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Graph Attention Multi-Layer Perceptron

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arXiv 2022年

作者： Zhang, Wentao Yin, Ziqi Sheng, Zeang Li, Yang Ouyang, Wen Li, Xiaosen Tao, Yangyu 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 Tencent Inc China Beijing Institute of Technology China

Graph neural networks (GNNs) have achieved great success in many graph-based applications. However, the enormous size and high sparsity level of graphs hinder their applications under industrial scenarios. Although some scalable GNNs are proposed for large-scale graphs, they adopt a fixed k-hop neighborhood for each node, thus facing the over-smoothing issue when adopting large propagation depths for nodes within sparse regions. To tackle the above issue, we propose a new GNN architecture — Graph Attention Multi-Layer Perceptron (GAMLP), which can capture the underlying correlations between different scales of graph knowledge. We have deployed GAMLP in Tencent with the Angel platform 1, and we further evaluate GAMLP on both real-world datasets and large-scale industrial datasets. Extensive experiments on these 14 graph datasets demonstrate that GAMLP achieves state-of-the-art performance while enjoying high scalability and efficiency. Specifically, it outperforms GAT by 1.3% regarding predictive accuracy on our large-scale Tencent Video dataset while achieving up to 50× training speedup. Besides, it ranks top-1 on both the leader-boards of the largest homogeneous and heterogeneous graph (i.e., ogbn-papers100M and ogbn-mag) of Open Graph Benchmark 2 Copyright © 2022, The Authors. All rights reserved.

关键词： Graph neural networks

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

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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

Towards Dynamic and Safe Configuration Tuning for Cloud databases

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arXiv 2022年

作者： Zhang, Xinyi Wu, Hong Li, Yang Tan, Jian Li, Feifei Cui, Bin School of CS Key Laboratory of High Confidence Software Technologies Peking University China Database and Storage Laboratory Damo Academy Alibaba Group China Center for Data Science Peking University National Engineering Laboratory for Big Data Analysis and Applications China China

Configuration knobs of database systems are essential to achieve high throughput and low latency. Recently, automatic tuning systems using machine learning methods (ML) have shown to find better configurations compared to experienced database administrators (DBAs). However, there are still gaps to apply the existing systems in production environments, especially in the cloud. First, they conduct tuning for a given workload within a limited time window and ignore the dynamicity of workloads and data. Second, they rely on a copied instance and do not consider the availability of the database when sampling configurations, making the tuning expensive, delayed, and unsafe. To fill these gaps, we propose OnlineTune, which tunes the online databases safely in changing cloud environments. To accommodate the dynamicity, OnlineTune embeds the environmental factors as context feature and adopts contextual Bayesian Optimization with context space partition to optimize the database adaptively and scalably. To pursue safety during tuning, we leverage the black-box and the white-box knowledge to evaluate the safety of configurations and propose a safe exploration strategy via subspace adaptation. We conduct evaluations on dynamic workloads from benchmarks and real-world workloads. Compared with the state-of-the-art methods, OnlineTune achieves 14.4%∼165.3% improvement on cumulative performance while reducing 91.0%∼99.5% unsafe configuration recommendations. Copyright © 2022, The Authors. All rights reserved.

关键词： database systems

data Processing Technology for Network Abnormal Traffic Detection 4

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Data Processing Technology for Network Abnormal Traffic Dete...

4th International Conference on Machine Learning and big data Analytics for IoT Security and Privacy, SPIoT 2023

作者： Wang, Kun Fu, Yu Duan, Xueyuan Xu, Jianqiao Liu, Taotao Department of Information Security Naval University of Engineering Wuhan430033 China School of Mathematics and Information Engineering Xinyang Vocational and Technical College Xinyang464000 China College of Computer and Information Technology Xinyang Normal University Xinyang464000 China Henan Key Laboratory of Analysis and Applications of Education Big Data Xinyang Normal University Xinyang464000 China

At present, the threats to network security are also increasing, among which abnormal traffic detection is the key link to ensure network security. Traditional detection methods based on signature or threshold are often difficult to adapt to the increasingly complex network environment and new attack methods. Therefore, this paper optimizes and improves the data processing technology, proposes a network ATD method based on particle swarm optimization(PSO) algorithm, and explores in detail the traffic data collection and pre-processing, the feature recognition of abnormal traffic, the application of PSO algorithm, real-time monitoring and response mechanism. The results of two sets of simulation experiments are as follows: compared with the traditional model, the accuracy rate of ATD of the improved algorithm is increased by 7.2% on average, and the detection time is reduced by 7.35s on average. This method not only enhances the adaptability of the model to new attacks, but also improves the degree of automation of detection. © 2024 The Authors.

