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arXiv 2023年

作者： Akter, Mst Shapna Rodriguez-Cardenas, Juanjose Rahman, Md Mostafizur Shahriar, Hossain Rahman, Akond Wu, Fan Dept. of Intelligence and Robotics Systems University of West Florida United States Dept. of Information Technology Kennesaw State University United States Dept. of Information Technology University of West Florida United States Center of Cyber Security University of West Florida United States Dept. of Computer Science and Software Engineering Auburn University Auburn United States Dept of Computer Science Tuskegee University Tuskegee United States

The field of DevOps security education necessitates innovative approaches to effectively address the ever-evolving challenges of cybersecurity. In adopting a student-centered approach, there is the need for the design and development of a comprehensive set of hands-on learning modules. In this paper, we introduce hands-on learning modules that enable learners to be familiar with identifying known security weaknesses, based on taint tracking to accurately pinpoint vulnerable code. To cultivate an engaging and motivating learning environment, our hands-on approach includes a pre-lab, hands-on and post lab sections. They all provide introduction to specific DevOps topics and software security problems at hand, followed by practicing with real world code examples having security issues to detect them using tools. The initial evaluation results from a number of courses across multiple schools show that the hands-on modules are enhancing the interests among students on software security and cybersecurity, while preparing them to address DevOps security vulnerabilities. © 2023, CC BY.

关键词： computer aided instruction

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Enhancing Facial Recognition in Visual Prostheses using Region of Interest Magnification and Caricaturing

Enhancing Facial Recognition in Visual Prostheses using Regi...

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International Conference on computer and Applications (ICCA)

作者： Hesham M. Moneer Reham H. Elnabawy Seif Eldawlatly Digital Media Eng. and Technology Dept. German University in Cairo Cairo Egypt Computer and Systems Eng. Dept. Ain Shams University Cairo Egypt Computer Science and Eng. Depr. The American University in Cairo Cairo Egypt

Blindness is a prevalent disability with significant personal and societal consequences. While medical advancements offer treatment options, severe damage to the retina, optic nerve, or brain may remain untreated. Visual prostheses are implantable medical devices that aim at providing limited vision to such individuals. However, such prostheses offer low spatial resolution making activities like reading, facial recognition, and navigation challenging. This work aims to enhance implantees’ ability to recognize faces through real-time scene preprocessing, machine learning and computer vision techniques. Virtual-reality visual models simulating prosthetic vision were tested on normally/corrected sighted subjects, investigating the use of histogram equalization for contrast enhancement, facial region magnification, and caricaturing of facial features. Results revealed that histogram equalization with magnification increases facial recognition accuracy by 60%, distinguishability accuracy by 50%, and accuracy of seeing facial details by 90%. In contrast, adding facial caricaturing improved the accuracies by 66.66%, 25%, and 75% for recognizability, distinguishability, and seeing facial details, respectively. Consequently, the combination of visual field histogram equalization, face magnification, and optional caricaturing can be considered as a promising enhancement approach that could enhance the quality of vision perceived through visual prostheses.

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POCS-based Image Compression: An Empirical Examination

POCS-based Image Compression: An Empirical Examination

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International Conference on Green technology and Sustainable Development (GTSD)

作者： Truong-Dong Do Le-Anh Tran Thanh-Dat Nguyen Nghe-Nhan Truong Dong-Chul Park My-Ha Le Dept. of Aerospace Systems Engineering Sejong University Seoul South Korea Dept. of Electronics Engineering Myongji University Yongin South Korea Dept. of Research and Development OCST Co. Ltd. Seoul South Korea Dept. of Computer Science & Engineering Sejong University Seoul South Korea Dept. of Electrical & Electronics Engineering HCMC University of Technology & Education Ho Chi Minh City Vietnam

