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2024 International Conference on Artificial Intelligence and Digital Technology, ICAIDT 2024

作者： Yan, Shengnan Shen, Yihui Minjiang University School of Computer and Control Engineering Fujian China Fujian Medical University College of Arts and Sciences Fujian China

ISBN: (纸本)9798350386905

To address the issues of lacking datasets and low recognition accuracy for paint film defects, this paper proposes a denoising diffusion implicit model (DDIM) for data augmentation of paint film defects and innovatively suggests a classification method combining Selective Kernel Networks (SKNet) with SqueezeNet model. Initially, the DDIM is used for dataset expansion, followed by an evaluation of the similarity between generated and captured paint film defect images using multiscale structural similarity (MS-SSIM). The training and generation effects of DDIM are then compared with those of DCGAN. In the SqueezeNet model, a Selective Kernel module is added following the fire7 module to enhance the model's attention mechanism. The results show that all types of paint film defect images generated by DDIM have MS-SSIM indices above 0.64, with most exceeding 0.7. The combined approach of Selective Kernel Networks(SKNet) and SqueezeNet outperforms other attention mechanisms, achieving an accuracy above 96.3%. The method demonstrates promising prospects for paint film defect detection, enhancing identification efficiency and accuracy while reducing detection costs, and is applicable to mobile or embedded devices. © 2024 IEEE.

关键词： Classification (of information)

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Machine Learning-Based Analysis of Tennis Match Processes 6

Machine Learning-Based Analysis of Tennis Match Processes

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6th International Symposium on Robotics and Intelligent Manufacturing Technology, ISRIMT 2024

作者： Li, Ke Yang, Nuo Yu, Yan School of Economics and Finance Hohai University Nanjing China College of Computer Science and Software Engineering Hohai University Nanjing China College of Geography and Remote Sensing Hohai University Nanjing China

ISBN: (纸本)9798331528942

Prediction is an important topic in machine learning. Given the random nature of sports events, they are meaningful experimental subjects for conducting research. In this paper, we focused on answering three key questions: (1) How to determine which player is performing better at a specific time? (2) What is the impact of so-called 'momentum' on the outcome of a match? And (3) How to determine which player has the advantage at a certain point in the match. Firstly, we construct the wavelet neural network, in which Spearman's correlation coefficient is 0.7069, to decompose the performance of a player in real-time into relevant quantifiable features, evaluating which player is performing better at the moment. Then, we identified the main features affecting momentum and proposed the Additive-Multiplicative Model. The results confirm the momentum role in determining the outcome of a player's match. Finally, we established a random forest model with 30 decision trees. The accuracy and recall rates are 83.3% and 89.0%, respectively. Our study shows high accuracy and robustness in the outcome of sports events prediction and demonstrates promising potential for further applications. © 2024 IEEE.

关键词： Adversarial machine learning

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Towards Improving the Quality of Requirement and Testing Process in Agile software Development:An Empirical Study

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computers, Materials & Continua 2024年第9期80卷 3761-3784页

作者： Irum Ilays Yaser Hafeez Nabil Almashfi Sadia Ali Mamoona Humayun Muhammad Aqib Ghadah Alwakid University Institute of Information Technology Pir Mehr Ali Shah Arid Agriculture UniversityRawalpindi46000Pakistan Department of Software Engineering College of Computer and Information SciencesJouf UniversityAl Jouf72388Saudi Arabia School of Arts Humanities and Social Sciences University of RoehamptonLondonSW155PJUK Department of Computer Science College of Computer and Information SciencesJouf UniversityAl Jouf72388Saudi Arabia

software testing is a critical phase due to misconceptions about ambiguities in the requirements during specification,which affect the testing ***,it is difficult to identify all faults in *** requirement changes continuously,it increases the irrelevancy and redundancy during *** to these challenges;fault detection capability decreases and there arises a need to improve the testing process,which is based on changes in requirements *** this research,we have developed a model to resolve testing challenges through requirement prioritization and prediction in an agile-based *** research objective is to identify the most relevant and meaningful requirements through semantic analysis for correct change *** compute the similarity of requirements through case-based reasoning,which predicted the requirements for reuse and restricted to error-based ***,the apriori algorithm mapped out requirement frequency to select relevant test cases based on frequently reused or not reused test cases to increase the fault detection ***,the proposed model was evaluated by conducting *** results showed that requirement redundancy and irrelevancy improved due to semantic analysis,which correctly predicted the requirements,increasing the fault detection rate and resulting in high user *** predicted requirements are mapped into test cases,increasing the fault detection rate after changes to achieve higher user ***,the model improves the redundancy and irrelevancy of requirements by more than 90%compared to other clustering methods and the analytical hierarchical process,achieving an 80%fault detection rate at an earlier ***,it provides guidelines for practitioners and researchers in the modern *** the future,we will provide the working prototype of this model for proof of concept.

