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检索条件"任意字段=International Conference on Measurement and Modeling of Computer Systems"

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20th international conference on Information systems Security, ICISS 2024

20th International Conference on Information Systems Securit...

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20th international conference on Information systems Security, ICISS 2024

ISBN: (纸本)9783031800191

The proceedings contain 28 papers. The special focus in this conference is on Information systems Security. The topics include: S-RFUP: Secure Remote Firmware Update Protocol;securing Virtual Reality Apps Inter-process Communication;Securing the Web: Analysis of HTTP Security Headers in Popular Global Websites;Countering Subscription Concealed Identifier (SUCI)-Catchers in Cellular Communications;BP-MAP: A Secure and Convenient Mutual Authentication Protocol;effects of Soft-Domain Transfer and Named Entity Information on Deception Detection;Paving the Way: Advancing V2X Safety Through Innovative Attack Generation and Analysis Framework (V2X-SAF);from Traits to Threats: Learning Risk Indicators of Malicious Insider Using Psychometric Data;Identifying Insecure Network Configurations Through Attack modeling and Explainable AI;QIris: Quantum Implementation of Rainbow Table Attacks;insights from Running 24 Static Analysis Tools on Open Source Software Repositories;REMEDII: Robust Malware Detection with Iterative and Intelligent Adversarial Training;semantics-Based Static Vulnerability Detection in Solidity Using Abstract Interpretation;web Privacy Perceptions Amongst Indian Users;Enabling Privacy in IT Service Operations;Privacy-Preserving Photo Sharing: An SSI Use Case;zone Recovery Attack on a Secure Privacy-Preserving Ride-Matching Protocol;Making EULA Great Again: A Novel Nudge Mechanism to Improve Readability, User Attention and Awareness;a Decoupling Mechanism for Transaction Privacy;Protecting Ownership of Trained DNN Models with Zero-Knowledge Proofs;MALAI: ML-Based Attack on Learning with Error Problem;patch Based Backdoor Attack on Deep Neural Networks;integrating Crypto-Based Payment systems for Data Marketplaces: Enhancing Efficiency, Security, and User Autonomy;IntelliSOAR: Intelligent Alert Enrichment Using Security Orchestration Automation and Response (SOAR);inTrust: An Asset Monitoring, Analysis and Vulnerability Assessment System for Zero Trust Network.

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Waste Sorting System Using AI: Development of An Embedded Solution for Efficient Waste Management

Waste Sorting System Using AI: Development of An Embedded So...

引用

international Image Processing, Applications and systems conference (IPAS)

作者： Amal Remili Mouna Zouari Mahdi Jihen Frikha Elleuch Ahmed Fakhfakh Dorra Sellami Electrical Engineering Department National Engineering School of Sfax Sfax Tunisia Sfax Digital Research Center Sfax Tunisia

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

Waste is causing a serious environmental problem. Waste management and circular economy offers a best alternative for preserving the environment and protecting nature resources. This paper presents an AI-based system for waste identification and sorting, particularly focusing on plastic, which is the less decomposed waste, using computer vision techniques. The data, used for system validation, comprises publicly available datasets and a locally collected dataset, devoted to plastic classification and detection. Performance metrics demonstrate that YOLOv8 outperforms other deep learning solutions, in real-time waste detection and is well suited for implementation on a Raspberry Pi 4 Model B. The system outperforms also existing methods in terms of efficiency, accuracy, and speed, and is a promising solution for addressing the global waste management challenge.

