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41st IMAC, A Conference and Exposition on Structural Dynamics, 2023

作者： Yotsui, Haruki Matsuoka, Kodai Kaito, Kiyoyuki Department of Global Architecture Graduate School of Engineering Osaka University Osaka Suita-City Japan Data Analytics in Information and Communication Technology Division Railway Technical Research Institute Tokyo Kokubunji-City Japan

ISBN: (纸本)9783031366628

The resonance of railway bridges that occurs under high-speed trains passage is an important academic and practical issue. The bridge design code in Europe (Eurocode) sets an upper limit of 5 m/s2 on the bridge deck acceleration for a slab type track. However, the performance of high-speed railway bridges regarding train-induced deck acceleration specified in Eurocode has not been sufficiently verified. In this study, a slab track type PC box girder bridge in Japanese high-speed railway is targeted, and multipoint acceleration measurement is conducted. Based on the measurement results, the global and deck local modal characteristics are identified with ERA method, and the maximum deck acceleration is evaluated. As a result, the maximum deck acceleration of the bridge is much lower than the Eurocode limit value of 5 m/s2 even under the resonance condition. Unlike previous reports for European composite box girder bridges, the main deck vibration modes of the bridge are over 40 Hz, thus the deck local vibration did not affect the evaluation of deck acceleration. © 2024, The Society for Experimental Mechanics, Inc.

关键词： Acceleration

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AI Fairness-From Machine Learning to Federated Learning

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Computer Modeling in Engineering & Sciences 2024年第5期139卷 1203-1215页

作者： Lalit Mohan Patnaik Wenfeng Wang Consciousness Studies Program School of HumanitiesNational Institute of Advanced StudiesBangalore560012India Research Institute of Intelligent Engineering and Data Applications Shanghai Institute of TechnologyShanghai201418China Research Center of Ecology and Environment of Central Asia Chinese Academy of SciencesUrumqi830011China Applied Nonlinear Science Lab Anand International College of EngineeringJaipur391320India London Institute of Technology The ASE-London CTI of SCOLondonCR26EQUK Sino-Indian Joint Research Center of AI and Robotics The IMT InstituteBhubaneswar752054India

This article reviews the theory of fairness in AI-frommachine learning to federated learning,where the constraints on precision AI fairness and perspective solutions are also *** a reliable and quantitative evaluation of AI fairness,many associated concepts have been proposed,formulated and ***,the inexplicability of machine learning systems makes it almost impossible to include all necessary details in the modelling stage to ensure *** privacy worries induce the data unfairness and hence,the biases in the datasets for evaluating AI fairness are *** imbalance between algorithms’utility and humanization has further reinforced *** for federated learning systems,these constraints on precision AI fairness still *** solution is to reconcile the federated learning processes and reduce biases and imbalances accordingly.

关键词： Formulation evaluation classification constraints imbalance biases

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Analysis of the Classification of Medical Device Software in the AI Act Proposal 2

Analysis of the Classification of Medical Device Software in...

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Workshops at the 2nd International Conference on Hybrid Human-Artificial Intelligence, HHAI-WS 2023

作者： Cepeda Zapata, Karla A. Patil, Ritesh Ward, Tomás Loughran, Róisín McCaffery, Fergal Regulated Software Research Centre Dundalk Institute of Technology Dublin Road Dundalk Ireland Insight SFI Research Centre for Data Analytics School of Computing Dublin City University Dublin Ireland

The Artificial Intelligence Act (AIA) proposal by the European Commission (EC) is considered the first legal attempt to harmonize rules for AI systems. The proposal is designed to regulate AI systems in different European economic sectors, including Medical Devices (MD). This paper aims to examine the classification of AI systems in the AIA and their alignment with the Medical Device Regulation (MDR). The analysis focuses on Software as a Medical Device (SaMD) and Software in a Medical Device (SiMD), excluding general-purpose AI systems and machinery products (i.e., driven systems and safety components), as investigation in the Machinery Directive legislation is required. The strategy is to identify the classification conditions for AI systems by mapping key terms and definitions related to Article 6 in the AIA. Then, these conditions are translated into propositions suitable for the MD domain and presented in a flow chart for discussion. The primary source of information for the analysis is the MDR and the AIA, considering the latest revisited version of the proposal (Presidency compromise text, document 11124/22). We conclude this paper by discussing the classification pathway for SaMD and SiMD according to the AIA and additional discussion on terminology-related concerns and suggestions. © 2023 Copyright for this paper by its authors.

