检索结果-内蒙古大学图书馆

9th International Conference on Information, Cybernetics, and Computational Social Systems, ICCSS 2023

作者： Cai, Hai-Chun Lin, Yue-Na Zhang, Chun-Yang School of Computer and Data Science Fuzhou University Fuzhou China

ISBN: (纸本)9798350342239

In recent years, graph neural network has become the main paradigm for solving graph analysis tasks, which can easily process high-dimensional data and has a powerful fitting capability. Recent works on graph neural networks have successfully transferred the convolution network in computer vision to graph. Graph convolution network (GCN) has become the classical network framework in graph neural networks due to its simple aggregation approach and favorable theoretical support. When the original graph data are constructed, an adjacency matrix is used to represent the topology, where 0 or 1 indicates whether there is a connection between nodes. Moreover, GCN aggregates node attributes only depends on adjacency matrix. Although it can learn a mapping function, its message propagation mechanism is fixed for a given adjacency matrix. However, for specific downstream tasks, we expect to propagate messages relevant to the downstream task, while a fixed aggregation mode cannot handle this. To this end, we propose a graph convolution neural network with a learnable message propagation mechanism. The original adjacency matrix is adjusted through a learnable weight, so that the message propagation mechanism better adapts to downstream tasks. Experimental results show that the proposed model achieves significant performance in node classification. © 2023 IEEE.

关键词： Graph neural networks

来源：评论

学校读者我要写书评

暂无评论

A survey on dynamic graph processing on GPUs: concepts, terminologies and systems

引用

Frontiers of computer science 2024年第4期18卷 1-23页

作者： Hongru GAO Xiaofei LIAO Zhiyuan SHAO Kexin LI Jiajie CHEN Hai JIN National Engineering Research Center for Big Data Technology and System/Services Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China Zhejiang Lab Hangzhou 311121China

Graphs that are used to model real-world entities with vertices and relationships among entities with edges,have proven to be a powerful tool for describing real-world problems in *** most real-world scenarios,entities and their relationships are subject to constant *** that record such changes are called dynamic *** recent years,the widespread application scenarios of dynamic graphs have stimulated extensive research on dynamic graph processing systems that continuously ingest graph updates and produce up-to-date graph analytics *** the scale of dynamic graphs becomes larger,higher performance requirements are demanded to dynamic graph processing *** the massive parallel processing power and high memory bandwidth,GPUs become mainstream vehicles to accelerate dynamic graph processing ***-based dynamic graph processing systems mainly address two challenges:maintaining the graph data when updates occur(i.e.,graph updating)and producing analytics results in time(i.e.,graph computing).In this paper,we survey GPU-based dynamic graph processing systems and review their methods on addressing both graph updating and graph *** comprehensively discuss existing dynamic graph processing systems on GPUs,we first introduce the terminologies of dynamic graph processing and then develop a taxonomy to describe the methods employed for graph updating and graph *** addition,we discuss the challenges and future research directions of dynamic graph processing on GPUs.

关键词： dynamic graphs graph processing graph algorithms GPUs

来源：评论

学校读者我要写书评

暂无评论

SHAPRFs: SHapley Additive eXplanation-Based Random Forests Algorithm for Classification Problems 4th

SHAPRFs: SHapley Additive eXplanation-Based Random Forests A...

引用

4th International Conference on Frontiers in Computing and Systems, COMSYS 2023

作者： Jain, Nishant Shukla, Shipra Department of Computer Science and Engineering School of Computer Science and Engineering Manipal University Jaipur Jaipur303007 India School of Information Security and Data Science Manipal University Jaipur Jaipur303007 India

