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Evaluating the Performance of Sidecar-based and Sidecarless Cloud-native Service Mesh Solutions 18

Evaluating the Performance of Sidecar-based and Sidecarless ...

引用

18th IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2024

作者： Hasanth, Karumuri Meher Nadig, Deepak Purdue University West Dept. of Computer and Information Technology LafayetteIN47907 United States

ISBN: (纸本)9798350391725

The software development landscape is rapidly evolving, marked by the shift from monolithic applications to microservices architectures and the adoption of agile methodologies. While developing microservices has become more effortless, the challenges of maintaining and deploying them persist, specifically with managing the complex dependencies associated with multiple microservice deployments. With an increasing demand for container orchestrators and Kubernetes emerging as the industry standard, networking microservices remains a key challenge. Service mesh, a solution to this problem, consists of a control and a data plane, with the former responsible for managing the components of the latter. Data planes in most service meshes are implemented using sidecar proxies, posing specific challenges when injected into application pods. A potential solution to these challenges lies in the sidecarless approach. This paper aims to explore the eBPF-based service mesh, Cilium, which eliminates the need for a sidecar while encompassing most service mesh capabilities. Further, we benchmark Cilium's performance, analyze its effectiveness, and compare its performance with Istio and Linkerd. Our evaluations show that Linkerd outperforms other solutions with respect to response times, with Linkerd performing 29.85 % and 63.43% better than Cilium and Istio, respectively. © 2024 IEEE.

关键词： Application programs

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Deep Learning Approach with a Convolutional Neural Network for Landslide Detection Using Remotely Sensed Images

Deep Learning Approach with a Convolutional Neural Network f...

引用

2024 International Conference on Science, Engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Pucoe, Gloria Obagbuwa, Ibidun Christiana Sol Plaatje University Dept. Computer Science and Information Technology Kimberley South Africa

ISBN: (纸本)9798350358155

A landslide is a natural hazard that has become prevalent, especially in these times of climate change. Detecting a natural hazard is critical as it can prevent costs and fatalities. In this study, we investigate the efficiency of algorithms in detecting landslides. We implement this investigation using convolutional neural networks to classify extracted features from satellite images to detect landslides. This study investigates the use of convolutional neural networks to classify, segment, detect and landslides in an area. Two experiments were conducted;the first was to evaluate the performance of the deep learning model when the learning rate changes along with the number of epochs, and the second was to evaluate the accuracy of the deep learning model. For experiment 1, performance was better due to a higher epoch number per learning rate by monitoring the loss, and it was then used for experiment 2. Adam optimizer was used to build a deep learning CNN model, and after 20 epochs of training, the model gave an F1-score area of 65.09%, recall area of 81.13%, and precision of 57.44%. The model can detect potential landslide areas from LiDAR image input data. © 2024 IEEE.

关键词： Convolutional neural networks

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Wine Quality Prediction Using Supervised and Unsupervised Machine Learning Techniques

Wine Quality Prediction Using Supervised and Unsupervised Ma...

引用

2024 International Conference on Science, Engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Pucoe, Gloria Obagbuwa, Ibidun Christiana Sol Plaatje University Dept. Computer Science and Information Technology Kimberley South Africa

ISBN: (纸本)9798350358155

Wine quality and its assurance are crucial for the wine-making business. The traditional method of measuring the quality of wine is expensive and time-consuming. Innovative measures, such as using machine learning tools, can replace the methods used to determine the quality of wine. Key features or attributes must be identified to predict the quality of the wine. It is important to note that not all wines possess these relevant features due to factors such as origin, chemical composition, etc. For this reason, this work explored identifying the key features required for wine quality. The project also aimed to investigate two machine learning techniques for classifying wine quality when provided with the same data (red and white wine). This project also aims to study the impact of supervised and unsupervised machine learning on the same dataset. We used a random forest and a support vector machine (SVM) to classify wine quality under supervised learning. The unsupervised machine learning algorithm used is principal component analysis (PCA). The models that predicted wine quality the best according to metrics were supervised machine learning models. According to the results of the investigation, supervised machine learning is the best classifier, being a random forest with an accuracy score of 71% for white wine and 74% for red wine, as opposed to unsupervised machine learning, which scored less. © 2024 IEEE.

关键词： Adversarial machine learning

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Strategies for Helping Anchor-Based Trackers Learn re-ID Features for Smart City Surveillance

Strategies for Helping Anchor-Based Trackers Learn re-ID Fea...

