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12th International Electrical engineering Congress, iEECON 2024

作者： Yodrot, Teerapon Sutacha, Chalermkiat Orachon, Teerapong Jangjongdee, Nattapon Boonyasuwanno, Sompol Rajamangala University Of Technology Rattanakosin Faculty Of Industry And Technology Department Of Electrical Engineering Technology Prachuap Khiri Khan Thailand Ubon Ratchathani Rajabhat University Faculty Of Industrial Technology Department Of Computer Network Engineering Ubon Ratchathani Thailand Kosen Of King Mongkut's Institute Of Technology Ladkrabang Department Of Computer Engineering Bangkok Thailand

ISBN: (纸本)9798350383591

This study investigates a hybrid machine learning approach for the classification of cucumber leaf diseases, combining the strengths of pre-trained Convolutional Neural networks (CNNs) and Support Vector Machines (SVM). Focusing on a publicly available dataset, we evaluated four CNN architectures - VGG16, ResNet50, AlexNet, and EfficientNetV2 - for feature extraction, followed by SVM for the final classification task. The performance of each model was assessed using F1 scores and confusion matrices, with ResNet50 yielding the highest F1 score, indicative of a strong balance between precision and recall. VGG16 and AlexNet showed potential for improvement, which could be realized through model tuning or integration into an ensemble framework. EfficientNetV2, despite a slightly lower F1 score, highlighted the importance of further training to enhance class distinction. The research underlines the importance of a hybrid approach, which harnesses deep learning's feature extraction capabilities with SVM's classification strength, to improve the accuracy of disease detection in cucumber plants. © 2024 IEEE.

关键词： Convolutional neural networks

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Scale-Based Slicer Placement in Elastic Optical networks

Scale-Based Slicer Placement in Elastic Optical Networks

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2023 Opto-Electronics and Communications Conference, OECC 2023

作者： Kitsuwan, Nattapong Pavarangkoon, Praphan The University of Electro-Communications Department of Computer and Network Engineering Tokyo Japan King Mongkut's Institute of Technology Ladkrabang School of Information Technology Bangkok Thailand

ISBN: (纸本)9781665462136

This paper proposes a scale-based slicer placement scheme to reduce bandwidth blocking rate (BBR) in elastic optical networks (EONs). In EONs, a slicing-stitching technology is adopted to overcome a spectrum slot allocation rule that the spectrum slots of the same request must be consecutive on the same link. This technology is done by slicers to split a spectrum band into multiple sub-spectrum components. In EONs with limited slicers, determining equally the number of slicers on every node may not result in a low BBR since the traffic volume on each node is not the same. We investigate and determine the number of slicers needed for each node as a scale-based policy. The simulation results show that our scheme achieves a BBR reduction of 91%, compared to the conventional scheme. © 2023 IEEE.

关键词： Fiber optic networks

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Mobility-Aware Multi-Task Migration and Offloading Scheme for Internet of Vehicles

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Chinese Journal of Electronics 2023年第6期32卷 1192-1202页

作者： LI Xujie TANG Jing XU Yuan SUN Ying College of Computer and Information Hohai University State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Key Lab for Wireless Sensor Network and Communication Shanghai Institute of Microsystem School of Information Engineering Jiangsu Open University

In Internet of vehicles, vehicular edge computing(VEC) as a new paradigm can effectively accomplish various tasks. Due to limited computing resources of the roadside units(RSUs), computing ability of vehicles can be a powerful supplement to computing resources. Then the task to be processed in data center can be offloaded to the vehicles by the RSUs. Due to mobility of the vehicles, the tasks will be migrated among the RSUs. How to effectively offload multiple tasks to the vehicles for processing is a challenging problem. A mobility-aware multi-task migration and offloading scheme for Internet of vehicles is presented and analyzed. Considering the coupling between migration and offloading, the joint migration and offloading optimization problem is formulated. The problem is a NP-hard problem and it is very hard to be solved by the conventional methods. To tackle the difficult problem, the idea of alternating optimization and divide and conquer is introduced. The problem can be decoupled into two sub-problems: computing resource allocation problem and vehicle node selection problem. If the vehicle node selection is given, the problem can be solved based on Lagrange function. And if the allocation of computing resource is given, the problem turns into a 0-1 integer programming problem, and the linear relaxation of branch bound algorithm is introduced to solve it. Then the optimization value is obtained through continuous iteration. Simulation results show that the proposed algorithm can effectively improve system performance.

关键词： Costs Computational modeling System performance Simulation Programming Multitasking Resource management

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Remote Sensing Target Recognition Model Based on Small Sample Image Generation 4

Remote Sensing Target Recognition Model Based on Small Sampl...

