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学者姓名：张勇
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Abstract ：
Crowd counting is very important in many fields such as public safety, urban planning, and is essential for the intelligent transportation systems. Due to the complexity and diversity of traffic scenes, point-level annotations for pedestrians would cost much human labor. Weakly supervised crowd counting methods are more suitable for these scenes, considering they only require count-level annotations. However, ignoring the uneven distribution of cross-distance crowd region density and multi-scale pedestrian head, existing weakly supervised methods can not achieve similar counting performance as fully supervised crowd counting methods. To solve these issues, we propose a novel multi-level dynamic graph convolutional networks for weakly supervised crowd counting. Within this network, a multi-level region dynamic graph convolutional module is designed to mine the cross-distance intrinsic relationship between crowd regions. A feature enhancement module is used to enhance crowd semantic information. In addition, we design a coarse grained multi-level feature fusion module to aggregate multi-scale pedestrian information. Experiments are conducted on five well-known benchmark crowd counting datasets, achieving state-of-the-art results compared to existing weakly supervised methods and competitive results compared to fully supervised methods.
Keyword ：
transformer transformer Crowd counting Crowd counting graph convolutional network graph convolutional network weakly supervised weakly supervised
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| GB/T 7714 | Miao, Zhuangzhuang , Zhang, Yong , Ren, Hao et al. Multi-Level Dynamic Graph Convolutional Networks for Weakly Supervised Crowd Counting [J]. | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS , 2023 . |
| MLA | Miao, Zhuangzhuang et al. "Multi-Level Dynamic Graph Convolutional Networks for Weakly Supervised Crowd Counting" . | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2023) . |
| APA | Miao, Zhuangzhuang , Zhang, Yong , Ren, Hao , Hu, Yongli , Yin, Baocai . Multi-Level Dynamic Graph Convolutional Networks for Weakly Supervised Crowd Counting . | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS , 2023 . |
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Abstract ：
Social link is an important index to understand master students' mental health and social ability in educational management. Extracting hidden social strength from students' rich daily life behaviors has also become an attractive research hotspot. Devices with positioning functions record many students' spatiotemporal behavior data, which can infer students' social links. However, under the guidance of school regulations, students' daily activities have a certain regularity and periodicity. Traditional methods usually compare the co-occurrence frequency of two users to infer social association but do not consider the location-intensive and time-sensitive in campus scenes. Aiming at the campus environment, a Spatiotemporal Entropy-Based Analyzing (S-EBA) model for inferring students' social strength is proposed. The model is based on students' multi-source heterogeneous behavioral data to calculate the frequency of co-occurrence under the influence of time intervals. Then, the three features of diversity, spatiotemporal hotspot and behavior similarity are introduced to calculate social strength. Experiments show that our method is superior to the traditional methods under many evaluating criteria. The inferred social strength is used as the weight of the edge to construct a social network further to analyze its important impact on students' education management.
Keyword ：
Social link Social link campus big data campus big data AI for education AI for education social network social network data mining data mining
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| GB/T 7714 | Li, Mengran , Zhang, Yong , Li, Xiaoyong et al. Inferring student social link from spatiotemporal behavior data via entropy-based analyzing model [J]. | INTELLIGENT DATA ANALYSIS , 2023 , 27 (1) : 137-163 . |
| MLA | Li, Mengran et al. "Inferring student social link from spatiotemporal behavior data via entropy-based analyzing model" . | INTELLIGENT DATA ANALYSIS 27 . 1 (2023) : 137-163 . |
| APA | Li, Mengran , Zhang, Yong , Li, Xiaoyong , Lin, Xuanqi , Yin, Baocai . Inferring student social link from spatiotemporal behavior data via entropy-based analyzing model . | INTELLIGENT DATA ANALYSIS , 2023 , 27 (1) , 137-163 . |
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Abstract ：
Real-time passenger-flow anomaly detection at all metro stations is a very critical task for advanced Internet management. Robust principal component analysis (RPCA) based method has often been employed for anomaly detection task of multivariate time series data. However, it ignores the spatio-temporal features of regular passenger-flow patterns, resulting in a decrease in the accuracy of anomaly detection. In this paper, RT-STRPCA model integrating temporal periodicity and spatial similarity is proposed to address the above issues. RT-STRPCA model detects anomalies by decomposing the observation data into normal component and anomaly component. The spatio-temporal constraints are taken into account to extract anomalies more accurately. The real-time anomaly detection are realized by a sliding window. The performance of RT-STRPCA model is evaluated on synthetic datasets and real-world datasets. The experimental results on synthetic datasets demonstrate that the method achieves more accurate real-time anomaly detection than baseline approaches and the result on real-world datasets verify the utility and effectiveness of the proposed method.