ISBN: (纸本)9789819726103

"Tree-based ensemble algorithms" (TEAs) are extensively employed for classification and regression problems. However, existing TEAs lag behind the trade-off between TEA interpretability and achieving cutting-edge accuracy when applied to real-time applications. This paper proposes a new TEA for bridging the gap between real-time applications and the trade-off between TEA interpretability and cutting-edge accuracy. The proposed algorithm is based on the SHapley Additive exPlanation (SHAP) value, which is one of the most advanced cooperative game solutions for quantifying each feature’s contribution to the model prediction. As a result, we call our proposed algorithm the SHapley additive explanation-based Random Forest algorithm (SHAPRFs). The proposed SHAPRFs compute the dominion of feature alliances using the SHAP value to determine the significance of each feature in the dataset. Following suit, the SHAPRFs divide the features into two subsets based on the importance of each feature. Then it employs the Roulette wheel selection algorithm to generate decision trees (DTs) in the forest. Five benchmark datasets are used to test the SHAPRFs algorithm. The experimental results show that the proposed SHAPRFs outperform three well-known and benchmark classifiers by a significant margin, namely, Breiman random forest (BRF), deep forest, and support vector machines (SVM). © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Decision trees

来源：评论

学校读者我要写书评

暂无评论

UNSUPERVISED SAMPLE SELECTION FOR ACTIVE LEARNING WITH QUADRATIC programMING

Journal of Applied and Numerical Optimization

引用

Journal of Applied and Numerical Optimization 2024年第3期6卷 339-350页

作者： Wang, Yunbin Song, Na Wang, Shiping College of Computer and Data Science Fuzhou University Fuzhou350108 China School of Mechanical Electrical and Information Engineering Putian University Putian351100 China

Graph Neural Networks (GNNs), which gained popularity recently, is facing the problem of reducing the cost of acquiring large datasets. Although a portion of the work combining GNN with active learning has been moderately successful, there are still some shortcomings in this research area. Most of the studies using clustering methods can only obtain local optima, and some of them suffer from the problem of difficult convergence. Moreover, the result of clustering is often undesired if the amount of data in each class is not balanced. Some methods obtain higher performance by combining multiple metrics, but are limited by the adverse effects of under-training the initial network. For these reasons, we propose an active learning framework based on quadratic programming in this paper. This framework transforms the sample sampling process into an optimal solution problem, which can obtain the global optimal solution and avoid the problem of hard convergence. Experimental results on several datasets demonstrate that the proposed method outperforms other baselines. © 2024 Journal of Applied and Numerial Optimization.

关键词： Active learning

来源：评论

学校读者我要写书评

暂无评论

Blockchain-Empowered Asynchronous Federated Reinforcement Learning for IoT-Based Traffic Trajectory Prediction

引用

IEEE Internet of Things Journal 2025年第11期12卷 17095-17109页

作者： Wang, Bin Tian, Zhao Tang, Fengxiao Pan, Heng She, Wei Liu, Wei Zhengzhou University School of Computer and Artificial Intelligence Zhengzhou450002 China Zhengzhou University School of Cyber Science and Engineering Zhengzhou450002 China Central South University School of Computer Science and Engineering Changsha410083 China Zhongyuan University of Technology School of Cyber Space Security Zhengzhou450007 China Zhengzhou University School of Cyber Science and Engineering Zhengzhou Key Laboratory of Blockchain and Data Intelligence Zhengzhou450002 China

Vehicle trajectory prediction plays a crucial role in IoT-based intelligent transportation systems, which can effectively address key issues, such as driving safety and multivehicle collaboration. However, the sensitivity of trajectory data and the reluctance of data holders to share it constrain the prediction model’s ability to capture vehicle behavior patterns in different scenarios. To address the above problems, we propose a blockchain-enabled asynchronous federated proximal policy optimization framework (BE-AFPPO) for the trajectory prediction of self-driving vehicles. First, we propose a curiosity proximal policy optimization (C-PPO) algorithm. The method utilizes a driven exploration strategy to actively motivate the intelligent agent to explore the unknown state space. The avoidance policy model reaches a local optimum when processing trajectory data. In addition, we design historical gated recurrent unit (GRU) and future GRU as input layers. The target’s historical motion features and future trajectory features are extracted, respectively. Then, various data is received through asynchronous federated learning. This model can fully learn the vehicle’s behavior patterns in different scenarios, which improves prediction accuracy. Based on this, we develop a blockchain-based dynamic group practical Byzantine fault tolerance (DG-PBFT) consensus algorithm. This enhances the credibility and integrity of the data while enriching the sources of trajectory data. Finally, we perform the experiments on the publicly available dataset nuScenes. The results demonstrate that the proposed method improves the robustness and accuracy of trajectory prediction. © 2014 IEEE.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

