检索结果-内蒙古大学图书馆

INFORMATION GAIN PROPAGATION: A NEW WAY TO GRAPH ACTIVE LEARNING WITH SOFT labELS

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Zhang, Wentao Wang, Yexin You, Zhenbang Cao, Meng Huang, Ping Shan, Jiulong Yang, Zhi Cui, Bin School of CS Peking University China Apple United States National Engineering Laboratory for Big Data Analysis and Applications Institute of Computational Social Science Peking University Qingdao China

Graph Neural Networks (GNNs) have achieved great success in various tasks, but their performance highly relies on a large number of labeled nodes, which typically requires considerable human effort. GNN-based Active Learning (AL) methods are proposed to improve the labeling efficiency by selecting the most valuable nodes to label. Existing methods assume an oracle can correctly categorize all the selected nodes and thus just focus on the node selection. However, such an exact labeling task is costly, especially when the categorization is out of the domain of individual expert (oracle). The paper goes further, presenting a soft-label approach to AL on GNNs. Our key innovations are: i) relaxed queries where a domain expert (oracle) only judges the correctness of the predicted labels (a binary question) rather than identifying the exact class (a multi-class question), and ii) new criteria of maximizing information gain propagation for active learner with relaxed queries and soft labels. Empirical studies on public datasets demonstrate that our method significantly outperforms the state-of-the-art GNN-based AL methods in terms of both accuracy and labeling cost. Copyright © 2022, The Authors. All rights reserved.

关键词： Graph neural networks

Explore user neighborhood for real-time e-commerce recommendation

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Xie, Xu Sun, Fei Yang, Xiaoyong Yang, Zhao Gao, Jinyang Ou, Wenwu Cui, Bin Peking University China Center for Data Science Peking University National Engineering Laboratory for Big Data Analysis and Applications China Institute of Computational Social Science Peiking University Qingdao China Alibaba Group China

Recommender systems play a vital role in modern online services, such as Amazon and Taobao. Traditional personalized methods, which focus on user-item (UI) relations, have been widely applied in industrial settings, owing to their efficiency and effectiveness. Despite their success, we argue that these approaches ignore local information hidden in similar users. To tackle this problem, user-based methods exploit similar user relations to make recommendations in a local perspective. Nevertheless, traditional user-based methods, like userKNN and matrix factorization, are intractable to be deployed in the real-time applications since such transductive models have to be recomputed or retrained with any new interaction. To overcome this challenge, we propose a framework called self-complementary collaborative filtering (SCCF) which can make recommendations with both global and local information in real time. On the one hand, it utilizes UI relations and user neighborhood to capture both global and local information. On the other hand, it can identify similar users for each user in real time by inferring user representations on the fly with an inductive model. The proposed framework can be seamlessly incorporated into existing inductive UI approach and benefit from user neighborhood with little additional computation. It is also the first attempt to apply user-based methods in real-time settings. The effectiveness and efficiency of SCCF are demonstrated through extensive offline experiments on four public datasets, as well as a large scale online A/B test in Taobao. © 2021, CC BY-SA.

关键词： Collaborative filtering

Efficient Diversity-Driven Ensemble for Deep Neural Networks

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Zhang, Wentao Jiang, Jiawei Shao, Yingxia Cui, Bin Center for Data Science Peking University National Engineering Laboratory for Big Data Analysis and Applications China Peking University China ETH Zurich Switzerland School of Computer Science Beijing University of Posts and Telecommunications China

The ensemble of deep neural networks has been shown, both theoretically and empirically, to improve generalization accuracy on the unseen test set. However, the high training cost hinders its efficiency since we need a sufficient number of base models and each one in the ensemble has to be separately trained. Lots of methods are proposed to tackle this problem, and most of them are based on the feature that a pre-trained network can transfer its knowledge to the next base model and then accelerate the training process. However, these methods suffer a severe problem that all of them transfer knowledge without selection and thus lead to low diversity. As the effect of ensemble learning is more pronounced if ensemble members are accurate and diverse, we propose a method named Efficient Diversity-Driven Ensemble (EDDE) to address both the diversity and the efficiency of an ensemble. To accelerate the training process, we propose a novel knowledge transfer method which can selectively transfer the previous generic knowledge. To enhance diversity, we first propose a new diversity measure, then use it to define a diversity-driven loss function for optimization. At last, we adopt a Boosting-based framework to combine the above operations, such a method can also further improve diversity. We evaluate EDDE on Computer Vision (CV) and Natural Language Processing (NLP) tasks. Compared with other well-known ensemble methods, EDDE can get highest ensemble accuracy with the lowest training cost, which means it is efficient in the ensemble of neural networks. Copyright © 2021, The Authors. All rights reserved.

