检索结果-内蒙古大学图书馆

18th International Conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

作者： Wang, Yong Xiong, Yi Yang, Tianyu Shen, Yiran College of Computer Science and Technology Harbin Engineering University Harbin China Modeling and Emulation in E-Government National Engineering Laboratory Harbin China School of Software Shandong University Jinan China

ISBN: (纸本)9783031714696

As the global population continues to age, there is a concurrent rise in the number of individuals experiencing cognitive impairment and dementia, underscoring the critical necessity to address their hospice needs and quality of life concerns. Numerous studies have underscored the positive impact of exposure to outdoor environments on their overall condition and well-being. However, cognitive challenges often hinder their capacity to perform basic tasks independently, necessitating frequent assistance from onsite professionals. Complicating matters further, the complexity of outdoor settings often results in difficulties in identifying and recognizing personnel. This paper introduces Profinder, a system designed to assist individuals with cognitive impairment in identifying occupational personnel at specific outdoor locations and accessing essential assistance. Utilizing advanced deep learning techniques, Profinder seamlessly integrates foreground and background information, incorporates attention mechanisms, and harnesses siamese networks for efficient information decoupling, thereby achieving occupational recognition on smart mobile devices with an accuracy rate exceeding 90%. Additionally, a meticulously curated dataset dedicated to occupation recognition was devel0oped to facilitate and validate this research endeavor. By providing a robust auxiliary tool for individuals with cognitive impairment, this research endeavors to enhance their autonomy and overall quality of life, while also laying a solid foundation for further exploration and innovation in this burgeoning field, both within academia and industry. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Neurodegenerative diseases

来源：评论

学校读者我要写书评

暂无评论

Dynamic Graph Convolutional Networks with Spatiotemporal Missing Pattern Awareness

Dynamic Graph Convolutional Networks with Spatiotemporal Mis...

引用

2025 Ieee International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Pang, Bingheng Liang, Zhuoxuan Li, Wei Zheng, Xiangping Abdein, Rokia College of Computer Science and Technology Harbin Engineering University Harbin China Modeling and Emulation in E-Government National Engineering Laboratory Harbin Engineering University Harbin China Qingdao Innovation and Development Base of Harbin Engineering University Qingdao China

ISBN: (纸本)9798350368741

Missing data is ubiquitous phenomenon in the time series community, significantly challenging forecasting due to incomplete ground truth and sparse data. Most previous Multivariate Time Series Forecasting with Missing Values (MTSFMV) approaches usually assume static missing patterns, neglecting the dynamic changes over time and space, leading to suboptimal forecasting results. To tackle these challenges, we propose novel STMPANets, which are capable of perceiving time-varying spatiotemporal missing patterns to refine the forecasting sequences. Specifically, we decompose the series into seasonal trend components, allowing STMPANets to highlight inherent sequence properties and adapt to missing patterns. We then propose a Multi-granularity Conditional Partial TCN (MGCPT) to regulate the imputation rate of missing values over time, modeling temporal correlation. Additionally, we design an Adaptive Dynamic GCN (ADGCN) to capture spatial dependencies by perceiving dynamic missing patterns. extensive experiments demonstrate that STMPANets outperform state-of-the-art models. © 2025 Ieee.

关键词： Dynamic graphs Missing patterns Multivariate time series forecasting Time series

来源：评论

学校读者我要写书评

暂无评论

Scmaskgan: masked multi-scale CNN and attention-enhanced GAN for scRNA-seq dropout imputation

引用

BMC bioinformatics 2025年第1期26卷 130页

作者： You Wu Li Xu Xiaohong Cong Hanxiao Li Yanli Li College of Computer Science and Technology Harbin Engineering University Harbin China. National Engineering Laboratory for Modeling and Emulation in E-Government Beijing China. College of Computer Science and Technology Harbin Engineering University Harbin China. xuli@***. National Engineering Laboratory for Modeling and Emulation in E-Government Beijing China. xuli@***. College of Information Technology University of New South Wales Sydney Australia.