A novel lightweight deep learning approach for simultaneousoptic cup and optic disc segmentation in glaucoma detection

引用

Mathematical Biosciences and Engineering 2024年第4期21卷 5092-5117页

作者： Song, Yantao Zhang, Wenjie Zhang, Yue Institute of Big Data Science and Industry Shanxi University Taiyuan030006 China School of Computer and Information Technology Shanxi University Taiyuan030006 China

Glaucoma is a chronic neurodegenerative disease that can result in irreversible vision loss if not treated in its early ***-to-disc ratio is a key criterion for glaucoma screening and diagnosis, and it isdetermined by dividing the area of the optic cup (OC) by that of the optic disc (OD) in fundus images. Consequently, the automatic and accurate segmentation oftheOC and OD is a pivotal step in glaucoma detection. In recent years, numerous methods have resulted ingreat success on this task. However, most existing methods either have unsatisfactory segmentation accuracy orhigh time costs. In this paper, we propose a lightweight deep-learning architecture for the simultaneous segmentation oftheOC and OD, where we haveadoptedfuzzy learning andamulti-layer perceptron to simplify the learning complexity and improve segmentation accuracy. Experimental results demonstrate the superiority of our proposed method as compared tomost state-of-the-art approaches in terms of both training time and segmentation accuracy. © 2024the Author(s).

关键词： Multilayer neural networks

来源：评论

学校读者我要写书评

暂无评论

DCTNets: Deep crowd transfer networks for an approximate crowd counting

引用

Cognitive Robotics 2022年 2卷 96-111页

作者： Ali, Arslan Ou, Weihua Kanwal, Saima School of Big Data and Computer Science Guizhou Normal University Guiyang China School of Computer Science Lanzhou University of Technology Lanzhou China

Due to the numerous real-world applications of the crowd counting job, it has become a popular research topic. Modern crowd counting systems have a sophisticated structure and employ a filter on a big image size, making them difficult to use. Because these technologies are computationally intensive and difficult to implement in small surveillance systems, they are not appropriate for use in small surveillance systems. They also function poorly in a variety of sizes and densities, as well. Transfer learning and deep convolutional neural network architecture are used to create a modest but efficient network, which we describe herein. We named the proposed crowd counting architecture deep crowd transfer network (DCTNets) since it incorporates both deep learning and transfer learning principles into a single system. Among DCTNets’ key components are a detection module that is based on mask R-CNNs and an estimate module that is based on deep convolutional neural networks. In the first step, we apply transfer learning to the Mask R-CNN model using the datasets ShanghaiTech, JHU-CROWD++, and UCF-QNRF. After that, we train and evaluate the complete architecture on these datasets using the transfer learning results. Input images are sent through a Mask R-CNN, which counts individuals and segments the counted region, then through an estimation network, which estimates the population size, and finally through a merge of the outputs from the two models. According to the findings of comparative tests, the proposed model outperforms existing state-of-the-art approaches on the ShanghaiTech, JHU-CROWD++, and UCF-QNRF datasets. © 2022

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Comparison of Spelling Error Correction Algorithms for the Indonesian Language 11

Comparison of Spelling Error Correction Algorithms for the I...

