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Using negative exponential function to characterize built-up land density along slope gradient for 19 urban areas in China

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Journal of Mountain science 2024年第11期21卷 3859-3870页

作者： SONG Yufei PENG Qiuzhi LU Jiating LYU Leting PENG Fengcan Faculty of Land Resources Engineering Kunming University of Science and TechnologyKunming 650093China Surveying and Mapping Geo-informatics Technology Research Center on Plateau Mountains of Yunnan Higher Education Kunming 650093China Yunnan Natural Resources and Planning Intelligence Innovation Laboratory Kunming 650093China School of Geographical Sciences Liaoning Normal UniversityDalian 116029China

In China,numerous cities are expanding into sloping land,yet the quantitative distribution patterns of urban built-up land density along the slope gradient remain unclear,limiting the understanding of sloping land *** this paper,a simple negative exponential function was presented to verify its applicability in 19 typical sloping urban areas in *** function fits well for all case urban areas(R^(2)≥0.951,p<0.001).The parameters of this function clearly describe two fundamental attributes:initial value a and decline rate *** 2000 and 2020,a tends to increase,while b tends to decrease in all urban areas,confirming the hypothesis of mutual promotion between flatland densification and sloping land *** regression analysis indicates that the built-up land density and the ruggedness of background land can explain 70.7%of a,while the average slope ratio of built-up land to background land,the built-up land density and the built-up land area can explain 82.1%of *** work provides a quantitative investigative tool for distribution of urban built-up land density along slope gradient,aiding in the study of the globally increasing phenomenon of sloping land urbanization from a new perspective.

关键词： Negative exponential function Urban built up land density Slope gradient Sloping urban area Sloping land urbanization Mountain area

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Compression through extraction of learned parameters from images in de-correlated image space

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Iran Journal of Computer science 2024年第2期7卷 259-277页

作者： Hasnat, Abul Halder, Santanu Bhattacharjee, Debotosh Government College of Engineering and Textile Technology West Bengal Berhampore India Government College of Engineering and Leather Technology West Bengal Kolkata India Computer Science and Engineering Jadavpur University Kolkata 700032 India Center for Basic and Applied Science Faculty of Informatics and Management University of Hradec Kralove Rokitanskeho 62 Hradec Kralove 500 03 Czech Republic

Image compression is a class of algorithms that reduces the storage space requirement for a digital image. Lossy image compression techniques achieve higher compression but the visual quality of the decompressed image is degraded many times. Decompressed images lose their visual appeal due to compression artifacts. These compression artifacts are introduced due to the quantization step of the compression phase. We developed a lossy image compression technique that works on the spatial domain and de-correlated color model. For the luminance channel compression, the modified Vector Quantization method is used. In the case of chrominance channels, a feature vector is built for each pixel using the neighborhood statistics and cluster information of the pixel. For all the pixels of the image, using these feature vectors, a training dataset is formed. For the training of an artificial neural network (ANN), a feature vector of a pixel is used as the input and its respective chrominance value is used as the target output. Two training datasets are used to train two ANNs separately—one for the Cb channel and one for the Cr channel. These two trained ANNs are stored as the compressed form for the chrominance channels. During the decompression process, first, the luminance channel is reconstructed. Later, for each chrominance channel, the respective trained ANN predicts the chrominance values for each pixel. Thus, the whole image is reconstructed. The method has been tested on the benchmark images and also color images from the UCID v.2 database. The experimental result shows that the method successfully avoids the blocking artifacts in the reconstructed images. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： JPEG JPEG2000 Lossy image compression Neighborhood Statistics PSNR SSIM Vector quantization

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Efficient Time-Delay System Optimization with Auto-Configured Metaheuristics

Efficient Time-Delay System Optimization with Auto-Configure...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Senkerik, Roman Byrski, Aleksander Guzowski, Hubert Janku, Peter Kadavy, Tomas Oplatkova, Zuzana Kominkova Matusu, Radek Pluhacek, Michal Pekar, Libor Smolka, MacIej Viktorin, Adam Tomas Bata University A.I. Lab Faculty of Applied Informatics Department of Informatics and Artificial Intelligence Zlin Czech Republic Tomas Bata University Faculty of Applied Informatics Department of Automation and Control Engineering Zlin Czech Republic Institute of Computer Science Agh University of Science and Technology Krakow Poland