引用

2024 IEEE International Conference on Consumer Electronics, ICCE 2024

作者： Chen, Xiu-Zhi Li, Mu-Chuan Chen, Yen-Lin National Taipei University of Technology Dept. of Computer Science & Information Engineering Taipei Taiwan

ISBN: (纸本)9798350324136

Re-identification has become a crucial issue in computer vision today as it allows for tracking objects in both continuous and discontinuous scenarios. Despite achieving perfect detection results, anchor-based trackers encountered difficulties in effectively learning re-identification features, due to various issues. This research proposes strategies aimed at improving the capability of anchor-based trackers to learn high-quality re-identification (re-ID) features. The model developed through our strategies can extract more distinct features and achieve almost 0.57 Multiple Object Tracking Accuracy (MOTA) on MOT20, even under a limited training dataset. This result indicates that our proposed strategies hold potential for improving the performance of anchor-based trackers. © 2024 IEEE.

关键词： Anchor-Based Tracker Re-Identification Smart City Surveillance

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Enhancing Train Transportation in Sri Lanka: A Smart IOT Based Multi-Subsystem Approach Using MQTT

Enhancing Train Transportation in Sri Lanka: A Smart IOT Bas...

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2024 IEEE Region 10 Conference, TENCON 2024

作者： Balasingham, Dhanushka Samarathunga, Sadeesha Bandara, Anuththara Arachchige, Gayantha Godakanda Gamage, Narmada Wijayaraja, Jaliya L. Sri Lanka Institute of Information Technology Dept. of Computer Systems Engineering Sri Lanka

ISBN: (纸本)9798350350821

This research proposes a system as a solution for the challenges faced by Sri Lanka's historic railway system, such as scheduling delays, overcrowding, manual ticketing, and management inefficiencies. It proposes a multi-subsystem approach, incorporating GPS tracking, RFID-based e-ticketing, seat reservation, and vision-based people counting. The GPS-based real-time train tracking system performs accurately within 24 meters, with the MQTT protocol showing twice the speed of the HTTP-based system. All subsystems use the MQTT protocol to enhance efficiency, reliability, and passenger experience. The study's data and methodology demonstrate the effectiveness of these innovations in improving scheduling, passenger flow, and overall system performance, offering promising solutions for modernizing Sri Lanka's railway infrastructure. © 2024 IEEE.

关键词： Global positioning system

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Multispectral Images and IoT Based Tea Plantation Monitoring: A Proposed System Architecture 5

Multispectral Images and IoT Based Tea Plantation Monitoring...

引用

5th International Conference on Advanced Research in Computing, ICARC 2025

作者： Kariyawasam, K.P.W.D.V. Kahandagamage, P.N. Fernando, M.R.R. Fernandopulle, J.M. Jayasinghearachchi, Vishan Dissanayaka, Kapila Dept. of Software Engineering Sri Lanka Dept. of Information Technology Sri Lanka Dept. of Computer Science Sri Lanka

ISBN: (纸本)9798331530983

In recent years, the Sri Lankan tea industry has fallen behind its competitors in the global tea market. This decline is caused by the challenges in productivity and resource management due to the limitations of traditional crop monitoring methods. This study presents a prototype system architecture that integrates multispectral imagery and IoT technologies to enhance plantation monitoring. The proposed system uses drones to capture high-resolution multispectral images and IoT devices to collect real-time environmental data, providing a comprehensive approach to assessing plant yield and detecting stress. Built on a hexagonal architecture, the system emphasizes modularity, reliability, and scalability by ensuring a clear separation of core functionalities from external components. This design facilitates easy adaptation to various crop types and agricultural contexts, enabling flexibility in deployment and use. This flexible framework can serve as a blueprint for improving decision-making and management practices across diverse plantation environments. © 2025 IEEE.

关键词： Risk perception

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Relating Muscle Activity and Mouse Sensitivity in FPS Game Players Using Surface EMG Signals 5

Relating Muscle Activity and Mouse Sensitivity in FPS Game P...

引用

5th International Conference on Biomedical Engineering, IBIOMED 2024

作者： Prajapati, Vaibhav Turlapaty, Anish C. Sarma, Himangshu Ghorai, Mrinmoy Indian Institute of Information Technology Dept. of Electronics and Communication Engineering Sri City India Indian Institute of Information Technology Dept. of Computer Science and Engineering Sri City India

ISBN: (纸本)9798350389265

In the domain of first-person shooter (FPS) gaming, mouse sensitivity is crucial for performance. This paper presents an analysis showing the correlation between specific muscle activity and mouse movement. We analyzed mouse movements from twelve subjects with varying sensitivity levels. A new dataset electromyography analysis of human activity database 8 (EMAHA-DB8), is introduced which involved four mouse designs and two game modes: Medium (normal movement) and Hard (aggressive movement). Sensitivity settings were standardized using effective dots per inch (eDPI), calculated as Mouse DPI multiplied by in-game sensitivity. Hand muscle activity was measured using five Noraxon Ultium wireless surface electromyography (sEMG) sensors with Ag/AgCl electrodes. Various regression models, including Random Forest, Support Vector Regressor, K-Nearest Neighbors Regressor, MLP Regressor, and Gradient Boost Regressor, were used to predict eDPI from muscle movement patterns. Sequential feature selection identified the top ten features, highlighting the most significant muscles for predicting sensitivity. The results demonstrate that the Extensor Carpi Radialis (ECR) is the most active muscle, followed by the Extensor Digitorum (ED), while playing the chosen FPS game. These findings give insights for ergonomic and personalized equipment design to enhance gaming experience and performance. © 2024 IEEE.

