Concurrency and Computation-Practice & Experience最新文献

{"title":"A Comprehensive Reinforcement Learning Framework for Priority-Aware Data Center Scheduling Optimization and QOS-Defined Buffer Management","authors":"Vinu Josephraj,&nbsp;Wilfred Franklin Sundara Raj","doi":"10.1002/cpe.70028","DOIUrl":"https://doi.org/10.1002/cpe.70028","url":null,"abstract":"<div>\u0000 \u0000 <p>A network architecture's fundamental components include buffering designs and policies for their effective administration. Strong incentives exist to test and implement new regulations, but there are few opportunities to alter much more than minor details. We describe Open Queue, a new specification language that enables the expression of management rules and virtual buffering architectures that represent a broad range of economic models. Open Queue provides various comparators and basic functions that make it easy for users to create whole buffering structures and policies. It provides examples of Open Queue buffer management strategies and provides empirical evidence of how they affect performance in different scenarios. Through all of these efforts, minimizing network usage, avoiding network congestion, which ensures QoS (Quality of Service), and making the most use of the current route is regarded as the main problems. Common traffic engineering methods like Equal Cost Multipath (ECMP) don't address the state of the network at the moment or offer a mouse flow solution. The proposed solution to this issue is the implementation of a Deep Reinforcement Learning (DRL) based Priority-Aware Data Center Scheduling Algorithm (PADCS), which leverages AHP-TOPSIS to update previous experience using prioritized experiences replay, monitor workload categorization, and receive real-time environmental feedback. Finally, the suggested algorithm determines the optimal route through the network for every flow depending on the kind of current flows in order to increase customer happiness and improve QoS. The evaluation findings show that, under various traffic scenarios, the DRL-PADCS algorithm lowers average throughput and normalized total throughput, link usage, average round-trip time, and packet loss rate in comparison to ECMP.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 4-5","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143446848","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 Slip Control of Distributed Electric Drive Vehicles Based on Improved PSO-BPNN-PID","authors":"Huipeng Chen,&nbsp;Xinglei Yu,&nbsp;Shaopeng Zhu,&nbsp;Zhijun Wu,&nbsp;Chou Jay Tsai Chien,&nbsp;Junjie Zhu,&nbsp;Rougang Zhou","doi":"10.1002/cpe.70002","DOIUrl":"https://doi.org/10.1002/cpe.70002","url":null,"abstract":"<div>\u0000 \u0000 <p>The distributed electric drive vehicle is a highly nonlinear and time-varying system. To address the issue of drive slip control under varying driving forces and road surface coefficients, a novel drive slip control strategy is proposed, which considers axle load transfer during vehicle acceleration. The strategy employs an improved PSO algorithm to obtain optimal parameters for the BP neural network, uses the BP neural network for forward propagation to calculate PID parameters in real-time, and adjusts the weight matrix through backward propagation to achieve real-time adaptive PID control for vehicle slip. Experimental results indicate that this strategy improves the ITAE index by 13.6% and response time by 74.8% compared to the anti-saturation PID.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 4-5","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143439048","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":"Fv-SFL: A Contrastive Learning-Based Feature Sharing Method for Reducing the Effect of Label Skewed Data Heterogeneity in Federated Medical Imaging","authors":"Soumyaranjan Panda,&nbsp;Vikas Pareek,&nbsp;Sanjay Saxena","doi":"10.1002/cpe.8379","DOIUrl":"https://doi.org/10.1002/cpe.8379","url":null,"abstract":"<div>\u0000 \u0000 <p>Deep learning plays a crucial role in medical image analysis. Traditionally, it involves the collection of patient images at a central location. For this reason, centralized approaches have encountered technical challenges such as data security vulnerabilities, data transfer bottlenecks, limited data diversity, and government regulatory hurdles like HIPAA and GDPR. Federated Learning presents an alternative approach by allowing model training without sharing patient data from client hospitals. However, it faces challenges such as label-skewed data heterogeneity due to variations in population characteristics, biases, and disease prevalence among hospitals, which leads to performance drift during model training. We propose a framework called Feature vector sharing-based Federated Learning (Fv-SFL) to address this issue by combining a novel contrastive learning-based feature-sharing method and distribution-discrepancy-based aggregation. This introduces a local learning approach incorporating class-wise feature vectors for federated learning. These vectors, defined as the average vectors of representations within distinct classes, allow for the utilization of clients' knowledge to refine local training. In addition to adjusting server aggregation, we integrate a distribution discrepancy method to calculate the weight for each client for server aggregation. We evaluate