检索结果-内蒙古大学图书馆

Endogenous Security Formal Definition,Innovation Mechanisms,and Experiment Research in Industrial Internet

Tsinghua science and technology 2024年第2期29卷 492-505页

作者： Hongsong Chen Xintong Han Yiying Zhang School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China

With the rapid development of information technologies,industrial Internet has become more open,and security issues have become more *** endogenous security mechanism can achieve the autonomous immune mechanism without prior ***,endogenous security lacks a scientific and formal definition in industrial ***,firstly we give a formal definition of endogenous security in industrial Internet and propose a new industrial Internet endogenous security architecture with cost ***,the endogenous security innovation mechanism is clearly ***,an improved clone selection algorithm based on federated learning is ***,we analyze the threat model of the industrial Internet identity authentication scenario,and propose cross-domain authentication mechanism based on endogenous key and zero-knowledge *** conduct identity authentication experiments based on two types of blockchains and compare their experimental *** on the experimental analysis,Ethereum alliance blockchain can be used to provide the identity resolution services on the industrial *** of Things Application(IOTA)public blockchain can be used for data aggregation analysis of Internet of Things(IoT)edge ***,we propose three core challenges and solutions of endogenous security in industrial Internet and give future development directions.

关键词： industrial Internet endogenous security architecture federated learning blockchain

来源：评论

学校读者我要写书评

暂无评论

Weakly-supervised instance co-segmentation via tensor-based salient co-peak search

引用

Frontiers of computer science 2024年第2期18卷 83-92页

作者： Wuxiu QUAN Yu HU Tingting DAN Junyu LI Yue ZHANG Hongmin CAI School of Computer Science Guangdong Polytechnic Normal UniversityGuangzhou 510665China School of Computer Science and Engineering South China University of TechnologyGuangzhou 510006China

Instance co-segmentation aims to segment the co-occurrent instances among two *** task heavily relies on instance-related cues provided by co-peaks,which are generally estimated by exhaustively exploiting all paired candidates in point-to-point ***,such patterns could yield a high number of false-positive co-peaks,resulting in over-segmentation whenever there are mutual *** tackle with this issue,this paper proposes an instance co-segmentation method via tensor-based salient co-peak search(TSCPS-ICS).The proposed method explores high-order correlations via triple-to-triple matching among feature maps to find reliable co-peaks with the help of co-saliency *** proposed method is shown to capture more accurate intra-peaks and inter-peaks among feature maps,reducing the false-positive rate of co-peak *** having accurate co-peaks,one can efficiently infer responses of the targeted *** on four benchmark datasets validate the superior performance of the proposed method.

关键词： weakly-supervised co-segmentation co-peak tensormatching deep network instance segmentation

来源：评论

学校读者我要写书评

暂无评论

ISHD:Intelligent Standing Human Detection of Video Surveillance for the Smart Examination Environment

引用

computer Modeling in engineering & sciences 2023年第10期137卷 509-526页

作者： Wu Song Yayuan Tang Wenxue Tan Sheng Ren School of Computer and Electrical Engineering Hunan University of Arts and SciencesChangde415000China School of Information Engineering Hunan University of Science and TechnologyYongzhou425119China School of Computer Science and Engineering Central South UniversityChangsha410083China

In the environment of smart examination rooms, it is important to quickly and accurately detect abnormal behavior(human standing) for the construction of a smart campus. Based on deep learning, we propose an intelligentstanding human detection (ISHD) method based on an improved single shot multibox detector to detect thetarget of standing human posture in the scene frame of exam room video surveillance at a specific examinationstage. ISHD combines the MobileNet network in a single shot multibox detector network, improves the posturefeature extractor of a standing person, merges prior knowledge, and introduces transfer learning in the trainingstrategy, which greatly reduces the computation amount, improves the detection accuracy, and reduces the trainingdifficulty. The experiment proves that the model proposed in this paper has a better detection ability for the smalland medium-sized standing human body posture in video test scenes on the EMV-2 dataset.

