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Near-Optimal Content Service Algorithm with Procurement and Placement in Edge Networks

Near-Optimal Content Service Algorithm with Procurement and ...

引用

2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Yang, Chun-An Chen, Shih-Chieh Peng, Yi-Hsuan Chen, Yu-Wen Kuo, Jian-Jhih Tsai, Ming-Jer National Tsing Hua University Dept. of Computer Science Hsinchu Taiwan National Chung Cheng University Dept. of Computer Science and Information Engineering Chiayi Taiwan New York City College of Technology Computer Systems Technology BrooklynNY United States

ISBN: (纸本)9798350351255

Telecom carriers have announced new content services by making a partnership with content providers and offered popular video streaming to customers. Via the integration with edge networks, telecom carriers can procure various contents from content providers and place them on edge servers proximate to end users to serve real-time requests with high bandwidth and ultra-low latency. Nevertheless, it is challenging to consider the content procurement, placement, and services jointly due to the user preference, user distribution, storage capacity of edge server, economic costs, etc. Telecom carriers would like to balance the procuring, placing, and transfer costs. To address this problem, the paper formulates an optimization problem and then proposes an approximation algorithm. Finally, the simulation results manifest that our algorithm outperforms other baselines. © 2024 IEEE.

关键词： Video streaming

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Acoustic Device for Detecting Red Palm Weevil Using Deep Learning and IoT

Acoustic Device for Detecting Red Palm Weevil Using Deep Lea...

引用

2023 International Conference on Microelectronics, ICM 2023

作者： Atia, Maryam M. Gouda, Amr I. Ismail, Mahmoud G. Salem, Mohammed A.-M Elghany, Mohamed A. Abd German University in Cairo Computer Engineering Dept. Egypt German University in Cairo Electronics Engineering Dept. Egypt Tu Darmstadt Integrated Electronic Systems Lab Germany

ISBN: (纸本)9798350380828

The Red Palm Weevil (RPW) is a major threat to the date palm industry, requiring early detection strategies. This paper presents a comprehensive exploration of two bioacoustic sensor prototypes tailored for the early detection of RPW. The first prototype employs a Raspberry Pi 4 coupled with a Convolutional Neural Network (CNN), achieving a detection accuracy of 99.02%. The second device, built with an ESP32-WROVER module, integrates a Simple Neural Network (SNN) for audio classification and attains an accuracy of 98.79%. This design's affordability and low power consumption make it particularly suited for large-scale deployment in agricultural settings. This paper delves deeper into the bioacoustic sensor technology used, the dataset chosen for CNN and SNN models training, and provides a comparison between the two device designs in terms of cost and efficiency. Our project's commitment to iterative improvement aims to provide an effective solution to counteract RPW's destructive effects on date palm cultivation. © 2023 IEEE.

关键词： Deep learning

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Automated Optical Inspection for Quality Control in PCBA assembly lines: a case study for Point of Sale Devices Production Lines 6

Automated Optical Inspection for Quality Control in PCBA ass...

引用

6th International Congress on Human-computer Interaction, Optimization and Robotic Applications, HORA 2024

作者： Aras, Kerem Saif, Syed Saad Giuseppi, Alessandro Coskun, Vedat R&D Center Token Financial Technologies Istanbul Turkey Dept. of Computer Control and Management of Rome La Sapienza Rome Italy Dept. of Software Engineering Istanbul Atlas University Istanbul Turkey

ISBN: (纸本)9798350394634

This paper reports on the design and development of a customized Automated Optical Inspection (AOI) solution aimed at detecting defects in a production line related to the correct mounting of integrated circuits. Contrary to most solutions avail- able on the market, the developed system relies on deep learning to be able to perform detailed real-time visual inspections of components without the need to compare the captured photos with any reference images/golden sample. The proposed solution was designed to also provide good generalization capabilities, accommodating visual changes in the environment and in the structure of the component being produced. A custom testing machine was built in order to perform real-time inferences and validate the simulation results in a real-world setting. © 2024 IEEE.

