Adaptive spatio-temporal graph learning for bus station profiling
- Hou, Mingliang, Xia, Feng, Chen, Xin, Saikrishna, Vidya, Chen, Honglong
- Authors: Hou, Mingliang , Xia, Feng , Chen, Xin , Saikrishna, Vidya , Chen, Honglong
- Date: 2024
- Type: Text , Journal article
- Relation: ACM Transactions on Spatial Algorithms and Systems Vol. 10, no. 3 (2024), p.
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- Description: Understanding and managing public transportation systems require capturing complex spatio-temporal correlations within datasets. Existing studies often use predefined graphs in graph learning frameworks, neglecting shifted spatial and long-term temporal correlations, which are crucial in practical applications. To address these problems, we propose a novel bus station profiling framework to automatically infer the spatio-temporal correlations and capture the shifted spatial and long-term temporal correlations in the public transportation dataset. The proposed framework adopts and advances the graph learning structure through the following innovative ideas: (1) designing an adaptive graph learning mechanism to capture the interactions between spatio-temporal correlations rather than relying on pre-defined graphs, (2) modeling shifted correlation in shifted spatial graphs to learn fine-grained spatio-temporal features, and (3) employing self-attention mechanism to learn the long-term temporal correlations preserved in public transportation data. We conduct extensive experiments on three real-world datasets and exploit the learned profiles of stations for the station passenger flow prediction task. Experimental results demonstrate that the proposed framework outperforms all baselines under different settings and can produce meaningful bus station profiles. © 2024 held by the owner/author(s).
Coupled attention networks for multivariate time series anomaly detection
- Xia, Feng, Chen, Xin, Yu, Shuo, Hou, Mingliang, Liu, Mujie, You, Linlin
- Authors: Xia, Feng , Chen, Xin , Yu, Shuo , Hou, Mingliang , Liu, Mujie , You, Linlin
- Date: 2024
- Type: Text , Journal article
- Relation: IEEE Transactions on Emerging Topics in Computing Vol. 12, no. 1 (2024), p. 240-253
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- Description: Multivariate time series anomaly detection (MTAD) plays a vital role in a wide variety of real-world application domains. Over the past few years, MTAD has attracted rapidly increasing attention from both academia and industry. Many deep learning and graph learning models have been developed for effective anomaly detection in multivariate time series data, which enable advanced applications such as smart surveillance and risk management with unprecedented capabilities. Nevertheless, MTAD is facing critical challenges deriving from the dependencies among sensors and variables, which often change over time. To address this issue, we propose a coupled attention-based neural network framework (CAN) for anomaly detection in multivariate time series data featuring dynamic variable relationships. We combine adaptive graph learning methods with graph attention to generate a global-local graph that can represent both global correlations and dynamic local correlations among sensors. To capture inter-sensor relationships and temporal dependencies, a convolutional neural network based on the global-local graph is integrated with a temporal self-attention module to construct a coupled attention module. In addition, we develop a multilevel encoder-decoder architecture that accommodates reconstruction and prediction tasks to better characterize multivariate time series data. Extensive experiments on real-world datasets have been conducted to evaluate the performance of the proposed CAN approach, and the results show that CAN significantly outperforms state-of-the-art baselines. © 2013 IEEE.
- Authors: Xia, Feng , Chen, Xin , Yu, Shuo , Hou, Mingliang , Liu, Mujie , You, Linlin
- Date: 2024
- Type: Text , Journal article
- Relation: IEEE Transactions on Emerging Topics in Computing Vol. 12, no. 1 (2024), p. 240-253
- Full Text:
- Reviewed:
- Description: Multivariate time series anomaly detection (MTAD) plays a vital role in a wide variety of real-world application domains. Over the past few years, MTAD has attracted rapidly increasing attention from both academia and industry. Many deep learning and graph learning models have been developed for effective anomaly detection in multivariate time series data, which enable advanced applications such as smart surveillance and risk management with unprecedented capabilities. Nevertheless, MTAD is facing critical challenges deriving from the dependencies among sensors and variables, which often change over time. To address this issue, we propose a coupled attention-based neural network framework (CAN) for anomaly detection in multivariate time series data featuring dynamic variable relationships. We combine adaptive graph learning methods with graph attention to generate a global-local graph that can represent both global correlations and dynamic local correlations among sensors. To capture inter-sensor relationships and temporal dependencies, a convolutional neural network based on the global-local graph is integrated with a temporal self-attention module to construct a coupled attention module. In addition, we develop a multilevel encoder-decoder architecture that accommodates reconstruction and prediction tasks to better characterize multivariate time series data. Extensive experiments on real-world datasets have been conducted to evaluate the performance of the proposed CAN approach, and the results show that CAN significantly outperforms state-of-the-art baselines. © 2013 IEEE.
