Predicting the next Point-of-Interest (POI) is crucial for location-based services. In this paper, we propose the Time-enhanced Sequence Prediction Model (TSPM) to improve the accuracy of next POI recommendations by i...
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This paper explores the implementation of batch sampling techniques in semi-supervised federated learning (SSFL), particularly focusing on environments where labeled data is scarce and primarily resides on the server ...
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The semiconductor foundry industry faces challenges in managing diverse customer demands and complex manufacturing processes. Variations in the chemical vapor deposition (CVD) process affect transistor parameters and ...
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ISBN:
(纸本)9798350362206;9798350362190
The semiconductor foundry industry faces challenges in managing diverse customer demands and complex manufacturing processes. Variations in the chemical vapor deposition (CVD) process affect transistor parameters and yield. Siemens' Calibre (R) software with artificial intelligence / machine learning ( AI/ML) techniques create a virtual metrology (VM) model that outperforms traditional methods. An advanced process control (APC) system, incorporating design features and real-time data, improves process capability and reduces film thickness variations in high-mix product foundry fabs, as confirmed by control simulations.
Weakly supervised semantic segmentation (WSSS) using only image-level labels has gradually become an emerging research hotspot in the field of computer vision in recent years due to its low annotation cost. Existing m...
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Weakly supervised semantic segmentation (WSSS) using only image-level labels has gradually become an emerging research hotspot in the field of computer vision in recent years due to its low annotation cost. Existing methods rely on Class Activation Maps (CAMs) from specific classification models to locate target regions. However, the classifiers tend to focus on the most discriminative regions of the input image and assign higher weights to these areas, leading to the problem of incomplete CAM target regions. To address this issue, we design a Siamese feature aggregation network, named SFA-Net, which introduces contextual information to activate more complete target regions while suppressing the similarly adjacent background regions. Specifically, the context-aware module of SFA-Net consists of two key components: a multi-scale adaptive aggregation sub-module and a contextual linkage sub-module. These components work synergistically to uncover potential target features and identify global target areas. Additionally, we design a background activation suppression loss to minimize false activations in the background regions by measuring the similarity between the target object and background regions at the boundary. Extensive experiments on the challenging PASCAL VOC 2012 and COCO 2014 datasets show that our SFA-Net outperforms other state-of-the-art methods.
A novel technique in early Parkinson's disease diagnosis with the use of a quantum long short-term memory model is described in this paper. Quantum computing, therefore, coupled with deep learning, seeks to improv...
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The menu interaction methods in VR, such as floating menus, are still considered unnatural. A solution is proposed in this paper where menus are tightly attached to the user's palm. Firstly, use UV mapping technol...
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The main challenge for billiard players, regardless of their skill level, is to develop a strategy that allows them to consecutively pocket balls in a single turn, commonly known as 'closing the table.' This s...
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Underwater acoustic data classification has received significant attention from the research community in recent years because of its potential applications in underwater object detection and classification. Underwate...
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Amidst the swift expansion of mobile Internet, the issue of personal information exploitation within mobile applications, particularly those related to finance, has emerged as a grave concern. Presently, the inspectio...
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