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Traffic flow prediction is a pivotal element in Intelligent Transportation Systems (ITSs) that provides significant opportunities for real-world applications. Capturing complex and dynamic spatio-temporal patterns within traffic data remains a significant challenge for traffic flow prediction. Different approaches to effectively modeling complex spatio-temporal correlations within traffic data have been proposed. These approaches often rely on a single model to capture temporal dependencies, which neglects the varying influences of different time periods on traffic flow. Additionally, these models frequently utilize either static or dynamic graphs to represent spatial dependencies, which limits their ability to address complex and overlapping spatial relationships. Moreover, some approaches struggle to fully capture spatio-temporal variations, leading to the exclusion of critical information and ultimately resulting in suboptimal prediction performance. Thus, this paper introduces the Adaptive Spatio-Temporal Attention-Based Multi-Model (ASTAM), an architecture designed to capture spatio-temporal dependencies within traffic data. The ASTAM employs multi-temporal gated convolution with multi-scale temporal input segments to model complex non-linear temporal correlations. It utilizes static and dynamic parallel multi-graphs to facilitate the modeling of complex spatial dependencies. Furthermore, this model incorporates a spatio-temporal self-attention mechanism to adaptively capture the dynamic and long-term spatio-temporal variations in traffic flow. Experiments conducted on four real-world datasets reveal that the proposed architecture outperformed 13 baseline approaches, achieving average reductions of 5.0% in MAE, 13.28% in RMSE, and 6.46% in MAPE across four datasets. [ABSTRACT FROM AUTHOR]

Within the realm of social recommendation, a recommender system can enhance its performance through the use of social information among users. Due to the abundance of redundant information in user interactions and social connections, it affects the performance of recommendation results negatively. Existing recommendation models do not distinguish the influence of different users and different friends. To solve this problem, this paper introduces a new recommendation framework, user preference and social relationship-aware light graph convolutional networks (USLGCN). The proposed framework distinguishes between users based on their interactions with items and social relationships to enhance recommendation accuracy. Specifically, we design a subgraph classification strategy that divides the user–item interaction graph and social graph into different subgraphs to capture the impact of various user types on items and friends, thereby reducing negative information and enhancing model resilience. On top of that, we also design a graph fusion module that enhances recommendation performance by fusing data from multiple subgraphs together. Experiments on public datasets show that USLGCN exhibits a 2.6% increase in recall accuracy compared to other social recommendation methods. [ABSTRACT FROM AUTHOR]

Network botnet attacks have been increasing rapidly because of the widespread use of interconnected Internet of Things (IoT) devices. These devices can be used for many malicious actions, such as phishing, fraud, data theft, and distributed computing attacks against IoT networks. The traditional methods of botnet detection fail to capture the relationships between network nodes that exhibit coordinated behavior. In this paper, we introduce a novel Graph-based Modified Attention with Convolutional Neural Network (GrMA-CNN) for the effective detection of botnet attacks. The novelty of GrMA-CNN lies in its integration of spectral and spatial layers within a Graph Convolutional Network (GCN). It combines the GCN with a modified attention mechanism to effectively capture relationships and coordinated behaviours among IoT devices in graph-structured data. The approach extract features from network flow traffic using hybrid feature selection techniques, which include mutual information, correlation analysis, and principal component analysis. The extracted features are then processed through a GCN, with spectral and spatial layers that operates directly on graph-structured data. In this context, each IoT device and its associated features are represented as nodes, while the relationships between these devices are modelled as edges in the graph. The robustness of the model is verified on different datasets, such as N-BaIoT, BoT-IoT, CTU-13, and CICIDS. The proposed model obtained an accuracy of 99.1% on N-BaIoT, 99.2% on BoT-IoT, 99.15% on CTU-13, and 99.3% on CICIDS datasets. Further the model has achieved an average precision of 98.82%, a recall of 99.02%, and F1-score of 98.51%. The performance comparison demonstrates that the proposed model outperforms state-of-the-art botnet detection methods, including DNN, SGDC, WCC, and IHHO-NN with high detection rate. [ABSTRACT FROM AUTHOR]

Green logistics and environmentally-friendly logistics necessitates transport system to be reliable for delivery. The reliability of transport system is usually measured by travel time reliability (TTR). Compared with the TTR on a single road or path, the TTR of trip (delivery) seems more important for managers and logistics operators in decision making. To estimate the trip-based reliability, this paper firstly defines the trip-based reliability as 'the arriving late risk between an OD pair'. In the trip-based reliability, OD pair rather than path or road is chosen as the object, which is different from the existing TTR. Aggregating the trips with the same origin in a specific time interval, we then introduce network travel risk (NTR) to evaluate the reliability of zone. Further, this paper develops a temporal graph neural network with heterogeneous features (TGCNHF) to provide the real-time NTR. In this model, features are divided into tendency-based and periodicity-based and handled respectively by two 1-D convolution layers on time axis. After stacking the length of time intervals to 1, a graph convolution is employed to extract the spatial correlation. Then, a fully connected layer with a SoftMax function accomplishes the NTR prediction. To test the proposed TGCNHF, a real-world travel time dataset collected in Beijing main urban area is used in comparison. The results show that our TGCNHF model can extract the spatio-temporal correlation from traffic data and the predictions overperform the state-of-art baselines on real-world traffic datasets. [ABSTRACT FROM AUTHOR]

