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Highly efficient compact verifiable shuffle scheme based on QA-NIZK proof
Published 2015-09-01“…To protect the privacy of voters in e-voting,votes should be shuffled by a series of mix servers.To guarantee the honesty of mix servers,verifiable shuffle scheme was needed.However the proof size of existed CVS (compact veri-fiable shuffle) scheme was dependent on the number of mix servers and the number of voters,which could be very ineffi-cient when there were lots of mix servers and voters.A new CVS scheme was presented with the proof size of only O(1),i.e.constant no matter how many mix servers and voters were involved.The construction is based on an efficient proof system QA-NIZK (quasi-adaptive non-interactive zero knowledge) presented recently.It also points out that the QA-NIZK proof system is malleable,which is of independent interest.…”
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Fedrtid: an efficient shuffle federated learning via random participation and adaptive time constraint
Published 2024-12-01“…However, with the frequent interaction of model parameters between the client and the server, the client will consume a large amount of network and arithmetic resources, and resource-constrained clients can hardly maintain model security while ensuring the efficiency of collaborative user training. Therefore, we propose FedRtid, a shuffle differential privacy federated learning scheme with random participation and adaptive time constraints, to improve the efficiency of collaborative user training while considering model privacy. …”
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Advancements in Coded Computation: Integrating Encoding Matrices with Data Shuffling for Enhanced Data Transmission Efficiency
Published 2025-01-01“…This paper analyzes various data shuffling methods, their integration with encoding matrices, and their impact on computational efficiency and data transmission. …”
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Lightweight Models for Real-Time Steganalysis: A Comparison of MobileNet, ShuffleNet, and EfficientNet
Published 2024-12-01“…This research evaluates MobileNet, ShuffleNet, and EfficientNet for such tasks, using the BOSSbase-1.01 dataset. …”
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DMM-YOLO: A high efficiency soil fauna detection model based on an adaptive dynamic shuffle mechanism
Published 2025-08-01“…Abstract Soil fauna play a critical role in maintaining ecosystem functions and assessing environmental health, making accurate and efficient detection essential. Therefore, this paper proposes an improved algorithm based on You Only Look Once (YOLO) v9, which enhances feature capture capability while reducing parameters by 33.6%. …”
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Towards Efficient Serverless MapReduce Computing on Cloud-Native Platforms
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Rock image classification based on improved EfficientNet
Published 2025-05-01Subjects: Get full text
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Efficient Task Scheduling Approach in Edge-Cloud Continuum based on Flower Pollination and Improved Shuffled Frog Leaping Algorithm
Published 2024-02-01“…Therefore, there is a dire need for an efficient metaheuristic algorithm for task scheduling. …”
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LENet: A Lightweight and Efficient High-Resolution Network for Human Pose Estimation
Published 2025-01-01“…In order to address this issue, we propose a lightweight and efficient network (LENet), which is not only capable of learning comprehensive information under a lightweight architecture for real-time human pose estimation, but also has a faster inference speed than other common lightweight networks, so that the decision-making efficiency and user experience of real-time systems would be improved. …”
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An efficient medical data encryption scheme using selective shuffling and inter-intra pixel diffusion IoT-enabled secure E-healthcare framework
Published 2025-02-01“…The CCS generates the chaotic key sequences to shuffle the medical image row-wise and column-wise. …”
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An EfficientNetv2-based method for coal conveyor belt foreign object detection
Published 2025-01-01Subjects: Get full text
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An explainable deep learning model for diabetic foot ulcer classification using swin transformer and efficient multi-scale attention-driven network
Published 2025-02-01“…The second track involves the Efficient Multi-Scale Attention-Driven Network (EMADN), which leverages Light-weight Multi-scale Deformable Shuffle (LMDS) and Global Dilated Attention (GDA) blocks to extract local features efficiently. …”
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An electricity price optimization model considering time-of-use and active distribution network efficiency improvements
Published 2025-01-01“…We aimed to optimize energy costs, improve system response speed, and reduce price volatility, thereby achieving more efficient energy utilization and economic benefits.…”
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Design of shuffling protocol based on additive secret sharing
Published 2024-08-01“…Aiming at the problems such as lack of specific algorithms for process implementation, using public keys in most of the solutions, low efficiency in dealing with large-scale data sets, and lack of applicability, a unilateral shuffling protocol was proposed, and on this basis, a shuffling protocol based on additive secret sharing was designed. …”
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Image encryption scheme based on thorp shuffle and pseudo dequeue
Published 2025-04-01“…The results show that the algorithm proposed in this paper has high encryption efficiency and security and performs excellently in terms of anti-attack performance.…”
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Exploiting Circular Shifts for Efficient Chaotic Image Encryption
Published 2025-01-01“…The permutation step combines actions of bit level rearranging, and bit shuffling. Circular shift operations are used for bit shuffling, to reduce the execution time. …”
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Spiking Residual ShuffleNet-Based Intrusion Detection in IoT Environment
Published 2025-01-01“…After detecting an intrusion, an attack is mitigated to eliminate the malicious node. The SR-ShuffleNet model achieves impressive accuracy and efficiency in identifying and mitigating attacks in IoT systems.…”
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A Secure and Efficient White-Box Implementation of SM4
Published 2024-12-01“…We employ fourth-order non-linear encoding to reduce the loss of efficiency while utilizing a random sequence to shuffle lookup table access, thereby severing the potential link between memory data and the intermediate values of SM4. …”
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Enhancing Distributed Machine Learning through Data Shuffling: Techniques, Challenges, and Implications
Published 2025-01-01“…In distributed machine learning, data shuffling is a crucial data preprocessing technique that significantly impacts the efficiency and performance of model training. …”
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