Single-cell RNA sequencing (scRNA-seq) enables high-resolution analysis of cellular heterogeneity, but dropout events, where gene expression is undetected in individual cells, present a significant challenge. We propose scMASKGAN, which transforms matrix imputation into a pixel restoration task to improve the recovery of missing gene expression data. Specifically, we integrate masking, convolutional neural networks (CNNs), attention mechanisms, and residual networks (ResNets) to effectively address dropout events in scRNA-seq data. The masking mechanism ensures the preservation of complete cellular information, while convolution and attention mechanisms are employed to capture both global and local features. Residual networks augment feature representation and effectively mitigate the risk of model overfitting. Additionally, cell-type labels are incorporated as constraints to guide the methods in learning more accurate cellular features. Finally, multiple experiments were conducted to evaluate the methods' performance using seven different data types and scRNA-seq data from ten neuroblastoma samples. The results demonstrate that the data imputed by scMASKGAN not only perform excellently across various evaluation metrics but also significantly enhance the effectiveness of downstream analyses, enabling a more comprehensive exploration of underlying biological information.

关键词： Data imputation Deep learning Single-cell RNA sequencing

来源：评论

学校读者我要写书评

暂无评论

SSSLN:Multivariate Time Series Forecasting via Collaborative Dynamic Graph Learning

引用

Neural Networks 2025年 188卷 107485页

作者： Liang, Zhuoxuan Li, Wei Wang, Zhihong Zheng, Xiangping Pang, Bingheng College of Computer Science and Technology Harbin Engineering University Harbin150001 China Modeling and Emulation in E-Government National Engineering Laboratory Harbin Engineering University Harbin150001 China

Multivariate time series (MTS) forecasting has achieved notable progress through graph modeling. However, existing approaches often face two key challenges. First, traditional dynamic graph learning (DGL) methods typically maintain dynamic graphs directly on the original MTS data, ignoring the distinct temporal patterns (e.g., trend and seasonality) that arise at different scales. Second, existing DGL methods often fail to capture interdependencies between graph structures evolving at multiple temporal scales, which can limit their ability to model complex spatio-temporal dynamics. To address these limitations, we propose the Synergistic Spatial Semantic Learning Network (SSSLN), a novel DGL-based framework for MTS forecasting. Specifically, our method (1) decomposes MTS data into trend and seasonal components and constructs separate dynamic graphs for each component to capture their unique temporal patterns, and (2) introduces a synergistic spatial semantic learning paradigm that explicitly aligns the spatial semantics of these graphs to guide their evolution. This design enables our model to fully exploit multi-scale information and uncover spatio-temporal dependencies across different scales. extensive experiments on six real-world datasets across various domains demonstrate the effectiveness of our approach (e.g., the improvement in RSe score reaching as high as 5.913%). © 2025

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Dynamic Graph Convolutional Networks with Spatiotemporal Missing Pattern Awareness

Dynamic Graph Convolutional Networks with Spatiotemporal Mis...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Bingheng Pang Zhuoxuan Liang Wei Li Xiangping Zheng Rokia Abdein College of Computer Science and Technology Harbin Engineering University Harbin China Modeling and Emulation in E-Government National Engineering Laboratory Harbin Engineering University Harbin China Qingdao Innovation and Development Base of Harbin Engineering University Qingdao China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Missing data is ubiquitous phenomenon in the time series community, significantly challenging forecasting due to incomplete ground truth and sparse data. Most previous Multi-variate Time Series Forecasting with Missing Values (MTSFMV) approaches usually assume static missing patterns, neglecting the dynamic changes over time and space, leading to suboptimal forecasting results. To tackle these challenges, we propose novel STMPANets, which are capable of perceiving time-varying spatiotemporal missing patterns to refine the forecasting sequences. Specifically, we decompose the series into seasonal trend components, allowing STMPANets to highlight inherent sequence properties and adapt to missing patterns. We then propose a Multi-granularity Conditional Partial TCN (MGCPT) to regulate the imputation rate of missing values over time, modeling temporal correlation. Additionally, we design an Adaptive Dynamic GCN (ADGCN) to capture spatial dependencies by perceiving dynamic missing patterns. extensive experiments demonstrate that STMPANets outperform state-of-the-art models.