关键词： Requirement prediction software testing agile software development semantic analysis case-based reasoning

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UAV-based Reliable Optical Wireless Communication via Cooperative Multi-agent Reinforcement Learning Approach 29

UAV-based Reliable Optical Wireless Communication via Cooper...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Liu, Jiangang Luo, Hanjiang Ruby, Rukhsana Wang, Haoran Tao, Hang Wu, Kaishun Shandong University of Science and Technology College of Computer Science and Engineering Qingdao China Shenzhen University College of Computer Science and Software Engineering Shenzhen China The Hong Kong University of Science and Technology Guangzhou China

ISBN: (纸本)9798350330717

In marine wireless sensor networks, swarms of unmanned aerial vehicles (UAVs) based optical communication system can be leveraged to transmit underwater real-time monitoring data which enables a variety of potential maritime missions, such as video streaming for underwater surveillance and target tracking. However, due to the high directionality of optical beams and the mechanical instability of UAVs caused by the wind, the optical link via UAVs as relays is very fragile. To deal with the challenge, in this study, we model the problem as a partially observed Markov decision process (POMDP), and propose a novel link maintenance scheme based on the cooperative multi-agent deep deterministic policy gradient (MADDPG) approach to control the swarms of UAVs, which integrates the UAV dynamics model and the optical communication model. In this scheme, multiple UAVs act as agents controlling their own states in real-time under the complex wind field, in order to keep mechanical stability through cooperation, and dynamically maintain the reliability of the optical links which maximize communication performance to achieve reliable end-to-end optical communication and reduce energy consumption. Through numerical simulations, it demonstrates that the proposed optimization scheme is effective and achieves robust performance in terms of communication quality and energy consumption. © 2023 IEEE.

关键词： Free-Space Optical Multi-agent Reinforcement Learning Partially Observable Markov Decision Process Unmanned Aerial Vehicles

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Journal of computer Science & Technology 2022年第1期37卷 231-251页

作者： Jian-Zhe Zhao Xing-Wei Wang Ke-Ming Mao Chen-Xi Huang Yu-Kai Su Yu-Chen Li Software College Northeastern UniversityShenyang 110169China State Key Laboratory of Synthetical Automation for Process Industries Northeastern UniversityShenyang 110819China College of Computer Science and Engineering Northeastern UniversityShenyang 110819China

Differential privacy(DP)is widely employed for the private data release in the single-party *** utility could be degraded with noise generated by ubiquitous data correlation,and it is often addressed by sensitivity reduction with correlation ***,increasing multiparty data release applications present new challenges for existing *** this paper,we propose a novel correlated differential privacy of the multiparty data release(MP-CRDP).It effectively reduces the merged dataset's dimensionality and correlated sensitivity in two steps to optimize the *** also propose a multiparty correlation analysis *** on the prior knowledge of multiparty data,a more reasonable and rigorous standard is designed to measure the correlated degree,reducing correlated sensitivity,and thus improve the data ***,by adding noise to the weights of machine learning algorithms and query noise to the release data,MP-CRDP provides the release technology for both low-noise private data and private machine learning *** experiments demonstrate the effectiveness and practicability of the proposed method on the utilized Adult and Breast Cancer datasets.

关键词： correlated differential privacy multiparty data release machine learning

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A method of monitoring and locating eggs laid by breeding geese based on photoelectric sensing technology

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Information Processing in Agriculture 2022年第3期9卷 406-416页

作者： Yidan Xu Qiuju Xie Liwei Wang College of Mechanical and Automotive Engineering Zhaoqing UniversityZhaoqing526061China School of Computer Science and Software Zhaoqing UniversityZhaoqing 526061China

On the current breeding goose farm,the detection of individual egg laying mainly depends on some judgement experiences of farm *** present,there have been some egg laying detection systems developed with images and weighing sensors,which only signal the eggs being laid,but no egg position being ***,the detection rate of the system is not high due to environment limitations like dim light of the goose ***,to solve these problems mentioned above,an intelligent detection and positioning system is pro-posed by integrating technologies of the Radio Frequency(RF)and photoelectric sensors,together with the geometric calculation *** this research,individual egg laying information of breeding geese in a non-cage state was examined to improve the level of auto-matic detection and positioning in the field of breeder egg *** results showed that an accurate detection and positioning of an egg in a nest filled with the artificial turf could be achieved under some conditions:the height of sensor is 3.5 cm from the bottom plate of the egg laying nest,the spacing of the photoresistor module is 5 cm,and the external light intensity is less than 110 *** also shown that the error of the goose egg position recognition is 0.443 cm with a suitable level of straw in the ***,the monitoring system and positioningmethod that was developed in this research could provide a reference for the analysis of individual egg laying behavior,and could result in an improvement in the automatic egg collection for the breeding geese production.

