Concurrency and Computation-Practice & Experience最新文献

{"title":"Automated Bisimulation-Based Similarity Measurement in Heterogeneous Information Networks","authors":"Yongjie Liang,&nbsp;Wujie Hu,&nbsp;Junjie Wu,&nbsp;Jinzhao Wu","doi":"10.1002/cpe.70310","DOIUrl":"https://doi.org/10.1002/cpe.70310","url":null,"abstract":"<div>\u0000 \u0000 <p>Heterogeneous information networks (HINs) serve as effective models for information systems characterized by diverse types of objects and relationships. Evaluating similarities among objects is crucial in various data mining applications, such as web search, label prediction, and clustering tasks. This paper presents BiSim, a novel similarity measurement method tailored for HINs. By harnessing the concept of bisimulation, BiSim evaluates node similarity by integrating both macroscopic and microscopic levels of bisimulation. Unlike existing metrics that rely on predefined metapaths, BiSim provides a universal approach to assess the structural and semantic similarity simultaneously in HINs. We thoroughly investigate BiSim's mathematical properties and demonstrate its effectiveness through comprehensive experimentation across diverse data mining tasks.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 25-26","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145223974","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intrusion Detection System in IoT 5G Networks Based on LSSVM and Harmony Search Optimization","authors":"Ali Hamzah Najim,&nbsp;Hussein Ali Rasool,&nbsp;Amjed Abbas Ahmed,&nbsp;Naglaa F. Soliman","doi":"10.1002/cpe.70297","DOIUrl":"https://doi.org/10.1002/cpe.70297","url":null,"abstract":"<div>\u0000 \u0000 <p>5G-powered Internet of Things devices, there has been a major challenge in making its network infrastructures safe against the rising wave of cyber threats. In the context of 5G-IoT networks, the traditional intrusion detection systems (IDS) tend to have problems with real-time detection, class imbalance, and adaptive patterns of the attacks. In this study, a new hybrid system of Least Squares Support Vector Machine (LSSVM) and the Harmony Search Optimization Algorithm is proposed as a new intrusion detection framework capable of improving sensitivity and stable intrusion detection. Also, Principal Component Analysis (PCA) is used to decrease the feature dimensionality and get rid of the redundancy. The suggested model is tested with the use of reasonable botnet and adversarial traffic circumstances in the IoT-23 dataset. As far as the detection of such underrepresented attacks as U2R/R2L is concerned, SMOTE is used to balance the classes. Results demonstrate that the LSSVM + HSOA model achieves superior detection performance with an accuracy of 99.27%, significantly outperforming standard SVM and Random Forest baselines. The framework also shows improved recall for minority attack classes, affirming its suitability for complex and imbalanced IoT traffic. Future work will address real-time deployment challenges, such as latency and adversarial evasion, through lightweight model adaptations and distributed learning. This study contributes a practical and scalable approach to securing modern 5G-IoT networks.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 25-26","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145223973","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Blockchain-Based Platform for Information Security and Visual Management in Coffee Trading","authors":"G. T. S. Ho,&nbsp;Yuk Ming Tang,&nbsp;Wei Ting Kuo,&nbsp;Valerie Tang","doi":"10.1002/cpe.70299","DOIUrl":"https://doi.org/10.1002/cpe.70299","url":null,"abstract":"<div>\u0000 \u0000 <p>The wider impact of blockchain is considered one of the core technologies for the next generation of intelligent web that adopts information technologies to enhance food safety and security, thereby enhancing sustainable food trading. This technology can be adopted in a wide range of applications such as healthcare, logistics, food trading, etc. Among these, coffee trading is one of the sectors that can benefit from blockchain due to its traceability and security features. Traditional coffee trading faces challenges due to geographical and cultural differences, leading to unfair practices and trust issues. Currently, there is no reliable and secure platform in the coffee trading business. This article introduces “Cof3Lib,” a coffee trading platform integrating decentralization and blockchain to provide food safety and trading security. To our knowledge, it's one of the first blockchain-based platforms addressing traditional coffee trading challenges. It ensures transparency, traceability, visibility, and accuracy in coffee trading operations and logistics management. By leveraging blockchain technology, it aims to revolutionize the food industry, creating a more equitable and efficient trading ecosystem for food technology.