Search Results - "graph neural network"

With the widespread application of the Go language, the demand for vulnerability detection in Go programs is increasing. Existing detection models and methods have deficiencies in extracting source code features of Go programs and mainly focus on detecting concurrency vulnerabilities. In response to...

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Online recruitment platforms are transforming talent acquisition paradigms, where a precise person-job fit plays a pivotal role in intelligent recruitment systems. However, current methodologies predominantly rely on coarse-grained semantic analysis, failing to address the textual structural depende...

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Transformer-based architectures have gained popularity across various domains, including graph representation learning. However, selecting an optimal transformer configuration remains challenging, as attention-based models are highly sensitive to parameter choices and input value ranges. Even state-...

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Knowledge tracing (KT) models learners’ evolving knowledge states to predict future performance, serving as a fundamental component in personalized education systems. However, existing methods suffer from data sparsity challenges, resulting in inadequate representation quality for low-frequency know...

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The numerical simulation of Homann flow, a classical problem in fluid dynamics involving axisymmetric flow over a solid plate, is essential for understanding various boundary layer phenomena. Due to the prominence of this flow and its computationally intensive nature when dealing with turbulent regi...

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Recently, person re-identification (re-ID) with weakly labeled or unlabeled data has drawn much attention in open-set and cross-domain re-ID systems especially for the attribute auxiliary weakly supervised person re-ID. Although state-of-the-art clustering-based methods have achieved good performanc...

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Reproductive toxicity is a concern critical to human health and chemical safety assessment. Recently, the U.S. Food and Drug Administration announced plans to assess toxicity with artificial intelligence-based computational models instead of animal studies in “a win-win for public health and ethics....

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Classical literary works have attracted extensive attention in modern society because of their unique cultural memory and aesthetic value. However, due to the long history and evolution of language, how to accurately grasp its connotation, especially its emotional color, has always been a dilemma fo...

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This paper proposes a deeply integrated model called CAS-GNN, aiming to solve the collaborative path-planning problem for multi-agent vehicles operating in dynamic environments. Our proposed model integrates CAS-UNet and Graph Neural Network (GNN), and, by introducing a dynamic edge enhancement modu...

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Virulence factors (VFs), produced by pathogens, facilitate pathogenic microorganisms to invade, colonize, and damage the host cells. Accurate VF identification advances pathogenic mechanism understanding and provides novel anti-virulence targets. Existing models primarily utilize protein sequence fe...

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Multi-robot systems are significant in decision-making capabilities and applications, but avoiding collisions during movement remains a critical challenge. Existing decentralized obstacle avoidance strategies, while low in computational cost, often fail to ensure safety effectively. To address this...

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Predicting global ionospheric total electron content (TEC) is critical for high-precision GNSS applications, but some existing models fail to jointly capture spatial heterogeneity and multiscale temporal trends. To address the problem, this work proposes a spatio-temporal graph neural network (STGNN...

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Drug combination therapies have shown promising therapeutic efficacy in complex diseases and demonstrated the potential to reduce drug resistance. However, the vast number of possible drug combinations makes it difficult to screen them all in traditional experiments. Although computational models ha...

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With the development of innovation and entrepreneurship education, the classification and integration of educational resources have become the key to improving education quality. However, traditional methods cannot deal with complex and multi-dimensional educational resource data. Therefore, this st...

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Dual polarimetric SAR is capable of reflecting the biophysical and geometrical information of terrain with open access data availability. When it is combined with time-series observations, it can effectively capture the dynamic evolution of scattering characteristics of crops in different growth cyc...

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Sparse borehole sampling at contaminated sites results in sparse and unevenly distributed data on soil pollutants. Traditional interpolation methods may obscure local variations in soil contamination when applied to such sparse data, thus reducing the interpolation accuracy. We propose an adaptive g...

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The recognition of point patterns in spatial vector data has important applications in geographic mapping and formation recognition. However, the application of traditional methods to spatial vector data faces two difficulties. Firstly, these data are low signal-to-noise ratio data in which the poin...

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Accurate traffic flow forecasting is essential for developing intelligent transportation systems (ITSs) to reduce congestion, optimize road management, and improve safety. While data-driven traffic prediction approaches have shown high accuracy, they rely heavily on precise measurements, making them...

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In the 3D target detection technology based on point cloud, there are problems like high cost of point cloud calculation and large gap between target scales, which lead to low target detection efficiency. In response, this paper proposes a 3D object detection algorithm based on fusion sampling and g...

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This work presents a novel Multiple Object Visual Tracking (MOT) approach integrating Conformalized Graph Neural Network (CF-GNN) with Narwhal Optimizer (NO) for enhanced tracking accuracy and efficiency in IoT and Mobile Edge Computing (MEC) applications. Addressing challenges like noise, occlusion...

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