关键词： Deep learning object detection video surveillance of exam room smart examination environment

来源：评论

学校读者我要写书评

暂无评论

OCRBench: on the hidden mystery of OCR in large multimodal models

引用

science China(Information sciences) 2024年第12期67卷 23-35页

作者： Yuliang LIU Zhang LI Mingxin HUANG Biao YANG Wenwen YU Chunyuan LI Xu-Cheng YIN Cheng-Lin LIU Lianwen JIN Xiang BAI School of Artificial Intelligence and Automation Huazhong University of Science and Technology School of Electronic and Information Engineering South China University of Technology Microsoft Research School of Computer & Communication Engineering University of Science and Technology Beijing Institute of Automation Chinese Academy of Sciences School of Software Engineering Huazhong University of Science and Technology

Large models have recently played a dominant role in natural language processing and multimodal vision-language learning. However, their effectiveness in text-related visual tasks remains relatively unexplored. In this paper, we conducted a comprehensive evaluation of large multimodal models, such as GPT4V and Gemini, in various text-related visual tasks including text recognition, scene text-centric visual question answering(VQA), document-oriented VQA, key information extraction(KIE), and handwritten mathematical expression recognition(HMER). To facilitate the assessment of optical character recognition(OCR) capabilities in large multimodal models, we propose OCRBench, a comprehensive evaluation benchmark. OCRBench contains 29 datasets, making it the most comprehensive OCR evaluation benchmark available. Furthermore, our study reveals both the strengths and weaknesses of these models, particularly in handling multilingual text, handwritten text, non-semantic text, and mathematical expression *** importantly, the baseline results presented in this study could provide a foundational framework for the conception and assessment of innovative strategies targeted at enhancing zero-shot multimodal *** evaluation pipeline and benchmark are available at https://***/Yuliang-Liu/Multimodal OCR.

关键词： large multimodal model OCR text recognition scene text-centric VQA document-oriented VQA key information extraction handwritten mathematical expression recognition

来源：评论

学校读者我要写书评

暂无评论

AsyCo: an asymmetric dual-task co-training model for partial-label learning

引用

science China(Information sciences) 2025年第5期68卷 332-347页

作者： Beibei LI Yiyuan ZHENG Beihong JIN Tao XIANG Haobo WANG Lei FENG College of Computer Science Chongqing University State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences University of Chinese Academy of Sciences School of Software Technology Zhejiang University School of Computer Science and Engineering Nanyang Technological University

Partial-label learning(PLL) is a typical problem of weakly supervised learning, where each training instance is annotated with a set of candidate labels. Self-training PLL models achieve state-of-the-art performance but suffer from error accumulation problems caused by mistakenly disambiguated instances. Although co-training can alleviate this issue by training two networks simultaneously and allowing them to interact with each other, most existing co-training methods train two structurally identical networks with the same task, i.e., are symmetric, rendering it insufficient for them to correct each other due to their similar limitations. Therefore, in this paper, we propose an asymmetric dual-task co-training PLL model called AsyCo,which forces its two networks, i.e., a disambiguation network and an auxiliary network, to learn from different views explicitly by optimizing distinct tasks. Specifically, the disambiguation network is trained with a self-training PLL task to learn label confidence, while the auxiliary network is trained in a supervised learning paradigm to learn from the noisy pairwise similarity labels that are constructed according to the learned label confidence. Finally, the error accumulation problem is mitigated via information distillation and confidence refinement. Extensive experiments on both uniform and instance-dependent partially labeled datasets demonstrate the effectiveness of AsyCo.

关键词： machine learning weakly supervised learning partial-label learning co-training models candidate label sets

来源：评论

学校读者我要写书评

暂无评论

When Software Security Meets Large Language Models:A Survey

引用

IEEE/CAA Journal of Automatica Sinica 2025年第2期12卷 317-334页

作者： Xiaogang Zhu Wei Zhou Qing-Long Han Wanlun Ma Sheng Wen Yang Xiang IEEE the School of Computer and Mathematical Sciences the University of Adelaide the School of Science Computing and Engineering Technologies Swinburne University of Technology

