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arXiv 2025年

作者： Sun, Qingshi Justin, Nathan Gómez, Andrés Vayanos, Phebe Department of Industrial & Systems Engineering USC United States Department of Computer Science USC United States Center for Artificial Intelligence in Society USC United States

Logistic regression models are widely used in the social and behavioral sciences and in high-stakes domains, due to their simplicity and interpretability properties. At the same time, such domains are permeated by distribution shifts, where the distribution generating the data changes between training and deployment. In this paper, we study a distributionally robust logistic regression problem that seeks the model that will perform best against adversarial realizations of the data distribution drawn from a suitably constructed Wasserstein ambiguity set. Our model and solution approach differ from prior work in that we can capture settings where the likelihood of distribution shifts can vary across features, significantly broadening the applicability of our model relative to the state-of-the-art. We propose a graph-based solution approach that can be integrated into off-the-shelf optimization solvers. We evaluate the performance of our model and algorithms on numerous publicly available datasets. Our solution achieves a 408x speed-up relative to the state-of-the-art. Additionally, compared to the state-of-the-art, our model reduces average calibration error by up to 36.19% and worst-case calibration error by up to 41.70%, while increasing the average area under the ROC curve (AUC) by up to 18.02% and worst-case AUC by up to 48.37%. Copyright © 2025, The Authors. All rights reserved.

关键词： Logistic regression

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Progressive learning with multi-scale attention network for cross-domain vehicle re-identification

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science China(Information sciences) 2022年第6期65卷 33-47页

作者： Yang WANG Jinjia PENG Huibing WANG Meng WANG School of Computer Science and Information Engineering Hefei University of Technology Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Information Science and Technology College Dalian Maritime University School of Cyber Security and Computer Hebei University

Vehicle re-identification(reID) aims to identify vehicles across different cameras that have nonoverlapping views. Most existing vehicle reID approaches train the reID model with well-labeled datasets via a supervised manner, which inevitably causes a severe drop in performance when tested in an unknown domain. Moreover, these supervised approaches require full annotations, which is limiting owing to the amount of unlabeled data. Therefore, with the aim of addressing the aforementioned problems, unsupervised vehicle reID models have attracted considerable attention. It always adopts domain adaptation to transfer discriminative information from supervised domains to unsupervised ones. In this paper, a novel progressive learning method with a multi-scale fusion network is proposed, named PLM, for vehicle reID in the unknown domain, which directly exploits inference from the available abundant data without any annotations. For PLM, a domain adaptation module is employed to smooth the domain bias, which generates images with similar data distribution to unlabeled target domain as “pseudo target samples”. Furthermore, to better exploit the distinct features of vehicle images in the unknown domain, a multi-scale attention network is proposed to train the reID model with the “pseudo target samples” and unlabeled samples; this network embeds low-layer texture features with high-level semantic features to train the reID model. Moreover, a weighted label smoothing(WLS) loss is proposed, which considers the distance between samples and different clusters to balance the confidence of pseudo labels in the feature learning module. Extensive experiments are carried out to verify that our proposed PLM achieves excellent performance on several benchmark datasets.

关键词： data adaptation module weighted label smoothing loss multi-scale attention network vehicle re-identification

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Stylometry Analysis of Multi-authored Documents for Authorship and Author Style Change Detection

arXiv

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arXiv 2024年

作者： Zamir, Muhammad Tayyab Ayub, Muhammad Asif Gul, Asma Ahmad, Nasir Ahmad, Kashif Abasyn University Islamabad Campus Pakistan Department of Computer Systems Engineering University of Engineering and Technology Peshawar Pakistan NUST Business School National University of Science and Technology Islamabad Pakistan University of Western Australia Australia Department of Computer Science Munster Technological University Cork Ireland

