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18th International computer Conference on Wavelet Active Media technology and Information Processing, ICCWAMTIP 2021

作者： Agyemang, Isaac Osei Zhang, Xiaoling Adjei-Mensah, Isaac Agbley, Bless Lord Y. Fiasam, Linda Delali Mawuli, Bernard Cobbinah Sey, Collins University of Electronic Science and Technology of China School of Information and Communication Engineering China University of Electronic Science and Technology of China School of Information and Software Engineering China University of Electronic Science and Technology of China School of Computer Science and Engineering China

ISBN: (纸本)9781665413640

Resource-constrained edge nodes are ubiquitous in industrial settings yet are challenged by limited computing resources. Leveraging computational advantage and perceptual awareness of Gabor filters, a hybrid classifier to mitigate computational requirements in the context of classification of key components of civil structures which are essential in the proactive structural assessment, specific repairs, and maintenance post-construction is given. Deployment of the hybrid classifier using the CoreML framework exhibits favorable classification accuracy and robustness as compared to contemporary state-of-the-art classifiers. © 2021 IEEE.

关键词： Deep neural networks

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An Intelligent Method for Detecting Jute Pests Using Deep Convolutional Neural Networks

An Intelligent Method for Detecting Jute Pests Using Deep Co...

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IoT Based Control Networks and Intelligent Systems (ICICNIS), International Conference on

作者： S. Vignesh S. Prabhu R. Sampathkumar M. Thillai Rani J. Rejina Parvin G. Uma Maheswari R. Saravanakumar Department of Computer Applications Karpagam Academy of Higher Education Coimbatore Tamil Nadu India Department of Mechatronics Engineering KPR Institute of Engineering and Technology Coimbatore Tamil Nadu India Department of Electronics and Communication Engineering Sri Krishna College of Technology Coimbatore Tamil Nadu India Department of Electronics and Communication Engineering Sri Krishna College of Engineering and Technology Coimbatore Tamil Nadu India Department of Computer Science and Engineering RMK College of Engineering and Technology Chennai Tamil Nadu India Iconix Software Solution Tirunelveli Tamil Nadu India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518103

Pest attacks pose a serious threat to the production of jute and other significant crops. Jute farmers often utilize their visual sense and hands-on expertise to distinguish between multiple diseases that seem to be identical. The intelligent model we built for the identification of jute pests was based on a deep convolutional neural network (DCNN). This practical difficulty led to the development of this concept. The proposed DCNN model can automatically identify jute pest concerns with high speed and accuracy based on image recognition. In addition, images of the four most common jute bugs are included in an organized image graphics collection. Our model yields a DCNN accuracy is 96.72% for the four most significant jute pest kinds. Further proof of the model's effectiveness is provided by the accuracy, recall, F1-score, and confusion matrix metrics.

关键词： Performance evaluation Visualization Accuracy Operating systems Production Pest control Feature extraction software Convolutional neural networks Overfitting

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Evaluating LoRaWAN performance in intentional and unintentional DoS attacks by legacy 900MHz network devices

Evaluating LoRaWAN performance in intentional and unintentio...

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International Conference on Advanced Communication Technologies and Networking (CommNet)

作者： Seokhyeon Bang Junyoung Jang Minju Ro Yuseon Choi Doyong Kwon Kangyeon Lee Anthony Smith School of Computer Science and Engineering Chung-Ang University Seoul Korea Department of Software Engineering Chonnam National University Gwangju Korea School of Computer Science and Engineering Kyungpook National University Daegu Korea Department of Computer and Information Technology Purdue University Indiana United States

The deployment of LoRaWAN on the Internet of Things (IoT) has increased since its advent and LoRaWAN now predominates the IoT market over other Low Powered Wide Area Networks (LPWAN). However, since LoRaWAN uses Chirp Spread Spectrum (CSS), it is susceptible to wideband jamming attacks. In this paper, we demonstrate with experiments and concrete numerical results that jamming US915 LoRaWAN frequency is possible by the usual data transmission and reception process of 900MHz Canopy, one of the legacy 900MHz network device. Intentional attack is possible in the same manner. The experiments emulate the real-world environment operated in medical and agriculture industries, in outdoor and indoor conditions, respectively. In addition, this paper introduces and utilizes the novel metric, Jamming Effect (JE), that indicates the network performance of wireless networks that spread the data on air.

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An Approach for Detecting Pneumonia from Chest X-Ray Image Using Convolution Neural Network

An Approach for Detecting Pneumonia from Chest X-Ray Image U...