ISBN: (纸本)9798350337020

This paper presents an experimental study that compares the performance of four selected metaheuristic algorithms for optimizing a time delay system model. Time delay system models are complex and challenging to optimize due to their inherent characteristics, such as non-linearity, multi-modality, and constraints. The study includes an explanation of the choice and core functionality of the selected algorithms, which are both baseline and state-of-the-art variants of self-organizing migrating algorithm (SOMA), state-of-the-art variant from the Success-History-based Adaptive Differential Evolution family of algorithms, with emphasis on diverse search (DISH algorithm), and Covariance Matrix Adaptation Evolution Strategy (CMA-ES) algorithm. The hyperparameters of the metaheuristic algorithms were set using the iRace automatic algorithm configuration framework. The paper emphasizes the importance of metaheuristic algorithms in control engineering for time-delay systems to develop more effective and efficient control strategies and precise model identifications. The experimental results highlight the effectiveness of the state-of-the-art algorithms with specific adaptive mechanisms like population organization process, diverse search and adaptation mechanisms ensuring a gradual transition from exploration to exploitation. Overall, this study contributes to understanding the challenges and advantages of using metaheuristic algorithms in control engineering for time delay systems. The results provide valuable insights into the performance of modern metaheuristic algorithms and can help guide the selection of appropriate adaptive mechanisms of metaheuristics. © 2023 IEEE.

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Optimization Accuracy of CNN Model by Utilizing CLAHE Parameters in Image Classification Problems

Optimization Accuracy of CNN Model by Utilizing CLAHE Parame...

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2023 International Conference on Networking, Electrical engineering, Computer science, and technology, IConNECT 2023

作者： Wanto, Anjar Yuhandri, Yuhandri Okfalisa, Okfalisa Universitas Putra Indonesia Yptk Padang Information Technology Faculty of Computer Science Padang Indonesia Universitas Putra Indonesia Yptk Padang Faculty of Computer Science Department of Information Technology Padang Indonesia Universitas Islam Negeri Sultan Syarif Faculty of Science and Technology Department Informatics Engineering Kasim Riau Indonesia

ISBN: (纸本)9798350331172

This study aims to optimize popular Convolutional Neural Network (CNN) models, such as ResNet50, InceptionV4, VGG19, MobileNetV1, MobileNetV2, MobileNetV3 Small, and MobileNetV3 Large, by utilizing the CLAHE (Contrast Limited Adaptive Histogram Equalization) parameter. This research focuses on the problem of classifying retinal fundus images, which have challenges in analyzing the structure and features of the picture. This study compared the performance of CNN models, which were optimized using the CLAHE method as image pre-processing. The fundus retinal image data used is a publicly available dataset. The optimization process is done by changing the parameters in the CNN model and applying the CLAHE technique to improve image quality before the classification process. The results showed that using the CLAHE parameter significantly contributed to improving the performance of the CNN model in classifying retinal fundus images. Some CNN models optimized with CLAHE produce better accuracy than the unoptimized models. There are also variations in performance between the different models, with some models providing better results in classifying retinal fundus images. This study provides new insights regarding using CLAHE parameters in optimizing the CNN model for retinal fundus image classification problems. The results of this research can be the basis for further development in medical image processing and pattern recognition for more accurate and practical diagnostic applications. © 2023 IEEE.

关键词： Neural network models

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Risk Countermeasures Based on Five Whys Analysis Considering Offensive Security 12

Risk Countermeasures Based on Five Whys Analysis Considering...