Keyword ：
traffic modelling traffic modelling real-time systems real-time systems management and control management and control intelligent transportation systems intelligent transportation systems time series time series
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| GB/T 7714 | Wei, Xiulan , Zhang, Yong , Zhang, Xinyu et al. Real-time passenger flow anomaly detection in metro system [J]. | IET INTELLIGENT TRANSPORT SYSTEMS , 2023 , 17 (10) : 2020-2033 . |
| MLA | Wei, Xiulan et al. "Real-time passenger flow anomaly detection in metro system" . | IET INTELLIGENT TRANSPORT SYSTEMS 17 . 10 (2023) : 2020-2033 . |
| APA | Wei, Xiulan , Zhang, Yong , Zhang, Xinyu , Ge, Qibin , Yin, Baocai . Real-time passenger flow anomaly detection in metro system . | IET INTELLIGENT TRANSPORT SYSTEMS , 2023 , 17 (10) , 2020-2033 . |
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Abstract ：
Weakly supervised crowd counting involves the regression of the number of individuals present in an image, using only the total number as the label. However, this task is plagued by two primary challenges: the large variation of head size and uneven distribution of crowd density. To address these issues, we propose a novel Hypergraph Association Crowd Counting (HACC) framework. Our approach consists of a new multi-scale dilated pyramid module that can efficiently handle the large variation of head size. Further, we propose a novel hypergraph association module to solve the problem of uneven distribution of crowd density by encoding higher-order associations among features, which opens a new direction to solve this problem. Experimental results on multiple datasets demonstrate that our HACC model achieves new state-of-the-art results.
Keyword ：
Crowd counting Crowd counting uneven distribution of crowd density uneven distribution of crowd density hypergraph neural network hypergraph neural network hypergraph association hypergraph association
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| GB/T 7714 | Li, Bo , Zhang, Yong , Zhang, Chengyang et al. Hypergraph AssociationWeakly Supervised Crowd Counting [J]. | ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS , 2023 , 19 (6) . |
| MLA | Li, Bo et al. "Hypergraph AssociationWeakly Supervised Crowd Counting" . | ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS 19 . 6 (2023) . |
| APA | Li, Bo , Zhang, Yong , Zhang, Chengyang , Piao, Xinglin , Yin, Baocai . Hypergraph AssociationWeakly Supervised Crowd Counting . | ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS , 2023 , 19 (6) . |
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Abstract ：
Crowd counting is a hot issue in visual data processing. It also plays an important role in the field of video surveillance, social security, and traffic control. However, most of the existing crowd counting methods always adopt a mount of training data or point-level annotation to learn the mapping relationships between images and density maps, which would cost much human labor. In this paper, we propose a new weakly semi-supervised crowd counting method which uses less count-level data for data training. In particular, we extend the classical smoothness assumption and design a many-to-many Region Feature Smoothness Assumption to deal with the uneven density distribution problem within crowd region. Further, we adopt hypergraph representation to explore the complex high-order relationship for different crowd regions. Besides, we design a multi-scale dynamic hypergraph convolutional module and hyperedge contrastive loss. Extensive experiments have been conducted on five public datasets. The experimental results show that the proposed method outperforms the state-of-the-art ones.
