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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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Cardinality estimation for property graph queries with gated learning approach on the graph database

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Multimedia Tools and Applications 2025年第11期84卷 9159-9183页

作者： He, Zhenzhen Yu, Jiong Du, Xusheng Guo, Binglei Li, Ziyang Li, Zhe School of Information Science and Engineering Xinjiang University Urumqi China School of Computer Engineering Hubei University of Arts and Science Xiangyang China School of Software Xinjiang University Urumqi China Department of Electronic and Information Engineering The Hong Kong Polytechnic University Hong Kong

With the increasing complexity of graph query processing tasks, it is difficult for users to obtain the accurate cardinality before or during the execution of query tasks. Accurate estimate query cardinality is crucial for property graph data model, which usually involves entities, multiple joins, and various types of properties (key-value pairs). While learning-based approaches have been used in query optimization, estimating the cardinality of property graph queries is still particularly challenging. In this paper, we first formally represent each property graph query and then divide the estimation process into three phases: query execution, training data generation, and model training. Secondly, we construct a data pool to deal the updating query workloads in the training data generation phase. Thirdly, we utilize the gate mechanism to develop a cardinality estimation framework based on deep learning neural networks for property graph queries in the Neo4j database, and we propose a hybrid loss function to optimize the training process. Finally, we adopt a method of pre-aggregating the underlying data to speed up query execution in the first phase. The result of the experiment on two real-world datasets shows that our optimized methods can effectively improve the estimation accuracy of the deep learning model, and our query estimator outperforms other deep learning models compared in terms of estimation accuracy. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Query processing

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Loop Subgraph-Level Greedy Mapping Algorithm for Grid Coarse-Grained Reconfigurable Array

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Tsinghua science and Technology 2023年第2期28卷 330-343页

作者： Naijin Chen Fei Cheng Chenghao Han Jianhui Jiang Xiaoqing Wen School of Computer and Information Science Anhui Polytechnic UniversityWuhu 241000China School of Software Engineering Tongji UniversityShanghai 201804China Department of Computer Science and Networks Kyushu Institute of TechnologyFukuoka 820-8502Japan

To solve the problem of grid coarse-grained reconfigurable array task mapping under multiple constraints,we propose a Loop Subgraph-Level Greedy Mapping(LSLGM)algorithm using parallelism and processing element *** the constraint of a reconfigurable array,the LSLGM algorithm schedules node from a ready queue to the current reconfigurable cell array *** mapping a node,its successor’s indegree value will be dynamically *** its successor’s indegree is zero,it will be directly scheduled to the ready queue;otherwise,the predecessor must be dynamically *** the predecessor cannot be mapped,it will be scheduled to a blocking *** dynamically adjust the ready node scheduling order,the scheduling function is constructed by exploiting factors,such as node number,node level,and node *** with the loop subgraph-level mapping algorithm,experimental results show that the total cycles of the LSLGM algorithm decreases by an average of 33.0%(PEA44)and 33.9%(PEA_(7×7)).Compared with the epimorphism map algorithm,the total cycles of the LSLGM algorithm decrease by an average of 38.1%(PEA_(4×4))and 39.0%(PEA_(7×7)).The feasibility of LSLGM is verified.

关键词： Grid Coarse-Grained Reconfigurable Array(GCGRA) mapping loop subgraph scheduling

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AI-Enhanced Secure Data Aggregation for Smart Grids with Privacy Preservation

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computers, Materials & Continua 2025年第1期82卷 799-816页

作者： Congcong Wang Chen Wang Wenying Zheng Wei Gu School of Software Nanjing University of Information Science and TechnologyNanjing210044China School of Information Science and Engineering Zhejiang Sci-Tech UniversityHangzhou310018China State Key Laboratory of Public Big Data Guizhou UniversityGuiyang550025China School of Computer Science and Technology(School of Artificial Intelligence) Zhejiang Sci-Tech UniversityHangzhou310018China School of Computer Science(School of Cyber Science and Engineering) Nanjing University of Information Science and TechnologyNanjing210044China

As smart grid technology rapidly advances,the vast amount of user data collected by smart meter presents significant challenges in data security and privacy *** research emphasizes data security and user privacy concerns within smart ***,existing methods struggle with efficiency and security when processing large-scale *** efficient data processing with stringent privacy protection during data aggregation in smart grids remains an urgent *** paper proposes an AI-based multi-type data aggregation method designed to enhance aggregation efficiency and security by standardizing and normalizing various data *** approach optimizes data preprocessing,integrates Long Short-Term Memory(LSTM)networks for handling time-series data,and employs homomorphic encryption to safeguard user *** also explores the application of Boneh Lynn Shacham(BLS)signatures for user *** proposed scheme’s efficiency,security,and privacy protection capabilities are validated through rigorous security proofs and experimental analysis.

