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Generalized Two-Point Visual Control Model of Human Steering for Accurate State Estimation1

ASME Letters in Dynamic Systems and Control 2025年第1期5卷 011004页

作者： Mai, Rene E. Sears, Katherine Roessling, Grace Julius, Agung Mishra, Sandipan Department of Mechanical Aerospace and Nuclear Engineering Rensselaer Polytechnic Institute TroyNY12180 United States Department of Cognitive Science Rensselaer Polytechnic Institute TroyNY12180 United States Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute TroyNY12180 United States

We derive and validate a generalization of the two-point visual control model, an accepted cognitive science model for human steering behavior. The generalized model is needed as current steering models are either insufficiently accurate or too complex for online state estimation. We demonstrate that the generalized model replicates specific human steering behavior with high precision (85% reduction in modeling error) and integrate this model into a human-as-advisor framework where human steering inputs are used for state estimation. As a benchmark study, we use this framework to decipher ambiguous lane markings represented by biased lateral position measurements. We demonstrate that, with the generalized model, the state estimator can accurately estimate the true vehicle state, providing lateral state estimates with under 0.15 m error across participants. However, without the generalized model, the estimator cannot accurately estimate the vehicle's lateral state. © 2024 by ASME.

关键词： Man machine systems

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Exponential Decay of Laminated Beam with Nonlinear Time-varying Delay and Microtemperature Effect

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Acta Mathematicae Applicatae Sinica 2025年第1期41卷 270-285页

作者： Imene LARIBI Ali KRELIFA Djamel OUCHENANE Fares YAZID Salah BOULAARAS Salah ZITOUNI Department of Mathematics Djilali Bounaama University of Khemis MilianaKhemis MilianaAlgeria Computer Science and Mathematics Laboratory Amar Telidji UniversityLaghouat 03000Algeria Laboratory of Pure and Applied Mathematics Amar Telidji UniversityLaghouat 03000Algeria Department of Mathematics College of ScienceQassim UniversityBuraydah 51452Saudi Arabia Department of Mathematics and Informatics University Mohamed-Cherif MessaadiaSouk AhrasAlgeria

This research paper addresses a topic of interest to many researchers and engineers due to its effective applications in various industrial *** focuses on the thermoelastic laminated beam model with nonlinear structural damping,nonlinear time-varying delay,and microtemperature *** primary goal is to establish the stability of the *** achieve this,and under suitable hypotheses,we demonstrate energy decay and construct a Lyapunov functional that leads to our results.

关键词： structural damping energy decay laminated beam microtemperature time-varying delay partial differential equations nonlinear equations

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Evaluating Deep Learning Models for Breast Cancer Classification: A Comparative Study

Evaluating Deep Learning Models for Breast Cancer Classifica...

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Medical Imaging 2025: Digital and Computational Pathology

作者： Eskandari, Sania Eslamian, Ali Cheng, Qiang Department of Electrical and Computer Engineering University of Kentucky Lexington United States Department of Computer Science University of Kentucky Lexington United States Institute for Biomedical Informatics University of Kentucky Lexington United States

ISBN: (纸本)9781510686045

The classification of histopathological images is crucial for the early and precise detection of breast cancer. This study investigates the efficiency of deep learning models in distinguishing between Invasive Ductal Carcinoma (IDC) and non-IDC in histopathology slides. We conducted a thorough comparison examination of eight sophisticated models: ResNet-50, DenseNet-121, ResNeXt-50, Vision Transformer (ViT), GoogLeNet (Inception v3), EfficientNet, MobileNet, and SqueezeNet. This analysis was carried out using a large dataset of 277,524 image patches. Our research makes a substantial contribution to the field by offering a comprehensive assessment of the performance of each model. We particularly highlight the exceptional efficacy of attention-based mechanisms in the ViT model, which achieved a remarkable validation accuracy of 94%, surpassing conventional convolutional networks. This study highlights the promise of advanced machine learning approaches in clinical settings, offering improved precision as well as efficiency in breast cancer diagnosis. © 2025 SPIE.