CenGCN : centralized convolutional networks with vertex imbalance for scale-free graphs
- Xia, Feng, Wang, Lei, Tang, Tao, Chen, Xin, Kong, Xiangjie, Oatley, Giles, King, Irwin
- Authors: Xia, Feng , Wang, Lei , Tang, Tao , Chen, Xin , Kong, Xiangjie , Oatley, Giles , King, Irwin
- Date: 2023
- Type: Text , Journal article
- Relation: IEEE Transactions on Knowledge and Data Engineering Vol. 35, no. 5 (2023), p. 4555-4569
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- Description: Graph Convolutional Networks (GCNs) have achieved impressive performance in a wide variety of areas, attracting considerable attention. The core step of GCNs is the information-passing framework that considers all information from neighbors to the central vertex to be equally important. Such equal importance, however, is inadequate for scale-free networks, where hub vertices propagate more dominant information due to vertex imbalance. In this paper, we propose a novel centrality-based framework named CenGCN to address the inequality of information. This framework first quantifies the similarity between hub vertices and their neighbors by label propagation with hub vertices. Based on this similarity and centrality indices, the framework transforms the graph by increasing or decreasing the weights of edges connecting hub vertices and adding self-connections to vertices. In each non-output layer of the GCN, this framework uses a hub attention mechanism to assign new weights to connected non-hub vertices based on their common information with hub vertices. We present two variants CenGCN_D and CenGCN_E, based on degree centrality and eigenvector centrality, respectively. We also conduct comprehensive experiments, including vertex classification, link prediction, vertex clustering, and network visualization. The results demonstrate that the two variants significantly outperform state-of-the-art baselines. © 1989-2012 IEEE.
- Authors: Xia, Feng , Wang, Lei , Tang, Tao , Chen, Xin , Kong, Xiangjie , Oatley, Giles , King, Irwin
- Date: 2023
- Type: Text , Journal article
- Relation: IEEE Transactions on Knowledge and Data Engineering Vol. 35, no. 5 (2023), p. 4555-4569
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- Reviewed:
- Description: Graph Convolutional Networks (GCNs) have achieved impressive performance in a wide variety of areas, attracting considerable attention. The core step of GCNs is the information-passing framework that considers all information from neighbors to the central vertex to be equally important. Such equal importance, however, is inadequate for scale-free networks, where hub vertices propagate more dominant information due to vertex imbalance. In this paper, we propose a novel centrality-based framework named CenGCN to address the inequality of information. This framework first quantifies the similarity between hub vertices and their neighbors by label propagation with hub vertices. Based on this similarity and centrality indices, the framework transforms the graph by increasing or decreasing the weights of edges connecting hub vertices and adding self-connections to vertices. In each non-output layer of the GCN, this framework uses a hub attention mechanism to assign new weights to connected non-hub vertices based on their common information with hub vertices. We present two variants CenGCN_D and CenGCN_E, based on degree centrality and eigenvector centrality, respectively. We also conduct comprehensive experiments, including vertex classification, link prediction, vertex clustering, and network visualization. The results demonstrate that the two variants significantly outperform state-of-the-art baselines. © 1989-2012 IEEE.
Digital twin mobility profiling : a spatio-temporal graph learning approach
- Chen, Xin, Hou, Mingliang, Tang, Tao, Kaur, Achhardeep, Xia, Feng
- Authors: Chen, Xin , Hou, Mingliang , Tang, Tao , Kaur, Achhardeep , Xia, Feng
- Date: 2022
- Type: Text , Conference paper
- Relation: 23rd IEEE International Conference on High Performance Computing and Communications, 7th IEEE International Conference on Data Science and Systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021, Hainan, China, 20-22 December 2021, Proceedings 2021 IEEE 23rd International Conference on High Performance Computing & Communications, 7th International Conference on Data Science & Systems 19th International Conference on Smart City 7th International Conference on Dependability in Sensor, Cloud & Big Data Systems & Applications p. 1178-1187
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- Description: With the arrival of the big data era, mobility profiling has become a viable method of utilizing enormous amounts of mobility data to create an intelligent transportation system. Mobility profiling can extract potential patterns in urban traffic from mobility data and is critical for a variety of traffic-related applications. However, due to the high level of complexity and the huge amount of data, mobility profiling faces huge challenges. Digital Twin (DT) technology paves the way for cost-effective and performance-optimised management by digitally creating a virtual representation of the network to simulate its behaviour. In order to capture the complex spatio-temporal features in traffic scenario, we construct alignment diagrams to assist in completing the spatio-temporal correlation representation and design dilated alignment convolution network (DACN) to learn the fine-grained correlations, i.e., spatio-temporal interactions. We propose a digital twin mobility profiling (DTMP) framework to learn node profiles on a mobility network DT model. Extensive experiments have been conducted upon three real-world datasets. Experimental results demonstrate the effectiveness of DTMP. © 2021 IEEE.
