Concurrency and Computation-Practice & Experience最新文献

We introduce $��{�\text{DR}�}_{�\pi �}^{�\phi �}�$, a novel routing calculus specifically designed to dynamically update routing tables using distance-vector routing updates in distributed computing networks. This calculus features a three-tier syntactic structure where routers form an undirected graph representing their connectivity, which does not need to be a complete graph. The nodes hosting processes are directly connected to certain routers. A key aspect of our calculus is the periodic and real-time exchange of updates of the routing table between adjacent routers, ensuring that routers consistently provide the optimal path for message transmission between communicating processes. Our calculus offers a more accurate representation of actual distributed networks within a process-algebraic framework. To validate our approach, we demonstrate that the equivalence between well-formed configurations in reduction semantics can be achieved through bi-simulation-based equivalence over labeled transition systems (LTSs), and vice versa.

Multi-Object Tracking (MOT) is promising for UAV applications but faces challenges such as small targets, occlusions, motion blur, and cluttered backgrounds. This work proposes a one-shot MOT framework with a retrospective matching architecture for UAV ground tracking. Using FairMOT as the baseline avoids two-stage redundancy, enhancing UAV deployment efficiency. The lightweight retrospective matching network, comprising feature extraction, sparse graph tracking, and a refinement module, leverages historical information to recover tracking failures, improving continuity and accuracy with low computational cost. Experiments on public benchmarks show superior tracking performance and real-time efficiency.

This study presents DFSplat, a feed-forward 3D Gaussian Splatting model utilizing depth feature fusion for the high-quality reconstruction of a 3D scene from sparse multiview data and the production of novel view images. DFSplat enhances the robustness of depth prediction and the quality of geometric reconstruction by incorporating a pre-trained monocular depth estimation module into the multiview feature matching branch, thereby addressing the limitations of current methods for multiview depth estimation in complex scenes. The method employs a content-guided attention (CGA) module to adaptively integrate monocular depth features with multiview cost-volume features, addressing the fusion difficulty arising from the disparity in encoding between low-level and high-level features. Experiments conducted on the extensive RealEstate10K and ACID data sets demonstrate that DFSplat surpasses current methodologies in PSNR, SSIM, and LPIPS measures, attaining state-of-the-art performance. The innovation integrates the global consistency of monocular depth estimation with the local precision of multiview matching, optimizing 3D Gaussian parameters prediction through an efficient fusion strategy, thereby offering a novel approach for high-quality scene reconstruction in sparse view scenarios.

Accurate forecasting of air pollutant concentrations is crucial for environmental protection and public health. This paper proposes STAG, a novel spatiotemporal architecture for multi-step air quality prediction. The model features a symmetric inner–outer layer structure that integrates Stacked Fluctuation-Weighted Multi-Head Self-Attention (SF-MHSA)and Graph Networks (GNs), effectively capturing complex temporal patterns and inter-pollutant relationships while maintaining computational efficiency through parameter sharing. We introduce a multi-order loss function incorporating first and second-order difference errors to enhance prediction stability and trend accuracy. Additionally, a stationarity correction mechanism is designed to mitigate distribution drift in long-term forecasting. Comprehensive interpretability analyses validate the model's capability to learn meaningful spatiotemporal dependencies. Forecasting results on Shenzhen air quality data further demonstrate that STAG achieves superior performance against state-of-the-art benchmarks in both accuracy and efficiency (lowest $��A�Q�I�$ prediction MSE 366.68 vs. the second best of 477.76). The proposed framework provides an effective solution for air quality forecasting with potential applications in other environmental monitoring domains.

Accurate segmentation of skin lesion images is essential for diagnosing and treating skin disorders. While current research primarily aims to enhance segmentation accuracy through the use of complex network models, the large size of these models restricts their practical application in clinical settings. To address this challenge, we propose SMedt (skin-medical transformer), a high-precision, parameter-efficient model for skin lesion image segmentation. SMedt combines CNN and transformer architectures within a dual-branch structure to extract both global and local features effectively. The model's global branch employs a dual-attention mechanism that integrates channel and spatial attention, along with skip connection cross attention (SCCA) between the encoder and decoder layers, to enhance global feature decoding. The local branch incorporates an All-aggregation decoder (all decoder) method, enabling the capture of multi-scale features, while pyramid stacking improves the extraction of local features across different channel dimensions. We evaluated SMedt on the ISIC2016 and ISIC2018 datasets. On the ISIC2016 dataset, the model achieved a 0.17% improvement in the Dice coefficient over the second-best model, FAT-Net, while reducing model parameters by 91%. On the ISIC2018 dataset, SMedt improved the Dice coefficient by 1.1% over the state-of-the-art Efficient UNet, while reducing model parameters by 73.26%. Compared with the latest lightweight model UCM-Net, SMedt improves segmentation accuracy (Dice) by 1.92% on ISIC2018, achieving a better balance between accuracy and model size. By leveraging the strengths of both CNN and Transformer models, SMedt maintains high segmentation accuracy while keeping a low parameter count, thereby enhancing diagnostic accuracy and clinical efficiency.

