计算机科学最新文献

{"title":"A Lightweight Detection and Recognition Framework for cigarette laser code","authors":"Wei Zhou ,&nbsp;Honggang Li ,&nbsp;Li Zheng ,&nbsp;Shan Xiao ,&nbsp;Jun Yi","doi":"10.1016/j.engappai.2025.111777","DOIUrl":"10.1016/j.engappai.2025.111777","url":null,"abstract":"<div><div>Automatic detection and recognition of laser codes on cigarette case is important in distinguishing the authenticity of cigarettes. However, detecting and recognizing cigarette laser codes presents a challenging industrial problem due to the intricate background of cigarette images. In this paper, a Lightweight Detection and Recognition Framework (LDRF) is proposed to detect and recognize cigarette laser code. The LDRF model consists of the Lightweight Detection Network (LDNet) and the Lightweight Recognition Network (LRNet). In the LDNet stage, a lightweight feature extraction network is proposed to extract features of the cigarette code area. Furthermore, a bidirectional feature pyramid network (BiFPN) feature fusion structure is introduced to tackle the multi-scale feature fusion challenge in cigarette code detection scenarios. Notably, alignment and normalization of all features channels are conducted to reduce computational requirements in the post-processing stage, enabling precise detection performance while significantly reducing parameters and computational complexity. In the LRNet stage, an integrated network architecture is designed to enhance the fusion of visual and temporal features. Furthermore, a combination of bidirectional temporal convolutional network (BiTCN) and Transformer is employed in the feature extraction stage to differentiate between background and characters, as well as capture the interdependence among different characters. Specifically, DownSampling is utilized to adjust the size of input images and Merging or Combining methods are applied at each stage to capture multi-level features. Experimental results demonstrate that the proposed LDRF method provides better performance than state-of-the-art models, and achieves trade-off between accuracy and speed.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"160 ","pages":"Article 111777"},"PeriodicalIF":7.5,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144702861","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Enhanced knowledge graph cascade learning model for cyber–physical systems","authors":"Shumao Zhang,&nbsp;Jie Xu,&nbsp;Haodiao Xie,&nbsp;Qiuru Fu,&nbsp;Ke Miao,&nbsp;Shixue Cheng,&nbsp;Zelei Wu","doi":"10.1016/j.engappai.2025.111802","DOIUrl":"10.1016/j.engappai.2025.111802","url":null,"abstract":"<div><div>Recently, the application prospects of knowledge graph technology in cyber–physical systems (CPS) have attracted considerable attention. However, knowledge graph data in various CPS domains are typically collected from sensors or through manual efforts, which inevitably results in incomplete and unreliable data, thereby impacting the performance of downstream task models. This issue is often overlooked in existing studies. This paper proposes an enhanced knowledge graph cascade learning model for CPS. The model performs cascaded and iterative learning of both graph structure and graph representation. By optimizing the graph structure and incorporating hierarchical learning of graph-structured information, the proposed model enhances feature propagation and aggregation during representation learning. Experiments show that our model achieves outstanding results: compared to the baseline models, our approach achieves an average improvement of 2.7% in accuracy on the node classification task and 1.35% in MRR on the link prediction task.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"160 ","pages":"Article 111802"},"PeriodicalIF":7.5,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144703432","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"General construction of integer codes correcting single error in t2-TQAM","authors":"T. Alexandrova ,&nbsp;H. Kostadinov ,&nbsp;N. Manev","doi":"10.1016/j.compeleceng.2025.110572","DOIUrl":"10.1016/j.compeleceng.2025.110572","url":null,"abstract":"<div><div>We propose a general construction of codes over the ring <span><math><msub><mrow><mi>Z</mi></mrow><mrow><mi>A</mi></mrow></msub></math></span> of integers modulo <span><math><mrow><mi>A</mi><mo>=</mo><msup><mrow><mi>t</mi></mrow><mrow><mn>2</mn></mrow></msup><mo>+</mo><mn>1</mn></mrow></math></span> that are capable to correct single errors of types that are dominant in communication based on triangular quadrature amplitude modulation constellation with <span><math><msup><mrow><mi>t</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span> signal points.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"127 ","pages":"Article 110572"},"PeriodicalIF":4.0,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144702624","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 industrial defect detection based on hybrid residual attention with modified generative adversarial network and convolutional neural network model","authors":"Asadulla Ashurov,&nbsp;Hongchun Qu","doi":"10.1016/j.compeleceng.2025.110580","DOIUrl":"10.1016/j.compeleceng.2025.110580","url":null,"abstract":"<div><div>Detecting and classifying industrial defects continues to pose significant issues in Industry 4.0 era, principally due to constraints in managing data scarcity, variability in fault characteristics, and the inadequacy of traditional models to adapt to different and dynamic situations. Contemporary approaches frequently encounter difficulties in producing dependable synthetic data, effectively extracting essential features, and attaining robust performance in industrial applications. This paper presents a hybrid residual attention generative adversarial network with convolutional neural networks (RAtGAN-CNN) model to address these constraints. The RAtGAN-CNN framework combines residual blocks and attention mechanisms with a generative adversarial network to produce high-quality synthetic samples that replicate the complex distributions of real defects. This approach