Real-time and accurate traffic flow forecasting plays a crucial role in transportation systems and holds great significance for urban traffic planning, traffic management, traffic control, and more. The most difficult challenge is the extraction of temporal features and spatial correlations of nodes in traffic flow forecasting. Meanwhile, graph convolutional networks has shown good performance in extracting relational spatial dependencies in existing methods. However, it is difficult to accurately mine the hidden spatial-temporal features of the traffic network by using graph convolution alone. In this paper, we propose a multi-attention gated temporal graph convolution network (MATGCN) for accurately forecasting the traffic flow. Firstly, we propose a gated multi-modal temporal convolution(MTCN) to handle the long-term series of the raw traffic data. Then, we use an efficient channel attention module(ECA) to extract temporal features. For the complexity of the spatial structure of traffic roads, we develop multi-attention graph convolution module (MAGCN)including graph convolution and graph attention to further extract the spatial features of a road network. Finally, extensive experiments are carried out on several public traffic datasets, and the experimental results show that our proposed algorithm outperforms the existing methods. [ABSTRACT FROM AUTHOR]

• Multi-pattern connection graph convolutional networks can effectively identify essential tremor with resting tremor and tremor-dominant Parkinson's disease. • Different connection modes may provide distinct discriminative information for diagnosis. • The occipital network and basal ganglion-temporal lobe networks appear to be tremor-related networks for rET and tPD, respectively. Essential tremor with resting tremor (rET) and tremor-dominant Parkinson's disease (tPD) share many similar clinical symptoms, leading to frequent misdiagnoses. Functional connectivity (FC) matrix analysis derived from resting-state functional MRI (Rs-fMRI) offers a promising approach for early diagnosis and for exploring FC network pathogenesis in rET and tPD. However, methods relying solely on a single connection pattern may overlook the complementary roles of different connectivity patterns, resulting in reduced diagnostic differentiation. Therefore, we propose a multi-pattern connection Graph Convolutional Network (MCGCN) method to integrate information from various connection modes, distinguishing between rET and healthy controls (HC), tPD and HC, and rET and tPD. We constructed FC matrices using three different connectivity modes for each subject and used these as inputs to the MCGCN model for disease classification. The classification performance of the model was evaluated for each connectivity mode. Subsequently, gradient-weighted class activation mapping (Grad-CAM) was used to identify the most discriminative brain regions. The important brain regions identified were primarily distributed within cerebellar-motor and non-motor cortical networks. Compared with single-pattern GCN, our proposed MCGCN model demonstrated superior classification accuracy, underscoring the advantages of integrating multiple connectivity modes. Specifically, the model achieved an average accuracy of 88.0% for distinguishing rET from HC, 88.8% for rET from tPD, and 89.6% for tPD from HC. Our findings indicate that combining graph convolutional networks with multi-connection patterns can not only effectively discriminate between tPD, rET, and HC but also enhance our understanding of the functional network mechanisms underlying rET and tPD. [ABSTRACT FROM AUTHOR]

Kitchen waste images encompass a wide range of garbage categories, posing a typical multi-label classification challenge. However, due to the complex background and significant variations in garbage morphology, there is currently limited research on kitchen waste classification. In this paper, we propose a multi-head attention-driven dynamic graph convolution lightweight network for multi-label classification of kitchen waste images. Firstly, we address the issue of large model parameterization in traditional GCN methods by optimizing the backbone network for lightweight model design. Secondly, to overcome performance losses resulting from reduced model parameters, we introduce a multi-head attention mechanism to mitigate feature information loss, enhancing the feature extraction capability of the backbone network in complex scenarios and improving the correlation between graph nodes. Finally, the dynamic graph convolution module is employed to adaptively capture semantic-aware regions, further boosting recognition capabilities. Experiments conducted on our self-constructed multi-label kitchen waste classification dataset MLKW demonstrate that our proposed algorithm achieves a 8.6% and 4.8% improvement in mAP compared to the benchmark GCN-based methods ML-GCN and ADD-GCN, respectively, establishing state-of-the-art performance. Additionally, extensive experiments on two public datasets, MS-COCO and VOC2007, showcase excellent classification results, highlighting the strong generalization ability of our algorithm. [ABSTRACT FROM AUTHOR]

