检索结果-内蒙古大学图书馆

Multi-task regression learning for survival analysis via prior information guided transductive matrix completion

Frontiers of Computer Science 2020年第5期14卷 99-112页

作者： Lei Chen Kai Shao Xianzhong Long Lingsheng Wang Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and TelecommunicationsNanjing210023China MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing University of Aeronautics and AstronauticsNanjing211106China

Survival analysis aims to predict the occurrence time of a particular event of interest,which is crucial for the prognosis analysis of ***,due to the limited study period and potential losing tracks,the observed data inevitably involve some censored instances,and thus brings a unique challenge that distinguishes from the general regression *** addition,survival analysis also suffers from other inherent challenges such as the high-dimension and small-sample-size *** address these challenges,we propose a novel multi-task regression learning model,i.e.,prior information guided transductive matrix completion(PigTMC)model,to predict the survival status of the new ***,we use the multi-label transductive matrix completion framework to leverage the censored instances together with the uncensored instances as the training samples,and simultaneously employ the multi-task transductive feature selection scheme to alleviate the overfitting issue caused by high-dimension and small-sample-size *** addition,we employ the prior temporal stability of the survival statuses at adjacent time intervals to guide survival ***,we design an optimization algorithm with guaranteed convergence to solve the proposed PigTMC ***,the extensive experiments performed on the real microarray gene expression datasets demonstrate that our proposed model outperforms the previously widely used competing methods.

关键词： survival analysis matrix completion multi-task regression transductive learning multi-task feature selection

来源：评论

学校读者我要写书评

暂无评论

data-Driven Intersection Safety Evaluation Methodology and Analysis 23

Data-Driven Intersection Safety Evaluation Methodology and A...

引用

23rd COTA International Conference of Transportation Professionals: Emerging data-Driven Sustainable Technological Innovation in Transportation, CICTP 2023

作者： Wu, Pingping Lu, Guangquan Liu, Miaomiao Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control School of Transportation Science and Engineering Beihang Univ. Beijing China Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control School of Transportation Science and Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang Univ. Beijing China Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies Southeast Univ. Nanjing China

ISBN: (数字)9780784485040

ISBN: (纸本)9780784485040

With the development of information and sensing technologies, a wide variety of intersection traffic data is available. The purpose of this paper is to make full use of video and commercial vehicle trajectory data to deeply explore the intersection safety status. In this paper, based on different sample sizes, different sampling frequencies, and different accuracies of data, the safety analysis of intersections is carried out from micro and macro aspects based on post-encroachment time (PET) and vehicle deceleration behavior, respectively. On the one hand, we use the full-sample and high-precision trajectory data extracted from video to analyze the safety of each region of intersection based on PET. On the other hand, using the trajectory data with low penetration rate and low sampling frequency extracted from the positioning data of the online car-hailing, a distribution fit, and non-parametric test are conducted for the vehicle deceleration behavior, and then the overall safety state of the intersection is evaluated. The results can provide ideas for the identification of intersection risk points and the application of different kinds of data on intersection safety evaluation. © ASCE.

关键词： Commercial vehicles

来源：评论

学校读者我要写书评

暂无评论

First-Order Meta-Learning in Node Classification with Graph Convolutional Network 4

First-Order Meta-Learning in Node Classification with Graph ...

引用

4th International Conference on Artificial Intelligence and big data, ICAIBD 2021

作者： Cao, Jing Xu, Yi Song, Xuening School of Information and Software Engineering University of Electronic Science and Technology of China China Trusted Cloud Computing and Big Data Key Laboratory of Sichuan Province Chengdu China

ISBN: (纸本)9780738131702

In recent years, research on graph representation learning within graph neural networks has made great progress. Among them, graph convolutional neural networks (GCN), which is based on spectral domain, have drawn great attention and become one of the mainstream graph embedding approaches. However, few-shot learning on graphs has little related work, as we know. Toward this problem, we propose a meta-learning method on graphs, FOML-GCN (First order meta-learning on GCN), aimed to be applied in various kinds of models of graph convolutional networks. As many common approaches in meta-learning, FOML-GCN involves two phases, meta-training and meta-testing. By learning the potential Knowledge from a small number of samples in existing classes during meta-training, our method enhances the ability in node classification of the base model by training within new few data that only appear in the meta-testing step. The experiment on two benchmark datasets shows that our FOML-GCN performs better than base GCN and other models of graph network in few-shot node classification tasks. © 2021 IEEE.