关键词： Waste management measurement computer vision Computational modeling Image processing Real-time systems Plastics Artificial intelligence System validation Sorting

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Image Captioning using CNN and LSTMs

Image Captioning using CNN and LSTMs

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Intelligent systems and Computational Networks (ICISCN), international conference on

作者： Rajeel Ahmad Ansari Divyapratap Singh Chauhan B. Jothi Krishnaveni S Department of Computational Intelligence SRM Institute of Science and Technology Kattankulathur India

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

Image captioning is a pivotal field in AI, enabling machines to generate descriptive text for images, with applications in accessibility, content creation, and human-computer interaction. This study identifies the shortcomings of the conventional CNN-RNN model by proposing an advanced architecture that incorporates InceptionV3, LSTM, attention mechanism, and beam search. The image features are extracted using Incep-tionV3; captions are generated using LSTMs, where an attention mechanism is used to guide the model to identify important regions of the image for each word. Beam search has been employed to choose the most optimal captions. The suggested model is tested on the Flickr8k data set with the help of the BLEU metric. The outcomes show that the caption relevance, diversity, and interpretability of the model are enhanced, with BLEU-1 and BLEU-2 scores of 0.5365 and 0.3218 respectively, demonstrating its potential in real-world applications such as providing accessibility and machine learning-based content generation.

关键词： Training measurement Human computer interaction Technological innovation Attention mechanisms Computational modeling Feature extraction Intelligent systems Long short term memory Tuning

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Learning Diffusion Model from Noisy measurement using Principled Expectation-Maximization Method

Learning Diffusion Model from Noisy Measurement using Princi...

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2025 IEEE international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Bai, Weimin Tang, Weiheng Ye, Enze Chen, Siyi Chen, Wenzheng Sun, He Academy for Advanced Studies Peking University China School of Physics Peking University China College of Future Technology Peking University China Wangxuan Institute of Computer Technology Peking University China

ISBN: (纸本)9798350368741

Diffusion models have demonstrated exceptional ability in modeling complex image distributions, making them versatile plug-and-play priors for solving imaging inverse problems. However, their reliance on large-scale clean datasets for training limits their applicability in scenarios where acquiring clean data is costly or impractical. Recent approaches have attempted to learn diffusion models directly from corrupted measurements, but these methods either lack theoretical convergence guarantees or are restricted to specific types of data corruption. In this paper, we propose a principled expectation-maximization (EM) framework that iteratively learns diffusion models from noisy data with arbitrary corruption types. Our framework employs a plug-and-play Monte Carlo method to accurately estimate clean images from noisy measurements, followed by training the diffusion model using the reconstructed images. This process alternates between estimation and training until convergence. We evaluate the performance of our method across various imaging tasks, including inpainting, denoising, and deblurring. Experimental results demonstrate that our approach enables the learning of high-fidelity diffusion priors from noisy data, significantly enhancing reconstruction quality in imaging inverse problems. © 2025 IEEE.

关键词： Bayesian inference Computational imaging diffusion models image priors inverse problems

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Real-Time Blood Pressure Monitoring and Hypertension Prediction based on Multitask Temporal Convolutional Network 1

Real-Time Blood Pressure Monitoring and Hypertension Predict...

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1st international conference on Intelligent systems and Computational Networks, ICISCN 2025

作者： Musnmusawi, Muntather Deepa, R. Shankar, Smitha Sharath Nirupashri, G. Khanam, P. Katheeja The Islamic University of Al Diwaniyah College of Technical Engineering Department of Computers Techniques Engineering Al Diwaniyah Iraq St.Joseph's Institute of Technology Department of Information Technology Chennai India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India Dhanalakshmi Srinivasan College of Engineering and Technology Department of Bio Medical Engineering Mamallapuram India Mohan Babu University Department of Computer Science and Engineering Tirupati India

ISBN: (纸本)9798331529246

In recent days, blood pressure monitoring and hypertension prediction have become essential for effective measurement and management. Continuously tracking blood pressure helps in making informed decisions like changing medication or dosage. However, some challenges are raised while monitoring such as blood pressure readings being prone to errors, variability in measurements if a patient moves and environmental noise. Therefore, this research proposes Multitask Temporal Convolutional Network (MTCN) for realtime blood pressure monitoring and hypertension prediction. Initially, data is collected from Medical Information Mart for Intensive Care III (MIMIC-III) and preprocessed by using Min- Max Normalization which rescales numeric data to definite range. After that, features like time frequency coefficients, power spectral density, Scalogram features are extracted by using Continuous Wavelet Transform (CWT). Finally, blood pressure monitoring and hypertension prediction is done by using proposed MTCN which provides accurate hypertension prediction and personalized risk assessments. The proposed MTCN achieved better results in terms of Mean Absolute Error (MAE) of 2.01, Root Mean Square Error (RMSE) of 3.26 when compared with existing Random Forest Regression model. © 2025 IEEE.