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 25-26","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145223972","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Portable PGAS-Based GPU-Accelerated Branch-And-Bound Algorithms at Scale","authors":"Guillaume Helbecque,&nbsp;Ezhilmathi Krishnasamy,&nbsp;Tiago Carneiro,&nbsp;Nouredine Melab,&nbsp;Pascal Bouvry","doi":"10.1002/cpe.70321","DOIUrl":"https://doi.org/10.1002/cpe.70321","url":null,"abstract":"<p>The Branch-and-Bound (B&amp;B) technique plays a key role in solving many combinatorial optimization problems, enabling efficient problem-solving and decision-making in a wide range of applications. It incrementally constructs a tree by building candidates to the solutions and abandoning a candidate as soon as it determines that it cannot lead to an optimal solution. With modern problems growing increasingly large, accelerating B&amp;B algorithms through parallelization has become a critical challenge for handling large solution spaces. At the same time, modern parallel computing systems themselves are becoming larger, more heterogeneous, and more diverse, requiring programming approaches capable of effectively exploiting such complexity. To address these challenges, this work presents a GPU-accelerated B&amp;B algorithm based on the Partitioned Global Address Space (PGAS) programming model, implemented using the Chapel language. The PGAS-based design is motivated by the high-level abstraction provided by this programming model, which favors programmability, whereas vendor-neutral GPU features of the Chapel language favor GPU portability. The algorithm uses a pool-based approach for generality and exploits a dynamic load balancing mechanism for performance scalability. Extensive experimentation on the N-Queens and permutation flowshop scheduling problems demonstrated both code performance and code portability of the proposed algorithm on several GPU architectures compared to optimized CUDA-based implementations. Moreover, the strong scaling efficiency of the proposed algorithm is investigated on a TOP500 pre-exascale supercomputer up to 1024 GPUs.</p>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 25-26","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cpe.70321","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145223782","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Approximate Block Diagonalization of Symmetric Matrices Using the D-Wave Advantage Quantum Annealer","authors":"Koushi Teramoto,&nbsp;Evgeniy Mishchenko,&nbsp;Keisuke Kawamura,&nbsp;Shuhei Kudo,&nbsp;Yasuhiko Takenaga,&nbsp;Yusaku Yamamoto","doi":"10.1002/cpe.70301","DOIUrl":"https://doi.org/10.1002/cpe.70301","url":null,"abstract":"<div>\u0000 \u0000 <p>Approximate block diagonalization is a problem of transforming a given symmetric matrix as close to block diagonal as possible by symmetric permutations of its rows and columns. This problem arises as a preprocessing stage of various scientific calculations and has been shown to be NP-complete. In this paper, we consider solving this problem approximately using the D-Wave Advantage quantum annealer. For this purpose, several steps are needed. First, we have to reformulate the problem as a quadratic unconstrained binary optimization (QUBO) problem. Second, the QUBO has to be embedded into the physical qubit network of the quantum annealer. Third, and optionally, reverse annealing for improving the solution can be applied. We propose two QUBO formulations and four embedding strategies for the problem and discuss their advantages and disadvantages. Through numerical experiments, it is shown that the combination of domain-wall encoding and D-Wave's automatic embedding is the most efficient in terms of usage of physical qubits, while the combination of one-hot encoding and automatic embedding is superior in terms of the probability of obtaining a feasible solution. It is also shown that reverse annealing is effective in improving the solution for medium-sized problems.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 25-26","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145223778","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Developing an End-to-End 3D X-Ray Ptychography Workflow Using Surrogate Models","authors":"Ryota Koda,&nbsp;Keichi Takahashi,&nbsp;Hiroyuki Takizawa,&nbsp;Nozomu Ishiguro,&nbsp;Yukio Takahashi","doi":"10.1002/cpe.70308","DOIUrl":"https://doi.org/10.1002/cpe.70308","url":null,"abstract":"<p>Recently, X-ray ptychography has attracted significant attention as a non-destructive imaging technique with high spatial resolution. However, its application to real-time imaging is limited by the long execution time required for iterative phase retrieval, which reconstructs sample images from diffraction patterns. To address this issue, deep learning-based surrogate models have been proposed to accelerate iterative phase retrieval by directly predicting sample images. While these surrogate models