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NGAT: attention in breadth and depth exploration for semi-supervised graph representation learning Research Articles

Jianke HU, Yin ZHANG,yinzh@zju.edu.cn

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 3,   Pages 409-421 doi: 10.1631/FITEE.2000657

Abstract: Recently, graph neural networks (GNNs) have achieved remarkable performance in representation learningon graph-structured data.However, as the number of network layers increases, GNNs based on the neighborhood aggregation strategyTo alleviate oversmoothing, we propose a nested graph network (NGAT), which can work in a semi-supervised

Keywords： Graph learning     Semi-supervised learning     Node classification     Attention    

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Video summarization with a graph convolutional attention network Research Articles

Ping Li, Chao Tang, Xianghua Xu,patriclouis.lee@gmail.com

Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 6,   Pages 902-913 doi: 10.1631/FITEE.2000429

Abstract: To address the above problem, we propose a graph convolutional attention network (GCAN) for .consists of two parts, embedding learning and , where embedding learning includes the temporal branch and graphIn particular, GCAN uses dilated temporal convolution to model local cues and temporal self-attentionIt learns graph embedding via a multi-layer to reveal the intrinsic structure of frame samples.The part combines the output streams from the temporal branch and graph branch to create the context-aware

Keywords： 时序学习；自注意力机制；图卷积网络；上下文融合；视频摘要    

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Classifying multiclass relationships between ASes using graph convolutional network

Frontiers of Engineering Management   Pages 653-667 doi: 10.1007/s42524-022-0217-1

Abstract: We then introduce new features and propose a graph convolutional network (GCN) framework, AS-GCN, toThe proposed framework considers the global network structure and local link features concurrently.

Keywords： autonomous system     multiclass relationship     graph convolutional network     classification algorithm     Internet    

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Prediction and cause investigation of ozone based on a double-stage attention mechanism recurrent neuralnetwork

Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 2, doi: 10.1007/s11783-023-1621-4

Abstract:

● Used a double-stage attention mechanism model to predict ozone.

Keywords： Ozone prediction     Deep learning     Time series     Attention     Volatile organic compounds    

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A knowledge-guided and traditional Chinese medicine informed approach for herb recommendation Research Article

Zhe JIN, Yin ZHANG, Jiaxu MIAO, Yi YANG, Yueting ZHUANG, Yunhe PAN,11521043@zju.edu.cn,yinzh@zju.edu.cn

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 10,   Pages 1416-1429 doi: 10.1631/FITEE.2200662

Abstract: (TCM) is an interesting research topic in China’s thousands of years of history. With the recent advances in artificial intelligence technology, some researchers have started to focus on learning the TCM prescriptions in a data-driven manner. This involves appropriately recommending a set of herbs based on patients’ symptoms. Most existing models disregard TCM domain knowledge, for example, the interactions between symptoms and herbs and the TCM-informed observations (i.e., TCM formulation of prescriptions). In this paper, we propose a knowledge-guided and TCM-informed approach for . The knowledge used includes path interactions and co-occurrence relationships among symptoms and herbs from a generated from TCM literature and prescriptions. The aforementioned knowledge is used to obtain the discriminative feature vectors of symptoms and herbs via a . To increase the ability of herb prediction for the given symptoms, we introduce TCM-informed observations in the prediction layer. We apply our proposed model on a TCM prescription dataset, demonstrating significant improvements over state-of-the-art methods.

Keywords： Traditional Chinese medicine     Herb recommendation     Knowledge graph     Graph attention network    

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financially constrained small- and medium-sized enterprises based on a multi-relation translational graphattention network Research Article

Qianqiao LIANG, Hua WEI, Yaxi WU, Feng WEI, Deng ZHAO, Jianshan HE, Xiaolin ZHENG, Guofang MA, Bing HAN

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 3,   Pages 388-402 doi: 10.1631/FITEE.2200151

Abstract: financing needs among SMEs, which motivates us to fully exploit the multi-relation enterprise social networkTo address these challenges, we propose a graph neural network named Multi-relation tRanslatIonal GrapHaTtention network (M-RIGHT), which not only models the of financing needs along different relation

Keywords： Financing needs exploration     Graph representation learning     Transfer heterogeneity     Behavior heterogeneity    

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Negative weights in network time model

