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YOLO-RLC:An Advanced Target-Detection Algorithm for Surface Defects of Printed Circuit Boards Based on YOLOv5

computers, Materials & Continua 2024年第9期80卷 4973-4995页

作者： Yuanyuan Wang Jialong Huang Md Sharid Kayes Dipu Hu Zhao Shangbing Gao Haiyan Zhang Pinrong Lv School of Computer and Software Engineering Huaiyin Institute of TechnologyHuai’an223003China Huaiyin Institute of Technology Laboratory for Internet of Things and Mobile Internet Technology of Jiangsu ProvinceHuai’an223001China Jiangsu Kesheng Xuanyi Technology Co. Ltd.Huai’an223300China

Printed circuit boards(PCBs)provide stable connections between electronic ***,defective printed circuit boards may cause the entire equipment system to malfunction,resulting in incalculable ***,it is crucial to detect defective printed circuit boards during the generation *** detection methods have low accuracy in detecting subtle defects in complex background *** order to improve the detection accuracy of surface defects on industrial printed circuit boards,this paper proposes a residual large kernel network based on YOLOv5(You Only Look Once version 5)for PCBs surface defect detection,called YOLO-RLC(You Only Look Once-Residual Large Kernel).Build a deep large kernel backbone to expand the effective field of view,capture global informationmore efficiently,and use 1×1 convolutions to balance the depth of the model,improving feature extraction efficiency through reparameterization *** neck network introduces a bidirectional weighted feature fusion network,combined with a brand-new noise filter and feature enhancement extractor,to eliminate noise information generated by information fusion and recalibrate information from different channels to improve the quality of deep *** the aspect ratio of the bounding box to alleviate the issue of specificity *** training and testing on the PCB defect dataset,our method achieved an average accuracy of 97.3%(mAP50)after multiple experiments,which is 4.1%higher than YOLOv5-S,with an average accuracy of 97.6%and an Frames Per Second of *** comparative analysis also proves the superior performance and feasibility of YOLO-RLC in PCB defect detection.

关键词： Deep learning PCB defect detection large kernel noise filtering weighted fusion YOLO

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Enhancing Security and Privacy in Distributed Face Recognition Systems through Blockchain and GAN Technologies

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computers, Materials & Continua 2024年第5期79卷 2609-2623页

作者： Muhammad Ahmad Nawaz Ul Ghani Kun She Muhammad Arslan Rauf Shumaila Khan Javed Ali Khan Eman Abdullah Aldakheel Doaa Sami Khafaga School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu611731China Department of Computer Science University of Science&TechnologyBannu28100Pakistan Department of Computer Science University of HertfordshireHatfieldAL109ABUK Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

The use of privacy-enhanced facial recognition has increased in response to growing concerns about data securityand privacy in the digital age. This trend is spurred by rising demand for face recognition technology in a varietyof industries, including access control, law enforcement, surveillance, and internet communication. However,the growing usage of face recognition technology has created serious concerns about data monitoring and userprivacy preferences, especially in context-aware systems. In response to these problems, this study provides a novelframework that integrates sophisticated approaches such as Generative Adversarial Networks (GANs), Blockchain,and distributed computing to solve privacy concerns while maintaining exact face recognition. The framework’spainstaking design and execution strive to strike a compromise between precise face recognition and protectingpersonal data integrity in an increasingly interconnected environment. Using cutting-edge tools like Dlib for faceanalysis,Ray Cluster for distributed computing, and Blockchain for decentralized identity verification, the proposedsystem provides scalable and secure facial analysis while protecting user privacy. The study’s contributions includethe creation of a sustainable and scalable solution for privacy-aware face recognition, the implementation of flexibleprivacy computing approaches based on Blockchain networks, and the demonstration of higher performanceover previous methods. Specifically, the proposed StyleGAN model has an outstanding accuracy rate of 93.84%while processing high-resolution images from the CelebA-HQ dataset, beating other evaluated models such asProgressive GAN 90.27%, CycleGAN 89.80%, and MGAN 80.80%. With improvements in accuracy, speed, andprivacy protection, the framework has great promise for practical use in a variety of fields that need face recognitiontechnology. This study paves the way for future research in privacy-enhanced face recognition systems, emphasizingt