- Authors: Chen, Xin , Hou, Mingliang , Tang, Tao , Kaur, Achhardeep , Xia, Feng
- Date: 2022
- Type: Text , Conference paper
- Relation: 23rd IEEE International Conference on High Performance Computing and Communications, 7th IEEE International Conference on Data Science and Systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021, Hainan, China, 20-22 December 2021, Proceedings 2021 IEEE 23rd International Conference on High Performance Computing & Communications, 7th International Conference on Data Science & Systems 19th International Conference on Smart City 7th International Conference on Dependability in Sensor, Cloud & Big Data Systems & Applications p. 1178-1187
- Full Text:
- Reviewed:
- Description: With the arrival of the big data era, mobility profiling has become a viable method of utilizing enormous amounts of mobility data to create an intelligent transportation system. Mobility profiling can extract potential patterns in urban traffic from mobility data and is critical for a variety of traffic-related applications. However, due to the high level of complexity and the huge amount of data, mobility profiling faces huge challenges. Digital Twin (DT) technology paves the way for cost-effective and performance-optimised management by digitally creating a virtual representation of the network to simulate its behaviour. In order to capture the complex spatio-temporal features in traffic scenario, we construct alignment diagrams to assist in completing the spatio-temporal correlation representation and design dilated alignment convolution network (DACN) to learn the fine-grained correlations, i.e., spatio-temporal interactions. We propose a digital twin mobility profiling (DTMP) framework to learn node profiles on a mobility network DT model. Extensive experiments have been conducted upon three real-world datasets. Experimental results demonstrate the effectiveness of DTMP. © 2021 IEEE.
Urban region profiling with spatio-temporal graph neural networks
- Hou, Mingliang, Xia, Feng, Gao, Haoran, Chen, Xin, Chen, Honglong
- Authors: Hou, Mingliang , Xia, Feng , Gao, Haoran , Chen, Xin , Chen, Honglong
- Date: 2022
- Type: Text , Journal article
- Relation: IEEE Transactions on Computational Social Systems Vol. 9, no. 6 (2022), p. 1736-1747
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- Description: Region profiles are summaries of characteristics of urban regions. Region profiling is a process to discover the correlations between urban regions. The learned urban profiles can be used to represent and identify regions in supporting downstream tasks, e.g., region traffic status estimation. While some efforts have been made to model urban regions, representation learning with awareness of graph-structured data can improve the existing methods. To do this, we first construct an attribute spatio-temporal graph, in which a node represents a region, an edge represents mobility across regions, and a node attribute represents a region's point of interest (PoI) distribution. The problem of region profiling is reformulated as a representation learning problem based on attribute spatio-temporal graphs. To solve this problem, we developed URGENT, a spatio-temporal graph learning framework. URGENT is made up of two modules. The graph convolutional neural network is used in the first module to learn spatial dependencies. The second module is an encoding-decoding temporal learning structure with self-attention mechanism. Furthermore, we use the learned representations of regions to estimate region traffic status. Experimental results demonstrate that URGENT outperforms major baselines in estimation accuracy under various settings and produces more meaningful results. © 2014 IEEE.
A federated learning-based license plate recognition scheme for 5G-enabled Internet of vehicles
- Kong, Xiangjie, Wang, Kailai, Hou, Mingliang, Hao, Xinyu, Shen, Guojiang, Chen, Xin, Xia, Feng
- Authors: Kong, Xiangjie , Wang, Kailai , Hou, Mingliang , Hao, Xinyu , Shen, Guojiang , Chen, Xin , Xia, Feng
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Transactions on Industrial Informatics Vol. 17, no. 12 (Dec 2021), p. 8523-8530
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- Description: License plate is an essential characteristic to identify vehicles for the traffic management, and thus, license plate recognition is important for Internet of Vehicles. Since 5G has been widely covered, mobile devices are utilized to assist the traffic management, which is a significant part of Industry 4.0. However, there have always been privacy risks due to centralized training of models. Also, the trained model cannot be directly deployed on the mobile device due to its large number of parameters. In this article, we propose a federated learning-based license plate recognition framework (FedLPR) to solve these problems. We design detection and recognition model to apply in the mobile device. In terms of user privacy, data in individuals is harnessed on their mobile devices instead of the server to train models based on federated learning. Extensive experiments demonstrate that FedLPR has high accuracy and acceptable communication cost while preserving user privacy.
- Authors: Kong, Xiangjie , Wang, Kailai , Hou, Mingliang , Hao, Xinyu , Shen, Guojiang , Chen, Xin , Xia, Feng
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Transactions on Industrial Informatics Vol. 17, no. 12 (Dec 2021), p. 8523-8530
- Full Text:
- Reviewed:
- Description: License plate is an essential characteristic to identify vehicles for the traffic management, and thus, license plate recognition is important for Internet of Vehicles. Since 5G has been widely covered, mobile devices are utilized to assist the traffic management, which is a significant part of Industry 4.0. However, there have always been privacy risks due to centralized training of models. Also, the trained model cannot be directly deployed on the mobile device due to its large number of parameters. In this article, we propose a federated learning-based license plate recognition framework (FedLPR) to solve these problems. We design detection and recognition model to apply in the mobile device. In terms of user privacy, data in individuals is harnessed on their mobile devices instead of the server to train models based on federated learning. Extensive experiments demonstrate that FedLPR has high accuracy and acceptable communication cost while preserving user privacy.
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