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12th IEEE Global Conference on Consumer Electronics, GCCE 2023

作者： Tanimoto, Shigeaki Nakajima, Ryusei Goromaru, Hideki Hatashima, Takashi Kanai, Atsushi Chiba Institute of Technology Faculty of Social System Science Chiba Japan Nippon Telegraph and Telephone Corporation Ntt Social Informatics Laboratories Tokyo Japan Hosei University Faculty of Science and Engineering Tokyo Japan

ISBN: (纸本)9798350340181

As digital transformation progresses, security incidents are becoming more sophisticated and more numerous. In general, it is not clear who the attackers are or what their motives may be. In such a situation, the defender is at a keen disadvantage because the only thing to do is simply take countermeasures to protect itself. The concept of "offensive security"has emerged as a way to overcome the attacker's advantage. This approach takes on the attacker's thought processes and other factors and predicts what methods and technologies would be most advantageous to it. Developing countermeasures in advance based on the concept of offensive security thus enables risk countermeasures to be incorporated in advance. At present, specific countermeasures based on the concept of offensive security have yet to be sufficiently studied. We therefore developed a new risk countermeasure utilizing the concept of offensive security based on the attacker's point of view. Specifically, we identified common factors by extracting the "factors behind"20 types of cyber-attacks - including targeted attacks, which are cyber-attacks that have a major impact on today's society - using a "five whys"analysis. We then utilized the results as a basis for creating risk countermeasures and clarified their effectiveness through qualitative evaluation. © 2023 IEEE.

关键词： Risk assessment

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Innovative Strategies for Preventing Online Child Grooming: A Multilingual Web Application 22

Innovative Strategies for Preventing Online Child Grooming: ...

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22nd International Conference on Emerging eLearning Technologies and Applications, ICETA 2024

作者： Kupcová, Eva Pleva, Matúš Haluška, Renát Sokolová, Zuzana Gajdoš, Patrik Bours, Patrick Technical University Košice Faculty of Electrical Engineering and Informatics Department of Electronics and Multimedia Communications Košice Slovakia Norwegian University of Science and Technology Department of Information Security and Communication Technology Gjøvik Norway

ISBN: (纸本)9798331527716

This article investigates modern approaches to preventing online child grooming, emphasizing raising awareness and analyzing current trends. It focuses on identifying emerging threats and examining existing prevention methods and available data within established models. Based on these findings, a multilingual web application was developed at the Technical University of Košice to protect children from online grooming in Slovakia and beyond. The web application allows users to assess online conversations and statements to determine whether they are safe or show signs of grooming, vulgarity, blackmail, or other related behaviors. The platform is available in three languages: English, Slovak, and Norwegian. The results from testing the app, collected from unique users, are presented and analyzed in the final section. © 2024 IEEE.

关键词： Tweets

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Improving DL-Learner on a Malware Detection Use Case 36

Improving DL-Learner on a Malware Detection Use Case

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36th International Workshop on Description Logics, DL 2023

作者： Bisták, Tomáš Švec, Peter Kluka, Ján Šimko, Alexander Balogh, Štefan Homola, Martin Department of Applied Informatics Faculty of Mathematics Physics and Informatics Comenius University Mlynská dolina Bratislava842 48 Slovakia Institute of Computer Science and Mathematics Faculty of Electrical Engineering and Information Technology Slovak University of Technology Ilkovicova 3 Bratislava812 19 Slovakia

Automation of malware characterization has become increasingly important for early malware detection over the past decades. Since it is crucial to be able to perform malware detection transparently, explainable machine-learning methods may be considered particularly suitable for this application. In our previous work, we thus employed algorithms for learning description-logics concept expressions to obtain structured characterizations of malicious software. The results of our initial experiments, however, lead us to several inconsistencies in the behaviour of the studied algorithms included in DL-Learner, a widely adopted framework for concept learning. Hence, in this work, we make a couple of corrections to DL-Learner, along with a few further enhancements, and repeat the experiments to empirically assess the effects of our changes. The outcomes show that the modified algorithms can mark an improvement in terms of the predictability of their behaviour as well as evaluation metrics. © 2023 CEUR-WS. All rights reserved.

关键词： Malware

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Implementing Linear Regression to Improve the Performance of Low-Cost Water Filling Machine 1

Implementing Linear Regression to Improve the Performance of...