关键词： Breeding geese Egg laying monitoring Goose egg location Photoelectric sensing technology RFID technology

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Robust copy-move detection and localization of digital audio based CFCC feature

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Multimedia Tools and Applications 2025年第11期84卷 9573-9589页

作者： Wang, Dongyu Li, Xiaojie Shi, Canghong Niu, Xianhua Xiong, Ling Wu, Hanzhou Qian, Qing Qi, Chao School of Computer and Software Engineering Xihua University Chengdu610039 China The College of Computer Science Chengdu University of Information Technology Chengdu610225 China School of Information Shanghai University Shanghai200444 China School of Information Guizhou University of Finance and Economics Guiyang550000 China

Copy-move forgery is a common audio tampering technique in which users copy the contents of one speech and paste them into another region of the same speech signal, thus achieving the effect of tampering with the semantics. To verify the authenticity of the audio, this paper proposes a method to detect and localize audio copy-move forgery based on the cochlear filter of cochlear filter cepstral coefficients (CFCC) feature. The pitch tracking algorithm is used to distinguish the voiced and unvoiced segments in the audio, and then the CFCC coefficients are extracted for each voiced segment. The CFCC feature simulates the entire transmission process of signals in the cochlear basilar membrane using wavelet transformation. Finally, we use Pearson correlation coefficients (PCCs) and dynamic time warping (DTW) in combination to compare the similarity of voiced segments, accurately determining the tampered locations in the audio through threshold judgment. Through extensive experiments on relevant datasets, this algorithm achieves a precision rate of 98.39% and a recall rate of 98.00% in detecting audio without post-processing. Even when detecting audio that has undergone different post-processing, the precision and recall rates remain above 90%. Compared to existing methods, this approach not only achieves precise localization of replicated segments but also demonstrates superior experimental results. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Semantics

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Bagging R-CNN: Ensemble for Object Detection in Complex Traffic Scenes 48

Bagging R-CNN: Ensemble for Object Detection in Complex Traf...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Li, Pengteng He, Ying Yin, Dongfu Yu, F Richard Song, Pinhao Shenzhen University College of Computer Science and Software Engineering Shenzhen China Shenzhen China KU Leuven Belgium

ISBN: (纸本)9781728163277

Generic object detection methods have achieved preferable results, but it is still challenging to detect objects from complicated traffic scenes like extreme illumination and adverse weather. The existing methods are not robust enough to be extended to new complex traffic scenes. To address this issue, we leverage the idea of ensemble learning for strong robustness and propose a novel Bagging R-CNN framework. Specially, we design a bagging classification branch that uses adaptive sampling to train base learners and make them different from each other. The final predictions are the ensemble of the base learners, achieving strong robustness to the challenging objects. For localizing more accurately, a progressive regression branch is proposed in which bounding boxes are continuously optimized for high quality. Extensive experiment results on TJU-DHD-traffic and Pascal VOC datasets show that our Bagging R-CNN achieves superior detection accuracy over state-of-the-art methods. The source code can be found at https://***/PungTeng/BaggingRCNN. © 2023 IEEE.

关键词： Object detection

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Research on Method of On-Orbit Evolution for Aerospace control software 10th

Research on Method of On-Orbit Evolution for Aerospace Contr...

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10th International Conference on Signal and Information Processing, Network and computers, ICSINC 2022

作者： Li, XiaoFeng Dong, XiaoGang Gu, Bin Zhao, Ming Zhong, RuiMing Li, Yi Wang, Jing Beijing Institute of Control Engineering Beijing100190 China Software College Northwestern Polytechnical University Xi’an710072 China

ISBN: (纸本)9789811999673

This paper summarizes the development of software evolution technology. Based on the fundamental concepts of software evolution, we deeply analyzed the motivation and evolution process of the on-orbit evolution for aerospace control software. Five key technologies in on-orbit evolution of aerospace control software are emphasized, including architecture modeling, unified description of adaptive elements, construction of on-orbit evolution knowledge base, on-orbit evolution requirement perception and event identification mechanism, and policy-based adaptive decision-making mechanism. In conclusion, we point out the challenges and development trends of the on-orbit evolution of aerospace control software. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Orbits

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COVID 19 and Information Technology: A Systematic Review 23

COVID 19 and Information Technology: A Systematic Review

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23rd International Conference on Electronic Business, ICEB 2023

作者： Alharbi, Hadeel Hamilton, John R. Maxwell, Stephen J. Computer Science and Software Engineering College Hail University Saudi Arabia James Cook University Cairns Australia

The COVID pandemic has driven innovations that have made information technology all-pervasive in the lives of most individuals. This systematic review seeks to determine the key themes that reflect these advances. The demographics of the publications reviewed were also examined. A total of 84 peer-reviewed and conference proceedings publications were selected from the years 2020-2022. NVivo 12 Plus was used to construct word relationship trees and a word frequency cloud. It was found that there are four main themes: information management;public health management;technological advances;and services. The word cloud revealed that data, health, learning, and social were the most frequently associated words with information technology and COVID. Most affiliations were based in the United States, with 79 authors, followed by China with 27 and India with 21 authors. The average number of authors on a publication was 3.59. Future research into the ethical impacts of online monitoring and real-world surveillance is needed. As data management systems are increasingly being used by corporations and governments to combat and restrict the spread of misinformation, who decides the truth needs to be clearly transparent? © 2023 International Consortium for Electronic Business. All rights reserved.

关键词： Information management

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