Search Results (1,564)

Conventional methods for calculating sortie mission reliability of shipborne vehicle layouts suffer from excessive computational overhead, long runtimes, and large labeled data requirements. To address these limitations, this work proposes a specialized graph neural network architecture tailored for limited-data small-sample scenarios, denoted as the Small-Sample Graph Neural Network (SS-GNN). The proposed SS-GNN integrates multi-relational graph convolutional layers, an adaptive attention weighting mechanism, small-sample regularization techniques, and an uncertainty quantification module to accurately capture the heterogeneous multidimensional dependencies between vehicles. To further improve learning performance under data-scarce conditions, we employ a hybrid training strategy combining meta-learning-based pretraining, contrastive learning for representation enhancement, knowledge distillation, and transfer learning. Experimental results demonstrate that SS-GNN substantially outperforms traditional reliability calculation methods, classical machine learning models, and state-of-the-art GNN baselines across three key dimensions: predictive accuracy, computational efficiency, and generalization robustness, while also providing theoretically grounded uncertainty estimates for all predictions. This work provides both a theoretical foundation and a practical technical framework for shipborne vehicle reliability prediction and offers a generalizable solution for small-sample graph regression tasks in industrial domains. Future work will focus on extending the approach to extremely low-data regimes via specialized few-shot learning algorithms, incorporating dynamic relation modeling for time-varying sortie processes, and integrating domain knowledge graphs to broaden its operational applicability. Full article

Cross-institutional collaborative fraud detection is essential for combating increasingly sophisticated financial fraud, yet privacy regulations and data silos severely constrain knowledge sharing among institutions. This study aims to develop a privacy-preserving framework that enables effective collaborative fraud detection while protecting raw data, with particular emphasis on exploiting symmetry properties in federated architectures and graph topology analysis. We propose an Adaptive Federated Graph Attention Network (FedGAT), which employs spatio-temporal graph attention mechanisms to capture topological structures and dynamic fraud patterns within institutional transaction networks. The framework introduces a symmetric similarity matrix derived from graph topological features, where the symmetry property ($s_{ij}=s_{ji}$) ensures consistent and unbiased measurement of structural relationships between any pair of institutions. Based on this symmetric similarity metric, an adaptive weighted aggregation mechanism is designed for cross-institutional parameter fusion, enabling balanced knowledge transfer that respects the symmetric collaborative relationship among participating institutions. The symmetric information exchange protocol between local institutions and the central server further guarantees equitable contribution and benefit distribution throughout the federated learning process. The framework is evaluated on the Elliptic Bitcoin transaction dataset and the IEEE-CIS fraud detection dataset, with recall rate and false positive rate as primary performance metrics. Results show that FedGAT achieves a recall of 0.85 and a false-positive rate of 0.038 in single-institution detection, representing approximately 40% and 70% improvements over existing methods, respectively. In collaborative detection across five virtual institutions, the symmetry-aware adaptive aggregation mechanism enables all participants to achieve performance gains exceeding 15% while completely eliminating negative transfer effects observed in simple averaging approaches. This work contributes a novel symmetry-based federated learning framework that balances privacy protection with detection performance, advancing the literature on cross-institutional financial risk management. Full article

With the rapid growth of fashion e-commerce, fashion attribute recognition has emerged as a critical research area in computer vision. Existing methods face two primary problems: (1) building multi-task models, leading to complex network architectures; (2) the overlooking of semantic relationships and spatial positional dependencies between fashion attributes. To address these issues, this paper proposes SLAR-Net, a novel hierarchical multi-label classification network that effectively fuses spatial and semantic information for improved recognition performance. Specifically, SLAR-Net adopts a progressive, hierarchical architecture. Firstly, we introduce a lightweight backbone network enhanced with a custom-designed attention mechanism to extract low-level image features. Secondly, we innovatively construct an adjacency matrix to represent the relative spatial orientations of attributes, which is then employed by a graph convolutional network to model mid-level spatial positional features. Thirdly, we design a graph embedding matrix that captures attribute dependency relationships, leveraging a neural network to learn high-level semantic representations. Finally, we propose a custom multi-head attention mechanism to fuse spatial and semantic features, facilitating enhanced feature interaction and improving recognition performance. Experimental results on fashion attribute and benchmark datasets demonstrate that SLAR-Net outperforms state-of-the-art methods in recognition accuracy, validating the effectiveness of the proposed hierarchical architecture and fusion strategy. Full article