关键词： Facial recognition privacy protection blockchain GAN distributed systems

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EPM: Evolutionary Perception Method for Anomaly Detection in Noisy Dynamic Graphs

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IEEE Transactions on Knowledge and Data Engineering 2025年第7期37卷 4035-4048页

作者： Wang, Huan Chen, Junyang Wu, Yirui Leung, Victor C. M. Wang, Di King Abdullah University of Science and Technology Division of Computer Electrical and Mathematical Sciences and Engineering Saudi Arabia Huazhong Agricultural University College of Informatics China Shenzhen University College of Computer Science and Software Engineering China Hohai University College of Computer and Information China

With the rapid expansion of interactions across various domains such as knowledge graphs and social networks, anomaly detection in dynamic graphs has become increasingly critical for mitigating potential risks. However, existing anomaly detection methods often assume noise-free dynamic graphs, overlooking the prevalence of noisy dynamic graphs in real-world applications. Specifically, noisy dynamic graphs affected by structural noises-such as spurious and missing nodes and edges-struggle to consistently provide reliable structural evidence for anomaly detection. To tackle this challenge, we propose an Evolutionary Perception Method (EPM) for identifying anomalous nodes in noisy dynamic graphs by resisting the interference of structural noises. EPM primarily consists of two components: a dynamic fitter and a filtering reviser. The dynamic fitter characterizes the interaction dynamics of nodes that removes and generates links at each period as a multiple superposition state, utilizing various link prediction algorithms to fit evolutionary mechanisms. Additionally, the filtering reviser designs evolutional entropies to quantify the evolutional uncertainty in multiple superposition states, further designing the Kalman filter to optimize these entropies. Extensive experiments show that the proposed EPM method surpasses state-of-the-art approaches in detecting anomalous nodes in noisy dynamic graphs. © 1989-2012 IEEE.

关键词： Kalman filters

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Modeling Implicit Emotion and User-specific Context for Malevolence Detection in Mental Health Counseling Dialogues

Modeling Implicit Emotion and User-specific Context for Male...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Xu, Bo Qiao, Xuening Zhang, Xiaokun Wan, Jiahui Liu, Xinyue Zong, Linlin Lin, Hongfei Dalian University of Technology School of Computer Science Dalian China Dalian University of Technology School of Software Dalian China

ISBN: (纸本)9798350386226

Generative conversational agents, driven by large language models, have gained widespread popularity. However, a significant drawback lies in their tendency to produce uncontrollable and unpredictable contents, thereby increasing the risk of generating malevolent responses that potentially exacerbate users' mental health issues. Although existing research on malevolence detection in dialogues addressed the modeling of interaction patterns in dialogues, the implicitly expressed emotion and user-specific context are often neglected. Addressing this gap, we propose a hypergraph-enhanced context modeling approach for detecting malevolence in mental health counseling dialogues. Our approach harnesses the emotion reasoning capabilities of large language models to generate implicit emotional prompts. Employing hypergraph neural networks, our approach effectively integrates emotional context, user-specific context, and interactive context, fusing them into high-order semantic representations using hypergraph convolution. Experimental results on two benchmark datasets, MDRDC and Dialogue Safety, demonstrate the superiority of our model over state-of-the-art baseline models, particularly in complex contextual scenarios. © 2024 IEEE.

关键词： Semantics

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A Virtual Texture technology Realized on Low Computational Power Platforms 11

A Virtual Texture Technology Realized on Low Computational P...

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11th International Conference on Dependable Systems and Their Applications, DSA 2024

作者： Ma, Jinzhao Fang, Zhenyu Zheng, Jiangbin Cao, Jun School of Computer Software Xi'an China Nanjing RuiYue Technology Ltd. Nanjing China

ISBN: (纸本)9798331532390

Texture is a fundamental component of computer graphics. Virtual texture technology is essential for facilitating large-scale texture loading and real-time rendering of natural terrain. To address the limitations posed by low-computing-power platforms and enable extensive texture loading, it is imperative to identify an efficient texture loading methodology. This article will present a dynamic loading virtual texture technology designed for low-computing-power platforms, aimed at achieving large-scale texture rendering on lightweight devices. © 2024 IEEE.