the effectiveness of our method for both multiclass and binary classification tasks by conducting experiments on two distinct datasets. Firstly, assess the method's performance on a multiclass classification task using the Ham10000 dataset. Secondly, evaluate its efficacy on a binary classification task using the COVID-QU-Ex dataset. Across various methods, Fv-SFL consistently outperforms other federated learning methods, indicating its superior performance compared to alternative approaches. This framework effectively mitigates performance drift issues during model training caused by label-skewed data heterogeneity by utilizing feature vector sharing-based contrastive learning methods and discrepancy-based global aggregation. Additionally, Fv-SFL outperforms traditional FL methods by optimizing resource utilization with reasonable communication costs.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 4-5","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143446808","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":"Improved Co-DETR With Dropkey and Its Application to Hot Work Detection","authors":"Yuting Zhang,&nbsp;Yangfeng Wu,&nbsp;Huang Xu,&nbsp;Yajun Xie,&nbsp;Yan Zhang","doi":"10.1002/cpe.70020","DOIUrl":"https://doi.org/10.1002/cpe.70020","url":null,"abstract":"<div>\u0000 \u0000 <p>Although ViT has achieved significant success in the field of image classification, research on ViT-based object detection algorithms is still in its early stages, and their application in real-world scenarios is limited. Furthermore, algorithms based on ViT or Transformer are prone to overfitting issues when training data is scarce. While CO-DETR has achieved state-of-the-art object detection precision on the COCO dataset leaderboard, the ViT-based CO-DETR also suffers from overfitting problems, which affect its detection precision on smaller datasets. Based on the study of ViT-based object detection algorithms, a new object detection algorithm termed DC-DETR (DropKey Co-DETR) was proposed in this paper. It builds upon CO-DETR and introduces a regularization method called DropKey into the Transformer attention mechanism. By randomly dropping part of the Key during the attention phase, the network is encouraged to capture global information about the target object. This method effectively alleviates the overfitting problem in ViT for object detection tasks, improving the model's precision and generalization ability. To validate the effectiveness and practical applicability of DC-DETR in environments with limited computational resources, a dataset for hot work scenarios was collected and annotated. Based on this dataset, performance tests were conducted on the DC-DETR, CO-DETR, and YOLOv5 algorithms. The test results indicate that the proposed DC-DETR algorithm exhibits superior performance, with detection precision improving by 0.7% compared to CO-DETR and by 5.7% compared to YOLOv5. The detection speed is the same as CO-DETR, and only 2.9 ms slower than YOLOv5. The experiments demonstrate that the proposed DC-DETR algorithm achieves a balance between precision and speed, making it well-suited for practical object detection applications.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 4-5","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143439050","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":"Modified Cryptosystem-Based Authentication Protocol for Internet of Things in Fog Networks","authors":"S. Kanthimathi,&nbsp;R. Sivakami,&nbsp;B. Indira","doi":"10.1002/cpe.70024","DOIUrl":"https://doi.org/10.1002/cpe.70024","url":null,"abstract":"<div>\u0000 \u0000 <p>The advanced architecture called fog-driven IoT, positioned between the centralized cloud platform and IoT devices, aims to expand storage, computing, and network capabilities to the Internet edges. This setup ensures that services and resources from fog nodes are easily accessible and in proximity to the end-users and devices, reduces latency, enhances mobility, and provides location awareness. However, despite its benefits, the fog computing paradigm inherits security and privacy issues like those found in cloud computing. These concerns encompass challenges like message replay, impersonation, spoofing, man-in-the-middle attacks, and physical capture of IoT devices, posing potential risks to the system's security and privacy. In order to address these challenges, a new authentication protocol is proposed in this study, which encompasses five key phases: “node registration, fog server registration, node authentication, fog server authentication, and fail-safe authentication.” It begins with node registers on fog servers (FSs), establishing a foundation for trust and identity verification. The protocol then scales to authenticate the fog network, which consists of multiple FSs, each undergoes authentication within the cloud server, to ensure robustness and reliability across distributed servers. A significant innovation lies in the third phase, where mutual authentication is achieved using the Modified Blowfish (MBF) algorithm, promoting secure communication between FSs and nodes while ensuring stronger encryption and better protection against attacks. The fourth phase extends authentication mechanisms to the FS in which intra-fog authentication is done by the IKM scheme and inter-fog authentication is done by the IECC mechanism to manage cryptographic keys effectively within fog nodes and also enhance security in communication between different fog nodes. Additionally, a fail-safe authentication phase provides emergency response capabilities against potential attacks, bolstering the protocol's resilience. The proposed method's performance is validated against other well-known techniques to prove the supremacy of the method. At 75% data variation, the IECC scheme attained a better KCA attack value of 0.152, which surpasses the result of ECC, RSA, Blowfish, Fernet, ElGamal, NTRU, and CP-ABE. This potentially underscores the model's effectiveness in protecting data against known cryptographic vulnerabilities contrasting to other traditional techniques.