The key to improving the performance of graph convolutional networks (GCN) is to fully explore the correlation between neighboring and distant information. Aiming at the over-smoothing problem of GCN, in order to make full use of the relationship among features, graphs and labels, a graph residual generation network based on multi-information aggregation (MIA-GRGN) is proposed. Firstly, aiming at the defects of GCN, we design a deep initial residual graph convolution network (DIRGCN), which connects the initial input through residuals, so that each layer node retains part of the information of the initial features, ensuring the localization of the graph structure and effectively alleviating the problem of over-smoothing. Secondly, we propose a random graph generation method (RGGM) by utilizing graph edge sampling and negative edge sampling, and optimize the supervision loss function of DIRGCN in the form of generation framework. Finally, applying RGGM and DIRGCN as inference modules for modeling hypotheses and obtaining approximate posterior distributions of unknown labels, an optimized loss function is obtained, we construct a multi-information aggregation MIA-GRGN that combines graph structure, node characteristics and label joint distribution. Experiments on benchmark graph classification datasets show that MIA-GRGN achieves better classification results compared with the benchmark models and mainstream models, especially for datasets with less dense edge relationships between nodes. [ABSTRACT FROM AUTHOR]

Pedestrian trajectory prediction holds significant research value in various fields, such as autonomous driving, autonomous service robots, and human flow monitoring. Two key challenges in pedestrian trajectory prediction are the modeling of pedestrian social interactions and movement factors. Previous methods have not utilized interpretable information to explore complex situations when modeling social interactions. These methods also focus too much on temporal interactions at each moment when modeling movement factors and are therefore susceptible to slight motion changes. To solve the above problems, we propose an Interpretable Masked Graph Convolution Network (IMGCN) for pedestrian trajectory prediction. The IMGCN utilizes interpretable information such as the pedestrian view area, distance, and motion direction to intelligently mask interaction features, resulting in more precise modeling of social interaction and movement factors. Specifically, we design a spatial and a temporal branch to model pedestrians' social interaction and movement factors, respectively. Within the spatial branch, the view-distance mask module masks pedestrian social interaction by determining whether the pedestrian is within a certain distance and view area to achieve more accurate interaction modeling. In the temporal branch, the motion offset mask module masks pedestrian temporal interaction according to the offset degree of their motion direction to achieve accurate modeling of movement factors. Ultimately, the 2D Gaussian distribution parameters of future trajectory points are predicted by the temporal convolution networks for multi-modal trajectory prediction. On the ETH, UCY and SDD datasets, our proposed method outperforms the baseline models in terms of average displacement error and final displacement error. The code is publicly available at . [ABSTRACT FROM AUTHOR]

Non-rigid 3D shape correspondence aims to establish dense correspondences between two non-rigidly deformed 3D shapes. However, the variability and symmetry of non-rigid shapes usually lead to mismatches due to shape deformation, topological changes, or data with severe noise. To finding an accurate correspondence between 3D dynamic shapes for the local deformation complexity, this article proposes a Relation Constrained Capsule Graph Network (RC-CGNet), which combines global and local features by encouraging the relation constraints between the embedding feature space and the input shape space based on the functional maps framework. Specifically, we design a Diffusion Graph Attention Network (DGANet) to segment the surface into parts with correct edge boundary between two regions. The Minimum Spanning Tree (MST) of geodesic curves among the singularities obtained from the segmented parts is added as relation constraints, which can compute isometric correspondences in both direct and symmetric directions. Besides that, the relation-and-attention constrained neural networks are designed to learn the shape correspondence via attention-aware CapsNet and functional maps under relation constraints. To improve the convergence speed and matching accuracy, we propose an optimized residual network structure based on the Nesterov Accelerated Gradient (NAG) to extract local features, and use graph convolution structure to extract global features. Moreover, a lightweight Gated Attention Module (GAM) is designed to fuse global and local features to obtain a richer feature representation. Since the capsule network has better spatial reasoning ability than the traditional convolutional neural network, our novel network architecture is a dual-route capsule network based on Routing Attention Fusion Block (RAFB), filtering low-discriminative capsules from a holistic view by exploiting geometric hierarchical relationships of semantic parts. Experiments on open datasets show that our method has excellent accuracy and wide adaptability. [ABSTRACT FROM AUTHOR]

The task of pedestrian trajectory prediction remains challenging due to variable scenarios, complex social interactions, and uncertainty in pedestrian motion. Previous trajectory prediction research only models from the time domain, which makes it difficult to accurately capture the global and detailed features of complex pedestrian social interactions and the uncertainty of pedestrian movement. These methods also ignore the relationship between scene features and the potential motion patterns of pedestrians. Therefore, we propose a wavelet transform graph convolution network to obtain accurate pedestrian potential motion patterns through time-frequency analysis. We first construct spatial and temporal graphs, then obtain the attention score matrices through the self-attention mechanism in the time domain and combine them with the scene features. Then, we utilize the two-dimensional discrete wavelet transform to generate low-frequency and high-frequency components for representing global and detailed features of spatial-temporal interactions. These components are then further processed using asymmetric convolution, and the wavelet transform adjacency matrix is obtained through the inverse wavelet transform. We then employ graph convolution to combine the graph and the adjacency matrix to obtain spatial and temporal interaction features. Finally, we design the wavelet transform temporal convolution network to directly predict the two-dimensional Gaussian distribution parameters of the future trajectory. Extensive experiments on the ETH, UCY, and SDD datasets demonstrate that our method outperforms the state-of-the-art methods in prediction performance. [ABSTRACT FROM AUTHOR]