关键词： Wavelet transforms

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DocHFormer: Document Image Dewarping via Harmonized modeling of Hierarchical Priors 27th

DocHFormer: Document Image Dewarping via Harmonized Modelin...

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27th international conference on Pattern Recognition, ICPR 2024

作者： Zhou, Xinyue Li, Guanting Jiang, Nanfeng Wang, Da-Han Zhang, Xu-Yao Zhu, ShunZhi School of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen361024 China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783031781186

Document Image Dewarping (DID) task aims to address the issue of geometry distortion and improve image quality. In this paper, we propose a simple but effective method, named DocHFormer, that can take hierarchical priors features of images, including document image mask and coordinate positions, as additional information to realize accurate representation. To better exploit these fused information for dewarping, we take them into a harmonized space random shuffle operation, which can stochastically rearrange the pixels across spatial space and further use inverse operation to recover the original order. This way can adapt to allocate each feature pixel with equal probability and thus make full use of multi-type features. Furthermore, we introduce this mechanism into local self-attention to use linear complexity to input resolution and also design a new feed-forward network with structural modeling to boost representation. With the help of the above components, our proposed DocHFormer can achieve competitive performance with lower complexity and also outperform the existing state-of-the-art on several popular datasets. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image enhancement

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Big Data for Enhancing Cybersecurity in Enterprise Environments Proactive Threat Detection and Prevention 3

Big Data for Enhancing Cybersecurity in Enterprise Environme...

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3rd IEEE international conference on Automation and Computation, AUTOCOM 2025

作者： Chityala, Upender Reddy Shnain, Ammar H. Govindaraj, Manoj Johri, Prashant Kuppuraj, T. Devi, N.Lavanya Equinox It Solutions Llc United States College of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Management Studies Chennai India School of Computing Science and Engineering Galgotias University Greater Noida India Karpagam Academy of Higher Education Department of Computer Applications Coimbatore India Psna College of Engineering and Technology Department of Ece India

ISBN: (纸本)9798331542375

This research explores the integration of big data technologies and machine learning to enhance cybersecurity in enterprise environments focusing on proactive threat detection and prevention. Traditional security systems often struggle to keep up with the growing complexity and volume of cyber threats, necessitating a more advanced, data-driven approach. The proposed methodology leverages real-time data processing, predictive modeling, and anomaly detection to improve threat detection accuracy, reduce response times, and enhance system scalability. By collecting and analyzing data from various network sources, including logs from security devices and endpoint protection software, the system detects potential threats more efficiently than conventional methods. Machine learning algorithms such as supervised and unsupervised learning, along with big data platforms like Apache Spark, enable real-time analysis of large volumes of data, ensuring timely identification of both known and unknown cyber threats. The results demonstrate significant improvements in detection accuracy, system scalability, and incident resolution time, with the proposed methodology outperforming traditional security systems in key performance metrics. This research highlights the potential of big data-driven cybersecurity solutions in addressing modern challenges and offers a framework for organizations to implement more effective, scalable, and proactive security measures to safeguard their digital assets. © 2025 IEEE.

关键词： Cyber attacks

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MFE-Net: A remote sensing image dehazing model for the visual system of a flight simulator 6

MFE-Net: A remote sensing image dehazing model for the visua...