关键词： Graph neural networks

来源：评论

学校读者我要写书评

暂无评论

WMAJL: Watcher-Mediated Attention Joint Learning Model for Multimodal Relation Extraction

WMAJL: Watcher-Mediated Attention Joint Learning Model for M...

引用

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

作者： Yunrui Dong Guiduo Duan Tianxi Huang Yunhao Li School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Laboratory of Intelligent Collaborative Computing University of Electronic Science and Technology of China Chengdu China Trusted Cloud Computing and Big Data Key Laboratory of Sichuan Province Chengdu China Chengdu Textile College College of Humanities and General Education Chengdu China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In the domain of Multimodal Relation Extraction (MRE), we present the $\color{Red}{\text{W}}$atcher-$\color{Red}{\text{M}}$ediated $\color{Red}{\text{A}}$ttention $\color{Red}{\text{J}}$oint $\color{Red}{\text{L}}$earning Model ($\color{Red}{\text{WMAJL}}$), a novel approach addressing the challenges of modality alignment noise, cross-modal fusion disparity, preservation of textual relative position information, and the distinctiveness of classification labels. WMAJL employs an integrative framework leveraging contrastive learning and variational autoencoder constraints to mitigate modality alignment noise by prioritizing relevant semantic data and effectively reducing extraneous noise that does not contribute to the task. The model’s innovative architecture includes a mediator watcher, which facilitates enhanced cross-modal fusion by enabling nuanced information exchange between textual and visual modalities while preserving the unique characteristics of each modality. Additionally, the design of auxiliary tasks, such as Named Entity Recognition (NER), and output supervision constructs loss functions that preserve relative position information, ensuring a precise depiction of entity relationships throughout the multilayer encoding processes. A key differentiator of WMAJL is its label-centric self-information loss technique, inspired by InfoNCE, which trains the model to cluster similar relation labels in semantically coherent areas, thereby optimizing classification label uniqueness by discerning subtle differences among relation types. The synergistic application of these strategies has led to a significant enhancement of WMAJL’s performance, as evidenced by its state-of-the-art F1 score of $\color{Red}{84.93\%}$ on the MNRE dataset. This achievement surpasses existing benchmarks and sets a new standard for multimodal knowledge extraction, underscoring WMAJL’s potential to revolutionize the MRE landscape.

关键词： Resistance Visualization Technological innovation Noise Autoencoders Semantics Contrastive learning Nonhomogeneous media Speech processing Standards

来源：评论

学校读者我要写书评

暂无评论

MFCM-DTI model of multimodal feature fusion: prediction of drug-target interaction

MFCM-DTI model of multimodal feature fusion: prediction of d...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Wenjun Li Wanjun Ma Mengyun Yang Xiwei Tang Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and Technology Changsha China School of Computer Science Hunan First Normal University Changsha China School of Intelligent Manufacturing Hunan First Normal University Changsha China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Drug repositioning is a vital area of biomedicine, where confirming interactions between drugs and specific targets is essential for establishing the efficacy of pharmaceutical agents. Traditional in vitro screening methods have limitations, prompting the use of computer simulations as an effective alternative for predicting drug-target interactions (DTI). This approach has gained significant attention in the scientific community. In this study, we introduce MFCM-DTI, a DTI prediction model that employs multimodal features to accurately capture the intricate interactions between drug molecular structures and key amino acids of target proteins. Our results demonstrate that MFCM-DTI outperforms existing models in prediction accuracy and robustness. Furthermore, MFCM-DTI has been successfully used to predict interactions between key SARS-CoV-2 proteins and existing drugs, providing a solid foundation for developing therapeutic agents against SARS-CoV-2 infection. This study underscores the broad applicability and strong predictive capabilities of MFCM-DTI in drug-protein interaction prediction, opening new avenues for research. The predicted drug targets and interaction data offer valuable insights for future experimental validation and clinical trials, potentially driving innovation in the biomedical field.

关键词： Drugs Proteins Technological innovation Accuracy Predictive models Feature extraction Solids Robustness Coronaviruses Diffusion tensor imaging

来源：评论

学校读者我要写书评

暂无评论

Hybrid Convolutional-Transformer Neural Network for Driver Distraction Detection

Hybrid Convolutional-Transformer Neural Network for Driver D...