Software security poses substantial risks to our society because software has become part of our life. Numerous techniques have been proposed to resolve or mitigate the impact of software security issues. Among them, software testing and analysis are two of the critical methods, which significantly benefit from the advancements in deep learning technologies. Due to the successful use of deep learning in software security, recently,researchers have explored the potential of using large language models(LLMs) in this area. In this paper, we systematically review the results focusing on LLMs in software security. We analyze the topics of fuzzing, unit test, program repair, bug reproduction, data-driven bug detection, and bug triage. We deconstruct these techniques into several stages and analyze how LLMs can be used in the stages. We also discuss the future directions of using LLMs in software security, including the future directions for the existing use of LLMs and extensions from conventional deep learning research.

关键词： Large language models (LLMs) software analysis software security software testing

来源：评论

学校读者我要写书评

暂无评论

Steel Surface Defect Detection Algorithm Based on S-YOLOv8

IAENG International Journal of Computer Science

引用

IAENG International Journal of computer science 2025年第3期52卷 644-652页

作者： Zhang, Xu Cui, Wenhua Tao, Ye Shi, Tianwei School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan China

Steel, being a widely utilized material in industrial production, holds a pivotal role in ensuring product safety and longevity. Hence, the exploration and implementation of steel surface defect detection technology carry significant importance. This paper introduces a steel surface defect detection algorithm based on S-YOLOv8. The algorithm, rooted in YOLOv8n as a benchmark model, initially incorporates a shift-wise shift operator in the backbone network. This introduction notably enhances accuracy compared to conventional CNN models while markedly reducing computational demands. Furthermore, the utilization of the SF-Neck framework, integrating the scale sequence feature fusion module (SSFF) and triple feature encoder module (TFE) in the head network, enriches the network’s multi-scale information extraction capabilities. Subsequently, the adoption of the WIoU loss function enhances the overall detector performance. Lastly, the integration of the SEAM occlusion attention module refines the detection head segment of the YOLOv8 algorithm, effectively addressing defect occlusion challenges. Experiments conducted on the NEU-DET dataset reveal that the mAP value of the S-YOLOv8 model reaches an impressive 84.2%. Comparative analysis with other mainstream algorithms demonstrates a substantial enhancement in detection accuracy, alongside a reduction in instances of leakage and misdetection. Consequently, this study charts a new technical trajectory for quality control within the steel manufacturing industry. © (2025), (International Association of Engineers). All rights reserved.

关键词： Benchmarking

来源：评论

学校读者我要写书评

暂无评论

Spontaneous grain refinement effect of rare earth zinc alloy anodes enables stable zinc batteries

引用

National science Review 2024年第7期11卷 252-262页

作者： Manjing Chen Yuxiang Gong Yunxiang Zhao Yexin Song Yan Tang Zhiyuan Zeng Shuquan Liang Peng Zhou Bingan Lu Xiaotan Zhang Jiang Zhou School of Materials Science&Engineering Hunan Provincial Key Laboratory of Electronic Packaging and Advanced Functional MaterialsCentral South University Institute of Materials Research Tsinghua Shenzhen International Graduate School Tsinghua University Department of Materials Science and Engineering City University of Hong Kong Hunan Provincial Key Defense Laboratory of High Temperature Wear-Resisting Materials and Preparation Technology Hunan University of Science and Technology School of Physics and Electronics Hunan University School of Chemistry and Materials Science University of Science and Technology of China Suzhou Institute for Advanced Research University of Science and Technology of China