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6th international conference on Geoscience and Remote Sensing Mapping, GRSM 2024

作者： Ge, Wenyi Wang, Bo Liu, Qi Qin, Xiaolin Tan, Shihan Wang, Shengjie Yuan, Xia College of Computer Science Chengdu University of Information Technology Chengdu610255 China Chengdu Institute of Computer Applications Chinese Academy of Sciences Chengdu610213 China Jiuzhou Air Trafic Control Sichuan Jiuzhou Investment Holding GroupCo. Ltd Mianyang621000 China The Key Laboratory of Flight Techniques and Flight Safety Civil Aviation Flight University of China Guanghan618307 China

ISBN: (纸本)9781510688018

Obtaining high-quality remote sensing images is crucial in generating a three-dimensional terrain for flight simulators. However, due to the presence of haze and other impact factors, collected remote sensing images usually suffer from blurred details, which limits its applicability real-world applications. To address this issue a deep learning-based dehazing model is proposed to advance hazy remote sensing images to build flight simulator visual systems. The encoder-decoder architecture is proposed to perform the image dehazing, in which an improved feature extraction module is designed to extract both global and local semantic features across multi-resolutions. In addition, the pixel-pixel and region-region mechanism are innovatively designed to achieve deep mapping relationships and further enhance image dehazing. Experimental results demonstrate that the proposed model outperforms other comparative baselines, achieving high-confidence dehazing in multi-scene remote sensing images, and with good generality and applicability in multi-resolution flight simulators and multi-terrain remote sensing. © 2025 SPIE.

关键词： Flight simulators

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Overvoltage Mitigation Filter Using High-Frequency Cable modeling in Long Transmission Lines for Silicon Carbide Inverter systems

Overvoltage Mitigation Filter Using High-Frequency Cable Mod...

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Annual IEEE conference on Applied Power Electronics conference and Exposition (APEC)

作者： Yun-Jin Lee Kyo-Beum Lee Department of Electrical and Computer Engineering Ajou University Suwon South Korea

ISBN: (数字)9798331516116

ISBN: (纸本)9798331516123

This paper analyzes a design of overvoltage mitigation filter using high-frequency cable modeling in long transmission lines for silicon carbide inverter systems. The SiC inverter systems operating with long transmission lines may experience overvoltage caused by voltage reflection, leading to insulation breakdown. The proposed method to address the problem of overvoltage occurring on long transmission lines involves connecting a RLC filter at the end of the cable to reduce the overvoltage. The RLC filter is calculated by deriving the characteristic impedance using the parameters of the cable modeling. The validity of cable modeling and designing overvoltage mitigation filter is verified through simulation results.

关键词： Analytical models Impedance measurement Power cables Silicon carbide Prevention and mitigation Power cable insulation Transmission line measurements Inverters Impedance Voltage control

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Applied Mathematical Modelling in the Physics Problem-Solving Classroom 4th

Applied Mathematical Modelling in the Physics Problem-Solvin...

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4th international conference on Numerical Computations: Theory and Algorithms, NUMTA 2023

作者： Serpe, Annarosa Department of Mathematics and Computer Science University of Calabria CS Rende Italy

ISBN: (纸本)9783031812460

This paper aims to contribute to the literature on planning and implementing mathematical modelling tasks in the higher education curriculum. Specifically, we propose and discuss a lesson plan carried out within the course of Mathematics Education II - Master’s Degree in Mathematics, to train and qualify students on how to capture the interplay between Physics and Mathematics in the perspective of mathematical modelling and problem-solving. The goal is to make transparent what is meant by modelling by showing how it is possible to develop integrated learning environments beyond the traditional boundaries between subjects both at the subject level and at the level of required pedagogy, illustrating this characterisation with an example regarding a theme that ‘subsumes’ the two disciplines. It is important in the educational training of future teachers to explore ways based on influential theoretical expositions of the concept of interdisciplinarity. This is especially true when examples of didactic transposition are provided where Mathematics and Physics are understood as forms of knowledge that have relationships and differences, which are deeply intertwined and which co-evolve, mutually generating new problems that lead to permeating the labile boundaries between them to generate new knowledge. These examples of didactic transposition, therefore, are rich in interdisciplinarity connections, which show students the relevant aspects of the two disciplines from a historical and epistemological point of view and their connections. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Students

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