关键词： Artificial intelligence

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Rethinking Node-wise Propagation for Large-scale Graph Learning

arXiv

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arXiv 2024年

作者： Li, Xunkai Su, Daohan Ma, Jingyuan Zhang, Wentao Wu, Zhengyu Li, Rong-Hua Wang, Guoren Beijing Institute of Technology Beijing China Peking University National Engineering Laboratory for Big Data Analytics and Applications Beijing China

Scalable graph neural networks (GNNs) have emerged as a promising technique, which exhibits superior predictive performance and high running efficiency across numerous large-scale graph-based web applications. However, (i) Most scalable GNNs tend to treat all nodes with the same propagation rules, neglecting their topological uniqueness;(ii) Existing node-wise propagation optimization strategies are insufficient on web-scale graphs with intricate topology, where a full portrayal of nodes’ local properties is required. Intuitively, different nodes in web-scale graphs possess distinct topological roles, and therefore propagating them indiscriminately or neglect local contexts may compromise the quality of node representations. To address the above issues, we propose Adaptive Topology-aware Propagation (ATP), which reduces potential high-bias propagation and extracts structural patterns of each node in a scalable manner to improve running efficiency and predictive performance. Remarkably, ATP is crafted to be a plug-and-play node-wise propagation optimization strategy, allowing for offline execution independent of the graph learning process in a new perspective. Therefore, this approach can be seamlessly integrated into most scalable GNNs while remain orthogonal to existing node-wise propagation optimization strategies. Extensive experiments on 12 datasets have demonstrated the effectiveness of ATP. Copyright © 2024, The Authors. All rights reserved.

关键词： Graph neural networks

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AdaFGL: A New Paradigm for Federated Node Classification with Topology Heterogeneity

AdaFGL: A New Paradigm for Federated Node Classification wit...

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International Conference on data Engineering

作者： Xunkai Li Zhengyu Wu Wentao Zhang Henan Sun Rong-Hua Li Guoren Wang Beijing Institute of Technology Beijing China Peking University National Engineering Labratory for Big Data Analytics and Applications Beijing China

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Recently, Federated Graph Learning (FGL) has attracted significant attention as a distributed framework based on graph neural networks, primarily due to its capability to break data silos. Existing FGL studies employ community split on the homophilous global graph by default to simulate federated semisupervised node classification settings. Such a strategy assumes the consistency of topology between the multi-client subgraphs and the global graph, where connected nodes are highly likely to possess similar feature distributions and the same label. However, in real-world implementations, the varying perspectives of local data engineering result in various subgraph topologies, posing unique heterogeneity challenges in FGL. Unlike the well-known label Non-independent identical distribution (Non-iid) problems in federated learning, FGL heterogeneity essentially reveals the topological divergence among multiple clients, namely homophily or heterophily. To simulate and handle this unique challenge, we introduce the concept of structure Non-iid split and then present a new paradigm called Adaptive Federated Graph Learning (AdaFGL), a decoupled two-step personalized approach. To begin with, AdaFGL employs standard multi-client federated collaborative training to acquire the federated knowledge extractor by aggregating uploaded models in the final round at the server. Then, each client conducts personalized training based on the local subgraph and the federated knowledge extractor. Extensive experiments on the 12 graph benchmark datasets validate the superior performance of AdaFGL over state-of-the-art baselines. Specifically, in terms of test accuracy, our proposed AdaFGL outperforms baselines by significant margins of 3.24 % and 5.57 % on community split and structure Non-iid split, respectively.