achieve significant speed-ups, they typically ignore the time needed for model training and dataset preparation, which can diminish their benefits. Consequently, conventional iterative phase retrieval may outperform surrogate-based approaches in end-to-end performance. This study aims to implement real-time X-ray ptychography using surrogate models that explicitly incorporate model training and dataset preparation into the workflow. Specifically, we propose a method that constructs a sample-specific surrogate model on-the-fly using a small subset of observed diffraction patterns and uses its predictions as initial estimates for iterative phase retrieval. The proposed method is up to 2.72 times faster than conventional iterative phase retrieval, even when including training and dataset preparation times. Moreover, the proposed method ensures that the reconstructed images satisfy physical constraints. Comprehensive performance evaluations further demonstrate that the trade-off between model accuracy and preparation time is critical for optimizing the total execution time in the X-ray ptychography workflow.</p>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 25-26","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cpe.70308","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145223781","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Fault Diagnosis Method for Centrifugal Compressors Based on Ontology and Bayesian Network Fusion Reasoning","authors":"Xinxin Zhou,&nbsp;Ruixin Bao,&nbsp;Jian Zhu,&nbsp;Qinglong Hu,&nbsp;Xiangguang Sun,&nbsp;Yong Chen,&nbsp;Tianxiang Zeng","doi":"10.1002/cpe.70278","DOIUrl":"https://doi.org/10.1002/cpe.70278","url":null,"abstract":"<div>\u0000 \u0000 <p>As a core industrial equipment, the stable operation of centrifugal compressors is crucial to production. Current research on its fault diagnosis mostly focuses on structured monitoring data, with insufficient mining of unstructured operation and maintenance experience data. To address this, this paper constructs an intelligent diagnosis model integrating ontology knowledge reasoning, knowledge graph modeling, and Bayesian Network (BN), realizing cross-modal fault accurate localization and root cause analysis through the deep integration of “knowledge + probability.” Firstly, an ontology knowledge model is established based on fault information mined from unstructured data (such as fault reports and maintenance records), enabling standardized expression and semantic association of fault knowledge. The model is then imported into the Neo4j database, and specific fault information files are exported through Python queries to serve as the basic data for BN reasoning. Next, a probabilistic reasoning model between fault components and symptoms is built based on BN. Combining expert experience and historical data, the node conditional probabilities are determined to describe the uncertainty of fault propagation. Finally, a fusion method of ontology and BN is designed: ontology reasoning is used to optimize the BN structure, and intelligent diagnostic reasoning is realized through dynamic updating of posterior probabilities. Experiments using fault reports of centrifugal compressors from a certain enterprise show that the proposed fusion model can improve the interpretability and dynamic reasoning ability of fault diagnosis. Case verification demonstrates that the fault recognition accuracy of this method reaches 85%, indicating good performance. Therefore, this research provides a feasible solution for utilizing unstructured operation and maintenance data, enhancing the practicality of intelligent diagnosis for complex industrial equipment, which can shorten fault downtime, reduce maintenance costs, and thus has practical application value.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 25-26","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145223776","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adaptive Event-Driven Key Management for Securing Cloud Data Against Key Exposure","authors":"Atul Kumar Singh,&nbsp;Kriti Bhushan","doi":"10.1002/cpe.70329","DOIUrl":"https://doi.org/10.1002/cpe.70329","url":null,"abstract":"<div>\u0000 \u0000 <p>Ensuring data confidentiality and integrity in dynamic cloud storage environments is a growing challenge, particularly in the face of key exposure threats. Traditional key management schemes, which rely on periodic updates, introduce significant vulnerabilities due to long windows of exposure between key rotations. Also, it often requires high computational overhead from re-encrypting entire datasets during key updates and frequently depends on third-party auditors for integrity verification, which can compromise privacy. However, a major research gap remains in developing a scalable, efficient, and auditor-free key management protocol that can adapt in real time to evolving cloud access patterns. In this paper, we propose a novel Dynamic