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17th International Symposium on Operational Research in Slovenia, SOR 2023

作者： Ferš, Nataša Ošep Zamuda, Aleš University of Maribor Faculty of Electrical Engineering and Computer Science Slovenia

ISBN: (纸本)9789616165617

In this work, using Modular Differential Evolution (Modular DE), we investigate the impact of parameter memory size (MS) on the search performance of Success-history based parameter adaptation (L-SHADE) for Differential Evolution. To benchmark it, we use the blackbox optimization benchmarking (BBOB) test suite from the COmparing Continuous Optimizers in a black-box setting (COCO) platform. We run the algorithm on all 24 functions in BBOB and observe results separately, on each of those function. Experiment and assessment performance are done with Iterative Optimization Heuristics Profiler (IOHprofiler). © Proceedings of the 17th International Symposium on Operational Research in Slovenia, SOR 2023. All rights reserved.

关键词： Iterative methods

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On the Relationship Between Linter Warning Density and Software Maintainability: An Empirical Study of JavaScript Projects 23

On the Relationship Between Linter Warning Density and Softw...

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6th International Conference on Software engineering and Information Management, ICSIM 2023

作者： Heričko, Tjaša Šumak, Boštjan Faculty of Electrical Engineering and Computer Science University of Maribor Slovenia

ISBN: (纸本)9781450398237

A common practice in software development is to include linters, static analysis tools that warn developers about potential issues in the code, in the software quality assurance process. Actionable warnings generated by linters upon violations of defined rules help detect, resolve, and reduce coding errors, quality flaws, code style inconsistencies, and deviations from best coding practices and conventions. However, little empirical evidence exists to fully understand the relationship between linter warnings and external software quality factors. To this end, an empirical investigation of the source code of 40 open-source JavaScript project releases was conducted to study whether there is a relation between software maintainability measured by the Maintainability Index and the density of linter warnings per Logical Lines of Code. The findings suggest a very weak to strong negative correlation between warning density and the value of the Index at a project- and a module-level. Changes in warning density between projects only slightly inversely correspond to changes in maintainability. Additionally, a statistically significant difference in maintainability was found between projects defining linters in their manifest file and those that do not, in favor of the former. © 2023 ACM.

关键词： Software design

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Solving 100-Digit Challenge with Score 100 by Extended Running Time and Parallel Benchmarking 17

Solving 100-Digit Challenge with Score 100 by Extended Runni...

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17th International Symposium on Operational Research in Slovenia, SOR 2023

作者： Zamuda, Aleš University of Maribor Faculty of Electrical Engineering and Computer Science Slovenia

ISBN: (纸本)9789616165617

This paper assesses Distance-based Success History Differential Evolution for 100-Digit Challenge and Numerical Optimization Scenarios (DISHchain3e+12). DISHchain3e+12 algorithm is based on DISH algorithm, using a Differential Evolution (DE) parameters control mechanism based on Euclidean distance between trial and parent in a search space to weigh Lehmer means in scaling and crossover historical parameter memories. In this paper, the maximum number of fitness evaluations and initial population size parameters are tailored in the DISHchain3e+12 algorithm per problem, since the benchmark used allows tuning up to two of the same parameters independently for each problem. © Proceedings of the 17th International Symposium on Operational Research in Slovenia, SOR 2023. All rights reserved.

关键词： Population statistics

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Robustness of generative AI detection: adversarial attacks on black-box neural text detectors

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International Journal of Speech Technology 2024年第4期27卷 861-874页

作者： Fishchuk, Vitalii Braun, Daniel Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Enschede Netherlands Department of High-tech Business and Entrepreneurship University of Twente Enschede Netherlands

The increased quality and human-likeness of AI generated texts has resulted in a rising demand for neural text detectors, i.e. software that is able to detect whether a text was written by a human or generated by an AI. Such tools are often used in contexts where the use of AI is restricted or completely prohibited, e.g. in educational contexts. It is, therefore, important for the effectiveness of such tools that they are robust towards deliberate attempts to hide the fact that a text was generated by an AI. In this article, we investigate a broad range of adversarial attacks in English texts with six different neural text detectors, including commercial and research tools. While the results show that no detector is completely invulnerable to adversarial attacks, the latest generation of commercial detectors proved to be very robust and not significantly influenced by most of the evaluated attack strategies. © The Author(s) 2024.

关键词： Generative adversarial networks

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On Challenges and Opportunities in the Translation of Deep Neural Networks into Finite Automata 5

On Challenges and Opportunities in the Translation of Deep N...

