Concurrency and Computation-Practice & Experience最新文献

The proliferation of Open Radio Access Network (O-RAN) architectures in 5G/6G networks introduces unprecedented cybersecurity challenges. Strategic base station deployment constitutes a fundamental determinant of network security posture and cyberattack resilience. In this paper, a novel cybersecurity-driven deployment strategy for resilient base station positioning using an intelligent Resilient Ant Colony Optimization (iResACO) algorithm. The algorithm integrates security considerations directly into deployment optimization, employing bio-inspired collective intelligence to discover patterns that balance coverage efficiency with attack resilience. Through extensive simulations in a 3.6 km $��\times �$ 3.6 km urban environment in Riyadh, Saudi Arabia, experimental results demonstrate superior performance achieving 92.04% overall effectiveness with 96.0% coverage probability and 100% critical infrastructure protection. Under various cyberattack scenarios ranging from random to coordinated sophisticated attacks, the algorithm maintains coverage above 87% while preserving complete protection of critical facilities. The proposed approach provides a practical framework for deploying secure, resilient 5G/6G networks capable of withstanding evolving cyber threats while ensuring uninterrupted service to essential infrastructure.

As model parameters increase exponentially, distributed training has become essential for advancing modern deep neural networks. Megatron-LM, an efficient distributed training framework developed by NVIDIA, enables the training of trillion-parameter models on thousands of GPUs by integrating tensor, pipeline, and data parallelism. Its computational efficiency has established it as a foundational tool for training large-scale models. Rapid identification of optimal parallel configurations for specific GPU clusters is critical for maximizing computational resource utilization, with training time prediction serving as a key evaluation metric. The high cost and limited availability of high-performance GPUs, particularly those based on NVIDIA architectures, have made the construction of large-scale heterogeneous clusters a practical solution to resource and cost constraints. However, existing prediction methods do not reliably or efficiently account for the computational and communication complexities inherent in heterogeneous GPU clusters. To address this gap, HATP (Heterogeneous-Aware Time Predictor) is introduced as a novel performance prediction method specifically designed for heterogeneous GPU clusters. For any given parallel configuration, HATP rapidly and accurately simulates execution times to inform the optimization of parallel strategies. To address communication differences among heterogeneous GPUs, comprehensive experimental analyses are conducted and analytical expressions are derived to characterize the communication frequency patterns in Megatron-LM's parallel strategies. This work presents the first systematic quantification of communication operations within Megatron-LM framework, ensuring that performance predictions remain highly accurate even in complex, heterogeneous environments. Furthermore, to account for computational differences among heterogeneous GPUs, a layer-level computational performance acquisition scheme is proposed to reduce the impact of fine-grained operator overlap and additional memory operations. Experimental results demonstrate that HATP achieves an average prediction accuracy of 97.41% in isomorphic environments, surpassing the current state-of-the-art method, ACEso. HATP also attains an average accuracy of 96.04% in heterogeneous data parallel and pipeline parallel configurations, representing the first extension of training time prediction capabilities to heterogeneous environments.

The increasing application of unmanned aerial vehicles (UAVs) in diverse domains demands highly robust and autonomous path-planning algorithms capable of navigating complex and dynamic environments. To address the multifaceted challenges posed by obstacle avoidance, energy constraints, and environmental uncertainty, this work proposes an ensemble of learning strategies for the optimal path planning of UAVs. We introduce a modular particle swarm optimization and differential evolution (PSO-DE) ensemble framework and systematically investigate the impact of multiple learning and adaptation strategies, such as chaotic parameter adaptation, opposition-based learning (OBL), and a range of DE mutation schemes, to enhance the optimization process. We perform extensive experimentation across 16 carefully designed scenarios with varying complexity against ten competitive algorithms. We demonstrate that the integration of the PSO-DE hybrid with the opposition-based learning (OBLPSODE) achieves faster convergence while maintaining superior solution quality across all scenarios. The proposed OBLPSODE algorithm substantially outperforms other hybrid variants in both computational efficiency and path optimality, particularly excelling in cluttered environments where traditional algorithms often converge prematurely. Beyond algorithmic contributions, this work provides critical complexity analysis identifying obstacle-checking operations as the primary computational bottleneck in UAV path planning. The findings offer practical guidance for deploying UAVs in real-world applications and establish transferable design principles for developing adaptive meta-heuristics in complex optimization domains.

