{"title":"关于《DL-SCA：基于深度学习的类内剪辑混合数据增强方法》的撤稿通知 [Physical Communication 63 (2024) 102288]","authors":"Weiguang Liu","doi":"10.1016/j.phycom.2024.102448","DOIUrl":null,"url":null,"abstract":"<div><p>This article has been retracted: please see Elsevier Policy on Article Withdrawal (<span><span>https://www.elsevier.com/locate/withdrawalpolicy</span><svg><path></path></svg></span>).</p><p>This article has been retracted at the request of the Editor-in-Chief.</p><p>The authors plagiarised content from a manuscript that was submitted to another journal. The title of the original manuscript is, “Intra-class CutMix Data Augmentation based Deep Learning Side Channel Attacks”, and was submitted by authors, Runlian Zhanga, Yu Moa, Zhaoxuan Pana, Hailong Zhangb, Yongzhuang Weia, Xiaonian Wua.</p><p>One of the conditions of submission of a paper for publication is that authors declare explicitly that their work is original. Reuse of any data should be appropriately cited. As such this article represents a severe abuse of the scientific publishing system. The scientific community takes a very strong view on this matter and apologies are offered to readers of the journal that this was not detected during the submission process.</p><p>a Guangxi Key Laboratory of Cryptography and Information Security, Guilin University of Electronic Technology.</p><p>b State Key Laboratory of Information Security, Institute of Information Engineering Chinese Academy.</p></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"66 ","pages":"Article 102448"},"PeriodicalIF":2.0000,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1874490724001666/pdfft?md5=19bb08529fa603fc9da0ad335bf62985&pid=1-s2.0-S1874490724001666-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Retraction Notice to “DL-SCA: An deep learning based approach for Intra-class CutMix Data Augmentation” [Physical Communication 63 (2024) 102288]\",\"authors\":\"Weiguang Liu\",\"doi\":\"10.1016/j.phycom.2024.102448\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>This article has been retracted: please see Elsevier Policy on Article Withdrawal (<span><span>https://www.elsevier.com/locate/withdrawalpolicy</span><svg><path></path></svg></span>).</p><p>This article has been retracted at the request of the Editor-in-Chief.</p><p>The authors plagiarised content from a manuscript that was submitted to another journal. The title of the original manuscript is, “Intra-class CutMix Data Augmentation based Deep Learning Side Channel Attacks”, and was submitted by authors, Runlian Zhanga, Yu Moa, Zhaoxuan Pana, Hailong Zhangb, Yongzhuang Weia, Xiaonian Wua.</p><p>One of the conditions of submission of a paper for publication is that authors declare explicitly that their work is original. Reuse of any data should be appropriately cited. As such this article represents a severe abuse of the scientific publishing system. The scientific community takes a very strong view on this matter and apologies are offered to readers of the journal that this was not detected during the submission process.</p><p>a Guangxi Key Laboratory of Cryptography and Information Security, Guilin University of Electronic Technology.</p><p>b State Key Laboratory of Information Security, Institute of Information Engineering Chinese Academy.</p></div>\",\"PeriodicalId\":48707,\"journal\":{\"name\":\"Physical Communication\",\"volume\":\"66 \",\"pages\":\"Article 102448\"},\"PeriodicalIF\":2.0000,\"publicationDate\":\"2024-07-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S1874490724001666/pdfft?md5=19bb08529fa603fc9da0ad335bf62985&pid=1-s2.0-S1874490724001666-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Physical Communication\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1874490724001666\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Physical Communication","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1874490724001666","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
Retraction Notice to “DL-SCA: An deep learning based approach for Intra-class CutMix Data Augmentation” [Physical Communication 63 (2024) 102288]
This article has been retracted: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/locate/withdrawalpolicy).
This article has been retracted at the request of the Editor-in-Chief.
The authors plagiarised content from a manuscript that was submitted to another journal. The title of the original manuscript is, “Intra-class CutMix Data Augmentation based Deep Learning Side Channel Attacks”, and was submitted by authors, Runlian Zhanga, Yu Moa, Zhaoxuan Pana, Hailong Zhangb, Yongzhuang Weia, Xiaonian Wua.
One of the conditions of submission of a paper for publication is that authors declare explicitly that their work is original. Reuse of any data should be appropriately cited. As such this article represents a severe abuse of the scientific publishing system. The scientific community takes a very strong view on this matter and apologies are offered to readers of the journal that this was not detected during the submission process.
a Guangxi Key Laboratory of Cryptography and Information Security, Guilin University of Electronic Technology.
b State Key Laboratory of Information Security, Institute of Information Engineering Chinese Academy.
期刊介绍：
PHYCOM: Physical Communication is an international and archival journal providing complete coverage of all topics of interest to those involved in all aspects of physical layer communications. Theoretical research contributions presenting new techniques, concepts or analyses, applied contributions reporting on experiences and experiments, and tutorials are published.
Topics of interest include but are not limited to:
Physical layer issues of Wireless Local Area Networks, WiMAX, Wireless Mesh Networks, Sensor and Ad Hoc Networks, PCS Systems; Radio access protocols and algorithms for the physical layer; Spread Spectrum Communications; Channel Modeling; Detection and Estimation; Modulation and Coding; Multiplexing and Carrier Techniques; Broadband Wireless Communications; Wireless Personal Communications; Multi-user Detection; Signal Separation and Interference rejection: Multimedia Communications over Wireless; DSP Applications to Wireless Systems; Experimental and Prototype Results; Multiple Access Techniques; Space-time Processing; Synchronization Techniques; Error Control Techniques; Cryptography; Software Radios; Tracking; Resource Allocation and Inference Management; Multi-rate and Multi-carrier Communications; Cross layer Design and Optimization; Propagation and Channel Characterization; OFDM Systems; MIMO Systems; Ultra-Wideband Communications; Cognitive Radio System Architectures; Platforms and Hardware Implementations for the Support of Cognitive, Radio Systems; Cognitive Radio Resource Management and Dynamic Spectrum Sharing.
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应助结果提醒方式：
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