关键词： Breast cancer Deep learning Tumor growth modeling Medical imaging Pathology Alternate lighting of surfaces computer engineering computer science Health informatics

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Driver Drowsiness Detection Using Swin Transformer and Diffusion Models for Robust Image Denoising

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IEEE Access 2025年 13卷 71880-71907页

作者： El-Nabi, Samy Abd Ibrahim, Ahmed F. El-Rabaie, El-Sayed M. Hassan, Osama F. Soliman, Naglaa F. Ramadan, Khalil F. El-Shafai, Walid Faculty of Computer Science and Engineering Department of Artificial Intelligence Engineering South Sinai 46511 Egypt Menoufia University Faculty of Electronic Engineering Department of Electronics and Electrical Communications Engineering Menouf32952 Egypt Suez Canal University Faculty of Computers and Informatics Information Systems Department Ismailia41522 Egypt Princess Nourah bint Abdulrahman University College of Computer and Information Sciences Department of Information Technology P.O. Box 84428 Riyadh11671 Saudi Arabia Computer Science Department Riyadh11586 Saudi Arabia

With the rapid development of intelligent transportation systems and growing emphasis on driver safety, real-time detection of driver drowsiness has become a critical area of research. This study presents a robust and scalable driver drowsiness detection framework that integrates a Swin Transformer-based deep learning model with a diffusion model for image denoising. While conventional convolutional neural networks (CNNs) are effective in standard vision tasks, they often suffer performance degradation in real-world driving scenarios due to noise, poor lighting, motion blur, and adversarial attacks. To address these challenges, the proposed model focuses on eye-state detection, specifically, prolonged eye closure, as a primary indicator of driver disengagement and fatigue. Our system introduces a novel preprocessing stage using a denoising diffusion model built on a U-Net encoder-decoder architecture, effectively mitigating the impact of Gaussian noise and adversarial perturbations. Additionally, we incorporate adversarial training with Fast Gradient Sign Method (FGSM) and Projected Gradient Descent (PGD) attacks, demonstrating significant improvements in classification accuracy and resilience. Evaluations are conducted on two benchmark datasets, Eye-Blink and Closed Eyes in the Wild (CEW), under both clean and noisy conditions. Comparative experiments show that the proposed system outperforms several state-of-the-art models, including ViT, ResNet50V2, InceptionV3, MobileNet, DenseNet169, and VGG19, in terms of accuracy (up to 99.82%), PSNR (up to 41.61 dB), and SSIM (up to 0.984), while maintaining competitive inference times suitable for practical deployment. Moreover, a detailed sensitivity analysis of data augmentation strategies reveals that techniques such as rotation and horizontal flip substantially enhance the model’s generalization across variable visual inputs. The system also demonstrates improved robustness under real-world black-box scenarios and adver

关键词： Sensitivity analysis

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Polynomial Regret Concentration of UCB for Non-Deterministic State Transitions

arXiv

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arXiv 2025年

作者： Cömer, Can Blüml, Jannis Derstroff, Cedric Kersting, Kristian Computer Science Department Technical University Darmstadt Germany Centre for Cognitive Science Germany

Monte Carlo Tree Search (MCTS) has proven effective in solving decision-making problems in perfect information settings. However, its application to stochastic and imperfect information domains remains limited. This paper extends the theoretical framework of MCTS to stochastic domains by addressing non-deterministic state transitions, where actions lead to probabilistic outcomes. Specifically, building on the work of Shah, Xie, and Xu (2022), we derive polynomial regret concentration bounds for the Upper Confidence Bound algorithm in multi-armed bandit problems with stochastic transitions, offering improved theoretical guarantees. Our primary contribution is proving that these bounds also apply to non-deterministic environments, ensuring robust performance in stochastic settings. This broadens the applicability of MCTS to real-world decision-making problems with probabilistic outcomes, such as in autonomous systems and financial decision-making. © 2025, CC BY-SA.

关键词： Monte Carlo methods

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Diversity, Equity, and Inclusion in Computing science: Culture is the Key, Curriculum Contributes 2024

Diversity, Equity, and Inclusion in Computing Science: Cultu...