关键词： Assembly

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A Survey of Health Management System for On-The-Fly Repairing of Concurrency Errors in Airborne software 2

A Survey of Health Management System for On-The-Fly Repairin...

引用

2nd International Workshop on Intelligent software engineering, ISE 2023

作者： Lee, Junho Go, Seongyun Choi, Eu-Teum Lee, Seongjin Dept. of Aerospace Software Engineering Gyeongsang National University Jinju Korea Republic of Dept. of Aerospace and Software Engineering Gyeongsang National University Jinju Korea Republic of Convergence Research Center for Materials and Mechanical Systems Jinju Korea Republic of Dept. of AI Convergence Engineering Gyeongsang National University Jinju Korea Republic of

Concurrency errors are known for their difficulty of debugging and reproducing prior to execution. Undetected concurrency errors could result in nondeterministic executions that deviate from the programmer’s intent and significantly undermining the reliability of the program. To prevent functional failure in airborne software, on-the-fly repairing of concurrency errors is crucial. This paper surveys the Health Management System for on-the fly repairing of concurrency errors in airborne software and motivates the future work in concurrent airborne software. © 2023 Copyright for this paper by its authors.

关键词： Errors

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A Semisupervised Approach to Predicting a Twitch Streamer's Growth based on the Game Streamed

A Semisupervised Approach to Predicting a Twitch Streamer's ...

引用

2022 IEEE International Conference on Big Data, Big Data 2022

作者： Dybka, Andrew Kocjan, Dominic Ajila, Samuel A. Carleton University Dept. of Systems and Computer Engineering Ottawa Canada

ISBN: (纸本)9781665480451

Games live streaming is growing rapidly as a form of entertainment. A game streamer will like to know what game to stream in order to attract huge number of viewers and followers which in turn will generate sizable income for the streamer. Using streamer's metrics, the main goal of this research work is to design and develop a set of resources that a streamer can use to maximize the number of viewers and followers for a particular game and when to play the game. This research develops two models using machine learning techniques that can be used by game streamers to maximum the returns on investment. When both model predictions are presented as percentage, Model 1 using regression algorithms provides a MAE of 5.48 meaning the prediction has an error within 5.48% of the streamer's total follower count. Also, Model 1 has 85.46% of its predictions' absolute error less than or equal to 5. Similarly Model 2 with 2.53 MAE and 87.68% of its predictions' absolute error less than or equal to 5. © 2022 IEEE.

关键词： Forecasting

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Analysis of Energy Potential of Micro Wind Turbines at Three Locations in the Central-Eastern Part of the Bulgarian Danube River Region 5

Analysis of Energy Potential of Micro Wind Turbines at Three...

引用

5th IEEE International Conference on Communications, Information, Electronic and Energy systems, CIEES 2024

作者： Valov, Nikolay Gabrovska-Evstatieva, Katerina Stoyanov, Ivaylo Kaneva, Tsvetelina Kadirova, Seher Evstatiev, Boris Mihailov, Nicolay University of Ruse "Angel Kanchev" Dept. of Automation and Electronics Ruse Bulgaria University of Ruse "Angel Kanchev" Dept. of Computer Science Ruse Bulgaria University of Ruse "Angel Kanchev" Dept. of Electrical Power Engineering Ruse Bulgaria University of Ruse "Angel Kanchev" Dept. of Computer Systems and Technologies Ruse Bulgaria

ISBN: (纸本)9798350352863

The share of renewable energy sources in the energy mix worldwide is gradually increasing. And even though they all have a highly variable nature, they can be forecasted statistically. The wind energy potential was investigated in this article in the Bulgarian Danube region, located between Ruse and Silistra. Two years of meteorological data, including the wind speed at 10 m height and the ambient temperature was used to estimate the average wind energy for each month of the year at three geographic locations - Ruse, Staro Selo (near Tutrakan), and Calarasi, Romania (near Silistra). The results showed that Staro Selo has the highest energy potential, reaching up to 0.75 kWh/day/m2 in February, followed by Calarasi with 0.38 kWh/day/m2. The city of Ruse showed the lowest wind potential with the highest average monthly value reaching only 0.0027 kWh/day/m2. The obtained results showed that the regions around Tutrakan and Silistra are appropriate for the application of micro wind turbines. © 2024 IEEE.

