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arXiv 2024年

作者： Huroon, Aamer Mohamed Donmez, Baris Huang, Yu-Chih Kurt, Gunes Karabulut Wang, Li-Chun EECS International Graduate Program National Yang Ming Chiao Tung University Taiwan Institute of Communications Engineering National Yang Ming Chiao Tung University Taiwan Department of Electrical and Computer Engineering National Yang Ming Chiao Tung University Taiwan Dept. of Electrical Engineering Polytechnique Montréal Montréal Canada

This study introduces a novel approach to enhance communication networks in the cislunar space by leveraging Reconfigurable Intelligent Surfaces (RIS). Using the ability of RIS to dynamically control electromagnetic waves, this paper tackles the challenges of signal attenuation, directivity, and divergence in cislunar missions, primarily caused by immense distances and that Earth-based station transmitters do not always face the Moon. A new optimization problem is formulated, whose objective is to maximize the received signal-to-noise ratio (SNR) for Earth-to-Moon communications. We derive a closed-form solution to the problem of determining the optimal RIS phase shift configuration based on the effective area of the RIS. Through extensive simulations, this paper demonstrates how optimal adjustments in RIS phase shifts can significantly enhance signal integrity, hinting at the substantial potential of RIS technology to revolutionize long-distance cislunar communication. Copyright © 2024, The Authors. All rights reserved.

关键词： Moon

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LogVm: Variable Semantics Miner for Log Messages

LogVm: Variable Semantics Miner for Log Messages

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IEEE International Conference on software Reliability engineering Workshops (ISSRE Wksp)

作者： Yintong Huo Yuxin Su Michael Lyu Dept. of Computer Science & Engineering The Chinese University of Hong Kong Hong Kong China School of Software Engineering Sun Yat-sen University Zhuhai China

ISBN: (数字)9781665476799

ISBN: (纸本)9781665476805

Modern automated log analytics rely on log events without paying attention to variables. However, variables, such as the return code (e.g., “404”) in logs, are noteworthy for their specific semantics of system running status. To unlock the critical bottleneck of mining such semantics from log messages, this study proposes LogVM with three components: (1) an encoder to capture the context information; (2) a pair matcher to resolve variable semantics; and (3) a word scorer to disambiguate different semantic roles. The experiments over seven widely-used software systems demonstrate that Log Vm can derive rich semantics from log messages. We believe such uncovered variable semantics can facilitate downstream applications for system maintainers.

关键词： Codes Conferences Semantics software systems software reliability Data mining

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In-dept. Analysis of Automated Acne Disease Recognition and Classification

arXiv

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arXiv 2025年

作者： Jeny, Afsana Ahsan Junayed, Masum Shah Mia, Md Robel Islam, Md Baharul Department of Computer Engineering Bahcesehir University Istanbul Turkey Department of CSE University of Connecticut StorrsCT06269 United States Department of CSE Daffodil International University Dhaka Bangladesh Dept. of Computing & Software Engineering Florida Gulf Coast University Fort MyersFL33965 United States

Facial acne is a common disease, especially among adolescents, negatively affecting both physically and psychologically. Classifying acne is vital to providing the appropriate treatment. Traditional visual inspection or expert scanning is time-consuming and difficult to differentiate acne types. This paper introduces an automated expert system for acne recognition and classification. The proposed method employs a machine learning-based technique to classify and evaluate six types of acne diseases to facilitate the diagnosis of dermatologists. The pre-processing phase includes contrast improvement, smoothing filter, and RGB to L ∗ a ∗ b color conversion to eliminate noise and improve the classification accuracy. Then, a clustering-based segmentation method, k-means clustering, is applied for segmenting the disease-affected regions that pass through the feature extraction step. Characteristics of these disease-affected regions are extracted based on a combination of gray-level co-occurrence matrix (GLCM) and Statistical features. Finally, five different machine learning classifiers are employed to classify acne diseases. Experimental results show that the Random Forest (RF) achieves the highest accuracy of 98.50%, which is promising compared to the state-of-the-art methods. © 2025, CC BY.

关键词： Expert systems

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Robust and Reliable Liveness Detection Models for Facial Recognition Systems

Robust and Reliable Liveness Detection Models for Facial Rec...