Audio descriptions present a tool that helps blind audience members assist theater performances by conveying visual information, such as actors' gestures. However, its high production process cost and effort limit its availability. To address this, we propose a computer vision based system for automated actor gestures recognition, using the state-of-the-art spatio-temporal graph convolution networks (ST-GCNs) for skeleton-based action recognition via transfer learning technique. Hence, we evaluated the transferability of three pre-trained ST-GCNs: the first proposed spatio-temporal graph convolution network (ST-GCN), convolution network of two-stream adaptive graphs (2s-AGCN), and the multi-scale disentangled unified graph convolution network (MS-G3D). We used NTU-RGBD action benchmark as the source domain and collected a novel dataset: TS-RGBD, to serve as the target domain. We then proposed two configurations to accommodate the diversity between the source and target domains. Results showed that ST-GCNs exhibit positive transferability enhancing the models' recognition performance in theatre contexts, promoting automated system for gesture accessibility in theaters. [ABSTRACT FROM AUTHOR]

Graph convolution subspace clustering has been widely used in the field of hyperspectral image (HSI) unsupervised classification due to its ability to aggregate neighborhood information. However, existing methods focus on using graph convolution techniques to design feature extraction functions, ignoring the mutual optimization of the graph convolution operator and the self-expression coefficient matrix, leading to suboptimal clustering results. In addition, these methods directly construct graphs on raw data, which may be easily affected by noises and then degrade the clustering performance, as the constructed topology is not credible for the training procedure. To address these issues, we propose a novel method called spatial-spectral adaptive graph convolutional subspace clustering (S2AGCSC). We employ the reconstruction coefficient matrix to devise a graph convolutional operator with adjacency matrix, which collaboratively computes both the feature representations and coefficient matrix, and the graph-convolutional operator is updated iteratively and adaptively during training. In addition, we harness a combination of spectral and spatial features to introduce additional view information to help learn more robust features and generate more refined superpixels. Experimental validation on three HSI datasets confirms the efficacy of S2AGCSC.

Abstract Background The integration of multi-omics data through deep learning has greatly improved cancer subtype classification, particularly in feature learning and multi-omics data integration. However, key challenges remain in embedding sample structure information into the feature space and designing flexible integration strategies. Results We propose MoAGL-SA, an adaptive multi-omics integration method based on graph learning and self-attention, to address these challenges. First, patient relationship graphs are generated from each omics dataset using graph learning. Next, three-layer graph convolutional networks are employed to extract omic-specific graph embeddings. Self-attention is then used to focus on the most relevant omics, adaptively assigning weights to different graph embeddings for multi-omics integration. Finally, cancer subtypes are classified using a softmax classifier. Conclusions Experimental results show that MoAGL-SA outperforms several popular algorithms on datasets for breast invasive carcinoma, kidney renal papillary cell carcinoma, and kidney renal clear cell carcinoma. Additionally, MoAGL-SA successfully identifies key biomarkers for breast invasive carcinoma.

Background: The integration of multi-omics data through deep learning has greatly improved cancer subtype classification, particularly in feature learning and multi-omics data integration. However, key challenges remain in embedding sample structure information into the feature space and designing flexible integration strategies. Results: We propose MoAGL-SA, an adaptive multi-omics integration method based on graph learning and self-attention, to address these challenges. First, patient relationship graphs are generated from each omics dataset using graph learning. Next, three-layer graph convolutional networks are employed to extract omic-specific graph embeddings. Self-attention is then used to focus on the most relevant omics, adaptively assigning weights to different graph embeddings for multi-omics integration. Finally, cancer subtypes are classified using a softmax classifier. Conclusions: Experimental results show that MoAGL-SA outperforms several popular algorithms on datasets for breast invasive carcinoma, kidney renal papillary cell carcinoma, and kidney renal clear cell carcinoma. Additionally, MoAGL-SA successfully identifies key biomarkers for breast invasive carcinoma. [ABSTRACT FROM AUTHOR]

The financial transaction relationships between existing entities are complex and diverse. In this situation, traditional risk control methods mainly ignored such complex and implicit relationship characteristics, remaining difficult to cope with complex and ever-changing financial risks. To address this issue, this paper proposes a novel relational graph convolution network (GCN)-based smart risk recognition model for financial transactions. Firstly, the classic GCN is simplified based on spatiotemporal effect. Then, feature extraction is conducted for financial transaction data, and a transformer encoder-based GCN model is proposed for risk recognition. The proposed model in this work is named as graph transformer graph convolutional network (GT-GCN) for short. In addition, fuzzy evaluation method is added into it. Finally, some experiments are conducted on real-world financial transaction data to make validation for the proposed GT-GCN. The research results indicate that the GT-GCN can not only effectively identify risks in financial transactions, but also has high accuracy and predictive ability. The application of GT-GCN to actual datasets also has good scalability and adaptability, and it can be resiliently extended into many other fields. [ABSTRACT FROM AUTHOR]

