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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 ...

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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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End-To-End Knowledge Graph Construction and Reasoning Using Path Based Metrics 1

End-To-End Knowledge Graph Construction and Reasoning Using ...

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Shejole, Rohit Dofe, Sanket Shejole, Prachi Veermata Jijabai Technological Institute Dept. of Electrical Engineering Mumbai India Veermata Jijabai Technological Institute Dept. of Computer Engineering and Information Technology Mumbai India D.Y. Patil College of Engineering Dept. of Mechanical Engineering Pune India

ISBN: (纸本)9798350356816

The schema-less nature of knowledge graphs has led to their widespread adoption. It makes it possible for knowledge graphs to expand without interruption and to add new entities and relationships as needed. Knowledge graphs are now an effective tool for providing textual data with semantics and representing knowledge as a labelled directed graph. Every item, entity, and user are represented as nodes in a knowledge graph, which is then created by connecting the nodes that communicate with one another through edges. Knowledge graphs can hold a variety of more comprehensive types of information and have a wealth of natural semantics. Its mode of expression is similar to that of natural language. Nevertheless, there is still no accepted definition or format for expressing knowledge graphs. © 2024 IEEE.

关键词： Knowledge graph

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Enhancing Online Learning Monitoring with Novel Image Recognition Method Using Dlib for Eye Feature Detection 12

Enhancing Online Learning Monitoring with Novel Image Recogn...

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12th International Conference on Awareness Science and technology, iCAST 2023

作者： Yeh, ChunHsiu Shen, Wei-Cheng Chi, Hung-Yu Lin, Chin-En Chen, Jong-Shin Dept. of Information Engineering and Computer Science Feng Chia University Taichung Taiwan College of Computer Science Jimei University Xiamen China Dept. of Information Communication and Engineering ChaoYang University of Technology Taichung Taiwan

ISBN: (纸本)9798350324693

In the post-pandemic era, online learning has become a prevalent mode of education. To ensure effective monitoring of learners' engagement in online courses, this study proposes a novel method for real-time monitoring of learners' learning focus. It is divided into three main components: The first component is eye detection. We utilize Dlib technology and leverage pre-trained facial landmark detection to accurately identify the positions of the eyes. Through image processing and the application of appropriate thresholds (thresholds set above 190), this enables precise outlining of eye contours and recognition of eye movement characteristics, even during rapid eye movements or from varying angles. The second component is gaze estimation. By using adaptive thresholds for pupil segmentation and applying erosion and dilation to reduce image noise and enhance essential features, we can effectively determine pupil positions within images taken under varying photographic resolutions and lighting conditions. Angle calculation is subsequently employed to determine whether learners are concentrating on online learning courses. The third component involves an anti-cheating mechanism. By utilizing pupil-centered corneal reflex (PCCR) technology to address potential cheating behaviors of learners, an anti-cheating mechanism is proposed to detect and counter fraudulent tactics such as photos and makeup. Experimental results demonstrate that this study is capable of effectively monitoring learners' learning status and accurately determining whether learners exhibit appropriate learning attitudes and behaviors. This empowers a comprehensive assessment of learners' learning outcomes during the period of online learning. © 2023 IEEE.

关键词： Eye movements

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Masked Face Detection and Recognition Using a Unified Feature Extractor 5

Masked Face Detection and Recognition Using a Unified Featur...

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5th International Conference on Advancements in Computational Sciences, ICACS 2024

作者： Iftikhar, Aroobah Shaukat, Arslan Tariq, Rimsha College of Eme Dept of Computer and Software Engineering Islamabad Pakistan Information Technology University Dept of Computer Engineering Lahore Pakistan

ISBN: (纸本)9798350394764

The research field of computer vision has recently taken an interest in the active computer vision problem of masked face recognition due to the COVID-19 pandemic. The use of face masks as a preventative measure against the spread of COVID-19 has presented a new challenge for the technology of face recognition. Masked Face Recognition (MFR) is a specific type of facial occlusion issues that obstruct vital facial features such as the mouth, nose, or chin. The purpose of research on Masked Face Detection and Recognition is to fine-tune a pre-trained model that can more accurately recognize masked faces and detect whether the person is wearing a mask or not, which can be advantageous in various applications, like security and surveillance, healthcare, retail, law enforcement, the workplace, and social media. This paper proposes an approach to enhance the performance of the single neural network architecture as a unified model capable of both detection and recognition of masked face images by achieving 99% and 98% respectively, on the MFR2 dataset. Also, a detection model is proposed that is evaluated on publicly available datasets for the detection of masked faces, which are the MDMFR dataset, the Kaggle face mask detection dataset, and the Facedatahybrid dataset. The findings of this research offer valuable insights into the potential of a pre-trained network with transfer learning to make an efficient masked face detection and recognition system and pave the way for future research in this area. © 2024 IEEE.

