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第25届中国控制与决策会议

作者： Xiwei Liu Cheng Cheng Department of Computer Science and Technology Tongji University and the Key Laboratory of Embedded System and Service Computing Ministry of Education

ISBN: (纸本)9781467355339

In this paper, we investigate the synchronization problem for nonlinearly coupled networks under periodically intermittent pinning control, where the coupling matrix denoting network topology is assumed to be symmetric. A sufficient condition to guarantee global synchronization is presented. Moreover, a centralized adaptive intermittent control is designed and its validity is rigorously proved. Finally, some numerical examples are presented to demonstrate the correctness of obtained theoretical results.

关键词： Synchronization, Consensus, Complex networks, Nonlinearly coupled, Periodically, Intermittent pinning, Adaptive control.

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Cluster synchronization of nonlinearly coupled networks via periodically intermittent pinning control

Cluster synchronization of nonlinearly coupled networks via ...

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第三十二届中国控制会议

作者： LIU Xiwei CHENG Jiujun Department of Computer Science and Technology Tongji Universityand the Key Laboratory of Embedded System and Service ComputingMinistry of Education

ISBN: (纸本)9781479900305

In this paper,the cluster synchronization problem for nonlinearly coupled networks under periodically intermittent pinning control is *** first,a sufficient condition to guarantee cluster synchronization is ***,an adaptive intermittent control algorithm is designed to the control strength and its validity is rigorously ***,some numerical examples are presented to demonstrate the correctness of obtained theoretical results.

关键词： Cluster synchronization Adaptive Nonlinearly coupled Periodically Intermittent pinning.

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A transactional-behavior-based hierarchical gated network for credit card fraud detection

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IEEE/CAA Journal of Automatica Sinica 2025年

作者： Xie, Yu Zhou, MengChu Liu, Guanjun Wei, Lifei Zhu, Honghao De Meo, Pasquale College of Information Engineering Shanghai Maritime University Shanghai201306 China School of Information and Electronic Engineering Zhejiang Gongshang University Hangzhou310018 China Helen and John C. Hartmann Department of Electrical and Computer Engineering New Jersey Institute of Technology NewarkNJ07102 United States Key Laboratory of Embedded System and Service Computing Ministry of Education Department of Computer Science Tongji University Shanghai201804 China College of Computer Science and Information Engineering Bengbu University Bengbu233030 China Department of Ancient and Modern Civilizations University of Messina Messina98166 Italy

The task of detecting fraud in credit card transactions is crucial to ensure the security and stability of a financial system, as well as to enforce customer confidence in digital payment systems. Historically, credit card companies have used rule-based approaches to detect fraudulent transactions, but these have proven inadequate due to the complexity of fraud strategies and have been replaced by much more powerful solutions based on machine learning or deep learning algorithms. Despite significant progress, the current approaches to fraud detection suffer from a number of limitations: for example, it is unclear whether some transaction features are more effective than others in discriminating fraudulent transactions, and they often neglect possible correlations among transactions, even though they could reveal illicit behaviour. In this paper, we propose a novel credit card fraud detection (CCFD) method based on a transaction behaviour-based hierarchical gated network. First, we introduce a feature-oriented extraction module capable of identifying key features from original transactions, and such analysis is effective in revealing the behavioural characteristics of fraudsters. Second, we design a transaction-oriented extraction module capable of capturing the correlation between users' historical and current transactional behaviour. Such information is crucial for revealing users' sequential behaviour patterns. Our approach, called transactional-behaviour-based hierarchical gated network model (TbHGN), extracts two types of new transactional features, which are then combined in a feature interaction module to learn the final transactional representations used for CCFD. We have conducted extensive experiments on a real-world credit card transaction dataset with an increase in average F1 between 1.42% and 6.53% and an improvement in average AUC between 0.63% and 2.78% over the state of the art. © 2025 Institute of Electrical and Electronics Engineers Inc.. All right

关键词： Deep learning

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Automatic Web services composition algorithm based on optimal matching

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Journal of Central South University 2011年第4期18卷 1169-1177页

作者：王俊丽丁志军侯玉兵 Engineering Research Center for Enterprise Digital Technology Ministry of EducationTongji University Key Laboratory of Embedded System and Service Computing Ministry of EducationTongji University Shanghai Branch China Information Technology Designing and Consulting Institute