关键词： Training Network topology Federated learning Collaboration Benchmark testing data engineering Graph neural networks

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BIM: Improving Graph Neural Networks with Balanced Influence Maximization 40

BIM: Improving Graph Neural Networks with Balanced Influence...

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40th IEEE International Conference on data Engineering, ICDE 2024

作者： Zhang, Wentao Gao, Xinyi Yang, Ling Cao, Meng Huang, Ping Shan, Jiulong Yin, Hongzhi Cui, Bin Peking University Center for Machine Learning Research China Institute of Advanced Algorithms Research Shanghai China National Engineering Labratory for Big Data Analytics and Applications The University of Queensland Australia Peking University Key Lab of High Confidence Software Technologies China Apple Inc. Institute of Computational Social Science Peking University Qingdao China

ISBN: (纸本)9798350317152

The imbalanced data classification problem has aroused lots of concerns from both academia and industry since data imbalance is a widespread phenomenon in many real-world scenarios. Although this problem has been well researched from the view of imbalanced class samples, we further argue that graph neural networks (GNNs) expose a unique source of imbalance from the influenced nodes of different classes of labeled nodes, i.e., labeled nodes are imbalanced in terms of the number of nodes they influenced during the influence propagation in GNNs. To tackle this previously unexplored influence-imbalance issue, we connect social influence maximization with the imbalanced node classification problem and propose balanced influence maximization (BIM). Specifically, BIM greedily assigns the pseudo label to the node which can maximize the number of influenced nodes in GNN training while making the influence of each class more balance. Experimental results on five public datasets demonstrate the effectiveness of our method in relieving the influence-imbalance issue. For example, when training a GCN with an imbalance ratio of 0.1, BIM significantly outperforms the most competitive baseline by 0.6% -9.8% in five public datasets in terms of the F1 score. © 2024 IEEE.

关键词： Graph neural networks

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NC-ALG: Graph-Based Active Learning under Noisy Crowd 40

NC-ALG: Graph-Based Active Learning under Noisy Crowd

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40th IEEE International Conference on data Engineering, ICDE 2024

作者： Zhang, Wentao Wang, Yexin You, Zhenbang Li, Yang Cao, Gang Yang, Zhi Cui, Bin Center for Machine Learning Research Peking University China Key Lab of High Confidence Software Technologies Peking University China Institute of Advanced Algorithms Research Shanghai China Institute of Computational Social Science Peking University Qingdao China National Engineering Labratory for Big Data Analytics and Applications China TEG Tencent Inc. Department of Data Platform China Beijing Academy of Artificial Intelligence China

ISBN: (纸本)9798350317152

Graph Neural Networks (GNNs) have achieved great success in various data mining tasks but they heavily rely on a large number of annotated nodes, requiring considerable human efforts. Despite the effectiveness of existing GNN-based Active Learning (AL) methods, they assume that the annotated labels are always correct, which is contradictory to the error-prone labeling process in a practical crowdsourcing environment. Besides, due to this impractical assumption, existing works only focus on optimizing the node selection in AL but neglect optimizing the labeling process. Therefore, we present NC-ALG, the first GNN-based AL framework that optimizes both the node selection and node labeling process under a noisy crowd. For node selection, NC-ALG introduces a new measurement to model influence reliability and an effective influence maximization objective to select nodes. For node labeling, NC-ALG significantly reduces the labeling cost by considering the model-predicted labels and the labels of mirror nodes. To the best of our knowledge, this is the first attempt to consider GNN-based AL under the practical noisy crowd. Empirical studies on public datasets demonstrate that NC-ALG significantly outperforms existing methods in terms labeling efficiency. Notably, it only takes NC-ALG one-third of the labeling budget that the competitive baseline GRAIN needs to achieve an accuracy of 70.7 % on PubMed. © 2024 IEEE.