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 4-5","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143439049","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":"A Multi-Objective Decision-Making Neural Network: Effective Structure and Learning Method","authors":"Shu-Rong Yan,&nbsp;Mohadeseh Nadershahi,&nbsp;Wei Guo,&nbsp;Ebrahim Ghaderpour,&nbsp;Ardashir Mohammadzadeh","doi":"10.1002/cpe.70031","DOIUrl":"https://doi.org/10.1002/cpe.70031","url":null,"abstract":"<p>Decision Neural Networks significantly improve the performance of complex models and create more transparent and accountable decision-making systems that can be trusted in critical applications. However, their performance strongly depends on the amount of data and the learning algorithm. This article describes the development of a simplified structure and training algorithm based on the Levenberg–Marquardt algorithm to enhance the decision neural network's training and assess the utility function's efficacy in multi-objective issues. The suggested algorithm converges faster than traditional algorithms. Also, the designed scheme combines gradient descent with the Gauss-Newton method, allowing it to escape shallow local minima more effectively than other similar techniques. Numerical examples demonstrate how well the suggested method estimates linear utility functions, even complicated and nonlinear ones. Additionally, the findings of applying the enhanced decision neural network to multi-objective decision-making issues show that this instructional technique produces responses with higher quality and faster convergence. By applying the designed scheme to a multi-objective problem with seven primary answers, it is shown that accuracy is improved by more than 20%.</p>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 4-5","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cpe.70031","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143439051","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":"TPST: A Traffic Flow Prediction Model Based on Spatial–Temporal Identity","authors":"Yuchen Hou,&nbsp;Buqing Cao,&nbsp;Jianxun Liu,&nbsp;Changyun Li,&nbsp;Min Shi","doi":"10.1002/cpe.70011","DOIUrl":"https://doi.org/10.1002/cpe.70011","url":null,"abstract":"<div>\u0000 \u0000 <p>With the constant dynamics of temporal dependence and spatial correlation, the interaction between them has become intricate. Existing work attempts to model precise temporal dependency and spatial correlation to make their interactions more accurate but ignores the importance of understanding how the two interact with each other. Thus, this article mines deeper into their interaction mechanism and proposes a new traffic prediction model called traffic flow prediction model based on spatial–temporal identity (TPST). It provides a new way named the spatial–temporal identity mechanism to model spatial–temporal interactions, which convert complex temporal dependence and spatial correlation into their identity information. Meanwhile, in order to improve spatial–temporal interaction resolution of the model, the method utilizes the down-sampling cross-convolution technique to contain more spatial–temporal history information and parses spatial–temporal interactions at different granularity. Experiments conducted with four real traffic flow datasets show that TPST consistently outperforms the other seven benchmark models, providing higher prediction accuracy with lower computational cost.</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":"143439036","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 Integrated Framework for COVID-19 Classification Based on Classical and Quantum Transfer Learning from a Chest Radiograph","authors":"","doi":"10.1002/cpe.70015","DOIUrl":"https://doi.org/10.1002/cpe.70015","url":null,"abstract":"<p>\u0000 <b>RETRACTION:</b> <span>M. J. Umer</span>, <span>J. Amin</span>, <span>M. Sharif</span>, <span>M. A. Anjum</span>, <span>F. Azam</span>, and <span>J. H. Shah</span>, “ <span>An Integrated Framework for COVID-19 Classification Based on Classical and Quantum Transfer Learning from a Chest Radiograph</span>,” <i>Concurrency and Computation: Practice and Experience</i> <span>34</span>, no. <span>20</span> (<span>2022</span>): e6434, \u0000https://doi.org/10.1002/cpe.6434.</p><p>The above article, published online on 29 June 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. 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.70015","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143438990","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":"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}