Keyword ：
crowd counting crowd counting social security social security hypergraph hypergraph semi-supervised semi-supervised
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| GB/T 7714 | Miao, Zhuangzhuang , Zhang, Yong , Piao, Xinglin et al. Region feature smoothness assumption for weakly semi-supervised crowd counting [J]. | COMPUTER ANIMATION AND VIRTUAL WORLDS , 2023 , 34 (3-4) . |
| MLA | Miao, Zhuangzhuang et al. "Region feature smoothness assumption for weakly semi-supervised crowd counting" . | COMPUTER ANIMATION AND VIRTUAL WORLDS 34 . 3-4 (2023) . |
| APA | Miao, Zhuangzhuang , Zhang, Yong , Piao, Xinglin , Chu, Yi , Yin, Baocai . Region feature smoothness assumption for weakly semi-supervised crowd counting . | COMPUTER ANIMATION AND VIRTUAL WORLDS , 2023 , 34 (3-4) . |
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Abstract ：
Graph convolutional networks (GCN) have been applied in the traffic flow forecasting tasks with the graph capability in describing the irregular topology structures of road networks. However, GCN based traffic flow forecasting methods often fail to simultaneously capture the short-term and long-term temporal relations carried by the traffic flow data, and also suffer the over-smoothing problem. To overcome the problems, we propose a hierarchical traffic flow forecasting network by merging newly designed the long-term temporal Transformer network (LTT) and the spatio-temporal graph convolutional networks (STGC). Specifically, LTT aims to learn the long-term temporal relations among the traffic flow data, while the STGC module aims to capture the short-term temporal relations and spatial relations among the traffic flow data, respectively, via cascading between the one-dimensional convolution and the graph convolution. In addition, an attention fusion mechanism is proposed to combine the long-term with the short-term temporal relations as the input of the graph convolution layer in STGC, in order to mitigate the over-smoothing problem of GCN. Experimental results on three public traffic flow datasets prove the effectiveness and robustness of the proposed method.
Keyword ：
Predictive models Predictive models Task analysis Task analysis transformer transformer traffic data forecasting traffic data forecasting Transformers Transformers Graph convolutional networks Graph convolutional networks Convolution Convolution Forecasting Forecasting Network topology Network topology Roads Roads
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| GB/T 7714 | Huo, Guangyu , Zhang, Yong , Wang, Boyue et al. Hierarchical Spatio-Temporal Graph Convolutional Networks and Transformer Network for Traffic Flow Forecasting [J]. | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS , 2023 , 24 (4) : 3855-3867 . |
| MLA | Huo, Guangyu et al. "Hierarchical Spatio-Temporal Graph Convolutional Networks and Transformer Network for Traffic Flow Forecasting" . | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 24 . 4 (2023) : 3855-3867 . |
| APA | Huo, Guangyu , Zhang, Yong , Wang, Boyue , Gao, Junbin , Hu, Yongli , Yin, Baocai . Hierarchical Spatio-Temporal Graph Convolutional Networks and Transformer Network for Traffic Flow Forecasting . | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS , 2023 , 24 (4) , 3855-3867 . |
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Abstract ：
The traffic data corrupted by noise and missing entries often lead to the poor performance of Intelligent Transportation Systems (ITS), such as the bad congestion prediction and route guidance. How to efficiently impute the traffic data is an urgent problem. As a classic deep learning method, Generative Adversarial Network (GAN) achieves remarkable success in image recovery fields, which opens up a new way for the traffic data imputation. In this paper, we propose a novel spatio-temporal GAN model for the traffic data imputation (STGAN). Firstly, we design the generative loss and center loss, which not only minimizes the reconstructed errors of the imputed entries, but also ensures each imputed entry and its neighbors conform to the local spatio-temporal distribution. Then, the discriminator uses the convolution neural network classifier to judge whether the imputed matrix conforms to the global spatio-temporal distribution. As for the network architecture of the generator, we introduce the skip-connection to keep all well preserved data unchanged, and employ the dilated convolution to capture the spatio-temporal correlation in the traffic data. The experimental results show that our proposed method obviously outperforms other competitive traffic data imputation methods.