关键词： Smart grid data security privacy protection artificial intelligence data aggregation

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Leveraging user's preference and social circle for personalized recommendation via matrix factorization with sub-linear convergence rate

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Journal of Intelligent and Fuzzy Systems 2024年第1-2期47卷 1-13页

作者： Qiu, Laixiang Zhou, Wang Tian, Ying Ul Haq, Amin Ahmad, Sultan School of Computer and Software Engineering Xihua University Chengdu China Intelligent Policing Key Laboratory of Sichuan Province Sichuan Police College LuZhou China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Alkharj Saudi Arabia

Recommender systems aim to filter information effectively and recommend useful sources to match users' requirements. However, the exponential growth of information in recent social networks may cause low prediction accuracy for recommendation systems. This article proposes a unified personalized recommendation architecture referred to as PSRec, which incorporates user preference and social relationship into matrix factorization framework. Specifically, PSRec generates two collections for textual reviews and contextual information respectively, and performs preference learning for each user via the Latent Dirichlet Allocation topic model. Moreover, PSRec exploits the inner relations within the social circle for recommendation, including direct trust relationship and indirect trust relationship. Additionally, it's certificated that PSRec can converge at a sub-linear rate via theoretical analysis. Experimental results over DoubanMovie, CiaoDVDs and Yelp demonstrate the superiority of the proposed PSRec, which can achieve significant improvements and provide much better user experience while compared with other benchmark models. © 2024 - IOS Press. All rights reserved.

关键词： Matrix factorization

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An Efficient and Fast Area Optimization Approach for Mixed Polarity Reed-Muller Logic Circuits

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Chinese Journal of Electronics 2024年第5期33卷 1165-1180页

作者： Yuhao ZHOU Zhenxue HE Jianhui JIANG Xiaojun ZHAO Fan ZHANG Limin XIAO Xiang WANG School of Software Engineering Tongji University Key Laboratory of Agricultural Big Data of Hebei Province Hebei Agricultural University School of Computer Science and Engineering Beihang University School of Electronic and Information Engineering Beihang University

Area has become one of the main bottlenecks restricting the development of integrated circuits. The area optimization approaches of existing XNOR/OR-based mixed polarity Reed-Muller(MPRM) circuits have poor optimization effect and efficiency. Given that the area optimization of MPRM logic circuits is a combinatorial optimization problem, we propose a whole annealing adaptive bacterial foraging algorithm(WAA-BFA), which includes individual evolution based on Markov chain and Metropolis acceptance criteria, and individual mutation based on adaptive probability. To address the issue of low conversion efficiency in existing polarity conversion approaches, we introduce a fast polarity conversion algorithm(FPCA). Moreover, we present an MPRM circuits area optimization approach that uses the FPCA and WAA-BFA to search for the best polarity corresponding to the minimum circuits area. Experimental results demonstrate that the proposed MPRM circuits area optimization approach is effective and can be used as a promising EDA tool.

关键词： Integrated circuits Microorganisms Annealing Logic circuits Circuits Benchmark testing Delays

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Survey and Tutorial on Hybrid Human-Artificial Intelligence

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Tsinghua science and Technology 2023年第3期28卷 486-499页

作者： Feifei Shi Fang Zhou Hong Liu Liming Chen Huansheng Ning School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China School of Computer Science and Software Engineering East China Normal UniversityShanghai 200062China Faculty of Computing Ulster UniversityNewtownabbey BT370QBUK

The growing computing power,easy acquisition of large-scale data,and constantly improved algorithms have led to a new wave of artificial intelligence(AI)applications,which change the ways we live,manufacture,and do *** with this development,a rising concern is the relationship between AI and human intelligence,namely,whether AI systems may one day overtake,manipulate,or replace *** this paper,we introduce a novel concept named hybrid human-artificial intelligence(H-AI),which fuses human abilities and AI capabilities into a unified *** presents a challenging yet promising research direction that prompts secure and trusted AI innovations while keeping humans in the loop for effective *** scientifically define the concept of H-AI and propose an evolution road map for the development of AI toward *** then examine the key underpinning techniques of H-AI,such as user profile modeling,cognitive computing,and human-in-the-loop machine ***,we discuss H-AI’s potential applications in the area of smart homes,intelligent medicine,smart transportation,and smart ***,we conduct a critical analysis of current challenges and open gaps in H-AI,upon which we elaborate on future research issues and directions.