关键词： Computing power

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Is Large Language Model Good at Triple Set Prediction? An Empirical Study 15

Is Large Language Model Good at Triple Set Prediction? An Em...

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15th IEEE International Conference on Knowledge Graph, ICKG 2024

作者： Yuan, Yuan Xu, Yajing Zhang, Wen Zhejiang University School of Software Technology Ningbo China Zhejiang University College of Computer Science of Technology Hangzhou China

ISBN: (纸本)9798331508821

The core of the Knowledge Graph Completion (KGC) task is to predict and complete the missing relations or nodes in a KG. Common KGC tasks are mostly about inferring unknown elements with one or two elements being known in a triple. In comparison, the Triple Set Prediction (TSP) task is a more realistic knowledge graph completion task. It aims to predict all elements of unknown triples based on the information from known triples. In recent years, large language models (LLMs) have exhibited significant advancements in language comprehension, demonstrating considerable potential for KGC tasks. However, the potential of LLM on the TSP task has not yet to be investigated. Thus in this paper we proposed a new framework to explore the strengths and limitations of LLM in the TSP task. Specifically, the framework consists of LLM-based rule mining and LLM-based triple set prediction. The relation list of KG embedded within rich semantic information is first leveraged to prompt LLM in the generation of rules. This process is both efficient and independent of statistical information, making it easier to mine effective and realistic rules. For each subgraph, the specified rule is applied in conjunction with the relevant triples within that subgraph to guide the LLM in predicting the missing triples. Subsequently, the predictions from all subgraphs are consolidated to derive the complete set of predicted triples on KG. Finally, the method is evaluated on the relatively complete CFamily dataset. The experimental results indicate that when LLMs are required to adhere to a large amount of factual knowledge to predict missing triples, significant hallucinations occurs, leading to a noticeable decline in performance. To further explore the causes of this phenomenon, this paper presents a comprehensive analysis supported by a detailed case study. The datasets and code for experiments are available at https://***/zjukg/LLM-based-TSP. © 2024 IEEE.

关键词： Knowledge graph

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Masked Face Detection and Recognition Using a Unified Feature Extractor 5

Masked Face Detection and Recognition Using a Unified Featur...

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5th International Conference on Advancements in Computational Sciences, ICACS 2024

作者： Iftikhar, Aroobah Shaukat, Arslan Tariq, Rimsha College of Eme Dept of Computer and Software Engineering Islamabad Pakistan Information Technology University Dept of Computer Engineering Lahore Pakistan

ISBN: (纸本)9798350394764

The research field of computer vision has recently taken an interest in the active computer vision problem of masked face recognition due to the COVID-19 pandemic. The use of face masks as a preventative measure against the spread of COVID-19 has presented a new challenge for the technology of face recognition. Masked Face Recognition (MFR) is a specific type of facial occlusion issues that obstruct vital facial features such as the mouth, nose, or chin. The purpose of research on Masked Face Detection and Recognition is to fine-tune a pre-trained model that can more accurately recognize masked faces and detect whether the person is wearing a mask or not, which can be advantageous in various applications, like security and surveillance, healthcare, retail, law enforcement, the workplace, and social media. This paper proposes an approach to enhance the performance of the single neural network architecture as a unified model capable of both detection and recognition of masked face images by achieving 99% and 98% respectively, on the MFR2 dataset. Also, a detection model is proposed that is evaluated on publicly available datasets for the detection of masked faces, which are the MDMFR dataset, the Kaggle face mask detection dataset, and the Facedatahybrid dataset. The findings of this research offer valuable insights into the potential of a pre-trained network with transfer learning to make an efficient masked face detection and recognition system and pave the way for future research in this area. © 2024 IEEE.