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3rd International Conference on Intelligent Computing and Optimization, ICO 2020

作者： Kar, Susmita Akhtar, Nasim Rahman, Mostafijur Department of Computer Science and Engineering Dhaka University of Engineering and Technology Gazipur Dhaka Bangladesh Department of Software Engineering Daffodil International University Dhaka Bangladesh

ISBN: (纸本)9783030681531

Pneumonia is one of the most cynical problems to human beings all over the world and detecting the presence of pneumonia in an early stage is very necessary to avoid Premature Death. According to the World Health Organization above 4 million sudden deaths happen each year from domiciliary air pollution correlated diseases inclusive of pneumonia. Generally, pneumonia can be identified using chest X-ray images that are performed by an expert radiologist. But only rely on the radiologist sometimes blocks the treatment because of detecting diseases from the chest X-ray images which requires human effort, experience, and time. In this case, computer-aided diagnosis (CAD) system is required for identifying pneumonia from chest X-ray images automatically. In this research, a modified model is proposed using Convolution Neural Network (CNN) model to train sample data to relegate and diagnose the presence of pneumonia from an amassment of chest X-ray images. From the experimental result, it is found that the proposed model performs better results (89%) compared to other related existing algorithms. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deep learning

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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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Investigating the Impact of Bug Dependencies on Bug-Fixing Time Prediction

Investigating the Impact of Bug Dependencies on Bug-Fixing T...

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International Symposium on Empirical software engineering and Measurement, ESEM

作者： Chenglin Li Yangyang Zhao Yibiao Yang Yuming Zhou Liming Nie Zuohua Ding School of Computer Science and Technology Zhejiang Sci-Tech University Hangzhou China State Key Lab for Novel Software Technology Nanjing University Nanjing China School of Computer Science and Engineering Nanyang Technological University Singapore

Background: Bug dependencies refer to the link relationships between bugs and related issues, which are commonly observed in software evolution. It has been found that bugs with bug dependencies often take longer time to be resolved than other bugs without any dependencies. Despite the potential impact of bug dependencies on bug-fixing time, previous studies use traditional metrics without considering bug dependencies to build bug-fixing time prediction models. As a result, there is currently little empirical evidence to support the use of bug dependencies in improving prediction accuracy. Aims: We aim to conduct a comprehensive empirical study to investigate the value of considering bug dependencies for bug-fixing time prediction. Method: We define a set of bug dependency metrics based on bug dependencies. We first investigate the correlation between bug dependency metrics and bug-fixing time to investigate whether bugs with more complex dependencies are more time-consuming to be fixed. Next, we employ principal component analysis to study whether bug dependency metrics capture additional dimensions of a bug compared to traditional metrics. Finally, we build multivariate prediction models to explore whether considering bug dependencies can improve the effectiveness of bug-fixing time prediction. Results: The experimental results suggest that: (1) bugs with more complex dependencies require more time to be fixed; (2) bug dependency metrics are complementary to traditional metrics; (3) considering bug dependencies can improve the effectiveness of bug-fixing time prediction. Conclusions: These findings highlight the importance of considering bug dependencies in bug-fixing time prediction, and provide valuable insights into the potential impact of bug dependencies on software development processes.

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Incentive-driven Federated Learning in Mobile Edge Networks

Incentive-driven Federated Learning in Mobile Edge Networks

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International Conference on Distributed Computing Systems Workshop

作者： Yanlang Zheng Huan Zhou Liang Zhao Shouzhi Xu Victor C. M. Leung Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering College of Computer and Information Technology China Three Gorges University Yichang China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

Federated Learning (FL) is proposed as a privacy-preserving distributed learning methodology that can better protect the privacy and reduce communication costs. To stimulate sufficient User Equipments (UEs) to participate in FL, proper incentives need to be designed for FL. Existing incentive mechanisms do not jointly consider UE selection and local learning accuracy optimization to reduce the training expenditure. This paper designs a reverse auction-based incentive mechanism for FL to minimize the training expenditure of Base Station (BS). To this end, we first propose a Greedy Winner Determination (GWD) algorithm to select UEs with the minimum bidding prices. Then, we incorporate the Particle Swarm Optimization (PSO)-based local learning accuracy optimization into UE selection to further reduce the training expenditure of BS. In addition, we design a Vickrey Clarke Groves (VCG)-based payment rule to determine the payment to each participating UE. The simulation experiments show that our proposed PSO with Winner Determination (PSOWD) algorithm is superior to other existing methods in different scenarios.

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Leveraging Blockchain-as-a-Certificate Authority for Authentication in 6G-Enabled Spatial Crowdsourcing Drone Services

Leveraging Blockchain-as-a-Certificate Authority for Authent...