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29th Working Group Reports on Innovation and Technology in computer science Education, ITiCSE WGR 2024

作者： Toti, Giulia Lindner, Peggy Gao, Alice Baghban Karimi, Ouldooz Engineer, Rutwa Hur, Jinyoung Mcneill, Fiona Reckinger, Shanon Robinson, Rebecca Sollazzo, Anna Wicentowski, Richard Department of Computer Science University of British Columbia VancouverBC Canada Department of Information Science Technology University of Houston HoustonTX United States Department of Computer Science University of Toronto TorontoON Canada School of Computing Science Simon Fraser University SurreyBC Canada Department of Mathematical and Computational Sciences University of Toronto Mississauga MississaugaON Canada Department of Computer Science University of Illinois Urbana-Champaign Urbana-ChampaignIL United States School of Informatics University of Edinburgh Edinburgh United Kingdom Department of Computer Science University of Illinois Chicago ChicagoIL United States Department of Human Centred Computing Monash University ClaytonVIC Australia School of Computing Science University of Glasgow United Kingdom Computer Science Department Swarthmore College SwarthmorePA United States

ISBN: (纸本)9798400712081

Undergraduate computer science programs worldwide struggle to attract and retain underrepresented students for many reasons. Culture, stereotype threats, uneven gender and racial representations, lack of role models, and uncertain career prospects for minority groups are among the many reasons behind this situation. Many computer science programs are trying to change course through strategies to foster equity, diversity, and inclusion (EDI), aimed at improving outreach, recruitment, admissions, and retention of underrepresented students. EDI approaches may also include modifications to the undergraduate computer science curriculum. However, if not properly planned, these modifications risk amplifying existing stereotypes rather than producing positive change [38]. In this study, through an extensive literature review, a rigorous curriculum analysis of 49 computer science programs across the globe, and qualitative and quantitative analysis of surveys and interviews bringing in the voices of 613 students and 30 educators participating from around the world, we explore equity, diversity, and inclusion in the computer science curriculum. We highlight the role of inclusive content and course design, discuss program flexibility, and the impact of inclusive courses and program design in attracting and retaining historically marginalized students. Finally, we provide concrete steps to make computing science undergraduate curricula more appealing to a diverse audience. © 2024 Copyright held by the owner/author(s).

关键词： Students

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Transforming Medical Practice: Harnessing the Power of Big Data and Machine Learning for Predictive Precision Medicine

Transforming Medical Practice: Harnessing the Power of Big D...

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2025 IEEE Symposium on Computational Intelligence in Health and Medicine, CIHM 2025

作者： Theocharopoulos, Panagiotis C. Bersimis, Sotiris Georgakopoulos, Spiros V. Tasoulis, Sotiris K. Plagianakos, Vassilis P. Department of Computer Science and Biomedical Informatics University of Thessaly Greece Department of Business Administration University of Piraeus Greece Department of Mathematics University of Thessaly Greece

ISBN: (纸本)9798331508333

Machine learning (ML) techniques have been widely applied in precision medicine for the early identification of high-risk individuals using Electronic Health Records (EHRs). However, real-world EHR data often contain missing values and inconsistent patient follow-ups, creating challenges for predictive modeling. This study proposes an ensemble approach that leverages various machine learning models, such as Long Short-Term Memory (LSTM) networks, Recurrent Neural Networks (RNNs), and Random Forest (RF) models, combined through a weighted averaging scheme. To address the issue of missing data caused by irregular clinical visits, the Multivariate Imputation by Chained Equations (MICE) algorithm is used for data imputation. The ensemble model is evaluated on a real-world EHR dataset and demonstrates improved performance in predicting health outcomes. These results highlight the potential of ensemble learning and advanced imputation methods to enhance predictive accuracy, offering a robust solution for analyzing incomplete EHR data. This study provides a foundation for developing future precision medicine models that incorporate multiple clinical tests for individualized risk assessment. © 2025 IEEE.

关键词： Biomedical engineering

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Smart real-time detection of risky roads using vehicles trajectories for intelligent transportation

International Journal of Cognitive Computing in Engineering

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International Journal of cognitive Computing in Engineering 2025年 6卷 370-379页

作者： Eldawy, Eman O. Abdalla, Mohammed Mokhtar, Hoda M.O. Hendawi, Abdeltawab AbdelAziz, Amr M. Minia Egypt Faculty of Computers and Information Minia University Egypt Faculty of Computers and Artificial Intelligence Beni-Suef University Egypt Egypt University of Informatics Egypt Department of Computer Science and Statistics University of Rhode Island United States

