Deep hashing retrieval has gained widespread use in big data retrieval due to its robust feature extraction and efficient hashing process. However, training advanced deep hashing models has become more expensive due t...
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Effective congestion control algorithms (CCAs) are crucial for the smooth operation of Internet communication infrastructure. CCAs adjust transmission rates based on congestion signals, optimizing resource utilization...
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ISBN:
(数字)9798350368741
ISBN:
(纸本)9798350368758
Effective congestion control algorithms (CCAs) are crucial for the smooth operation of Internet communication infrastructure. CCAs adjust transmission rates based on congestion signals, optimizing resource utilization and user experience. However, existing studies, both rule-based and learning-based CCAs, often struggle with generalization and underperform when deployed in real-world environments. When applied to unseen network conditions, hand-crafted schemes or pre-trained models may experience significant performance degradation. To address this challenge, we propose MetaCon, a novel adaptive Internet congestion control approach based on meta-reinforcement learning. MetaCon leverages knowledge learned from prior scenarios to quickly adapt to new environments. Experimental results show that MetaCon outperforms existing algorithms by exhibiting superior generalization and achieving better transmission performance across a wide variety of network conditions.
The given manuscript delves into the realm of materials science and engineering, specifically focusing on the utilization of different derivatives of graphene reinforced in matrix material (DGMM’s) to enhance mechani...
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This paper uses semantic web and ontology techniques to predict the risk analysis of patients with diabetes mellitus. The data is collected from patients through personal interaction and by accessing their previous me...
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ISBN:
(数字)9798331523923
ISBN:
(纸本)9798331523930
This paper uses semantic web and ontology techniques to predict the risk analysis of patients with diabetes mellitus. The data is collected from patients through personal interaction and by accessing their previous medical history. However, during personal interactions, medical staff often fail to gather all the necessary details for an accurate diagnosis, and patients may lose their previous medical records, which hinders doctors' ability to gain a full understanding of the problem. To address this issue, ontology techniques are employed, and additional details are collected from patients, such as their food habits, smoking history, alcohol consumption, sexual activity, and cardiovascular health. Using the collected data, ontology and semantic web technologies are applied to predict the risk analysis of diabetic patients. The results are visualized in a graph, providing a more comprehensive and accurate assessment of the patients' health risks.
Biofuel from microalgae is a promising alternative that can substitute fossil fuels because they are renewable and eco-friendly. The growth of microalgae is influenced by numerous culture parameters, and temperature i...
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Accurate segmentation of breast tumor boundaries is essential for effective breast cancer diagnosis. Many convolutional and transformer-based models have been proposed for the semantic segmentation of Breast UltraSoun...
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ISBN:
(数字)9798331520526
ISBN:
(纸本)9798331520533
Accurate segmentation of breast tumor boundaries is essential for effective breast cancer diagnosis. Many convolutional and transformer-based models have been proposed for the semantic segmentation of Breast UltraSound (BUS) images. However, transformer-based segmentation models are challenging to train on small medical datasets, and breast anatomical information is rarely incorporated into these models to enhance their performance. In this study, we propose AnatoSegNet, a novel hybrid network that integrates a CNN-based U-shaped architecture with a novel breast Anatomical Attention Module for BUS image segmentation. The proposed attention module introduces a novel differential transformer and a bias matrix that emphasizes the layer structure of BUS images while capturing long-range dependencies, thereby improving the network's feature extraction capabilities. The proposed model is evaluated on two public BUS image datasets and achieves superior tumor IoU and F1 scores compared to state-of-the-art methods. The code is available at https://***/kuanhuang0624/AnatoSegNet.
Accurately synthesizing talking face videos and capturing fine facial features for individuals with long hair presents a significant challenge. To tackle these challenges in existing methods, we propose a decomposed p...
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ISBN:
(数字)9798350368741
ISBN:
(纸本)9798350368758
Accurately synthesizing talking face videos and capturing fine facial features for individuals with long hair presents a significant challenge. To tackle these challenges in existing methods, we propose a decomposed per-embedding Gaussian fields (DEGSTalk), a 3D Gaussian Splatting (3DGS)-based talking face synthesis method for generating realistic talking faces with long hairs. Our DEGSTalk employs Deformable Pre-Embedding Gaussian Fields, which dynamically adjust pre-embedding Gaussian primitives using implicit expression coefficients. This enables precise capture of dynamic facial regions and subtle expressions. Additionally, we propose a Dynamic Hair-Preserving Portrait Rendering technique to enhance the realism of long hair motions in the synthesized videos. Results show that DEGSTalk achieves improved realism and synthesis quality compared to existing approaches, particularly in handling complex facial dynamics and hair preservation. Our code is available at https://***/CVI-SZU/DEGSTalk.
Decentralized governance is going through radical changes due to the innovative use of blockchain which allows decisions to be made in a transparent, secure and more im- portantly, immutable way, without the authority...
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Communication barriers between deaf and hearing individuals can be mitigated through advancements in sign language recognition (SLR) systems. These SLR systems can also improve the user experience of deaf people when ...
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The global real estate market is a significant asset class, which was valued at over $6.27 billion dollars in 2020. It is anticipated to grow at a compound annual growth rate (CAGR) of 6.4% between 2021 and 2028. Due ...
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