Emotion recognition enables machines to more acutely perceive and understand users' emotional states, thereby offering more personalized and natural interactive experiences. Given the regularity of the responses of brain activity to human cognitive processes, we propose a powerful and novel dual-stream multi-level graph convolution network (DMGCN) with the ability to capture the hierarchies of connectivity between cerebral cortex neurons and improve computational efficiency. This consists of a hierarchical dynamic geometric interaction neural network (HDGIL) and multi-level feature fusion classifier (M2FC). First, the HDGIL diversifies representations by learning emotion-related representations in multi-level graphs. Subsequently, M2FC integrates advantages from methods for early and late feature fusion and enables the addition of more details to final representations from EEG samples. We conducted extensive experiments to validate the superiority of our model over numerous state-of-the-art (SOTA) baselines in terms of classification accuracy, the efficiency of graph embedding and information propagation, achieving accuracies of 98.73%, 95.97%, 72.74% and 94.89% for our model as well as increases of up to 0.59%, 0.32%, 2.24% and 3.17% over baselines on the DEAP-Arousal, DEAP-Valence, DEAP and SEED datasets, respectively. Additionally, these experiments demonstrated the effectiveness of each module for emotion recognition tasks. [ABSTRACT FROM AUTHOR]

Group recommendation involves suggesting items or activities to a group of users based on their collective preferences or characteristics. Graph contrastive learning is a technique used to learn representations of items and users in a graph structure. Although contrastive learning-based recommendation techniques reduce the data sparsity problem by extracting general features from raw data and also make the representation of user-item bipartite graph augmentations more consistent, the factors contributing to improving the performance of this technique are still not fully understood. Meanwhile, graph augmentations have little importance in contrastive learning-based recommendation and are relatively unreliable. The eXtremely Simple Graph Contrastive Learning (XSimGCL) provides novel insights into the effect of contrastive learning on recommendation, where views for contrastive learning are created through a simple yet effective noise-based embedding augmentation. Although XSimGCL infers the final group decision by dynamically aggregating the preferences of group members and includes various types of interaction, the performance of supervised learning is reduced due to the data sparsity problem, and as a result, the efficiency of group preference representation is limited. To address this challenge, we developed a Group Recommendation model based on XsimGCL in this study (GR-GCL). GR-GCL is inspired by the Light Graph Convolution Network (LightGCN) to realize simultaneous learning of multiple graphs, where initial embedding is considered the only update parameter. Also, GR-GCL improves group recommendation by applying cross-layer contrastive learning in the XSimGCL model by representing more diverse entities. The rationality analysis of our proposed GR-GCL has been performed on several datasets from both analytical and empirical perspectives. Although our model is very simple, it performs better in group recommendations by adjusting the uniformity of representations learned from counterparts based on contrastive learning. [ABSTRACT FROM AUTHOR]

Skeleton-based human action recognition has great potential for human behavior analysis owing to its simplicity and robustness in varying environments. This paper presents a spatial and temporal attention-enhanced graph convolution network (STAEGCN) for human action recognition. The spatial-temporal attention module in the network uses convolution embedding for positional information and adopts multi-head self-attention mechanism to extract spatial and temporal attention separately from the input series of the skeleton. The spatial and temporal attention are then concatenated into an entire attention map according to a specific ratio. The proposed spatial and temporal attention module was integrated with an adaptive graph convolution network to form the backbone of STAEGCN. Based on STAEGCN, a two-stream skeleton-based human action recognition model was trained and evaluated. The model performed better on both NTU RGB+D and Kinetics 400 than 2s-AGCN and its variants. It was proven that the strategy of decoupling spatial and temporal attention and combining them in a flexible way helps improve the performance of graph convolution networks in skeleton-based human action recognition. [ABSTRACT FROM AUTHOR]

Spatial temporal forecasting of urban sensors is essentially important for many urban systems, such as intelligent transportation and smart cities. However, due to the problem of hardware failure or network failure, there are some missing values or missing monitoring sensors that need to be interpolated. Recent research on deep learning has made substantial progress on imputation problem, especially temporal aspect (i.e., time series imputation), while little attention has been paid to spatial aspect (both dynamic and static) and long-term temporal dependencies. In this article, we proposed a spatial temporal imputation model, named Long Short-Term Graph Convolution Networks (LSTGCN), which includes gated temporal extraction (GTE) module, multi-head attention-based temporal capture (MHAT) module, long-term periodic temporal encoding (LPTE) module, and bidirectional spatial graph convolution (BSGC) module. The GTE adopts a gated mechanism to filter short-term temporal information, while the MHAT utilizes position encoding to enhance the difference of each timestamps, then use multi-head attention to capture short-term temporal dependency. The BSGC is adopted to handle with spatial relationships between sensor nodes. And we design a periodic encoding technique to process long-term temporal dependencies. The BSGC handles spatial relationships between sensor nodes, and a periodic encoding technique is used to process long-term temporal dependencies. Our experimental analysis includes completion and forecasting tasks, as well as transfer and ablation analyses. The results show that our proposed model outperforms state-of-the-art baselines on real-world datasets. [ABSTRACT FROM AUTHOR]