Keyword ：
Generative adversarial networks Generative adversarial networks Task analysis Task analysis Data models Data models Generators Generators Image reconstruction Image reconstruction Matrix decomposition Matrix decomposition traffic data imputation traffic data imputation Data mining Data mining Correlation Correlation generative adversarial network generative adversarial network
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| GB/T 7714 | Yuan, Ye , Zhang, Yong , Wang, Boyue et al. STGAN: Spatio-Temporal Generative Adversarial Network for Traffic Data Imputation [J]. | IEEE TRANSACTIONS ON BIG DATA , 2023 , 9 (1) : 200-211 . |
| MLA | Yuan, Ye et al. "STGAN: Spatio-Temporal Generative Adversarial Network for Traffic Data Imputation" . | IEEE TRANSACTIONS ON BIG DATA 9 . 1 (2023) : 200-211 . |
| APA | Yuan, Ye , Zhang, Yong , Wang, Boyue , Peng, Yuan , Hu, Yongli , Yin, Baocai . STGAN: Spatio-Temporal Generative Adversarial Network for Traffic Data Imputation . | IEEE TRANSACTIONS ON BIG DATA , 2023 , 9 (1) , 200-211 . |
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Abstract ：
With the powerful learning ability of deep convolutional networks, deep clustering methods can extract the most discriminative information from individual data and produce more satisfactory clustering results. However, existing deep clustering methods usually ignore the relationship between the data. Fortunately, the graph convolutional network can handle such relationships, opening a new research direction for deep clustering. In this paper, we propose a cross-attention based deep clustering framework, named Cross-Attention Fusion based Enhanced Graph Convolutional Network (CaEGCN), which contains four main modules: the cross-attention fusion module which innovatively concatenates the Content Auto-encoder module (CAE) relating to the individual data and Graph Convolutional Auto-encoder module (GAE) relating to the relationship between the data in a layer-by-layer manner, and the self-supervised model that highlights the discriminative information for clustering tasks. While the cross-attention fusion module fuses two kinds of heterogeneous representation, the CAE module supplements the content information for the GAE module, which avoids the over-smoothing problem of GCN. In the GAE module, two novel loss functions are proposed that reconstruct the content and relationship between the data, respectively. Finally, the self-supervised module constrains the distributions of the middle layer representations of CAE and GAE to be consistent. Experimental results on different types of datasets prove the superiority and robustness of the proposed CaEGCN.