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4th International Symposium on computer engineering and Intelligent Communications, ISCEIC 2023

作者： Chen, Lei Sun, Liting Space Engineering University School of Space Information Beijing China Beijing Institute of Computer Technology and Application General Department of Network and Information Security Beijing China

ISBN: (纸本)9798350306323

In recent years, remote sensing object recognition methods based on deep learning have received extensive attention. Deep learning methods need to use a large number of labeled data to carry out network training. However, the cost of labeling remote sensing image data is very high. Therefore, how to use a small number of labeled target images to train the remote sensing target classifier with high performance is an urgent problem to be solved. In view of the above problems, this paper has carried out the following work. Firstly, based on WGAN-GP, this paper optimizes the constraint conditions of neural network and proposes a depth generation model, namely CCWGAN-GP, for remote sensing image generation. Secondly, this paper proposes a remote sensing target classification model SCLNet, which makes full use of the original one-hot label and the generated soft classification label to train the neural network. Experimental results show that the proposed model can generate highly similar images to real images, and can significantly improve the performance of the classifier under training conditions with only a few labels. © 2023 IEEE.

关键词： Remote sensing

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Failed Yet Stored: A First Look at DNS Negative Caching

Failed Yet Stored: A First Look at DNS Negative Caching

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2024 IEEE International Performance, Computing, and Communications Conference, IPCCC 2024

作者： Zeng, Meng Zhu, Yujia Li, Baiyang Zhang, Yuedong Liu, Qingyun Fang, Binxing Chinese Academy of Sciences Institute of Information Engineering China University of Chinese Academy of Sciences School of Cyber Security China National Computer Network Emergency Response Technical Team/Coordination of China China School of Computer Science and Technology ShenZhen China

ISBN: (纸本)9798350367942

Caching is a critical method for enhancing the efficiency and the security of the Domain Name System (DNS). Initially, only successful domain name resolution results were cached. To mitigate failures in DNS transactions (e.g. NXDomain), the IETF proposed standards, further developed into RFC 9520 as of December 2023. In addition to the basic implementation, RFC 9520 standardizes more sophisticated forms of negative caching. This new standard aims to reduce redundant query retries in DNS traffic and protect resolvers from Denial of Service (DoS) *** this study, we present a comprehensive examination of the specific implementations of Negative Caching in resolvers. We designed and validated a method for measuring negative caching and conducted experiments on 44 public resolvers, including their Do53, DoH, and DoT interfaces. Our findings indicate that while public resolvers generally implement various types of negative caching, some exhibit unexpected cache handling behaviors when encountering specific negative responses. Additionally, we discovered that most public resolvers modify the TTL value of negative responses before returning them to clients. Despite the lack of explicit TTL values for newly specified negative responses, we devised a method to approximate the default TTL values used by public resolvers. © 2024 IEEE.

关键词： Denial-of-service attack

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6GAI: Active IPv6 Address Generation via Adversarial Training with Leaked Information 27

6GAI: Active IPv6 Address Generation via Adversarial Trainin...

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27th International Conference on computer Supported Cooperative Work in Design, CSCWD 2024

作者： Jiao, Liang Zhu, Yujia Zhang, Wenxiu Zhao, Lei Zhou, Yi Liu, Qingyun Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China National Computer Network Emergency Response Technical Team Coordination Center of China Shandong Branchs Beijing China School of Cyber Security University of Chinese Academy of Sciences National Computer Network Beijing China School of Information Science And Engineering University of Jinan Jinan China National Computer Network Emergency Response Technical Team Coordination Center of China Guangdong Branch Guangdong China

ISBN: (纸本)9798350349184

Global IPv6 scanning has always been a challenge for researchers because of the limited network speed and computational power. In this paper, we introduce 6GAI to implement more efficient target address generation. 6GAI is built with Generative Adversarial Net (GAN) integrated with Convolutional Bottleneck Attention Module (CBAM). 6GAI allows the discriminative net to leak generated address's high-level features extracted by CBAM to the generative net, while the generative net incorporates such informative signals into all generation steps through an additional Manger module, which takes the extracted features of current generated address nybbles and outputs a latent vector to guide the Worker module for active IPv6 address generation. This work outperformed the state-of-the-art target generation algorithms on two datasets including one public dataset and one independently collected dataset. © 2024 IEEE.

关键词： Deep learning

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Advancing Zero-Shot Digital Human Quality Assessment through Text-Prompted Evaluation

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IEEE Transactions on Image Processing 2025年 34卷 3503-3517页

作者： Zhang, Zicheng Sun, Wei Zhou, Yingjie Wu, Haoning Li, Chunyi Min, Xiongkuo Liu, Xiaohong Zhai, Guangtao Lin, Weisi Shanghai Jiao Tong University Institute of Image Communication and Network Engineering Shanghai200240 China Nanyang Technological University S-Lab Singapore Shanghai Jiao Tong University John Hopcroft Center Shanghai200240 China Nanyang Technological University School of Computer Science and Engineering Singapore

