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3rd International Conference on Image Processing, computer Vision and Machine Learning, ICICML 2024

作者： Feng, Zhining Sun, Shibao Shi, Nianfeng Wang, Guoqiang Henan University of Science and Technology School of Information Engineering Luoyang China Henan University of Science and Technology School of Software Luoyang China Henan Key Laboratory of Green Building Materials Manufacturing and Intelligent Equipment School of Computer Information Engineering Luoyang Institute of Science and Technology Luoyang China

ISBN: (纸本)9798350355413

To address the issues of low accuracy, high number of parameters and computational cost in strip steel surface defect detection algorithms, a lightweight strip steel surface defect detection algorithm based on YOLOv8s is proposed. Initially, the Cross-stage Focused Linear Attention (C2F-FLA) module is introduced to enhance the feature extraction capability of the backbone network. Subsequently, to improve the capture of multi-scale defect contours and reduce the model's parameters and computational complexity, a Wavelet Convolution-Bidirectional Weighted Feature Pyramid Network (WT-BiFPN) is employed in the model's Neck section for adaptive fusion of features at different resolutions. Finally, Haar Wavelet Downsampling (HWD) is utilized to retain more defect-related information, further reducing the parameters and computational complexity. The proposed method was validated on the NEU-DET dataset, and the results demonstrate that compared to YOLOv8s, this approach achieves a 4.1% increase in mAP, a 44.4% reduction in parameters, and a 32.8% decrease in computational complexity. The method ensures lightweight implementation while improving detection accuracy, offering more possibilities for defect detection on edge terminal devices. © 2024 IEEE.

关键词： NP-hard

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Lightweight Strip Steel Surface Defect Detection Algorithm Based on YOLOv8s

Lightweight Strip Steel Surface Defect Detection Algorithm B...

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Image Processing, computer Vision and Machine Learning (ICICML), International Conference on

作者： Zhining Feng Shibao Sun Nianfeng Shi Guoqiang Wang School of Information Engineering Henan University of Science and Technology Luoyang China School of Software Henan University of Science and Technology Luoyang China School of Computer and Information Engineering Luoyang Institute of Science and Technology Henan Key Laboratory of Green Building Materials Manufacturing and Intelligent Equipment Luoyang China

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

To address the issues of low accuracy, high number of parameters and computational cost in strip steel surface defect detection algorithms, a lightweight strip steel surface defect detection algorithm based on YOLOv8s is proposed. Initially, the Cross-stage Focused Linear Attention (C2F-FLA) module is introduced to enhance the feature extraction capability of the backbone network. Subsequently, to improve the capture of multi-scale defect contours and reduce the model’s parameters and computational complexity, a Wavelet Convolution-Bidirectional Weighted Feature Pyramid Network (WT-BiFPN) is employed in the model’s Neck section for adaptive fusion of features at different resolutions. Finally, Haar Wavelet Downsampling (HWD) is utilized to retain more defect-related information, further reducing the parameters and computational complexity. The proposed method was validated on the NEU-DET dataset, and the results demonstrate that compared to YOLOv8s, this approach achieves a 4.1% increase in mAP, a 44.4% reduction in parameters, and a 32.8% decrease in computational complexity. The method ensures lightweight implementation while improving detection accuracy, offering more possibilities for defect detection on edge terminal devices.

关键词： Strips Adaptation models Accuracy Surface waves Computational modeling Feature extraction Scattering parameters Steel Computational complexity Defect detection

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Fuzzy-Classical Linguistic Concept Acquisition Approach Based on Attribute Topology 2nd

Fuzzy-Classical Linguistic Concept Acquisition Approach Base...

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2nd International Conference on Artificial Intelligence Logic and Applications, AILA 2022

作者： Pang, Kuo Kang, Ning Zou, Li Lu, Mingyu Information Science and Technology College Dalian Maritime University Dalian116026 China Software Engineering Institute East China Normal University Shanghai200062 China School of Computer Science and Technology Shandong Jianzhu University Jinan250102 China

