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RadioModRec-l: A Large Scale Radio Frequency Dataset for Automatic Modulation Recognition Research 4

RadioModRec-l: A Large Scale Radio Frequency Dataset for Aut...

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4th International Conference on Emerging Trends in Networks and computer Communications, ETNCC 2024

作者： Adetiba, Emmanuel Olasina, Jamiu R. Obiyemi, Obiseye Covenant University Covenant Applied Informatics and Communication Africa Center of Excellence Dept. of Electrical and Information Engineering Ota Nigeria HRA Institute for Systems Science Durban University of Technology Durban4001 South Africa Federal Polytechnic Ilaro Dept. of Computer Engineering Ilaro Nigeria Durban University of Technology Space Science Centre Dept. of Electrical Power Engineering Durban South Africa

ISBN: (纸本)9798350353266

Access to well-curated large datasets remains a significant bottleneck in AI-based research within wireless communication. Rapid advancements in neighbouring fields, such as computer vision and natural language processing, are largely due to the availability of extensive open-access datasets. However, similar progress has not been observed in wireless communication. To address this gap, we curated a comprehensive dataset for fifteen digital modulation schemes, including 4QAM, 16QAM, 64QAM, 256QAM, 8PSK, 16PSK, 32PSK, 64PSK, 128PSK, 256PSK, CPFSK, DBPSK, DQPSK, GFSK, and GMSK. Our dataset considers Rayleigh and Rician channel models under Additive White Gaussian Noise (A WGN) with SNRs ranging from -20dB to + 20dB in 5dB increments. The data samples were converted to constellation signal images and carefully pre-processed. Named RadioModRec, this dataset provides a valuable resource for researchers to train and evaluate AI models. It is freely accessible on Kaggle, promoting further innovation in the wireless communication domain. © 2024 IEEE.

关键词： Signal to noise ratio

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Cybersecurity Certification for Professional Training: An Overview

Cybersecurity Certification for Professional Training: An Ov...

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IEEE Education engineering (EDUCON)

作者： Dimitrios Kallergis Theodoros Karvounidis Kitty Kioskli Christos Douligeris Dept. of Informatics & Computer Engineering University of West Attica Attica Greece Dept. of Informatics University of Piraeus Piraeus Greece trustilio BV Amsterdam The Netherlands

ISBN: (数字)9798331539498

ISBN: (纸本)9798331539504

The cybersecurity industry faces a significant skills gap, with a shortage of qualified professionals to meet the growing demand for cyber talent. Although there is a wide variety of cybersecurity professional trainings available, these trainings use different approaches and the certificates obtained are not compatible with each other. Contrary to the United States, there is no European Authority responsible for approving these trainings. The situation becomes even more complex when considering sector-specific trainings. This paper sheds light on the certification landscape regarding relevant, organisations and bodies at the international and European level. The standards, the criteria, and the processes regarding professional certifications are discussed. The manuscript concludes with a proposal of a sector-agnostic schema for a cybersecurity professional training programme.

关键词： Industries Europe Proposals computer security engineering education Certification Standards Faces

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Mining Road Traffic Accident Data for Prediction of Accident Severity

Mining Road Traffic Accident Data for Prediction of Accident...

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2023 International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2023

作者： Bahiru, Tadesse Kebede Manjula, V.S. Akele, Tadesse Birara Tesfaw, Engdaw Ayalew Belay, Tadesse Destaw Kombolcha Institute of Technology Wollo University College of Informatics Dept. of Information Technology Amhara Kombolcha Ethiopia Kombolcha Institute of Technology Wollo University College of Informatics Pg Dept of Computer Science Amhara Kombolcha Ethiopia Kombolcha Institute of Technology Wollo University College of Informatics Dept. of Information System Amhara Kombolcha Ethiopia Wachamo University School of Computing and Informatics College of Engineering and Technology Dept. of Information Technology SSNPR Hosaena Ethiopia

ISBN: (纸本)9781665474511

As World Health Organization reports show more than 1.3 million people die each year from traffic accidents and more than twenty to fifty million people are harmed by non-fatal accidents. Understanding and identifying the major factors of road traffic accidents can help the stakeholders to take appropriate measures to minimize the accidents. Support Vector Machine (SVM), J48, and Naïve Bayes (NB) classification techniques are implemented to build a model that predicts accident severities and to identify road traffic accident factors. As experimental findings showed weather conditions, number of lanes, road lighting conditions, and speed limit are the determinant factors that cause more accident severity. Finally, the classification and prediction performance of the models are compared using various performance evaluation techniques. J48 classifier has better classification performance than Support Vector Machine and Naïve Bayes classifiers. Naïve Bayes is the classifier that showed the lowest performance than others, but it has good performance in classifying fatal accident severities rather than serious and slight accident severities. © 2023 IEEE.