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5th Workshop on Artificial Intelligence and Formal Verification, Logic, Automata, and Synthesis, OVERLAY 2023

作者： Sälzer, Marco Alsmann, Eric Lange, Martin Faculty of Electrical Engineering and Computer Science University of Kassel Germany

The certification of safety properties and interpretation of neural networks is a topic of great concern. We recently devised an automata-based approach for addressing these tasks. It translates a neural network with ReLU activations into a finite automaton, capturing the input-output relation induced by the neural network. We report on a proof-of-concept implementation of this translation for a subclass neural networks, so called binarized neural networks, pointing out difficulties and opportunities of the proposed approach. © 2023 CEUR-WS. All rights reserved.

关键词： Finite automata

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Design of Double Closed Loop Control System for DC Motor Based on TX8C1010 Single Chip 2

Design of Double Closed Loop Control System for DC Motor Bas...

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2022 2nd International Conference on Mechatronics Technology and Aerospace engineering, ICMTAE 2022

作者： Wang, Zhouyi Yu, Jianding Lou, Yuanchao Faculty of Electrical Engineering and Computer Science Ningbo University China

In order to improve the operational strength and control precision of the DC motor, a double closed-loop control system based on TAIXIN TX8C1010 MCU is proposed. The single chip is based on a high-speed 8051 core, which is applied to home appliances and can realize the double closed-loop motor control algorithm. The given speed value provided by the potentiometer is compared with the real-time speed value and inputs the error to the speed loop function, which is based on the incremental PID algorithm, to calculate the reference current value. Then it is compared with the real-time current value and inputs the error into the current loop. The PWM signal duty cycle is determined by the output value of the current loop to adjust and stabilize the motor speed. The drive circuit outputs the PWM signal in the inverse amplification way to control the operation of the DC motor, and finally tests the function on the MAGTROL M-TEST7 motor test platform. The test results show that the control system has fast response speed, strong load capacity, good robustness and safety. Hence, TAIXIN TX8C1010 single-chip machine is feasible in the DC motor control. It can be improved to strengthen its load characteristics according to the parameters and applied to a variety of power DC motor control. © Published under licence by IOP Publishing Ltd.

关键词： Pulse width modulation

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Early Detection of Cyber-Physical Attacks on Electric Vehicles Fast Charging Stations Using Wavelets and Deep Learning

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical Systems 2024年 2卷 220-231页

作者： Abu-Nassar, Ahmad M. Morsi, Walid G. Electrical Computer and Software Engineering Department Faculty of Engineering and Applied Science OshawaONL1G 0C5 Canada

Transportation electrification plays an important role in the operation of the smart grid through the integration of the electric vehicle fast charging stations (EVFCSs), which allows the electric vehicles to provide regulation services to the grid through the vehicle-to-grid (V2G) concept. However, such an integration makes smart grid assets prone to cyber vulnerability threats. In this paper, a cyber-physical attack detection approach is developed to early detect such attacks. The proposed approach combines the continuous wavelet transform (CWT) and the convolution neural network (CNN) to provide an effective detection technique. The proposed detection approach has undergone rigorous testing that considered 420 realistic operational scenarios. Unlike in previous work, the proposed detection approach was found to be effective in automatically learning the salient features from the data as well as identifying the frequency bands that hold such features and using them in the classification process. Furthermore, this work investigated the cyber-attack detection accuracy using different time resolutions of smart meters. The results have shown that the proposed approach effectively detects cyber-physical attacks with an accuracy of 99.76% and a low computational time of 1.8 seconds. © 2024 IEEE.

关键词： Feature extraction

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Deep Learning Based Side-Channel Attack Detection for Mobile Devices Security in 5G Networks

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清华大学学报自然科学版（英文版） 2025年第3期30卷 1012-1026页

作者： Amjed A.Ahmed Mohammad Kamrul Hasan Ali Alqahtani Shayla Islam Bishwajeet Pandey Leila Rzayeva Huda Saleh Abbas Azana Hafizah Mohd Aman Nayef Alqahtani Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangi 43600Malaysia Imam Alkadhim University College Department of Computer Techniques EngineeringBaghdad 10066Iraq Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangi 43600Malaysia Department of Networks and Communications Engineering College of Computer Science and Information SystemsNajran UniversityNajran 61441Saudi Arabia Institute of Computer Science and Digital Innovation UCSI UniversityKuala Lumpur 56000Malaysia Department of Intelligent Systems and Cyber Security Astana IT UniversityAstana 20000Kazakstan Department of Computer Science College of Computer Science and EngineeringTaibah UniversityMadinah 42353Saudi Arabia Department of Electrical Engineering College of EngineeringKing Faisal UniversityAl-Hofuf 31982Saudi Arabia

