Concurrency and Computation-Practice & Experience最新文献

{"title":"RETRACTION: A Multi-modality Paradigm for CT and MRI Fusion with Applications of Quantum Image Processing","authors":"","doi":"10.1002/cpe.70017","DOIUrl":"https://doi.org/10.1002/cpe.70017","url":null,"abstract":"<p>\u0000 <b>RETRACTION:</b> <span>A. Dogra</span>, <span>C. K. Ahuja</span>, and <span>S. Kumar</span>, “ <span>A Multi-modality Paradigm for CT and MRI Fusion with Applications of Quantum Image Processing</span>,” <i>Concurrency and Computation: Practice and Experience</i> <span>34</span>, no. <span>20</span> (<span>2022</span>): e6610, \u0000https://doi.org/10.1002/cpe.6610.</p><p>The above article, published online on 23 September 2021 in Wiley Online Library (\u0000wileyonlinelibrary.com), has been retracted by agreement between the journal Editors, David W. Walker, Jinjun Chen, Nitin Auluck, and Martin Berzins; and John Wiley &amp; Sons Ltd. The retraction has been agreed on as the manuscript was found to be published solely on the basis of a compromised peer review process. The authors have been informed of the decision to retract.</p>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 4-5","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cpe.70017","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143438993","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":"RETRACTION: Brain Tumor Segmentation and Overall Survival Period Prediction in Glioblastoma Multiforme Using Radiomic Features","authors":"","doi":"10.1002/cpe.70014","DOIUrl":"https://doi.org/10.1002/cpe.70014","url":null,"abstract":"<p>\u0000 <b>RETRACTION:</b> <span>S. Das</span>, <span>S. Bose</span>, <span>G. K. Nayak</span>, <span>S. C. Satapathy</span>, and <span>S. Saxena</span>, “ <span>Brain Tumor Segmentation and Overall Survival Period Prediction in Glioblastoma Multiforme Using Radiomic Features</span>,” <i>Concurrency and Computation: Practice and Experience</i> <span>34</span>, no. <span>20</span> (<span>2022</span>): e6501, \u0000https://doi.org/10.1002/cpe.6501.</p><p>The above article, published online on 21 July 2021 in Wiley Online Library (\u0000wileyonlinelibrary.com), has been retracted by agreement between the journal Editors, David W. Walker, Jinjun Chen, Nitin Auluck, and Martin Berzins; and John Wiley &amp; Sons Ltd. The retraction has been agreed on as the peer review and publishing process was found to be manipulated. Furthermore, the authors included incoherent, meaningless, and irrelevant information in this article. The underlying dataset is not referenced correctly and a detailed description of the applied methods is missing so that the results cannot be considered reproducible. The authors have been informed of the decision to retract.</p>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 4-5","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cpe.70014","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143438989","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":"HCRP-OSD: Fine-Grained Open-Set Intrusion Detection Based on Hybrid Convolution and Adversarial Reciprocal Points Learning","authors":"Nengfu Cai,&nbsp;Yi Jiang,&nbsp;Haitao Zhang,&nbsp;Zhiwen Yang,&nbsp;Laicheng Zhong,&nbsp;Qi Chen","doi":"10.1002/cpe.70010","DOIUrl":"https://doi.org/10.1002/cpe.70010","url":null,"abstract":"<div>\u0000 \u0000 <p>Amidst the swift progression of network technology, unknown network attacks and malicious code iterations perpetually surface, thereby imposing augmented exigencies on the efficacy and innovativeness of intrusion detection systems within networks. Timely detection of unknown network attacks is critical to reducing the risk of significant damage to systems. This paper aims to develop an open-set intrusion detection model that is able to infer unknown network attacks and correctly classify known attacks. In order to enable the model to classify known attacks while inferring unknown attacks correctly, we consider the open space risk of unknown attacks when training the known attacks classification model. Specifically, we propose an open-set intrusion detection system, HCRP-OSD, consisting of three modules: Network flow feature extraction, hybrid convolutional network, and ARPL open-set intrusion detection. The network flow feature extraction module extracts data information from the original network traffic to avoid the loss of original information caused by manual design and selection of features. The hybrid convolutional network module learns distinguishable features between different known attacks. The hybrid convolutional network uses two learning channels, a two-dimensional CNN and a one-dimensional residual network, to obtain attack features from different angles. The features are aggregated at the output layer. The ARPL open-set intrusion detection module learns a set of vectors as reciprocal points for each known attack and maximizes the distance between the known attack and its reciprocal point during training. This increases the discrimination between known and unknown attacks while accurately classifying known attacks. Experiments on the dataset CICIDS2017 show that our method outperforms the baseline models. The AUROC for identifying unknown attacks is 97.59%. The classification accuracy for known attacks is 99.97%.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 4-5","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143439037","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":"RETRACTION: An Efficient Medical Image Super Resolution Based on Piecewise Linear Regression Strategy Using Domain Transform Filtering","authors":"","doi":"10.1002/cpe.70016","DOIUrl":"https://doi.org/10.1002/cpe.70016","url":null,"abstract":"<p>\u0000 <b>RETRACTION:</b> <span>D. C. Lepcha</span>, <span>B. Goyal</span>, <span>A. Dogra</span>, and <span>S.