关键词： Support vector machines

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WebRTC-based Resource Offloading in Smart Home Environments

WebRTC-based Resource Offloading in Smart Home Environments

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2022 IEEE International Conference on Consumer Electronics, ICCE 2022

作者： Jeong, Hunseop Lee, Taehyung Eom, Young Ik Sungkyunkwan University Dept. of Digital Media and Communications Engineering Suwon Korea Republic of Sungkyunkwan University Dept. of Computer Science and Engineering Suwon Korea Republic of College of Computing and Informatics Sungkyunkwan University Dept. of Electrical and Computer Engineering Suwon Korea Republic of

ISBN: (纸本)9781665441544

Web platforms face new demands for emerging applications, which use machine learning models such as pose recognition or object detection. These models require significant computing powers in processing enormous inputs such as images or audios for machine learning computation. These demands are also being generated in smart home appliances based on web platforms. Unfortunately, smart home appliances do not generally have built-in input devices, such as cameras or microphones, due to privacy issues and have limited performance compared to mobile devices. This paper proposes a WebRTC-based resource offloading system for web applications, which allows smart home appliances to use resources of nearby mobile devices as if the resources are their own. We implemented the proposed system, performed experiments on the resource offloading framework, and evaluated the performance using five computation-intensive web applications, which use a machine learning model. Our system was able to run machine learning models, through resource offloading to mobile devices, on smart home appliances without an attached camera, and achieved up to 1.5x speedup, compared to local execution with a camera. © 2022 IEEE.

关键词： Domestic appliances

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An Intelligent Controller for Demand-Side Management in Residential Electrical Networks

An Intelligent Controller for Demand-Side Management in Resi...

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Panhellenic Conference on Electronics and Telecommunications (PACET)

作者： Gregory Zigirkas John Kalomiros Dept. of Computer Informatics and Telecommunications Engineering International Hellenic University Serres Greece

A novel embedded controller is proposed for the management of electrical consumption in residential installations. The proposed system is designed to implement well-established DSM techniques. It can be placed directly into the electrical board of a residential circuit and can be connected to the smart grid. The controller is based on a PIC microcontroller and incorporates a series of perceptrons which adjust the duty cycle of PWM modules according to user defined preferences. The system is tested in laboratory conditions and is proved to be able to adjust the total current to a predefined threshold, without violating user comfort and priorities. The proposed embedded controller is a flexible solution in the form of a final product, ready for the smart grid and able to support microgrids.

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Cutlery Detection System With Camera and Yolov5

Cutlery Detection System With Camera and Yolov5

引用

International Electronics Symposium (IES)

作者： Gita Agraini Manullang Setiawardhana Bima Sena Bayu Dewantara Dept. of Informatics and Computer Engineering Politeknik Elektronika Negeri Surabaya Surabaya Indonesia

ISBN: (数字)9798350391992

ISBN: (纸本)9798350392005

The culinary business industry, such as restaurants, heavily relies on consumer experience. One aspect that can affect consumer experience is the service provided by the restaurant in supplying complete dining equipment. Misunderstandings may occur due to the lack of complete dining and drinking utensils in restaurants, leading to complaints from customers or guests. However, manually checking the completeness of dining equipment can be time-consuming and can easily lead to human error. Therefore, it is necessary to develop a system that capable to detect the completeness of dining equipment on restaurant tables. To address this, this research uses cameras and machine learning to build a system that can automatically recognize dining equipment on the dining table. The machine learning model used in this research is YOLOv5. In YOLOv5, two processes occur such as training and testing. The training process is carried out on an annotated dataset to produce a trained model. Then this model is tested in the testing process to evaluate its performance using a separate dataset different from the training dataset. The model run on Google Colab showed an accuracy rate of 90% during training and 93.75% during testing.

关键词： Training YOLO Industries Accuracy Machine learning Cameras Internet

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Evaluation of Pseudorandom Pixel Placement in Enhancing Face Recognition using VGG-Face 5

Evaluation of Pseudorandom Pixel Placement in Enhancing Face...

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5th International Conference on Image, Video Processing, and Artificial Intelligence, IVPAI 2023

作者： Hadis, Muhammad Sabirin Akita, Junichi Toda, Masashi Dept. of Electrical Engineering and Computer Science Kanazawa University Kakuma-machi Ishikawa Kanazawa920-1192 Japan Research and Education Institute for Semiconductors and Informatics Kumamoto University Kumamoto860-8555 Japan

ISBN: (纸本)9781510674660

In efforts to enhance face recognition performance, techniques ranging from super-resolution methods to the use of Local Binary Pattern (LBP) and deep learning have been explored. Among these, the pseudorandom pixel placement (PSE) technique has demonstrated potential in face recognition. Nevertheless, its testing was previously limited to just 8 subjects. This study undertakes a comprehensive evaluation of the PSE technique with a larger sample, utilizing 2000 subjects from the DigiFACE1M dataset and leveraging the state-of-the-art VGG-Face deep learning model. Through experiments involving 10 different PSE patterns on 144.000 face images, our findings indicate that, compared to Regular Pixel Placement (REG), PSE achieved an improvement in average accuracy by 1.05%, reduced the standard deviation by 1.47%, and resulted in 31 additional subjects achieving 100% accuracy. We conclude that PSE consistently outperforms REG in face recognition tasks using the VGG-Face model across the majority of tested scenarios. © 2024 SPIE. All rights reserved.