关键词： Face recognition

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An Efficient Heterogeneous Ring Signcryption Scheme for Wireless Body Area Networks

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computer Systems Science & Engineering 2023年第11期47卷 2061-2078页

作者： Qingqing Ning Chunhua Jin Zhiwei Chen Yongliang Xu Huaqi Lu Faculty of Computer&Software Engineering Huaiyin Institute of TechnologyHuai’an233003China

Wireless body area networks(WBANs)are an emerging technology for the real-time monitoring of physiological *** provide a mechanism for collecting,storing,and transmitting physiological data to healthcare ***,the open wireless channel and limited resources of sensors bring security *** ensure physiological data security,this paper provides an efficient Certificateless Public Key Infrastructure Heterogeneous Ring Signcryption(CP-HRSC)scheme,in which sensors are in a certificateless cryptosystem(CLC)environment,and the server is in a public key infrastructure(PKI)*** could solve the limitations of key escrow in identity-based cryptography(IBC)and certificate management for public keys in *** PKI is suited for the server because it is widely used on the ***,this paper designs a ring signcryption method that allows the controller to anonymously encrypt physiological data on behalf of a set of sensors,but the server does not exactly know who the sensor *** construction of this paper can achieve anonymity,confidentiality,authentication,non-repudiation,and integrity in a logically single *** the computational Diffie-Hellman(CDH)problem,the formal security proof is provided in the random oracle model(ROM).This paper demonstrates that this scheme has indistinguishability against adaptive chosen ciphertext attacks(IND-CCA2)and existential unforgeability against adaptive chosen message attacks(EUF-CMA).In terms of computational cost and energy usage,a comprehensive performance analysis demonstrates that the proposed scheme is the most *** to the three existing schemes,the computational cost of this paper’s scheme is reduced by about 49.5%,4.1%,and 8.4%,and the energy usage of our scheme is reduced by about 49.4%,3.7%,and 14.2%,respectively.

关键词： Wireless body area networks certificateless cryptosystem public key infrastructure security ring singncryption

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Quick Hidden Layer Size Tuning in ELM for Classification Problems

Mendel

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Mendel 2024年第1期30卷 1-14页

作者： Albtoush, Audi Fernandez-Delgado, Manuel Maarouf, Haitham Al Nawaiseh, Asmaa Jameel Faculty of Computer Science and Information Technology Jerash University Jordan Santiago De Compostela University Spain Software Engineering Department Mut’ah university Jordan

The extreme learning machine is a fast neural network with outstanding performance. However, the selection of an appropriate number of hidden nodes is time-consuming, because training must be run for several values, and this is undesirable for a real-time response. We propose to use moving average, exponential moving average, and divide-and-conquer strategies to reduce the number of training’s required to select this size. Compared with the original, constrained, mixed, sum, and random sum extreme learning machines, the proposed methods achieve a percentage of time reduction up to 98% with equal or better generalization ability. © 2024, Brno University of technology. All rights reserved.

关键词： Knowledge acquisition

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A Preference-Based Multi-Objective Evolutionary Algorithm with Local Pareto Front Modeling

A Preference-Based Multi-Objective Evolutionary Algorithm wi...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Zhao, Peipei Wang, Liping Wang, Hui College of Computer Science Technology College of Software Zhejiang University of Technology Hangzhou China

ISBN: (纸本)9798350337020

Most existing multi-objective evolutionary algorithms (MOEAs) have difficulties in approximating the whole Pareto Fronts with complicated geometries. However, the decision maker (DM) may only be interested in a small portion of the front, referred to as the region of interest (ROI). Bearing this in mind, this paper develops a preference-based MOEA with local Pareto Front (PF) estimation to address the above issues. We first modify the r-dominance relation, which is not affected by the position of the user reference point. The modified non-r-dominated solutions are then used as training data during the optimization process to model the PF of the ROI. The estimated local front can be used to drive the search toward the preferred PF region. Experimental results demonstrate the effectiveness of our proposed algorithm on a variety of benchmark problems with different kinds of PFs. © 2023 IEEE.

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