-H. Wang</span>, “ <span>An Efficient Medical Image Super Resolution Based on Piecewise Linear Regression Strategy Using Domain Transform Filtering</span>,” <i>Concurrency and Computation: Practice and Experience</i> <span>34</span>, no. <span>20</span> (<span>2022</span>): e6644, \u0000https://doi.org/10.1002/cpe.6644.</p><p>The above article, published online on 4 October 2021 in Wiley Online Library (\u0000wileyonlinelibrary.com), has been retracted by agreement between the journal Editors, David W. Walker, Jinjun Chen, Nitin Auluck, and Martin Berzins; and John Wiley &amp; Sons Ltd. The retraction has been agreed on as the peer review and publishing process was found to be manipulated. Furthermore, a detailed investigation of this manuscript revealed that parts of this manuscript are unambiguously fabricated, and so the conclusions of this manuscript are substantially compromised. The authors have been informed of the decision to retract.</p>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 4-5","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cpe.70016","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143438991","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":"Load-Aware Multi-Objective Optimization of Controller and Datastore Placement in Distributed Sdns","authors":"Kang Xingyuan,&nbsp;Keichi Takahashi,&nbsp;Chawanat Nakasan,&nbsp;Kohei Ichikawa,&nbsp;Hajimu Iida","doi":"10.1002/cpe.70007","DOIUrl":"https://doi.org/10.1002/cpe.70007","url":null,"abstract":"<div>\u0000 \u0000 <p>In distributed Software Defined Networking (SDN), multiple controllers need to maintain a consistent view of the network state among themselves using consensus algorithms, introducing additional communication overhead and network delay, especially in large-scale networks. Therefore, optimizing controller placement presents significant challenges, as it must account not only for the delay between switches and controllers but also for the delay introduced by consensus algorithms. Additionally, SDN controllers have limited capacity in terms of the number of switches they can manage and the network events they can process. Improper placement of controllers can lead to longer message processing times, increased queuing delays, or even controller failures. Thus, achieving balanced workloads among controllers is essential. This study introduces and validates a practical Flow Setup Time (FST) model to measure controller response times. We proposed an advanced multi-objective optimization approach that incorporates the Variance of Load Balancing (VOLB), to determine the optimal placements of controllers and datastore nodes involved in processing consensus algorithms. Furthermore, we applied this optimization method to different types of real networks from the Internet Topology Zoo dataset. Based on experimental findings, we identified key factors to consider when selecting optimal placement strategies, including the trade-offs between the number of controllers, the number of datastore nodes, FST, and VOLB.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 4-5","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143423902","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":"Multi-Objective Dynamic Registry Provisioning in Edge Computing Infrastructures","authors":"Lucas Roges,&nbsp;Tiago Ferreto","doi":"10.1002/cpe.70006","DOIUrl":"https://doi.org/10.1002/cpe.70006","url":null,"abstract":"<div>\u0000 \u0000 <p>The rise of mobile devices and IoT sensors has led to the development of latency-sensitive, data-oriented, and resource-intensive applications. These devices often lack sufficient processing and storage capabilities, but cloud computing can provide the necessary support. However, the distance to cloud data centers causes communication delays. Edge computing addresses this by bringing computational resources closer to users, and improving latency and bandwidth. Container-based virtualization further enhances QoS and QoE due to its lightweight nature, enabling faster application provisioning and reduced resource overhead. Despite these benefits, container provisioning faces significant overhead during content downloads from registries. Existing solutions often focus only on reducing provisioning time, neglecting critical metrics like latency and resource usage. Some approaches use complex techniques like registry migration, which can harm network utilization. We introduce MODyn, a multi-objective dynamic provisioning strategy for container registries. MODyn allocates container registries to edge servers based on infrastructure analysis, eliminating the need for resource migration. Our evaluation shows that MODyn balances provisioning time, latency, and resource usage, optimizing container-based application deployment in edge computing.