关键词： hybrid human-artificial intelligence(H-AI) Internet of Things(IoT) artificial intelligence(AI)

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A PPO-based Channel Hopping Sequence Framework for Time Slot Channel Hopping 24

A PPO-based Channel Hopping Sequence Framework for Time Slot...

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12th International Conference on Communications and Broadband Networking, ICCBN 2024

作者： He, Zhihang Niu, Xianhua Qi, Chao Wang, Yaoxuan Zhou, Enzhi Zeng, Bosen School of Computer and Software Engineering Xihua University China School of Computer Science Chengdu Normal University China

ISBN: (纸本)9798400717109

As a key communication technology in IEEE 802.15.4, Time Slot Channel Hopping (TSCH) enhances transmission reliability and interference immunity by scheduling of time slots and channel assignments. This paper presents a TSCH framework designed to improve packet delivery ratio and anti-interference performance by generating optimal channel hopping sequences that achieve the Lempel-Greenberger bound. The framework employs the Proximal Policy Optimization (PPO) algorithm to predict channel quality, thereby facilitating the selection of higher quality channels in response the dynamic interference changes. Simulation results demonstrate a 7.9% improvement in packet delivery ratio performance compared to previous studies. © 2024 ACM.

关键词： Frequency hopping

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More and Larger Auxiliary Feature-Guided Spatial-Temporal Super-Resolution for Rendered Sequences 17th

More and Larger Auxiliary Feature-Guided Spatial-Temporal S...

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17th Asian Conference on computer Vision, ACCV 2024

作者： Zheng, Lijie Liang, Xiao School of Computer Science and Software Engineering Southwest Petroleum University Chengdu610500 China

ISBN: (纸本)9789819609628

The post-processing rendered sequences improves the quality of the sequences and shortens the time of the rendering phase. However, most of the current post-processing methods for sequences are suitable for video. Directly transferring these methods to rendered sequences cannot obtain high-quality results. To address this problem, we propose an end-to-end spatial-temporal super-resolution network for rendered sequences, which improves rendering efficiency by simultaneously implementing frame interpolation (FI) and super-resolution. In the FI task, accurately inferring results that closer to the real motion state of the target frames is the key and difficult point to ensure the generation effect. For this issue, we design an auxiliary feature-guided interpolation (AFGI) module. By introducing the auxiliary features corresponding to the target frames, AFGI module provides the real motion state of the target frames to the network. In the part of aggregating contextual information, we propose a weighted aggregation upsampling (WAUpS) module. Aggregation is selectively performed based on the correlation between incoming information and the current frame. WAUpS module effectively prevents irrelevant information from affecting the super-resolution results, which is a problem with the direct aggregation methods used previously. At the same time, WAUpS module combines the upsampled features with the corresponding high-resolution auxiliary features. This addition provides the output with rich detail textures and other key information, further improving the overall processing quality. Experimental results show that compared with state-of-the-art (SOTA) methods, our method not only obtains high-quality rendered sequences processing results, but also effectively improves the rendering efficiency. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Rendering (computer graphics)

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EmoNet: A Convolutional Neural Network and Transfer Learning-Based Framework for Facial Micro-Expression Recognition and Emoticonification 5

EmoNet: A Convolutional Neural Network and Transfer Learning...

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5th International Conference on Artificial Intelligence and Electromechanical Automation, AIEA 2024

作者： Gong, Rui Wang, Zhongyi Zhao, Jie Southwest Petroleum University School of Computer Science and Software Engineering Chengdu China

ISBN: (纸本)9798350366174

With the rising popularity of online social interactions, emojis play a pivotal role in communication, effectively conveying people's emotions. Hence, accurately converting facial micro-expressions into corresponding emojis becomes crucial. However, current micro-expression recognition technology faces challenges of low accuracy and poor generalization. To address these issues, this paper innovatively proposes a method that combines transfer learning with convolutional neural networks. This approach significantly enhances the model's generalization capability and recognition accuracy. Experimental results demonstrate notable improvements in both accuracy and robustness in micro-expression recognition, effectively overcoming common problems found in traditional methods, such as overfitting and sample imbalance. © 2024 IEEE.

关键词： Convolutional neural networks

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