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Domain Adaptation in Visual Reinforcement Learning via Self-Expert Imitation with Purifying Latent Feature

Domain Adaptation in Visual Reinforcement Learning via Self-...

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2024 International conference on Intelligent Robots and systems

作者： Chen, Lin Huang, Jianan Zhou, Zhen Wang, Yaonan Mo, Yang Miao, Zhiqiang Zeng, Kai Feng, Mingtao Wang, Danwei Hunan Univ Sch Elect & Informat Engn Changsha 410082 Peoples R China Natl Engn Res Ctr Robot Visual Percept & Control Changsha 410082 Peoples R China Xidian Univ Sch Comp Sci & Technol Xian 710126 Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Nanyang Ave Singapore 639798 Singapore

ISBN: (纸本)9798350377712;9798350377705

Generalizing visual reinforcement learning is fundamental to robot visual navigation, involving the acquisition of a policy from interactions with source environments to facilitate adaptation to analogous, yet unfamiliar target environments. Recent advancements capitalize on data augmentation techniques, self-supervised learning methods, and the generative adversarial network framework to train policy neural networks with enhanced generalizability. However, current methods, upon extracting domain-general latent features, further utilize these features to train the reinforcement learning policy, resulting in a decline in the performance of the learned policy guiding the agent to accomplish tasks. To tackle these challenges, a framework of self-expert imitation with purifying latent features was devised, empowering the policy to achieve robust and stable zero-shot generalization performance in visually similar domains previously unseen, without diminishing the performance of guiding the agent to accomplish tasks. The extraction method of domain-general latent features is proposed to enhance their quality based on the variational autoencoder. Extensive experiments have shown that our policy, compared with state-of-the-art counterparts, does not diminish the performance of the policy guiding the agent to accomplish tasks after generalization.

关键词： Adversarial machine learning

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An Optimized Hybrid Deep Learning Framework for Monitoring Botnet Attacks in IoT networks 5

An Optimized Hybrid Deep Learning Framework for Monitoring B...

引用

5th International conference on IoT Based control networks and Intelligent systems, ICICNIS 2024

作者： Manimaran, A. Sathish Kumar, P.J. Kartheesan, L. Kumutha, D. Surendran, R. Karpaga Vinayaga College of Engineering and Technology Technical Sciences Department of Electronics and Communication Engg Chengalpattu India Panimalar Engineering College Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr.Sangunthala R&D Department of Computer Science and Engineering Institute of Science and Technology Chennai India Jeppiaar Institute of Technology Department of Electronics and Communication Engg Sriperumudur India Saveetha School of Engineering Department of Computer Science and Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518097

The serious threat of botnet attacks in the IOT world today can be effectively addressed with deep learning (DL). However, to train the model, large and complex data sets are required, which adds cost and necessitates expensive hardware to perform intricate mathematical operations. The IoT plays a significant challenge in memory constrained by implementing the DL. The hybrid Principal Component Analysis (PCA) and Autoencoder is proposed to measures the performance to lower the IoT network traffic data and consumes bulky information. Following that, reduced datasets are examined and trained using hybrid Long Short Term Memory (LSTM) and Convolutional Neural network (CNN) algorithms. The hybrid deep learning algorithms attain the feature to enhance the precision, accuracy, recall, and F1 scores 96.51%, 100%, 96.51%, and 98.22%, which were higher than the conventional methods. The model's performance is analyzed using extensive experiments. © 2024 IEEE.

