Traditional autonomous navigation methods for mobile robots mainly rely on geometric feature-based LiDAR scan-matching algorithms, but in complex environments, this method is often affected due to the presence of movi...
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Saliency maps play a major role in understanding the decision-making process of 3D models by illustrating the importance of individual points from the input to model predictions. However, saliency maps typically suffe...
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The rapid growth of machine learning(ML)across fields has intensified the challenge of selecting the right algorithm for specific tasks,known as the Algorithm Selection Problem(ASP).Traditional trial-and-error methods...
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The rapid growth of machine learning(ML)across fields has intensified the challenge of selecting the right algorithm for specific tasks,known as the Algorithm Selection Problem(ASP).Traditional trial-and-error methods have become impractical due to their resource *** Machine Learning(AutoML)systems automate this process,but often neglect the group structures and sparsity in meta-features,leading to inefficiencies in algorithm recommendations for classification *** paper proposes a meta-learning approach using Multivariate Sparse Group Lasso(MSGL)to address these *** method models both within-group and across-group sparsity among meta-features to manage high-dimensional data and reduce multicollinearity across eight meta-feature *** Fast Iterative Shrinkage-Thresholding Algorithm(FISTA)with adaptive restart efficiently solves the non-smooth optimization *** validation on 145 classification datasets with 17 classification algorithms shows that our meta-learning method outperforms four state-of-the-art approaches,achieving 77.18%classification accuracy,86.07%recommendation accuracy and 88.83%normalized discounted cumulative gain.
3D object tracking has become a popular research topic because of its broad application prospects. However, it remains a challenging task to advance the trustworthiness of deep trackers, caused by the complex network ...
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Combat effectiveness of unmanned aerial vehicle(UAV)formations can be severely affected by the mission execution *** the practical execution phase,there are inevitable risks where UAVs being destroyed or targets faile...
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Combat effectiveness of unmanned aerial vehicle(UAV)formations can be severely affected by the mission execution *** the practical execution phase,there are inevitable risks where UAVs being destroyed or targets failed to be *** improve the mission reliability,a resilient mission planning framework integrates task pre-and re-assignment modules is developed in this *** the task pre-assignment phase,to guarantee the mission reliability,probability constraints regarding the minimum mission success rate are imposed to establish a multi-objective optimization *** an improved genetic algorithm with the multi-population mechanism and specifically designed evolutionary operators is used for efficient *** in the task-reassignment phase,possible trigger events are first analyzed.A real-time contract net protocol-based algorithm is then proposed to address the corresponding emergency *** the dual objective used in the former phase is adapted into a single objective to keep a consistent combat *** cases of different scales demonstrate that the two modules cooperate well with each *** the one hand,the pre-assignment module can generate high-reliability mission schedules as an elaborate mathematical model is *** the other hand,the re-assignment module can efficiently respond to various emergencies and adjust the original schedule within a *** corresponding animation is accessible at ***/video/BV12t421w7EE for better illustration.
In the era of rapid development of artificial intelligence technologies, traditional teaching models are unable to meet the employment needs of enterprises, and talent cultivation in universities faces more challenges...
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The effectiveness of hybrid deep learning models in detecting network intrusions on imbalanced datasets was tested. Conventional IDS often misses rare attacks due to class imbalance. Three models were evaluated: CNN+D...
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As one of the most popular blockchain platforms supporting smart contracts, Ethereum has caught the interest of both investors and criminals. Differently from traditional financial scenarios, executing Know Your Custo...
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In edge computing, the Zero-Trust Security Model (ZTSM), as a key enabling technology for next-generation networks, plays a crucial role in providing authentication for addressing data sharing concerns, such as freque...
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Testing is an inevitable part of any softwareengineering process to ensure quality and reliability. Model-based testing is a successful approach for the automated generation of test cases but requires a model of the ...
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