effectively addresses data scarcity and is trained concurrently with a discriminator through adversarial learning, thereby enhancing data diversity and reducing overfitting in situations with limited labeled data. The lightweight design guarantees appropriateness for real-time industrial applications, fulfilling the demands of computationally limited situations. The model employs a lightweight convolutional neural network (CNN) that utilizes a modified residual block to boost feature extraction, while its attention mechanism concentrates on critical defect areas to improve detection accuracy. These methodologies empower the RAtGAN-CNN to operate effectively across many datasets and settings, particularly excelling in situations with sparse or highly variable input data. The framework is evaluated on a binary image classification dataset of industrial casting defects, attaining a competitive accuracy above 99% on the validation set, with a lightweight model size of 12.5 MB and an average inference time of 18.5 ms per image on a single GPU. Metrics including precision, recall, and F1-score illustrate the approach’s robustness, underpinned by thorough evaluation via confusion matrices and loss-accuracy curves.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"127 ","pages":"Article 110580"},"PeriodicalIF":4.0,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144702625","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 graph regularized overlapping community discovery framework with three-way decisions","authors":"Xiaoyang Zou ,&nbsp;Jinxin Cao ,&nbsp;Hengrong Ju ,&nbsp;Weiping Ding ,&nbsp;Lu Liu ,&nbsp;Fuxiang Chen ,&nbsp;Di Jin","doi":"10.1016/j.ins.2025.122525","DOIUrl":"10.1016/j.ins.2025.122525","url":null,"abstract":"<div><div>Community detection is essential for complex network analysis. Most existing approaches focus on hard community partitioning, and a few have investigated overlapping community structures, which are important but difficult to handle in practical applications. This paper presents a graph regularization-based framework for overlapping community detection, which integrates topological information and applies a theoretical three-way decision method to handle uncertain knowledge. The proposed models, <span><math><mrow><mtext>GNMFO</mtext><mi>_</mi><mtext>TW</mtext></mrow></math></span>, <span><math><mrow><mtext>GYNMFO</mtext><mi>_</mi><mtext>TW</mtext></mrow></math></span>, and <span><math><mrow><mtext>GAEO</mtext><mi>_</mi><mtext>TW</mtext></mrow></math></span>, employ NMF, YNMF, and AEs with graph regularization terms for initial partitioning. The membership degrees of each node across different communities are then used for re-partitioning through three-way decisions. These models apply subspace clustering principles to incorporate basic network structure. To address the limitations caused by sparse network topology, the graph regularization terms encourage similar community membership among connected or nearby nodes, resulting in more coherent communities. In addition, three-way decisions, guided by node structural similarity, detect overlapping clusters and participating vertices. The proposed models not only identify community memberships but also reveal the overlapping community structures within networks. Empirical evaluations across both artificial and empirical networks indicate that our method outperforms existing advanced overlapping community detection techniques.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"720 ","pages":"Article 122525"},"PeriodicalIF":8.1,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144703725","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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":"Decentralized key distribution versus on-demand relaying for QKD networks","authors":"Maria Alvarez Roa;Catalina Stan;Sebastian Verschoor;Idelfonso Tafur Monroy;Simon Rommel","doi":"10.1364/JOCN.547793","DOIUrl":"https://doi.org/10.1364/JOCN.547793","url":null,"abstract":"Quantum key distribution (QKD) allows the distribution of secret keys for quantum-secure communication between two distant parties, vital in the quantum computing era in order to protect against quantum-enabled attackers. However, overcoming rate-distance limits in QKD and the establishment of quantum key distribution networks necessitate key relaying over trusted nodes. This process may be resource-intensive, consuming a substantial share of the scarce QKD key material to establish end-to-end secret keys. Hence, an efficient scheme for key relaying and the establishment of end-to-end key pools is essential for practical and extended quantum-secured networking. In this paper, we propose and compare two protocols for managing, storing, and distributing secret key material in QKD networks, addressing challenges such as the success rate of key requests, key consumption, and overhead resulting from relaying. We present an innovative, fully decentralized key distribution strategy as an alternative to the traditional hop-by-hop relaying via trusted nodes, where three experiments are considered to evaluate performance metrics under varying key demand. Our results show that the decentralized pre-flooding approach achieves higher success rates as application demands increase. This analysis highlights the strengths of each approach in enhancing QKD network performance, offering valuable insights for developing robust key distribution strategies in different scenarios.","PeriodicalId":50103,"journal":{"name":"Journal of Optical Communications and Networking","volume":"17 8","pages":"732-742"},"PeriodicalIF":4.0,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144704948","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Federated learning with three-way decisions for privacy-preserving multicloud resource scheduling","authors":"Chunmao Jiang,&nbsp;Lirun Su","doi":"10.1016/j.asoc.2025.113634","DOIUrl":"10.1016/j.asoc.2025.113634","url":null,"abstract":"<div><div>This paper introduces the Federated Three-Way Decision System (F3WDS), a novel framework for multicloud resource scheduling that integrates federated