Keyword ：
Image segmentation Image segmentation Clustering methods Clustering methods graph convolutional network graph convolutional network Cross-attention fusion mechanism Cross-attention fusion mechanism Smoothing methods Smoothing methods Task analysis Task analysis Image reconstruction Image reconstruction Deep learning Deep learning Data mining Data mining deep clustering deep clustering
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| GB/T 7714 | Huo, Guangyu , Zhang, Yong , Gao, Junbin et al. CaEGCN: Cross-Attention Fusion Based Enhanced Graph Convolutional Network for Clustering [J]. | IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING , 2023 , 35 (4) : 3471-3483 . |
| MLA | Huo, Guangyu et al. "CaEGCN: Cross-Attention Fusion Based Enhanced Graph Convolutional Network for Clustering" . | IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 35 . 4 (2023) : 3471-3483 . |
| APA | Huo, Guangyu , Zhang, Yong , Gao, Junbin , Wang, Boyue , Hu, Yongli , Yin, Baocai . CaEGCN: Cross-Attention Fusion Based Enhanced Graph Convolutional Network for Clustering . | IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING , 2023 , 35 (4) , 3471-3483 . |
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Abstract ：
Crowd simulation has been widely used in evacuation exercises, games or movie manufacturing, and many other fields. How to plan reasonable trajectories for pedestrians in a scene is always one of the critical problems in crowd simulation. Traditional simulation methods have the problem of large differences between simulated and actual trajectories, and it is difficult to generate near-real and reasonable multimodal pedestrian trajectories. In this paper, we propose a novel method utilizing generative models for crowd simulation: GAN with Incubator and Extender (IE-GAN). This data-driven model learns the movement laws of pedestrians from real datasets, and simulates a full movement trajectory for the "dummy" without corresponding situations in the dataset through a unique model architecture. In our method, the generated initial trajectory and further trajectories constitute the full trajectory of the "dummy". Incubator networks based on long-term memory network (LSTM) are used to generate the initial trajectory, and the further trajectory is generated by the Extender, which is based on a generative adversarial network (GAN). The experimental results show that the trajectories generated by our model can approach real human's trajectories.
Keyword ：
Pedestrian trajectory Pedestrian trajectory Generative adversarial networks Generative adversarial networks Crowd simulation Crowd simulation Deep learning Deep learning
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| GB/T 7714 | Lin, Xuanqi , Liang, Yuchen , Zhang, Yong et al. IE-GAN: a data-driven crowd simulation method via generative adversarial networks [J]. | MULTIMEDIA TOOLS AND APPLICATIONS , 2023 . |
| MLA | Lin, Xuanqi et al. "IE-GAN: a data-driven crowd simulation method via generative adversarial networks" . | MULTIMEDIA TOOLS AND APPLICATIONS (2023) . |
| APA | Lin, Xuanqi , Liang, Yuchen , Zhang, Yong , Hu, Yongli , Yin, Baocai . IE-GAN: a data-driven crowd simulation method via generative adversarial networks . | MULTIMEDIA TOOLS AND APPLICATIONS , 2023 . |
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Abstract ：
Community detection aims to identify dense subgroups of nodes within a network. However, in real-world networks, node attributes are often missing, making traditional methods less effective. In networks with missing attributes, the main challenge of community detection is to deal with the missing attribute information efficiently and use network structure information to make accurate predictions. This article proposes an innovative method called contrastive sampling-aggregating transformer (CSAT) for community detection in attribute-missing networks. CSAT incorporates the contrastive learning principle to capture hidden patterns among nodes and to aggregate information from different samples to create a more robust and accurate methodology for community detection. Specifically, CSAT utilizes a sampling and propagation strategy to obtain different samples and smooth attribute features of the network structure and leverages the Transformer architecture to model the pairwise relationships between nodes. Therefore, our method can address the attribute-missing issue by integrating the auxiliary information from both the network structure and other sources. Extensive experiments on several benchmark datasets demonstrate CSAT's superior performance compared to the state-of-the-art methods for community detection.
Keyword ：
community detection community detection graph contrastive learning graph contrastive learning Transformer Transformer deep graph representation learning deep graph representation learning Attribute-missing graph Attribute-missing graph
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| GB/T 7714 | Li, Mengran , Zhang, Yong , Zhang, Wei et al. CSAT: Contrastive Sampling-Aggregating Transformer for Community Detection in Attribute-Missing Networks [J]. | IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS , 2023 . |
| MLA | Li, Mengran et al. "CSAT: Contrastive Sampling-Aggregating Transformer for Community Detection in Attribute-Missing Networks" . | IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS (2023) . |
| APA | Li, Mengran , Zhang, Yong , Zhang, Wei , Zhao, Shiyu , Piao, Xinglin , Yin, Baocai . CSAT: Contrastive Sampling-Aggregating Transformer for Community Detection in Attribute-Missing Networks . | IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS , 2023 . |
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