ISBN: (数字)9798350386073

ISBN: (纸本)9798350386080

This paper investigates the applicability of the Pro-jection onto Convex Set (POCS)-based clustering algorithm to image compression tasks. The POCS-based clustering approach treats all data points in a given dataset as non-intersecting convex sets and performs POCS-based parallel projections from each cluster prototype onto corresponding member data points to minimize an objective function and update cluster prototypes. The POCS-based clustering algorithm has been proven to be able to yield promising results against other prevailing clustering approaches in terms of convergence time and clustering error on general clustering tasks. In this study, a comparison of various clustering schemes for image compression applications has been conducted. The evaluations and analyses on various standard test images verify that the POCS-based clustering algorithm can perform competitively against other conventional clustering methods in image compression problems.

关键词： Image coding Clustering methods Clustering algorithms Prototypes Linear programming Sustainable development Standards

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An Efficient GPU-based Implementation for Noise Robust Sound Source Localization

arXiv

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arXiv 2025年

作者： Lin, Zirui Takigahira, Masayuki Terakado, Naoya Gulzar, Haris Busto, Monikka Roslianna Eda, Takeharu Itoyama, Katsutoshi Nakadai, Kazuhiro Amano, Hideharu Dept. of Systems and Control Engineering School of Engineering Tokyo Institute of Technology Tokyo Japan Honda Research Institute Japan Co. Ltd. Saitama Japan NTT Software Innovation Center Tokyo Japan Dept. of Information and Computer Science Keio University Kanagawa Japan

Robot audition, encompassing Sound Source Localization (SSL), Sound Source Separation (SSS), and Automatic Speech Recognition (ASR), enables robots and smart devices to acquire auditory capabilities similar to human hearing. Despite their wide applicability, processing multi-channel audio signals from microphone arrays in SSL involves computationally intensive matrix operations, which can hinder efficient deployment on Central Processing Units (CPUs), particularly in embedded systems with limited CPU resources. This paper introduces a GPU-based implementation of SSL for robot audition, utilizing the Generalized Singular Value Decomposition-based Multiple Signal Classification (GSVD-MUSIC), a noise-robust algorithm, within the HARK platform, an open-source software suite. For a 60-channel microphone array, the proposed implementation achieves significant performance improvements. On the Jetson AGX Orin, an embedded device powered by an NVIDIA GPU and ARM®Cortex™-A78AE v8.2 64-bit CPUs, we observe speedups of 4645.1× for GSVD calculations and 8.8× for the SSL module, while speedups of 2223.4× for GSVD calculation and 8.95× for the entire SSL module on a server configured with an NVIDIA A100 GPU and AMD EPYC™ 7352 CPUs, making real-time processing feasible for large-scale microphone arrays and providing ample capacity for real-time processing of potential subsequent machine learning or deep learning tasks. © 2025, CC BY-NC-ND.

关键词： Acoustic generators

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Unsupervised Graph-Generative Network-based PV Condition Monitoring systems

Unsupervised Graph-Generative Network-based PV Condition Mon...

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IEEE General Meeting Power& Energy Society

作者： Sarah Allahmoradi Shahabodin Afrasiabi Xiaodong Liang Mousa Afrasiabi Jamshid Aghaei Chi Yung Chung Dept. Electrical and Computer Engineering University of Saskatchewan Saskatoon Canada Department of Electrical Engineering School of Energy Systems Lappeenranta University of Technology Lappeenranta Finland Department of Electrical and Computer Engineering The Hong Kong Polytechnique University Hong Kong China

ISBN: (数字)9798350381832

ISBN: (纸本)9798350381849

Condition monitoring plays a pivotal role in the photovoltaic (PV) system’s operation. To overcome issues in conventional PV condition monitoring, in this paper, an unsupervised graph-generative network-based PV condition monitoring system is proposed by considering Gaussian/non-Gaussian noises. The designed network has the ability to handle an imbalanced dataset and high dimensional signals, establish the correlation between time-varying signals in PV systems, and learn spatial-temporal features. The proposed system is validated through an experimental dataset by considering seven abnormal conditions, including PV array faults, PV control system faults, and grid faults. The proposed method appears to be effective under Gaussian/non-Gaussian noises with an accuracy higher than 98%. The superiority of the proposed graph-generative network is demonstrated by comparing with well-known deep- and shallow-based PV condition monitoring systems with 5%- 30% improvements in terms of accuracy and reliability.