Vessel re-identification (ReID) plays a critical role in maritime surveillance by matching vessels across different camera views. Compared with person or vehicle ReID, vessel ReID faces unique challenges due to subtle interclass differences and large intraclass variations caused by viewpoint changes. These issues are further exacerbated by the highly similar appearances of vessels and the lack of fine-grained identity cues commonly found in other ReID tasks. To address these challenges, we propose a spatial-channel fusion network (SCF-Net), a dual-branch deep framework that integrates a spatial-channel fusion (SCF) module and a feature refinement and alignment (FRA) module. The SCF module captures interdependent relationships between spatial and channel dimensions, enabling the network to emphasize discriminative regions while suppressing irrelevant background information. The FRA module refines high-dimensional embeddings into a compact representation and enforces intraclass similarity via a learnable multilayer perceptron (MLP) and a supervised mean squared error (MSE) loss. By jointly optimizing the two branches and the FRA output, SCF-Net effectively learns both interclass discrimination and intraclass compactness. Extensive experiments demonstrate that SCF-Net achieves competitive performance on public vessel ReID benchmarks, highlighting its effectiveness in handling subtle interclass differences and large intraclass variations.

Tunnel face instability prediction represents a critical technical challenge in underground engineering, particularly during tunnel boring machine (TBM) excavation under complex geological conditions. This study proposes the Multi-modal Gaussian Cross-Attention Fusion (MGCAF) algorithm, which integrates physics-constrained Gaussian processes with cross-attention mechanisms to achieve intelligent tunnel collapse prediction. The MGCAF framework reconstructs the traditional prediction paradigm by treating earth pressure balance chamber pressure as the primary prediction target rather than an input parameter, while incorporating first-principles constraints of TBM cutting mechanisms into kernel function design. The algorithm employs a dual-pathway architecture that fuses TBM operational parameters through temporal modeling, processes geological radar images via deep feature extraction, and achieves cross-modal information fusion through physics-constrained cross-attention mechanisms. Dynamic kernel optimization enables real-time adaptive parameter adjustment through multi-source gradient feedback. Validation results based on the Yinsong Water Diversion Tunnel project (20 km length, 9 collapse events) demonstrate that the algorithm achieves high-precision prediction with R² = 0.8330, successfully predicting major collapse locations with approximately 20-m accuracy. Comparative analysis against baseline methods (Transformer, Gaussian Process, Random Forest, XGBoost) indicates that MGCAF exhibits superior performance in engineering reliability (0.95) and ROC-AUC (0.765) metrics. Generalization testing on the 2025 Los Angeles Wilmington Sewage Outfall Tunnel confirms the algorithm's cross-domain applicability. Ablation experiments reveal that the cross-attention mechanism serves as the primary performance driver, while uncertainty quantification provides interpretable risk assessment for TBM operations in heterogeneous geological environments.

The growing demand for data sharing in domains such as health care highlights limitations in existing solutions, including low search efficiency, coarse-grained access control, and heavy decryption overhead on users. To address these challenges, this paper proposes a hierarchical searchable attribute-based encryption scheme with outsourced decryption for blockchain-based data sharing (OD-H-SABE). OD-H-SABE introduces a hierarchical attribute structure alongside an outsourced decryption mechanism that offloads computationally intensive bilinear operations to the cloud server. Consequently, users only need to perform a single lightweight operation to complete decryption, significantly alleviating the computational burden. Furthermore, the scheme integrates searchable encryption with multi-keyword aggregate hashing, enabling efficient search with constant complexity regardless of the number of keywords. Leveraging the transparency and immutability of blockchain, smart contracts verify the integrity of results returned from the cloud server, ensuring data security and trustworthiness throughout the sharing process. Theoretical and experimental analyses demonstrate that OD-H-SABE achieves notable advantages over traditional schemes in terms of security, search efficiency, and computational overhead. For example, compared to MKS-VABE and BEM-ABSE, OD-H-SABE reduces encryption and user-side decryption overhead by approximately 20% and 42%, respectively. This makes it a practical and lightweight solution for constructing secure and efficient blockchain-based data-sharing platforms.