引用

11th International Conference on Information and Education Technology, ICIET 2023

作者： Yanfi, Yanfi Setiawan, Reina Soeparno, Haryono Budiharto, Widodo Bina Nusantara University BINUS Graduate Program Computer Science Jakarta Indonesia Bina Nusantara University BINUS Graduate Program Computer Science School of Computer Science Computer Science Department Jakarta Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta Indonesia

ISBN: (纸本)9781665465489

Edit distance as a string measurement metric is often used to help detect misspellings in languages. This paper aims to compare two string spelling error correction algorithms for the Indonesian language. The N-gram, Jaro-Winkler distance, and Levenshtein distance algorithms are used to determine whether they can accurately correct typological errors in the Indonesian language. Moreover, this study utilized KNIME tools to process the data from beginning to end. The data was retrieved from news in Indonesia. After the experiment on N from 1 to 12, the results obtained for the comparative analysis proved that Jaro-Winkler distance performed better than Levenshtein distance for comparing smaller strings like words and names. However, Levenshtein distance performs as well as Jaro-Winkler distance started from four strings. Finally, both Jaro-Winkler distance and Levenshtein distance algorithm got the best performance accuracy for eight strings with an accuracy of 99.52 percent. The result of this study is also presented that both algorithms can support word error correction for the Indonesian language. © 2023 IEEE.

关键词： Error correction

来源：评论

学校读者我要写书评

暂无评论

Adaptive Feature Selection on Graph 9

Adaptive Feature Selection on Graph

引用

9th International Conference on Information, Cybernetics, and Computational Social Systems, ICCSS 2023

作者： Lin, Yue-Na Zhang, Chun-Yang School of Computer and Data Science Fuzhou University Fuzhou China

ISBN: (纸本)9798350342239

Graphs have become a widely-used tool to model data with relationships in real life for a long time. To discover the important contents in the graph, many graph neural networks (GNNs) have been come up with. Nevertheless, these models tend to adopt ReLU as their activation function for its nonlinearity, effectiveness, and efficiency. Owing to its own deficiency, it would cause many generated feature elements to be zero, which would miss part significant features. To overcome this problem, an adaptive weight vector to tune the features was provided. By limiting the elements of the weight vector, it can be a better substitute for ReLU. Besides, such a weight vector adaptively measures the importance of each feature element to work as a feature selection operator. To show the function of the weight vector, we examine a GCN with the weight vecotor in node classification, and it exhibits overall improvements over three well-known citation networks. © 2023 IEEE.

关键词： Feature Selection

来源：评论

学校读者我要写书评

暂无评论

Inside and Inside: Efficient Anomaly Detection by Fully Capturing the Detailed Dynamics

Inside and Inside: Efficient Anomaly Detection by Fully Capt...

引用

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

作者： Tang, Ziyu Zhou, Xiren Chen, Ao Liu, Shikang Wei, Chuyang Chen, Huanhuan School of Computer Science and Technology University of Science and Technology of China Hefei China School of Artificial Intelligence and Data Science University of Science and Technology of China Hefei China

ISBN: (纸本)9798350368741

Anomaly detection in sequential signals is gaining prominence, especially with limited training data and timeliness requirements. Fully extracting the data-inside changing information, we propose a novel Wavelet-Enhanced Reservoir Computing framework (WE-Res). Our framework uses Discrete Wavelet Transform (DWT) to decompose signals for multi-level detail and trend extraction recursively. Each level is fitted using an Echo State Network (ESN) respectively, extracting its inside dynamic features into fitted models. Integrating these dynamic features creates a "multi-level dynamic feature" that enhances signal representation, aiding in distinguishing between normal and anomalous patterns. We further introduce a dual-objective optimization to refine ESNs' reservoirs, increasing the fitting accuracy and improving category discrimination among the captured features. Validation on real-world data confirms our method's effectiveness, especially in data-limited scenarios and less training time compared to recent baselines. © 2025 IEEE.

关键词： Anomaly detection Dynamic Feature Model-Based Methods Reservoir computing Time-series signal

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：