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IAENG International Journal of computer science 2023年第1期50卷 152页

作者： Jing, XiuPing Tian, Ying School of Computer Science and Software Engineering University of Science and Technology Liaoning Liaoning 114051 China

Vehicle detection based on deep learning plays a vital role in various fields, such as autopilot and intelligent transportation. Moreover, it presents a major development direction to computer vision in recent years. Lightweight vehicle detection aims to reduce the burden of computer storage and computing, including exploration of network structure and computing efficiency. But it is difficult to cope with complex and changeable traffic scenes. The urgent problem is to enhance lightweight network performance while maintaining inference speed. In this context, this study proposes an improved Yolox-nano to alleviate the above-mentioned problems by splitting the vanilla convolution unit into two parts and utilizing fewer convolution kernels to generate several feature maps. Subsequently, several linear transformations are further applied to generate cheap feature maps efficiently. The cheap convolutional unit Ghost Module can control feature map ratio of vanilla convolution and linear transformation. The activation function in Ghost Module is improved by SiLU, which has a more powerful nonlinear ability. In addition, the convolution and the batch normalization in Ghost Module can be fused in the pre-processing stage to speed up inference time. Experimental results show that the improved network provides 4.9% and 3.1% mAP on Pascal VOC and MS COCO vehicle datasets. The improved network enhances the performance and maintains an efficient inference speed, and detection results become further competitive using Yolox-nano © 2023, IAENG International Journal of computer *** Rights Reserved.

关键词： Convolution

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PCB Surface Defect Detection based on YOLOv8n

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2024年第12期51卷 2017-2025页

作者： You, Rui Wang, Zhifeng School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China

In the electronic manufacturing industry, accurate detection of PCB defects is crucial as it directly impacts product quality and reliability. The primary challenges in PCB defect detection include missed detections and false alarms, particularly concerning micro-defects. This study proposes an enhanced PCB defect detection algorithm based on YOLOv8, which incorporates the Global Attention Module (GAM), Partial Convolution Layer (PConv3), and a multi-head detection strategy. The GAM improves the model’s sensitivity to micro-defects by capturing and weighting global context information through spatial and channel attention mechanisms applied to the input feature map. The PConv3 optimizes feature extraction, minimizing false alarms due to information loss. The multi-head detection strategy identifies defects at varying scales, preserving detailed information and enhancing the detection of small-sized defects. Experimental results demonstrate that the improved algorithm achieves a 3.6% increase in average precision while meeting real-time detection requirements. © (2024), (International Association of Engineers). All rights reserved.

关键词： Deep Learning Global Attention Module Multi-head Detection PCB Defect Detection PConv3 YOLOv8

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Cyber Security Threats on Multifunctional Devices and Mitigation Techniques 2

Cyber Security Threats on Multifunctional Devices and Mitiga...

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2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023

作者： Al-Dmour, Nidal A. Farooq, Umer Sarwar, Irfan Iqbal, Muhammad Waseem Aqeel, Muhammad Khan, Wasim Ahmad Al Hamadi, Hussam Mutah University College of Engineering Department of Computer Engineering Jordan LGU Department of Computer Science Lahore Pakistan Superior University Department of Computer Science Lahore54000 Pakistan Superior University Department of Software Engineering Lahore54000 Pakistan NCBA&E Lahore School of Computer Science Pakistan Applied Science Research Center Applied Science Private University Amman11937 Jordan University of Dubai College of Engineering and IT PO Box 14143 United Arab Emirates

ISBN: (纸本)9798350335644

The risks discussed in this document are related to smart printers/ fax/ photocopiers/ or e-mail-enabled smart devices, also known as "document centers,". These devices can be purchased outright or rented every month. When an item reaches its end of life, it is either donated to a non-profit organization or returned to the manufacturer (OEM) or supplier at the end of a lease arrangement. These network-interconnected devices have an inherent susceptibility that most IT operations teams or information security professionals are unaware of. This effort will raise awareness of the dangers of losing a company's data. This discussion will focus on reported vulnerabilities, previous discussions, and given the author's view on the potential danger. Create instructions for potential users on how to use and disassemble these devices safely to prevent losing sensitive data. Lastly, the technological structure of two potential attack vectors against a document center of an organization will be demonstrated in this study. The study was conducted in government as well as the private sector, medium-sized enterprises (300 employees of each entity) settings, outside of normal business hours, with special authorizations and engagement, limiting the scope to a minimal set of devices. © 2023 IEEE.

关键词： Sensitive data

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ALETHEIA: Robust large-scale spectrum auctions against false-name bids 15

ALETHEIA: Robust large-scale spectrum auctions against false...