The Chengdu–Chongqing Twin-City Economic Circle (CC-TCEC), located in the Sichuan Basin, frequently experiences persistent winter PM_2.5 pollution due to basin-constrained ventilation and strong meteorology–emission coupling. Using daily PM_2.5 observations from 113 monitoring stations with a strict two-year training and one-year testing split, we develop hybrid spatiotemporal forecasting models that couple a graph neural network (GCN/GAT) for inter-station spatial dependence learning with a temporal backbone (LSTM/Transformer) for evolving concentration dynamics. We adopt a rolling one-day-ahead forecasting scheme using a 7-day look-back window. Across 12-month, 6-month, and 3-month evaluation windows, the meteorology-augmented Multi-GAT-Transformer shows a slight but consistent advantage over the other tested variants, suggesting potential benefits of attention-based spatial weighting and long-range temporal self-attention under nonstationary basin pollution regimes. Spatiotemporal mappings derived from the best-performing configuration suggest that elevated winter PM_2.5 is mainly associated with low-lying areas such as the Chengdu Plain, industry clusters, and dense urban cores, with peaks that also coincide with the New Year and the pre-Lunar New Year period, suggesting a possible contribution from elevated traffic and production activity. These impacts are amplified by winter stagnation (low winds, high humidity, limited precipitation). From a policy perspective, the results support sustainability-oriented winter haze management by enabling early episode warning and hotspot prioritization. Full article

On 12 July 2016, the ruling on the South China Sea Arbitration was announced and rapidly drew worldwide attention, turning the event into a major international hotspot. Quantifying the dynamics of such hotspot events and understanding the evolution of media-based inter-actor networks during major shocks are of substantial research interest. Viewing these interactions as dynamic networks, we analyze the time-varying actor interaction structure surrounding the arbitration using the Global Database of Events, Location and Tone (GDELT), a large-scale media-based event database with global coverage since 1979. We extract nearly 30,000 events related to the arbitration from 5 July to 25 July 2016, constructing daily cooperation and conflict networks to quantify structural changes via network size and degree-entropy dynamics. To further reveal actor-level structural roles, we learn node embeddings on each daily network via an entropy-driven graph representation learning scheme and perform embedding-based clustering with automatically selected cluster numbers, visualized via t-SNE. The results show that key dates in the event window are associated with pronounced structural shifts in the networks, including changes in participation breadth, degree-distribution heterogeneity, and clearer differentiation and reconfiguration of actor roles, with distinct patterns between cooperation and conflict networks. These findings demonstrate the potential of massive media event data for characterizing structural responses and actor-role evolution in event-driven inter-actor networks. Full article

Machining process route planning (MPRP) is vital for autonomous manufacturing yet remains challenging under complex, multi-dimensional engineering constraints. This paper proposes an attention-augmented deep reinforcement learning (DRL) framework to achieve intelligent process orchestration. First, an Optional Process Attribute Adjacency Graph (OPAAG) is established to formally model the “feature–process–resource–constraint” coupling, enhancing the agent’s perception of manufacturing semantics. The architecture synergistically integrates Graph Attention Networks (GAT) to perceive spatial benchmark dependencies and a Transformer-based encoder to capture sequential resource correlations within variable-length machining chains. Furthermore, a dynamic action masking mechanism is integrated to guarantee a 100% constraint satisfaction rate during both training and inference stages. Experimental evaluations across diverse part geometries demonstrate that the proposed method offers significant advantages in cost optimization, inference efficiency, and topological stability compared to traditional heuristic algorithms and standard DRL models. By effectively distilling the search space and maintaining action feasibility, the framework provides an efficient and robust solution for autonomous process planning in complex industrial scenarios. Full article