关键词： Bot (internet)

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LaMa network architecture search for image inpainting 12

LaMa network architecture search for image inpainting

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12th International Scientific and Practical conference "Information control systems and Technologies", ICST 2024

作者： Kolodochka, Dmytro Polyakova, Marina Nesteriuk, Oleksandr Makarichev, Victor Odesa Polytechnic National University 1 Shevchenko Ave. Odesa65044 Ukraine National Aerospace University "Kharkiv Aviation Institute" 17 Chkalova Street Kharkiv61000 Ukraine

The neural architecture search problem is to obtain a neural network architecture with a version of the selected block that has the best performance according to a pre-selected evaluation strategy compared to other alternative versions. The aim of the paper is to improve the performance of image inpainting using neural architecture search by applying the wavelet transform to the LaMa network. Analyzing the results of experiments on researching the performance of image inpainting using the developed software it was noticed that the inpainting was better for images containing significant areas of uniform intensity, fine-grained or structural texture. Fragments of images, including complex textures or detailed patterns were inpainted worse. The proposed technique for searching neural architecture for image inpainting based on LaMa differs in the ratio of image inpainting time and the quality of the reconstructed image. Inpainting of images with large masks based on the LaMa network is improved by applying the wavelet transform. In particular, the quality of filling the missing areas with image edges and small details is improved. In addition, it was researched the dependence of the quality of generating of details and edges of objects in the image on the properties of the image textures, which can be described by texture descriptors. Prospect for further research is prediction the effectiveness of the image inpainting with the LaMa networks depending on the estimated values of original image texture descriptors and missing areas size. © 2024 Copyright for this paper by its authors Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0)

关键词： Image texture

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TCP Westwood Simulation and Analysis on Wireless Sensor network for Building Structure Condition Monitoring Topology

TCP Westwood Simulation and Analysis on Wireless Sensor Netw...

引用

2024 International conference on Information Technology systems and Innovation, ICITSI 2024

作者： Bossa, M.Alif Gibran A. Huda, Hilal Sutiyo Hossen, Md. Jakir Telkom University School of Computing Bandung Indonesia CAATIS Tel-U School of Computing Bandung Indonesia Selangor Malaysia

ISBN: (纸本)9798331511470

The TCP Westwood congestion control algorithm was designed to improve data transfer efficiency in LTE networks. It can be applied to optimize data transmission in structural health monitoring topologies using Wireless Sensor networks (WSN). To evaluate data transmission performance in a WSN-based structural monitoring topology, simulations of TCP Westwood were run using network Simulator 2 (NS2). The results indicate that TCP Westwood optimizes bandwidth usage and reduces congestion, thereby enhancing data transmission performance in WSN. This allows WSNs to transmit data more quickly and efficiently, improving the quality of structural health monitoring. Additionally, by lowering bandwidth usage, TCP Westwood helps reduce WSN operational costs and maximizes network resource utilization. The study applies TCP Westwood in a building structure monitoring topology with various parameters such as the number of nodes, distance between nodes, and congestion levels. Simulation results show that TCP Westwood, particularly in topologies with more nodes and greater distances between them, enhances WSN data transmission by optimizing bandwidth usage and reducing congestion. Overall, the study demonstrates that TCP Westwood improves WSN data transmission performance in structural health monitoring, enabling faster and more efficient data transmission and reducing operational costs. © 2024 IEEE.

关键词： Structural health monitoring

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On the performance of Non-Terrestrial networks to Support the internet of Things 56

On the Performance of Non-Terrestrial Networks to Support th...

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56th Asilomar conference on Signals, systems, and Computers

作者： Wang, Dengke Traspadini, Alessandro Giordani, Marco Alouini, Mohamed-Slim Zorzi, Michele King Abdullah Univ Sci & Technol Thuwal Saudi Arabia Univ Padua Dept Informat Engn Padua Italy

ISBN: (纸本)9781665459068

The advent of the internet of Things (IoT) era, where billions of devices and sensors are becoming more and more connected and ubiquitous, is putting a strain on traditional terrestrial networks, that may no longer be able to fulfill service requirements efficiently. This issue is further complicated in rural and remote areas with scarce and low-quality cellular coverage. To fill this gap, the research community is focusing on non-terrestrial networks (NTNs), where Unmanned Aerial Vehicles (UAVs), High Altitude Platforms (HAPs) and satellites can serve as aerial/space gateways to aggregate, process, and relay the IoT traffic. In this paper we demonstrate this paradigm, and evaluate how common Low-Power Wide Area network (LPWAN) technologies, designed and developed to operate for IoT systems, work in NTNs. We then formalize an optimization problem to decide whether and how IoT traffic can be offloaded to LEO satellites to reduce the burden on terrestrial gateways.