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16th ACM International Symposium on Mobile Ad Hoc Networking and Computing, MobiHoc 2015

作者： Wang, Qinhui Ye, Baoliu Tang, Bin Xu, Tianyin Guo, Song Lu, Sanglu Zhuang, Weihua National Key Laboratory for Novel Software Technology Nanjing University China Department of Military Management Army Command College China Department of Computer Science and Engineering U.C. San Diego United States School of Computer Science and Engineering University of Aizu Japan Department of Electrical and Computer Engineering University of Waterloo Canada

ISBN: (纸本)9781450334891

Auction is a promising approach for dynamic spectrum access in Cognitive Radio Networks. Existing auction mechanisms are mainly proposed to be strategy-proof to stimulate bidders to reveal their valuations of spectrum truthfully. However, they would suffer significantly from a new cheating pattern named false-name bids, where a bidder can manipulate the auction by submitting bids under multiple fictitious names. We show such false-name bid cheating is easy to make but hard to be detected in dynamic spectrum auctions. To resolve this issue, we propose ALETHEIA, a novel flexible, false-name-proof auction framework for large-scale dynamic spectrum access. ALETHEIA has the following important features: (1) it not only guarantees strategy-proofness but also resists false-name bids, (2) it enables spectrum reuse across a large number of bidders, (3) it provides the bidders the flexibility of diverse demand formats, and (4) it incurs low computational overhead. Simulation results show that ALETHEIA achieves both high spectrum redistribution efficiency and auction efficiency. Copyright © 2015 ACM.

关键词： Efficiency

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MSKI-Net: Towards modality-specific knowledge interaction for glioma survival prediction

MSKI-Net: Towards modality-specific knowledge interaction fo...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Su, Ran Xiao, Jin Cui, Hui Xuan, Ping Feng, Xikang Wei, Leyi Jin, Qiangguo Tianjin University School of Computer Software College of Intelligence and Computing Tianjin China La Trobe University Department of Computer Science and Information Technology Melbourne Australia Shantou University School of Engineering Department of Computer Science China Northwestern Polytechnical University School of Software China Macao Polytechnic University AIDD Faculty of Applied Science China Xiamen University School of Information Xiamen China Yangtze River Delta Research Institute Northwestern Polytechnical University Taicang China

ISBN: (纸本)9798350386226

Gliomas hold a prominent position in neurooncology due to their high malignancy and poor survival rates. Accurately predicting the prognosis and survival risk of glioma patients is crucial for clinical treatment. Recent advances in survival prediction methods emphasize the importance of integrating complementary information from diverse modalities while neglecting the significant modality gap between pathological images and genomic data. To address this issue, we propose a modality-specific knowledge interaction network (MSKI-Net), which integrates whole slide images (WSI), RNA-Seq gene expression data, and copy number variation (CNV) data for glioma survival analysis. The MSKI-Net consists of a modality-specific feature enhancement (MSFE) module, a modality-interactive cross-attention (MICA) module, and a modality-specific knowledge-guided representation learning (MSKR) module. The three modules collaborate by complementing modality-specific features with modality-agnostic knowledge to improve the learning capability of MSKI-Net. Furthermore, we construct a dataset named TCGAmm, which combines WSI, RNA-Seq, and CNV data from The Cancer Genome Atlas (TCGA) to address the issue of data scarcity. Extensive experiments demonstrate that MSKI-Net achieves superior performance in predicting the survival risk of glioma cancer. © 2024 IEEE.

关键词： Gene expression

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Agent-monitored anticipatory multisimulation: A systems engineering approach for threat-management training

Agent-monitored anticipatory multisimulation: A systems engi...

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24th European Modeling and Simulation Symposium, EMSS 2012

作者： Ören, Tuncer I. Yilmaz, Levent University of Ottawa School of Electrical Eng. and Computer Science Ottawa ON K1N 6N5 Canada Auburn University Computer Science and Software Engineering Auburn AL 36849 United States

ISBN: (纸本)9788897999096

Agent-monitored multisimulation provides a powerful paradigm to conceive and perform experimentations not possible by traditional simulation techniques. Multisimulation was developed for simulation-based conflict management and training purposes. Anticipatory behavior has several advantages over reactive behavior. Monitoring multisimulation studies by agents bring additional benefits. First, overviews of several types of threats and the relevance and importance of anticipation are given. Then, the possibilities offered by multisimulation and agent-monitored anticipatory multisimulation are presented. Afterwards, the appropriateness of agent-monitored anticipatory multisimulation is shown for several types of synchronous or asynchronous threat management and training.