关键词： Face recognition

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Adaptive Forward Error Correction for Voice over IP with Random Linear Network Coding 2

Adaptive Forward Error Correction for Voice over IP with Ran...

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2nd IEEE Virtual Conference on Communications, VCC 2024

作者： Su, Yi-Chiuan Wu, Quincy National Chi Nan University Dept. of Computer Science and Information Engineering Nantou Taiwan

ISBN: (纸本)9798331530099

This paper proposes the use of Random Linear Network Coding (RLNC) as an erasure code in the forward error correction (FEC) mechanism for Internet telephony. It also designs and implements a forward error correction algorithm applied to the Real-time Transport Protocol (RTP), which we call Adaptive Systematic RLNC (AS-RLNC). The algorithm learns the current packet loss rate of the network through the Real-time Transport Control Protocol (RTCP) and dynamically adjusts the number of redundant packets sent. Compared with traditional forward error correction methods based on block codes, this approach can enhance the reliability of data transmission and improve audio quality by 31%. © 2024 IEEE.

关键词： Internet telephony

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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...

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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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Using Blockchain for Incentive-Driven FHIR-based Health Record Exchange 24

Using Blockchain for Incentive-Driven FHIR-based Health Reco...

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39th Annual ACM Symposium on Applied Computing, SAC 2024

作者： Rosar, Moritz Chuang, Kun-Ta Dept. of Computer Science and Information Engineering National Cheng Kung University Tainan Taiwan

ISBN: (纸本)9798400702433

Medical data is highly sensitive data, and patients are particularly concerned about the exposure of their identities [1]. A study found that 64% of participants were agreeable to sharing their health data when anonymized, highlighting the importance of privacy in medical data sharing [5]. © 2024 Copyright held by the owner/author(s).

关键词： Sensitive data

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Enhanced Streetlight Management: Using IoT and ESP32 for Automated Fault Detection 5

Enhanced Streetlight Management: Using IoT and ESP32 for Aut...

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5th IEEE India Council International Subsections Conference, INDISCON 2024

作者： Bhisikar, Swati Jagtap, Prabodh Sonawane, Shilpa Sawant, Hrushikesh Pisal, Prerana Rajarshi Shahu College of Engineering Dept. of Electronics and Telecommunication Pune India Vishwakarma Institute of Information Technology Dept. of Information Technology Pune India Rajarshi Shahu College of Engineering Dept. of Automation and Robotics Pune India

ISBN: (纸本)9798350376753

The core focus of this paper is to implement a cutting-edge system for intelligent streetlight management, leveraging an automotive monitoring system to identify and rectify any faults within the streetlight infrastructure. Rapid response of the proposed fault detection system will help save the time of the linemen, which is wasted in manually detecting the faults at streetlights, and will help to reduce the workload of the linemen. Also, most of the time, the detection of the problem mainly depends on the grievances of the local people. Thus, this causes delays in the maintenance of the streetlights, wastage of power, and problems for the local people. So, to reduce such problems, we have developed an automatic detection system for streetlight faults. During nighttime, this system will detect faults such as whether the lamp is ON or OFF, or if a wire cut is in the circuit. As the fault is detected, the information will be provided to the authorized person;the information will contain the type of fault that occurred in the streetlight system. © 2024 IEEE.

关键词： Fault detection

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NLP Powered Restaurant Chatbot for Exceptional Customer Engagement 2

NLP Powered Restaurant Chatbot for Exceptional Customer Enga...

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2nd International Conference on Advancement in Computation and computer Technologies, InCACCT 2024

作者： Goyal, Diya Guru Prasad, M.S. Garg, Ishika Tewari, Shashank Pandu N, K. Asha, P.N. Dept. of Computer Science and Engineering Dehradun India Srinivas University Institute of Engineering & Technology Dept. of Computer Science and Engineering Surathkal India REVA University School of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350371314

technology is very volatile in nature, in a way that it keeps on changing daily. Restaurants are no different in the effects of this changing scenario. The primary objective of this research paper is to explore the historical trajectory of restaurants, analyze the current trends shaping the industry, and delve into the potential of chatbot integration to overcome existing challenges. Chatbots can play a pivotal role in enhancing the performance of businesses as per the research done. An integrated Dialog Flow application has been proposed, incorporating Rule-based grammar matching and M.L matching algorithms. This proposed work is trying to make NLP-based chatbots which may expedite the endeavor of businesses to produce and become profitable. © 2024 IEEE.

关键词： Python

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Wine Quality Prediction Using Supervised and Unsupervised Machine Learning Techniques

Wine Quality Prediction Using Supervised and Unsupervised Ma...

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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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