关键词： Windmill

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An Architectural Approach for Enhanced Data Interoperability Across Building systems 7

An Architectural Approach for Enhanced Data Interoperability...

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7th Conference on Cloud and Internet of Things, CIoT 2024

作者： Yefi, Peter Ejaz, Sikandar Menon, Ramanunni Parakkal Eicker, Ursula Guéhéneuc, Yann-Gaël Concordia University Computer Science and Software Engineering Dept. Montreal Canada Concordia University Institute for Information Systems Engineering Montreal Canada Institute for Solar Research German Aerospace Center Jülich Germany Concorida University NextGen Cities Institute Montreal Canada

ISBN: (纸本)9798350356717

Access to building data is crucial for creating portable applications and improving building operation and energy efficiency. Ensuring data transmutability is essential for facilitating research and overcoming the diverse data representation, management, and collection methods across various building management systems (BMSs). Different BMSs use multiple interfaces for data exchange. Some BMSs provide Application Programming Interfaces (APIs) for data exchanges, while others have gateways connecting to cloud services for application subscription and data access. This study introduces a five-layered architecture that describes how client applications, researchers, and other interested stakeholders can exchange data with different BMSs. Our objective is to help researchers and practitioners understand the various ways of accessing transmutable (capable of being transformed into a compatible format) data from BMSs, regardless of the building management system a building uses. Through transmutable access to building systems' data, we aim to enable transferable energy-efficient-related applications for buildings. We evaluate our work with a building model of the Varennes Library in Varennes, QC, Canada. The building model includes information about the building, floors, rooms, electric meter and historical time series data of the meter, weather station and its historical time series data. The model also has carbon dioxide concentration sensors, temperature sensors, humidity sensors, and historical time series data of the sensors. We include two test client programs to show how they access the transmutable data of the building through a model. We conclude that the five-layered architecture facilitates the exchange of transmutable data across diverse BMSs, making it a valuable tool for researchers and practitioners. © 2024 IEEE.

关键词： Middleware

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Automatic Network Intrusion Detection System Using Machine Learning and Deep Learning

Automatic Network Intrusion Detection System Using Machine L...

引用

2024 International Conference on Artificial Intelligence and Mechatronics System, AIMS 2024

作者： Mynuddin, Mohammed Khan, Sultan Uddin Chowdhury, Zayed Uddin Islam, Foredul Islam, Md Jahidul Hossain, Mohammad Iqbal Ahad, Dewan Mohammed Abdul North Carolina A and T State University Dept. of Electrical Engineering United States Data Scientist Data Bid Machine Inc. Associate Power Systems Engineer-II Open Systems International Inc. Tuskegee University Dept. of Electrical Engineering United States University of North Carolina at Charlotte Dept. of Electrical and Computer Engineering United States