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Frontiers of Information Technology (FIT)

作者： Haris Anjum Usama Arshad Raja Hashim Ali Zain Ul Abideen Muhammad Huzaifa Shah Talha Ali Khan Ali Zeeshan Ijaz Abu Bakar Siddique Muhammad Imad Faculty of Computer Science & Engineering GIK Institute of Engg. Sciences & Technology Topi Pakistan Dept. of Technology & Software Engineering University of Europe for Applied Sciences Berlin Germany BCMaterials Basque Center for Materials Applications & Nanostructure Spain

Facial Liveness Detection is instrumental in combating fraudulent practices and identity theft by differentiating genuine faces from forgeries. Given that facial recognition is now an integral part of many sectors like banking and law enforcement, liveness detection has become a vital aspect for maintaining the trustworthiness of these applications. Unfortunately, the utility of prevalent facial liveness detection models is constrained by their lack of generalizability to novel, unseen data, which presents a substantial impediment to practical applications. To mitigate this issue, our research endeavored to construct a reliable and precise facial liveness detection model that can effectively navigate this challenge and ensure consistent performance across diverse datasets. The devised model exhibited excellent accuracy, which attests to its robustness. This research underscores the advantages of employing deep learning methods such as Convolutional Neural Networks (CNNs) as they significantly enhance the model’s accuracy. We intend for our findings to drive further research and progress in this critical field, by laying the groundwork for future work that integrates multiple liveness detection methods and collects more varied training data to develop an even more resilient and efficient system.

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Further Experimental Evidence of the Dead Matter Has Memory Conjecture in Capacitive Devices

arXiv

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arXiv 2022年

作者： Allagui, Anis Zhang, Di Elwakil, Ahmed Dept. of Sustainable and Renewable Energy Engineering University of Sharjah United Arab Emirates Dept. of Mechanical and Materials Engineering Florida International University MiamiFL33174 United States Dept. of Electrical Engineering and Computer Science Khalifa University United Arab Emirates Dept. of Electrical and Software Engineering University of Calgary Alberta Canada

This study provides new sets of experimental results supporting Westerlund's conjecture that Dead Matter Has Memory [1]. Memory effects in the dynamic response of electric double-layer capacitors (EDLCs) that integrate its prior history of stimulation and state have been experimentally observed and reported in a few recent studies. The different excitation signals used to quantify such effects in these studies aimed at charging a device to the same voltage value and the exact same accumulated charge level but in different manners. Having reached the same unique voltage-charge point, it was observed that different yet repeatable discharge patterns occur, proving the existence of memory. The aim of this work is to provide further experimental evidence of the inherent memory effect in EDLCs in response to time-varying stationary input excitations with different statistical properties. In particular, different sets of charging voltage waveforms composed of fixed dc values with superimposed uniformly-distributed random fluctuations of different amplitudes were created and used to charge the same EDLC device to a unique voltage-charge point. The duration of these signals was the same but with different values of variance around the mean value. We observed different time-charge responses depending on the extent of the noise level in these charging waveforms. This is interpreted and discussed in the context of inherent memory using fractional-order voltage-charge equations of non-ideal capacitors. © 2022, CC BY.

关键词： Supercapacitor

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Deep learning based classification of papaya disease recognition 3

Deep learning based classification of papaya disease recogni...

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3rd International Conference on Intelligent Sustainable Systems, ICISS 2020

作者： Hossen, Md. Sagar Haque, Imdadul Islam, Md. Saif Ahmed, Md. Tanvir Nime, Md. Jannati Islam, Md. Ashiqul Daffodil International University Dept. of Computer Science and Engineering Dhaka Bangladesh Daffodil International University Dept. of Software Engineering Dhaka Bangladesh

ISBN: (纸本)9781728170893

Every year many of farmers had to face a huge amount of loss due to the papaya disease. From the last several years, thousands of researchers pay attention to the papaya disease recognition system to reduce the losses of farmers. Farmers have no prior knowledge about the detection technique to mitigate the disease. They found the diseases when the papaya already affected and wasted. Due to the loss of cultivation, many of them are now frightening to take steps about planting papaya. On this matter, a research has been performed with the advancement of deep learning technology to detect and classify the papaya disease. CNN model is used according to Keras API. This model becomes reliable with fully connected, where classification is completed and all the process is a deep learning based. It took the fixed size of image 200x200 RGB image as input. © 2020 IEEE.

关键词： Deep learning

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Detection of Ophthalmic Disorders Through Deep Feature Fusion

Detection of Ophthalmic Disorders Through Deep Feature Fusio...