learning with the three-way decision theory to address the challenges of resource heterogeneity, decision uncertainty, and data privacy. By combining privacy-preserving collaborative learning with nuanced decision-making (positive, boundary, and negative regions), the F3WDS optimizes resource allocation across multiple cloud providers while adhering to strict data sovereignty requirements. We provide rigorous theoretical guarantees, including convergence analysis, privacy bounds, and performance bounds, to demonstrate the reliability of the system. Extensive experiments on synthetic and real-world datasets demonstrate that F3WDS achieves significant improvements over state-of-the-art methods: 5%–14% higher resource utilization, 60% lower privacy loss, and 30% reduced cross-cloud latency. The framework’s scalability, robustness to stragglers, and favorable privacy-utility trade-off make it a solution for privacy-sensitive multicloud environments, with implications for future research on distributed computing and privacy-aware resource management.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"183 ","pages":"Article 113634"},"PeriodicalIF":7.2,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144711627","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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":"Measuring building information modeling user satisfaction by using active interpretable machine learning","authors":"Wei-Chih Wang,&nbsp;Shyn-Chang Huang,&nbsp;Hsu-Pin Wang,&nbsp;Minh-Tu Cao","doi":"10.1016/j.asoc.2025.113663","DOIUrl":"10.1016/j.asoc.2025.113663","url":null,"abstract":"<div><div>Accurately predicting building information modeling (BIM) user satisfaction (US) is essential for proactively addressing implementation challenges, ensuring effective adoption, and maximizing return on investment in BIM technologies in construction projects. Accordingly, this study developed advanced, interpretable boosting ensemble models to predict BIM US by integrating the forensic-based investigation (FBI) algorithm with gradient boosting machine, light gradient boosting machine, adaptive boosting (AdaBoost), extreme gradient boosting, and random forest algorithms. To validate the proposed models and establish a dataset, a comprehensive survey was conducted on 70 construction projects in Taiwan that used BIM technologies to support design work. Subsequently, the synthetic minority oversampling technique (SMOTE) was integrated into the proposed models to address the data imbalance problem. The results indicated that among all models, the FBI-AdaBoost-SMOTE model exhibited the highest performance, achieving accuracy, precision, recall, and F1 scores of 88.6 %, 90.6 %, 88.6 %, and 87.8 %, respectively. The FBI-AdaBoost model based on Shapley additive explanations identified contextual analysis and visualization, project scale, and cost estimates as key determinants of BIM US. Overall, this study presents an advanced machine learning framework for predicting BIM US and identifying key influencing factors for BIM US. It also provides actionable insights for stakeholders to enhance BIM implementation and user experience. In addition, this study highlights the potential of predictive modeling for optimizing the adoption of BIM in the architecture, engineering, and construction industry.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"183 ","pages":"Article 113663"},"PeriodicalIF":7.2,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144711630","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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","authors":"","doi":"10.1111/coin.70110","DOIUrl":"https://doi.org/10.1111/coin.70110","url":null,"abstract":"<p><b>RETRACTION</b>: <span>A. Rajendran</span> and <span>M. Rajappa</span>, “ <span>Efficient Signal Selection Using Supervised Learning Model for Enhanced State Restoration</span>,” <i>Computational Intelligence</i> <span>37</span> no. <span>3</span> (<span>2021</span>): <span>1141</span>–<span>1154</span>, https://doi.org/10.1111/coin.12344.</p><p>The above article, published online on 17 June 2020 in Wiley Online Library (wileyonlinelibrary.com) has been retracted by agreement between the journal Editor-in-Chief, Diana Inkpen; and Wiley Periodicals LLC. The article was published as part of a guest-edited issue. Following an investigation by the publisher, all parties have concluded that this article was accepted solely on the basis of a compromised peer review process. The editors have therefore decided to retract the article. The authors do not agree with the retraction.</p>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"41 4","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/coin.70110","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144695770","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","authors":"","doi":"10.1111/coin.70109","DOIUrl":"https://doi.org/10.1111/coin.70109","url":null,"abstract":"<p><b>RETRACTION</b>: <span>L. Sun</span>, <span>X. Xu</span>, <span>Y. Yang</span>, <span>W. Liu</span>, and <span>J. Jin</span>, “ <span>Knowledge Mapping of Supply Chain Risk Research Based on CiteSpace</span>,” <i>Computational Intelligence</i> <span>36</span> no. <span>4</span> (<span>2020</span>): <span>1686</span>–<span>1703</span>, https://doi.org/10.1111/coin.12306.</p><p>The above article, published online on 04 March 2020 in Wiley Online Library (wileyonlinelibrary.com) has been retracted by agreement between the journal Editor-in-Chief, Diana Inkpen; and Wiley Periodicals LLC. The article was published as part of a guest-edited issue. Following an investigation by the publisher, all parties have concluded that this article was accepted solely on the basis of a compromised peer review process. The editors have therefore decided to retract the article. The authors have been informed of the retraction.</p>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"41 4","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/coin.70109","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144695772","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}