Concurrency and Computation-Practice & Experience最新文献

Brain tumors (BT) are considered a major health challenge around the world, which needs early detection; therefore, effective treatment strategies can be planned. This kind of cancer greatly diminishes the patient's quality and lifespan, which opens a gateway for early diagnosis and effective treatment. The medical professionals need assistance with this difficult and error-prone process; it is also mandatory to augment the interpretability and accuracy of the recognition model. To achieve such a goal, a hybrid deep learning model superior with explainable AI is introduced in the proposed framework, which performs brain tumor classification and model interpretation from MRI. The proposed study involves four key steps: Pre-processing, segmentation, classification, and analysis. The input images are initially pre-processed using a median-boosted Kuan Filtering (Me-KF) to remove any noise in the data and improve the subsequent segmentation procedure. After pre-processing, the Extended Multi-Inception Attention U-Net (ExMIA_U-Net) technique is added to effectively separate the brain tumor region. Finally, a deep learning method based on Convolution Attentive assisted EfficientNetB0 (CA-EfficientNetB0) is presented to categorize the many categories of brain tumors, comprising gliomas, meningiomas, pituitary tumors and normal tumors. This model uses Shapley additive explanation (SHAP), Local interpretable model-agnostic explanations (LIME), and Gradient-weighted Class Activation Mapping (Grad-CAM) for model interpretation. The proposed model uses a brain tumor classification dataset. In the results section, the proposed model is compared to many other prevailing schemes and it achieves 99.45% accuracy, 99.16% precision, 98.97% recall, and a 99.06% F1-score. The results show that an efficient, interpretable, robust and better model is developed for brain tumor classification.

In online social networks, identifying influential users is crucial for maintaining network stability and accelerating information dissemination. However, most of the existing research evaluate the influence of users according to the topological structure of local or global networks, which ignores the historical information and social interactions of users. In this work, we introduce a novel algorithm called MADS-UC to pinpoint the influential super-spreaders of information. Specifically, three kinds of activation influence between each pair of users are defined to better portray the mutual interactions with each other, and the proposed measure makes full consideration of the topological and historical information of networks, and the social interactions of users. Then, a hybrid multi-attribute decision-making algorithm is put forward to evaluate the influence of users, while the obtained three kinds of activation influence are introduced as basic indicators, and the weight for each indicator is determined from both subjective and objective dimensions. Finally, to achieve a good balance between algorithm accuracy and time complexity, the influential users are identified by considering the interactions between a user and other users within its influence range. We collect seven real-world datasets from the Sina Weibo platform in 2024 about mobile product Honor, and a series of experiments is conducted to validate the effectiveness of the MADS-UC algorithm. Experimental results show that MADS-UC surpasses six widely used algorithms in robustness, sensitivity analysis, and distinguishing capability, which is useful in accelerating the dissemination of information in the product marketing process.

Brain tumors (BT) are considered a major health challenge around the world, which needs early detection; therefore, effective treatment strategies can be planned. This kind of cancer greatly diminishes the patient's quality and lifespan, which opens a gateway for early diagnosis and effective treatment. The medical professionals need assistance with this difficult and error-prone process; it is also mandatory to augment the interpretability and accuracy of the recognition model. To achieve such a goal, a hybrid deep learning model superior with explainable AI is introduced in the proposed framework, which performs brain tumor classification and model interpretation from MRI. The proposed study involves four key steps: Pre-processing, segmentation, classification, and analysis. The input images are initially pre-processed using a median-boosted Kuan Filtering (Me-KF) to remove any noise in the data and improve the subsequent segmentation procedure. After pre-processing, the Extended Multi-Inception Attention U-Net (ExMIA_U-Net) technique is added to effectively separate the brain tumor region. Finally, a deep learning method based on Convolution Attentive assisted EfficientNetB0 (CA-EfficientNetB0) is presented to categorize the many categories of brain tumors, comprising gliomas, meningiomas, pituitary tumors and normal tumors. This model uses Shapley additive explanation (SHAP), Local interpretable model-agnostic explanations (LIME), and Gradient-weighted Class Activation Mapping (Grad-CAM) for model interpretation. The proposed model uses a brain tumor classification dataset. In the results section, the proposed model is compared to many other prevailing schemes and it achieves 99.45% accuracy, 99.16% precision, 98.97% recall, and a 99.06% F1-score. The results show that an efficient, interpretable, robust and better model is developed for brain tumor classification.

