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On the Security of Key-Aggregate Searchable Encryption

Journal of Cyber Security and Mobility 2024年第3期13卷 565-584页

作者： Wen, Jing Li, Haifeng Lan, Caihui School of Information Engineering Lanzhou City University Lanzhou730070 China School of Computer and Information Science Qinghai University of Science and Technology Xining810016 China Department of Information Technology Qinghai University Xining810016 China

The sharing of encrypted data in cloud computing is an essential functionality with countless applications in our everyday life. However, the issue of how to securely, efficiently and flexibly share encrypted data in multi-user settings has not been well solved. As a promising and elegant technique, the key-aggregate searchable encryption (KASE) scheme can efficiently support selective sharing of a large number of documents with a set of users using only a single, constant-size authorization key (i.e., the aggregated key). However, by conducting cryptanalysis on existing KASE schemes, we classify the attack methods into two types: offline keyword guessing attacks and authorization abuse. For the former attacks, we first employ the known keyword guessing attack methods to cryptanalyze several existing KASE schemes. Furthermore, we propose two novel keyword guessing attack methods, namely (1) Keyword guessing attack by modifying ciphertext and (2) Keyword guessing attack by constructing verification equation. For the latter attacks, we first utilized the known authorization abuse attack methods to cryptanalyze several existing KASE schemes. Furthermore, we develop a novel attack method in which the attacker can independently upgrade their own authorization and gain enhanced search privileges without colluding with multiple authorized users. © 2024 River Publishers.

关键词： Aggregates

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An enhanced AMBTC for color image compression using color palette

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Multimedia Tools and Applications 2024年第11期83卷 31783-31803页

作者： Xiong, Lizhi Zhang, Mengtao Yang, Ching-Nung Kim, Cheonshik School of Computer Science Nanjing University of Information Science and Technology Nanjing China Department of Computer Science and Information Engineering National Dong Hwa University Hualien Taiwan Department of Computer Engineering Sejong University Seoul Korea Republic of

Digital image has been used in various fields as an essential carrier. Many color images have been constantly produced since their more realistic description, which takes up much storage space and network bandwidth. Thus, color image compression has become an essential key technology. Absolute Moment Block Truncation Coding (AMBTC) has been widely studied as one of the classical image compression methods. However, in the existing methods, the visual quality of the reconstructed images and the compression rate are all relatively low. Therefore, this paper proposes an enhanced AMBTC for color image compression using a color palette. In the proposed method, the K-means clustering algorithm is utilized for training the image's palette pattern. The color palette obtained by K-mean will be more suitable for reconstructing this image than the standard color palette, and the visual quality will be higher. The six clustered central pixels are matched with the palette through a color difference formula, and the obtained index values are used as the quantization levels. Huffman coding is used to build a bitmap to achieve a higher compression rate, that is, a lower bit rate. At last, a block of a color image can be represented by six index values and a bitmap. Experimental results and theoretical analysis demonstrate that the proposed method has better visual quality and bit rate than similar schemes. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023.

关键词： Color

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TransFuseNetS: A new Image Medical Segmentation Method based on TransFuse Architecture 24

TransFuseNetS: A new Image Medical Segmentation Method based...

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2024 International Conference on computer Vision and Deep Learning, CVDL 2024

作者： Chen, Wei Wang, Zhiheng School of Computer and Information Engineering Heilongjiang University of Science and Technology Heilongjiang China School of Computer and Information Engineering Heilongjiang University of Science and Technology Anhui China

ISBN: (纸本)9798400718199

In medical images, image segmentation is a very important method, which can accurately locate and analyze the lesions and tissues. However, due to the complexity of medical images and noise, accurate and robust segmentation is still a difficult problem. In view of the above problems, this project intends to use TransFuseNetS under the framework of Transfuse for image medical segmentation. TransFuseNetS further optimizes and strengthens the Fusion architecture. First of all, this project intends to design a more complex coding architecture and propose an overall attention strategy at the channel level to improve the feature representation and understandability of the image. Secondly, the linear fusion method of two directions is used to make the multi-level images more comprehensive and rich, so as to improve the identification and segmentation of multi-scale images. Through a large number of experiments, we find that TransFuseNetS can effectively improve the recognition rate of CT images. Compared with the traditional artificial neural network, the average error of IOU and ACC of artificial neural network is reduced by 0.1571, and the average error is reduced by 0.0029. The research results of this project will lay a foundation for the research and application in the fields of medical image processing and medical image processing. © 2024 ACM.

