{"title":"SatBFT: An Efficient and Scalable Consensus Protocol for Blockchain-Enabled Space-Air-Ground Integrated Network","authors":"Haolin Zhang, Youping Zhao","doi":"10.1109/tccn.2025.3547786","DOIUrl":"https://doi.org/10.1109/tccn.2025.3547786","url":null,"abstract":"","PeriodicalId":13069,"journal":{"name":"IEEE Transactions on Cognitive Communications and Networking","volume":"10 1","pages":""},"PeriodicalIF":8.6,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143546701","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":"Anti-Swing Control for Double-Pendulum Overhead Cranes: From Underactuated to FAS Configuration","authors":"Yang Gao, Zhongcai Zhang, Nan Jiang, Yuqiang Wu","doi":"10.1109/tie.2025.3544192","DOIUrl":"https://doi.org/10.1109/tie.2025.3544192","url":null,"abstract":"","PeriodicalId":13402,"journal":{"name":"IEEE Transactions on Industrial Electronics","volume":"13 1","pages":""},"PeriodicalIF":7.7,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143546708","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mohamed Afouene Melki, Mohammad Shehab, Mohamed-Slim Alouini
{"title":"AUV Trajectory Learning for Underwater Acoustic Energy Transfer and Age Minimization","authors":"Mohamed Afouene Melki, Mohammad Shehab, Mohamed-Slim Alouini","doi":"10.1109/jiot.2025.3547733","DOIUrl":"https://doi.org/10.1109/jiot.2025.3547733","url":null,"abstract":"","PeriodicalId":54347,"journal":{"name":"IEEE Internet of Things Journal","volume":"14 2 1","pages":""},"PeriodicalIF":10.6,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143546750","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":"RoundImage: Towards Secure Graphical Password Authentication via Rounded Image Selection in IoT","authors":"Xinyuan Qin, Wenjuan Li, Philip Rosenberg","doi":"10.1109/jiot.2025.3547816","DOIUrl":"https://doi.org/10.1109/jiot.2025.3547816","url":null,"abstract":"","PeriodicalId":54347,"journal":{"name":"IEEE Internet of Things Journal","volume":"35 1","pages":""},"PeriodicalIF":10.6,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143546754","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":"A comprehensive transplanting of black-box adversarial attacks from multi-class to multi-label models","authors":"Zhijian Chen, Qi Zhou, Yujiang Liu, Wenjian Luo","doi":"10.1007/s40747-025-01805-z","DOIUrl":"https://doi.org/10.1007/s40747-025-01805-z","url":null,"abstract":"<p>Adversarial examples generated by perturbing raw data with carefully designed, imperceptible noise have emerged as a primary security threat to artificial intelligence systems. In particular, black-box adversarial attack algorithms, which only rely on model input and output to generate adversarial examples, are easy to implement in real scenarios. However, previous research on black-box attacks has primarily focused on multi-class classification models, with relatively few studies on black-box attack algorithms for multi-label classification models. Multi-label classification models exhibit significant differences from multi-class classification models in terms of structure and output. The former can assign multiple labels to a single sample, with these labels often exhibiting correlations, while the latter classifies a sample as the class with the highest confidence. Therefore, existing multi-class attack algorithms cannot directly attack multi-label classification models. In this paper, we study the transplantation methods of multi-class black-box attack algorithms to multi-label classification models and propose the multi-label versions for eight classic black-box attack algorithms, which include three score-based attacks and five decision-based (label-only) attacks, for the first time. Experimental results indicate that the transplanted black-box attack algorithms demonstrate effective attack performance across various attack types, except for extreme attacks. Especially, most transplanted attack algorithms achieve more than 60% success rate on the ML-GCN model and more than 30% on the ML-LIW model under the hiding all attack type. However, the performance of these transplanted attack algorithms shows variation among different attack types due to the correlations between labels.</p>","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"17 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143538781","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":"Identification of zinc stripping defects from cathode plate based on deep learning","authors":"Tao Liu , Yibin Liu , Jian Chen , Jin Gong","doi":"10.1016/j.engappai.2025.110448","DOIUrl":"10.1016/j.engappai.2025.110448","url":null,"abstract":"<div><div>During hydro-zinc smelting, the cathode plates are attached by with residual zinc or discarded due to damaged insulation strips and edging strips. Such defects limit the recycling of cathode plates. Current manual observation leads to low accuracy and speed of recognition owing to perception biases. Therefore, this work applied computer vision and deep learning semantic segmentation technology to realize the defect recognition of cathode plates. Firstly, a semantic segmentation dataset on cathode plates was constructed for training and testing the model. Then a network of attention mechanism and multiscale feature fusion (AMNet) was proposed to detect the defects. In AMNet, the encoder-decoder jump connection architecture was designed to fuse low-level and high-level features. A channel attention module was incorporated to enhance focus on the channels with important information, and the newly proposed multiscale feature extraction module was used to solve the problem of target multiscale capture. Through related parameter selection experiments, the final AMNet achieved 95.12% and 97.73% for Mean Intersection over Union (MIoU) and mean pixel accuracy (MPA), respectively. These values are 3.24 and 1.74 percentage points higher than DeepLabv3+.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"148 ","pages":"Article 110448"},"PeriodicalIF":7.5,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143535298","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":"Active Fault Detection in Static Systems","authors":"Joshua A. Taylor, Alejandro D. Domínguez-García","doi":"10.1109/tac.2025.3547848","DOIUrl":"https://doi.org/10.1109/tac.2025.3547848","url":null,"abstract":"","PeriodicalId":13201,"journal":{"name":"IEEE Transactions on Automatic Control","volume":"2 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143546141","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}