Aiming at the problems of insufficient utilization of information about elite particles in archive and instability of particle motion in the population in the multi-objective artificial physics optimization algorithm ...
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ISBN:
(数字)9798350380286
ISBN:
(纸本)9798350380293
Aiming at the problems of insufficient utilization of information about elite particles in archive and instability of particle motion in the population in the multi-objective artificial physics optimization algorithm (MOAPO) in solving multiobjective optimization problems, A multi-objective artificial physics optimization algorithm based on two-phase search (TPMOAPO) is proposed. To begin with, the algorithm improves the calculation of the mass of particles, so that the strength and weakness of the particles can be accurately transformed into the corresponding masses while improving the efficiency of particle mass calculation. Next, a two-phase search strategy is proposed, which makes the algorithm have strong exploration ability in the first phase, and the second phase gradually enhances the exploitation capability with iterations, which solves the problem of instability motion of particles in the search process. Finally, the simulated binary crossover (SBX) and polynomial-based mutation (PM) operators are adopted in the archive to further enhance the search capability of the algorithm. For verifying the performance of TP-MOAPO, 21 benchmark functions were selected to compare with the classical multi-objective particle swarm optimization algorithms: MOPSO, dMOPSO, SMPSO, MMOPSO, and NMPSO, and the experimental results show the superiority of TP-MOAPO in these functions.
Split learning is a neural network training approach that can overcome the limitations of traditional deep neural networks in edge artificial intelligence environments. It offers the advantage of privacy protection be...
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Split learning is a neural network training approach that can overcome the limitations of traditional deep neural networks in edge artificial intelligence environments. It offers the advantage of privacy protection because it transmits intermediate features that are calculated via the client-side model and the client does not need to send the original input data to the server. However, concerns remain regarding data privacy leakage because an attacker can still attempt model inversion attacks based on the intermediate features. We introduce several shortcomings of existing defense techniques for such attacks and present a new defense approach called TrapMI. The proposed method can induce an attacker to generate a class-specific target image that appears different from the original image when inverting the input image. We analyze the performance through quantitative and qualitative evaluations. Furthermore, the AutoGenerator is proposed to overcome the problem whereby the client cannot perform modulation that requires the target image because the class of the input image is unknown during this phase. De-identified images are automatically modulated in the inference phase using this approach. The proposed method was evaluated on two datasets, three classification models, and three split points. Its resistance was measured using a deeper and stronger inverse model than those in previous studies. Overall, the proposed method ensures data privacy protection at a significantly higher level while maintaining a similar task performance to that of existing defense technologies.
History of code elements is essential for software maintenance tasks. However, code refactoring is one of the main causes that makes obtaining a consistent view on code evolution difficult as renaming or moving source...
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This research focuses on forecasting solar irradiance for the year 2025 by applying Artificial Neural Network (ANN) based on historical data collected over three years (2022-2024) for the humidity, earth temperature, ...
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With the rise of multi-modal large language models, accurately extracting and understanding textual information from video content-referred to as video-based optical character recognition (Video OCR)-has become a cruc...
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Previous studies on joint optimization of computation offloading and service caching policies in Mobile Edge Computing (MEC) have often neglected the impact of dependency-aware subtasks, edge server resource constrain...
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Previous studies on joint optimization of computation offloading and service caching policies in Mobile Edge Computing (MEC) have often neglected the impact of dependency-aware subtasks, edge server resource constraints, and multiple users on policy formulation. To remedy this deficiency, this paper proposes a many-objective joint optimization dependencyaware task offloading and service caching model (MaJDTOSC). MaJDTOSC considers the impact of dependencies between subtasks on the joint optimization problem of task offloading and service caching in multi-user, resource-constrained MEC scenarios, and takes the task completion time, energy consumption, subtask hit rate, load variability, and storage resource utilization as optimization objectives. Meanwhile, in order to better solve MaJDTOSC, a many-objective evolutionary algorithm TSMSNSGAIII based on a three-stage mating selection strategy is proposed. Simulation results show that TSMSNSGAIII exhibits an excellent and stable performance in solving MaJDTOSC with different number of users setting and can converge faster. Therefore, it is believed that TSMSNSGAIII can provide appropriate sub -task offloading and service caching strategies in multi-user and resource-constrained MEC scenarios, which can greatly improve the system offloading efficiency and enhance the user experience.
Many real-world data can be modeled as heterogeneous graphs that contain multiple types of nodes and edges. Meanwhile, due to excellent performance, heterogeneous graph neural networks (GNNs) have received more and mo...
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Background: Drug-target binding affinity (DTA) prediction can accelerate the drug screening process, and deep learning techniques have been used in all facets of drug research. Affinity prediction based on deep learni...
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Poisson's equation is one of the most popular partial differential equation (PDE), which is widely used in image processing, computer graphics and other fields. However, solving a large-scale Poisson's equatio...
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The Winograd Schema Challenge (WSC) is a popular benchmark for commonsense reasoning. Each WSC instance has a component that corresponds to the mention of the correct answer option of the two options in the context. W...
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