ISBN: (纸本)9798350350524

In recent years, the popularity of network intrusion detection systems (NIDS) has surged, driven by the widespread adoption of cloud technologies. Given the escalating network traffic and the continuous evolution of cyber threats, the need for a highly efficient NIDS has become paramount for ensuring robust network security. Typically, intrusion detection systems utilize either a pattern-matching system or leverage machine learning for anomaly detection. While pattern-matching approaches tend to suffer from a high false positive rate (FPR), machine learning-based systems, such as SVM and KNN, predict potential attacks by recognizing distinct features. However, these models often operate on a limited set of features, resulting in lower accuracy and higher FPR. In our research, we introduced a deep learning model that harnesses the strengths of a Convolutional Neural Network (CNN) combined with a Bi-directional LSTM (Bi-LSTM) to learn spatial and temporal data features. The model, evaluated using the NSL-KDD dataset, exhibited a high detection rate with a minimal false positive rate. To enhance accuracy, K-fold cross-validation was employed in training the model. This paper showcases the effectiveness of the CNN with Bi-LSTM algorithm in achieving superior performance across metrics like accuracy, F1-score, precision, and recall. The binary classification model trained on the NSL-KDD dataset demonstrates outstanding performance, achieving a high accuracy of 99.5% after 10-fold cross-validation, with an average accuracy of 99.3%. The model exhibits remarkable detection rates (0.994) and a low false positive rate (0.13). In the multiclass setting, the model maintains exceptional precision (99.25%), reaching a peak accuracy of 99.59% for $\text{k-value} =10$. Notably, the Detection Rate for $\text{k-value}=10$ is 99.43%, and the mean False Positive Rate is calculated as 0.214925. © 2024 IEEE.

关键词： Classification (of information)

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Intrusion Detection System Based on Machine Learning Techniques: A Survey 2

Intrusion Detection System Based on Machine Learning Techniq...

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2nd International Conference on Advances in engineering Science and Technology, AEST 2022

作者： Sheet, Omar I. Ibrahim, Laheeb M. University of Mosul College of Computer Sciences & Math Software Engineering Dept. Iraq

ISBN: (纸本)9798350334906

The volume of network traffic data has become so big and complicated as a result of the development in Internet-based services that it is extremely difficult to process using typical data processing techniques. Due to the enormous and complicated nature of network traffic data, fast and effective cybersecurity intrusion detection is a highly difficult task. In order to detect hostile traffic as soon as feasible, a realistic cyber security intrusion detection system should be able to handle a huge volume of network traffic data as quickly as *** paper studies a classification-based intrusion detection algorithms based on Machine Learning for speedy and effective intrusion detection in huge network traffic, including Support Vector Machines (SVM), Random Forests(RF), Decision Trees(DT), Naïve Bayes (NB), Deep Neural Network (DNN), Extreme Gradient Boosting (XG Boost), Nondominated Sorting Genetic Algorithm (NSGA2), as well as Deep Belief Network (DBN) and artificial neural network(AI). It has been applied to ACTUAL TIME The KDD dataset, KDD Cup 99 dataset, NSL-KDD dataset, CICIDS-2017 dataset, CICIDS-2018 dataset, ISCX Dataset, CICAndMal2017 dataset and UNSW-NB15 to compare performance based on rating Intrusion detection systems which are assessed in terms of training time, prediction time, accuracy. © 2022 IEEE.

关键词： Intrusion detection

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Improvement of Protection Factor for a Controlled Laboratory computer Network Using Artificial Intelligence

Improvement of Protection Factor for a Controlled Laboratory...

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2023 International Conference on engineering, Science and Advanced Technology, ICESAT 2023

作者： Daoud, Raid W. Aziz, Enas F. Abdullah, Zaki M. Saleh, Ziyaad H. Northern Technical University Electrical Tech. Dept. Kirkuk Iraq Northern Technical University Computer Systems Dept. Kirkuk Iraq College of Petroleum Processes Engineering Petroleum Systems and Control Dept. Tikrit Iraq

ISBN: (纸本)9798350344400

computer networks provide exceptional privacy, primarily for testing and other educational activities. However, the security of computer networks is an exceptionally touchy subject, mainly when applied in labs and tests for educational purposes. Any software system created on a server typically requires a username and password to access a collection of computers on a particular network. The proposed work devised and implemented a neural network feedback network with four inputs (I.P. address, MAC address, user number, and time) and an output that represents the user's acceptance or rejection. A few instances of the data and numbers needed to train the neural network include the lab's address, the student's name, the academic stage's address, etc. Good results were obtained through the company's training and education, which was tested on more than 200 models, and the response aligned with the data. No user can access the network without first being recognized by the proposed network by comparing the pre-trained data to ensure they are authorized to take the exam or enter for any other reason. © 2023 IEEE.

关键词： Network security

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