In recent years, the increasing use of Artificial Intelligence (AI)-based text generation tools has posed new challenges in document provenance, authentication, and authorship detection. However, advancements in stylometry have provided opportunities for automatic authorship and author change detection in multi-authored documents using style analysis techniques. Style analysis can serve as a primary step towards document provenance and authentication through authorship detection. This paper investigates three key tasks of style analysis: (i) classification of single and multi-authored documents, (ii) single change detection, which involves identifying the point where the author switches, and (iii) multiple author-switching detection in multi-authored documents. We formulate all three tasks as classification problems and propose a merit-based fusion framework that integrates several state-of-the-art natural language processing (NLP) algorithms and weight optimization techniques. We also explore the potential of special characters, which are typically removed during pre-processing in NLP applications, on the performance of the proposed methods for these tasks by conducting extensive experiments on both cleaned and raw datasets. Experimental results demonstrate significant improvements over existing solutions for all three tasks on a benchmark dataset. © 2024, CC BY.

关键词： Classification (of information)

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WARA-PS:a research arena for public safety demonstrations and autonomous collaborative rescue robotics experimentation

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Autonomous Intelligent systems 2021年第1期1卷 104-134页

作者： Olov Andersson Patrick Doherty Mårten Lager Jens-Olof Lindh Linnea Persson Elin A.Topp Jesper Tordenlid Bo Wahlberg Department of Computer and Information Science Linköping UniversityLinköpingSweden Saab Kockums MalmöSweden Division of Decision and Control Systems School of Electrical Engineering and Computer ScienceKTH Royal Institute of TechnologyStockholmSweden Department of Computer Science Lund UniversityLundSweden Saab Combitech AB LinköpingSweden Division of Automatic Control Department of Electrical EngineeringLinköping UniversityLinköpingSweden

A research arena(WARA-PS)for sensing,data fusion,user interaction,planning and control of collaborative autonomous aerial and surface vehicles in public safety applications is *** objective is to demonstrate scientific discoveries and to generate new directions for future research on autonomous systems for societal *** enabler is a computational infrastructure with a core system architecture for industrial and academic *** includes a control and command system together with a framework for planning and executing tasks for unmanned surface vehicles and aerial *** motivating application for the demonstration is marine search and rescue operations.A state-of-art delegation framework for the mission planning together with three specific applications is also *** first one concerns model predictive control for cooperative rendezvous of autonomous unmanned aerial and surface *** second project is about learning to make safe real-time decisions under uncertainty for autonomous vehicles,and the third one is on robust terrain-aided navigation through sensor fusion and virtual reality tele-operation to support a GPS-free positioning system in marine *** research results have been experimentally evaluated and demonstrated to industry and public sector audiences at a marine test *** would be most difficult to do experiments on this large scale without the WARA-PS research ***,these demonstrator activities have resulted in effective research dissemination with high public visibility,business impact and new research collaborations between academia and industry.

关键词： Autonomous systems Intelligent system architectures Research demonstration arena Autonomous drones Autonomous marine vessels Public safety and security Collaborative robotics

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End-Face Position Measurement of Work Piece During Press Work Using Surface Wave Reflection

End-Face Position Measurement of Work Piece During Press Wor...

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IEEE Symposium (IUS) Ultrasonics

作者： Wenke Hu Eikou Nakazawa Jie Zheng Norio Tagawa Ming Yang Electrical Engineering and Computer Science Tokyo Metropolitan University Tokyo Japan Mechanical Systems Engineering Tokyo Metropolitan University Tokyo Japan

In press operations, accurate measurement of the edge position of the workpiece between the bolster and die leads to improved machining accuracy and productivity. In general, laser ranging is highly accurate, but ultrasonic ranging is effective considering the cost and ease of installation of a measurement system. In this study, we investigated high-precision ranging of workpiece edge positions based on surface wave reflections and their super-resolution. Surface waves are dispersive, which degrades the reflected waveform and affects the ranging accuracy. Therefore, we evaluated the effectiveness of suppressing the waveform degradation using dispersion-compensating ultrasonic transmission. This dispersion-compensating transmission was also found to be highly effective in super-resolving the echo pulse.