A novel layered method was proposed to solve the problem of Web services *** this method,services composition problem was formally transformed into the optimal matching problem of every layer,then optimal matching problem was modeled based on the hypergraph theory,and solved by computing the minimal transversals of the ***,two optimization algorithms were designed to discard some useless states at the intermediary steps of the composition *** effectiveness of the composition method was tested by a set of experiments,in addition,an example regarding the travel services composition was also *** experimental results show that this method not only can automatically generate composition tree whose leaf nodes correspond to services composition solutions,but also has better performance on execution time and solution quality by adopting two proposed optimization algorithms.

关键词： Web services services composition optimal matching hypergraph theory

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Feedback-based Coarse Time-Granularity POI Recommendation and Itinerary Planning

Feedback-based Coarse Time-Granularity POI Recommendation an...

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2021 China Automation Congress, CAC 2021

作者： Zong, Jie Ding, Zhijun The Key Laboratory of Embedded System and Service Computing Ministry of Education TONGJI University Shanghai China

ISBN: (纸本)9781665426473

For tourists, planning their own travel itinerary to a strange city is really challenging. Although there are many researches on Point of Interests (POIs) recommendation and itinerary planning, two problems occur in currently proposed recommendation algorithms. First, the time granularity of the algorithms is too fine. POI recommendation should be considered within a more coarse-grained time factor. For example, a visitor wouldn't like to visit a water park in winter, although he appears to love such places. However, seldom researches have taken this into consideration. Second, most of the algorithms build their models based on the tourists' trajectory, which means, they did not consider users' feedback. In fact, quite a number of tourists do not like their travel experience and give negative feedbacks after their visits. In this situation, we shouldn't recommend similar POIs to this user because he may not like such kind of scenic spots at all. To address these two problems, this paper proposes an algorithm called Feedback-based Coarse Time-Granularity POI Recommendation, which can extract information from comments and distinguish tourists' attitudes towards the POIs they have visited. At the same time, the coarse-grained time factor is considered while making recommendations. In addition, an itinerary planning algorithm is proposed. Experiments show that our algorithm can reach SOTA. © 2021 IEEE

关键词： Parks

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CONSISTENT REPRESENTATION LEARNING ACROSS MODALITIES FOR ZERO-SHOT IMAGE RECOGNITION

CONSISTENT REPRESENTATION LEARNING ACROSS MODALITIES FOR ZER...

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2021 IEEE International Conference on Multimedia and Expo, ICME 2021

作者： Wang, Yu Zhao, Sev-Jue Key Laboratory of Embedded System and Service Computing Ministry of Education China HaiBa Technology China

ISBN: (纸本)9781665438643

Zero-shot learning (ZSL) recently has drawn widespread attention due to the demand for scalability of object recognition in real scenes. Existing approaches typically focus on directly learning various mapping functions from the visual space to the semantic space or vice versa. However, these methods fail to explicitly capture consistent representations reflecting the nature of different modalities of the same object, which results in the deterioration of the domain shift problem in the ZSL context during the testing stage. To this end, a consistent representation learning mechanism across visual modalities and the associated semantic modalities based on common subspace learning is proposed in this paper. We further impose an orthogonal constraint on the subspace for informative representations, as well as 21-norm regularization terms on projection matrices for automatic feature selection. Finally, an iterative process based on the ALM algorithm with an alternating direction strategy is displayed to resolve the proposed formulation. Extensive experimental results on four popular datasets demonstrate that our algorithm is promising. © 2021 IEEE

关键词： Zero-shot learning

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STMGT: A Long-Term Traffic Forecasting Method Based on Spatial-Temporal Multi-View Graph Transformer 24

STMGT: A Long-Term Traffic Forecasting Method Based on Spati...