Indeed, risky roads have a negative impact on traffic by causing road injuries with fatalities, which can lead to negative emotional, social, and economic influences on humans, countries, and the world. Additionally, taxi and rideshare passengers prefer to move on familiar and safe roads. Therefore, to ensure the high quality of transportation services, it is required to follow secure roads to avoid poorly maintained roads, areas with high incidences of car accidents, neighborhoods with high crime rates, and places with a history of terrorist attacks or civil unrest. In this regard, discovering risky roads is a need. This paper introduces a real-time framework, named RiskyMove, that helps drivers and passengers to follow safe roads and avoid risky once that are not safe for travel. Mainly, the RiskyMove framework employs a probabilistic method based on a Minimum Adaptive Viterbi (MAV) algorithm to identify risky paths during the trip and alarm the drivers to take precautions. An experimental evaluation of the RiskyMove with a real dataset of the movement of cabs in San Francisco illustrates the effectiveness of the proposed framework. © 2025 The Authors

关键词： Quality of service

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Identify spoofing attacks in Internet of Things (IoT) environments using machine learning algorithms

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Journal of High Speed Networks 2025年第1期31卷 61-70页

作者： Vajrobol, Vajratiya Saxena, Geetika Jain Pundir, Amit Singh, Sanjeev B. Gupta, Brij Gaurav, Akshat Rahaman, Mosiur Institute of Informatics and Communication University of Delhi New Delhi India Department of Electronics Maharaja Agrasen College University of Delhi New Delhi India Department of Computer Science and Information Engineering Asia University Taichung Taiwan Dehradun India UCRD Chandigarh University Chandigarh India Department of Computer Science Ronin Institute MontclairNJ United States International Center for AI and Cyber Security Research and Innovations Asia University Taichung Taiwan

With the growing adoption of Internet of Things (IoT) devices, security concerns are becoming increasingly urgent. Protecting IoT systems from cyberattacks is crucial to safeguard sensitive information. Spoofing, particularly Domain Name System (DNS) and Address Resolution Protocol (ARP) spoofing, is a type of attack that can manipulate network traffic and compromise data integrity. DNS spoofing redirects users to fraudulent websites by altering domain name resolutions, while ARP spoofing tricks the network by associating a legitimate internet protocol address with a malicious MAC address, allowing attackers to intercept or modify communication. This study aims to develop an efficient method for detecting these types of spoofing attacks in IoT environments using machine learning techniques. The results show that the random forest algorithm outperforms other models, achieving remarkable performance with a 95.1% accuracy, a precision score of 95.2%, and a strong F1 score of 95.1%. A key contribution of this research is the simultaneous detection of both DNS and ARP spoofing within a unified framework, utilizing a comprehensive set of 46 features. These findings underscore the importance of ensuring robust protection against spoofing attacks to maintain the security and integrity of IoT systems. © The Author(s) 2025.

关键词： Adversarial machine learning

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Matching Cuts in Graphs of High Girth and H-Free Graphs

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Algorithmica 2025年 1-23页

作者： Feghali, Carl Lucke, Felicia Paulusma, Daniël Ries, Bernard University of Lyon EnsL CNRS LIP Lyon Cedex 0769342 France Department of Informatics University of Fribourg Fribourg Switzerland Department of Computer Science Durham University Durham United Kingdom

The (Perfect) Matching Cut problem is to decide if a connected graph has a (perfect) matching that is also an edge cut. The Disconnected Perfect Matching problem is to decide if a connected graph has a perfect matching that contains a matching cut. Both Matching Cut and Disconnected Perfect Matching are NP-complete for planar graphs of girth 5, whereas Perfect Matching Cut is known to be NP-complete even for subcubic bipartite graphs of arbitrarily large fixed girth. We prove that Matching Cut and Disconnected Perfect Matching are also NP-complete for bipartite graphs of arbitrarily large fixed girth and bounded maximum degree. Our result for Matching Cut resolves a 20-year old open problem. We also show that the more general problem d-Cut, for every fixed d≥1, is NP-complete for bipartite graphs of arbitrarily large fixed girth and bounded maximum degree. Furthermore, we show that Matching Cut, Perfect Matching Cut and Disconnected Perfect Matching are NP-complete for H-free graphs whenever H contains a connected component with two vertices of degree at least 3. Afterwards, we update the state-of-the-art summaries for H-free graphs and compare them with each other, and with a known and full classification of the Maximum Matching Cut problem, which is to determine a largest matching cut of a graph G. Finally, by combining existing results, we obtain a complete complexity classification of Perfect Matching Cut for H-subgraph-free graphs where H is any finite set of graphs. © The Author(s) 2025.

关键词： Graphic methods

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