ISBN: (纸本)9789811975097

There is a large amount of fuzzy linguistic-valued data in real life. For the complexity of fuzzy linguistic concept acquisition, in this paper, we propose a Fuzzy-classical Linguistic Concept Lattice Acquisition (FLCLC) approach based on attribute topology. Specifically, the fuzzy-classical linguistic concept induced operator is proposed according to the fuzzy linguistic formal context. On this basis, the fuzzy linguistic attribute topology is generated by the fuzzy linguistic coupling relationship between attributes. Furthermore, in order to improve the interpretability of the fuzzy-classical linguistic concept acquisition process, we traverse the weight paths of the coarse fuzzy linguistic attribute topology, and the set of attributes obtained by traversing the paths is the intents of the fuzzy-classical linguistic concept, and the weights on the paths are the extents of the fuzzy-classical linguistic concept, resulting in the fuzzy-classical linguistic concept. Finally, examples are used to demonstrate the effectiveness and practicality of our proposed approach. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Topology

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Broadband usage for rural communities in the North Island of Aotearoa New Zealand

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Intelligent and Converged Networks 2022年第3期3卷 244-259页

作者： Ming Xiang William Liu Edmund Lai Jairo Gutierrez Luca Chiaraviglio Jinsong Wu Control Science and Engineering South China University of TechnologyGuangzhou 510300Chinaand also with the South China Sea Institute of Planning and Environmental ResearchMinistry of Natural ResourcesGuangzhou 510300China Department of Computer Science and Software Engineering School of EngineeringComputer and Mathematical SciencesAuckland University of TechnologyAuckland 1010New Zealand Department of Electronic Engineering University of Rome“Tor Vergata”Rome 00133Italy Consorzio Nazionale Interuniversitario per le Telecomunicazioni(CNIT) Parma 43124Italy Department of Electrical Engineering Universidad de ChileSantiago 92101Chile

New Zealand government’s Rural Broadband Initiative(RBI)aims to invest 400 million New Zealand dollar to provide 99%of New Zealanders with access to 50 Mbps peak broadband speed,with the remaining 1%at 10 Mbps by *** 2017,stage 1 of the RBI has been completed,and research is needed to find out the level of usage,proficiency,and productivity achieved by ***,a survey was carried out to learn whether the rural residents and their communities are making good use of the new fast broadband *** survey collected data from 217 rural residents from North Island,and the results indicate that about half of the interview respondents are satisfied with the new broadband speed and the reliability which is offered by RBI stage ***,there is about 28%of the respondents disagreed with *** majority of the Internet uses for rural residents are information searching,reading news,online entertainment,and online *** that,only a small proportion of respondents know how to utilize the Internet in their work/business and benefit/profit from it,e.g.,using cloud technology capabilities and online marketing campaigns,*** ***,we argue that information and communications technologies(ICT)adoption is not only the availability of the infrastructure but also the beneficial outcomes of Internet *** other words,Internet skills in-depth training and education need to catch up with the infrastructure deployment,which is useful to fuel the digital productivity and inclusion for booming rural *** survey data-driven findings presented in this paper could serve as a reference to inform government policymakers and those who wish to create,invest,and take actions to speed up the economic and social growth of rural communities in Aotearoa New Zealand through the Internet while shifting from the Internet speed and traffic volume-driven to a more effective Internet connectivity and value-added driven rural economy.

关键词： data driven approach data-supported decision and policy making digital exclusion information and communications technologies(ICT)adoption effective connectivity rural broadband usage rural economic development

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A Survey of Medical Vision-and-Language Applications and Their Techniques

arXiv

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arXiv 2024年

作者： Chen, Qi Zhao, Ruoshan Wang, Sinuo Phan, Vu Minh Hieu van den Hengel, Anton Verjans, Johan Liao, Zhibin To, Minh-Son Xia, Yong Chen, Jian Xie, Yutong Wu, Qi Australian Institute for Machine Learning University of Adelaide Australia School of Software Engineering South China University of Technology China South Australia Medical Imaging Australia School of Computer Science & Engineering Northwestern Polytechnical University China

Medical vision-and-language models (MVLMs) have attracted substantial interest due to their capability to offer a natural language interface for interpreting complex medical data. Their applications are versatile and have the potential to improve diagnostic accuracy and decision-making for individual patients while also contributing to enhanced public health monitoring, disease surveillance, and policy-making through more efficient analysis of large data sets. MVLMS integrate natural language processing with medical images to enable a more comprehensive and contextual understanding of medical images alongside their corresponding textual information. Unlike general vision-and-language models trained on diverse, non-specialized datasets, MVLMs are purpose-built for the medical domain, automatically extracting and interpreting critical information from medical images and textual reports to support clinical decision-making. Popular clinical applications of MVLMs include automated medical report generation, medical visual question answering, medical multimodal segmentation, diagnosis and prognosis and medical image-text retrieval. Here, we provide a comprehensive overview of MVLMs and the various medical tasks to which they have been applied. We conduct a detailed analysis of various vision-and-language model architectures, focusing on their distinct strategies for cross-modal integration/exploitation of medical visual and textual features. We also examine the datasets used for these tasks and compare the performance of different models based on standardized evaluation metrics. Furthermore, we highlight potential challenges and summarize future research trends and directions. The full collection of papers and codes is available at: https://***/MVLMs. © 2024, CC BY.