关键词： Condition monitoring Photovoltaic systems Accuracy Correlation Noise Probabilistic logic Control systems Robustness Time-varying systems Monitoring

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

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

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International Conference on Emerging Trends in Networks and computer Communications (ETNCC)

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

ISBN: (数字)9798350353266

ISBN: (纸本)9798350353273

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.

关键词： Wireless communication Technological innovation Transfer learning Stochastic processes Rician channels Market research Natural language processing Optimization Residual neural networks Radio spectrum management

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COVID-19 Infection Detection in X-Ray Images Using Enhanced Image Segmentation and Machine Learning Models 25

COVID-19 Infection Detection in X-Ray Images Using Enhanced ...

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25th International Arab Conference on Information technology, ACIT 2024

作者： Nahar, Khalid M.O. Ebrahim Megdad, Mohammad Al Mamlook, Rabia Emhamed Aljaidi, Mohammad Nahar, Besan K.M. Nahar, Mohammad K. M. Anbarkhan, Samar Hussni Khafajeh, Hayel Amer, Waleed Arab Open University Faculty of Computer Studies P.O. Box 84901 Riyadh11681 Saudi Arabia Yarmouk University Computer Science Department Irbid21163 Jordan University of Zawia Dept. of Mechanical and Industrial Engineering Zawia Libya Zarqa University Faculty of Information Technology Department of Computer Science Zarqa13110 Jordan University of Business and Technology College of Engineering Jeddah21448 Saudi Arabia Yarmouk University Faculty of Medicine Irbid Jordan Jordan University of Science and Technology Faculty of Applied Medical Science Irbid21163 Jordan Northern Border University Information Systems Department Rafha76322 Saudi Arabia Zarqa University Faculty of Information Technology Department of Artificial Intelligence Zarqa13110 Jordan Zarqa University Faculty of Information Technology Department of Cybersecurity Zarqa13110 Jordan

ISBN: (纸本)9798331540012

The COVID-19 pandemic has shown a significant challenge to healthcare associations worldwide demanding the progress of reliable diagnostic techniques. Chest X-ray imaging is an easily available and cost-effective tool for evaluating lung defects combined with COVID-19. This study aims to introduce a novel COVID-19 infection detection and recognition method in X-ray images through image segmentation. By employing machine learning (ML) methods, particularly CNN-based U-Net architectures, the lung sections are accurately segmented, and infection sections are classified based on specific radiological features. The model was trained on a large dataset of annotated X-ray images assisting it to distinguish between healthy lungs, COVID-19-affected lungs, and other lung pathologies. The proposed method apprehended a segmentation accuracy of 96.6% with a Dice coefficient of 94.36% offering a potential solution for mechanical detection and 98.4% with KNN analysis of COVID-19 infection in chest X-rays in that way assisting radiologists in making faster and more accurate diagnoses. © 2024 IEEE.

关键词： COVID-19

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Controlled Dropout for Uncertainty Estimation

Controlled Dropout for Uncertainty Estimation

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IEEE International Conference on systems, Man and Cybernetics

作者： Mehedi Hasan Ibrahim Hossain Ashikur Rahman Saeid Nahavandi Institute for Intelligent Systems Research and Innovation Deakin University Geelong VIC Australia Dept. of Computer Science and Engineering Bangladesh University of Engineering and Technology Dhaka Bangladesh

