Vision is essential for human navigation. The World Health Organization (WHO) estimates that 43.3 million people were blind in 2020, and this number is projected to reach 61 million by 2050. Modern scene understanding...
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With the rapid deployment of storage services, secure and efficient user authorization and revocation data shared through the cloud have become a grand challenge hindering cloud data *** previous direct and indirect u...
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With the rapid deployment of storage services, secure and efficient user authorization and revocation data shared through the cloud have become a grand challenge hindering cloud data *** previous direct and indirect user revocation schemes implemented large-scale user revocation, they faced heavy communication and computational costs. To address these challenges, this study presents a new encryption scheme that combines ciphertext-policy attribute-based encryption(CP-ABE) with puncturable encryption to achieve efficient and flexible user revocation. We design a proxy server to reduce the computational overhead in the decryption phase. Because the puncture process is performed on a semi-honest cloud, we use the digital signature method to verify the correctness of its operation. Furthermore, we prove the security of our scheme under the chosen-plaintext attack(CPA), and compare it with other schemes to highlight its advantages. Numerical analysis and experimental simulation results reveal that our scheme is more suitable than other schemes for use in a cloud environment for user revocation.
With the development of the Internet, the volume of information is expanding rapidly, and the complex information makes it particularly important to extract information quickly and intelligently. Event extraction algo...
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Over the past few years,the application and usage of Machine Learning(ML)techniques have increased exponentially due to continuously increasing the size of data and computing *** the popularity of ML techniques,only a...
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Over the past few years,the application and usage of Machine Learning(ML)techniques have increased exponentially due to continuously increasing the size of data and computing *** the popularity of ML techniques,only a few research studies have focused on the application of ML especially supervised learning techniques in Requirement Engineering(RE)activities to solve the problems that occur in RE *** authors focus on the systematic mapping of past work to investigate those studies that focused on the application of supervised learning techniques in RE activities between the period of 2002–*** authors aim to investigate the research trends,main RE activities,ML algorithms,and data sources that were studied during this ***-five research studies were selected based on our exclusion and inclusion *** results show that the scientific community used 57 *** those algorithms,researchers mostly used the five following ML algorithms in RE activities:Decision Tree,Support Vector Machine,Naïve Bayes,K-nearest neighbour Classifier,and Random *** results show that researchers used these algorithms in eight major RE *** activities are requirements analysis,failure prediction,effort estimation,quality,traceability,business rules identification,content classification,and detection of problems in requirements written in natural *** selected research studies used 32 private and 41 public data *** most popular data sources that were detected in selected studies are the Metric Data Programme from NASA,Predictor Models in software Engineering,and iTrust Electronic Health Care System.
Traditional information theory provides a valuable foundation for Reinforcement Learning (RL), particularly through representation learning and entropy maximization for agent exploration. However, existing methods pri...
Using unmanned aerial vehicles (UAV) for large-scale scene sampling is a prevalent application in UAV vision. However, there are certain factors that can influence the quality of UAV sampling, such as the lack of text...
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The distribution of data has a significant impact on the results of *** the distribution of one class is insignificant compared to the distribution of another class,data imbalance *** will result in rising outlier val...
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The distribution of data has a significant impact on the results of *** the distribution of one class is insignificant compared to the distribution of another class,data imbalance *** will result in rising outlier values and ***,the speed and performance of classification could be greatly *** the above problems,this paper starts with the motivation and mathematical representing of classification,puts forward a new classification method based on the relationship between different classification *** with the vector characteristics of the actual problem and the choice of matrix characteristics,we firstly analyze the orderly regression to introduce slack variables to solve the constraint problem of the lone *** we introduce the fuzzy factors to solve the problem of the gap between the isolated points on the basis of the support vector *** introduce the cost control to solve the problem of sample ***,based on the bi-boundary support vector machine,a twostep weight setting twin classifier is *** can help to identify multitasks with feature-selected patterns without the need for additional optimizers,which solves the problem of large-scale classification that can’t deal effectively with the very low category distribution gap.
Light field (LF) camera sensors often face a trade-off between angular resolution and spatial resolution when shooting. High spatial resolution image arrays often result in lower angular resolution, and vice versa. In...
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Space-time video super-resolution (STVSR) is a comprehensive task comprising two subtasks: video super resolution in space dimension and video frame interpolation in time dimension. Conventional decoupled two-stage ap...
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ISBN:
(纸本)9798350359329;9798350359312
Space-time video super-resolution (STVSR) is a comprehensive task comprising two subtasks: video super resolution in space dimension and video frame interpolation in time dimension. Conventional decoupled two-stage approaches tend to overlook the intrinsic correlation between the two tasks. Overcoming this challenge requires the development of a unified model capable of simultaneously implementing space-time superresolution across arbitrary scales. Most existing models are confined to training on fixed space upsampling scales or specific frame-rate videos, resulting in limited generalization capabilities for flexible space-time super-resolution scenarios. In response to this limitation, our approach draws inspiration from continuous implicit neural representation. We propose an enhanced Implicit Neural Alignment Network (INAN) based on the VideoINR framework, encompassing feature refinement, precise motion flow estimation, and multi-scale feature fusion to optimize the final implicit neural decoding. Our extensive experimental evaluations on multiple benchmarks underscore the efficacy of the INAN model, indicate its superior performance compared to prior STVSR methods.
Pseudo-Boolean (PB) constraints are highly expressive, and many combinatorial optimization problems can be modeled using pseudo-Boolean optimization (PBO). It is recognized that stochastic local search (SLS) is a powe...
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