Mobile devices within Fifth Generation(5G)networks,typically equipped with Android systems,serve as a bridge to connect digital gadgets such as global positioning system,mobile devices,and wireless routers,which are vital in facilitating end-user communication ***,the security of Android systems has been challenged by the sensitive data involved,leading to vulnerabilities in mobile devices used in 5G *** vulnerabilities expose mobile devices to cyber-attacks,primarily resulting from security ***-permission apps in Android can exploit these channels to access sensitive information,including user identities,login credentials,and geolocation *** such attack leverages"zero-permission"sensors like accelerometers and gyroscopes,enabling attackers to gather information about the smartphone's *** underscores the importance of fortifying mobile devices against potential future *** research focuses on a new recurrent neural network prediction model,which has proved highly effective for detecting side-channel attacks in mobile devices in 5G *** conducted state-of-the-art comparative studies to validate our experimental *** results demonstrate that even a small amount of training data can accurately recognize 37.5％of previously unseen user-typed ***,our tap detection mechanism achieves a 92％accuracy rate,a crucial factor for text *** findings have significant practical implications,as they reinforce mobile device security in 5G networks,enhancing user privacy,and data protection.

关键词： Fifth Generation(5G)networks smartphone information leakage Side-Channel Attack(SCA) deep learning

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Lung Cancer Classification from CT Scan Images Using the LVQ Algorithm and GLCM Feature Extraction with Spatial Filters

Lung Cancer Classification from CT Scan Images Using the LVQ...

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2024 International Conference on Information Technology and Computing, ICITCOM 2024

作者： Ahmad, Imam Tri Prastowo, Agung Indra Borman, Rohmat Tonggiroh, Mursalim Jusman, Yessi Universitas Teknokrat Indonesia Faculty of Engineering and Computer Science Bandarlampung Indonesia Universitas Yapis Papua Faculty of Computer Science Jayapura Indonesia Universitas Muhammadiyah Yogyakarta Department of Electrical Engineering Faculty of Engineering Yogyakarta Indonesia

ISBN: (纸本)9798350379839

Lung cancer is the leading cause of cancer-related fatalities in Indonesia, primarily due to late-stage diagnoses. This study aims to develop a model that employs image processing to classify lung cancer from CT scan images. The system combines the Learning Vector Quantization (LVQ) algorithm with the Gray Level Co-occurrence Matrix (GLCM) feature extraction method and utilizes spatial filtering techniques such as low pass, median, and high pass filters. The LVQ algorithm effectively manages non-linear data and forms complex decision boundaries through the distribution of prototype vectors. The findings show that the median filtering technique achieved the highest accuracy of 90.14% at a GLCM angle of 0°, demonstrating its effectiveness in reducing noise while maintaining crucial details for accurate classification. This suggests that the horizontal texture orientation may be more critical for analyzing lung images in this dataset. © 2024 IEEE.

关键词： Lung cancer

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Adaptive Boosting Method for Mitigating Ethnicity and Age Group Unfairness

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SN computer science 2024年第1期5卷 10页

作者： Colakovic, Ivona Karakatič, Sašo Faculty of Electrical Engineering and Computer Science University of Maribor Koroška cesta 46 Maribor 2000 Slovenia

Machine learning algorithms make decisions in various fields, thus influencing people’s lives. However, despite their good quality, they can be unfair to certain demographic groups, perpetuating socially induced biases. Therefore, this paper deals with a common unfairness problem, unequal quality of service, that appears in classification when age and ethnicity groups are used. To tackle this issue, we propose an adaptive boosting algorithm that aims to mitigate the existing unfairness in data. The proposed method is based on the AdaBoost algorithm but incorporates fairness in the calculation of the instance’s weight with the goal of making the prediction as good as possible for all ages and ethnicities. The results show that the proposed method increases the fairness of age and ethnicity groups while maintaining good overall quality compared to traditional classification algorithms. The proposed method achieves the best accuracy in almost every sensitive feature group. Based on the extensive analysis of the results, we found that when it comes to ethnicity, interestingly, White people are likely to be incorrectly classified as not being heroin users, whereas other groups are likely to be incorrectly classified as heroin users. © 2023, The Author(s).

关键词： Boosting Classification Fairness Machine learning

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