关键词： Decision trees

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Version Controlling of User Content in Learning Management Systems for Supporting the Teaching/Learning Process 8

Version Controlling of User Content in Learning Management S...

引用

8th International Conference on information technology Research, ICITR 2023

作者： Firdhous, Mohamed Fazil Mohamed Elbreiki, Walid Wijesiriwardana, Chaman University of Moratuwa Dept. of Information Technology Moratuwa Sri Lanka College of Computer Technology Dept. of Computer Science Benghazi Libya

ISBN: (纸本)9798350359503

E-Learning or online learning has become an integral part of the educational system all over the world in recent times. The importance of online learning is really felt by almost everybody in the recent COVID19 pandemic as no physical activity could be conducted that required people to meet or gather at a specific place. Learning or Content Management Systems play an important role in any online learning system as most of the teaching and learning activities occur in a disconnected or asynchronous form. One of the main features of any learning management systems is collection and distribution of learning/teaching materials as well as other documents. In this paper, the authors present the results of an in dept. study carried out on the need for version controlling of user uploaded documents to the learning management systems. Also, presented is a document validation technique with a pilot implementation that can effectively compare and cross validate two uploaded files. © 2023 IEEE.

关键词： E-learning

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Utilizing AI-powered Drones for Efficient Flood Rescue Operations 3

Utilizing AI-powered Drones for Efficient Flood Rescue Opera...

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3rd International Conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Kumar, M. Jaswanth Bitra, Dhanush Titus, Rohan Jazz, Dill Kiliroor, Cinu C. Dept. of Electronics and Communication Indian Institute of Information Technology Kottayam Kerala Kottayam India Dept. of Computer Science Indian Institute of Information Technology Kottayam Kerala Kottayam India

ISBN: (纸本)9798331532420

In times of severe earthquakes and floods, rapid and effective aid is crucial. Traditional methods often face challenges due to accessibility issues and the vast scale of *** proposed Autonomous Aerial Humanitarian Assistance and Disaster Relief (A2-HADR) System aims to transform disaster response through the use of drone technology. The proposed solution addresses the obstacles in rescue operations by employing drones equipped with Artificial Intelligence (AI) to provide immediate support. The advanced system utilizes state-of-the-art computer Vision technology to autonomously detect people and obstacles from heights of 50-100 meters at various angles and deliver essential supplies like food, clothing, and rescue gear to those in need. We have executed and assessed a range of advanced object detection algorithms, such as YOLOv8, YOLOv9, and Detectron2. After thorough evaluation, Detectron2 emerged as the most effective model among those tested, showcasing exceptional accuracy and resilience. © 2024 IEEE.

关键词： Drones

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Data loss in quantum image representation methods 27

Data loss in quantum image representation methods

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27th International Conference on Knowledge Based and Intelligent information and Engineering Sytems, KES 2023

作者： Werner, Krzysztof Kordasz, Michal Michalczuk, Agnieszka Cyran, Krzysztof Dept. of Computer Graphics Vision and Digital Systems Silesian University of Technology Gliwice44-100 Poland

Quantum Image Representation is a well-studied area in quantum informatics. The first proposed algorithm was the qubit lattice method introduced in 2003. This method was not harnessing the full potential of the quantum system - using as many qubits as there were pixels in the image. The most commonly used methods right now are Flexible Representation of Quantum Images (FRQI) proposed in 2011, and Novel Enhanced Quantum Representation (NEQR) proposed in 2013, which uses superposition to encode a monochrome image on a quantum computer using a reduced number of qubits. In this paper, we want to compare these two methods, alongside Local Phase Image Quantum Encoding (LPIQE) method proposed in 2020, to see which method produces images with minimal data loss. Another comparison will be done with image encoded using the LPIQE method with Phase Distortion Unraveling (PDU) error mitigation method, which is done entirely on a classical computer. LPIQE implementation was intended for using the photonic quantum system since the phase-shift is easy to produce, and it will be implemented on real, photonic quantum computers after further development. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： FRQI LPIQE NEQR PDU quantum computing Quantum image encoding quantum information theory quantum sampling

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