Financial markets exhibit high levels of non-stationarity and structural heterogeneity, which pose significant challenges to reinforcement learning (RL)-based portfolio optimization methods. To address these challenges, this paper proposes a Structure-Aware Deep Reinforcement Learning (SADRL) framework for cross-market portfolio optimization. The proposed framework explicitly models market structural dynamics through a structure encoder that identifies latent market regimes, while a policy learner adapts investment strategies accordingly. This dual-level learning mechanism enables the model to generalize across heterogeneous markets and remain stable under regime shifts. Extensive experiments on multiple cross-market datasets demonstrate that SADRL achieves superior risk-adjusted returns and improved robustness compared with conventional RL-based baselines. These findings highlight the potential of structure-aware learning for developing intelligent and adaptive decision-making systems in financial markets.

Fog presents a substantial latent hazard to highway traffic safety, significantly impairing drivers' visibility, thereby elevating the risk of high-speed collisions. While driving, occlusion of multiple targets against complex backgrounds diminishes the detection rate of vehicle detectors. Many existing vehicle detection methods depend on bounding box representations for vehicle identification, limiting their capacity to provide accurate localization, particularly in foggy highway conditions. To enable early warnings of preceding vehicles in fog, this article proposes AG-YOLOv10n, a novel vehicle detection method for foggy environments. This approach improves the model's adaptability to fog-induced target features by replacing standard convolutional layers with AKConv and incorporating the GCAM gated convolutional attention module to enhance the extraction of locally salient information, thereby improving vehicle recognition accuracy in fog. Simultaneously, the DeepSORT tracking algorithm is enhanced, with AG-YOLOv10n replacing the traditional Faster R-CNN detector, and combined with the Kalman filter and Hungarian matching mechanism to achieve stable tracking of vehicle targets. The proposed method enhances the accuracy, recall rate, and average precision of the baseline model by 1.4%, 0.6%, and 1.1%, respectively, on the foggy vehicle dataset. The results demonstrate that the proposed method effectively improves detection accuracy, real-time performance, and system robustness while maintaining the model's lightweight nature, which holds significant practical application for highway fog driving safety.

In the context of security protection against false data injection attacks (FDIAs) in power grids, traditional federated learning effectively utilizes decentralized data resources for distributed training and achieves global collaboration. However, during the model aggregation process, it often overlooks or drowns out local sparse key features, significantly increasing the risk of missed detection of specific attack patterns. To address this issue, this paper proposes a personalized detection framework based on federated learning. Initially, the bidirectional transformer detection (BTD) model detection algorithm is deployed on the client side and trained on local data. Subsequently, through personalized federated learning, the client dynamically combines the weights of the global and local models to generate a personalized detection model. The framework employs a collaborative optimization mechanism of “global knowledge sharing and local feature adaptation” to effectively mitigate the feature drowning problem while strictly safeguarding data privacy. Compared to existing methods, this approach significantly enhances detection accuracy and robustness against differentiated attack patterns, thereby establishing a more reliable security defense system for smart grids.

Telecommunication fraud has escalated in complexity due to evolving adversarial strategies that exploit dynamic communication patterns, multimodal signals, and semantic manipulation. Recent developments in deep learning and graph-based modeling have shown promise; however, existing systems struggle to simultaneously capture temporal dependencies, relational feature structures, and linguistic nuances embedded in modern fraud activities. Addressing these limitations, this study proposes an Improved Graph Convolutional Network–Bidirectional LSTM (IGCN–Bi-LSTM) framework integrated within a unified signal-to-text and multi-perspective feature-engineering pipeline for high-accuracy fraud detection. The system begins by converting raw telecommunication signals into structured textual representations through a CNN-driven signal-to-text encoder, enabling the extraction of temporal–spectral patterns. These sequences are subsequently enriched through a comprehensive feature-engineering module that synthesizes linguistic markers, statistical descriptors, lexical indicators, and semantic embeddings. The hybrid IGCN–Bi-LSTM model then jointly learns higher-order relational dependencies among features and bidirectional temporal patterns, while an adaptive score-level fusion mechanism optimally weights model outputs for robust classification. Experiments were conducted using a high-quality synthetic Fraud Detection Transactions Dataset comprising 50,000 transactions with 21 heterogeneous attributes covering behavioral, contextual, financial, and security-related characteristics. Extensive preprocessing, normalization, and stratified data partitioning ensured reliable training of the hybrid model in a GPU-accelerated environment. The proposed model demonstrated substantial improvements over baseline methods by effectively capturing weakly correlated, high-dimensional features and rare-event patterns. Performance evaluation using precision-recall metrics confirmed the superiority of the IGCN–Bi-LSTM fusion, particularly in highly imbalanced scenarios where conventional accuracy metrics fail to reflect true detection capability.