Multimodal named entity recognition (MNER) leverages both textual and visual information to improve entity recognition, particularly in unstructured scenarios such as social media. While existing approaches predominantly rely on raster images (e.g., JPEG, PNG), scalable vector graphics (SVG) offer unique advantages in resolution independence and structured semantic representation—an underexplored potential in multimodal learning. To fill this gap, we propose MNER-SVG, the first framework that incorporates SVG as a visual modality and enhances it with ChatGPT-generated auxiliary knowledge. Specifically, we introduce a Multimodal Similar Instance Perception Module that retrieves semantically relevant examples and prompts ChatGPT to generate contextual explanations. We further construct a Full-Text Graph and a Multimodal Interaction Graph, which are processed via Graph Attention Networks (GATs) to achieve fine-grained cross-modal alignment and feature fusion. Finally, a Conditional Random Field (CRF) layer is employed for structured decoding. To support evaluation, we present SvgNER, the first MNER dataset annotated with SVG-specific visual content. Extensive experiments demonstrate that MNER-SVG achieves state-of-the-art performance with an F1 score of 82.23%, significantly outperforming both text-only and existing multimodal baselines. This work validates the feasibility and potential of integrating vector graphics and large language model-generated knowledge into multimodal NER, opening a new research direction for structured visual semantics in fine-grained multimodal understanding. Full article

The screw pump is a critical device for elevating downhole petroleum to the surface, and screw pump failure can significantly disrupt the production of oil wells. Due to the complex structure of the screw pump, the same pump fault can cause different changes in the monitoring parameters, and different faults can also cause the same parameter change. In consequence of the complexity, it requires a large amount of labeled data for a diagnosis model to achieve fault diagnosis of a screw pump in practical application. Aiming for this kind of condition, we discovered the dynamic coupling effect between multi-source information through detailed research on the collected data of screw pumps. To fully leverage the information dynamic coupling (IDC) effect, a semi-supervised learning graph attention network (SSL-GAT) fault diagnosis method is proposed. This approach integrates the semi-supervised learning framework and graph attention neural network for the fault diagnosis of a screw pump. The experimental validation of the SSL-GAT method demonstrates its outstanding performance in screw pump fault diagnosis. Full article

Forecasting monthly precipitation in mountainous terrain poses challenges that push conventional deep learning approaches to their limits: convective processes operate locally while orographic effects span entire drainage basins. We compare three architecture families on precipitation prediction across the Colombian Andes: ConvLSTM (convolutional recurrent), FNO-ConvLSTM (spectral–temporal), and GNN-TAT (graph attention LSTM). Using CHIRPS v2.0 and SRTM topography for Boyacá department (61 × 65 grid, 3965 nodes), we evaluate 39 configurations across feature bundles (BASIC, KCE elevation clusters, and PAFC autocorrelation lags) and horizons from 1 to 12 months. GNN-TAT matches ConvLSTM accuracy ($R^2$: 0.628 vs. 0.642; RMSE: 82.29 vs. 79.40 mm) with 95% fewer parameters (∼98K vs. 2.1M). Across configurations, GNN-TAT produces a lower mean RMSE (92.12 vs. 112.02 mm; $p=0.015$) and a 74.7% lower variance. The explicit graph structure, with edges weighted by elevation similarity, appears to reduce sensitivity to hyperparameter choices. Pure FNO struggles with precipitation’s spatial discontinuities ($R^2=0.206$), though adding a ConvLSTM decoder recovers much of the lost skill ($R^2=0.582$). Elevation clustering improves GNN-TAT significantly ($p=0.036$) but not ConvLSTM, suggesting that feature design should match the spatial encoding paradigm. ConvLSTM achieves peak accuracy on local patterns; GNN-TAT provides robust predictions with interpretable spatial reasoning. These complementary strengths motivate stacking ensembles that combine grid-based and graph-based representations. Full article