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Benchmarking GAN-Based vs Classical Data Augmentation on Biomedical Images 27th

Benchmarking GAN-Based vs Classical Data Augmentation on Bi...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Mantegna, Massimiliano Tronchin, Lorenzo Tortora, Matteo Soda, Paolo Unit of Computer Systems and Bioinformatics Department of Engineering University Campus Bio-Medico of Rome Rome Italy Department of Naval Electrical Electronics and Telecommunications Engineering University of Genova Via all’Opera Pia 11a Genoa16145 Italy Department of Diagnostics and Intervention Radiation Physics Biomedical Engineering Umeå University Umeå Sweden Department of Computing Science Umeå University Umeå Sweden Messina Italy

ISBN: (纸本)9783031876592

The medical field faces significant data shortages due to the high image acquisition and maintenance costs. Data Augmentation aims to mitigate this by increasing data availability and enhancing image generalization. However, traditional DA methods often produce data with limited quality and diversity. Generative Adversarial Networks present a promising alternative, offering potential solutions to data scarcity issues. This paper evaluates the impact of GAN-based data augmentation in medical imaging and provides a benchmark for the efficacy of synthetic data in downstream classification tasks. To this aim, we performed a wide set of tests using three different GAN architectures on six 2D datasets from the standardized MedMNIST biomedical image collection, conducting a total of 696 experiments. Our results reveal that while GAN-based DA methods show promise with low-dimensional datasets, traditional DA methods still outperform them. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Mammography

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Secrecy Performance Analysis of Space-to-Ground Optical Satellite Communications

arXiv

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arXiv 2024年

作者： Nguyen, Thang V. Pham, Thanh V. Pham, Anh T. Ngoc, Dang T. Wireless Systems and Applications Lab. Posts and Telecommunications Institute of Technology Hanoi Viet Nam Department of Mathematical and Systems Engineering Shizuoka University Japan Department of Computer Science and Engineering The University of Aizu Japan

To cope with the scarcity of radio frequency (RF) counterparts, free-space optics (FSO) based on satellite communications technologies have recently gained a lot of interest. Firstly, this investigates the security of space-to-ground intensity modulation/direct detection FSO satellite communications in various effects from the physical world, such as propagation loss, beam misalignment, cloud attenuation, and fading caused by atmospheric turbulence. Next, A wiretap channel composed of a genuine broadcaster Alice (i.e., the satellite), a legitimate user Bob, and an eavesdropper Eve is considered over turbulent channels characterized by the Fisher-Snedecor F distribution. In addition, the closed-form expressions of the secrecy performance metrics, including average secrecy capacity, secrecy outage probability, and strictly positive secrecy capacity, are derived in our study. Finally, the numerical findings indicate that the satellite altitude of 600 km should be used to achieve a secrecy outage of roughly 50% for the whole turbulence regime. In addition, high levels of cloud liquid water content (CLWC) could improve the secrecy performance as it is shown that, the average secrecy capacity increases by about 12% when the CLWC increases from 1 mg/m3 to 2 mg/m3 © 2024, CC BY.

关键词： Atmospheric turbulence

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Intelligent health model for medical imaging to guide laymen using neural cellular automata

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Scientific Reports 2025年第1期15卷 1-19页

作者： Sharma, Sandeep Kumar Chowdhary, Chiranji Lal Sharma, Vijay Shankar Rasool, Adil Khan, Arfat Ahmad Department of Computer and Communication Engineering Manipal University Jaipur Ajmer Road Rajasthan Jaipur 302007 India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore 632014 India Department of Computer Bakhtar University Kabul Afghanistan Department of Computer Science College of Computing Khon Kaen University Khon Kaen Thailand