关键词： Adaptation models Correlation Graph convolutional networks Time series analysis Signal processing Market research Imputation Spatiotemporal phenomena Forecasting Speech processing

来源：评论

学校读者我要写书评

暂无评论

Self-Supervised Uncertainty-Guided Refinement for Robust Joint Optical Flow and Depth estimation

Self-Supervised Uncertainty-Guided Refinement for Robust Joi...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Rokia Abdein Wei Li Chenghao Li Xiangping Zheng Rahul Yadav College of Computer Science and Technology Harbin Engineering University Harbin China Modeling and Emulation in E-Government National Engineering Laboratory Harbin Engineering University Harbin China Qingdao Innovation and Development Base of Harbin Engineering University Qingdao China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Jointly estimating the optical flow and depth tasks in real-world scenes presents considerable hurdles due to some phenomena, such as occlusion, ambiguous textures, and illumination variation. The lack of guidance from the labeled data makes these challenges harder to overcome. This paper presents a novel approach to learning the regions with high uncertainties in a self-supervised manner. Our method allows the network to learn these regions by leveraging its predictions’ confidence. The uncertainties are then utilized in the estimation and refinement processes for optical flow and depth. We also introduce a novel uncertainty-guided smoothness regularization technique that leverages the uncertainty map to increase the robustness through concentrating the smoothness on regions with low confidence scores. Our results in the KITTI real-world dataset demonstrate the effectiveness of our approach for enhancing the predictions, particularly in the challenging scenes, showcasing the potential of our approach for real-world applications.

关键词： Uncertainty Smoothing methods Depth measurement estimation Lighting Signal processing Robustness Acoustics Speech processing Optical flow

来源：评论

学校读者我要写书评

暂无评论

A Real-Time, Robust, and Versatile Visual-SLAM Framework Based on Deep Learning Networks

引用

Ieee Transactions on Instrumentation and Measurement 2025年 74卷

作者： Zhang, Xiao Dong, Hongbin Zhang, Haoxin Zhu, Xiaozhou Li, Shuaixin Deng, Baosong Harbin Engineering University College of Computer Science and Technology National Engineering Laboratory for E-Government Modeling and Simulation Harbin150009 China Defense Innovation Institute Chinese Academy of Military Science Beijing100850 China Sun Yat-sen University College of Systems Science and Engineering Guangzhou510275 China Intelligent Game and Decision Laboratory Beijing100166 China

In this article, we investigate the paradigm of deep learning techniques to enhance the performance of visual-based simultaneous localization and mapping (vSLAM) systems, particularly in challenging environments. By leveraging deep feature extraction and matching methods, we propose a robust, versatile hybrid visual SLAM framework, Rover-SLAM, aimed at improving adaptability in adverse conditions, such as dynamic lighting conditions, areas with weak textures, and camera jitter. Building on excellent learning-based algorithms of recent years, we designed from scratch a novel system that uses the same feature extraction and matching approaches for all SLAM tasks. Our system supports multiple modes, including monocular, stereo, monocular-inertial, and stereo-inertial configurations, offering flexibility to address diverse real-world scenarios. Through comprehensive experiments conducted on publicly available datasets and self-collected data, we demonstrate the superior performance of our Rover-SLAM system compared to the state-of-the-art (SOTA) approaches. We also conducted an in-depth analysis of the integration of visual SLAM with deep learning methods quantitatively to provide insights for future research endeavors in this domain. The experimental results showcase the system's capability of achieving higher localization accuracy and robust tracking performance. © 1963-2012 Ieee.