关键词： Circuit simulation

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Motor imagery enhances performance beyond the imagined action

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Proceedings of the National Academy of sciences of the United States of America 2025年第20期122卷 e2423642122页

作者： Magdalena Gippert Pei-Cheng Shih Tobias Heed Ian S Howard Mina Jamshidi Idaji Arno Villringer Bernhard Sehm Vadim V Nikulin Department of Neurology Max Planck Institute for Human Cognitive and Brain Sciences Leipzig 04103 Germany. Sony Computer Science Laboratories Tokyo 141-0022 Japan. Department of Psychology and Centre for Cognitive Neuroscience University of Salzburg Salzburg 5020 Austria. School of Engineering Computing and Mathematics Faculty of Science and Engineering University of Plymouth Plymouth PL4 8AA United Kingdom. Machine Learning Group Berlin Institute for the Foundations of Learning and Data Berlin 10587 Germany. Machine Learning Group Institute of Software Engineering and Theoretical Computer Science Electrical Engineering and Computer Science Faculty Technical University Berlin Berlin 10587 Germany. Department of Neurology Martin Luther University of Halle-Wittenberg Halle (Saale) 06120 Germany.

Motor imagery is frequently utilized to improve the performance of specific target movements in sports and rehabilitation. In this study, we show that motor imagery can facilitate learning of not only the imagined target movements but also sequentially linked overt movements. Hybrid sequences comprising imagined and physically executed segments allowed participants to learn specific movement characteristics of the executed segments when they were consistently associated with specific imagined segments. Electrophysiological recordings revealed that the degree of event-related synchronization in the alpha and beta bands during a basic motor imagery task was correlated with imagery-evoked motor learning. Thus, both behavioral and neural evidence indicate that motor imagery's benefits extend beyond the imagined movements, improving performance in linked overt movements. This provides decisive evidence for the functional equivalence of imagined and overt movements and suggests applications for imagery in sports and rehabilitation.

关键词： adaptation force field motor imagery motor learning motor sequence

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On detecting differences between groups 03

On detecting differences between groups

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9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '03

作者： Webb, Geoffrey I. Butler, Shane Newlands, Douglas School of Computer Science and Software Engineering Bldg 26 Monash University Clayton VIC. 3800 Australia School of Information Technology Deakin University Geelong VIC. 3126 Australia

ISBN: (纸本)9781581137378

Understanding the differences between contrasting groups is a fundamental task in data analysis. This realization has led to the development of a new special purpose data mining technique, contrast-set mining. We undertook a study with a retail collaborator to compare contrast-set mining with existing rule-discovery techniques. To our surprise we observed that straightforward application of an existing commercial rule-discovery system, Magnum Opus, could successfully perform the contrast-set-mining task. This led to the realization that contrast-set mining is a special case of the more general rule-discovery task. We present the results of our study together with a proof of this conclusion. Copyright 2003 ACM.

关键词： Data mining

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A comparison of the four evolutionary programming implementations used as function optimizers

A comparison of the four evolutionary programming implementa...

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2010 International Conference on E-Health Networking, Digital Ecosystems and Technologies, EDT 2010

作者： Luan I, Shaojun Gao I, Xuedong Qi II, Bing School of Economics and Management University of Science and Technology BeiJing Beijing China Department of Computer Science and Software Engineering 107 Dunstan Hall Auburn University AL 36849-5347 United States

ISBN: (纸本)9781424455157

In this artical, four evolutionary programming algorithms (Standard EP, Meta-EP, Continuous Standard EP and Continuous Meta-EP algorithms) are applied to test to maximizing a solution against a highly multimodal test objective function. We will use the same population size to compare the effectiveness of these four algorithms according to their success rate, average number of function evaluation and average best fitness. We experiments with different population size. ©2010 IEEE.

关键词： Evolutionary algorithms

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Face Detection Based on Improved Multi-task Cascaded Convolutional Neural Networks

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2024年第2期51卷 67-74页

作者： Jia, Siyu Tian, Ying College of Computer and Software Engineering University of Science and Technology Liaoning Liaoning 114051 China School of Computer and Software Engineering University of Science and Technology Liaoning Liaoning 114051 China

With the development of deep learning and computer vision, face detection has achieved rapid progress owing. Face detection has several application domains, including identity authentication, security protection, media, and entertainment. Although multi-task cascaded convolutional neural networks (MTCNN) have high accuracy and robustness, the model has the disadvantages of large parameters and computational overhead in the real scene due to the complexity of the real scene and the constraints of hardware facilities. Therefore, the development of an improved network model is crucial. This paper improves the MTCNN model by reducing the number of parameters and the computational overhead and using better model parameters to locate the key points of the face. This model improves the accuracy and robustness of the face age estimation. The WiderFace and CelebA datasets are used for training. The final face detection accuracy reaches 98.7% while simultaneously reducing the number of model parameters to 70% under the same conditions. This model meets the application needs of modern society for face detection and demonstrates the efficiency and accuracy of the improved network model. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Face recognition

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