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5th International Conference on Artificial Intelligence and Computer Engineering, ICAICE 2024

作者： Zheng, Fengkai The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China

ISBN: (纸本)9798400718007

Accurate long-term traffic forecasting is crucial for urban traffic management and planning. Existing methods focus mainly on short-term predictions and struggle with long-term spatial-temporal dependencies due to computational complexities. We propose a Spatial-Temporal Multi-View Graph Transformer (STMGT) based on spatial-temporal sequence decomposition. STMGT enhances long-term forecasting by decomposing traffic sequences into temporal position embeddings, periodicity-trend features, and residual components. This approach captures long-term correlations while managing computational cost by dividing data into multiple time blocks. A multi-head graph attention mechanism is used to explore dynamic spatial correlations. Experimental results on real-world datasets show that STMGT significantly outperforms baseline models, proving it effective for long-term traffic prediction. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： graph attention mechanism

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Edge-weight-Based link prediction in heterogeneous graph 19

Edge-weight-Based link prediction in heterogeneous graph

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19th IEEE International Conference on Networking, Sensing and Control, ICNSC 2022

作者： Zong, Jie Ding, Zhijun The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China

ISBN: (纸本)9781665472432

Link prediction is to predict whether there is a link between two nodes in the graph, it is a very important application and plays a great role in various industries. In recent years, with the development of graph neural network technology, many algorithms make effort to study the expression of each node from the original graph data and use them to infer new links. However, most of the existing algorithms have a common problem when facing heterogeneous graphs, which is, they do not consider the weight of edges in graphs. Instead, they put all their energies into computing node features. Although a few algorithms such as RGCN are trying to take the influence of different link types into account while extracting node features, these implicit feature extractions do not start from the global information, but just calculate independently for each node. In other words, in these algorithms, even the same type of links will be abstracted into different features on different nodes. This is obviously inconsistent with reality. On the same map, the feature of the same link should be relatively fixed and should not be changed just because of different positions. In addition, when the current graph neural network algorithm is applied to link prediction, the link type to be predicted must be specified in advance, which makes the algorithm extremely inflexible. In order to solve these problems, we propose an edge weight calculation algorithm that extracts the edge feature from the whole graph. We also propose the edge-weight-based link prediction algorithm. By introducing edge weight into the MLP, there is no need to specify the target link type at the beginning of model training. It improves both the performance and efficiency of the link prediction model. Experiments on two datasets show that this edge-weight-based link prediction algorithm performs better than current algorithms and reaches SOTA. © 2022 IEEE.

关键词： Graph neural networks

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Adaptive Random Forest with Dynamic Detectors for Evolving Data Stream Classification 23

Adaptive Random Forest with Dynamic Detectors for Evolving D...

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9th International Conference on computing and Artificial Intelligence, ICCAI 2023

作者： Jin, Xuancheng Zhang, Yaying Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China

ISBN: (纸本)9781450399029

Evolving data streams containing concept drift breaks the assumption that data are independent and identically distributed (IID) in traditional machine learning models. A series of ensemble-based models have been adapted to data stream learning tasks due to the abilities of dynamic partially update and cooperate with drift detection methods. Adaptive Random Forests (ARF), one of the state-of-the-art in the stream setting, encounters high resource requirements in both efficiency and memory like all ensemble methods. We are committed to reducing running cost without compromising ARF’s performance. In this paper, we propose the Adaptive Random Forest with Dynamic Detectors (ARFDD) which utilizes diversity measurement to combine homogeneous detectors and split heterogeneous ones accordingly, leads to lower computing costs and thus speed up in applications. Experiments on all 8 synthetic and 4 real datasets show a great CPU-time thrift while avoiding performance degradation. © 2023 Copyright held by the owner/author(s).

关键词： Learning systems

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Implementation and Analysis of Probabilistic Methods for Gate-Level Circuit Reliability Estimation

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Tsinghua Science and Technology 2007年第S1期12卷 32-38页

作者：王真江建慧杨光 Key Laboratory of Embedded Systems and Service Computing Ministry of Education of Shanghai Department of Computer Science and Technology Tongji University

The development of VLSI technology results in the dramatically improvement of the performance of integrated circuits. However, it brings more challenges to the aspect of reliability. Integrated circuits become more susceptible to soft errors. Therefore, it is imperative to study the reliability of circuits under the soft error. This paper implements three probabilistic methods (two pass, error propagation probability, and probabilistic transfer matrix) for estimating gate-level circuit reliability on PC. The functions and performance of these methods are compared by experiments using ISCAS85 and 74-series circuits.

关键词： soft error soft error rate signal reliability failure probability reliability estimation

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