关键词： internet of Things (IoT) non-terrestrial network (NTN) Low-Power Wide Area networks (LPWANs) SigFox LoRa NB-IoT offloading

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A Wireless Sensor network Integrated with Edge Computing for Real-Time Crop Monitoring and Automated Irrigation 18

A Wireless Sensor Network Integrated with Edge Computing for...

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18th International conference on Telecommunication systems, Services, and Applications, TSSA 2024

作者： Garma, Angeline D. Cruz, Febus Reidj G. School of Eece Mapúa University Manila Philippines

ISBN: (纸本)9798331527686

Cloud-based smart agriculture systems struggle with real-time processing and connectivity in remote areas. This study integrates edge computing with WSNs to create a real-time crop monitoring and auto-irrigation system. The edge node handles RF communication with sensor and actuator nodes and connects to a LoRaWAN gateway provided by Packetworx using an SX1276 LoRa chip at 868 MHz. This design allows efficient data collection, local processing, and automated control without needing constant internet access. Implemented in Rosario, Batangas, Philippines, the system monitors soil moisture, NPK levels, temperature, and humidity, and controls irrigation via five solenoid valves. Each node operates for about 10.3 hours, extended by solar panels. Connectivity tests indicate better signal quality with higher gateways and increased packet loss at greater distances. © 2024 IEEE.

关键词： Solenoid valves

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Distributed Data Sharing and Access control in Industrial IoT Using Blockchain Technology 5

Distributed Data Sharing and Access Control in Industrial Io...

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5th International conference on Computer Engineering and Intelligent control, ICCEIC 2024

作者： Li, Rui Sun, Yan Liu, Caiyun Wen, Ying Liu, Yitong Fujian Provincial Industrial and Information Industry Development Research Center Fujian Fuzhou China China Industrial Control Systems Cyber Emergency Reponse Team Beijing China

ISBN: (纸本)9798331507992

With the rapid development of the Industrial internet of Things (IIoT), secure and efficient data sharing has become crucial for enabling industrial automation and smart transformation. However, existing centralized solutions face limitations in scalability, latency, and data privacy. To address these challenges, this paper proposes a blockchain-based distributed data-sharing and access control system tailored for resource-constrained industrial edge networks. The system adopts a three-layer architecture that integrates edge computing and blockchain technology to provide decentralized, low-latency data management. A novel smart contract framework is introduced to enforce multi-level access control and support fine-grained permissions for intra-domain and cross-domain data sharing. Experimental results show the system's improved performance in data access efficiency and throughput, offering a robust solution for real-time, secure IIoT applications. © 2024 IEEE.

关键词： Smart contract

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Application of a Neural network to Feed Bag Tag Detection systems 26

Application of a Neural Network to Feed Bag Tag Detection Sy...

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2024 IEEE International conference on Automation/26th Congress of the Chilean Association of Automatic control, ICA-ACCA 2024

作者： Loaiza, Luis Villota, Wellington Penaranda, Vicente Cardenas, Michelle Vega, Nino Salesian Polytechnic University Electronics and Automation Guayaquil Ecuador Institute Iitp Catholic University Guayaquil Ecuador Mechatronic Salesian Polytechnic University Guayaquil Ecuador

ISBN: (纸本)9798350378115

This paper describes the implementation of an advanced artificial intelligence-based system for the detection and labeling of feed sacks, using the Jetson Nano development board and the YOLOv8 neural network model. This system employs computer vision techniques to accurately identify the sacks and their side labels. Ensuring the presence of a label on each sack is crucial for product monitoring and quality control of the labeling process. The methodology involves a rigorous model training process, using data captured in real production conditions, and system configuration to optimize detection performance. The results obtained demonstrate the system's robustness and efficiency, with an accuracy rate of 98.84% in controlled conditions and 96.42% in a real production environment. This underscores the system's practicality and its potential to significantly enhance logistics and quality control processes in the shrimp feed industry. The technology's implementation can lead to improved product traceability and operational efficiency throughout the supply chain, potentially reducing costs, minimizing errors, and increasing end-customer satisfaction. © 2024 IEEE.

