This work explores advanced methodologies for encrypting images by utilizing one-dimensional Group Cellular Automata (1-D GCA) and S-Box techniques, with a focus on their pivotal role in ensuring the security of digit...
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ISBN:
(数字)9798331528140
ISBN:
(纸本)9798331528157
This work explores advanced methodologies for encrypting images by utilizing one-dimensional Group Cellular Automata (1-D GCA) and S-Box techniques, with a focus on their pivotal role in ensuring the security of digital images. The proposed approach amalgamates Cellular Automata and S-Box mechanisms within a framework of symmetric-key cryptography to augment the strength of encryption. Through the generation of pseudorandom sequences using 1-D GCA and the utilization of S-Box for substitution, the method accomplishes efficient pixel diffusion and confusion. The assessment criteria demonstrate noteworthy enhancements: Number of Pixels Change Rate (NPCR) at 99.9864%, Unified Average Changing Intensity (UACI) at 33.6130%, Peak Signal-to-Noise Ratio (PSNR) at 9.2336 dB, and Mean Squared Error (MSE) at 0.1193. These findings underscore the robustness of the algorithm against statistical and differential attacks. The study justifies the promise of integrating GCA and S-Box strategies to reinforce cryptographic systems, proposing future avenues for enhancing computational efficiency and broadening applicability across diverse digital contexts.
Semantic hashing is an effective technique to empower information retrieval. Currently, considerable efforts have been dedicated to generating high-quality hash codes by modeling document features using generative mod...
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A modern decentralized electric grid is a groundbreaking system that integrates demand response effortlessly and doesn't need major infrastructure changes. Within the decentralized domain, users independently cont...
A modern decentralized electric grid is a groundbreaking system that integrates demand response effortlessly and doesn't need major infrastructure changes. Within the decentralized domain, users independently control their power consumption according to the frequency of the grid. This is made possible by the use of reasonably priced devices like smart meters, which allow grid frequency to be measured from almost anywhere. Different data-level resampling strategies have been used to address the problem of data imbalance, while data normalization approaches have been used to reduce biased behavior among characteristics. The findings clearly show that in terms of the performance of classifiers, a balanced dataset performs better than an unbalanced one. Specifically, for unbalanced datasets, oversampling approaches are more effective than under sampling ones. With a precision level of 94.7 percent, the XGBoost algorithm is the best performer within the range of deep learning algorithms that are taken into consideration. Remarkably, XGBoost's accuracy forecast rises to 96.8% when paired with random oversampling. This improved model manages the volatility of renewable energy supplies and maximizes their use by accurately forecasting frequency variations in decentralized power networks. This model's predictive capacities have great potential to support the stability of distributed electricity grids, which will improve the distribution and administration of energy on a larger scale.
We study the problem of reconfiguring one minimum s-t-separator A into another minimum s-tseparator B in some n-vertex graph G containing two non-adjacent vertices s and t. We consider several variants of the problem ...
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Disentangled feature representation is essential for data-efficient learning. The feature space of deep models is inherently compositional. Existing β-VAE-based methods, which only apply disentanglement regularizatio...
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Person re-identification holds significant research value within supervised systems characterized by non-overlapping multiple cameras. In recent years, unsupervised learning has made notable strides and has gradually ...
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ISBN:
(数字)9798350394085
ISBN:
(纸本)9798350394092
Person re-identification holds significant research value within supervised systems characterized by non-overlapping multiple cameras. In recent years, unsupervised learning has made notable strides and has gradually approached the training efficacy of supervised learning. This paper focuses on exploring the influence and analysis of various sampling strategies on overall unsupervised training. We initially delineate a proxylevel memory bank scheme based on camera labels and employ a hard sample mining strategy for selecting negative pairs in a contrastive learning loss. Various sampling strategies, Random sampling, triplet sampling with dissimilar labels, and group sampling yield markedly distinct outcomes across three large-scale datasets, i.e. Market-1501, DukeMTMC-reID, and MSMT17. Detailed analysis and discussion of these results are provided in this study.
The rapid growth of Artificial Intelligence-Generated Content (AIGC) services has led to increased mobile user participation in related computations and interactions. This development has enabled AI-generated characte...
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Large-scale photovoltaic (PV) plant problem identification and diagnosis is expected to grow more difficult in the future as more and more plants of increasing capacity enter into existence. To keep large-scale PV ins...
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Tensor dimensionality reduction is one of the fundamental tools for modern datascience. To address the high computational overhead, fiber-wise sampled subtensors that preserve the original tensor rank are often used ...
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