引用

Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS), CAA Symposium on

作者： Penghua Li Qiyun Mou Jie Hou Yushan Tu Key Laboratory of Intelligent Computing for Big Data College of Automation Chongqing University of Posts and Telecommunications Chongqing China College of Automation Chongqing University of Posts and Telecommunications Chongqing China College of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China

Distracted driving causes a significant number of traffic accidents each year, making it a crucial area of research to monitor the driver’s state and prevent accidents. Although convolutional networks are widely used for detecting driver distraction, they face several issues, such as poor generalization ability, high model capacity, and response latency. To tackle these challenges, this paper introduces a nimble and low-latency algorithm named MViTCNet, which utilizes the MobileViT module for the identification of driver distraction while operating within constrained computational resources. The MViTCNet algorithm extracts local features and constructs an original feature map, which is fused with a global feature representation processed by a Transformer to capture finer details and obtain a larger receptive field. Additionally, the classic convolutional module MBConv is employed to reduce spatial redundancy, and downsampling is used to obtain low-resolution feature maps. By stacking multiple modules to learn more complex feature representations, the network structure increases in depth and width, resulting in a better fit for complex data distributions. Experiments on an embedded hardware platform show that MViTCNet achieves an acceptable trade-off, with an accuracy of 91.04% on the StateFarm dataset and 97.80% on the LDDB dataset at a latency of 28.9±5.3 milliseconds, outperforming existing methods.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Enhancing EEG-Based Cross-Subject Emotion Recognition via Adaptive Source Joint Domain Adaptation

引用

IEEE Transactions on Affective computing 2024年

作者： Liu, Ke Luo, Xin Zhu, Wenrui Yu, Zhuliang Yu, Hong Xiao, Bin Wu, Wei Chongqing University of Posts and Telecommunications School of Computer Science and Technology Chongqing400065 China Chongqing University of Posts and Telecommunications Key Laboratory of Big Data Intelligent Computing China South China University of Technology College of Automation Science and Engineering Guangzhou510641 China Shanghai Jiao Tong University School of Medicine Department of Neurology Songjjiang Hospital and Songjiang Research Institute Shanghai Key Laboratory of Emotions and Affective Disorders Shanghai201600 China

EEG emotion recognition is crucial in both human-machine interaction and healthcare. However, recognizing emotions across different subjects remains challenging due to individual variability. While existing multi-source domain adaptation methods have been utilized for cross-subject EEG emotion decoding, they often struggle with irrelevant or weakly relevant source domains, leading to negative transfer. Additionally, variations within subdomains are often neglected in these studies. We propose a joint domain adaptation method, Adaptive Source Joint Domain Adaptation (ASJDA) to address these issues. ASJDA utilizes an unsupervised adaptive source selection strategy to select a subset of source domains by evaluating the Jensen-Shannon divergence between the source and target domains, choosing those most relevant to the target. Subsequently, it implements joint domain adaptation with these chosen sources at both the domain and category subdomain levels. Our proposed method outperforms existing state-of-the-art methods, achieving cross-subject accuracies of 96.81% in SEED, 89.69% in SEED-IV, and 69.31% in DEAP. This work significantly advances the state of the art in EEG emotion recognition by effectively addressing the challenges of cross-subject variability. The source code for ASJDA is available at https://***/Pam098/ASJDA. © 2010-2012 IEEE.

关键词： adaptive source selection cross-subject domain adaptation EEG emotion recognition

来源：评论

学校读者我要写书评

暂无评论

Look Inside for More: Internal Spatial Modality Perception for 3D Anomaly Detection 39

Look Inside for More: Internal Spatial Modality Perception f...

引用

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Liang, Hanzhe Xie, Guoyang Hou, Chengbin Wang, Bingshu Gao, Can Wang, Jinbao College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen Audencia Financial Technology Institute Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Intelligent Manufacturing CATL Ningde China School of Computing and Artificial Intelligence Fuyao University of Science and Technology Fuzhou China School of Software Northwestern Polytechnical University Xi’an China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China