The key of current research on behavior recognition algorithms based on deep learning lies in enhancing the accuracy and stability of key point extraction, in order to achieve more accurate action recognition of targets. However, many current algorithms tend to just add attention mechanisms that appear to perform better in the feature extraction stage of the target, without considering the impact of different attention mechanisms on different models and tasks. Therefore, this study proposes an algorithmic model for pose estimation based on various attention mechanisms, which further highlights the importance of selecting an appropriate attention mechanism by comparing the impact of different attention mechanisms on the model. In addition, considering the stability of key point extraction, the initialization of the model is fine tuned to select a more suitable initialization method that improves the performance by increasing the category of weights on network layer judgments. Compared with the performance of the benchmark network model, the model enhances all evaluation metrics on both multiscale and no multiscale CrowdPose datasets, where the average accuracy improvement in both cases is more than 1%. [ABSTRACT FROM AUTHOR]

In the field of autonomous driving, trajectory prediction of traffic agents is an important and challenging problem. Fully capturing the complex spatio-temporal features in trajectory data is crucial for accurate trajectory prediction. This paper proposes a trajectory prediction model called multi-dimensional spatio-temporal feature fusion (MDSTF), which integrates multi-dimensional spatio-temporal features to model the trajectory information of traffic agents. In the spatial dimension, we employ graph convolutional networks (GCN) to capture the local spatial features of traffic agents, spatial attention mechanism to capture the global spatial features, and LSTM combined with spatial attention to capture the full-process spatial features of traffic agents. Subsequently, these three spatial features are fused using a gate fusion mechanism. Moreover, during the modeling of the full-process spatial features, LSTM is capable of capturing short-term temporal dependencies in the trajectory information of traffic agents. In the temporal dimension, we utilize a Transformer-based encoder to extract long-term temporal dependencies in the trajectory information of traffic agents, which are then fused with the short-term temporal dependencies captured by LSTM. Finally, we employ two temporal convolutional networks (TCN) to predict trajectories based on the fused spatio-temporal features. Experimental results on the ApolloScape trajectory dataset demonstrate that our proposed method outperforms state-of-the-art methods in terms of weighted sum of average displacement error (WSADE) and weighted sum of final displacement error (WSFDE) metrics. Compared to the best baseline model (S2TNet), our method achieves reductions of 4.37% and 6.23% respectively in these metrics. [ABSTRACT FROM AUTHOR]

Cervical cancer is a prevalent disease affecting the cervix cells in women and is one of the leading causes of mortality for women globally. The Pap smear test determines the risk of cervical cancer by detecting abnormal cervix cells. Early detection and diagnosis of this cancer can effectively increase the patient's survival rate. The advent of artificial intelligence facilitates the development of automated computer-assisted cervical cancer diagnostic systems, which are widely used to enhance cancer screening. This study emphasizes the segmentation and classification of various cervical cancer cell types. An intuitive but effective segmentation technique is used to segment the nucleus and cytoplasm from histopathological cell images. Additionally, handcrafted features include different properties of the cells generated from the distinct cervical cytoplasm and nucleus area. Two feature rankings techniques are conducted to evaluate this study's significant feature set. Feature analysis identifies the critical pathological properties of cervical cells and then divides them into 30, 40, and 50 sets of diagnostic features. Furthermore, a graph dataset is constructed using the strongest correlated features, prioritizes the relationship between the features, and a robust graph convolution network (GCN) is introduced to efficiently predict the cervical cell types. The proposed model obtains a sublime accuracy of 99.11% for the 40-feature set of the SipakMed dataset. This study outperforms the existing study, performing both segmentation and classification simultaneously, conducting an in-depth feature analysis, attaining maximum accuracy efficiently, and ensuring the interpretability of the proposed model. To validate the model's outcome, we tested it on the Herlev dataset and highlighted its robustness by attaining an accuracy of 98.18%. The results of this proposed methodology demonstrate the dependability of this study effectively, detecting cervical cancer in its early stages and upholding the significance of the lives of women. [ABSTRACT FROM AUTHOR]