Zoltán A. VATTAI, Levente MÁLYUSZ

Frontiers of Engineering Management 2022, Volume 9, Issue 2,   Pages 268-280 doi: 10.1007/s42524-020-0109-1

Abstract: Previous network techniques (CPM/PERT/PDM) did not support negative parameters and/or loops (potentiallyMonsieur Roy and John Fondahl implicitly introduced negative weights into network techniques to representrestrictions are represented by weighted arrows, we can release most restraints in constructing the graphincorporating the dynamic model of the inner logic of time plan), and a surprisingly flexible and handy networkreview the theoretical possibilities and technical interpretations (and use) of negative weights in network

Keywords： graph technique     network technique     construction management     scheduling    

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A multi-sensor relation model for recognizing and localizing faults of machines based on network analysis

Frontiers of Mechanical Engineering 2023, Volume 18, Issue 2, doi: 10.1007/s11465-022-0736-9

Abstract: Recently, advanced sensing techniques ensure a large number of multivariate sensing data for intelligent fault diagnosis of machines. Given the advantage of obtaining accurate diagnosis results, multi-sensor fusion has long been studied in the fault diagnosis field. However, existing studies suffer from two weaknesses. First, the relations of multiple sensors are either neglected or calculated only to improve the diagnostic accuracy of fault types. Second, the localization for multi-source faults is seldom investigated, although locating the anomaly variable over multivariate sensing data for certain types of faults is desirable. This article attempts to overcome the above weaknesses by proposing a global method to recognize fault types and localize fault sources with the help of multi-sensor relations (MSRs). First, an MSR model is developed to learn MSRs automatically and further obtain fault recognition results. Second, centrality measures are employed to analyze the MSR graphs learned by the MSR model, and fault sources are therefore determined. The proposed method is demonstrated by experiments on an induction motor and a centrifugal pump. Results show the proposed method’s validity in diagnosing fault types and sources.

Keywords： fault recognition     fault localization     multi-sensor relations     network analysis     graph neural network    

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Detecting large-scale underwater cracks based on remote operated vehicle and graph convolutional neuralnetwork

Wenxuan CAO; Junjie LI

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 11,   Pages 1378-1396 doi: 10.1007/s11709-022-0855-8

Abstract: The graph convolutional neural network (GCN) was used to segment the stitched image.

Keywords： underwater cracks     remote operated vehicle     image stitching     image segmentation     graph convolutionalneural network    

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Filter-cluster attention based recursive network for low-light enhancement Research Article

Zhixiong HUANG, Jinjiang LI, Zhen HUA, Linwei FAN,hzxcyanwind@163.com,lijinjiang@gmail.com-

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 7,   Pages 1028-1044 doi: 10.1631/FITEE.2200344

Abstract: To improve the visibility of low-light images, we propose a recurrent network based on (FCA), the mainThe network performs multi-stage recursive learning on low-light images, and then extracts deeper featureFCA and self-attention are used to highlight the low-light regions and important channels of the feature

Keywords： Low-light enhancement     Filter-cluster attention     Dense connection pyramid     Recursive network    

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A forwarding graph embedding algorithm exploiting regional topology information Article

Hong-chao HU, Fan ZHANG, Yu-xing MAO, Zhen-peng WANG

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 11,   Pages 1854-1866 doi: 10.1631/FITEE.1601404

Abstract: Network function virtualization (NFV) is a newly proposed technique designed to construct and managenetwork functions dynamically and efficiently.Allocating physical resources to the virtual network function forwarding graph is a critical issue inWe formulate the forwarding graph embedding (FGE) problem as a binary integer programming problem, whichto increase the revenue and decrease the cost to a service provider (SP) while considering limited network

Keywords： Network function virtualization     Virtual network function     Forwarding graph embedding    

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Attention-based efficient robot grasp detection network Research Article

Xiaofei QIN, Wenkai HU, Chen XIAO, Changxiang HE, Songwen PEI, Xuedian ZHANG,xiaofei.qin@usst.edu.cn,obmmd_zxd@163.com

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 10,   Pages 1430-1444 doi: 10.1631/FITEE.2200502