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1st International Conference on Advanced engineering and Technologies, ICONNIC 2023

作者： Wibawanto, Slamet Zaeni, Ilham Ari Elbaith Wardhana, Arya Kusuma Universitas Negeri Malang Faculty of Applied Science and Technology Malang Indonesia Universitas Negeri Malang Dept. of Electrical Engineering and Informatics Malang Indonesia

ISBN: (纸本)9798350306484

The manufacturing of bottled beverages, including mineral water, energy drinks, and soft drinks, relies heavily on filling water bottles. Automation can improve quality and precision by reducing human interference. A study has been conducted to design an automatic bottle filling system using a Programmable Logic Controller (PLC) and Arduino for Small and Medium Enterprises (SMEs). The Arduino-controlled machine is more suitable for SMEs use, but improvements are needed. The study was trying to improve the accuracy of filling machine using a linear regression method. A linear regression model was used due to its mathematical model is simple and easy to interpret. The model was tested by comparing the water volume delivered by a pump to the duration estimated by linear regression with the desired water volume. The manual model achieved an MAPE of 11.78%, while the linear regression model achieved an MAPE of 3.11 %. The models achieved high accuracy, making linear regression a suitable solution for low-cost water filling machines. © 2023 IEEE.

关键词： Filling

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Securing the Future after Pager Bombs:Lifecycle Protection of Smart Devices via Blockchain Intelligence

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IEEE/CAA Journal of Automatica Sinica 2024年第12期11卷 2355-2358页

作者： Fei Lin Qinghua Ni Jing Yang Juanjuan Li Nan Zheng Levente Kovács Radu Prodan Mariagrazia Dotoli Qing-Long Han Fei-Yue Wang the Department of Engineering Science Faculty of Innovation EngineeringMacao University of Science and TechnologyMacao 999078China the State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China the School of Artificial Intelligence University of Chinese Academy of SciencesBei-jing 100049China IEEE Obuda University John von Neumann Faculty of InformaticsBecsi Str.96/bH-1034 BudapestHungary the Institute of Information Technology University of Klagenfurt9020 Klagenfurt am WortherseeAustria the Department of Electrical and Information Engineering Polytechnic of Bari70126 BariItaly the School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourne VIC 3122Australia the State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190

THE Lebanese wireless device explosion incident has drawn widespread attention,involving devices such as pagers,walkie-talkies,and other common devices[1].This event has revealed and highlighted the security vulnerabilities in global supply chains from raw material manufacturing and distribution to the usage of devices and equipment,signaling the onset of a new wave of"supply chain warfare"[2].

关键词： Smart cycle chains

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Cybersecurity Revolution via Large Language Models and Explainable AI 17

Cybersecurity Revolution via Large Language Models and Expla...

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17th International Conference on Security of Information and Networks, SIN 2024

作者： Ghazal, Taher M. Janjua, Jamshaid Iqbal Abushiba, Walid Ahmad, Munir Ihsan, Anuam Al-Dmour, Nidal A. College of Arts & Science Applied Science University Manama Bahrain Center for Cyber Security Faculty of Information Science and Technology Selangor Malaysia Lahore Pakistan College of Engineering Applied Science University Bahrain College of Informatics Korea University Seoul02841 Korea Republic of College of Engineering Mutah University Department of Computer Engineering Jordan

ISBN: (纸本)9798331509736

Integrating Groundbreaking advancements in AI, like language models, interpretable AI, and machine learning, opens up a world of exciting new possibilities. The Evolving face of cybersecurity and Modern cyber threats are complex and well crafted;hence, conventional cybersecurity mechanisms show difficulty in staying relevant. LLMs, especially based on Transformer architecture will noticeably increase the accuracy and speed of detecting threats. Transparency and trust are increased by XAI approaches like SHAP and LIME, which offer facts about ML model predictions. This paper explores the literature that demonstrates the integration between XAI and LLMs in cybersecurity, exemplifying how this trinity of models has the potential to help attenuate errors producing reduced false positives and improve how we detect threats. Thinking about the possibilities the challenges including performance Explainability trade-offs, the need for common evaluation metrics, and the black-box nature of AI Models, remain in place. Solving these will help to enhance AI-driven solutions in cybersecurity. © 2024 IEEE.

关键词： Cybersecurity Explainable AI (XAI) Large Language Models (LLMs) Machine Learning (ML) Reliability Threat Detection Transformer Architecture Transparency

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