The swift spread of online social network rumors creates huge challenges for e-commerce stands, as purchaser conviction and the market can be permanently disrupted. This study proposed the ICRSbGCN, which is established on the ICRS (Ignorant, Confused, Rumor-monger, and Stifler) and the use of Graph Convolutional Networks (GCNs) to examine rumor propagation among cyber consumers. By adding confused consumers, the ICRSbGCN model accounted for more complexity or nuance to consumer behavior while also emulating the consumer reservation behaviors seen in the real-world context in considering misinformation. To derive the epidemic thresholds, equilibrium points, and stability conditions, the principles behind rumor spread dynamics were utilized. The results from numerical simulations demonstrate that rumors disappear when the basic reproduction number (Ro) is less than 1, while an infectious threshold with Ro greater than 1 reflects as endemic in cyberspace. These results also demonstrated the utility of the ICRSbGCN model to identify the critical influencers in an e-commerce domain and reduce misinformation. The ICRSbGCN model achieved 99.10% accuracy, 99.23% precision, 99.15% recall, and 99.43% F1-score while outperforming previously known techniques. The importance of targeting misinformation is emphasized specifically to protect consumer trust and uphold the e-commerce sphere through the proposed ICRSbGCN model. Overall, the ICRSbGCN model shows an accurate method of analyzing and reducing rumor spread in online social networks through finding influencers and predicting the misinformation spread across many states of a consumer.

Searchable Encryption (SE) enables users to perform searches on encrypted data while preserving data privacy. Since cloud servers are platforms that provide services for a large number of users, and data owners require access control over their data, SE schemes that support multi-user settings and access control are therefore more suitable for cloud storage. However, in existing SE schemes that support multi-user settings and access control, most only support single-keyword or conjunctive keyword searches, and the search time grows linearly with the total amount of data. These limitations negatively impact both the accuracy and efficiency of search operations. This work proposes an SE scheme specifically designed for multi-user settings. Data owners can enforce fine-grained access control policies, while a specialized retrieval structure allows the cloud to assist users in performing Boolean keyword searches with improved efficiency. The search complexity of the proposed scheme is $��O�\left(�r�\right)�$, where $��r�$ denotes the number of files relevant to the queried keyword. We demonstrate the scheme's effectiveness and practicality through performance analysis.

Private set operations (PSO) address key challenges in cross-organizational data sharing where multiple transport entities securely verify shared cargo manifests or delivery schedules without exposing sensitive information. Private set intersection (PSI) enables secure computation of common elements across private datasets, while its variant, PSI with cardinality (PSI-CA), reveals only the intersection size. The PSO protocol based on Diffie-Hellman (DH) key agreement has emerged as a prominent solution for communication-sensitive applications. However, current research efforts mostly concentrate on two-party implementations, which face inherent limitations in addressing the communication complexity when scaling to multiple participants. In this work, we firstly present E3PSI-CA, a novel three-party PSO protocol. This construction for the semi-honest model leverages bilinear maps in DH key agreement and Bloom Filters (BF) for compact set representation, achieving remarkable efficiency. By integrating private information retrieval (PIR) techniques, the party in E3PSI-CAcan efficiently compute the set intersection. To address the inherent accuracy limitations of the BF and extend security to the malicious model, we further propose an enhanced PSI scheme. This variant is built upon a “Ring” structure, within which we employ DH key agreement and OKVS data structure to achieve security against any single malicious party, while the OKVS simultaneously ensures perfect accuracy (zero false positives). We prove the security through the ideal/real simulation paradigm and conduct performance evaluations.

Zooplankton image classification is important for marine biodiversity monitoring and environmental research. Zooplankton image classification faces several challenges, including complex background noise, intraspecific phenotypic variation, interspecific morphological similarity and limited original data and insufficient training sets. To overcome these limitations, we propose an adaptive gate-enhanced multi-feature hybrid fusion-ShuffleNetV2(AHFNet) algorithm using a transfer learning framework. ShuffleNetV2 is a lightweight convolutional neural network that has been pre-trained on various large-scale datasets. Therefore, we adopt the pre-trained parameters of ShuffleNetV2 from the ImageNet dataset for fine-tuning, adapting it to zooplankton image classification. We combine three feature channels and concatenate them to form multi-source inputs to enhance feature complementarity. The three channels include the original feature image, global shape features obtained by Gaussian low-pass filtering, and local texture features obtained by Gabo filtering. We designed a hybrid fusion module with a conditional gating mechanism that dynamically fuses channel attention (via the Squeeze and excitation module) and spatial attention (via the Parallelized patch-aware mechanism). A pivotal aspect of our design is the phased network layout: we deploy a standalone SE module in Stage2 to suppress background noise, while the hybrid SE-PPA module in Stages3 and 4 performs joint channel recalibration and multi-scale spatial feature capture. This is the first work to adapt and synergize the PPA mechanism with SE for this specific task through optimized stage-wise integration. Experimental results demonstrate the effectiveness of our method, which achieves $��94.37�\%�$ accuracy on the Kaggle38 dataset and $��96�\%�$ on the WHOI25 dataset, with ablation studies conclusively validating our design choices.