Irreversible interfacial reactions at the anodes pose a significant challenge to the long-term stability and lifespan of zinc（Zn） metal batteries, impeding their practical application as energy storage devices. The plating and stripping behavior of Zn ions on polycrystalline surfaces is inherently influenced by the microscopic structure of Zn anodes, a comprehensive understanding of which is crucial but often overlooked. Herein, commercial Zn foils were remodeled through the incorporation of cerium（Ce）elements via the ‘pinning effect’ during the electrodeposition process. By leveraging the electron-donating effect of Ce atoms segregated at grain boundaries（GBs）, the electronic configuration of Zn is restructured to increase active sites for Zn nucleation. This facilitates continuous nucleation throughout the growth stage,leading to a high-rate instantaneous-progressive composite nucleation model that achieves a spatially uniform distribution of Zn nuclei and induces spontaneous grain refinement. Moreover, the incorporation of Ce elements elevates the site energy of GBs, mitigating detrimental parasitic reactions by enhancing the GB stability. Consequently, the remodeled ZnCe electrode exhibits highly reversible Zn plating/stripping with an accumulated capacity of up to 4.0 Ah cm-2in a Zn symmetric cell over 4000 h without short-circuit behavior. Notably, a ～0.4 Ah Zn||NH4V4O10pouch cell runs over 110 cycles with 83% capacity retention with the high-areal-loading cathode(≈20 mg cm-2). This refining-grains strategy offers new insights into designing dendrite-free metal anodes in rechargeable batteries.

关键词： grain refinement rare earth GB stability zinc metal batteries

来源：评论

学校读者我要写书评

暂无评论

PosParser: A Heuristic Online Log Parsing Method Based on Part-of-Speech Tagging

引用

IEEE Transactions on Big Data 2025年第3期11卷 1334-1345页

作者： Jiang, Jinzhao Fu, Yuanyuan Xu, Jian Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing210094 China

Log parsing, the process of transforming raw logs into structured data, is a key step in the complex computer system's intelligent operation and maintenance and therefore has received extensive attention. Among all log parsing methods, heuristic log parsing methods are lightweight and can work in a streaming mode to well meet the real-time parsing requirements. However, the existing log representations used in the heuristic log parsing methods are not powerful in distinguishing log messages, which leads to low parsing accuracy and weak generality. Inspired by trigger word extraction of the event detection task in natural language processing (NLP), this paper proposes an online log parser, named PosParser, which employs the part-of-speech (PoS) tagging to extract a function token sequence (FTS) as the log message representation, and then identify event templates of log messages through the FTS. Experimental results on sixteen logs from real systems demonstrate that the FTS is powerful in distinguishing log messages from different event templates, and PosParser not only performs better in terms of parsing accuracy than state-of-the-art methods but is also comparable to them in efficiency. © 2015 IEEE.

关键词： Real time systems

来源：评论

学校读者我要写书评

暂无评论

Web API Recommendation via Combining Adaptive Multichannel Graph Representation and xDeepFM Quality Prediction

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2024年第6期5卷 3218-3232页

作者： Cao, Buqing Qing, Yueying Zhou, Dong Xie, Xiang Kang, Guosheng Liu, Jianxun Fletcher, Kenneth K. Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan411201 China Guangdong University of Foreign Studies School of Information Science and Technology Guangzhou510420 China University of Massachusetts Computer Science BostonMA02125-3393 United States

With the increasing number of Web services, how to provide developers with Web APIs that meet their Mashup requirements accurately and efficiently has become a challenge. Even though the existing methods show improvements in service recommendation, the efficiency and accuracy (ACC) of them still need to be improved due to their limited representation in fuzing network topology and node feature of Web service, and the neglected higher-order feature interactions of Web service. To address this problem, this article proposes a Web APIs recommendation method via combining adaptive multichannel (AMC) graph representation and eXtreme deep factorization machine (xDeepFM) quality prediction. In this method, firstly, specific embedding and shared embedding in Web API node isomorphic network are extracted from the nodes' feature space, topology space, and the combination of the two spaces. Then, attention mechanism is used to adaptively learn the importance weight of each embedding. Next, these embeddings are adaptively integrated to generate the multichannel graph representation of Web APIs for service classification. Finally, aiming at the Web APIs in the service cluster, it utilizes xDeepFM to model and mine the complex feature interactions and predict and rank the scores of Web APIs for Mashup creation. The experimental results on the real datasets of ProgrammableWeb show that compared with DeepFM, wide and deep learning (WDL), FM supported neural network (FNN), neural factorization machine (NFM), and mixed logistic regression (MLR), the method proposed in this article has an average improvement in AUC of 2.3%, 7.9%, 8.0%, 9.6%, and 13.3%. © 2020 IEEE.

关键词： Network topology

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：