Abstract: important for robot grasping tasks, we propose an ; structured pixel-level grasp detection named the attention-basedefficient network (AE-GDN).Three spatial attention modules are introduced in the encoder stages to enhance the detailed information, and three channel attention modules are introduced in the stages to extract more semantic information

Keywords： Robot grasp detection     Attention mechanism     Encoder–     decoder     Neural network    

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Clean air captures attention whereas pollution distracts: evidence from brain activities

Frontiers of Environmental Science & Engineering 2023, Volume 18, Issue 4, doi: 10.1007/s11783-024-1801-x

Abstract:

● We find air pollution distracts attention and reveal the neurocognitive

Keywords： Air pollution     Attention     Disengagement     Performance     Event-related potential    

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Achieving Cognitive Mass Personalization via the Self-X Cognitive Manufacturing Network: An IndustrialKnowledge Graph- and Graph Embedding-Enabled Pathway

Xinyu Li, Pai Zheng, Jinsong Bao, Liang Gao, Xun Xu

Engineering 2023, Volume 22, Issue 3,   Pages 14-19 doi: 10.1016/j.eng.2021.08.018

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A Novel MILP Model Based on the Topology of a Network Graph for Process Planning in an Intelligent Manufacturing Article

Qihao Liu, Xinyu Li, Liang Gao

Engineering 2021, Volume 7, Issue 6,   Pages 807-817 doi: 10.1016/j.eng.2021.04.011

Abstract: paper proposes a novel mixed-integer linear programming (MILP) mathematical model by considering the networktopology structure and the OR nodes that represent a type of OR logic inside the network.

Keywords： Process planning     Network     Mixed-integer linear programming     CPLEX    

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Title Author Date Type Operation

NGAT: attention in breadth and depth exploration for semi-supervised graph representation learning

Jianke HU, Yin ZHANG,yinzh@zju.edu.cn

Journal Article

Video summarization with a graph convolutional attention network

Ping Li, Chao Tang, Xianghua Xu,patriclouis.lee@gmail.com

Journal Article

Classifying multiclass relationships between ASes using graph convolutional network

Journal Article

Prediction and cause investigation of ozone based on a double-stage attention mechanism recurrent neuralnetwork

Journal Article

A knowledge-guided and traditional Chinese medicine informed approach for herb recommendation

Zhe JIN, Yin ZHANG, Jiaxu MIAO, Yi YANG, Yueting ZHUANG, Yunhe PAN,11521043@zju.edu.cn,yinzh@zju.edu.cn

Journal Article

financially constrained small- and medium-sized enterprises based on a multi-relation translational graphattention network

Qianqiao LIANG, Hua WEI, Yaxi WU, Feng WEI, Deng ZHAO, Jianshan HE, Xiaolin ZHENG, Guofang MA, Bing HAN

Journal Article

Negative weights in network time model

Zoltán A. VATTAI, Levente MÁLYUSZ

Journal Article

A multi-sensor relation model for recognizing and localizing faults of machines based on network analysis

Journal Article

Detecting large-scale underwater cracks based on remote operated vehicle and graph convolutional neuralnetwork

Wenxuan CAO; Junjie LI

Journal Article

Filter-cluster attention based recursive network for low-light enhancement

Zhixiong HUANG, Jinjiang LI, Zhen HUA, Linwei FAN,hzxcyanwind@163.com,lijinjiang@gmail.com-

Journal Article

A forwarding graph embedding algorithm exploiting regional topology information

Hong-chao HU, Fan ZHANG, Yu-xing MAO, Zhen-peng WANG

Journal Article

Attention-based efficient robot grasp detection network

Xiaofei QIN, Wenkai HU, Chen XIAO, Changxiang HE, Songwen PEI, Xuedian ZHANG,xiaofei.qin@usst.edu.cn,obmmd_zxd@163.com

Journal Article

Clean air captures attention whereas pollution distracts: evidence from brain activities

Journal Article

Achieving Cognitive Mass Personalization via the Self-X Cognitive Manufacturing Network: An IndustrialKnowledge Graph- and Graph Embedding-Enabled Pathway

Xinyu Li, Pai Zheng, Jinsong Bao, Liang Gao, Xun Xu

Journal Article

A Novel MILP Model Based on the Topology of a Network Graph for Process Planning in an Intelligent Manufacturing

Qihao Liu, Xinyu Li, Liang Gao

Journal Article