Accurate pedestrian trajectory prediction and intention estimation are crucial for autonomous systems and intelligent transportation applications. However, existing methods often address these two highly correlated tasks in isolation and rely on static or heuristic interaction modeling, leading to insufficient adaptability and limited generalization capability in dynamic traffic scenarios. To this end, this paper proposes MTG-TPNet, a Multi-task dynamic Graph Transformer network for joint Trajectory Prediction and intention estimation. The research framework integrates three key innovations: First, a dynamic graph neural network enhanced with motion features, whose graph topology can be adaptively learned end-to-end based on semantic and motion contexts to accurately capture evolving interactions. Second, a multi-granularity attention mechanism that collaboratively fuses geometric proximity, semantic similarity, and physical hard constraints to achieve fine-grained modeling of spatiotemporal dependencies. Third, a dynamic correlation loss based on Bayesian uncertainty, which balances multi-task learning in an adaptive manner and encourages beneficial interactions across tasks. Extensive experiments on the publicly available PIE and ETH/UCY datasets demonstrate that MTG-TPNet achieves state-of-the-art performance. On the PIE dataset, the proposed model significantly outperforms the best baseline model in trajectory prediction metrics, achieving an Average Displacement Error (ADE) of 0.21 and a Final Displacement Error (FDE) of 0.29. This represents a 27.6% reduction in ADE while maintaining stability in intention estimation. Systematic ablation studies validate the effectiveness of each proposed module, with the model retaining an average performance of 69.3%. Furthermore, cross-dataset evaluations confirm its superior generalization capability. This study provides a powerful unified framework for robust pedestrian behavior understanding in complex urban traffic scenarios. Full article

Anti-money laundering (AML) in cryptocurrency networks presents significant challenges due to complex transactional relationships, severe class imbalance, and limited labeled data, which severely constrain the scalability and label efficiency of existing AML systems. Traditional machine learning approaches treat transactions independently and fail to capture the intricate network structures inherent in money laundering schemes. To address these limitations, we propose HeteroGCL, a heterogeneous graph contrastive learning framework for scalable and sustainable cryptocurrency AML. Our approach models cryptocurrency transactions as a heterogeneous graph with multiple node and edge types and integrates a heterogeneous graph attention network with a graph contrastive learning module. By leveraging unlabeled data through topology-aware and attribute-aware graph augmentations, HeteroGCL mitigates label scarcity while enabling scalable and label-efficient AML model training while reducing reliance on costly manual annotation. Extensive experiments on the Elliptic dataset demonstrate that HeteroGCL achieves superior performance over state-of-the-art baselines, achieving an F1-score of 0.824 and an AUC of 0.912, with a 4.7% improvement in F1-score compared to the CARE-GNN baseline. The results indicate that the proposed framework effectively captures complex money laundering patterns while supporting scalable deployment of AML systems and improving the economic and operational sustainability of blockchain AML infrastructures. Full article

Accurately predicting and controlling the attitude of a shield tunneling machine is critical for quality assurance in shield tunneling projects. Existing prediction methods utilize historical data to construct a machine learning framework to predict future attitude deviations. However, this method is poorly interpretable and lacks practical engineering guidance. Considering the shortcomings of this prediction method, this study suggests an innovative deep learning method called causal graph convolutional network (C-GCN-GRU), and the goal of this project is the improvement of the interpretability of the shield attitude prediction. The causal relationships between key attitude features of the shield machine are recognized and quantified by the PCMCI+ method. The found causal relationships are converted into collocation matrices to be input into a model consisting of GCN and GRU, and combined with multi-head causal attention to better forecast the shield machine attitude. The results trained on a dataset from the Karnaphuli River Tunnel Project in Bangladesh show that the accuracy of the four variables characterizing the shield attitude and position predicted by the C-GCN-GRU model outperforms that of the other four similar models and provides decision support for attitude and position adjustments in shield tunnels. Full article