关键词： Clinical research

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TR-LDA: An Improved Potential Topic Recognition Model 14th

TR-LDA: An Improved Potential Topic Recognition Model

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14th International Conference on Brain Inspired Cognitive Systems, BICS 2024

作者： Li, Anzhen Qing, Shufan Qin, Weijie Qin, Liwen Zhang, Jiawei Shi, Meilin Ren, Jinchang Feng, Mingchen College of Information Engineering Northwest A&F University Shaanxi Yangling712100 China School of Software Shanxi Agricultural University Taigu030801 China Beijing Institute of Computer Technology and Applications Beijing100006 China National Subsea Centre School of Computing and Engineering Robert Gordon University AberdeenAB21 0BH United Kingdom

ISBN: (纸本)9789819628841

Latent topic recognition is a technique for extracting hidden themes or patterns from textual data. And it plays an important role in popular application fields such as text mining and information retrieval. It can improve search accuracy, achieve personalized recommendations and automate document classification. At present, traditional natural language processing methods suffer from problems such as long processing time on large-scale datasets and insufficient consideration of the correlation between words in text, resulting in inaccurate and unstable recognition of potential themes and internal keywords in document sets. To address the aforementioned issues, this paper proposes an improved latent topic recognition model called TR-LDA. It aims to combine the TF-IDF model with the LDA model. At the same time, by integrating the TextRank algorithm, a semantic analysis module is constructed to consider the co-occurrence relationships of words within the text. The experimental results on the THUCNews Dataset indicate that the average F1 score increased from 0.4263 with the original LDA to 0.5127 with TR-LDA. Semantic consistency improved by 54.20%, and perplexity was reduced by 98.74%. Furthermore, there was a time saving of 69.08%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Semantics

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GeneDroid Fuzz: An Android Intent Fuzzing Method Based on Gene Mutation

GeneDroid Fuzz: An Android Intent Fuzzing Method Based on Ge...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Lu, Runfeng Sun, Yuzhu Sun, Haofeng Fu, Xiao Luo, Bin Du, Xiaojiang Shi, Jin Aitsaadi, Nadjib Guizani, Mohsen Nanjing University State Key Laboratory for Novel Software Technology Nanjing China Stevens Institute of Technology Department of Electrical and Computer Engineering HobokenNJ United States Nanjing University School of Information Management Nanjing China Université Paris-Saclay David Uvsq Versailles France Mohamed Bin Zayed University of Artificial Intelligence Machine Learning Department Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350351255

With the rapid expansion of mobile internet usage, the prevalence of the Android operating system on smartphones is steadily growing. However, improper utilization of the Intent mechanism within Android applications can result in security vulnerabilities. Presently, the majority of Android security testing methods, which rely heavily on fuzzing, are predominantly focused on UI interactions, lacking sufficient testing capabilities for Intents. The motivation of this paper is to find a more effective testing method to improve the security detection capabilities of Intents. This paper introduces an Intent fuzzing method based on genetic mutation principles. Initially, we establish an Intent seed library using a text classification model, followed by employing Jaccard distance and minimum edit distance to refine high-quality seeds. Subsequently, we augment the seeds through extensive mutation using genetic algorithms, generating numerous test cases that exhibit structural similarity but contain varied content. During testing, we compare the state before and after Intent testing using image similarity to detect anomalies. Experimental results demonstrate that this method effectively enhances test coverage and identifies potential issues in edge cases. This approach offers an efficient means of conducting Intent security testing and enhances Android app robustness and security. © 2024 IEEE.

关键词： Android Fuzzing Gene Mutation Intent

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Multi-channel Avionics Module Fault Classification Based on Inception-CBAM Network

Multi-channel Avionics Module Fault Classification Based on ...