Existing research extensively incorporates syntactic information, especially dependency trees, to enhance the performance of relation extraction tasks. However, relying solely on dependency information may not fully exploit the rich semantic and syntactic information contained in sentences, and not all information in dependency trees is substantively helpful for relation extraction. Therefore, this paper proposes the Dependent-Position Relation Graph Convolutional network with Hierarchical Attention (DPR-GHA) for relation extraction, a method that integrates dependency relations and position relations into a hierarchical attention mechanism to effectively capture the relations between entities in text. The method aims to capture rich semantic information and enhance the performance of relation extraction. Specifically, we introduce the dependency relations of sentences and position relations of words to model global dependencies and local features, respectively. Subsequently, a novel hierarchical attention mechanism is introduced into the Graph Convolutional Network (GCN), dynamically adjusting the weights between nodes based on the input of the graph convolutional layer. This adaptive information aggregation enables each node to aggregate information adaptively according to its context and the importance of neighboring nodes. The research results on the SemEval-2010 Task 8 and KBP37 datasets thoroughly validate the effectiveness of the proposed model, demonstrating its significant performance advantage in relation extraction tasks. [ABSTRACT FROM AUTHOR]

As a powerful tool for learning high-dimensional data representation, graph neural networks (GNN) have been applied to predict the remaining useful life (RUL) of rolling bearings. Existing GNN-based RUL prediction methods predominantly rely on constant pre-constructed graphs. However, the degradation of bearings is a dynamic process, and the dependence information between features may change at different moments of degradation. This article introduces a method for RUL prediction based on dynamic graph spatial-temporal dependence information extraction. The raw signal is segmented into multiple periods, and multiple features of each period data are extracted. Then, the correlation coefficient analysis is conducted, and the feature connection graph of each period is constructed based on different analytical results, thereby dynamically mapping the degradation process. The graph data is fed into graph convolutional networks (GCN) to extract spatial dependence between the graph node features in different periods. To make up for the shortcomings of GCN in temporal dependence extraction, the TimesNet module is introduced. TimesNet considers the two-dimensional changes of time series data and can extract the temporal dependence of graph data within and between different time cycles. Experimental results based on the PHM2012 dataset show that the average RUL prediction error of the proposed method is 17.4%, outperforming other comparative methods. [ABSTRACT FROM AUTHOR]

Copy number variation (CNV) refers to the number of copies of a specific sequence in a genome and is a type of chromatin structural variation. The development of the Hi‐C technique has empowered research on the spatial structure of chromatins by capturing interactions between DNA fragments. We utilized machine‐learning methods including the linear transformation model and graph convolutional network (GCN) to detect CNV events from Hi‐C data and reveal how CNV is related to three‐dimensional interactions between genomic fragments in terms of the one‐dimensional read count signal and features of the chromatin structure. The experimental results demonstrated a specific linear relation between the Hi‐C read count and CNV for each chromosome that can be well qualified by the linear transformation model. In addition, the GCN‐based model could accurately extract features of the spatial structure from Hi‐C data and infer the corresponding CNV across different chromosomes in a cancer cell line. We performed a series of experiments including dimension reduction, transfer learning, and Hi‐C data perturbation to comprehensively evaluate the utility and robustness of the GCN‐based model. This work can provide a benchmark for using machine learning to infer CNV from Hi‐C data and serves as a necessary foundation for deeper understanding of the relationship between Hi‐C data and CNV. [ABSTRACT FROM AUTHOR]

Prediction of Remaining Useful Life (RUL) for Rolling Element Bearings (REB) has attracted widespread attention from academia and industry. However, there are still several bottlenecks, including the effective utilization of multi-sensor data, the interpretability of prediction models, and the prediction across the entire life cycle, which limit prediction accuracy. In view of that, we propose a knowledge-based explainable life-cycle RUL prediction framework. First, considering the feature fusion of fast-changing signals, the Pearson correlation coefficient matrix and feature transformation objective function are incorporated to an Improved Graph Convolutional Autoencoder. Furthermore, to integrate the multi-source signals, a Cascaded Multi-head Self-attention Autoencoder with Characteristic Guidance is proposed to construct health indicators. Then, the whole life cycle of REB is divided into different stages based on the Continuous Gradient Recognition with Outlier Detection. With the development of Measurement-based Correction Life Formula and Bidirectional Recursive Gated Dual Attention Unit, accurate life-cycle RUL prediction is achieved. Data from self-designed test rig and PHM 2012 Prognostic challenge datasets are analyzed with the proposed framework and five existing prediction models. Compared with the strongest prediction model among the five, the proposed framework demonstrates significant improvements. For the data from self-designed test rig, there is a 1.66 % enhancement in Corrected Cumulative Relative Accuracy (CCRA) and a 49.00 % improvement in Coefficient of Determination (R2). For the PHM 2012 datasets, there is a 4.04 % increase in CCRA and a 120.72 % boost in R2. [Display omitted] • Advocate explainable knowledge-data integration for RUL prediction throughout the life cycle, leveraging multi-sensor data. • Introduce a novel unsupervised learning framework that integrates IGCA and CMSACG to construct HI. • A two-stage prediction framework, fusing MCLF and BR-GDAU, is proposed for predicting the life cycle RUL. • The effectiveness of the proposed framework is validated using both Self-Designed Experiment-Derived and PHM 2012 Prognostic Challenge Bearing Datasets. [ABSTRACT FROM AUTHOR]