关键词： Customer satisfaction

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Mitigating Cross-Technology Interference in Low-Power Wireless networks 33

Mitigating Cross-Technology Interference in Low-Power Wirele...

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33rd IEEE International conference on Computer Communications and networks (ICCCN)

作者： Dargie, Waltenegus Kidane, Zegeye Mekasha Tech Univ Dresden Inst Syst Architecture Fac Comp Sci Dresden Germany Max Planck Inst Radio Astron D-53902 Effelsberg Bad Muenstereif Germany

ISBN: (纸本)9798350348439;9798350384611

Emerging autonomous and semi-autonomous vehicles such as Unmanned Aerial Vehicles (UAV) and Unmanned Surface Vessels (USV) use unlicensed frequency bands to interact with their remote control stations and peers. Typically, these systems are cost-intensive and their safe operation is of paramount importance. As a result, they establish powerful wireless communication links. When low-power IoT sensing nodes operate nearby, their performance can be considerably affected by cross-technology interference arising from the powerful systems. Different coexistence strategies have been proposed to deal with cross-technology interference, including dynamic channel-hopping, low-level spectrum sensing and channel adaptation, channel blacklisting, and direct cross-technology communication. These approaches require advanced spectrum scanning, detection, and clustering as well as knowledge of low-level packet structure and modulation schemes. In this paper, we propose a packet transmission strategy relying on link quality statistics alone to deal with cross-technology interference. Our approach does not require intimate knowledge of modulation schemes;nor does it require the modification of any hardware components. Evaluation based on traces of field experiments show that our approach improves Packet Delivery Ratio (PDR) by more than 30% when compared to baseline results and by more than 20% when compared to state-of-the-art solutions.

关键词： Wireless sensor networks UAV link quality fluctuation Poisson Process internet of Things

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Enhancing internet Security Through Adaptive Access control Mechanisms 3

Enhancing Internet Security Through Adaptive Access Control ...

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3rd International conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Mohanprasath, R. Abhinav Shankar, H. Kanchana, M. SRM Institute of Science and Technology Department of Computing Technology Chengalpattu District Tamil Nadu India

ISBN: (纸本)9798350343632

Strong systems for regulating and safeguarding online activity are required due to the tremendous potential and difficulties brought about by the internet's fast expansion. In order to successfully restrict and manage internet use, this research article investigates the use of access control strategies. In order to reduce security risks and improve user responsibility, the research focuses on creating a thorough framework that incorporates access control measures. Using a multifaceted approach, the suggested system combines dynamic access control rules, Attribute-Based Access control (ABAC), and Role-Based Access control (RBAC). By using these strategies, companies may have fine-grained control over user rights and customize access privileges according to user roles, characteristics, and contextual variables. The system also uses behavior analysis and anomaly detection to spot and handle unusual user activity, which improves the system's overall security posture. Additionally, the study looks at the real-world effects of putting such a system in place in a variety of contexts, including public Wi-Fi networks, educational institutions, and business networks. performance evaluations, usability tests, and real-world deployment scenarios are all part of a comprehensive review of the suggested framework that verifies its effectiveness in various settings. The research's results add to the expanding corpus of knowledge on internet use management by providing a sophisticated grasp of the potential and difficulties related to access control strategies. © 2023 IEEE.

关键词： and network Security Extended ACL network Traffic Protocols Standard ACL

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