Unmanned aerial vehicles (UAVs) acting as mobile aerial edge platforms can deliver on-demand communication and computing for the Internet of Vehicles (IoV) via flexible deployment and line-of-sight (LoS) links, improving reliability and reducing latency. However, high vehicle mobility, time-varying channels, and limited onboard energy make task offloading and resource coordination challenging. This paper studies joint task offloading and resource allocation in a UAV-assisted IoV system, where the UAV selects its hovering position from discrete candidate sites each time slot and splits vehicular tasks between the UAV and a roadside unit (RSU) to relieve backhaul congestion and enhance edge resource utilization. Considering vehicle mobility, multi-stage queue dynamics, and UAV energy consumption for communication, computation, and movement, the online optimization of position selection, task splitting, and bandwidth allocation is formulated as a constrained Markov decision process (CMDP). The goal is to maximize the number of tasks completed within the latency deadlines while satisfying the UAV energy budget. To solve this CMDP, we propose a graph-attention-based constrained twin delayed deep deterministic policy gradient (GAT-CTD3) algorithm. A graph attention network captures spatial correlations and resource competition among active vehicles, while a Lagrangian TD3 framework enforces long-term energy constraints and improves learning stability via twin critics, delayed policy updates, and target smoothing. The simulation results demonstrate that it outperforms the comparative scheme in terms of task completion rate, delay, and energy consumption per completed task, and exhibits strong robustness in situations with dense traffic. Full article

As distributed terminals are increasingly integrated into modern power systems with high penetration of renewable energy and decentralized resources, access control mechanisms must support continuous and highly detailed trust assessment. Existing approaches based on machine learning primarily rely on network traffic features from a single source and analyze terminals in isolation, which limits their ability to capture complex device states and correlated attack behaviors. This paper presents a trust assessment framework for distributed power grid terminals that combines multidimensional behavioral modeling with dual domain graph neural networks. Behavioral features are collected from network traffic, runtime environment, and hardware or kernel events and are fused into compact representations through a variational autoencoder to mitigate redundancy and reduce computational overhead. Based on the fused features and observed communication relationships, two graphs are constructed in parallel: a feature domain graph reflecting behavioral similarity and a topological domain graph capturing communication structure between terminals. Graph convolution is performed in both domains to jointly model individual behavioral risk and correlation across terminals. A fusion mechanism based on attention is further introduced to adaptively integrate embeddings specific to each domain, together with a loss function that enforces both shared and complementary representations across domains. Experiments conducted on the CIC EV Charger Attack Dataset 2024 show that the proposed framework achieves a classification accuracy of 96.84%, while maintaining a recall rate above 95% for the low trust category. These results indicate that incorporating multidimensional behavior perception and dual domain relational modeling improves trust assessment performance for distributed power grid terminals under complex attack scenarios. Full article

Rotating machinery serves as a critical component in various engineering systems, making accurate prediction of its Remaining Useful Life (RUL) essential for ensuring operational stability. To address the technical limitations of mainstream RUL prediction models comprehensively capturing spatial correlations among multiple sensors, this paper proposes a multi-graph-structured spatiotemporal feature fusion model for RUL prediction of rotating machinery. Breaking through the constraints of constructing a single correlation graph, the model first builds two distinct graphs—a prior correlation graph based on the structural mechanism of the rotating machinery and a similarity correlation graph derived from monitoring data distribution characteristics. These dual-perspective graphs collectively characterize the potential spatial dependencies among multiple sensors. Subsequently, a Graph Attention Network (GAT) is introduced to aggregate spatial features from both graphs, and a feature concatenation fusion strategy is adopted to achieve a comprehensive representation of the inter-sensor spatial dependencies. Finally, a Long Short-Term Memory (LSTM) network is employed to extract temporal evolution features from the operational data. The effective fusion of these spatial and temporal features enhances the model’s RUL prediction performance. Simulation experiments conducted on the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset validated the robustness of the proposed method. Full article