Online social networks (OSNs) are an indispensable part of social communication where people connect and share information. Spammers and other malicious actors use the OSN's power to propagate spam content. In an OSN with mutual relations between nodes, two kinds of spammer detection methods can be employed: feature based and propagation based. However, both of these are incomplete in themselves. The feature-based methods cannot exploit mutual connections between nodes, and propagation-based methods cannot utilize the rich discriminating node features. We propose a hybrid model—Markov enhanced graph attention network (MEGAT)—using graph attention networks (GAT) and pairwise Markov random fields (pMRF) for the spammer detection task. It efficiently utilizes node features as well as propagation information. We experiment our GAT model with a smoother Swish activation function having non-monotonic derivatives, instead of the leakyReLU function. The experiments performed on a real-world Twitter Social Honeypot (TwitterSH) benchmark dataset and subsequent comparative analysis reveal that our proposed MEGAT model outperforms the state-of-the-art models in accuracy, precision–recall area under curve (PRAUC), and F1-score performance measures. [ABSTRACT FROM AUTHOR]

Studying gene regulatory networks (GRNs) is paramount for unraveling the complexities of biological processes and their associated disorders, such as diabetes, cancer, and Alzheimer's disease. Recent advancements in computational biology have aimed to enhance the inference of GRNs from gene expression data, a non-trivial task given the networks' intricate nature. The challenge lies in accurately identifying the myriad interactions among transcription factors and target genes, which govern cellular functions. This research introduces a cutting-edge technique, EGRC (Effective GRN Inference applying Graph Convolution with Self-Attention Graph Pooling), which innovatively conceptualizes GRN reconstruction as a graph classification problem, where the task is to discern the links within subgraphs that encapsulate pairs of nodes. By leveraging Spearman's correlation, we generate potential subgraphs that bring nonlinear associations between transcription factors and their targets to light. We use mutual information to enhance this, capturing a broader spectrum of gene interactions. Our methodology bifurcates these subgraphs into 'Positive' and 'Negative' categories. 'Positive' subgraphs are those where a transcription factor and its target gene are connected, including interactions among their neighbors. 'Negative' subgraphs, conversely, denote pairs without a direct connection. EGRC utilizes dual graph convolution network (GCN) models that exploit node attributes from gene expression profiles and graph embedding techniques to classify these. The performance of EGRC is substantiated by comprehensive evaluations using the DREAM5 datasets. Notably, EGRC attained an AUROC of 0.856 and an AUPR of 0.841 on the E. coli dataset. In contrast, the in silico dataset achieved an AUROC of 0.5058 and an AUPR of 0.958. Furthermore, on the S. cerevisiae dataset, EGRC recorded an AUROC of 0.823 and an AUPR of 0.822. These results underscore the robustness of EGRC in accurately inferring GRNs across various organisms. The advanced performance of EGRC represents a substantial advancement in the field, promising to deepen our comprehension of the intricate biological processes and their implications in both health and disease. [ABSTRACT FROM AUTHOR]

ABSTRACTSpatial interaction research is particularly important for geographical analyses, as it plays a crucial role in extracting travel patterns. However, previous studies on spatial interactions have not adequately considered regional population variations over time, resulting in insufficiently precise travel predictions. Moreover, the threshold of spatial correlations is difficult to determine. Existing studies have assumed fully connected spatial correlation matrices, which is not realistic. To address these limitations, we proposed the Self-paced Gaussian-Based Graph Convolutional Network (SG-GCN) to automatically estimate the threshold of spatial correlations for travel flow predictions. It incorporates a temporal dimension into spatial relationship matrices to enhance the accuracy of vehicle flow predictions. In particular, Gaussian-based GCN identifies patterns in a time series of regional flows, enabling more precise capturing of spatial relationships while fusing node and edge features. Building on this model, self-paced contrastive learning automatically sets thresholds to determine the presence or absence of spatial relationships. The model's performance was verified through two empirical case studies conducted in New York City, USA, and Ningbo, China, using 2.8 million bicycle-sharing records and 1.25 million taxi trip records, respectively. The proposed model helps delineate mobility patterns in cities of varying scales and with different modes of transportation.

To solve the spectrum scarcity problem, dynamic spectrum access (DSA) technology has emerged as a promising solution. Effectively implementing DSA demands accurate and efficient spectrum prediction. However, complex spatiotemporal correlation and heterogeneity in spectrum observations usually make spectral prediction arduous and even ambiguous. In this letter, we propose a spectrum prediction method based on an attention-aided graph convolutional neural network (AttGCN) to capture features in both spatial and temporal dimensions. By leveraging the attention mechanism, the AttGCN adapts its attention weights at different time steps and spatial positions, thus enabling itself to seize changes in spatiotemporal correlations dynamically. Simulation results show that the proposed spectrum prediction method performs better than baseline algorithms in long-term forecasting tasks.