关键词： Crowdsourcing

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AdaFGL: A New Paradigm for Federated Node Classification with Topology Heterogeneity

arXiv

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arXiv 2024年

作者： Li, Xunkai Wu, Zhengyu Zhang, Wentao Sun, Henan Li, Rong-Hua Wang, Guoren Beijing Institute of Technology Beijing China Peking University China National Engineering Labratory for Big Data Analytics and Applications Beijing China

Recently, Federated Graph Learning (FGL) has attracted significant attention as a distributed framework based on graph neural networks, primarily due to its capability to break data silos. Existing FGL studies employ community split on the homophilous global graph by default to simulate federated semi-supervised node classification settings. Such a strategy assumes the consistency of topology between the multi-client subgraphs and the global graph, where connected nodes are highly likely to possess similar feature distributions and the same label. However, in real-world implementations, the varying perspectives of local data engineering result in various subgraph topologies, posing unique heterogeneity challenges in FGL. Unlike the well-known label Non-independent identical distribution (Non-iid) problems in federated learning, FGL heterogeneity essentially reveals the topological divergence among multiple clients, namely homophily or heterophily. To simulate and handle this unique challenge, we introduce the concept of structure Non-iid split and then present a new paradigm called Adaptive Federated Graph Learning (AdaFGL), a decoupled two-step personalized approach. To begin with, AdaFGL employs standard multi-client federated collaborative training to acquire the federated knowledge extractor by aggregating uploaded models in the final round at the server. Then, each client conducts personalized training based on the local subgraph and the federated knowledge extractor. Extensive experiments on the 12 graph benchmark datasets validate the superior performance of AdaFGL over state-of-the-art baselines. Specifically, in terms of test accuracy, our proposed AdaFGL outperforms baselines by significant margins of 3.24% and 5.57% on community split and structure Non-iid split, respectively. Copyright © 2024, The Authors. All rights reserved.

关键词： Graph neural networks

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A Meta-learning System for Automatic Objects Tracking 4

A Meta-learning System for Automatic Objects Tracking

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4th International Conference on Applied Machine Learning, ICAML 2022

作者： Li, Qiaolin Wang, Wenfeng Zhang, Jingjing Research Institute of Intelligent Engineering and Data Applications Shanghai Institute of Technology Shanghai China Research Center of Artificial Intelligence and Robotics Interscience Institute of Management and Technology Bhubaneswar India

ISBN: (纸本)9781665462655

Recent years, reinforcement learning has been developed in many area and achieve high performance in many application. However, for huge amount data and training will make hard leaning for the agent of reinforcement learning, in the project, the agent of DDQN Network was integrated with LSTM meta learner for learning the game called Cartpole and LunarLander. It get a good performance for large train. © 2022 IEEE.

关键词： Reinforcement learning

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Optimizing User Interaction with Combined Avatars in BIM Systems 15

Optimizing User Interaction with Combined Avatars in BIM Sys...

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15th IEEE International Conference on Cognitive Infocommunications, CogInfoCom 2024

作者： Horváth, Ildikó Berki, Borbála Baranyi, Péter Corvinus Institute for Advanced Studies Institute of Data Analytics and Information Systems Corvinus University of Budapest Budapest Hungary Hungarian Research Network HUN-REN Budapest Hungary University of Pécs Faculty of Engineering and Information technology Pécs Hungary

ISBN: (纸本)9798350378245

Integrating virtual reality with Building Information Modeling (BIM) has been recognized for its numerous benefits in enhancing visualization, spatial recognition, distance and real-time collaboration, and efficiency in construction projects. However, there is a growing interest in exploring the cognitive aspects associated with using different types of avatars in these systems. This paper explores the cognitive implications of avatars in BIM systems, examining how they influence user experience, perception, decision-making, and overall cognitive load. © 2024 IEEE.

关键词： Virtual environments

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