关键词： Particle swarm optimization (PSO)

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

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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

Unsupervised domain adaptation in semantic segmentation based on pixel alignment and self-training

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arXiv 2021年

作者： Dong, Hexin Yu, Fei Zhao, Jie Dong, Bin Zhang, Li Center for Data Science Peking University Beijing China Center for Data Science in Health and Medicine Peking University Beijing China National Engineering Laboratory for Big Data Analysis and Applications Peking University Beijing China Peking University Beijing China

This paper proposes an unsupervised cross-modality domain adaptation approach based on pixel alignment and self-training. Pixel alignment transfers ceT1 scans to hrT2 modality, helping to reduce domain shift in the training segmentation model. Self-training adapts the decision boundary of the segmentation network to fit the distribution of hrT2 scans. Experiment results show that PAST has outperformed the non-UDA baseline significantly, and it received rank-2 on CrossMoDA validation phase Leaderboard with a mean Dice score of 0.8395. Copyright © 2021, The Authors. All rights reserved.

关键词： Pixels

EarthquakeAware: Visual Analytics for Understanding Human Impacts of Earthquakes from Social Media data

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EarthquakeAware: Visual Analytics for Understanding Human Im...

IEEE Symposium on Visual Analytics Science and Technology (VAST)

作者： Shuai Chen Sihang Li Liwenhan Xie Yi Zhong Yun Han Xiaoru Yuan Key Laboratory of Machine Perception Ministry of Education School of EECS Peking University National Engineering Laboratory for Big Data Analysis and Application Peking University

In this paper, we propose EathquakeAware, a visual analytics system for social media visualization. Our system supports analyzing geo-tagged messages from different perspectives, including location, keyword, account. It can summarize the evolution patterns of topics and reveal the discussing trend of different keywords to help evaluate situations in different regions. We take VAST Challenge 2019 Mini Challenge 3 as case studies to demonstrate its effectiveness in a fictitious analysis scenario.

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Spatial Commonsense Reasoning for Machine Reading Comprehension 9th

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Spatial Commonsense Reasoning for Machine Reading Comprehen...

19th International Conference on Advanced data Mining and applications, ADMA 2023

作者： Lin, Miaopei Wang, Meng-xiang Yu, Jianxing Wang, Shiqi Lai, Hanjiang Liu, Wei Yin, Jian School of Computer Science and Engineering Sun Yat-sen University Guangzhou China School of Artificial Intelligence Sun Yat-sen University Zhuhai China China National Institute of Standardization Beijing100088 China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China

ISBN: (纸本)9783031466632

This paper studies the problem of spatial commonsense reasoning for the machine reading comprehension task. Spatial commonsense is the human-shared but latent knowledge of object shape, size, distance, and position. Reasoning this abstract knowledge can facilitate machines better perceive their surroundings, which is crucial for general intelligence. However, this valuable topic is challenging and has been less studied. To bridge this research gap, we focus on this topic and propose a new method to realize spatial reasoning. Given a text, we first build a potential reasoning graph based on its parsing tree. To better support spatial reasoning, we retrieve the related commonsense entities and relations from external knowledge sources, including the pre-trained language model (LM) and knowledge graph (KG). LM covers all kinds of factual knowledge and KG has abundant commonsense relations. We then propose a new fusion method called LEGRN (LM Edge-GNN Reasoner Networks) to fuse the text and graph. LEGRN adopts layer-based attention to integrate the LM text encoder and KG graph encoder, which can capture correlations between LM text context and KG graph structure. Considering that spatial relations involve a variety of attributes, we propose an attribute-aware inferential network to deduce the correct answers. To evaluate our approach, we construct a new large-scale dataset named CRCSpatial, consisting of 40k spatial reasoning questions. Experiment results illustrated the effectiveness of our approach. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Large dataset

C olumnSGD: A Column-oriented Framework for Distributed Stochastic Gradient Descent

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C olumnSGD: A Column-oriented Framework for Distributed Stoc...

International Conference on data engineering

作者： Zhipeng Zhang Wentao Wu Jiawei Jiang Lele Yu Bin Cui Ce Zhang School of EECS & Key Laboratory of High Confidence Software Technologies (MOE) Peking University Microsoft Research Redmond USA ETH Zurich Switzerland Tencent Inc Center for Data Science Peking University & National Engineering Laboratory for Big Data Analysis and Applications

ISBN: (数字)9781728129037

ISBN: (纸本)9781728129044

Distributed machine learning (ML) has triggered tremendous research interest in recent years. Stochastic gradient descent (SGD) is one of the most popular algorithms for training ML models, and has been implemented in almost all distributed ML systems, such as Spark MLlib, Petuum, MXNet, and TensorFlow. However, current implementations often incur huge communication and memory overheads when it comes to large models. One important reason for this inefficiency is the row-oriented scheme (RowSGD) that existing systems use to partition the training data, which forces them to adopt a centralized model management strategy that leads to vast amount of data exchange over the network. We propose a novel, column-oriented scheme (ColumnSGD) that partitions training data by columns rather than by rows. As a result, ML model can be partitioned by columns as well, leading to a distributed configuration where individual data and model partitions can be collocated on the same machine. Following this locality property, we develop a simple yet powerful computation framework that significantly reduces communication overheads and memory footprints compared to RowSGD, for large-scale ML models such as generalized linear models (GLMs) and factorization machines (FMs). We implement ColumnSGD on top of Apache Spark, and study its performance both analytically and experimentally. Experimental results on both public and real-world datasets show that ColumnSGD is up to 930× faster than MLlib, 63× faster than Petuum, and 14× faster than MXNet.

关键词： Conferences data engineering Radio frequency