For cross-city traffic prediction, the significant heterogeneity of traffic data across cities and the requirement for privacy protection make it challenging for conventional centralized spatiotemporal graph modeling techniques to balance predictive performance and data security. Therefore, this paper proposes AT-SPNet, a personalized federated spatiotemporal modeling approach specifically designed for cross-city traffic prediction. This method decouples the spatiotemporal modeling paths through the construction of a shared temporal branch and a hidden local spatial branch, thereby mitigating the heterogeneity of cross-city traffic data while preserving privacy. In the temporal branch, Gated Recurrent Units and a multi-head attention mechanism are incorporated to capture temporal dependencies, and a Squeeze-and-Excitation module is employed to enhance the extraction of informative features. In the spatial branch, a Spatial Attention Fusion module based on a triple-attention mechanism is designed to capture spatial features from multiple spatial perspectives, combined with static graph convolution and dynamic graph attention to construct a dual-modal information fusion path. Furthermore, to alleviate the adverse effects of cross-city data heterogeneity in federated training, a personalized federated learning strategy is introduced, which enables differentiated fusion of client spatial features without sharing raw data. Experiments on four real-world traffic datasets demonstrate that AT-SPNet outperforms existing methods in both prediction accuracy and cross-city generalization, validating the effectiveness and practical applicability of the proposed approach for cross-city traffic prediction.

Remote procedure call (RPC) technology has become a cornerstone of modern cloud computing, enabling efficient and seamless communication between distributed services. In cloud infrastructures, where scalability, interoperability, and performance are critical, RPC frameworks play a key role in abstracting the complexities of network communication. Despite their ubiquity, relatively little up-to-date empirical research exists on the comparative performance of RPC frameworks across cloud environments. And, to our best knowledge, there is no comparative study in which different cloud platforms and RPC frameworks were directly compared. This paper addresses that gap by presenting the results of a series of experiments evaluating the performance and scalability of four major RPC frameworks—ONC RPC, gRPC, Web-RPC, and JSON-RPC—across the three most widely used cloud platforms: AWS, Azure, and Google Cloud. The experiments were based on a test suite comprising four distinct RPC call types with varying argument sizes and complexities. Each configuration was executed multiple times with client loads ranging from 1 to 8, with 300,000 runs per test. Total call latency was used as the primary performance measure and analyzed statistically. The results reveal a nuanced picture: while ONC RPC consistently delivers the best performance, no other framework or platform emerges as a clear overall leader. Across all cloud environments and workloads, ONC RPC consistently outperformed the other frameworks. It proved to be at least 20% faster than its nearest competitor and, in some cases, up to four times faster than the second best. By contrast, in short-argument tests, gRPC performed unexpectedly poorly; it often ranked close to or below the text-based frameworks. Particularly surprising was its poor performance under high load in Google Cloud—the platform where it could be expected to perform best. However, gRPC ranked second in the large-argument tests, while text-based frameworks show relatively poor performance as the argument size increases. We discuss and explain these findings in detail and provide guidelines for selecting the most suitable RPC technology for different use cases.

Public auditing is a method used to verify the integrity of data stored in the cloud without requiring access to the actual data. However, the advancement of quantum computers poses significant security threats to existing public auditing schemes, as these schemes are based on conventional cryptography hard problems, which are vulnerable to quantum attacks. To address this, NIST has launched the development of post-quantum cryptographic primitives and protocols. Among the various approaches, lattice-based cryptography (LBC) is considered one of the most promising candidates due to its strong security guarantees and inherent resistance to quantum attacks. Leveraging LBC, several researchers have proposed lattice-based public auditing (LBPA) schemes for cloud storage security based on lattice hardness assumptions. This paper provides a comprehensive survey of existing LBPA schemes for cloud storage, presenting a detailed taxonomy and analyzing their similarities, differences, and performance. Additionally, it highlights key challenges and outlines future research directions for designing efficient and secure public auditing schemes in the post-quantum era.

With the exponential growth of graph-structured data, single-machine efficient analysis has become increasingly impractical, making high-performance distributed graph computing systems indispensable. The efficacy of these systems hinges critically on high-quality graph partitioning. The edges of many graphs stemmed from real applications are uncertain, but many existing graph partitioning algorithms are only for deterministic graphs without considering uncertainty. This paper presents a novel partitioning algorithm, PAUG (Partitioning Algorithm for Uncertain Graphs), tailored for uncertain graphs. First, it formalizes the partitioning problem as an optimization task to minimize the cut-edge ratio while balancing load. Second, it introduces probabilistic similarity to quantify vertex relationships under uncertainty. Finally, it details the PAUG algorithm which consists of initial partition phase and score-function-guided refinement strategy. Experimental results shows that PAUG achieves an average 23.2% reduction in cut-edge ratio and a 26.2% improvement in load balance over state-of-the-art algorithms.