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IEEE Conference on Vehicular technology (VTC)

作者： Junaid Akram Ali Anaissi Sagar Sidana Deepak Kumar Rutvij H. Jhaveri School of Computer Science The University of Sydney NSW Australia TD School University of Technology Sydney Ultimo NSW Australia Principal Software Engineer McKinsey & Company Dallas USA Department of Computer Science and Engineering National Institute of Technology Meghalaya India School of Technology Pandit Deendayal Energy University India

ISBN: (数字)9798331517786

ISBN: (纸本)9798331517793

The integration of the Internet of Drone Things (IoDT) with spatial crowdsourcing, enhanced by 6G technology, has revolutionized environmental monitoring, particularly in managing Australian bushfires. This approach leverages drones’ mobility, multidimensional motion, and ease of deployment to gather real-time data from hazardous or inaccessible areas. However, the unsecured wireless communication channels and limited computational resources of drones in typical IoDT scenarios make them susceptible to cyber-attacks, including spoofing, GPS manipulation, impersonation, man-in-the-middle, and hijacking. To counter these threats, we propose a robust security protocol that utilizes blockchain technology augmented by Hyperelliptic Curve Cryptography (HECC). By employing blockchain as a Certificate Authority (CA) and treating transactions as certifications, our framework, DronCert, eliminates the need for traditional CAs or Trusted Third Parties (TTP). This decentralized approach, combined with the high-speed, low-latency capabilities of 6G, significantly enhances data transmission security within the IoDT network. A comprehensive security analysis demonstrates DronCert’s resilience against various attacks, such as Denial-of-Service (DoS), man-in-the-middle, replay, and unauthorized device representation.

关键词： Crowdsourcing 6G mobile communication Wireless communication Vehicular and wireless technologies Authentication Resists Blockchains Security Drones Resilience

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A New Application of Machine Learning: Detecting Errors in Network Simulations 22

A New Application of Machine Learning: Detecting Errors in N...

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Proceedings of the Winter Simulation Conference

作者： Maciej K. Wozniak Luke Liang Hieu Phan Philippe J. Giabbanelli Department of Intelligent Systems KTH Royal Institute of Technology Stockholm SWEDEN Department of Computer Science & Software Engineering Miami University Oxford OH USA

After designing a simulation and running it locally on a small network instance, the implementation can be scaled-up via parallel and distributed computing (e.g., a cluster) to cope with massive networks. However, implementation changes can create errors (e.g., parallelism errors), which are difficult to identify since the aggregate behavior of an incorrect implementation of a stochastic network simulation can fall within the distributions expected from correct implementations. In this paper, we propose the first approach that applies machine learning to traces of network simulations to detect errors. Our technique transforms simulation traces into images by reordering the network's adjacency matrix, and then training supervised machine learning models. Our evaluation on three simulation models shows that we can easily detect previously encountered types of errors and even confidently detect new errors. This work opens up numerous opportunities by examining other simulation models, representations (i.e., matrix reordering algorithms), or machine learning techniques.

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Leveraging Knowledge Distillation for Lightweight Skin Cancer Classification: Balancing Accuracy and Computational Efficiency

arXiv

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arXiv 2024年

作者： Islam, Niful Hasib, Khan Md Joti, Fahmida Akter Karim, Asif Azam, Sami Department of Computer Science and Engineering United International University United City Madani Avenue Badda Dhaka1212 Bangladesh Department of Computer Science and Software Engineering The University of Western Australia PerthWA6009 Australia Department of Computer Science and Engineering Bangladesh University of Business and Technology Dhaka1216 Bangladesh Ahsanullah University of Science and Technology Dhaka1208 Bangladesh Charles Darwin University NT0810 Australia

Skin cancer is a major concern to public health, accounting for one-third of the reported cancers. If not detected early, the cancer has the potential for severe consequences. Recognizing the critical need for effective skin cancer classification, we address the limitations of existing models, which are often too large to deploy in areas with limited computational resources. In response, we present a knowledge distillation based approach for creating a lightweight yet high-performing classifier. The proposed solution involves fusing three models, namely ResNet152V2, ConvNeXtBase, and ViT Base, to create an effective teacher model. The teacher model is then employed to guide a lightweight student model of size 2.03 MB. This student model is further compressed to 469.77 KB using 16-bit quantization, enabling smooth incorporation into edge devices. With six-stage image preprocessing, data augmentation, and a rigorous ablation study, the model achieves an impressive accuracy of 98.75% on the HAM10000 dataset and 98.94% on the Kaggle dataset in classifying benign and malignant skin cancers. With its high accuracy and compact size, our model appears to be a potential choice for accurate skin cancer classification, particularly in resource-constrained settings. Copyright © 2024, The Authors. All rights reserved.

关键词： Distillation

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