关键词： Stereocenters

来源：评论

学校读者我要写书评

暂无评论

Research on formalization of efficient query application problems with compound condition in software development

引用

The Journal of China Universities of Posts and Telecommunications 2017年第2期24卷 18-30页

作者： Yang Yuchen Wang Xiaofang Yin Guisheng College of Computer Science and Technology Harbin Engineering University National Engineering Laboratory for E-Government Modeling and Emulation Harbin Engineering University

This paper provides a formalized definition of the application problem of compound condition query （CCQ） and a formal method of applying requirements elicitation based on trace information space derived from trace algebra. With the formalized process of solving the application problem of CCQ, formal requirements specification of application of CCQ is given, a formalized and automatic mapping of the results of requirements elicitation to the formal requirements specification is performed, the software system model and the application code are developed. Through a sample application of comprehensive query on housing information, the feasibility of formalized and automatic software development for the application problem of CCQ is proved. The result has important implications for the other problems regarding formalization and automatic software development.

关键词： automatic programming formal specification requirements-based programming compound condition query

来源：评论

学校读者我要写书评

暂无评论

Complex graph neural networks for multi-hop propagation

引用

Neurocomputing 2025年

作者： Dong Xu Ziye Liu Fengming Li Yulong Meng College of Computer Science Harbin Engineering University Harbin 150000 Heilongjiang China Modeling and Emulation in E-Government National Engineering Laboratory Harbin Engineering University Harbin 150000 Heilongjiang China Xiaoxiang Laboratory School of Electrical and Information Engineering Hunan University Changsha Hunan China

Graph neural networks have made significant advancements over the years, enabling them to tackle a wide range of real-world tasks. However, many existing models still face persistent challenges, such as limitations in representing directed edges and difficulties in aggregating information from distant nodes. Aggregating distant node information typically requires stacking multiple layers, which can lead to over-smoothing and reduce the network’s effectiveness. To address these challenges, we propose a complex Laplacian matrix that effectively captures directed edge information and design an approximation method utilizing the Implicitly Restarted Lanczos (IRL) algorithm. This approach provides a low-rank approximation of the complex Laplacian matrix, facilitating fast convolution on both low-order and high-order nodes. Building on this foundation, we introduce the Complex Aggregating Graph Network (CAGN), which can aggregate both low-order and high-order node information without the need for multiple stacked layers. extensive experiments on multiple real-world datasets demonstrate that CAGN achieves strong performance in node classification and link prediction tasks, with particularly notable results in link prediction, validating the model’s effectiveness. The code is available on https://***/LaZuiYa/CAGN .

关键词：

来源：评论

学校读者我要写书评

暂无评论

Reparameterized Importance Sampling for Robust Variational Bayesian Neural Networks 41

Reparameterized Importance Sampling for Robust Variational B...

引用

41st International Conference on Machine Learning, ICML 2024

作者： Long, Yunfei Tian, Zilin Zhang, Liguo Xu, Huosheng College of Computer Science and Technology Harbin Engineering University Heilongjiang Harbin China Modeling and Emulation in E-Government National Engineering Laboratory Harbin Engineering University Heilongjiang Harbin China

Mean-field variational inference (MFVI) methods provide computationally cheap approximations to the posterior of Bayesian Neural Networks (BNNs) when compared to alternatives like MCMC. However, applying MFVI to BNNs encounters limitations due to the Monte Carlo sampling problem. This problem stems from two main issues. First, most samples do not accurately represent the most probable weights. Second, random sampling from variational distributions introduces high variance in gradient estimates, which can hinder the optimization process, leading to slow convergence or even failure. In this paper, we introduce a novel sampling method called Reparameterized Importance Sampling (RIS) to estimate the first moment in neural networks, reducing variance during feed-forward propagation. We begin by analyzing the generalized form of the optimal proposal distribution and presenting an inexpensive approximation. Next, we describe the sampling process from the proposal distribution as a transformation that combines exogenous randomness with the variational parameters. Our experimental results demonstrate the effectiveness of the proposed RIS method in three critical aspects: improved convergence, enhanced predictive performance, and successful uncertainty estimation for out-of-distribution data. Copyright 2024 by the author(s)

关键词： Importance sampling

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：