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engineering & Computing Technologies (ICECT), International Conference on

作者： Muhammad Toheed Hussain Muhammad Faraz Sumair Aziz Khushbakht Iqtidar Muhammad Umar Khan Gabriel Axel Montes Department of Electronics Engineering University of Engg. & Tech. Taxila Taxila Pakistan Faculty of Science and Technology University of Canberra Canberra ACT Australia Dept. of Computer & Software Engineering National University of Sciences & Tech. Islamabad Pakistan University of Newcastle Callaghan NSW Australia

ISBN: (数字)9798350349719

ISBN: (纸本)9798350349726

Given the rising prevalence of ophthalmic disorders, this study aims to meet the urgent need for an automated technique capable of diagnosing and treating these conditions. Our approach combines deep learning and machine learning techniques to develop a diagnostic system that can detect and classify seven distinct ophthalmic disorders. Manual diagnosis of ophthalmic disorders is labor-intensive and slow, worsened by a shortage of medical experts. In this paper, we propose a medical diagnostic system that utilizes deep features extracted from a ResNet-50 CNN, which has been trained on retinal images enhanced through contrast-limited adaptive histogram equalization (CLAHE). These deep features serve as input for the machine learning model. To address the class imbalance problem, we employed ADASYN to generate synthetic features for all classes. An Ensemble subspace KNN classifier, trained on these deep features, achieved a remarkable mean accuracy of 99.18% compared to other classifiers evaluated in this study. The proposed technique has the potential to assist medical experts in achieving timely and accurate diagnoses of ophthalmic diseases.

关键词： Deep learning computers Histograms Accuracy Manuals Feature extraction Retina

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A Guaranteed Approximation Algorithm for Scheduling Fork-Joins with Communication Delay

A Guaranteed Approximation Algorithm for Scheduling Fork-Joi...

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International Symposium on Parallel and Distributed Processing (IPDPS)

作者： Pierre-François Dutot Yeu-Shin Fu Nikhil Prasad Oliver Sinnen CNRS Inria Grenoble INP* LIG Univ. Grenoble Alpes Grenoble France Dept. of Electrical Computer & Software Engineering Parallel and Reconfigurable Computing Lab University of Auckland Auckland New Zealand

Scheduling task graphs with communication delay is a widely studied NP-hard problem. Many heuristics have been proposed, but there is no constant approximation algorithm for this classic model. In this paper, we focus on the scheduling of the important class of fork-join task graphs (describing many types of common computations) on homogeneous processors. For this sub-case, we propose a guaranteed algorithm with a $\left( {1 + \frac{m}{{m - 1}}} \right)$-approximation factor, where m is the number of processors. The algorithm is not only the first constant approximation for an important sub-domain of the classic scheduling problem, it is also a practical algorithm that can obtain shorter makespans than known heuristics. To demonstrate this, we propose adaptations of known scheduling heuristic for the specific fork-join structure. In an extensive evaluation, we then implemented these algorithms and scheduled many fork-join graphs with up to thousands of tasks and various computation time distributions on up to hundreds of processors. Comparing the obtained results demonstrates the competitive nature of the proposed approximation algorithm.

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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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Hybrid security scheme for a colored image using DNA and chaotic map

Hybrid security scheme for a colored image using DNA and cha...

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International Conference on Mathematics, Actuarial Science, computer Science and Statistics (MACS)

作者： Tayyaba Ajaz Humaira Ashraf Ghadah Alwakid Dept. of computer science and software Engineering International Islamic University Islamabad Islamabad Pakistan Department of Computer Science College of computer and information sciences Jouf university Al-Jouf Saudia Arabia

ISBN: (纸本)9781665460729

this paper proposes a color image encryption algorithm based on DNA encoding in conjunction with a 1D logistic map and a high-dimensional chaotic map. The XOR function is used to combine low and high-dimensional chaotic maps, resulting in a cipher image. For image encryption, the paper also proposes a system of confusion and diffusion. Confusion and Diffusion apply to high-dimensional chaotic maps. In a low-dimensional logistic map, the keyspace is smaller, and chaotic sequences are used directly from the chaotic map, which has an impact on the encryption process. Recent image encryption focuses primarily on a gray image scale. We present color image encryption that uses non-RGB color spaces like YCbCr, YIQ, HSV, and L*a*b*. The high connection between RGB color spaces red, green, and blue is commonly used in data storage and handling. RGB color spaces merge Luminance and Chroma channels so color analysis becomes more complex so we use non -RGB spaces. Using the logistic sine map or tent map association, different chaotic systems can be found by random number attacks and dictionary attacks. Gives the security analysis of encryption using DNA encoding and chaotic map and explains that it's vulnerable to plaintext-choice attacks we propose a new and more efficient approach and reduce time complexity then- existing approach and become encryption more secure.

关键词： Image color analysis Chaotic communication DNA Color Mobile communication Encoding Encryption

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