关键词： Image segmentation

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A Self-Adapting and Efficient Dandelion Algorithm and Its Application to Feature Selection for Credit Card Fraud Detection

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IEEE/CAA Journal of Automatica Sinica 2024年第2期11卷 377-390页

作者： Honghao Zhu MengChu Zhou Yu Xie Aiiad Albeshri IEEE School of Computer and Information Engineering Bengbu UniversityBengbu 233030China School of Information and Electronic Engineering Zhejiang Gongshang UniversityHangzhou 310018China Helen and John C.Hartmann the Department of Electrical and Computer EngineeringNew Jersey Institute of TechnologyNewark NJ 07102 USA College of Information Engineering Shanghai Maritime UniversityShanghai 201306China Department of Computer Science King Abdulaziz UniversityJeddah 21481Saudi Arabia

A dandelion algorithm(DA) is a recently developed intelligent optimization algorithm for function optimization problems. Many of its parameters need to be set by experience in DA,which might not be appropriate for all optimization problems. A self-adapting and efficient dandelion algorithm is proposed in this work to lower the number of DA's parameters and simplify DA's structure. Only the normal sowing operator is retained;while the other operators are discarded. An adaptive seeding radius strategy is designed for the core dandelion. The results show that the proposed algorithm achieves better performance on the standard test functions with less time consumption than its competitive peers. In addition, the proposed algorithm is applied to feature selection for credit card fraud detection(CCFD), and the results indicate that it can obtain higher classification and detection performance than the-state-of-the-art methods.

关键词： Credit card fraud detection(CCFD) dandelion algorithm(DA) feature selection normal sowing operator

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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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Text region detection algorithm for medical instruments screen based on improved DBNet 2

Text region detection algorithm for medical instruments scre...

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2nd International Conference on Pattern Recognition, Machine Vision and Intelligent Algorithms, PRMVIA 2024

作者： Liu, Jiaming Chen, Wei Heilongjiang University of Science and Technology School of Computer and Information Engineering Nantong China Heilongjiang University of Science and Technology School of Computer and Information Engineering Harbin China

ISBN: (纸本)9798350389746

Recognition of text in images has become increasingly important with the rapid development of cameras, and the main technology involved is Optical Character Recognition (OCR). In order to enhance the recognition accuracy of traditional text region detection algorithms for medical instrument screen data, which contains irregular and small objects, this paper introduces an improved scheme incorporating Adaptive Spatial Feature Fusion (ASFF) and Gaussian Error Linear Unit (GELU) activation function, and proposes the DBNeti model. The introduction of ASFF aims to address the limitations of fixed feature fusion in traditional text recognition algorithms and to enhance the performance of target detectors at different scales. The GELU is introduced as the activation function of the DBNet algorithm, utilizing its nonlinear characteristics, gradient preservation, and computational efficiency to enhance the expressiveness and generalization ability of the model. Experiments demonstrate that the improved model, DBNeti, has achieved optimal performance across all evaluation indices, including an Accuracy of 0.8189, a Recall of 0.3633, a Precision of 0.6858, and an F-measure of 0.4750. Compared to DBNet, DBNeti improves the Accuracy, Recall, Precision, and F-measure indices by 3.3%, 6.1%, 3.1%, and 4.2%, respectively. This study demonstrates the superiority of the DBNeti algorithm in the text recognition task. © 2024 IEEE.

关键词： Image segmentation

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Principle of subtraction ghost imaging in scattering medium

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Chinese Physics B 2023年第6期32卷 250-254页

作者：付芹白艳锋谭威黄贤伟南苏琴傅喜泉 College of Computer Science and Electronic Engineering Hunan UniversityChangsha 410082China School of Computer and Information Engineering Hunan University of Technology and BusinessChangsha 410205China

Scattering medium in light path will cause distortion of the light field,resulting in poor signal-to-noise ratio(SNR)of ghost *** disturbance is usually eliminated by the method of *** deduce the intensity fluctuation correlation function of the ghost imaging with the disturbance of the scattering medium,which proves that the ghost image consists of two correlated results:the image of scattering medium and the target *** effect of the scattering medium can be eliminated by subtracting the correlated result between the light field after the scattering medium and the reference light from ghost image,which verifies the theoretical *** research may provide a new idea of ghost imaging in harsh environment.