关键词： Deep neural networks

Design for intelligent control system of curtain based on arduino 1st

学校读者我要写书评

暂无评论

Design for intelligent control system of curtain based on ar...

1st International Conference on Smart Vehicular Technology, Transportation, Communication and applications, VTCA 2017

作者： Meng, Sheng-Hui Huang, An-Chi Lee, Chia-Jung Huang, Tian-Jiun Dal, Jian-Nan Fujian Provincial Key Lab of Big Data Mining and Applications Fujian University of Technology Fuzhou350118 China School of Information Science and Engineering Fujian University of Technology FuzhouFujian350118 China Department of Electrical Engineering National Sun Yat-Sen University Kaohsiung Taiwan Kaohsiung Municipal ChungShan Senior High School Kaohsiung Taiwan

In this research, a design for smart home curtains was proposed. The design is based on an Arduino UNO R3 development board equipped with an ESP8266-01 Wi-Fi module that communicates with an Android client application... 详细信息

ISBN: (纸本)9783319707297

关键词： HTTP

An efficient fifth-order interpolation-based Hermite WENO scheme for hyperbolic conservation laws

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Xie, Xiaoyang Tao, Zhanjing Jiao, Chunhai Zhang, Min College of Engineering Peking University Beijing100871 China School of Mathematics Jilin University Changchun130012 China National Engineering Laboratory for Big Data Analysis and Applications Peking University Beijing100871 China Chongqing Research Institute of Big Data Peking University Chongqing401121 China

In this paper, we develop a simple, efficient, and fifth-order finite difference interpolation-based Hermite WENO (HWENO-I) scheme for one- and two-dimensional hyperbolic conservation laws. We directly interpolate the solution and first-order derivative values and evaluate the numerical fluxes based on these interpolated values. We do not need the split of the flux functions when reconstructing numerical fluxes and there is no need for any additional HWENO interpolation for the modified derivative. The HWENO interpolation only needs to be applied one time which utilizes the same candidate stencils, Hermite interpolation polynomials, and linear/nonlinear weights for the solution and first-order derivative at the cell interface, as well as the modified derivative at the cell center. The HWENO-I scheme inherits the advantages of the finite difference flux-reconstruction-based HWENO-R scheme [Fan et al., Comput. Methods Appl. Mech. Engrg., 2023], including fifth-order accuracy, compact stencils, arbitrary positive linear weights, and high resolution. The HWENO-I scheme is simpler and more efficient than the HWENO-R scheme and the previous finite difference interpolation-based HWENO scheme [Liu and Qiu, J. Sci. Comput., 2016] which needs the split of flux functions for the stability and upwind performance for the high-order derivative terms. Various benchmark numerical examples are presented to demonstrate the accuracy, efficiency, high resolution, and robustness of the proposed HWENO-I scheme. Copyright © 2024, The Authors. All rights reserved.

关键词： Interpolation

Studying weariness prediction using SMOTE and random forests 3rd

学校读者我要写书评

暂无评论

Studying weariness prediction using SMOTE and random forests

3rd International Conference on Smart Computing and Communications, SmartCom 2018

作者： Weng, Yu Deng, Fengming Yang, Guosheng Chen, Liandong Yuan, Jie Gui, Xinkai Wang, Jue School of Information Engineering Minzu University of China Beijing100081 China National & Local Joint Engineering Lab for Big Data Analysis and Computing Technology Beijing100190 China State Grid Hebei Electric Power Company ShijiazhuangHebei050022 China Computer Network Information Center Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9783030057541

This article is aimed at the low accuracy of student weariness prediction in education and the poor prediction effect of traditional prediction models. It was established the SMOTE (Synthetic Minority Oversampling Technique) algorithm and random forest prediction models. This study puts forward to useing the SMOTE oversampling method to balance the data set and then use the random forest algorithm to train the classifier. By comparing the common single classifier with the ensemble learning classifier, it was found that the SMOTE and Random forest method performed more prominently, and the reasons for the increase in the AUC value after using the SMOTE method were analyzed. Using Massive Open Online Course (Mooc) synthesis student’s datasets, which mainly include the length of class, whether the mouse has moved, whether there is a job submitted, whether there is participating in discussions and completing the accuracy of the assignments. It is proved that this method can significantly improve the classification effect of classifiers, so teachers can choose appropriate teaching and teaching interventions to improve student’s learning outcomes. © 2018, Springer Nature Switzerland AG.