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 4-5","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143423901","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":"The DeepLabV3+ Algorithm Combined With the ResNeXt Network for Medical Image Segmentation","authors":"Yanyan Wu,&nbsp;Yajun Xie,&nbsp;Yintao Hong","doi":"10.1002/cpe.8386","DOIUrl":"https://doi.org/10.1002/cpe.8386","url":null,"abstract":"<div>\u0000 \u0000 <p>This paper presents a semantic segmentation algorithm for medical images, leveraging the DeepLabV3+ architecture in conjunction with the ResNeXt network. The proposed algorithm takes into account the correlation between each structure of lung images and the unique characteristics of image features. Firstly, the cavity convolution algorithm is employed to enhance the receptive field of the network's feature map without augmenting the number of network parameters. Then, the extraction of dense pixel features and the expansion of the receptive field for lung images are conducted using a Densely Connected Atrous Spatial Pyramid Pooling (DenseASPP) module integrated with the ResNeXt network, which is based on multi-scale feature fusion. This ultimately leads to improved refinement of the edges in segmented lung images. The algorithm has shown excellent performance in clinical applications, providing medical professionals with more precise and accurate data to inform diagnostic and treatment strategies. Our algorithm achieved Mean Pixel Accuracy (MPA) of 0.9866, Intersection Over Union (IOU) of 0.9886 and Mean Intersection over Union (MIoU) of 0.9761, which demonstrates superiority over other state-of-the-art algorithms.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 4-5","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143423900","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":"Synergistic Distributed CNN Model for Protein Classification With a Collaborative BSP Synchronization Based on LSTM Prediction","authors":"Tasnim Assali,&nbsp;Zayneb Trabelsi Ayoub,&nbsp;Sofiane Ouni","doi":"10.1002/cpe.70025","DOIUrl":"https://doi.org/10.1002/cpe.70025","url":null,"abstract":"<div>\u0000 \u0000 <p>Recently, distributed deep learning has been introduced as the new highly computational solution that could handle huge amounts of data and reduce training time. Especially when handling high-dimensional and complicated data, is very challenging, such as dealing with Genomics which is the most demanding in terms of data acquisition, storage, distribution, and analysis. However, Distributed deep learning has issues that need to be resolved. Focusing on the synchronization paradigm, BSP (Bulk Synchronous Parallel) is the most used model. Even so, it is demanding in terms of time due to an exigent problem called the straggler, where all the workers need to wait for the slowest worker to synchronize. Therefore, in this article, we propose a collaborative BSP (Collab-BSP) that aims to solve this issue by adopting LSTM for execution time prediction and implementing it with the Apache Spark environment. We proved the efficiency of our approach in reducing the waiting time and iteration time by 50% and 30%, respectively. Also, our approach demonstrated promising results while training a distributed CNN for protein classification with 98.82% accuracy and proved its capability to enhance distributed deep learning training.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 4-5","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143404676","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":"Difference and Influencing Factors of the City Logistics Development Level in China","authors":"Man Zhang,&nbsp;Zheng Kou","doi":"10.1002/cpe.70012","DOIUrl":"https://doi.org/10.1002/cpe.70012","url":null,"abstract":"<div>\u0000 \u0000 <p>Urban logistics is an important bridge connecting urban supply and demand, and its development level is crucial to urban economic development. To explore the development level, spatial and temporal differences, and influencing factors of urban logistics in China, this article uses the entropy weight method, Theil index, and hierarchical regression model to analyze the panel data of urban logistics in 31 provinces (autonomous regions and municipalities) in China. The research conclusion provides a reference for the government to formulate a balanced development policy for urban logistics. It has important practical significance for guiding logistics enterprises to adjust their regional distribution.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 4-5","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143404480","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":"An Efficient Parallel Ordered Depth-First Search Strategy for Directed Acyclic Graphs","authors":"Chuqi Yan,&nbsp;Jianqiang Huang","doi":"10.1002/cpe.70013","DOIUrl":"https://doi.org/10.1002/cpe.70013","url":null,"abstract":"<div>\u0000 \u0000 <p>With the advent of the big data era, accelerating the parallelization of Depth-First Search (DFS) has become pivotal for addressing the challenges posed by large-scale datasets and complex problems in contemporary applications. To improve the parallel processing performance of DFS on Directed Acyclic Graphs (DAGs) while maintaining the orderliness of traversal outcomes, this paper introduces an efficient Parallel Ordered Depth-First Search (PODFS). By leveraging the novel concepts of Clue Path, ParallelList, and the node attributes Level and Dis, PODFS achieves precise subgraph partitioning while preserving the ordered nature of parallel search results. After performing a one-time preprocessing on a specific graph, the proposed algorithm enables more efficient global traversals, achieving a speedup ranging from 6× to 12× on various real-world graph datasets while maintaining the orderedness of traversal results. These performance improvements are crucial for applications that require frequent, in-depth graph searches with a strict need to preserve traversal order.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 4-5","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143404481","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}