关键词： Pixels

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FLoW-Vision: Object Recognition and Pose Estimation System based on Three-Dimensional (3D) computer Vision 23

FLoW-Vision: Object Recognition and Pose Estimation System b...

引用

23rd International Electronics Symposium, IES 2021

作者： Putra, Vardyansyah Cahya Pratama Harsetya Apriandy, Kevin Ilham Pramadihanto, Dadet Barakbah, Ali Ridho Dept. of Informatics and Computer Engineering Electronic Engineering Indonesia

ISBN: (纸本)9781665443463

This paper presents a three-dimensional computer vision-based object recognition on FLoW-Vision in RoISC (formerly ER2C) has entered its second phase. Previously, the robot had a basic vision that was used to replicate 'human-like' visual skills using 2D computer vision. As a result of the above discussion, we proposed the design and implementation of an object recognition and pose estimation system based on three-dimensional computer vision to handle object recognition and pose estimation tasks in real-world environments simultaneously. In the object recognition process, a point-cloud segmentation method is used to obtain possible object clusters before starting the calculation of feature description. Then, a keypoints-based two-stage matching process is performed to speed up the computation of finding correspondences between the object clusters of the current scene and a colored point cloud model of an object. Next, a Hough voting algorithm is employed to filter out matching errors in the correspondence set and estimate the initial 3D pose of the object. Last process process the pose estimation from clustered object using RANSAC to search the largest surface as Z surface. Experimental validate the object recognition can work correctly with percentage 100% and pose estimation accuracy of the proposed system can work correctly with percentage 60% in a complex real-world scene. © 2021 IEEE.

关键词： Object detection

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Sentiment Analysis of Thesis Policy by Instagram Users Using Logistic Regression

Sentiment Analysis of Thesis Policy by Instagram Users Using...

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International Conference on Electrical engineering, computer Science and informatics (EECSI)

作者： Fathia Dwi Astuti Widodo Bambang Prasetya Adhi Dept. of Informatics and Computer Engineering Education Universitas Negeri Jakarta Jakarta Indonesia

ISBN: (数字)9798350355314

ISBN: (纸本)9798350355321

The requirements for university graduation were changed by the Minister of Education, Culture, Research and Technology in August 2023, where a thesis is no longer required and can be replaced with another form. The existence of this regulation has caused discussion in the community with various responses visible through social media Instagram. Instagram has become a forum for sharing the latest topics for the public, causing the emergence of online media accounts such as Folkative. This research aims to analyze sentiment regarding the policy of eliminating thesis obligations at universities through data collected from comments on Folkative's Instagram account posts. Sentiment analysis was carried out using Logistic Regression for classification, K-Fold Cross Validation for data validation, Confusion Matrix for evaluation, and ADASYN to resolve data imbalances. The data collected from the scraping process was 4,172 comments. After carrying out the classification and performance evaluation process, the best method produced was a combination of Logistic Regression and ADASYN achieving results of 89.6% accuracy, 91.5% precision, 87.7% recall, and f-1 score 89.5%. This shows that the performance of the Logistic Regression method is good and suitable for use as a classification model in this research.

关键词： Support vector machines Performance evaluation Logistic regression Sentiment analysis Accuracy Social networking (online) Media Regulation Web sites Multimedia communication

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Anomaly Detection With Quantified Explanations for Multivariate Time Series Networking Data

Anomaly Detection With Quantified Explanations for Multivari...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Marantis, Panagiotis Ramantas, Kostas Verikoukis, Christos Iquadrat Informatica S.L Barcelona Spain University of Patras Dept. of Computer Engineering and Informatics Patras Greece Athena Research Center Athens Greece Industrial Systems Institute Patras Greece

ISBN: (纸本)9798350351255

Next-generation telecommunication networks provide vast amounts of Multivariate Time Series monitoring data during their operation. Using these data to automatically detect anomalous system behavior is of critical importance to operators and Internet Service Providers, in order to analyze the network state and take mitigating actions to resolve possible faults. For the output of this Anomaly Detection process to be trusted, comprehensive and accurate explanations must be provided. These explanations are essential to make critical decisions that affect the network state. In this paper, we present a framework that utilizes reconstruction-based Deep Neural Network models to perform the anomaly detection task, while also using an explainability module that provides explanations of the results. The quality of the explanations is quantified through the use of interpretation labels. In addition, we provide a comparative analysis, performing Anomaly Detection on three different open-source datasets from the mobile networking domain, while testing multiple Deep Learning models and comparing their performance. The presented results show that our framework accurately detects various types of anomalies that can be present in mobile networks, while offering the flexibility to identify and select the model that offers the best performance for each dataset, in both the Anomaly Detection and explainability tasks. © 2024 IEEE.

关键词： DNNs Explainable Anomaly Detection Machine Learning Multivariate Time Series Next-Generation Networks Reconstruction-based Models

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