper proposes an approach for generating feature-preserving color pencil drawings from *** mimic the tonal style of color pencil drawings,which are much lighter and have relatively lower saturation than photographs,we devise a lightness enhancement mapping and a saturation reduction *** lightness mapping is a monotonically decreasing derivative function,which not only increases lightness but also preserves input photograph *** saturation is usually related to lightness,so we suppress the saturation dependent on lightness to yield a harmonious ***,two extremum operators are provided to generate a foreground-aware outline map in which the colors of the generated contours and the foreground object are *** experiments show that color pencil drawings generated by our method surpass existing methods in tone capture and feature preservation.

关键词： non-photorealistic rendering pencil drawings image editing feature preservation

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Navigating the Metaverse: A Study on Cyber Security Implications 8th

Navigating the Metaverse: A Study on Cyber Security Implicat...

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8th World Conference on Smart Trends in Systems Security and Sustainability, WorldS4 2024

作者： Pathirana, Parakum Amaranga School of Mathematics Computer Science & Engineering Liverpool Hope University Liverpool United Kingdom

ISBN: (纸本)9789819793266

Metaverse a term coined in 1992 has become a popular paradigm recently fueled by Zuckerberg’s press release in October 2021. The Metaverse promises a whole new set of opportunities and challenges for the society. A considerable amount of people’s time is spent on communicating via technology, and this trend has been fueled by COVID-19. With the current growth patterns, the Metaverse is likely to generate and store a trove of sensitive data. Thus, the critiques have raised concerns on the potential for bad actors with malicious intent to use it for activities such as fraud, misinformation, exploitation, etc. This chapter aims to provide an introduction to Metaverse and cyber security implications souring it. The chapter begins with an introduction presenting how the term "Metaverse" was coined and then proceeds to understand the current market leaders. This is followed by a brief on the main concepts behind the Metaverse together with the technologies used. In the last section, the discussion is on the known cyber security implications of Metaverse. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cyber attacks

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Enhanced YOLOv5s-Based Apple Detection for Harvesting Robots Using a Multi-Rotated Box Algorithm

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第18期49卷 191-204页

作者： Feng, Wen Jian Zhao, Yi Lin Xie, Yongsheng Wei, Linbing Guangxi Eco-engineering Vocational & Technical College Liuzhou Guangxi 545004 China School of Mathematics and Computer Science Guangxi Science & Technology Normal University Laibin546199 China

The apple picking robot makes use of a number of technologies, one of which is the apple target identification algorithm. When it comes to automated apple picking, the robots' optical systems are crucial. Generally speaking, it finds ripe apples by taking photographs of its environment, processing them, and then analyzing the findings. The inability of traditional vision algorithms to process complex backdrops hinders the efficiency of harvesting robots. The continuous development and refining of the CNN have led to a substantial improvement in its efficacy in target identification during the last several years. The current crop of apple recognition algorithms struggles to tell the difference between partially obscured apples and ones entirely concealed by tree branches. Direct use of the algorithm endangers the harvesting robot's mechanical arm, apples, as well as gripping end-effector. In response to this real-world issue, we provide a lightweight apple targets identification approach for picking robots based on enhanced YOLOv5s. This method can automatically identify which apples in an apple tree picture are graspable and which ones are not. This method is able to circumvent the impact of light transformation, in contrast to the conventional segmentation approach. When there is a lot of resemblance between the fruit and the backdrop, though, it becomes more challenging to get strong recognition results. With a recall rate of 98%, a detection speed of 47 f/s, and a mAP (mean Average Precision) of apple detection of 98.13%, the findings demonstrate that the YOLO v5 network has perfect properties. The YOLO v5 is able to simultaneously fulfill the accuracy and speed criteria of apple identification, in contrast to more conventional network models like Faster R-CNN and YOLO v4. The experiment culminates with the employment of the apple-harvesting robot that the researcher developed themselves. Results demonstrate that the robot has a harvesting success rate of 99.2% i

关键词： Fruits

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Light Weighted YOLO Vehicle Tracker for Toll Gate Using Centroid Algorithm 3rd

Light Weighted YOLO Vehicle Tracker for Toll Gate Using Cen...

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3rd International Conference on Emerging Electronics and Automation, E2A 2023

作者： Singh, Pankaj Pratap Prasad, Shitala Kalita, Gyanjyoti Das, Jahnu Mahanta, Pallab Jyoti Department of Computer Science and Engineering CIT Kokrajhar Assam India School of Mathematics and Computer Science IIT Goa Goa India

ISBN: (纸本)9789819730896

This paper focuses on vehicle detection at highway toll gates. The study involves the detection of vehicles within a custom dataset, which comprises four vehicle classes: car, truck, bus, and motorcycle. The YOLOv4 model is utilized for training and is complemented by the implementation of the Centroid tracking algorithm. In the proposed vehicle track dataset, each class comprises 1500 annotated images for training and 300 images in the testset. These images are sourced from the Google Open Images V6 dataset, and the OIDv4 toolkit is employed to scrape dataset images. The primary objective of this study is to train a vehicle detection model and evaluate the effectiveness of various tracking algorithms, with a specific focus on the centroid tracker for real-time application at the toll gates. Importantly, this model is designed to operate efficiently on conventional and cost-effective GPUs and demonstrates a commendable level of precision. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Image annotation

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