User interactions with items are driven by diverse intentions, and effectively modeling these intentions can greatly enhance recommendation systems’ efficacy. In this paper, we propose a Time-aware Intent Contrastive...
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The fair k-center and k-center with outliers problems are two important variants of the k-center problem in computerscience, which have attracted lots of attention. The previous best results for the above two problem...
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Skin lesion segmentation is a challenging task in computer-aided diagnosis, which is crucial for the early diagnosis of skin cancer. Convolutional Neural Networks (CNNs) have been successful in medical image segmentat...
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As a branch of object detection task, the detection of queuing people in the aerial images emerges as a new research direction in recent years. However, it remains challenging due to the complex background, variations...
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In this paper, we investigate the Grundy Coloring problem for graphs with a cluster modulator, a structure commonly found in dense graphs. The Grundy chromatic number, representing the maximum number of colors needed ...
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In this paper, we propose and study the parity-constrained k-supplier (PAR k-supplier) problem, generalizing the classical (unconstrained) k-supplier problem. In the PAR k-supplier problem, we are given a set of facil...
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In order to enhance the efficiency and accuracy of robots in automated production lines and address issues such as inaccurate positioning and limited real-time capabilities in robot-controlled grasping, a deep learnin...
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ISBN:
(纸本)9783031500749;9783031500756
In order to enhance the efficiency and accuracy of robots in automated production lines and address issues such as inaccurate positioning and limited real-time capabilities in robot-controlled grasping, a deep learning-based lightweight algorithm for robot object grasping is proposed. This algorithm optimizes the lightweight network GG-CNN2 as the base model. Firstly, the depth of the backbone network is increased, and transpose convolutions are replaced with dilated convolutions to enhance the network's feature extraction for grasping detection. Secondly, the ASPP module is introduced to obtain a wider receptive field and multi-scale feature information. Furthermore, the shallowfeature maps aremerged with the deep feature maps to incorporate more semantic and detailed information from the images. Experimental results demonstrate that the algorithm achieves an accuracy of 81.27% on the Cornell dataset. Compared to the original GG-CNN2 network, the accuracy has improved by 11.68%, achieving a balance between speed and accuracy. Finally, grasping verification is conducted on the Panda robot arm, with an average success rate of 89.62%, which validates the superiority of the algorithm and showcases the theoretical and practical value of this research.
This paper presents a GPU-based parallel implementation of the Enhanced Jaya (EJAYA) algorithm for solving large-scale systems of nonlinear equations. EJAYA is a gradient-free metaheuristic optimization scheme based o...
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This paper explores an extended applications’ cost function to model the willingness of Edge data centres to accommodate additional users in decentralized edge computing environments. By enhancing the Marginal Comput...
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Explanatory systems ("explainers") make the behavior of blackbox machine learning models more transparent. However, the results of different explainers ("explanations") are often inconsistent with ...
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