Digital humans have witnessed extensive applications in various domains, necessitating related quality assessment studies. However, there is a lack of comprehensive digital human quality assessment (DHQA) databases. To address this gap, we propose SJTU-H3D, a subjective quality assessment database specifically designed for full-body digital humans. It comprises 40 high-quality reference digital humans and 1,120 labeled distorted counterparts generated with seven types of distortions. The SJTU-H3D database can serve as a benchmark for DHQA research, allowing evaluation and refinement of processing algorithms. Further, we propose a zero-shot DHQA approach that focuses on no-reference (NR) scenarios to ensure generalization capabilities while mitigating database bias. Our method leverages semantic and distortion features extracted from projections, as well as geometry features derived from the mesh structure of digital humans. Specifically, we employ the Contrastive Language-Image Pre-training (CLIP) model to measure semantic affinity and incorporate the Naturalness Image Quality Evaluator (NIQE) model to capture low-level distortion information. Additionally, we utilize dihedral angles as geometry descriptors to extract mesh features. By aggregating these measures, we introduce the Digital Human Quality Index (DHQI), which demonstrates significant improvements in zero-shot performance. The DHQI can also serve as a robust baseline for DHQA tasks, facilitating advancements in the field. © 1992-2012 IEEE.

关键词： Mesh generation

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Smart Home Camera Fall Detection System 13th

Smart Home Camera Fall Detection System

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13th International Conference on computer engineering and networks, CENet 2023

作者： Ding, Ziqi Qian, Hanwei Wu, Zechen Liu, Wei Department of Computer Information and Network Security Jiangsu Police Institute Nanjing China Engineering Research Center of Electronic Data Forensics Analysis Jiangsu Province Nanjing China City University of Macau China

ISBN: (纸本)9789819992386

As China’s population ages, the issue of population aging has become increasingly severe. By the end of 2022, the population aged 60 and above in China has already reached 280 million, with an estimated 25 million falls occurring annually. Falls pose a serious threat to the health and well-being of the elderly. To address this problem, this article presents the design of an intelligent home camera fall detection system aimed at detecting falls and providing immediate assistance. The design incorporates streaming technology and image processing techniques, utilizing a Raspberry Pi camera placed in the home to collect data and stream it via RTMP. A remote server then pulls the video stream and performs fall detection using a combination of the Tiny-YOLOv3, AlphaPose, and ST-GCN algorithms. Upon detecting a fall, the system sends an immediate alert to designated individuals via the PushPlus platform, ensuring timely warnings and effectively safeguarding the safety of the elderly. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Fall detection

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Water Quality Assessment of Reservoirs in Sri Lanka 5

Water Quality Assessment of Reservoirs in Sri Lanka

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5th International Conference on Advancements in Computing, ICAC 2023

作者： Mudunkotuwa, Kavindi Senadeera, Chamath Gunawardana, Beuni Pathiraja, Onara Wijendra, Dinuka Fernando, Harinda Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Computer Systems and Network Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

This study presents a comprehensive assessment of water quality in Sri Lankan reservoirs using available data sources. The study focuses on the identification of microorganisms through image analysis, watercolor analysis in satellite-based images detection of water quality, and the inference of water quality index using relevant physical parameters. By analyzing images, the study quantifies microorganisms, providing insights into microbial diversity and potential risks. Image processing techniques enable the identification of contaminants based on watercolor analysis. Utilizing satellite data, it examines variations in water quality parameters, providing a comprehensive assessment of overall water quality. The derived water quality index, using relevant physical parameters, quantitatively assesses and compares water quality across different reservoirs. The findings contribute to understanding water quality in Sri Lankan reservoirs, supporting informed decision-making and sustainable water resource management. © 2023 IEEE.

关键词： Water quality

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Rapid Autonomy Transfer in Reinforcement Learning with a Single Pre- Trained Critic 36

Rapid Autonomy Transfer in Reinforcement Learning with a Sin...

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36th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2024

作者： Karim, Muhammad Faraz Deng, Yunjie Niu, Luyao Ramasubramanian, Bhaskar Alexiou, Michail Sahabandu, Dinuka Poovendran, Radha Mertoguno, Sukarno Georgia Institute of Technology AtlantaGA United States University of Washington Network Security Lab Department of Electrical and Computer Engineering SeattleWA United States Western Washington University Electrical and Computer Engineering BellinghamWA United States Kennesaw State University MariettaGA United States

ISBN: (纸本)9798331527235

Reinforcement learning (RL) is a well-studied framework to solve complex decision-making problems in unknown environments. The actor-critic model in RL facilitates autonomy transfer by allowing agents to iteratively update their policies using ongoing dynamic evaluations of value functions via a critic. In this paper, we examine the impact of using different pretrained critics on the performance of actor-critic algorithms. First, in any single given environment, we show that a pretrained critic can be effective in reducing the duration of an initial training phase, thereby accelerating convergence by a factor of up to 2×. In this setting, we identify the critical range of the number of episodes for which a critic will need to be trained in order for it to be an effective pretrained critic. Second, we show that a critic trained in one environment enables transfer of autonomy by aiding learning of behaviors in a different, yet related environment. We carry out extensive experiments on a bipedal locomotion task in the MuJoCo physics engine to verify our hypotheses. Our results in this paper mark the first step towards demonstrating the role and impact of pretrained critics to achieve rapid autonomy transfer for complex reinforcement learning tasks while minimizing costs associated with retraining in new environments. © 2024 IEEE.

关键词： Reinforcement learning

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