A layman in health systems is a person who doesn’t have any knowledge about health data i.e., X-ray, MRI, CT scan, and health examination reports, etc. The motivation behind the proposed invention is to help laymen to make medical images understandable. The health model is trained using a neural network approach that analyses user health examination data;predicts the type and level of the disease and advises precaution to the user. Cellular Automata (CA) technology has been integrated with the neural networks to segment the medical image. The CA analyzes the medical images pixel by pixel and generates a robust threshold value which helps to efficiently segment the image and identify accurate abnormal spots from the medical image. The proposed method has been trained and experimented using 10000+ medical images which are taken from various open datasets. Various text analysis measures i.e., BLEU, ROUGE, and WER are used in the research to validate the produced report. The BLEU and ROUGE calculate a similarity to decide how the generated text report is closer to the original report. The BLEU and ROUGE scores of the experimented images are approximately 0.62 and 0.90, claims that the produced report is very close to the original report. The WER score 0.14, claims that the generated report contains the most relevant words. The overall summary of the proposed research is that it provides a fruitful medical report with accurate disease and precautions to the laymen. © The Author(s) 2025.

关键词： Cellular automata Healthcare Layman Machine learning Medical image

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Exploring Equilibrium Strategies in Network Games with Generative AI

arXiv

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arXiv 2024年

作者： Yang, Yaoqi Du, Hongyang Sun, Geng Xiong, Zehui Niyato, Dusit Han, Zhu The College of Communications Engineering Army Engineering University PLA Nanjing210000 China The School of Computer Science and Engineering NTU Singapore The College of Computer Science and Technology Jilin University Changchun130012 China The College of Computing and Data Science Nanyang Technological University 639798 Singapore The Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore The Department of Electrical and Computer Engineering The University of Houston HoustonTX77004 United States

Game theory offers a powerful framework for analyzing strategic interactions among decision-makers, providing tools to model, analyze, and predict their behavior. However, implementing game theory can be challenging due to difficulties in deriving solutions, understanding interactions, and ensuring optimal performance. Traditional non-AI and discriminative AI approaches have made valuable contributions but struggle with limitations in handling large-scale games and dynamic scenarios. In this context, generative AI emerges as a promising solution because of its superior data analysis and generation capabilities. This paper comprehensively summarizes the challenges, solutions, and outlooks of combining generative AI with game theory. We start with reviewing the limitations of traditional non-AI and discriminative AI approaches in employing game theory, and then highlight the necessity and advantages of integrating generative AI. Next, we explore the applications of generative AI in various stages of the game theory lifecycle, including model formulation, solution derivation, and strategy improvement. Additionally, from game theory viewpoint, we propose a generative AI-enabled framework for optimizing machine learning model performance against false data injection attacks, supported by a case study to demonstrate its effectiveness. Finally, we outline future research directions for generative AI-enabled game theory, paving the way for its further advancements and development. © 2024, CC BY.

关键词： Game theory

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HRPDF: A Software-Based Heterogeneous Redundant Proactive Defense Framework for Programmable Logic Controller

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Journal of computer science & Technology 2021年第6期36卷 1307-1324页

作者： Ke Liu Jing-Yi Wang Qiang Wei Zhen-Yong Zhang Jun Sun Rong-Kuan Ma Rui-Long Deng State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou 450001China College of Control Science and Engineering Zhejiang UniversityHangzhou 310027China College of Computer Science and Technology Guizhou UniversityGuiyang 550025China School of Information Systems Singapore Management UniversitySingapore 689867Singapore

Programmable logic controllers(PLCs)play a critical role in many industrial control systems,yet face increasingly serious cyber *** this paper,we propose a novel PLC-compatible software-based defense mechanism,called Heterogeneous Redundant Proactive Defense Framework(HRPDF).We propose a heterogeneous PLC architecture in HRPDF,including multiple heterogeneous,equivalent,and synchronous runtimes,which can thwart multiple types of attacks against PLC without the need of external *** ensure the availability of PLC,we also design an inter-process communication algorithm that minimizes the overhead of *** implement a prototype system of HRPDF and test it in a real-world PLC and an OpenPLC-based device,*** results show that HRPDF can defend against multiple types of attacks with 10.22%additional CPU and 5.56%additional memory overhead,and about 0.6 ms additional time overhead.

关键词： industrial control system programmable logic controller proactive defense heterogeneous redundant architecture

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