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Electronic Communication and Artificial Intelligence (IWECAI), International Workshop on

作者： Lubin Yu YiXi Peng Xingjian Han Shuqi Liang Peiliang Yang Junbin Liu Shilie He China Electronic Product Reliability and Environmental Testing Research Institute Guangzhou China School of Software Engineering Sun Yat-Sen University Zhuhai China School of Computer Science and Technology Shandong University of Technology Zibo China

ISBN: (数字)9798350386943

ISBN: (纸本)9798350386950

In today's highly integrated and automated aviation field, the stable operation of avionics systems is the foundation for maintaining flight safety and efficient operations. Because of the shortcomings in research on fault classification of avionics modules, this study proposes an Inception-CBAM network model, aiming to improve the accuracy and efficiency of fault diagnosis. This model combines the powerful multi-scale feature extraction capability of the Inception network with the attention mechanism of the CBAM module, which greatly enhances the ability to identify complex fault patterns. Experimental verification shows that the model can effectively classify various faults, enhance the scientificity and systematicness of diagnosis, and contribute important technical support to the formulation of aviation safety maintenance and fault prevention strategies.

关键词： Fault diagnosis Systematics Attention mechanisms Prevention and mitigation Aerospace electronics Feature extraction Data models

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Learning from Mistakes: A Comprehensive Review of Knowledge Editing for Large Language Models

Learning from Mistakes: A Comprehensive Review of Knowledge ...

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IEEE International Conference on Smart Internet of Things (SmartIoT)

作者： Yanhong Li Chunling Fan Mingqing Huang Chengming Li Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen China School of Science Harbin Institute of Technology (Shenzhen) Shenzhen China School of Electronic and Communication Engineering Shenzhen Polytechnic University Shenzhen China School of Computer and Software Shenzhen Institute of Information Technology Shenzhen China

ISBN: (数字)9798350366440

ISBN: (纸本)9798350366457

In recent years, there has been a growing recognition that large language models like GPT-4 have the capability to store vast amounts of knowledge and possess extremely powerful abili-ties. However, these models are not immune to making mistakes. The knowledge stored within these models may be erroneous or outdated, leading to incorrect outputs. Consequently, it is imperative to implement methods to edit the knowledge within the models. In this paper, we primarily summarize and organize several key knowledge editing methods that have emerged in recent years. By drawing an analogy to the human process of correcting mistakes, we categorize these methods into three types: rote memorization, learning from examples, and thoroughly understanding. In addition, we briefly discuss the potential positive impact of researches on the knowledge mechanisms of large models on knowledge editing. By summarizing the existing researches on the knowledge of large models, we also briefly explore the role of fully utilizing knowledge mechanisms and resolving knowledge conflicts in enhancing the effectiveness of knowledge editing.

关键词： Reviews Large language models Face recognition Internet of Things Faces Immune system

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Bridging Trajectory-Aware Evolutionary Graph Learning and Large Language Models for Enhancing Navigability in Social Internet of Things

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IEEE Internet of Things Journal 2025年

作者： Bi, Xin Han, Zhubin Yao, Xin Zhao, Xiangguo Wang, Yu-Ping Yuan, Ye State Key Laboratory of Intelligent Deep Metal Mining and Equipment China Northeastern University School of Computer Science and Engineering Shenyang110819 China Northeastern University College of Software Shenyang110819 China Beijing Institute of Technology School of Computer Science and Technology Beijing100091 China

Social Internet of Things (SIoT) has emerged as a novel paradigm that enhances IoT service capabilities by leveraging device-level social relationships. However, the explosive growth of heterogeneous devices, the dynamic mobile device trajectories, and complex spatiotemporal interaction patterns severely hinder SIoT network navigability. Particularly, the device mobility and contextual diversity pose significant challenges to social relation classification, a critical task for efficient routing and service discovery. Existing approaches, primarily designed for static or homogeneous networks, fail to adequately capture the evolving contextual dependencies and the spatiotemporal heterogeneity in SIoT. To address these challenges, we propose Trajectory-Aware Graph LLM (TAGLLM), a novel framework that enhances SIoT navigability through context-aware relation classification. TAGLLM introduces a multi-feature fusion trajectory evolutionary graph encoder to jointly model complex device attributes, social relations, and dynamic trajectories. Furthermore, a structural graph-text token alignment strategy is designed to exploit the generalization ability and contextual understanding capabilities of Large Language Models (LLMs), enabling more effective modeling of heterogeneous and dynamic SIoT scenarios. Extensive experiments on real-world SIoT datasets demonstrate that TAGLLM outperforms state-of-the-art baselines across multiple evaluation metrics, highlighting its potential to push the frontier of graph learning and LLM integration in SIoT applications. © 2014 IEEE.

关键词： Network theory (graphs)

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