Decentralized wireless traffic prediction is crucial for intelligent communication systems. But, in the conventional federated learning framework, ensuring the global model's performance depends on frequent local-...
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The privacy of Cryptocurrencies are of great concern in various fields. Researches has shown that pseudonyms, which are used in Bitcoin, only provide weak privacy. The privacy of users may be put at risk under deanony...
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Open-Domain Question Answering (ODQA) systems often struggle with the quality of retrieved passages, which may contain conflicting information and be misaligned with the reader's needs. Existing retrieval methods ...
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The goal of crowd-counting techniques is to estimate the number of people in an image or video in real-time and accurately. In recent years, with the development of deep learning, the accuracy of the crowd-counting ta...
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The goal of crowd-counting techniques is to estimate the number of people in an image or video in real-time and accurately. In recent years, with the development of deep learning, the accuracy of the crowd-counting task has improved. However, the accuracy of the crowd-counting task in crowded scenes with large-scale variations still needs improvement. To address this situation, this paper proposes a novel crowd-counting network: Context-Scaled Fusion Network (CSFNet). The details include: (1) the design of the Multi-Scale Receptive Field Fusion Module (MRFF Module), which employs multiple dilated convolutional layers with different dilation rates and uses a fusion mechanism to obtain multi-scale hybrid information to generate higher quality feature maps;(2) the proposal of the Contextual Space Attention Module (CSA Module), which can obtain pixel-level contextual information and combine it with the attention map to enable the model to autonomously learn and focus on important regions, thereby achieving a reduction in counting error. In this paper, the model is trained and evaluated on five datasets: ShanghaiTech, UCF_CC_50, WorldExpo'10, BEIJING-BRT, and Mall. The experimental results show that CSFNet outperforms many state-of-the-art (SOTA) methods on these datasets, demonstrating its superior counting ability and robustness.
In Agile software delivery, complexity factors have become an important part that has been linked to the success of software projects, especially with highly structured plan-driven approaches. Although there are a ran...
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We propose a physics-constrained generative adversarial network, PCSAGAN, based on the self-attention mechanism for high-fidelity flow field reconstruction, which can generate high-resolution and high-precision volume...
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We propose a physics-constrained generative adversarial network, PCSAGAN, based on the self-attention mechanism for high-fidelity flow field reconstruction, which can generate high-resolution and high-precision volumes for two-dimensional time-varying datasets. PCSAGAN consists of two discriminators and one generator, capable of preserving temporal coherence and spatial characteristics. It avoids the heavy computation of multilayer convolution and obtains spatial flow evolution information by using the self-attention mechanism. A soft-constraint loss function is further employed to effectively utilize the underlying physical properties. Moreover, the dataset is generated from real-world simulations rather than artificial synthesis, which ensures generalizability to practical applications. We compare PCSAGAN against volume upscaling methods using BI, ESPCN and SSR-TVD, and demonstrate its effectiveness with several time-varying flow fields through quantitative and qualitative evaluations. Meanwhile, a re-analysis framework is introduced to enable potential later visualization and exploratory analysis of the reconstructed flow field data.
Due to the significant increase in data transmission speed and gradual increase in Doppler frequency shift, channel estimation accuracy has become one of the most prioritized considerations in many cases. Specifically...
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This study aims to revolutionize software defect prediction by leveraging deep learning (DL) techniques, specifically focusing on Convolutional Neural Networks (CNN) and Stack Sparse Autoencoders (SSAE). The research ...
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Improving access to health information and territorial resources can be a way to help strengthen health-related quality of life perception in disadvantaged communities. We argue that, in countries facing diverse econo...
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The challenges in implementing SAE Level 4/5 automated vehicles are manifold, with intersection navigation being a pervasive one. We analyze a novel road topology invented by a co-author of this paper, Xiayong Hu. The...
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ISBN:
(纸本)9798350348811;9798350348828
The challenges in implementing SAE Level 4/5 automated vehicles are manifold, with intersection navigation being a pervasive one. We analyze a novel road topology invented by a co-author of this paper, Xiayong Hu. The topology eliminates the need for traditional traffic control and cross-traffic at intersections, potentially improving the safety of autonomous driving systems. The topology, herein called the Zonal Road Topology, consists of unidirectional loops of road with traffic flowing either clockwise or counter-clockwise. Adjacent loops are directionally aligned with one another, allowing vehicles to transfer from one loop to another through a simple lane change. To evaluate the Zonal Road Topology, a one km(2) pilot-track near Changshu, China is currently being set aside for testing. In parallel, traffic simulations are being performed. To this end, we conduct a simulation-based comparison between the Zonal Road Topology and a traditional road topology for a generic Electric Vehicle (EV) using the Simulation for Urban MObility (SUMO) platform and MATLAB/Simulink. We analyze the topologies in terms of their travel efficiency, safety, energy usage, and capacity. Drive time, number of halts, progress rate, and other metrics are analyzed across varied traffic levels to investigate the advantages and disadvantages of the Zonal Road Topology. Our results indicate that vehicles on the Zonal Road Topology have a lower, more consistent drive time, make more progress, and halt less frequently, while using less energy on average. The Zonal Road Topology also has the capacity to support a greater amount of vehicles. These results become more prominent at higher traffic densities.
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