ISBN: (纸本)157735897X

3D anomaly detection has recently become a significant focus in computer vision. Several advanced methods have achieved satisfying anomaly detection performance. However, they typically concentrate on the external structure of 3D samples and struggle to leverage the internal information embedded within samples. Inspired by the basic intuition of why not look inside for more, we introduce a straightforward method named Internal Spatial Modality Perception (ISMP) to explore the feature representation from internal views fully. Specifically, our proposed ISMP consists of a critical perception module, Spatial Insight Engine (SIE), which abstracts complex internal information of point clouds into essential global features. Besides, to better align structural information with point data, we propose an enhanced key point feature extraction module for amplifying spatial structure feature representation. Simultaneously, a novel feature filtering module is incorporated to reduce noise and redundant features for further aligning precise spatial structure. Extensive experiments validate the effectiveness of our proposed method, achieving object-level and pixel-level AUROC improvements of 4.2% and 13.1%, respectively, on the Real3D-AD benchmarks. Note that the strong generalization ability of SIE has been theoretically proven and is verified in both classification and segmentation tasks. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Multi-feature Based Memory-Enhanced Knowledge Tracing 6th

Multi-feature Based Memory-Enhanced Knowledge Tracing

引用

6th International Conference on Computer Science and Educational Informatization, CSEI 2024

作者： Sun, Youwei Liu, Ruyi Miao, Qiguang Lu, Zixiang Zhao, Peipei Li, Ronghan School of Computer Science and Technology Xidian University Shaanxi Xi’an710071 China Xi’an Key Laboratory of Big Data and Intelligent Vision Shaanxi Xi’an710071 China Key Laboratory of Collaborative Intelligence Systems Ministry of Education Xidian University Xi’an710071 China

ISBN: (纸本)9789819637379

In the realm of intelligent education, knowledge tracing (KT) has emerged as a crucial tool for enhancing educational efficiency and enabling personalized learning. Traditional KT methods primarily rely on students’ historical response data to evaluate their mastery of specific skills and forecast future performance. However, focusing solely on basic interactions, skills and response outcomes, makes it challenging to capture the complexities of real learning environments and accurately reflect students’ genuine knowledge states. To address this issue, we propose an innovative multi-feature based memory-enhanced knowledge tracing (MMKT) method. The key innovation of the MMKT model lies in the introduction of ‘memory features’ and ‘interaction features’. Memory features account for features such as the interval between repeated responses, the total number of responses, and memory interference during the overall learning process. In contrast, interaction features include response time, number of attempts, and hints. To integrate these features, we employ a self-attention mechanism coupled with a memory enhancement module and a recurrent neural network. This approach effectively uncovers variations in knowledge states while balancing the influence of global and local interactions. Our experimental results from different public datasets emphasize the benefits of the MMKT model in predicting future performance, underscoring the significance of integrating multi-feature and real-world relevant memory improvements. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Students

来源：评论

学校读者我要写书评

暂无评论

Learning Counterfactual Explanation of Graph Neural Networks Via Generative Flow Network

SSRN

引用

SSRN 2023年

作者： He, Kangjia Liu, Li Zhang, Youmin Wang, Ye Liu, Qun Wang, Guoyin School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China Chongqing Key Laboratory of Computational Intelligence Chongqing China Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education Chongqing China

Counterfactual subgraphs explain Graph Neural Networks (GNNs) by answering the question: "How would the prediction change if a certain subgraph were absent in the input instance?" The differentiable proxy adjacency matrix is prevalent in current counterfactual subgraph discovery studies due to its ability to avoid exhaustive edge searching. However, a prediction gap exists when feeding the proxy matrix with continuous values and the thresholded discrete adjacency matrix to GNNs, compromising the optimization of the subgraph generator. Furthermore, the end-to-end learning schema adopted in the subgraph generator limits the diversity of counterfactual subgraphs. To this end, we propose CF-GFNExplainer, a flow-based approach for learning counterfactual subgraphs. CF-GFNExplainer employs a policy network with a discrete edge removal schema to construct counterfactual subgraph generation trajectories. Additionally, we introduce a reward and loss function designed to guide CF-GFNExplainer's optimization. The discrete adjacency matrix generated in each trajectory eliminates the prediction gap, enhancing the validity of the learned subgraphs. Furthermore, the multi-trajectories sampling strategy adopted in CF-GFNExplainer results in diverse counterfactual subgraphs. Extensive experiments conducted on synthetic and real-world datasets demonstrate the effectiveness of the proposed method in terms of validity and diversity. © 2023, The Authors. All rights reserved.

关键词： Graph neural networks

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：