Event-Driven Key Regeneration System that leverages Elliptic Curve Diffie-Hellman (ECDH) for secure key exchange and Advanced Encryption Standard—Galois/Counter Mode (AES-GCM) for combined encryption and integrity verification. Unlike conventional time- or session-based strategies, the proposed design uses statistically adaptive thresholding derived from real-time file access patterns to enable on-demand key regeneration and selective re-encryption, drastically reducing computational overhead. By re-encrypting only affected files, the system is optimized for large-scale, multi-tenant cloud environments. Furthermore, the proposed approach eliminates the need for external auditors, as integrity verification is performed internally via cryptographic mechanisms, ensuring both privacy and security. Experimental results show that the proposed system achieves an average key generation time of 2.3 ms, encryption latency of just 0.21 s for 100 MB files, and key regeneration times as low as 0.0012–0.0350 s, outperforming existing approaches by up to 80% in computational efficiency. The system scales efficiently in multi-tenant environments, maintaining low overhead with up to 100 users and providing near-linear performance even with 1000 concurrent encryption operations. These results demonstrate that the proposed adaptive, event-driven system offers enhanced protection against key exposure while maintaining low overhead, making it a viable and secure solution for modern cloud infrastructures.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 25-26","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145223779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Basketball Posture Recognition Using Neural Networks","authors":"Hui Zhang,&nbsp;Jianfeng Wang,&nbsp;Haishan Liu","doi":"10.1002/cpe.70261","DOIUrl":"https://doi.org/10.1002/cpe.70261","url":null,"abstract":"<div>\u0000 \u0000 <p>Recognizing and training basketball athletes on their postures is crucial for enhancing performance, preventing injuries, and optimizing movement efficiency on the court. Therefore, this paper employs a convolutional neural network (CNN) to recognize six training postures in basketball. In terms of model structure, four convolutional layers are designed to extract critical features for identifying the six postures. To maintain consistency between the extracted features and the original features, this work uses the optimal mass transport (OMT) map to derive the model's loss function. Finally, the proposed model is evaluated on image datasets. Experimental results demonstrate that the proposed model outperforms competing methods in recognizing the six training postures. We find that the loss function derived from the optimal mass transport map significantly improves the CNN's image recognition capabilities. This is because the OMT map preserves the geometric characteristics of the original data distribution to the greatest extent possible.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 25-26","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145223775","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Design of Incentive Mechanism for Node Collaboration in Hierarchical Federated Learning Based on Deep Reinforcement Learning","authors":"Zhuo Li,&nbsp;Yu Xin,&nbsp;Fangxing Geng","doi":"10.1002/cpe.70320","DOIUrl":"https://doi.org/10.1002/cpe.70320","url":null,"abstract":"<div>\u0000 \u0000 <p>With the rapid development of artificial intelligence, big data, and distributed computing technologies, hierarchical federated learning has emerged as a widely studied distributed machine learning framework. In hierarchical federated learning, edge servers are deployed between cloud servers and mobile devices, efficiently receiving local models from nearby mobile devices and performing edge model aggregation. Node collaboration in hierarchical federated learning can reduce training costs and improve model quality while protecting data privacy. However, data security risks and resource consumption during model training can reduce the willingness of mobile devices to participate. Additionally, collaborative nodes are often heterogeneous, facing issues such as skewed datasets and imbalanced capabilities. Therefore, this paper proposes a deep reinforcement learning-based incentive mechanism for node collaboration, aimed at maximizing node benefits. A node collaboration strategy optimization model is then constructed using the Markov decision process framework, and the NCIA algorithm, based on deep reinforcement learning networks, is designed. Finally, through extensive simulation experiments, the proposed NCIA algorithm is demonstrated to improve model accuracy by 5.28% and 14.22% compared with the CCEG and FedAvg algorithms, respectively.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 25-26","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145223810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}