Uncertainty quantification in a neural network is one of the most discussed topics for safety-critical applications. Though Neural Networks (NNs) have achieved state-of-the-art performance for many applications, they still provide unreliable point predictions, which lack information about uncertainty estimates. Among various methods to enable neural networks to estimate uncertainty, Monte Carlo (MC) dropout has gained much popularity in a short period due to its simplicity. In this study, we present a new version of the traditional dropout layer where we are able to fix the number of dropout configurations. As such, each layer can take and apply the new dropout layer in the MC method to quantify the uncertainty associated with NN predictions. We conduct experiments on both toy and realistic datasets and compare the results with the MC method using the traditional dropout layer. Performance analysis utilizing uncertainty evaluation metrics corroborates that our dropout layer offers better performance in most cases.

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Forecasting of Monkeypox Cases Using Optimized SARIMAX Based Model

Forecasting of Monkeypox Cases Using Optimized SARIMAX Based...

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Electronic Engineering (ICEEM), International Conference on

作者： Mahmoud Elshabrawy Marwa M. Eid Abdelaziz A. Abdelhamid El-Sayed M. El-Kenawy Abdelhameed Ibrahim Dept. of Communications & Electronics Delta Higher Institute of Engineering and Technology Mansoura Egypt Faculty of Artificial Intelligence Delta University for Science and Technology Mansoura Egypt Dep. of Computer Science Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt Computer Engineering and Control Systems Dept. Faculty of Engineering University Mansoura Egypt

This study presents a dipper-throated-based ant colony optimization (DTACO) with the Seasonal Auto-Regressive Integrated Moving Average with eXogenous factor (SARIMAX) model (DTACO+SARIMAX) to forecast monkeypox cases. The work optimizes the SARIMAX model using grid search cross-validation and fine-tunes its hyperparameters using DTACO to improve prediction accuracy. The suggested model's consistency and accuracy are considerable compared to previous studies. Comparisons with state-of-the-art models validate the proposed model's predictions. DTACO+SARIMAX can be used to control disease and monitor monkeypox. Healthcare organizations and governments can better manage and track the pandemic's course by offering accurate predictions, reducing public panic, and enabling effective pandemic planning. The Analysis of Variance (ANOVA) and Wilcoxon signed-rank tests are conducted on the proposed DTACO-SARIMAX model and compared models.

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Intelligent Operational Decision Support System for Sugar Factory Resource Efficiency

Intelligent Operational Decision Support System for Sugar Fa...

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International Conference on Modern Electrical and Energy systems (MEES)

作者： Nataliia Lutska Nataliia Zaiets Lidiia Vlasenko Stefan Junge Dept. of Automation and Computer Technologies of Control Systems National University of Food Technologies Kyiv Ukraine Dept. of Automation and Robotic Systems National University of Life and Environmental Sciences of Ukraine Kyiv Ukraine Dept. of Software Engineering and Cyber Security State University of Trade and Economics Kyiv Ukraine Dept. of Packaging Technology Berlin University of Applied Sciences Berlin Germany

With the economic crisis that is looming over the world due to changes in the energy sector and a reduction in the supply of certain types of products, energy and resource efficiency issues are becoming more relevant for the food industry. Also important is the preservation of the environment and the implementation of Cleaner Production trends. Most enterprises follow the concept of Industry 4.0 and increase efficiency through the implementation of intelligent methods, in particular machine learning. The paper proposes a neural network model of sugar production. The model makes it possible to predict the main estimates of resource efficiency used to correct the technological modes of production by the operator-technologist. The structure of the operational decision support system has been developed and is used by personnel to model and predict changes in resource efficiency estimates and KPIs calculated on their basis. Its feature is the integration of methods for achieving resource efficiency, improving environmental performance and ensuring KPIs. To do this, at the initial stage, an analysis of the main flows that affect resource efficiency and an assessment of their importance was carried out. Ensuring Production Performance is confirmed by the simulation and the adequacy of the created neural network accuracy of more than 95%.

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