关键词： ghost imaging image reconstruction techniques scattering medium

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A robust visual information hiding framework based on HVS pixel adaptive alpha blending (HPAAB) technique

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Multimedia Tools and Applications 2025年第14期84卷 13959-13982页

作者： Panda, Bishwabara Nayak, Manas Ranjan Mallick, Pradeep Kumar Basu, Abhishek School of Computer Engineering KIIT Deemed to be University Bhubaneswar India Department of ECE RCC Institute of Information Technology Kolkata India

The popularity of the Internet and digital consumer gadgets has fundamentally changed our society and daily lives by making digital data collection, transmission, and storage exceedingly easy and convenient. However, protecting these data and preventing unauthorized changes is an important task. This challenge has arisen in several domains, including copyright protection, content authentication, information concealment, and protected communications. Several academics have developed digital watermarking algorithms to solve this issue, aiming to implant hidden data (a watermark) in digital material to affix a mark on or seal the digital data content. The proposed approach builds a spatial domain image watermarking approach using entropy, a spectral residual pixel-based saliency map, and alpha blending. This method states that the value of entropy improves the concealing capacity. To increase imperceptibility, a spectral residual-based saliency map is used to discover less visible portions of the input images and progressively incorporate more information based on entropy data. Any modifications made other than those detectable regions will be less noticeable to viewers since they result from saliency, which decides which elements of an image are the most visible from a visual perspective. Lastly, utilizing the quality criteria, the mechanism's capability is analyzed for numerous existing frameworks. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Entropy

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Quantized Control for Input-to-State Stabilization of Discrete-Time Markov Jump Systems with Coding and Decoding Procedures

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IAENG International Journal of Applied Mathematics 2025年第1期55卷 26-33页

作者： Gao, Xiaohui Su, Yue Han, Chengyi Han, Jing Chen, Yebin School of Computer Science and Technology Anhui University of Technology Ma'anshan243032 China School of Computer Science and Technology Anhui University of Technology Ma'anshan243032 China School of Electrical and Information Engineering Wanjiang University of Technology Ma'anshan243032 China School of Computer Science and Technology Anhui University of Technology Ma'anshan243032 China School of Computer Science and Technology Anhui University of Technology Ma'anshan243032 China

This paper investigates the input-to-state stabilization of discrete-time Markov jump systems. A quantized control scheme that includes coding and decoding procedures is proposed. The relationship between the error in the system state before and after encoding and decoding, the quantization range, and the packet length is established. A criterion for inputto- state stability of the quantized closed-loop Markov jump system is obtained using a Lyapunov function and the Schur complement. The gains of the required quantized controller can be derived from a feasible solution to linear matrix inequalities. Finally, the proposed control scheme is validated using an operational amplifier circuit system. © (2025), (International Association of Engineers). All rights reserved.

关键词： Lyapunov functions

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Asynchronous Energy-to-peak Control for 2D Roesser-type Markov Jump Systems

IAENG International Journal of Computer Science

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IAENG International Journal of computer Science 2024年第11期51卷 1761-1768页

作者： Chen, Qing Zhang, Yu Han, Chengyi Chen, Ling Zhou, Jianping The School of Computer Science and Technology Anhui University of Technology Ma’anshan243032 China The School of Electrical and Information Engineering Wanjiang University of Technology Ma’anshan243032 China

This paper studies asynchronous energy-to-peak control for 2D Roesser-type Markov jump systems (RTMJSs). Given the practical challenge of obtaining the system state, output-feedback is utilized for closing the control loop instead of state feedback. The asynchronous behavior between the controlled plant and the controller is represented by a hidden Markov model. A sufficient condition for the stochastic stability and energy-to-peak disturbance attenuation (EPDA) of the closed-loop RTMJS is established using a Lyapunov function and Schur’s complement. Under fixed conditional probability, a scheme for asynchronous output-feedback controller design is proposed, employing variable replacement and matrix transformation to cope with the nonlinear terms. The present work is then expanded to the uncertain conditional probability scenario and a related controller design method is developed. Finally, the effectiveness of the proposed design methods is demonstrated through a thermal process. © (2024), (International Association of Engineers). All rights reserved.

关键词： Lyapunov functions

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