关键词： Students

Model Degradation Hinders Deep Graph Neural Networks

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Zhang, Wentao Sheng, Zeang Yin, Ziqi Jiang, Yuezihan Xia, Yikuan Gao, Jun Yang, Zhi Cui, Bin School of CS & Key Laboratory of High Confidence Software Technologies Peking University China Center for Data Science Peking University National Engineering Laboratory for Big Data Analysis and Applications China Beijing Institute of Technology China

Graph Neural Networks (GNNs) have achieved great success in various graph mining tasks. However, drastic performance degradation is always observed when a GNN is stacked with many layers. As a result, most GNNs only have shallow architectures, which limits their expressive power and exploitation of deep neighborhoods. Most recent studies attribute the performance degradation of deep GNNs to the over-smoothing issue. In this paper, we disentangle the conventional graph convolution operation into two independent operations: Propagation (P) and Transformation (T). Following this, the depth of a GNN can be split into the propagation depth (Dp ) and the transformation depth (Dt ). Through extensive experiments, we find that the major cause for the performance degradation of deep GNNs is the model degradation issue caused by large Dt rather than the over-smoothing issue mainly caused by large Dp. Further, we present Adaptive Initial Residual (AIR), a plug-and-play module compatible with all kinds of GNN architectures, to alleviate the model degradation issue and the over-smoothing issue simultaneously. Experimental results on six real-world datasets demonstrate that GNNs equipped with AIR outperform most GNNs with shallow architectures owing to the benefits of both large Dp and Dt, while the time costs associated with AIR can be ignored. Copyright © 2022, The Authors. All rights reserved.

关键词： Graph neural networks

Resilience-enhanced Power System Dispatch Amidst Extreme Blizzard Events

学校读者我要写书评

暂无评论

Resilience-enhanced Power System Dispatch Amidst Extreme Bli...

Power Science and Technology (ICPST), IEEE International Conference on

作者： Kuo Wang Jianxiao Wang Yiyang Song College of Engineering Peking University Beijing China National Engineering Laboratory for Big Data Analysis and Applications Peking University Beijing China Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong SAR China

ISBN: (数字)9798350349030

ISBN: (纸本)9798350349047

Enhancing the resilience of power systems against escalating threats from extreme blizzard events has become increasingly vital. Despite advances in research, there is a notable gap in modeling the mapping relationship between blizzard scenarios and the failure rates of power system components, alongside developing corresponding emergency dispatch strategies. To address this, our work presents a novel emergency dispatch strategy underpinned by a two-stage stochastic optimization model. This model integrates real-time weather forecasting and advanced stochastic methods to optimize the balance between electricity generation costs and the economic impacts of unserved energy during blizzard conditions, emphasizing the innovative mapping of blizzard scenarios to component failure rates. Employing the IEEE 30-bus test system demonstrates the strategy’s effectiveness in enhancing operational stability and reducing socioeconomic impacts, suggesting its potential for broad adoption in improving system resilience to extreme weather.

关键词： Fault tolerance Adaptation models Costs Stochastic processes Weather forecasting Power system stability Stability analysis

Design of Leakage Current Monitoring System for Low Voltage Power Grid Based on NB-IoT 6th

学校读者我要写书评

暂无评论

Design of Leakage Current Monitoring System for Low Voltage ...

6th International Conference on Advanced Machine Learning Technologies and applications, AMLTA 2021

作者： Xu, GuanWei Zheng, RongJin Chang, Kuo-Chi School of Information Science and Engineering Fujian University of Technology Fuzhou China Fujian Institute of Engineering Smart Grid Simulation Analysis and Integrated Control of Fujian University Engineering Research Center Fuzhou Fujian China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China College of Mechanical and Electrical Engineering National Taipei University of Technology Taipei Taiwan Department of Business Administration North Borneo University College Sabah Malaysia

ISBN: (纸本)9783030697167

Leakage current of low-voltage power grid lines will cause major safety hazards such as personal electric shock and electrical fire. Traditional leakage current protectors can not play their due protection role due to their quality problems and aging, and other reasons. When leakage current accidents occur, traditional manual troubleshooting is inefficient. Aiming at the safety of leakage current in a low-voltage power grid, a new leakage current monitoring system is designed based on NB-IoT technology. The system includes the detection and transmission of leakage current and the construction of the cloud platform, which can realize the real-time monitoring of leakage current in low-voltage power grid and prevent and solve the leakage current safety problems. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Leakage currents